The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
Tumor Microenvironment Characterization in Gastric Cancer Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures
Published version of a preprint
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“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]
Citations
Cites
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×
“The adjusted P value for multiple testing was calculated using the Benjamini–Hochberg correction (32).”
MethodInfluentialSubramanian, 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×
“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.”
Methodlimma 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
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×
“Next, the clusterProfiler R package (32) was adopted to annotate gene patterns.”
MethodAlgorithm AS 136: A K-Means Clustering Algorithm
Hartigan, J. A., & Wong, M. A. (1979) · Applied Statistics · doi:10.2307/2346830 · Cited by 14,523
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×
“TMB (nonsynonymous variants), which is significantly associated with efficacy of immunotherapy, was also evaluated with ROC analysis (50) in the IMvigor210 cohort (61).”
MethodRobust 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 13,224
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829
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 10,037 · cited 1×
“OncoPrint used to depict mutation landscape of TCGA-STAD cohort was constructed by ComplexHeatmap R package (52).”
MethodInferring 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×
“Moreover, the abundance of immune cells and other cells in the TME can be estimated using computational methods (16-18).”
Cited by
Sorted by citations
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 1,274
Self-citationZhang, B., Wu, Q., Li, B., Wang, D., Wang, L., & Zhou, Y. L. (2020) · Molecular Cancer · doi:10.1186/s12943-020-01170-0 · Cited by 940
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 395
“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).”
ResultGambardella, 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 337
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
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 236
“Sufficiently, the aforementioned data confirmed that the TMEscore might perform better in selecting candidate patients with GC that can benefit from ICB immunotherapy.”
BackgroundSelf-citationChong, 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 230
“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 198
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
“It might result in different immunotherapeutic responses [22, 25].”
BackgroundBreakthrough 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 197
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
“Over the last decade, several reports have highlighted that the GC microenvironment could be associated with the infiltration of tumor lymphocytes [5,59].”
BackgroundMacrophage 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
“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-citationKim, 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 165
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
“The strong infiltration of the lesion with CD8þ effector T cells (1, 9, 21, 26), tumor-suppressive macrophages (CD68þ and CD11cþ; refs.”
BackgroundMethodInfluentialStromal-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
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 133
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 130
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 121
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 113
“The results were visualized in the form of an immune network [20].”
BackgroundSelf-citationJia, 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 118
“Tumor-infiltrating immune cells are heterogeneous, exhibit functional and phenotypic plasticity, and may exert pro-tumorigenic and anti-tumorigenic effects [20].”
BackgroundZhao, 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 115
“A growing body of evidence illustrates the clinicopathological significance of changes in TME in predicting prognosis and therapeutic efficacy [17].”
BackgroundHu, 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 109
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
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 100
“org 19 associated with regulation of tumor immunosurveillance and therapeutic response of tumors (71, 72).”
BackgroundSelf-citationLe, T., Aronow, R. A., Kirshtein, A., & Shahriyari, L. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa219 · Cited by 98
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 111
“We identified a series of mutations associated with the TMEscore, including TP53 , FGFR3 , ERBB3 , and PIK3CA .”
Self-citationSystematic 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
Self-citationJiang, 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 101
Self-citationImmune 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
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
“defined three TME phenotypes and developed a quantifiable TMEscore as a prognostic biomarker to predict immunotherapeutic benefits [25].”
BackgroundA 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 94
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
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
Li, B., Jiang, Y., Li, G., Fisher, G. A., & Li, R. (2020) · JCI Insight · doi:10.1172/jci.insight.136570 · Cited by 89
“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…”
ResultMetin, C. U., & Özcan, G. (2022) · BMC Cancer · doi:10.1186/s12885-022-09736-5 · Cited by 83
“CAFs were identified as the greatest risk factor in tumor microenvironment phenotype with the poorest overall survival in gastric cancer patients [10].”
BackgroundIdentification 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
“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.”
BackgroundHuang, 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 85
“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.”
MethodCooperation between chemotherapy and immunotherapy in gastroesophageal cancers
Davern, M., & Lysaght, J. (2020) · Cancer Letters · doi:10.1016/j.canlet.2020.09.014 · Cited by 82
“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].”
BackgroundMeng, Z., Ren, D., Zhang, K., Zhao, J., Jin, X., & Wu, H. (2020) · Aging · doi:10.18632/aging.102931 · Cited by 78
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 82
“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…”
MethodLiu, 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
“Where i is m1A phenotyperelated genes’ expression (42, 43).”
BackgroundChu, G., Ji, X., Wang, Y., & Niu, H. (2023) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2023.06.001 · Cited by 76
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 75
“, lung or gastric cancer, to implement the TME in the tumor staging system [38,39].”
BackgroundBu, M. T., Chandrasekhar, P., Ding, L., & Hugo, W. (2022) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2022.108211 · Cited by 72
“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.”
BackgroundResultEarly 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 76
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
Wang, S., Pang, L., Liu, Z., & Meng, X. (2021) · BMC Cancer · doi:10.1186/s12885-021-08536-7 · Cited by 65
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
“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.”
MethodIdentification 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 64
“Zeng et al.(16) defined three GC subtypes based on immune cell infiltration patterns in the tumor microenvironment (TME).”
BackgroundMethodInfluentialChida, 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 66
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
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 68
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
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 64
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
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
“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).”
ResultLin, 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 61
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 61
“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].”
BackgroundMethodYang, 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
“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].”
MethodLin, 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 62
“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
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 57
“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.”
MethodIdentification 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 56
“, 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.”
MethodHu, Y., Han, J. H., Ding, S., Liu, S., & Wang, H. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.986384 · Cited by 55
“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.”
MethodChen, W., Ou, M., Tang, D., Dai, Y., & Du, W. (2020) · Journal of Immunology Research · doi:10.1155/2020/5494858 · Cited by 54
“Immune activation by B cell, T cell, natural cell, and dendritic cell contributes to the tumor suppression [27].”
BackgroundCLDN18.2 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
“Tumour-infiltrating immune cells exhibit diverse phenotypes and functions, which can have both protumorigenic and antitumorigenic effects [21].”
BackgroundImmune‐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
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 53
The Role of TAMs in Tumor Microenvironment and New Research Progress
Feng, Y., Ye, Z., Song, F., He, Y., & Liu, J. (2022) · Stem Cells International · doi:10.1155/2022/5775696 · Cited by 53
“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].”
BackgroundRetractedHe, Y., & Wang, X. (2020) · Annals of Translational Medicine · doi:10.21037/atm-20-922 · Cited by 53
“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.”
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
Baj, J., Brzozowska, K., Forma, A., Maani, A., Sitarz, R., & Portincasa, P. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21072544 · Cited by 53
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
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 49
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 51
Li, N., Wang, J., & Zhan, X. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.752643 · Cited by 50
“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…”
MethodClinical 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
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 48
“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).”
BackgroundLi, 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 45
“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.”
BackgroundInfluentialZhang, 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 47
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
“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].”
MethodComprehensive 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 41
Self-citationIdentification 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
Self-citationHuang, Y., Chen, X., Wang, L., Wang, T., Tang, X.-B., & Su, X. (2021) · Journal of Cancer · doi:10.7150/jca.52187 · Cited by 43
“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].”
MethodThe 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 45
“, 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…”
BackgroundResultInfluentialYang, Y. Q., Fan, Q., Huang, J., Wu, Y., Lin, H., & Zhang, Q. (2021) · Journal of Oncology · doi:10.1155/2021/9915312 · Cited by 42
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
Han, X., Guo, Y., Ye, H., Chen, Z., Hu, Q., Wei, X., Liu, Z., & Liang, C. (2024) · Breast Cancer Research · doi:10.1186/s13058-024-01776-y · Cited by 44
“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].”
BackgroundGu, W., Kim, M., Wang, L., Yang, Z., Nakajima, T., & Tsushima, Y. (2021) · International Journal of Biological Sciences · doi:10.7150/ijbs.61441 · Cited by 40
“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].”
MethodAdvancements in Cancer Immunotherapies
Roy, R., Singh, S. K., & Misra, S. (2022) · Vaccines · doi:10.3390/vaccines11010059 · Cited by 44
“Several reports have elucidated their clinicopathologic significance in predicting outcomes and therapeutic efficacy [2,3].”
BackgroundFeng, 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
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
Ji, Z., Ren, W., Wang, H., Gao, W. D., & Yuan, B. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.919083 · Cited by 38
“Eight genes (SIGLEC15, TIGIT, CD274, HAVCR2, PDCD1, CTLA4, LAG3 and PDCD1LG2) were used for immune checkpoint analysis (20).”
MethodSalnikov, M. Y., MacNeil, K. M., & Mymryk, J. S. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1358511 · Cited by 43
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
“An increasing number of evidences has elucidated the clinic pathological significance of TME in the prediction of treatment effects [9, 10].”
BackgroundGuan, 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 38
“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).”
BackgroundITGB1 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 41
Self-citationBi, 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
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 38
Self-citationSui, 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 40
“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
Metadata
- 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
- Venue
- Cancer Immunology Research
- Published
- 2019-05-01
- Volume
- 7
- Issue
- 5
- Pages
- 737-750
- ISSN
- 2326-6066 (ISSN-L)
- 2326-6074
- 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 › Oncology › 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
