Cited by 999Cancer Immunotherapy and BiomarkersGastric Cancer Management and OutcomesImmune cells in cancer

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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·2019·Cancer Immunology Research

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

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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.

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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).”
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×
“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
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.”
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
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).”
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 13,224
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
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).”
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×
“Moreover, the abundance of immune cells and other cells in the TME can be estimated using computational methods (16-18).”

Show all 76 references

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“The TME-related signatures were downloaded, and the scores were computed based on the published findings [17].”
Method
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Li, S., Li, K., Tian, F., Li, H., Xia, Q., Li, T., Dong, B., Li, D., Yu, J., Zhang, J., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1056144 · Cited by 25
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Huang, H., Huang, Z., Ge, J., Yang, J., Chen, J., Xu, B., Wu, S., Zheng, X. W., Chen, L., Zhang, X., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1150803 · Cited by 21
“have established the TME score, providing a valuable predictor for precision immunotherapy against GC (30, 31).”
Background
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Wu, C., Li, D., Cheng, X., Gu, H., Qian, Y., & Li, F. (2023) · Cell Cycle · doi:10.1080/15384101.2023.2231740 · Cited by 20
“In the past decade, the concept of tumor microenvironment has gradually attracted the attention of researchers [7,8].”
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Tumor microenvironment characterization in colorectal cancer to identify prognostic and immunotherapy genes signature
Guo, X., Lei, R., Zhou, Q.-N., Zhang, G., Hu, B.-L., & Liang, Y. (2023) · BMC Cancer · doi:10.1186/s12885-023-11277-4 · Cited by 20
“signature The “TMEscore” package [10, 18] was used to calculate the TMEscore and establish TME clusters for the gene datasets, which estimates the TMEscore based on gene expression.”
BackgroundMethodResultInfluential
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“The TME plays an important role in tumor progression and has an impact on the prediction of tumor prognosis.(30,31) Illustrating the fraction of stromal/immune cells could demonstrate the role of the TME in cancer and prognostic prediction.”
Background
Single cell and bulk RNA sequencing identifies tumor microenvironment subtypes and chemoresistance-related IGF1+ cancer-associated fibroblast in gastric cancer
Jia, X., Li, Z., Zhou, R., Feng, W., Yi, L., Zhang, H., Chen, B., Li, Q., Huang, S., & Zhu, X. (2024) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2024.167123 · Cited by 20
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Ju, M., Jin, Z., Yu, X. Q., Huang, C., Li, Y., Gao, Z., He, L., Huang, H., Chen, Z., Jia, S., et al. (2025) · Small · doi:10.1002/smll.202409321 · Cited by 22
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Zhou, H., Zheng, M., Shi, M., Wang, J., Huang, Z., Zhang, H., Zhou, Y., & Shi, J. (2021) · BMC Cancer · doi:10.1186/s12885-021-08655-1 · Cited by 19
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BackgroundSelf-citation
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Lin, Y., Huang, K., Cai, Z., Yide, C., Feng, L., Gao, Y., Zheng, W., Fan, X., Qiu, G., Zhuang, J., et al. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.884090 · Cited by 19
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“was applied to estimate the related effects of FBXO5 within TME of 33 cancers (14).”
Method
Comprehensive analysis of cuproptosis-related genes in prognosis, tumor microenvironment infiltration, and immunotherapy response in gastric cancer
Nie, H., Wang, H., Zhang, M., Ning, Y., Chen, X., Zhang, Z., Hu, X., Zhao, Q., Chen, P., Fang, J., et al. (2022) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-022-04474-4 · Cited by 19
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Liu, J., Zhong, L., Deng, D., Zhang, Y., Yuan, Q., & Shang, D. (2023) · Scientific Reports · doi:10.1038/s41598-023-33213-z · Cited by 19
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Ning, J., Sun, K., Fan, X., Jia, K., Meng, L., Wang, X., Li, H., Ma, R., Li, F., Li, F., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-34291-9 · Cited by 19
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Background
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MethodResult
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Retracted
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“Last, we applied an equation similar to the previous study (Sotiriou et al., 2006; Zeng et al., 2019) to define the metabolic score:”
Method
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Method
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Li, H., Lin, D., Yu, Z., Li, H., Zhao, S., Hainisayimu, T., Liu, L., & Wang, K. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.947802 · Cited by 18
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“Based on gene expression profiles, a recent study employed computational algorithms to predict immune-checkpoint blockade responses by estimating TME infiltration patterns of 1,524 tumors among gastric cancer patients (14).”
MethodSelf-citation
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Single-cell sequencing analysis reveals gastric cancer microenvironment cells respond vastly different to oxidative stress
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“comprehensively described all the characteristics of the gastric cancer microenvironment and made a detailed analysis of immune infiltration [20].”
Background
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Background
A large histological images dataset of gastric cancer with tumour microenvironment annotation for AI
Lou, S., Ji, J., Li, H., Zhang, X., Jiang, Y., Hua, M., Chen, K., Ge, K., Zhang, Q., Wang, L., et al. (2025) · Scientific Data · doi:10.1038/s41597-025-04489-9 · Cited by 18
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Lei, J.-X., Wang, R., Hu, C., Lou, X., Lv, M., Li, C., Gai, B., Wu, X., Dou, R., Cai, D., et al. (2024) · International Journal of Surgery · doi:10.1097/js9.0000000000001684 · Cited by 17
“…of gene sets that deposited genes associated with some biological processes were extracted from Mariathasan ’ s literature [25] and Zeng ’ s literature [26] , including epithelial-mesenchymal transition (EMT) markers, angiogenesis signature, TGF β /EMT markers, and immune activation signature.”
Method
Integrating multi-omics and machine learning survival frameworks to build a prognostic model based on immune function and cell death patterns in a lung adenocarcinoma cohort
Xie, Y., Chen, H., Tian, M., Wang, Z., Wang, L., Zhang, J., Wang, X., & Lian, C. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1460547 · Cited by 17
Self-citation
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Zeng, D., Yu, Y., Qiu, W., Ou, Q., Mao, Q., Jiang, L., Wu, J., Wu, J., Luo, H., Luo, P., et al. (2025) · Advanced Science · doi:10.1002/advs.202417593 · Cited by 17
Self-citation
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Fan, Y., Zhou, Y., Lou, M., Li, X., Zhu, X., & Yuan, K. (2022) · Journal of Inflammation Research · doi:10.2147/jir.s356841 · Cited by 17
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“SIGLEC15, IDO1, CD274, HAVCR2, PDCD1, CTLA4, LAG3, and PDCD1LG2 are transcripts related to immune checkpoints [21].”
Background
Neurexophilin 4 is a prognostic biomarker correlated with immune infiltration in bladder cancer
Sun, X., Xin, S., Jin, L., Zhang, Y., & Ye, L. (2022) · Bioengineered · doi:10.1080/21655979.2022.2085284 · Cited by 17
“Computer algorithms for transcriptomic data are now being widely used to determine dynamic immune components in TME of tumor patients [20,34].”
Method
COL3A1 : Potential prognostic predictor for head and neck cancer based on immune‐microenvironment alternative splicing
Shen, Y., Li, X., Wang, D., Zhang, L., Li, X., Su, L., Fan, X., & Yang, X. (2022) · Cancer Medicine · doi:10.1002/cam4.5170 · Cited by 17
IMPDH1, a prognostic biomarker and immunotherapy target that correlates with tumor immune microenvironment in pan-cancer and hepatocellular carcinoma
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“Three TME phenotypes were defined, and the TME score was constructed using principal component analysis algorithms according to previous work (25).”
Method
A precise molecular subtyping of ulcerative colitis reveals the immune heterogeneity and predicts clinical drug responses
Mo, S., Jin, B., Tseng, Y., Lin, L., Lin, L., Shen, X., Song, H., Kong, M., Luo, Z., Chu, Y., et al. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04326-w · Cited by 17
“biomarkers Boruta package was employed for the dimension reduction which applied the random forest algorithm to extract features and the order of them was scrambled to calculate the importance of features [26].”
Method
Genomic and immune microenvironment features influencing chemoimmunotherapy response in gastric cancer with peritoneal metastasis: a retrospective cohort study
Yu, P., Ding, G., Huang, X., Wang, C., Fang, J., Huang, L., Ye, Z., Xu, Q., Wu, X., Yan, J., et al. (2024) · International Journal of Surgery · doi:10.1097/js9.0000000000001281 · Cited by 17
“TMEscore A was associated with immune-related signatures, while TMEscore B was associated with stromal-relevant signatures [25] .”
Background
Tumor mutational burden related classifier is predictive of response to PD-L1 blockade in locally advanced and metastatic urothelial carcinoma
Wang, Y., Chen, L., Ju, L., Xiao, Y., & Wang, X. (2020) · International Immunopharmacology · doi:10.1016/j.intimp.2020.106818 · Cited by 17
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“Studies have shown that the tumor immune infiltrates correlated with prognosis and response to therapy (Zeng et al., 2019; Refolo et al., 2020).”
BackgroundSelf-citation
An angiogenesis-related three-long non-coding ribonucleic acid signature predicts the immune landscape and prognosis in hepatocellular carcinoma
Wang, W., Ye, Y., Zhang, X., Sun, W., & Bao, L. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e13989 · Cited by 17
“Molecular subtypes of tumours are associated with patient clinical outcomes and the TIME [63].”
Background
Spatial and functional dissection of cancer-associated fibroblasts-mediated immune modulation in H. pylori-associated gastric cancer
Chen, B., Tang, H., Zheng, X., Xie, F., Yu, P., Lyu, Y., Feng, T., Wu, J., Liu, J., Xu, Y., et al. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02490-9 · Cited by 20
“In line with prior researches, our findings confirm the existence of histology-specific immunoregulatory mechanisms in GC [32, 33].”
Identification of an IRGP Signature to Predict Prognosis and Immunotherapeutic Efficiency in Bladder Cancer
Zhang, L., Li, L., Zhan, Y., Zhu, Z., & Zhang, X. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.607090 · Cited by 15
Impact of human papillomavirus on the tumor microenvironment in oropharyngeal squamous cell carcinoma
Liu, X., Liu, P., Chernock, R. D., Kuhs, K. A. L., Lewis, J. S., Li, H., Gay, H. A., Thorstad, W. L., & Wang, X. (2021) · International Journal of Cancer · doi:10.1002/ijc.33849 · Cited by 16
Comprehensive Analysis of m6A Regulators Characterized by the Immune Cell Infiltration in Head and Neck Squamous Cell Carcinoma to Aid Immunotherapy and Chemotherapy
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“We then curated the final genes determined to conduct principal component analysis (PCA), and principal component 1 and 2 were extracted to construct the m(6)A score (44, 45).”
Method
The multi-omics analyses of acsl1 reveal its translational significance as a tumor microenvironmental and prognostic biomarker in clear cell renal cell carcinoma
Yang, Y., Liang, J., Zhao, J., Wang, X., Feng, D., Xu, H., Shen, Y., Zhang, Y., Dai, J., Wang, Z., et al. (2023) · Diagnostic Pathology · doi:10.1186/s13000-023-01384-y · Cited by 16
“Meanwhile, we picked SIGLEC15, TIGIT, CD274, HAVCR2, PDCD1, CTLA4, LAG3 and PDCD1LG2 as immune checkpoint-associated transcripts [47–49].”
Cuproptosis-related genes predict prognosis and trastuzumab therapeutic response in HER2-positive breast cancer
Sha, R., Dong, X., Yan, S., Dai, H., Sun, A., You, L., & Guo, Z. (2024) · Scientific Reports · doi:10.1038/s41598-024-52638-8 · Cited by 16
Identification of New Therapeutic Targets for Gastric Cancer With Bioinformatics
Yang, L., Wang, J., Zhang, T., Wang, H., & Li, L. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00865 · Cited by 16
“Studies have found that higher TME scores show improved immune treatment and prognosis (Junttila and de Sauvage, 2013; Lazar et al., 2018; Zeng et al., 2019).”
Association of the Psoriatic Microenvironment With Treatment Response
Wang, G., Miao, Y., Kim, N., Sweren, E., Kang, S., Hu, Z., & Garza, L. A. (2020) · JAMA Dermatology · doi:10.1001/jamadermatol.2020.2118 · Cited by 16
Epigenetic Age Acceleration of Stomach Adenocarcinoma Associated With Tumor Stemness Features, Immunoactivation, and Favorable Prognosis
Hong, C., Yang, S., Wang, Q., Zhang, S., Wu, W., Chen, J., Zhong, D., Li, M., Li, L., Li, J., et al. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.563051 · Cited by 16
Self-citation
Copy number variation analysis of m6A regulators identified METTL3 as a prognostic and immune‐related biomarker in bladder cancer
Wang, X., Yu, J., Chen, J., Hou, Y., Du, Z., Huang, H., Tang, S., Han, Y., Ding, C., & Xue, Z. (2021) · Cancer Medicine · doi:10.1002/cam4.3981 · Cited by 16
Identification of an Immune-Related Signature Predicting Survival Risk and Immune Microenvironment in Gastric Cancer
Dai, S., Liu, T., Liu, X., Li, X.-Y., Xu, K., Ren, T., & Luo, F. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.687473 · Cited by 15
Pan-Cancer Analysis Predicts FOXS1 as a Key Target in Prognosis and Tumor Immunotherapy
Liu, Y., Tu, M., & Wang, L. (2022) · International Journal of General Medicine · doi:10.2147/ijgm.s354195 · Cited by 16
“method, a previous study was employed as a reference to investigate TME-related biological processes.(13) Next, based on the TCGA database, we exploited the “Immune Gene” module of the TIMER2, ImmuCellAI platform, and referred to a previous study to estimate the relationship between FOXS1 expression and immune cell in…”
BackgroundMethod
Identification of m6A modification patterns and development of m6A–hypoxia prognostic signature to characterize tumor microenvironment in triple-negative breast cancer
Shen, X. P., Zhong, J., He, J., Han, J., & Chen, N. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.978092 · Cited by 16
An antigen processing and presentation signature for prognostic evaluation and immunotherapy selection in advanced gastric cancer
Wang, K.-W., Wang, M.-D., Li, Z.-X., Hu, B.-S., Wu, J.-J., Yuan, Z.-D., Wu, X., Yuan, Q.-F., & Yuan, F.-L. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.992060 · Cited by 16
Characterization of glycometabolism and tumor immune microenvironment for predicting clinical outcomes in gastric cancer
Shi, J., Wu, Z., Wu, X., Huangfu, L., Guo, T., Cheng, X., Han, J., Li, Z., Xing, X., & Ji, J. (2023) · iScience · doi:10.1016/j.isci.2023.106214 · Cited by 16
Comprehensive Characterization of Alternative mRNA Splicing Events in Glioblastoma: Implications for Prognosis, Molecular Subtypes, and Immune Microenvironment Remodeling
Zhao, L., Zhang, J., Liu, Z., Wang, Y., Xuan, S., & Zhao, P. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2020.555632 · Cited by 17
“included several genomic and transcriptomic datasets from patients with different cancer types treated with immunotherapy in their study to evaluate the efficiency of the tumor microenvironment score they developed in predicting the immunotherapeutic benefits (71).”
Self-citation
Phosphoserine phosphatase as a prognostic biomarker in patients with gastric cancer and its potential association with immune cells
Huang, M.-Y., Liu, X.-Y., Shao, Q., Zhang, X., Miao, L., Wu, X.-Y., Xu, Y.-X., Wang, F., Wang, H.-Y., Zeng, L., et al. (2022) · BMC Gastroenterology · doi:10.1186/s12876-021-02073-0 · Cited by 15
“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].”
Background
TME-guided deep learning predicts chemotherapy and immunotherapy response in gastric cancer with attention-enhanced residual Swin Transformer
Sang, S., Sun, Z., Zheng, W., Wang, W., Islam, M. T., Chen, Y., Yuan, Q., Cheng, C., Xi, S., Han, Z., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102242 · Cited by 15
“21,24 27 Therefore, developing a non-invasive and cost-effective model to assess chemotherapy response while integrating TME status to enhance model performance is imperative.”
Systematic evaluation of tumor microenvironment and construction of a machine learning model to predict prognosis and immunotherapy efficacy in triple-negative breast cancer based on data mining and sequencing validation
Gou, Q., Liu, Z., Xie, Y., Deng, Y., Ma, J., Li, J., & Zheng, H. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.995555 · Cited by 15
“A previous study depicted a vast TME landscape of gastric cancer and helped to provide new strategies for interpreting responses to immunotherapies (Zeng et al., 2019).”
Background
Lipid metabolism characterization in gastric cancer identifies signatures to predict prognostic and therapeutic responses
Zeng, J., Tan, H., Huang, B., Zhou, Q., Ke, Q., Dai, Y., Tang, J., Xu, B., Feng, J., & Yu, L. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.959170 · Cited by 15
“gz) were processed according to previous studies (Zeng et al., 2019; Chen et al., 2021).”
Method
Gene signature developed for predicting early relapse and survival in early-stage pancreatic cancer
Huang, L., Yuan, X., Zhao, L., Han, Q., Yan, H., Yuan, J., Guan, S., Xu, X., Dai, G., Wang, J., et al. (2023) · BJS Open · doi:10.1093/bjsopen/zrad031 · Cited by 15
m5C regulator-mediated methylation modification phenotypes characterized by distinct tumor microenvironment immune heterogenicity in colorectal cancer
Chen, Z., Li, Q., Lin, Y., Lin, S., Gao, J., & Chen, S. (2023) · Scientific Reports · doi:10.1038/s41598-023-37300-z · Cited by 16
AGAP2‐AS1 promotes the assembly of m6A methyltransferases and activation of the IL6 / STAT3 pathway by binding with WTAP in the carcinogenesis of gastric cancer
Nie, K., Zheng, Z., Li, J., Chang, Y., Deng, Z., Huang, W. J., & Li, X. (2023) · The FASEB Journal · doi:10.1096/fj.202301249r · Cited by 15
Mucosal microbiota characterization in gastric cancer identifies immune-activated–related transcripts relevant gastric microbiome signatures
Qian, C., Jiang, H., Peng, Z., Sun, X., & Zhang, J. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1435334 · Cited by 15
Self-citation
Characterization of Two Ferroptosis Subtypes With Distinct Immune Infiltration and Gender Difference in Gastric Cancer
Ma, J., Hu, X., Yao, Y., Wu, L., Sheng, C., Chen, K., & Liu, B. (2021) · Frontiers in Nutrition · doi:10.3389/fnut.2021.756193 · Cited by 15
“The advantage of this method is that the score is concentrated on the set with the largest block of well-correlated (or anticorrelated) genes in the set, while down-weighting contributions from genes that do not track with other set members (28, 29): ferroptosis score= ∑ (PC1i + PC2i) where i is the expression of subt…”
Method
Characterization of m6A regulator‐mediated methylation modification patterns and tumor microenvironment infiltration in acute myeloid leukemia
Han, S., Qi, J., Fang, K., Wang, H., Tang, Y., Wu, D., & Han, Y. (2022) · Cancer Medicine · doi:10.1002/cam4.4531 · Cited by 14
Pyroptosis-Mediated Molecular Subtypes are Characterized by Distinct Tumor Microenvironment Infiltration Characteristics in Breast Cancer
Xu, L., Hu, Y., & Liu, W. (2022) · Journal of Inflammation Research · doi:10.2147/jir.s349186 · Cited by 15
Ferroptosis Mediation Patterns Reveal Novel Tool to Implicate Immunotherapy and Multi-Omics Characteristics in Bladder Cancer
Liu, J., Zhang, Z., Zhang, W., Meng, L., Wang, J., Lv, Z., Xia, H., Wu, M. Y., Zhang, Y., & Wang, J. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.791630 · Cited by 16
Multi-omics landscape and clinical significance of a SMAD4 -driven immune signature: Implications for risk stratification and frontline therapies in pancreatic cancer
Wang, L., Liu, Z., Zhu, R., Liang, R., Wang, W., Li, J., Zhang, Y., Guo, C., Han, X., & Sun, Y. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.02.031 · Cited by 15
“The similar methods have also been implemented and validated in gastric and colorectal cancer [28,29].”
Method
Integrated Analysis of Methylomic and Transcriptomic Data to Identify Potential Diagnostic Biomarkers for Major Depressive Disorder
Xie, Y., Xiao, L., Chen, L., Zheng, Y., Zhang, C., & Wang, G. (2021) · Genes · doi:10.3390/genes12020178 · Cited by 17
“Integrated analyses of other omics are also common in medical research, namely, multi-genome [51], and multi-transcriptome [52] analyses.”
Background
Procollagen-Lysine, 2-Oxoglutarate 5-Dioxygenase Family: Novel Prognostic Biomarkers and Tumor Microenvironment Regulators for Lower-Grade Glioma
Gong, S., Wu, C., Köhler, F., Meixensberger, J., Schopow, N., & Kallendrusch, S. (2022) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2022.838548 · Cited by 16
“We considered CXCL10, CXCL9, GZMA, GZMB, PRF1, CD8A, TBX2, and TNF as immune-activating transcripts; IDO1, CD274, HAVCR2, PDCD1, CTLA4, LAG3, and PDCD1LG2 as immune checkpoint transcripts; and VIM, ACTA2, COL4A1, TGFBR2, ZEB1, CLDN3, SMAD9, TWIST1, and TGRB1 as transforming growth factor (TGF)β/epithelial-mesenchymal…”
Molecular characterization and prognostic implications of KRAS mutations in pancreatic cancer patients: insights from multi-cohort analysis
Jiang, Y., Mai, G., Zhao, X., Tang, M. X., Yang, P., Cheng, Q., Tian, H., Niu, Z., Wang, X., Wang, J., et al. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-01087-1 · Cited by 15
“In immune in ﬁ ltration analysis, low-risk patients show higher levels of CD4/CD8 T cells and Treg cells, which correlate with better prognosis across various cancers 31,32 .”
G Protein‐Coupled Receptor Signaling: Implications and Therapeutic Development Advances in Cancers
Khan, I. R., Khurshid, S., Almawash, S., Kumar, R., Akil, A. A., Bhat, A. A., & Macha, M. A. (2025) · MedComm · doi:10.1002/mco2.70375 · Cited by 15
Precision oncology in gastric cancer: Shaping the future of personalized treatment
Migliore, C., Fenocchio, E., Giordano, S., & Corso, S. (2025) · Cancer Treatment Reviews · doi:10.1016/j.ctrv.2025.103038 · Cited by 14
“Zeng and colleagues, using computational algorithms, analyzed the expression profile of more than 1500 gastric tumors and identified three distinct patterns of tumor TME infiltration, each characterized by a peculiar proportion of cancer-associated fibroblasts (CAF) and leukocytes [43].”
Development of a prognostic signature based on immune-related genes and the correlation with immune microenvironment in breast cancer
Dong, M., Cui, X., Wang, G., Zhang, Q., & Li, X. (2022) · Aging · doi:10.18632/aging.204158 · Cited by 14
“Finally, the m6Ascore was determined in a manner similar to the Genomic Grade Index (GGI) [21]: riskScore = ∑ (PC1i + PC2i), where i was the expression of overlapping IRGs with a significant difference in prognosis of BC patients.”
Method
PD-1 Relevant Genes Predict the Prognosis of Breast Cancer and Their Prediction Effect in Tumor Response to Immunotherapy
Xiao, Y., Jiang, J., Chen, Y., Huang, Y., & Wei, C. (2022) · ONCOLOGIE · doi:10.32604/oncologie.2022.026118 · Cited by 14
Identification and validation of pyroptosis-related gene landscape in prognosis and immunotherapy of ovarian cancer
Gao, L., Ying, F., Cai, J., Peng, M., Xiao, M., Sun, S., Zeng, Y., Xiong, Z., Cai, L., Gao, R., et al. (2023) · Journal of Ovarian Research · doi:10.1186/s13048-022-01065-2 · Cited by 15
“We performed a systematical search for immune checkpoint genes, immune chemokines, interleukin, interferon as well as epithelial-mesenchymal transition (EMT) -related genes, which were extracted to explore their relationship with Pyrsig score [35].”
Method
Diagnostic biomarker KIF23 is associated with immune infiltration and immunotherapy response in gastric cancer
Bai, M., & Liu, X. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1191009 · Cited by 14
Exosomes play a crucial role in remodeling the tumor microenvironment and in the treatment of gastric cancer
Tang, L., Zhang, W., Qi, T., Jiang, Z., & Tang, D. (2025) · Cell Communication and Signaling · doi:10.1186/s12964-024-02009-7 · Cited by 14
Tumor-infiltrating podoplanin + cells in gastric cancer: clinical outcomes and association with immune contexture
Liu, X., Cao, Y., Lv, K., Gu, Y., Jin, K., He, X., Fang, H., Fei, Y., Shi, M., Lin, C., et al. (2020) · OncoImmunology · doi:10.1080/2162402x.2020.1845038 · Cited by 14
“This may, partly, be attributed to the heterogeneous TME, T-cellinflamed gene-expression profile and different molecular subtypes.(15,16) Consequently, in order to further improve response rates and expand therapeutic effects, novel predictive biomarkers and potential immunoevasive mechanisms should be identified in g…”
BackgroundSelf-citation
Tumor Immunometabolism Characterization in Ovarian Cancer With Prognostic and Therapeutic Implications
Yang, M., Chen, G., Gao, K., & Wang, Y. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.622752 · Cited by 14
“2e-16; Figure 2C), suggesting T cell activation and effective immune response in C2 (39, 40).”
Background
Characterization of m6A RNA Methylation Regulators Predicts Survival and Immunotherapy in Lung Adenocarcinoma
Zhu, M., Cui, Y., Mo, Q., Zhang, J., Zhao, T., Xu, Y.-J., Wu, Z., Sun, D., Zhang, X., Li, Y., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.782551 · Cited by 14
“This method minimized the deprivation of information contained in the original index and reduced the indicators to be analyzed, thereby allowing a comprehensive analysis of the collected data; lastly, the m(6)A score was defined by performing a formula similar to that used in previous studies (48, 49).”
MethodSelf-citation
Fibroblast-derived LPP as a biomarker for treatment response and therapeutic target in gastric cancer
Wang, H., Wu, J., Ling, R., Li, F., Yang, Q., He, J., Lei, X., Wu, C., Zhang, G., Zheng, B., et al. (2022) · Molecular Therapy — Oncolytics · doi:10.1016/j.omto.2022.01.008 · Cited by 14
“Several studies have reported that the heterogeneity of the immune microenvironment within and among individual tumors is greatly responsible for such variations of treatment responses and patient outcomes.(7,8) For example, the tumor environment context observed at initially diagnosis could reflect the immune respons…”
Background
Identification of the Expression Patterns and Potential Prognostic Role of 5-Methylcytosine Regulators in Hepatocellular Carcinoma
Liu, Y., Zheng, S., Wang, T., Fang, Z., Kong, J., & Liu, J. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.842220 · Cited by 14
“The m5Cscore was defined using a method similar to the Genomic Grade Index (GGI) (Sotiriou et al., 2006; Zeng et al., 2019):”
Method
Immune-infiltrating signature-based classification reveals CD103+CD39+ T cells associate with colorectal cancer prognosis and response to immunotherapy
Luo, Y., Zong, Y., Hua, H., Gong, M., Peng, Q., Li, C., Neculai, D., & Zeng, X. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1011590 · Cited by 15
Cuproptosis-related gene FDX1 as a prognostic biomarker for kidney renal clear cell carcinoma correlates with immune checkpoints and immune cell infiltration
Yao, Y., Chen, H., Lou, M., & Chen, T. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1071694 · Cited by 14
“Referring to some high-quality literatures, the expression differences of immune checkpoint related genes (SIGLEC15, TIGIT, CD274, HAVCR2, PDCD1, CTLA4, LAG3 and PDCD1LG2) were compared between these two groups by “ggplots2” and “pheatmap” R packages (Ravi et al., 2018; Wang et al., 2019; Zeng et al., 2019; Yi et al.,…”
Result
Increased CD4/CD8 Lymphocyte ratio predicts favourable neoadjuvant treatment response in gastric cancer: A prospective pilot study
Skubleny, D., Lin, A., Garg, S., McLean, R., McCall, M., Ghosh, S., Spratlin, J. L., Schiller, D., & Rayat, G. R. (2023) · World Journal of Gastrointestinal Oncology · doi:10.4251/wjgo.v15.i2.303 · Cited by 14
Comprehensive Bioinformatics Analysis Identifies Tumor Microenvironment and Immune-related Genes in Small Cell Lung Cancer
Song, Y., Sun, Y., Sun, T., & Tang, R. (2020) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/1386207323666200407075004 · Cited by 14
Development and Validation of a Scoring System Based on 9 Glycolysis-Related Genes for Prognosis Prediction in Gastric Cancer
Luo, T., Du, Y., Duan, J., Liang, C., Chen, G., Jiang, K., Chen, Y., & Chen, Y. (2020) · Technology in Cancer Research & Treatment · doi:10.1177/1533033820971670 · Cited by 14
“In GC, the crosstalk between immune cells and tumor cells plays a vital role in predicting prognosis and immunotherapeutic outcomes.(29) After comparing the difference of tumorinfiltrating immune cells between high- and low-risk groups, we observed a high level of T cells gamma delta, monocytes, macrophages M2, and ma…”
Background
A signature of 24 aging‑related gene pairs predict overall survival in gastric cancer
Zhang, Y., Yan, Y., Ning, N., Shen, Z., & Ye, Y. (2021) · BioMedical Engineering OnLine · doi:10.1186/s12938-021-00871-x · Cited by 14
“on the role of tumor immune infiltrating cells in GC suggest that it can be used as independent prognostic markers [37, 38].”
Background

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 › 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

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

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