Tumor Microenvironment Characterization in Gastric Cancer Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures

Zeng, Dongqiang; Li, Meiyi; Zhou, Rui; Zhang, Jingwen; Sun, Huiying; Shi, Min; Bin, Jianping; Liao, Yulin; Rao, Jinjun; Liao, Wangjun

doi:10.1158/2326-6066.cir-18-0436

Cited by 1,000Cancer Immunotherapy and BiomarkersGastric Cancer Management and OutcomesImmune cells in cancer

Tumor Microenvironment Characterization in Gastric Cancer Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures

Zeng, Dongqiang, Li, Meiyi, Zhou, Rui, Zhang, Jingwen, Sun, Huiying, Shi, Min, Bin, Jianping, Liao, Yulin +2 more·2019·Cancer Immunology Research

DOI 10.1158/2326-6066.cir-18-0436

Published version of a preprint

Open this paper in CoChat — free account

Abstract

Tumor microenvironment (TME) cells constitute a vital element of tumor tissue. Increasing evidence has elucidated their clinicopathologic significance in predicting outcomes and therapeutic efficacy. Nonetheless, no studies have reported a systematic analysis of cellular interactions in the TME. In this study, we comprehensively estimated the TME infiltration patterns of 1,524 gastric cancer patients and systematically correlated the TME phenotypes with genomic characteristics and clinicopathologic features of gastric cancer using two proposed computational algorithms. Three TME phenotypes were defined, and the TMEscore was constructed using principal component analysis algorithms. The high TMEscore subtype was characterized by immune activation and response to virus and IFNγ. Activation of transforming growth factor β, epithelial–mesenchymal transition, and angiogenesis pathways were observed in the low TMEscore subtype, which are considered T-cell suppressive and may be responsible for significantly worse prognosis in gastric cancer [hazard ratio (HR), 0.42; 95% confidence interval (CI), 0.33–0.54; P < 0.001]. Multivariate analysis revealed that the TMEscore was an independent prognostic biomarker, and its value in predicting immunotherapeutic outcomes was also confirmed (IMvigor210 cohort: HR, 0.63; 95% CI, 0.46–0.89; P = 0.008; GSE78220 cohort: HR, 0.25; 95% CI, 0.07–0.89; P = 0.021). Depicting a comprehensive landscape of the TME characteristics of gastric cancer may, therefore, help to interpret the responses of gastric tumors to immunotherapies and provide new strategies for the treatment of cancers.

Integrity signals

Registry

Crossref · 10.1158/2326-6066.cir-18-0436

Cites

76 of 76 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Alteration of tumour immune microenvironment in gastric cancer is a pivotal strategy for predicting immunotherapeutic responses and prognosis.
“Recent studies reported that alteration of tumour immune microenvironment in gastric cancer is a pivotal strategy for predicting the immunotherapeutic responses and prognosis, which might provide new treatment strategies for patients [26, 27].”
From Phosphoserine phosphatase as a prognostic biomarker in patients with gastric cancer and its potential association with immune cells (2022) · cites this paper as [27]

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · cited 4× · see citing works
“The adjusted P value for multiple testing was calculated using the Benjamini–Hochberg correction (32).”
MethodInfluential
Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625 · cited 1× · see citing works
“Gene sets were downloaded from the MSigDB database of the Broad Institute (36), and HALLMARK gene sets were selected to perform quantification of pathway activity.”
Method
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458 · see citing works
clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters
Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012) · OMICS: A Journal of Integrative Biology · doi:10.1089/omi.2011.0118 · Cited by 36,259 · cited 2× · see citing works
“Next, the clusterProfiler R package (32) was adopted to annotate gene patterns.”
Method
Algorithm AS 136: A K-Means Clustering Algorithm
Hartigan, J. A., & Wong, M. A. (1979) · Applied Statistics · doi:10.2307/2346830 · Cited by 14,523 · see citing works
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697 · cited 3× · see citing works
“TMB (nonsynonymous variants), which is significantly associated with efficacy of immunotherapy, was also evaluated with ROC analysis (50) in the IMvigor210 cohort (61).”
Method
Robust enumeration of cell subsets from tissue expression profiles
Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., Hoang, C. D., Diehn, M., & Alizadeh, A. A. (2015) · Nature Methods · doi:10.1038/nmeth.3337 · Cited by 13,239 · see citing works
Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829 · see citing works
Complex heatmaps reveal patterns and correlations in multidimensional genomic data
Gu, Z., Eils, R., & Schlesner, M. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw313 · Cited by 10,122 · cited 1× · see citing works
“OncoPrint used to depict mutation landscape of TCGA-STAD cohort was constructed by ComplexHeatmap R package (52).”
Method
Inferring tumour purity and stromal and immune cell admixture from expression data
Yoshihara, K., Shahmoradgoli, M., Martínez, E., Vegesna, R., Kim, H., Torres-Garcia, W., Treviño, V., Shen, H., Laird, P. W., Levine, D. A., et al. (2013) · Nature Communications · doi:10.1038/ncomms3612 · Cited by 9,778 · cited 1× · see citing works
“Moreover, the abundance of immune cells and other cells in the TME can be estimated using computational methods (16-18).”
PD-1 Blockade in Tumors with Mismatch-Repair Deficiency
Le, D. T., Uram, J. N., Wang, H., Bartlett, B. R., Kemberling, H., Eyring, A. D., Skora, A. D., Luber, B. S., Azad, N. S., Laheru, D., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1500596 · Cited by 9,377 · see citing works
Microenvironmental regulation of tumor progression and metastasis
Quail, D. F., & Joyce, J. A. (2013) · Nature Medicine · doi:10.1038/nm.3394 · Cited by 7,994 · cited 1× · see citing works
“However, some studies have revealed the significance of tumor-related structures as well as upregulated signaling pathways in both cancer cells and the tumor microenvironment (TME) (3,4), suggesting that intercellular relationships are more important than genomic factors at the single-cell level (5,6).”
Background
Comprehensive molecular characterization of gastric adenocarcinoma
The Cancer Genome Atlas Research Network (2014) · Nature · doi:10.1038/nature13480 · Cited by 6,498 · see citing works
Feature Selection with the <b>Boruta</b> Package
Kursa, M. B., & Rudnicki, W. R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v036.i11 · Cited by 5,659 · cited 1× · see citing works
“Then, the random forest classification algorithm was used to perform dimension reduction in order to reduce noise or redundant genes (33).”
Method
TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells
Mariathasan, S., Turley, S. J., Nickles, D., Castiglioni, A., Yuen, K., Wang, Y., Kadel III, E. E., Koeppen, H., Astarita, J. L., Cubas, R., et al. (2018) · Nature · doi:10.1038/nature25501 · Cited by 5,400 · cited 9× · see citing works
“By applying ROC curve analysis (50), we also demonstrated the predictive value of the TMEscore for checkpoint blockade in four separate cohorts of patients withmetastatic urothelial cancer (13) treated with the anti–PD-L1 agent (atezolizumab), metastatic melanoma treated with anti–PD-1 (pembrolizumab), advanced melano…”
BackgroundMethodResultInfluential
affy—analysis of Affymetrix GeneChip data at the probe level
Gautier, L., Cope, L., Bolstad, B. M., & Irizarry, R. A. (2004) · Bioinformatics · doi:10.1093/bioinformatics/btg405 · Cited by 5,221 · cited 1× · see citing works
“The raw data for the dataset from Affymetrix were processed using the RMA algorithm for background adjustment in the Affy software package (23).”
Method
ggtree : an r package for visualization and annotation of phylogenetic trees with their covariates and other associated data
Yu, G., Smith, D. K., Zhu, H., Guan, Y., & Lam, T. T. (2016) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12628 · Cited by 4,860 · cited 1× · see citing works
“R package ggtree (51) was used to visualize phylogenetic trees of the TME signature genes.”
Method
Normality Tests for Statistical Analysis: A Guide for Non-Statisticians
Ghasemi, A., & Zahediasl, S. (2012) · International Journal of Endocrinology and Metabolism · doi:10.5812/ijem.3505 · Cited by 4,368 · cited 1× · see citing works
“Statistical analysis The normality of the variables was tested by the Shapiro—Wilk normality test (47).”
The biology and function of fibroblasts in cancer
Kalluri, R. (2016) · Nature Reviews Cancer · doi:10.1038/nrc.2016.73 · Cited by 4,366 · cited 2× · see citing works
“For example, differences in the compositions of resident cell types within the TME, including cytotoxic T cells, helper T cells, dendritic cells (DCs), tumor-associated macrophages, mesenchymal stem cells, and associated inflammatory pathways, have been reported in patients with cancer (5, 6, 9, 10).”
Background
TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data
Colaprico, A., Silva, T. C., Olsen, C., Garofano, L., Cava, C., Garolini, D., Sabedot, T. S., Malta, T. M., Pagnotta, S. M., Castiglioni, I., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1507 · Cited by 4,133 · cited 2× · see citing works
“Updated clinical data and sample information for TCGA-STAD samples were obtained from the Genomic Data Commons (https://portal.gdc.cancer.gov/) using the R package TCGAbiolinks (25).”
Tumour-associated macrophages as treatment targets in oncology
Mantovani, A., Marchesi, F., Malesci, A., Laghi, L., & Allavena, P. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2016.217 · Cited by 4,065 · cited 10× · see citing works
“org Cancer Immunol Res; 7(5) May 2019 745”
BackgroundInfluential
Macrophage plasticity and interaction with lymphocyte subsets: cancer as a paradigm
Biswas, S. K., & Mantovani, A. (2010) · Nature Immunology · doi:10.1038/ni.1937 · Cited by 3,840 · see citing works
Molecular and genetic properties of tumors associated with local immune cytolytic activity
Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G., & Hacohen, N. (2015) · Cell · doi:10.1016/j.cell.2014.12.033 · Cited by 3,825 · cited 1× · see citing works
“Thenumbers of predicted neoepitopes based on tumor-specific HLA typing, total mutations, andCYT signature score for eachpatientwere obtained for 263 STAD samples from the supplementary table of Rooney and colleagues (27).”
Background
IFN-γ–related mRNA profile predicts clinical response to PD-1 blockade
Ayers, M., Lunceford, J., Nebozhyn, M., Murphy, E., Loboda, A., Kaufman, D. R., Albright, A., Cheng, J. D., Kang, S. P., Shankaran, V., et al. (2017) · Journal of Clinical Investigation · doi:10.1172/jci91190 · Cited by 3,788 · see citing works
An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics
Liu, J., Lichtenberg, T., Hoadley, K. A., Poisson, L. M., Lazar, A. J., Cherniack, A. D., Kovatich, A. J., Benz, C. C., Levine, D. A., Lee, A. V., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.052 · Cited by 3,725 · see citing works
Self-citation
Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression
Becht, É., Giraldo, N. A., Lacroix, L., Buttard, B., Elarouci, N., Petitprez, F., Sèlves, J., Laurent‐Puig, P., Sautès‐Fridman, C., Fridman, W. H., et al. (2016) · Genome biology · doi:10.1186/s13059-016-1070-5 · Cited by 3,677 · cited 2× · see citing works
“Proportions of stromal cells were estimated by applying the Microenvironment Cell Populations-counter method, which allows for robust quantification of the absolute abundance of eight immune and two stromal cell populations in heterogeneous tissues from transcriptomic data (17).”
Method
Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial
Rosenberg, J. E., Hoffman-Censits, J., Powles, T., van der Heijden, M. S., Balar, A. V., Necchi, A., Dawson, N., O'Donnell, P. H., Balmanoukian, A., Loriot, Y., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(16)00561-4 · Cited by 3,669 · cited 3× · see citing works
“To explore the correlation between the TME signature and other relevant biological processes, we used gene sets curated by Mariathasan and colleagues (13), including (i) CD8 T-effector signature (11); (ii) antigen processingmachinery (37); (iii) immune-checkpoint; (iv) epithelial–mesenchymal transition (EMT) markers p…”
BackgroundMethod
Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma
Hugo, W., Zaretsky, J. M., Sun, L., Song, C., Moreno, B. H., Hu-Lieskovan, S., Berent-Maoz, B., Pang, J., Chmielowski, B., Cherry, G., et al. (2016) · Cell · doi:10.1016/j.cell.2016.02.065 · Cited by 3,448 · cited 4× · see citing works
“Genomic and clinical data sets with immune-checkpoint blockade Five genomic and transcriptomic data sets from patients with metastatic urothelial cancer (13) treatedwith ananti–PD-L1 agent (atezolizumab), patients with metastatic melanoma (43) treated with anti–PD-1 (pembrolizumab), patients with advanced melanoma tre…”
BackgroundMethodInfluential
Genomic classification of cutaneous melanoma
Akbani, R., Akdemir, K. C., Aksoy, B. A., Albert, M., Ally, A., Amin, S. B., Arachchi, H., Arora, A., Auman, J. T., Ayala, B., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.044 · Cited by 3,151 · see citing works
Comprehensive molecular characterization of urothelial bladder carcinoma
Perou, C. M. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/za09-dk57 · Cited by 2,992 · see citing works
Melanoma-intrinsic β-catenin signalling prevents anti-tumour immunity
Spranger, S., Bao, R., & Gajewski, T. F. (2015) · Nature · doi:10.1038/nature14404 · Cited by 2,830 · see citing works
Pan-tumor genomic biomarkers for PD-1 checkpoint blockade–based immunotherapy
Cristescu, R., Mogg, R., Ayers, M., Albright, A., Murphy, E., Yearley, J., Sher, X., Liu, X. Q., Lu, H., Nebozhyn, M., et al. (2018) · Science · doi:10.1126/science.aar3593 · Cited by 2,301 · cited 1× · see citing works
“crucial role in checkpoint inhibitor immunotherapy (12-14,61).”
The immune contexture in cancer prognosis and treatment
Fridman, W. H., Zitvogel, L., Sautès–Fridman, C., & Kroemer, G. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.101 · Cited by 2,263 · see citing works
Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes
Cristescu, R., Lee, J., Nebozhyn, M., Kim, K.-M., Ting, J. C., Wong, S. S., Liu, J., Yue, Y. G., Wang, J., Yu, K., et al. (2015) · Nature Medicine · doi:10.1038/nm.3850 · Cited by 2,223 · see citing works
Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data
Monti, S., Tamayo, P., Mesirov, J., & Golub, T. (2003) · Machine Learning · doi:10.1023/a:1023949509487 · Cited by 2,116 · cited 3× · see citing works
“A consensus clustering algorithm (28) was applied to define the cluster of genes, and principal component analysis (PCA) was conducted.”
BackgroundMethodInfluential
Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples
Wagner, G. P., Kin, K., & Lynch, V. J. (2012) · Theory in Biosciences · doi:10.1007/s12064-012-0162-3 · Cited by 2,085 · cited 1× · see citing works
“For TCGA dataset, RNA-sequencing data (FPKM values) were transformed into transcripts per kilobase million (TPM) values, which are more similar to those resulting from microarrays and more comparable between samples (24).”
Method
Gene Expression Profiling in Breast Cancer: Understanding the Molecular Basis of Histologic Grade To Improve Prognosis
Sotiriou, C. P., Wirapati, P., Loi, S., Harris, A. L., Fox, S., Smeds, J., Nordgren, H., Farmer, P. J., Praz, V., Haibe‐Kains, B., et al. (2006) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djj052 · Cited by 2,060 · cited 2× · see citing works
“After obtaining the prognostic value of each gene signature score, we applied amethod similar to GGI (35) to define the TMEscore of each patient: TMEscore ¼ X PC1i X PC1j where i is the signature score of clusters whose Cox coefficient is positive, and j is the expression of genes whose Cox coefficient is negative.”
Method
PD-1 and CTLA-4 combination blockade expands infiltrating T cells and reduces regulatory T and myeloid cells within B16 melanoma tumors
Curran, M. A., Montalvo, W., Yagita, H., & Allison, J. P. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0915174107 · Cited by 1,874 · cited 1× · see citing works
“Inhibition of immunological checkpoints with monoclonal antibodies that block the T-cell inhibitory molecules PD-L1 and PD-1 has emerged as an anticancer treatment with unprecedented and synergistic survival benefits (56).”
Background
TGFβ drives immune evasion in genetically reconstituted colon cancer metastasis
Tauriello, D. V. F., Palomo-Ponce, S., Stork, D., Berenguer-Llergo, A., Badia-Ramentol, J., Iglesias, M., Sevillano, M., Ibiza, S., Cañellas, A., Hernando-Momblona, X., et al. (2018) · Nature · doi:10.1038/nature25492 · Cited by 1,851 · see citing works
Safety and Efficacy of Pembrolizumab Monotherapy in Patients With Previously Treated Advanced Gastric and Gastroesophageal Junction Cancer: Phase 2 Clinical KEYNOTE-059 Trial
Fuchs, C. S., Doi, T., Jang, R. W., Muro, K., Satoh, T., Machado, M., Sun, W., Jalal, S. I., Shah, M. A., Metges, J.-P., et al. (2018) · JAMA Oncology · doi:10.1001/jamaoncol.2018.0013 · Cited by 1,784 · cited 2× · see citing works
“However, these responses occur only in a minority of patients, and several studies have found that PD-1 expression, PD-L1 expression, MSI status, and mutation load are not efficient biomarkers for predicting the benefits of immune-checkpoint blockade (15, 69, 70).”
Background
Comprehensive molecular characterization of clinical responses to PD-1 inhibition in metastatic gastric cancer
Kim, S. T., Cristescu, R., Bass, A. J., Kim, K., Odegaard, J. I., Kim, K., Liu, X. Q., Sher, X., Jung, H., Lee, M.-J., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0101-z · Cited by 1,665 · cited 3× · see citing works
“Several studies have indicated that EBVþ gastric cancer does not exhibit higher TMB or MSI, but can respond to immune-checkpoint therapy (15, 63), suggesting that the TMEscore may be more useful for predicting clinical benefits in patients with gastric cancer treated with immunotherapy than TMB or MSI.”
BackgroundMethod
MYC regulates the antitumor immune response through CD47 and PD-L1
Casey, S. C., Tong, L., Li, Y., Do, R., Walz, S., Fitzgerald, K. N., Gouw, A. M., Baylot, V., Gütgemann, I., Eilers, M., et al. (2016) · Science · doi:10.1126/science.aac9935 · Cited by 1,403 · see citing works
Programmed cell death 1 ligand 1 and tumor-infiltrating CD8 <sup>+</sup> T lymphocytes are prognostic factors of human ovarian cancer
Hamanishi, J., Mandai, M., Iwasaki, M., Okazaki, T., Tanaka, Y., Yamaguchi, K., Higuchi, T., Yagi, H., Takakura, K., Minato, N., et al. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0611533104 · Cited by 1,378 · see citing works
Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy
Davoli, T., Uno, H., Wooten, E. C., & Elledge, S. J. (2017) · Science · doi:10.1126/science.aaf8399 · Cited by 1,365 · see citing works
CD10+GPR77+ Cancer-Associated Fibroblasts Promote Cancer Formation and Chemoresistance by Sustaining Cancer Stemness
Su, S., Chen, J., Yao, H., Liu, J., Yu, S., Lao, L., Wang, M., Luo, M., Xing, Y., Chen, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.01.009 · Cited by 1,230 · see citing works
Self-citation
Epigenetic silencing of TH1-type chemokines shapes tumour immunity and immunotherapy
Peng, D., Kryczek, I., Nagarsheth, N., Zhao, L., Wei, S., Wang, W., Sun, Y., Zhao, E., Vatan, L., Szeliga, W., et al. (2015) · Nature · doi:10.1038/nature15520 · Cited by 1,201 · cited 1× · see citing works
“Gene cluster A was associated with high expression of TGF-β/EMT pathway-relevant mRNAs, whereas expression of Th1/cytotoxic T lymphocyte (CTL)-related mRNAs (49) (particularly CXCL10, CXCL9, IFNG, and GZMB) was relatively low (Fig.”
Background
Immunological hallmarks of stromal cells in the tumour microenvironment
Turley, S. J., Cremasco, V., & Astarita, J. L. (2015) · Nature Reviews Immunology · doi:10.1038/nri3902 · Cited by 1,140 · cited 1× · see citing works
“For example, differences in the compositions of resident cell types within the TME, including cytotoxic T cells, helper T cells, dendritic cells (DCs), tumor-associated macrophages, mesenchymal stem cells, and associated inflammatory pathways, have been reported in patients with cancer (5, 6, 9, 10).”
Background
Stromal gene expression defines poor-prognosis subtypes in colorectal cancer
Calon, A., Lonardo, E., Berenguer-Llergo, A., Espinet, E., Hernando-Momblona, X., Iglesias, M., Sevillano, M., Palomo-Ponce, S., Tauriello, D. V. F., Byrom, D., et al. (2015) · Nature Genetics · doi:10.1038/ng.3225 · Cited by 1,070 · cited 1× · see citing works
“mechanisms associated with the role of TME in immunotherapy response and resistance have been experimentally identified for some tumor types (4,13,14,20,21), to date, the comprehensive landscape of cells infiltrating the TME has not yet been elucidated.”
Background
Monitoring immune-checkpoint blockade: response evaluation and biomarker development
Nishino, M., Ramaiya, N. H., Hatabu, H., & Hodi, F. S. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.88 · Cited by 1,023 · cited 2× · see citing works
“The establishment of predictive biomarkers for checkpoint immunotherapy is, therefore, of importance in maximizing the therapeutic benefit (12, 15, 43).”
Background
Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures
Şenbabaoğlu, Y., Gejman, R. S., Winer, A. G., Liu, M., Allen, E. M. V., de Velasco, G., Miao, D., Ostrovnaya, I., Drill, E. N., Luna, A., et al. (2016) · Genome biology · doi:10.1186/s13059-016-1092-z · Cited by 949 · see citing works
Self-citation
A Molecular Taxonomy for Urothelial Carcinoma
Sjödahl, G., Lauss, M., Lövgren, K., Chebil, G., Guðjónsson, S., Veerla, S., Patschan, O., Aine, M., Fernö, M., Ringnér, M., et al. (2012) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-12-0077-t · Cited by 948 · cited 1× · see citing works
“13); (vi) DNA replication–dependent histones (13); (vii) selectmembers of theDDR-relevant gene set (39); (viii) Angiogenesis signature previously reported (40); (ix) cellcycle genes (KEGG); (x) WNT targets (41); (xi) cell-cycle regulators (42); (xii)mismatch repair (KEGG); (xiii) nucleotide excision repair (KEGG); (xi…”
Background
CMTM6 maintains the expression of PD-L1 and regulates anti-tumour immunity
Burr, M. L., Sparbier, C. E., Chan, Y.-C., Williamson, J. C., Woods, K., Beavis, P. A., Lam, E. Y. N., Henderson, M. A., Bell, C. C., Stolzenburg, S., et al. (2017) · Nature · doi:10.1038/nature23643 · Cited by 946 · cited 1× · see citing works
“Preclinical (64) and clinical (65) reports have described associations between individual altered genes and response or resistance to immune-checkpoint blockade.”
Background
Intrinsic subtypes of high-grade bladder cancer reflect the hallmarks of breast cancer biology
Damrauer, J. S., Hoadley, K. A., Chism, D. D., Fan, C., Tiganelli, C. J., Wobker, S. E., Yeh, J. J., Milowsky, M. I., Iyer, G., Parker, J. S., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1318376111 · Cited by 879 · cited 1× · see citing works
“To explore the correlation between the TME signature and other relevant biological processes, we used gene sets curated by Mariathasan and colleagues (13), including (i) CD8 T-effector signature (11); (ii) antigen processingmachinery (37); (iii) immune-checkpoint; (iv) epithelial–mesenchymal transition (EMT) markers p…”
Method
Integrated molecular analysis of tumor biopsies on sequential CTLA-4 and PD-1 blockade reveals markers of response and resistance
Roh, W., Chen, P.-L., Reuben, A., Spencer, C. N., Prieto, P. A., Miller, J. P., Gopalakrishnan, V., Wang, F., Cooper, Z. A., Reddy, S. M., et al. (2017) · Science Translational Medicine · doi:10.1126/scitranslmed.aah3560 · Cited by 859 · cited 1× · see citing works
“However, these responses occur only in a minority of patients, and several studies have found that PD-1 expression, PD-L1 expression, MSI status, and mutation load are not efficient biomarkers for predicting the benefits of immune-checkpoint blockade (15, 69, 70).”
BackgroundSelf-citation
Mutual regulation of tumour vessel normalization and immunostimulatory reprogramming
Tian, L., Goldstein, A., Wang, H., Ching Lo, H., Sun Kim, I., Welte, T., Sheng, K., Dobrolecki, L. E., Zhang, X., Putluri, N., et al. (2017) · Nature · doi:10.1038/nature21724 · Cited by 841 · cited 2× · see citing works
“These data offer mechanistic insights into the responses to immune-checkpoint blockade, suggesting that response to PD-L1 and PD-1 blockade is not only related to enhanced cytolytic activity, antigen processing, and IFNg pathway components (13, 75), but is also associated with inhibition of fibroblast activation, angi…”
Background
DNA polymerases and cancer
Lange, S. S., Takata, K.-I., & Wood, R. D. (2011) · Nature Reviews Cancer · doi:10.1038/nrc2998 · Cited by 615 · cited 1× · see citing works
“13); (vi) DNA replication–dependent histones (13); (vii) selectmembers of theDDR-relevant gene set (39); (viii) Angiogenesis signature previously reported (40); (ix) cellcycle genes (KEGG); (x) WNT targets (41); (xi) cell-cycle regulators (42); (xii)mismatch repair (KEGG); (xiii) nucleotide excision repair (KEGG); (xi…”
Background
Enhancing CD8+ T Cell Fatty Acid Catabolism within a Metabolically Challenging Tumor Microenvironment Increases the Efficacy of Melanoma Immunotherapy
Zhang, Y., Kurupati, R. K., Liu, L., Zhou, X., Zhang, G., Hudaihed, A., Filisio, F., Giles‐Davis, W., Xu, X., Karakousis, G. C., et al. (2017) · Cancer Cell · doi:10.1016/j.ccell.2017.08.004 · Cited by 614 · cited 1× · see citing works
“that are considered T-cell suppressive and exclusion (50-52) in TME gene cluster A (Fig.”
Background
Resting dendritic cells induce peripheral CD8+ T cell tolerance through PD-1 and CTLA-4
Probst, H. C., McCoy, K. D., Okazaki, T., Honjo, T., & van den Broek, M. F. (2005) · Nature Immunology · doi:10.1038/ni1165 · Cited by 554 · cited 2× · see citing works
“S2A–D): TMEcluster-A was characterized by increases in the infiltration of cancer-associated fibroblasts, M2 macrophages, resting DCs, and resting MCs (43-46) and exhibited variable decreases in other TME cell types; TMEcluster-B exhibited high infiltration of M0 macrophages,”
Background
Enhanced preclinical antitumor activity of M7824, a bifunctional fusion protein simultaneously targeting PD-L1 and TGF-β
Lan, Y., Zhang, D., Xu, C., Hance, K. W., Marelli, B., Qi, J., Yu, H., Qin, G., Sircar, A., Hernández, V. M., et al. (2018) · Science Translational Medicine · doi:10.1126/scitranslmed.aan5488 · Cited by 494 · see citing works
Loss of PTEN Is Associated with Resistance to Anti-PD-1 Checkpoint Blockade Therapy in Metastatic Uterine Leiomyosarcoma
George, S., Miao, D., Demetri, G. D., Adeegbe, D. O., Rodig, S. J., Shukla, S. A., Lipschitz, M., Amin‐Mansour, A., Raut, C. P., Carter, S. L., et al. (2017) · Immunity · doi:10.1016/j.immuni.2017.02.001 · Cited by 476 · cited 1× · see citing works
“Pre-clinical (54) and clinical (55) reports have described associations between individual altered genes and response or resistance to immune checkpoint blockade.”
Background
ImmunoScore Signature: A Prognostic and Predictive Tool in Gastric Cancer
Jiang, Y., Zhang, Q., Hu, Y., Li, T., Yu, J., Zhao, L., Ye, G., Deng, H., Mou, T., Cai, S., et al. (2018) · Annals of Surgery · doi:10.1097/sla.0000000000002116 · Cited by 458 · cited 2× · see citing works
“An increasing body of literature suggests a crucial role for the TME in cancer progression and therapeutic responses (7, 8).”
Background
Predictive Gene Signature in MAGE-A3 Antigen-Specific Cancer Immunotherapy
Ulloa-Montoya, F., Louahed, J., Dizier, B., Gruselle, O., Spiessens, B., Lehmann, F. F., Suciu, S., Kruit, W. H. J., Eggermont, A. M. M., Vansteenkiste, J., et al. (2013) · Journal of Clinical Oncology · doi:10.1200/jco.2012.44.3762 · Cited by 436 · cited 2× · see citing works
“Genomic and clinical data sets with immune-checkpoint blockade Five genomic and transcriptomic data sets from patients with metastatic urothelial cancer (13) treatedwith ananti–PD-L1 agent (atezolizumab), patients with metastatic melanoma (43) treated with anti–PD-1 (pembrolizumab), patients with advanced melanoma tre…”
Method
Clinical and genomic landscape of gastric cancer with a mesenchymal phenotype
Oh, S. C., Sohn, B. H., Cheong, J., Kim, S.-B., Lee, J. E., Park, K. C., Lee, S. H., Park, Y.-Y., Park, Y.-Y., Lee, H., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04179-8 · Cited by 428 · cited 1× · see citing works
“Genomic analysis has been the primary methodology used in international efforts to discover novel biological targets in gastric cancer (1, 2), although this method has not led to the successful discovery of distinct mechanisms.”
Method
Biostatistics series module 3: Comparing groups: Numerical variables
Hazra, A., & Gogtay, N. (2016) · Indian Journal of Dermatology · doi:10.4103/0019-5154.182416 · Cited by 354 · cited 1× · see citing works
“For comparisons of more than two groups, Kruskal–Wallis tests and one-way analysis of variance were used as nonparametric and parametric methods, respectively (48).”
Method
Control of the Immune Response by Pro-Angiogenic Factors
Voron, T., Marcheteau, E., Pernot, S., Colussi, O., Tartour, É., Taı̈eb, J., & Terme, M. (2014) · Frontiers in Oncology · doi:10.3389/fonc.2014.00070 · Cited by 325 · cited 2× · see citing works
“These data offer mechanistic insights into the responses to immune-checkpoint blockade, suggesting that response to PD-L1 and PD-1 blockade is not only related to enhanced cytolytic activity, antigen processing, and IFNg pathway components (13, 75), but is also associated with inhibition of fibroblast activation, angi…”
Background
Bifunctional immune checkpoint-targeted antibody-ligand traps that simultaneously disable TGFβ enhance the efficacy of cancer immunotherapy
Ravi, R., Noonan, K. A., Pham, V., Bedi, R., Zhavoronkov, A., Ozerov, I. V., Makarev, E., V. Artemov, A., Wysocki, P. T., Mehra, R., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02696-6 · Cited by 325 · see citing works
Mast cells, angiogenesis, and tumour growth
Ribatti, D., & Enrico Crivellato, B. (2010) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2010.11.010 · Cited by 232 · cited 2× · see citing works
“S2A–D): TMEcluster-A was characterized by increases in the infiltration of cancer-associated fibroblasts, M2 macrophages, resting DCs, and resting MCs (43-46) and exhibited variable decreases in other TME cell types; TMEcluster-B exhibited high infiltration of M0 macrophages,”
Background
Immune Activation and Benefit From Avelumab in EBV-Positive Gastric Cancer
Panda, A., Mehnert, J. M., Hirshfield, K. M., Riedlinger, G., Damare, S., Saunders, T., Kane, M., Sokol, L. O., Stein, M. N., Poplin, E. A., et al. (2017) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djx213 · Cited by 208 · cited 6× · see citing works
“Several studies have indicated that EBVþ gastric cancer does not exhibit higher TMB or MSI, but can respond to immune-checkpoint therapy (15, 63), suggesting that the TMEscore may be more useful for predicting clinical benefits in patients with gastric cancer treated with immunotherapy than TMB or MSI.”
BackgroundMethodInfluential
Gene expression profiles for a prognostic immunoscore in gastric cancer
Zeng, D., Zhou, R., Yu, Y., Luo, Y., Zhang, J., Sun, H., Bin, J., Liao, Y., Rao, J., Zhang, Y., et al. (2018) · British journal of surgery · doi:10.1002/bjs.10871 · Cited by 198 · cited 3× · see citing works
“S2E; Supplementary Table S6), which we established based on a lasso immune signature score model in a previous study (7) to predict survival outcomes in patients with gastric cancer.”
BackgroundMethodSelf-citation
Identification and Validation of Stromal Immunotype Predict Survival and Benefit from Adjuvant Chemotherapy in Patients with Muscle-Invasive Bladder Cancer
Fu, H., Zhu, Y., Wang, Y., Liu, Z., Zhang, J., Xie, H., Fu, Q., Dai, B., Ye, D., & Xu, J. (2018) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-2687 · Cited by 171 · cited 1× · see citing works
“Although several studies using these methodologies have explored the clinical utility of TME infiltrates (7, 19), and although several mechanisms associated with the role of TME in immunotherapy response and resistance have been experimentally identified for some tumor types (4, 13, 14, 20, 21), to date, the comprehen…”
BackgroundSelf-citation
Immune Response and the Tumor Microenvironment: How They Communicate to Regulate Gastric Cancer
Lee, K., Hwang, H. K., & Nam, K. T. (2014) · Gut and Liver · doi:10.5009/gnl.2014.8.2.131 · Cited by 162 · cited 1× · see citing works
“Although several studies using these methodologies have explored the clinical utility of TME infiltrates (7, 19), and although several mechanisms associated with the role of TME in immunotherapy response and resistance have been experimentally identified for some tumor types (4, 13, 14, 20, 21), to date, the comprehen…”
Background
Genomic landscape associated with potential response to anti-CTLA-4 treatment in cancers
Ock, C.-Y., Hwang, J., Keam, B., Kim, S.-B., Shim, J., Jang, H.-J., Park, S., Sohn, B. H., Cha, M., Ajani, J. A., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-01018-0 · Cited by 135 · cited 1× · see citing works
“Consistent with a previous study about an immune signature score (74), we observed a significantly higher TMEscores in responders than in nonresponders undergoing checkpoint blockade therapy.”
Result
Integrating Tumor and Stromal Gene Expression Signatures With Clinical Indices for Survival Stratification of Early-Stage Non–Small Cell Lung Cancer
Gentles, A. J., Bratman, S. V., Lee, L. J., Harris, J. P., Feng, W., Nair, R. V., Shultz, D. B., Nair, V. S., Hoang, C. D., West, R. B., et al. (2015) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djv211 · Cited by 77 · cited 1× · see citing works
“Although several studies using these methodologies have explored the clinical utility of TME infiltrates (7, 19), and although several mechanisms associated with the role of TME in immunotherapy response and resistance have been experimentally identified for some tumor types (4, 13, 14, 20, 21), to date, the comprehen…”
Background
Network analysis of immunotherapy-induced regressing tumours identifies novel synergistic drug combinations
Lesterhuis, W. J., Rinaldi, C., Jones, A. C., Rozali, E. N., Dick, I. M., Khong, A., Boon, L., Robinson, B. W. S., Nowak, A. K., Bosco, A., et al. (2015) · Scientific Reports · doi:10.1038/srep12298 · Cited by 68 · see citing works
Cognate Memory CD4+ T Cells Generated with Dendritic Cell Priming Influence the Expansion, Trafficking, and Differentiation of Secondary CD8+ T Cells and Enhance Tumor Control
Hwang, M. L., Lukens, J. R., & Bullock, T. N. J. (2007) · The Journal of Immunology · doi:10.4049/jimmunol.179.9.5829 · Cited by 40 · see citing works
maxstat: Maximally Selected Rank Statistics
Hothorn, T. (2001) · doi:10.32614/cran.package.maxstat · Cited by 24 · see citing works

Flagged first, then by citations

Sorted by citations

IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures
Zeng, D., Ye, Z., Shen, R., Yu, G., Wu, J., Xiong, Y., Zhou, R., Qiu, W., Huang, N., Sun, L., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.687975 · Cited by 1,263 · see citing works
Self-citation
m6A regulator-mediated methylation modification patterns and tumor microenvironment infiltration characterization in gastric cancer
Zhang, B., Wu, Q., Li, B., Wang, D., Wang, L., & Zhou, Y. L. (2020) · Molecular Cancer · doi:10.1186/s12943-020-01170-0 · Cited by 944 · see citing works
Single-Cell Genomic Characterization Reveals the Cellular Reprogramming of the Gastric Tumor Microenvironment
Sathe, A., Grimes, S. M., Lau, B. T., Chen, J., Suarez, C. J., Huang, R. J., Poultsides, G. A., & Ji, H. P. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-3231 · Cited by 381 · see citing works
“This resembles findings from recent studies in gastric cancer where molecular subtypes could be differentiated by gene signatures based on a combination of immunosuppression, immune activation, and stromal activation processes rather than the leucocyte fractions within the TME (48, 49).”
Result
The role of tumor-associated macrophages in gastric cancer development and their potential as a therapeutic target
Gambardella, V., Castillo, J., Tarazona, N., Gimeno-Valiente, F., Martínez‐Ciarpaglini, C., Cabeza-Segura, M., Roselló, S., Roda, D., Huerta, M., Cervantes, A. M. R., et al. (2020) · Cancer Treatment Reviews · doi:10.1016/j.ctrv.2020.102015 · Cited by 335 · see citing works
A risk model developed based on tumor microenvironment predicts overall survival and associates with tumor immunity of patients with lung adenocarcinoma
Wu, J., Li, L., Zhang, H., Zhao, Y., Zhang, H., Wu, S., & Xu, B. (2021) · Oncogene · doi:10.1038/s41388-021-01853-y · Cited by 255 · see citing works
Tumor microenvironment evaluation promotes precise checkpoint immunotherapy of advanced gastric cancer
Zeng, D., Wu, J., Luo, H., Li, Y., Xiao, J., Peng, J., Ye, Z., Zhou, R., Yu, Y., Wang, G., et al. (2021) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2021-002467 · Cited by 237 · see citing works
“Sufficiently, the aforementioned data confirmed that the TMEscore might perform better in selecting candidate patients with GC that can benefit from ICB immunotherapy.”
BackgroundSelf-citation
m6A regulator-based methylation modification patterns characterized by distinct tumor microenvironment immune profiles in colon cancer
Chong, W., Shang, L., Liu, J., Fang, Z., Du, F., Wu, H., Yang, L., Wang, Z., Chen, Y., Jia, S., et al. (2020) · Theranostics · doi:10.7150/thno.52717 · Cited by 229 · see citing works
“We then adopted a formula similar to previous studies to define the m6Sig score [55, 56]: m6Sig score= ∑(PC1i+PC2i), where is the expression of final determined m6A phenotype-related genes.”
Evolution of immune and stromal cell states and ecotypes during gastric adenocarcinoma progression
Wang, R., Song, S., Qin, J., Yoshimura, K., Peng, F., Chu, Y., Li, Y., Fan, Y., Jin, J., Dang, M., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.06.005 · Cited by 193 · see citing works
Necroptosis-Related lncRNAs: Predicting Prognosis and the Distinction between the Cold and Hot Tumors in Gastric Cancer
Zhao, Z., Liu, H., Zhou, X., Fang, D., Ou, X., Ye, J., Peng, J., & Xu, J. (2021) · Journal of Oncology · doi:10.1155/2021/6718443 · Cited by 189 · see citing works
“It might result in different immunotherapeutic responses [22, 25].”
Background
Breakthrough of solid tumor treatment: CAR-NK immunotherapy
Wang, W., Liu, Y., He, Z., Li, L., Liu, S., Jiang, M., Zhao, B., Deng, M., Wang, W., Ge, X., et al. (2024) · Cell Death Discovery · doi:10.1038/s41420-024-01815-9 · Cited by 182 · see citing works
Tumor-Associated Macrophages and Inflammatory Microenvironment in Gastric Cancer: Novel Translational Implications
Rihawi, K., Ricci, A. D., Rizzo, A., Brocchi, S., Marasco, G., Pastore, L. V., Llimpe, F. L. R., Golfieri, R., & Renzulli, M. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22083805 · Cited by 165 · see citing works
“Over the last decade, several reports have highlighted that the GC microenvironment could be associated with the infiltration of tumor lymphocytes [5,59].”
Background
Macrophage correlates with immunophenotype and predicts anti-PD-L1 response of urothelial cancer
Zeng, D., Ye, Z., Wu, J., Zhou, R., Fan, X., Wang, G., Huang, Y., Wu, J., Sun, H., Wang, M., et al. (2020) · Theranostics · doi:10.7150/thno.46176 · Cited by 157 · see citing works
“Other prevalent gene signature scores with respect to tumor microenvironment, tumor intrinsic pathway, and metabolism were calculated for each sample using the PCA algorithm [9, 39] (see the detailed procedure in the Supplementary Methods).”
BackgroundMethodInfluentialSelf-citation
Single-cell analysis of gastric pre-cancerous and cancer lesions reveals cell lineage diversity and intratumoral heterogeneity
Kim, J. H., Park, C., Kim, K. H., Kim, E. H., Kim, H., Woo, J. K., Seong, J. K., Nam, K. T., Lee, Y. C., & Cho, S. Y. (2022) · npj Precision Oncology · doi:10.1038/s41698-022-00251-1 · Cited by 157 · see citing works
Low-Dose Metformin Reprograms the Tumor Immune Microenvironment in Human Esophageal Cancer: Results of a Phase II Clinical Trial
Wang, S., Lin, Y., Xiong, X., Wang, L., Guo, Y., Chen, Y., Chen, S., Wang, G., Lin, P., Chen, H., et al. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-20-0113 · Cited by 151 · see citing works
“The strong infiltration of the lesion with CD8þ effector T cells (1, 9, 21, 26), tumor-suppressive macrophages (CD68þ and CD11cþ; refs.”
BackgroundMethodInfluential
Stromal-Immune Score Based Gene Signature: A Prognosis Stratification Tool in Gastric Cancer
Wang, H., Wu, X., & Chen, Y. (2019) · Frontiers in Oncology · doi:10.2139/ssrn.3409287 · Cited by 144 · see citing works
Adoptive CD8+ T cell therapy against cancer:Challenges and opportunities
Jiang, X., Jiang, X., Liu, M., Xing, H., Wang, Z., Huang, L., Mellor, A. L., Wang, W., & Wu, S. (2019) · Cancer Letters · doi:10.1016/j.canlet.2019.07.017 · Cited by 129 · see citing works
Microenvironment characterization and multi-omics signatures related to prognosis and immunotherapy response of hepatocellular carcinoma
Liu, F., Qin, L., Liao, Z., Song, J., Yuan, C., Liu, Y., Wang, Y., Xu, H., Zhang, Q., Pei, Y., et al. (2020) · Experimental Hematology and Oncology · doi:10.1186/s40164-020-00165-3 · Cited by 126 · see citing works
DKK1 Promotes Tumor Immune Evasion and Impedes Anti–PD-1 Treatment by Inducing Immunosuppressive Macrophages in Gastric Cancer
Shi, T., Zhang, Y., Wang, Y., Song, X., Wang, H., Zhou, X., Liang, K., Luo, Y., Che, K., Wang, X., et al. (2022) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-22-0218 · Cited by 120 · see citing works
Immune Cell Confrontation in the Papillary Thyroid Carcinoma Microenvironment
Xie, Z., Li, X., He, Y., Wu, S., Wang, S., Sun, J., He, Y., Lun, Y., & Zhang, J. (2020) · Frontiers in Endocrinology · doi:10.3389/fendo.2020.570604 · Cited by 115 · see citing works
“The results were visualized in the form of an immune network [20].”
BackgroundSelf-citation
Multiplex immunohistochemistry defines the tumor immune microenvironment and immunotherapeutic outcome in CLDN18.2-positive gastric cancer
Jia, K., Chen, Y., Sun, Y. L., Hu, Y., Jiao, L., Ma, J., Yuan, J., Qi, C., Li, Y., Gong, J., et al. (2022) · BMC Medicine · doi:10.1186/s12916-022-02421-1 · Cited by 116 · see citing works
“Tumor-infiltrating immune cells are heterogeneous, exhibit functional and phenotypic plasticity, and may exert pro-tumorigenic and anti-tumorigenic effects [20].”
Background
Impacts and mechanisms of metabolic reprogramming of tumor microenvironment for immunotherapy in gastric cancer
Zhao, L., Liu, Y., Zhang, S., Wei, L., Cheng, H., Wang, J., & Wang, J. (2022) · Cell Death and Disease · doi:10.1038/s41419-022-04821-w · Cited by 112 · see citing works
“A growing body of evidence illustrates the clinicopathological significance of changes in TME in predicting prognosis and therapeutic efficacy [17].”
Background
5mC regulator-mediated molecular subtypes depict the hallmarks of the tumor microenvironment and guide precision medicine in bladder cancer
Hu, J., Othmane, B., Yu, A., Li, H., Cai, Z., Chen, X., Ren, W., Chen, J., & Zu, X. (2021) · BMC Medicine · doi:10.1186/s12916-021-02163-6 · Cited by 107 · see citing works
Tumor Immune Microenvironment and Chemosensitivity Signature for Predicting Response to Chemotherapy in Gastric Cancer
Jiang, Y., Xie, J., Huang, W., Chen, H., Xi, S., Han, Z., Huang, L., Tian, L., Zhao, L., Hu, Y., et al. (2019) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-19-0311 · Cited by 105 · see citing works
Pan-Cancer Analysis Shows That ALKBH5 Is a Potential Prognostic and Immunotherapeutic Biomarker for Multiple Cancer Types Including Gliomas
Wei, C., Wang, B., Peng, D., Zhang, X., Li, Z., Luo, L., He, Y., Liang, H., Du, X., Li, S., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.849592 · Cited by 99 · see citing works
“org 19 associated with regulation of tumor immunosurveillance and therapeutic response of tumors (71, 72).”
BackgroundSelf-citation
A review of digital cytometry methods: estimating the relative abundance of cell types in a bulk of cells
Le, T., Aronow, R. A., Kirshtein, A., & Shahriyari, L. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa219 · Cited by 99 · see citing works
Enhancing immuno-oncology investigations through multidimensional decoding of tumor microenvironment with IOBR 2.0
Zeng, D., Fang, Y., Qiu, W., Luo, P., Wang, S., Shen, R., Gu, W., Huang, X., Mao, Q., Wang, G., et al. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100910 · Cited by 99 · see citing works
“We identiﬁed a series of mutations associated with the TMEscore, including TP53 , FGFR3 , ERBB3 , and PIK3CA .”
Self-citation
Systematic Investigation of Tumor Microenvironment and Antitumor Immunity With IOBR
Fang, Y., Kong, Y., Rong, G., Luo, Q., Liao, W., & Zeng, D. (2025) · Med Research · doi:10.1002/mdr2.70001 · Cited by 97 · see citing works
Self-citation
Nicotinamide metabolism face-off between macrophages and fibroblasts manipulates the microenvironment in gastric cancer
Jiang, Y., Wang, Y., Chen, G., Sun, F., Wu, Q., Huang, Q., Zeng, D., Qiu, W., Wang, J., Yao, Z., et al. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2024.05.013 · Cited by 96 · see citing works
Self-citation
Immune cell infiltration as a biomarker for the diagnosis and prognosis of digestive system cancer
Yang, S., Liu, T., Cheng, Y., Bai, Y., & Liang, G. (2019) · Cancer Science · doi:10.1111/cas.14216 · Cited by 93 · see citing works
Identification of a Tumor Microenvironment-relevant Gene set-based Prognostic Signature and Related Therapy Targets in Gastric Cancer
Cai, W., Dong, Z.-N., Fu, X.-T., Lin, L., Wang, L., Ye, G.-D., Luo, Q., & Chen, Y. (2020) · Theranostics · doi:10.7150/thno.47938 · Cited by 89 · see citing works
“defined three TME phenotypes and developed a quantifiable TMEscore as a prognostic biomarker to predict immunotherapeutic benefits [25].”
Background
A tumor microenvironment-based prognostic index for osteosarcoma
Wu, C., Gong, S., Duan, Y., Deng, C., Kallendrusch, S., Berninghausen, L. K., Osterhoff, G., & Schopow, N. (2023) · Journal of Biomedical Science · doi:10.1186/s12929-023-00917-3 · Cited by 89 · see citing works
Spatial dissection of tumour microenvironments in gastric cancers reveals the immunosuppressive crosstalk between CCL2+ fibroblasts and STAT3 -activated macrophages
Lee, S. H., Lee, D., Choi, J., Oh, H. J., Ham, I., Ryu, D., Lee, S.-Y., Han, D.-J., Kim, S., Moon, Y., et al. (2024) · Gut · doi:10.1136/gutjnl-2024-332901 · Cited by 94 · see citing works
Spatial Metabolomics Identifies Distinct Tumor-Specific Subtypes in Gastric Cancer Patients
Wang, J., Kunzke, T., Prade, V. M., Shen, J., Buck, A., Feuchtinger, A., Haffner, I., Luber, B., Liu, D. H. W., Langer, R., et al. (2022) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-21-4383 · Cited by 86 · see citing works
Natural killer cell and stroma abundance are independently prognostic and predict gastric cancer chemotherapy benefit
Li, B., Jiang, Y., Li, G., Fisher, G. A., & Li, R. (2020) · JCI Insight · doi:10.1172/jci.insight.136570 · Cited by 85 · see citing works
“Clinicopathologic and treatment information for patients in the GEP and IHC 602 cohorts 603 GEP cohorts IHC cohorts ACRG GSE15459 GSE84437 TCGA STAD SMU1 SMU2 SYSU Number of patients 300 192 433 415 247 234 272 Median age (range) 64 (24-86) 67 (23-92) 62 (27-86) 67 (30-90) 57 (23-90) 55 (21-77) 58 (24-82) Male (%) 199…”
Result
Comprehensive bioinformatic analysis reveals a cancer-associated fibroblast gene signature as a poor prognostic factor and potential therapeutic target in gastric cancer
Metin, C. U., & Özcan, G. (2022) · BMC Cancer · doi:10.1186/s12885-022-09736-5 · Cited by 85 · see citing works
“CAFs were identified as the greatest risk factor in tumor microenvironment phenotype with the poorest overall survival in gastric cancer patients [10].”
Background
Identification of an immunotherapy-responsive molecular subtype of bladder cancer
Song, B.-N., Kim, S., Mun, J.-Y., Choi, Y. D., Leem, S., & Chu, I. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.10.058 · Cited by 84 · see citing works
“Numerous investigations have been conducted on predictive biomarkers [9,10], but the underlying mechanisms governing the response to ICIs, which would be utilized to classify the most responsive patient subset, have yet to be fully elucidated.”
Background
Targeting glutamine metabolic reprogramming of SLC7A5 enhances the efficacy of anti-PD-1 in triple-negative breast cancer
Huang, R., Wang, H., Hong, J., Wu, J., Huang, O., He, J., Chen, W., Li, Y., Chen, X., Shen, K., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1251643 · Cited by 84 · see citing works
“We then de ﬁ ned the GMS utilizing a method similar to that used for establishing a GGI (18, 19): where i represents the expression of a GMG.”
Method
Cooperation between chemotherapy and immunotherapy in gastroesophageal cancers
Davern, M., & Lysaght, J. (2020) · Cancer Letters · doi:10.1016/j.canlet.2020.09.014 · Cited by 80 · see citing works
“remodelling (DCN, TIMP2, FOXF2, and MYH11), epithelial mesenchymal transition (EMT) (ACTA2, TGFB1L1, and SFRP1), cell adhesion and angiogenesis (PDGFRA, GREM1, and TMEM100), which are considered suppressive factors for T cells and may be responsible for significantly worsening prognosis in GC patients [71].”
Background
Using ESTIMATE algorithm to establish an 8-mRNA signature prognosis prediction system and identify immunocyte infiltration-related genes in Pancreatic adenocarcinoma
Meng, Z., Ren, D., Zhang, K., Zhao, J., Jin, X., & Wu, H. (2020) · Aging · doi:10.18632/aging.102931 · Cited by 79 · see citing works
The Mechanism of Stimulating and Mobilizing the Immune System Enhancing the Anti-Tumor Immunity
Wu, Z., Li, S., & Zhu, X. M. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.682435 · Cited by 81 · see citing works
“Through PCA algorithm, they scored the tumor microenvironment of gastric cancer patients, and found that the higher the tumor microenvironment score, the stronger the antitumor immune response-ability, the greater the potential of benefiting from immunotherapy, and the longer the survival time of gastric cancer patien…”
Method
Comprehensive of N1-Methyladenosine Modifications Patterns and Immunological Characteristics in Ovarian Cancer
Liu, J., Chen, C., Wang, Y., Qian, C., Wei, J., Xing, Y., & Bai, J. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.746647 · Cited by 77 · see citing works
“Where i is m1A phenotyperelated genes’ expression (42, 43).”
Background
Integrated multiomics analysis and machine learning refine molecular subtypes and prognosis for muscle-invasive urothelial cancer
Chu, G., Ji, X., Wang, Y., & Niu, H. (2023) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2023.06.001 · Cited by 77 · see citing works
Prognostic and Predictive Value of Tumor-infiltrating Leukocytes and of Immune Checkpoint Molecules PD1 and PDL1 in Clear Cell Renal Cell Carcinoma
Stenzel, P., Schindeldecker, M., Tagscherer, K. E., Foersch, S., Herpel, E., Hohenfellner, M., Hatiboglu, G., Alt, J., Thomas, C., Haferkamp, A., et al. (2019) · Translational Oncology · doi:10.1016/j.tranon.2019.11.002 · Cited by 76 · see citing works
“, lung or gastric cancer, to implement the TME in the tumor staging system [38,39].”
Background
The roles of TGF-β and VEGF pathways in the suppression of antitumor immunity in melanoma and other solid tumors
Bu, M. T., Chandrasekhar, P., Ding, L., & Hugo, W. (2022) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2022.108211 · Cited by 72 · see citing works
“Zeng et al. devised a combined score of the TME (termed “TMEscore”), which considers the immune-and stromal-activation scores, to predict the overall survival of gastric cancer patients.”
BackgroundResult
Early stage gastric adenocarcinoma: clinical and molecular landscapes
Hirata, Y., Noorani, A., Song, S., Wang, L., & Ajani, J. A. (2023) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-023-00767-w · Cited by 73 · see citing works
UPP1 promotes lung adenocarcinoma progression through the induction of an immunosuppressive microenvironment
Li, Y., Jiang, M., Aye, L., Luo, L., Zhang, Y., Xu, F., Wei, Y., Peng, D., He, X., Gu, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45340-w · Cited by 71 · see citing works
SERPINE1 associated with remodeling of the tumor microenvironment in colon cancer progression: a novel therapeutic target
Wang, S., Pang, L., Liu, Z., & Meng, X. (2021) · BMC Cancer · doi:10.1186/s12885-021-08536-7 · Cited by 65 · see citing works
CENPF/CDK1 signaling pathway enhances the progression of adrenocortical carcinoma by regulating the G2/M-phase cell cycle
Huang, Y., Li, D., Wang, L., Su, X., & Tang, X.-B. (2022) · Journal of Translational Medicine · doi:10.1186/s12967-022-03277-y · Cited by 66 · see citing works
“Sanguini diagram was drawn via the ‘ggalluval’ package [33] for displaying the distribution of the gene expression in survival status, ages, genders, stages, and other clinical characteristics for ACC.”
Method
Identification of gastric cancer subtypes based on pathway clustering
Li, L., & Wang, X. (2021) · npj Precision Oncology · doi:10.1038/s41698-021-00186-z · Cited by 62 · see citing works
“Zeng et al.(16) defined three GC subtypes based on immune cell infiltration patterns in the tumor microenvironment (TME).”
BackgroundMethodInfluential
Transcriptomic Profiling of MSI-H/dMMR Gastrointestinal Tumors to Identify Determinants of Responsiveness to Anti–PD-1 Therapy
Chida, K., Kawazoe, A., Suzuki, T., Kawazu, M., Ueno, T., Takenouchi, K., Nakamura, Y., Kuboki, Y., Kotani, D., Kojima, T., et al. (2022) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-22-0041 · Cited by 64 · see citing works
Novel Drug Delivery Systems as an Emerging Platform for Stomach Cancer Therapy
Hani, U., Osmani, R. A. M., Yasmin, S., Gowda, B. H. J., Ather, H., Ansari, M. Y., Siddiqua, A., Ghazwani, M., Fatease, A. M. A., Alamri, A. H., et al. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics14081576 · Cited by 66 · see citing works
First-Line Nivolumab and Relatlimab Plus Chemotherapy for Gastric or Gastroesophageal Junction Adenocarcinoma: The Phase II RELATIVITY-060 Study
Hegewisch‐Becker, S., Mendez, G. A., Chao, J., Němeček, R., Feeney, K., Cutsem, E. V., Al‐Batran, S., Mansoor, W., Maisey, N. R., Pazo-Cid, R., et al. (2024) · Journal of Clinical Oncology · doi:10.1200/jco.23.01636 · Cited by 65 · see citing works
A signature of 14 immune-related gene pairs predicts overall survival in gastric cancer
Zhao, E., Zhou, C., & Chen, S. (2020) · Clinical & Translational Oncology · doi:10.1007/s12094-020-02414-7 · Cited by 64 · see citing works
m 6 A modification patterns and tumor immune landscape in clear cell renal carcinoma
Zhong, J., Liu, Z., Cai, C., Duan, X., Deng, T., & Zeng, G. (2021) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2020-001646 · Cited by 63 · see citing works
HMGA1B/2 transcriptionally activated-POU1F1 facilitates gastric carcinoma metastasis via CXCL12/CXCR4 axis-mediated macrophage polarization
Tang, C. W., Lei, X., Xiong, L., Hu, Z., & Tang, B. (2021) · Cell Death and Disease · doi:10.1038/s41419-021-03703-x · Cited by 62 · see citing works
NOTCH3 is a Prognostic Factor and Is Correlated With Immune Tolerance in Gastric Cancer
Cui, Y., Li, Q., Li, W., Wang, Y., Lv, F., Shi, X., Tang, Z., Shen, Z., Hou, Y., Zhang, H., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2020.574937 · Cited by 62 · see citing works
“In contrast, TME cluster-C, which showed significant increases in the infiltration of CD8 T cells, M1 macrophages, and activated memory CD4 T cells, had a favorable outcome (10).”
Result
Immune cell infiltration signatures identified molecular subtypes and underlying mechanisms in gastric cancer
Lin, Y., Pan, X., Zhao, L., Yang, C., Zhang, Z., Wang, B., Gao, Z., Jiang, K., Ye, Y., Wang, S., et al. (2021) · npj Genomic Medicine · doi:10.1038/s41525-021-00249-x · Cited by 60 · see citing works
Pan‐cancer analyses reveal molecular and clinical characteristics of cuproptosis regulators
Wu, C., Tan, J., Wang, X., Qin, C., Long, W., Pan, Y., Li, Y., & Liu, Q. (2022) · iMeta · doi:10.1002/imt2.68 · Cited by 59 · see citing works
“Immunomodulators are crucial for the response to immunotherapy, and we collected some classical immune activation ‐ related genes, immune checkpoint ‐ related genes, and TGF ‐ β / EMT pathway ‐ related genes from a previous study by Zeng et al. [38].”
BackgroundMethod
Glucose transporter 3 (GLUT3) promotes lactylation modifications by regulating lactate dehydrogenase A (LDHA) in gastric cancer
Yang, H., Yang, S., He, J., Li, W., Zhang, A., Li, N., Zhou, G., & Sun, B. (2023) · Cancer Cell International · doi:10.1186/s12935-023-03162-8 · Cited by 60 · see citing works
“Analysis of the tumor microenvironment between the high and low GLUT3 expression groups using the ESTIMATE algorithm showed that the stromal score and immune score were significantly higher in the high GLUT3 expression group than in the low GLUT3 expression group [17, 18].”
Method
Histone deacetylase-mediated tumor microenvironment characteristics and synergistic immunotherapy in gastric cancer
Lin, Y., Jing, X., Chen, Z., Pan, X., Xu, D., Yu, X., Zhong, F., Zhao, L., Yang, C., Wang, B., et al. (2023) · Theranostics · doi:10.7150/thno.86928 · Cited by 60 · see citing works
“found that gastric cancer prognosis can be determined by the level of TME cell enrichment [8].”
Research progress on the immune microenvironment and immunotherapy in gastric cancer
Mou, P., Ge, Q., Sheng, R., Zhu, T.-F., Liu, Y., & Ding, K. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1291117 · Cited by 58 · see citing works
DNA methylation regulator-mediated modification patterns and tumor microenvironment characterization in gastric cancer
Qi, M., Lu, Y., Ruan, D., Yu, K., Chen, Y., Xiao, M., Wang, Y., Liu, Z., Xu, R., Ju, H., et al. (2021) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2021.03.023 · Cited by 56 · see citing works
“24 June 2021 published works.(26,38,44) All hallmark gene sets were downloaded from the Molecular Signature Database (MSigDB) to compare differences between DNA modification patterns.”
Method
Identification of HCC Subtypes With Different Prognosis and Metabolic Patterns Based on Mitophagy
Wang, Y., Wang, Z., Sun, J., & Qian, Y. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.799507 · Cited by 55 · see citing works
“, 2006), (Zeng et al., 2019) We calculated the mitophagyscore for each patient and divided them into the high-risk and low-risk groups based on the median in their respective cohorts.”
Method
Identification of ferroptosis-associated biomarkers for the potential diagnosis and treatment of postmenopausal osteoporosis
Hu, Y., Han, J. H., Ding, S., Liu, S., & Wang, H. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.986384 · Cited by 55 · see citing works
“Our FAG-Scoring scheme is calculated by adopting a formula similar to previous studies (15, 16): FAG-Score = ∑(PC1i+PC2i), where is the expression of phenotype-related genes determined from final ferroptosis.”
Method
Identification and Validation of Immune‐Related Gene Prognostic Signature for Hepatocellular Carcinoma
Chen, W., Ou, M., Tang, D., Dai, Y., & Du, W. (2020) · Journal of Immunology Research · doi:10.1155/2020/5494858 · Cited by 54 · see citing works
“Immune activation by B cell, T cell, natural cell, and dendritic cell contributes to the tumor suppression [27].”
Background
CLDN18.2 expression and its impact on prognosis and the immune microenvironment in gastric cancer
Wang, C., Wang, Y., Chen, J., Wang, Y., Pang, C., Chen, L., Li, Y., & Ma, Y. (2023) · BMC Gastroenterology · doi:10.1186/s12876-023-02924-y · Cited by 54 · see citing works
“Tumour-infiltrating immune cells exhibit diverse phenotypes and functions, which can have both protumorigenic and antitumorigenic effects [21].”
Background
Immune‐related signature predicts the prognosis and immunotherapy benefit in bladder cancer
Wang, Y., Chen, L., Yu, M., Fang, Y., Qian, K., Wang, G., Ju, L., Xiao, Y., & Wang, X. (2020) · Cancer Medicine · doi:10.1002/cam4.3400 · Cited by 53 · see citing works
tRNA-derived small RNAs: novel regulators of cancer hallmarks and targets of clinical application
Li, X., Liu, X., Zhao, D., Cui, W., Wu, Y., Zhang, C., & Duan, C. (2021) · Cell Death Discovery · doi:10.1038/s41420-021-00647-1 · Cited by 52 · see citing works
The Role of TAMs in Tumor Microenvironment and New Research Progress
Feng, Y.-W., Ye, Z.-Q., Song, F., He, Y., & Liu, J. (2022) · Stem Cells International · doi:10.1155/2022/5775696 · Cited by 50 · see citing works
“It represents most stromal cells in CAF TME, which can secrete collagen and cytokines, helping to form the structural framework of extracellular matrix [37, 38].”
Background
Retracted
Identification of molecular features correlating with tumor immunity in gastric cancer by multi-omics data analysis
He, Y., & Wang, X. (2020) · Annals of Translational Medicine · doi:10.21037/atm-20-922 · Cited by 50 · see citing works
“Despi te these pr ior explorat ions (20-22) , the identification of genetic and genomic features significantly correlating with GC immunity and immunotherapy response is worth further investigation.”
Development and validation of a novel ferroptosis-related gene signature for predicting prognosis and immune microenvironment in head and neck squamous cell carcinoma
He, F., Chen, Z., Deng, W., Zhan, T., Huang, X., Zheng, Y., & Yang, H. (2021) · International Immunopharmacology · doi:10.1016/j.intimp.2021.107789 · Cited by 51 · see citing works
Immunological Aspects of the Tumor Microenvironment and Epithelial-Mesenchymal Transition in Gastric Carcinogenesis
Baj, J., Brzozowska, K., Forma, A., Maani, A., Sitarz, E., & Portincasa, P. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21072544 · Cited by 50 · see citing works
Depiction of tumor stemlike features and underlying relationships with hazard immune infiltrations based on large prostate cancer cohorts
Zhang, C., Chen, T., Li, Z., Liu, A., Yang, X., Gao, Y., & Xu, D. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa211 · Cited by 51 · see citing works
Gastric Cancer Tumor Microenvironment Characterization Reveals Stromal-Related Gene Signatures Associated With Macrophage Infiltration
Wei, S., Lu, J., Lou, J., Shi, C., Mo, S., Shao, Y., Ni, J., Zhang, W., & Cheng, X. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00663 · Cited by 50 · see citing works
Cancer-associated fibroblasts: protagonists of the tumor microenvironment in gastric cancer
Ozmen, E., Demir, T. D., & Özcan, G. (2024) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2024.1340124 · Cited by 49 · see citing works
Identification of Immune-Related Gene Signatures in Lung Adenocarcinoma and Lung Squamous Cell Carcinoma
Li, N., Wang, J., & Zhan, X. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.752643 · Cited by 47 · see citing works
“Although various methods have been studied to predict the clinical responses and outcomes of lung cancers with immunotherapy based on immune-related gene profiling (33), the reliable and effective immune cells, immune-related genes, pathways, and immune-related gene signature during the progression process of LUAD and…”
Method
Clinical and pathological staging of gastric cancer: Current perspectives and implications
Zhu, Z., Gong, Y., & Xu, H. (2020) · European Journal of Surgical Oncology · doi:10.1016/j.ejso.2020.06.006 · Cited by 46 · see citing works
Mesenchymal stem cells induce PD‐L1 expression through the secretion of CCL5 in breast cancer cells
Es, H. A., Bigdeli, B., Zhand, S., Aref, A. R., Thiery, J. P., & Warkiani, M. E. (2020) · Journal of Cellular Physiology · doi:10.1002/jcp.30135 · Cited by 45 · see citing works
“The influence of tumor stromal cells in the regulation of hallmarks of cancer has been highlighted in various studies (De Boeck et al., 2013; Hanahan & Weinberg, 2011; Quail & Joyce, 2013; Zeng et al., 2019).”
Background
A molecular classification of gastric cancer associated with distinct clinical outcomes and validated by an XGBoost-based prediction model
Li, B., Zhang, F., Niu, Q., Liu, J. H., Yu, Y., Wang, P., Zhang, S., Zhang, H., & Wang, Z. (2022) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2022.12.014 · Cited by 44 · see citing works
“In addition, spatial metabolome- and immunome-driven classification methods have also been applied to identify GC.(18,19) Such studies may pave the way for the development of improved treatment strategies and personalized drugs for GC.”
BackgroundInfluential
An Inflammatory Checkpoint Generated by IL1RN Splicing Offers Therapeutic Opportunity for KRAS -Mutant Intrahepatic Cholangiocarcinoma
Zhang, M., Huang, Y., Pan, J., Sang, C., Lin, Y., Dong, L., Shen, X., Wu, Y. C., Song, G., Ji, S., et al. (2023) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-0282 · Cited by 45 · see citing works
Tumor mutation burden as a biomarker in resected gastric cancer via its association with immune infiltration and hypoxia
Wang, D., Wang, N., Li, X., Chen, X., Shen, B., Zhu, D., Zhu, L., Xu, Y., Yu, Y., & Shu, Y. (2021) · Gastric Cancer · doi:10.1007/s10120-021-01175-8 · Cited by 44 · see citing works
“GC cohorts in The Cancer Genome Atlas (TCGA) and the Asian Cancer Research Group (ACRG) were used as validation sets, and data were acquired and preprocessed as described elsewhere [12].”
Method
Comprehensive Characterization of Tumor Purity and Its Clinical Implications in Gastric Cancer
Lou, S., Zhang, J., Yin, X., Zhang, Y., Fang, T., Wang, Y., & Xue, Y. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.782529 · Cited by 43 · see citing works
Self-citation
Identification of crucial genes through WGCNA in the progression of gastric cancer
Liu, R., Liu, J., Cao, Q., Chu, Y., Chi, H., Zhang, J., Fu, J., Zhang, T., Fan, L., Liang, C., et al. (2024) · Journal of Cancer · doi:10.7150/jca.95757 · Cited by 42 · see citing works
Self-citation
Centromere Protein F (CENPF) Serves as a Potential Prognostic Biomarker and Target for Human Hepatocellular Carcinoma
Huang, Y., Chen, X., Wang, L., Wang, T., Tang, X.-B., & Su, X. (2021) · Journal of Cancer · doi:10.7150/jca.52187 · Cited by 42 · see citing works
“Sanguini diagram, which can be used to show the distribution trend of survival and expression of a gene in different stages, ages and other clinical characteristics for tumors, was built based on the 'ggalluval' package of R software [42].”
Method
The tumour immune microenvironment in oesophageal cancer
Davern, M., Donlon, N. E., Power, R. F., Hayes, C., King, R., Dunne, M. R., & Reynolds, J. V. (2021) · British Journal of Cancer · doi:10.1038/s41416-021-01331-y · Cited by 43 · see citing works
“, demonstrated that the TME in GC, could stratify responders and non-responders to PD-1 blockade.(121) Given the underlying genetic and molecular similarities of gastric adenocarcinomas and adenocarcinomas of the oesophagus,(19) this may be a promising approach to identify OC patients likely to benefit from immune che…”
BackgroundResultInfluential
Ferroptosis-Related Gene Signature Promotes Ovarian Cancer by Influencing Immune Infiltration and Invasion
Yang, Y. Q., Fan, Q., Huang, J., Wu, Y., Lin, H., & Zhang, Q. (2021) · Journal of Oncology · doi:10.1155/2021/9915312 · Cited by 42 · see citing works
Complete loss of miR-200 family induces EMT associated cellular senescence in gastric cancer
Yu, L., Cao, C., Li, X., Zhang, M., Gu, Q., Gao, H., Balic, J., Xu, D.-G., Zhang, L., Ying, L., et al. (2021) · Oncogene · doi:10.1038/s41388-021-02067-y · Cited by 37 · see citing works
Development of a machine learning-based radiomics signature for estimating breast cancer TME phenotypes and predicting anti-PD-1/PD-L1 immunotherapy response
Han, X., Guo, Y., Ye, H., Chen, Z., Hu, Q., Wei, X.-H., Liu, Z.-Y., & Liang, C.-H. (2024) · Breast Cancer Research · doi:10.1186/s13058-024-01776-y · Cited by 42 · see citing works
“As expected, future prospective direction might incorporate these three phenotypes, as well as possible additional immune phenotypes, as we gain a better understanding of breast cancer and its microenvironment immune function [12].”
Background
Multi-omics Analysis of Ferroptosis Regulation Patterns and Characterization of Tumor Microenvironment in Patients with Oral Squamous Cell Carcinoma
Gu, W., Kim, M., Wang, L., Yang, Z., Nakajima, T., & Tsushima, Y. (2021) · International Journal of Biological Sciences · doi:10.7150/ijbs.61441 · Cited by 41 · see citing works
“Further, this consensus clustering algorithm was used to identify the number of clusters, and the analysis included 1000 iterations to ensure the stability of the classification [24].”
Method
Advancements in Cancer Immunotherapies
Roy, R., Singh, S. K., & Misra, S. (2022) · Vaccines · doi:10.3390/vaccines11010059 · Cited by 41 · see citing works
“Several reports have elucidated their clinicopathologic significance in predicting outcomes and therapeutic efficacy [2,3].”
Background
Identification of a Novel Nomogram to Predict Progression Based on the Circadian Clock and Insights Into the Tumor Immune Microenvironment in Prostate Cancer
Feng, D., Xiong, Q., Zhang, F., Shi, X., Xu, H., Wei, W., Ai, J., & Yang, L. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.777724 · Cited by 40 · see citing works
A comprehensive analysis of PANoptosome to prognosis and immunotherapy response in pan-cancer
Zhuang, L., Sun, Q., Huang, S., Hu, L., & Chen, Q. (2023) · Scientific Reports · doi:10.1038/s41598-023-30934-z · Cited by 40 · see citing works
Molecular Subtyping Based on Cuproptosis-Related Genes and Characterization of Tumor Microenvironment Infiltration in Kidney Renal Clear Cell Carcinoma
Ji, Z., Ren, W., Wang, H., Gao, W. D., & Yuan, B. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.919083 · Cited by 39 · see citing works
“Eight genes (SIGLEC15, TIGIT, CD274, HAVCR2, PDCD1, CTLA4, LAG3 and PDCD1LG2) were used for immune checkpoint analysis (20).”
Method
The viral etiology of EBV-associated gastric cancers contributes to their unique pathology, clinical outcomes, treatment responses and immune landscape
Salnikov, M. Y., MacNeil, K. M., & Mymryk, J. S. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1358511 · Cited by 39 · see citing works
Exploring TCGA database for identification of potential prognostic genes in stomach adenocarcinoma
Zhou, L., Huang, W., Yu, H., Feng, Y., & Teng, X. (2020) · Cancer Cell International · doi:10.1186/s12935-020-01351-3 · Cited by 39 · see citing works
“An increasing number of evidences has elucidated the clinic pathological significance of TME in the prediction of treatment effects [9, 10].”
Background
Overexpression of PLXDC2 in Stromal Cell-Associated M2 Macrophages Is Related to EMT and the Progression of Gastric Cancer
Guan, Y., Du, Y., Wang, G., Gou, H., Xue, Y., Xu, J., Li, E., Chan, D. W., Wu, D., Xu, P., et al. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.673295 · Cited by 37 · see citing works
“The results proved that the TME of GC patients could help predict the response to immunotherapies and provide new strategies for treating GC (Zeng et al., 2019).”
Background
ITGB1 as a prognostic biomarker correlated with immune suppression in gastric cancer
Gu, W., Sun, H., Zhang, M., Mo, S., Tan, C., Ni, S., Yang, Z., Wang, Y., Sheng, W., & Wang, L. (2022) · Cancer Medicine · doi:10.1002/cam4.5042 · Cited by 39 · see citing works
Self-citation
Identification and Validation of Tumor Environment Phenotypes in Lung Adenocarcinoma by Integrative Genome-Scale Analysis
Bi, G., Chen, Z., Yang, X., Liang, J., Hu, Z., Bian, Y., Sui, Q., Li, R., Zhan, C., & Fan, H. X. (2019) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-020-02546-3 · Cited by 38 · see citing works
Characterization of the Immune Cell Infiltration Landscape in Esophageal Squamous Cell Carcinoma
Sui, Z., Wu, X., Du, L., Wang, H., Yuan, L., Zhang, J. V., & Yu, Z. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.879326 · Cited by 37 · see citing works
Self-citation
Integrative analysis of bulk and single-cell gene expression profiles to identify tumor-associated macrophage-derived CCL18 as a therapeutic target of esophageal squamous cell carcinoma
Sui, X., Chen, C., Zhou, X., Wen, X., Shi, C., Chen, G., Liu, J., He, Z., Yao, Y., Li, Y., et al. (2023) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-023-02612-5 · Cited by 37 · see citing works
“The TME, which reflects the immune response in a number of malignancies and plays a significant role in the genesis and progression of cancer [12, 38, 39].”
Background

The tumor microenvironment in gastrointestinal adenocarcinomas revealed a prognostic and immunotherapeutic biomarker
Zhang, Y., Li, L., Chu, F., Zhang, L., Zhang, L., Wu, H., & Li, K. (2022) · Aging · doi:10.18632/aging.204463 · Cited by 2 · see citing works
Immune cell infiltration signatures identified molecular subtypes and underlying mechanisms in gastric cancer
Lin, Y., Pan, X., Zhao, L., Yang, C., Zhang, Z., Wang, B., Gao, Z., Jiang, K., Ye, Y., Wang, S., et al. (2021) · npj Genomic Medicine · doi:10.1038/s41525-021-00249-x · Cited by 60 · see citing works
MCEMP1 as a Tumor Microenvironment and Immune Infiltration Related Gene for Prognostic Prediction in Advanced Gastric CancerPreprint
Wang, D., Gu, Y., Zhao, Y., & Li, Y. (2021) · Research Square · doi:10.21203/rs.3.rs-678280/v1
Identification of prognostic gene expression signatures based on the tumor microenvironment characterization of gastric cancer
Sang, Q., Dai, W., Yu, J., Chen, Y., Fan, Z., Liu, J., Li, F., Li, J., Wu, X., Hou, J., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.983632 · Cited by 2 · see citing works
Tumor Microenvironment Characterization for Assessment of Recurrence and Survival Outcome in Gastric Cancer to Predict Chemotherapy and Immunotherapy Response
Chen, Y., Sun, Z., Wan, L., Chen, H., Xi, T., & Jiang, Y. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.890922 · Cited by 24 · see citing works

Authors: Zeng, D., Li, M., Zhou, R., Zhang, J., Sun, H., Shi, M., Bin, J., Liao, Y., Rao, J., & Liao, W.
Affiliations: Nanfang Hospital (CN)
Southern Medical University (CN)
ORCID: Zeng, Dongqiang
Li, Meiyi
Zhou, Rui
Zhang, Jingwen
Sun, Huiying
Shi, Min
Bin, Jianping
Liao, Yulin
Liao, Wangjun
Venue: Cancer Immunology Research
Publisher: American Association for Cancer Research (AACR)
Published: 2019-05-01
Volume: 7
Issue: 5
Pages: 737-750
ISSN: 2326-6066 (ISSN-L)
2326-6074
DOI: 10.1158/2326-6066.cir-18-0436
OpenAlex: W2920600229
Semantic Scholar: 180648d1827a7e8c086f03b00ca69831a1ca538e
Cited by: 986
References: 76
Citations by year: 2026: 38 · 2025: 87 · 2024: 164 · 2023: 211 · 2022: 251 · 2021: 162 · 2020: 64 · 2019: 9
FWCI: 53.10
Type: journal article
Language: en
Open access: closed
Locations: Cancer Immunology Research (publishedVersion)
PubMed (publishedVersion)
Funders: National Natural Science Foundation of China (81772580)
Topics: Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Gastric Cancer Management and Outcomes
Life Sciences › Immunology and Microbiology › Immunology › Immune cells in cancer
Keywords: gastric cancer, tumor microenvironment, prognostic factors, immunotherapy response, immune cell infiltration, T cell suppression, epithelial-mesenchymal transition, angiogenesis, TGF-beta signaling, IFN-gamma response, gene expression signatures, IMvigor210
MeSH terms: Antineoplastic Agents, Immunological, Algorithms, Humans, Immunotherapy, Phenotype, Prognosis, Stomach Neoplasms, Gene Expression Regulation, Neoplastic, Tumor Microenvironment

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Tumor Microenvironment Characterization in Gastric Cancer Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Cites

Cited by

Related

Record

About these checks