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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Method
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Background
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Background
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Method
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Method
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Self-citation
Computational pathology approach for assessment of prognosis and immunotherapy response in pan-gastrointestinal cancer
Zhang, T., Sun, Z., Li, Z., Ahmad, M. U., Islam, M. T., Yang, X., Yang, X., Wu, L., Xie, J., Feng, W., et al. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-06181-3 · Cited by 5
“The tumor microenvironment (TME) has been increasingly recognized as a critical driver of cancer progression and therapy resistance [16, 17].”
A 107 Gene Nanostring Assay Effectively Translates the Cancer Genome Atlas, and Tumour Microenvironment Gastric Cancer Molecular Classification to a Patient‐Derived Organoid Model
Skubleny, D., Purich, K., Williams, T., McLean, D. R., Martins-Filho, S. N., Buttenschoen, K., Haase, E., McCall, M., Baker, K., Ghosh, S., et al. (2025) · Genes Chromosomes and Cancer · doi:10.1002/gcc.70090 · Cited by 5
Shaping the glioblastoma microenvironment to enhance CAR-NK immunotherapy
Tang, E., & Chen, J. Y. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1681966 · Cited by 5
Spatial atlas of gastric cancer reveals macrophage-driven stromal activation via ICAM1⁺ smooth muscle cells
Cheng, K., Xu, J., Yang, J., & Zhang, Z. (2026) · BMC Cancer · doi:10.1186/s12885-026-15647-6 · Cited by 5
“GC presents a prototypical case of tumor ecosystems shaped by extensive spatial and cellular heterogeneity [3, 6].”
Influential
Two Pyroptosis-Related Subtypes with Distinct Immune Microenvironment Characteristics and a Novel Signature for Predicting Immunotherapy Response and Prognosis in Uveal Melanoma
Lei, S., & Li, H. (2022) · Current Eye Research · doi:10.1080/02713683.2022.2048396 · Cited by 5
Role of SERCA3 in the Prognosis and Immune Function in Pan-Cancer
Li, J., Li, X., Huang, H., Tao, L., Zhang, C., Xie, Y., & Jiang, Y. (2022) · Journal of Oncology · doi:10.1155/2022/9359879 · Cited by 5
Identification of a basement membrane-related genes signature to predict prognosis, immune landscape and guide therapy in gastric cancer
Liu, Z., & Xin, L. (2023) · Medicine · doi:10.1097/md.0000000000035027 · Cited by 5
Immune checkpoints signature-based risk stratification for prognosis of patients with gastric cancer
Wu, Z., Li, G., Wang, W., Zhang, K., Fan, M., Jin, Y., & Lin, R. (2023) · Cellular Signalling · doi:10.1016/j.cellsig.2023.110976 · Cited by 5
Molecular subtyping based on immune cell marker genes predicts prognosis and therapeutic response in patients with lung adenocarcinoma
Liu, Z.-T., Shen, J., Lei, Y., Huang, Y., Zhao, G., Zheng, C., Wang, X., Wang, Y., Chen, L., Li, Z., et al. (2023) · BMC Cancer · doi:10.1186/s12885-023-11579-7 · Cited by 5
“Furthermore, the current study shows that molecular subtypes based on TME identification have significant advantages in predicting patient responsiveness to immunotherapy [14].”
Background
Ferroptosis-related LINC02535/has-miR-30c-5p/EIF2S1 axis as a novel prognostic biomarker involved in immune infiltration and progression of PDAC
Huang, Z., Li, M., Gu, B., Chen, J., Liu, S., Tan, P., & Fu, W. (2024) · Cellular Signalling · doi:10.1016/j.cellsig.2024.111338 · Cited by 5
Self-citation
Pan-cancer analysis of CREB3L1 as biomarker in the prediction of prognosis and immunotherapeutic efficacy
Lin, Z., Wu, Y., Xiao, X., Zhang, X., Wan, J., Zheng, T., Chen, H., Liu, T., & Tang, X. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.938510 · Cited by 5
“Developing novel predictive biomarkers for PD-1/PDL1 inhibitors therefore plays a key role in maximizing the immunotherapeutic efficacy (Zeng et al., 2019; Yi et al., 2022).”
Background
Cuprotosis Patterns Are Associated with Tumor Mutation Burden and Immune Landscape in Lung Adenocarcinoma
Liu, T., Cai, L., Hua, H., Jiang, X., Xu, X., Zhang, T., Huang, W., Li, Q., Bai, H., & Duan, J. (2022) · Journal of Oncology · doi:10.1155/2022/9772208 · Cited by 5
A signature of six-hypoxia-related genes to evaluate the tumor immune microenvironment and predict prognosis in gastric cancer
Zhou, K., Cai, C., Ding, G., He, Y., & Hu, D. (2022) · BMC Medical Genomics · doi:10.1186/s12920-022-01411-9 · Cited by 5
“Secondly, we did not take into account that the TME status may be various in different tumor regions, such as tumor core and infiltrating edges.”
BackgroundInfluential
Effectiveness and safety of anti-PD-1 monotherapy or combination therapy in Chinese advanced gastric cancer: A real-world study
Li, T., Liu, T., Zhao, L., Liu, L., Zheng, X., Wang, J., Zhang, F., & Hu, Y. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2022.976078 · Cited by 5
“Various responses to ICBs by different tumor types mainly arise from the diversity of tumor immune microenvironments (32, 33).”
Background
Analysis of angiogenesis-related subtypes of hepatocellular carcinoma and tumor microenvironment infiltration feature in hepatocellular carcinoma
Li, W., Wu, R., Zhang, S., Zhao, Y., Li, L., Su, Q., Su, Q., Mang, Y., Zhang, X., Dong, Y., et al. (2023) · Clinical & Translational Oncology · doi:10.1007/s12094-023-03084-x · Cited by 5
“Finally, we established an ARG score based on principal component analysis (PCA) using the following algorithm: ARG score = expression of a gene [1]×corresponding coefficient [1] + expression of a gene [2] ×corresponding coefficient [2] + expression of gene [n] × corresponding coefficient [n], where n is the expressio…”
Method
Comprehensive analysis of a novel RNA modifications-related model in the prognostic characterization, immune landscape and drug therapy of bladder cancer
Zhang, Z., Chen, P., & Yun, J. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1156095 · Cited by 5
Sintilimab Plus Fluorouracil, Leucovorin, Oxaliplatin and Docetaxel Regimen as Neoadjuvant Therapy for Resectable Gastric Cancer and Biomarker Exploration
Wei, J., Guo, X., Yang, X., Liu, J., Duan, Q., Tan, Y., Zhang, Q., Sun, T., Qi, C., Li, X., et al. (2023) · Future Oncology · doi:10.2217/fon-2022-0929 · Cited by 5
The Prognostic and Immune Significance of BZW2 in Pan-Cancer and its Relationship with Proliferation and Apoptosis of Cervical Cancer
Li, C., Li, Q., Li, L., Sun, S., & Xun, L. (2024) · Frontiers in Bioscience-Landmark · doi:10.31083/j.fbl2903097 · Cited by 5
“[21] to examine the correlation between BZW2 and the tumor microenvironment in a cohort of 33 tumors.”
Background
Oxidative stress-related genes score predicts prognosis and immune cell infiltration landscape characterization in breast cancer
Liu, D., & Fang, L. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e34046 · Cited by 5
Immunomodulatory behavior of CircRNAs in tumor microenvironment
LIANG, H., Yan, W., Liu, Z., HE, Y., HU, J., SHU, Z., LI, H., Othmane, B., REN, W., Quan, C., et al. (2024) · Oncology Research Featuring Preclinical and Clinical Cancer Therapeutics · doi:10.32604/or.2024.054623 · Cited by 5
“The TME performance and behavior may predict tumor response to immunotherapy [10,11], which contributes to predicting tumor prognosis and guiding the application of treatment regimens [12,13].”
Utilizing multi-omics analysis, a new signature has been identified and validated for predicting prognosis and response to immunotherapy in lung squamous cell carcinoma, which is based on tumor mutation burden
Wang, D., Wang, Y., Peng, Y., & Liang, P. (2025) · Discover Oncology · doi:10.1007/s12672-025-02166-2 · Cited by 5
“Blocking the T-cell inhibitory molecules PD-L1 and PD-1 with monoclonal antibodies, which inhibit immunologic checkpoints, has become a groundbreaking and synergistic anticancer therapy, leading to unprecedented survival advantages [17].”
Review: Gastric cancer: Basic aspects
Resende, C., Gomes, C. P., & Machado, J. C. (2020) · Helicobacter · doi:10.1111/hel.12739 · Cited by 5
Comprehensive Profiling Reveals Distinct Microenvironment and Metabolism Characterization of Lung Adenocarcinoma
Li, C., Tian, C., Liu, Y., Liang, J., Zeng, Y., Yang, Q., Liu, Y., Wu, D., Wu, J., Wang, J., et al. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.619821 · Cited by 5
“…(TME) or tumor cell metabolism, which might lead to an incomplete understanding of lung adenocarcinoma (Darvin et al., 2018; Sanchez-Vega et al., 2018; Chow et al., 2019; Havel et al., 2019; Zeng et al., 2019), as there’s a profound relationship between microenvironment and metabolism.”
The prognostic roles of CYP19A1 expression in bladder cancer patients of different genders
Cai, S., Feng, Y., Ye, J., Deng, Y., Cai, Z., Zhu, X., Liu, R., Zhang, Y., Zou, Z., Tang, Z., et al. (2021) · Translational Andrology and Urology · doi:10.21037/tau-21-400 · Cited by 4
“The function, ‘surv_cutpoint’, in the R package, ‘survminer’ was implemented to discover the optimal cut-off value of CYP19A1 using the maximally selected rank statistics, which is an outcome-oriented method that provides a value of a cut point that corresponds to the most significant relation with the outcome (25).”
Method
Identification of Gene-Set Signature in Early-Stage Hepatocellular Carcinoma and Relevant Immune Characteristics
Zhao, Q., Wongpoomchai, R., Chariyakornkul, A., Xiao, Z., & Pilapong, C. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.740484 · Cited by 5
“established a comprehensive TME model as a prognostic biomarker and immunotherapeutic benefit indicator of stomach cancer (32).”
Background
Identification of Grb2‐associated binding protein 3 expression to predict clinical outcomes and immunotherapeutic responses in lung adenocarcinoma
Wang, N., Zhou, X., Wang, X., & Zhu, X. (2022) · Journal of Biochemical and Molecular Toxicology · doi:10.1002/jbt.23166 · Cited by 5
Tumor Microenvironment Characterization in Breast Cancer and an Immune Cell Infiltration Score Development, Validation, and Application
Li, L. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.844082 · Cited by 5
“Through this algorithm, previous studies had comprehensively sketched the ICI landscape to predict prognoses and therapeutic benefits in other types of cancers, such as lung adenocarcinoma, gastric cancer, and head and neck squamous cell carcinoma (5, 12, 13).”
Result
m6A regulator-mediated methylation modification highlights immune infiltration patterns for predicting risk in hepatocellular carcinoma
Zhou, D., Wang, Y., Wei, W., Zhou, W., Gu, J., Kong, Y., Yang, Q., & Wu, Y. (2022) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-022-04255-z · Cited by 5
A Macrophage Differentiation‐Mediated Gene: DDX20 as a Molecular Biomarker Encompassing the Tumor Microenvironment, Disease Staging, and Prognoses in Hepatocellular Carcinoma
Yang, Y., Yang, M., Pang, H.-S., Qiu, Y., Sun, T., Wang, T., Shen, S. Y., & Wang, W. (2022) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2022/9971776 · Cited by 5
“A microenvironment comprehensive score [11] was calculated to investigate the effect of DDX20 on the tumor microenvironment (TME).”
The Combined Detection of Immune Genes for Predicting the Prognosis of Patients With Non-Small Cell Lung Cancer
Tian, W.-J., Liu, S., & Li, B. (2020) · Technology in Cancer Research & Treatment · doi:10.1177/1533033820977504 · Cited by 5
RNA modification patterns based on major RNA modifications define tumor microenvironment characteristics in glioblastoma
Li, G., Zhu, Y., Gu, J., Zhang, T., Wang, F., Huang, K., Gu, C., Xu, K., Zhan, R., & Shen, J. G. (2022) · Scientific Reports · doi:10.1038/s41598-022-14539-6 · Cited by 5
Multi-omics data integration in upper gastrointestinal cancers research: A review of concepts, approaches, and applicationPreprint
Mazloomnejad, R., Ahmadi, A., Piroozkhah, M., Omranian, A. Z., Zabihi, M. R., Mojarad, E. N., Kavousi, K., & Salehi, Z. (2023) · Research Square · doi:10.21203/rs.3.rs-2823707/v1 · Cited by 5
Cellular heterogeneity and communication networks in gastric cancer: Single-cell analysis reveals β-hydroxybutyrylation-associated genes and immune infiltration characteristics
Hu, Y., Xu, J., Lv, J., Qin, Y., Lu, Y., Cen, F., Yang, H., Chen, K., & Xia, S. (2025) · Translational Oncology · doi:10.1016/j.tranon.2025.102270 · Cited by 4
Constructing a novel mitochondrial metabolism-related genes signature to evaluate tumor immune microenvironment and predict survival of colorectal cancer
Wang, H., Zhang, K., & Ning, G. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1618471 · Cited by 4
SERPINB7 as a prognostic biomarker in cervical cancer: Association with immune infiltration and facilitation of the malignant phenotype
Wei, H., Zhang, R.-F., Zhao, Y., & Tong, X.-S. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e20184 · Cited by 4
Prognostic risk of immune-associated signature in the microenvironment of brain gliomas
Tao, Y., Zhu, J., Yu, X., Cong, H., Li, J., Cai, T., & Chen, Q. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1208651 · Cited by 4
“It accounts for more than 30% of the total intracranial tumors and 2% of adult cancers (Stupp et al., 2009; Gilbert et al., 2014; Hu et al., 2017; Zhang et al., 2018).”
Background
RETRACTED: Refining molecular subtypes and risk stratification of ovarian cancer through multi‐omics consensus portfolio and machine learning
Zhang, J., He, S., & Ying, H. (2024) · Environmental Toxicology · doi:10.1002/tox.24222 · Cited by 4
“13,15,16 To assess the prognostic significance of CMLS, we employed Kaplan – Meier survival curves.”
Self-citation
Retracted
Integrated Analysis Construct a Tumor-Associated Macrophage Novel Signature with Promising Implications in Predicting the Prognosis and Immunotherapeutic Response of Gastric Cancer Patients
Hua, X., Chen, Y., Niu, H., Li, X., Gai, X., & Cui, G. (2024) · Digestive Diseases and Sciences · doi:10.1007/s10620-024-08365-1 · Cited by 4
“We downloaded two PD-L1-treated datasets with clinical characteristics: IMvigor210 [20] and GSE78220 [21].”
Analysis of N6-Methyladenosine Modification Patterns and Tumor Immune Microenvironment in Pancreatic Adenocarcinoma
Liu, Y. H., Li, G., Yang, Y., Lu, Z., Wang, T., Wang, X., & Liu, J. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2021.752025 · Cited by 4
“The m6Ascore was defined using a method similar to Genomic Grade Index (GGI) (Sotiriou et al., 2006; Zeng et al., 2019): m6Ascore ∑(PC1 i + PC2 i) Where i is the expression of overlapping genes with significant prognosis-related DEGs the m6Aclusters.”
Method
m6A Regulator‐Based Exosomal Gene Methylation Modification Patterns Identify Distinct Microenvironment Characterization and Predict Immunotherapeutic Responses in Colon Cancer
Zheng, J., Feng, P., Chen, D., Zhang, C., Ji, Y., Liu, Y., Fan, X., & Li, J. (2022) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2022/9451480 · Cited by 4
“Furthermore, third, TNF, IFNG, TBX2, GZMB, CD8A, PRF1, GZMA, CXCL9, and CXCL10 were to be correlated with the pathways of immune activation [24, 25].”
Background
Prognosis, Immune Microenvironment Infiltration and Immunotherapy Response in Clear Cell Renal Cell Carcinoma Based on Cuproptosis-related Immune Checkpoint Gene Signature
Liu, G., Li, F., Ge, Y., Shi, Y., Ren, F., & Zhu, L. (2023) · Journal of Cancer · doi:10.7150/jca.88467 · Cited by 4
“…cell carcinoma treated with the anti– PD-1 agent Nivolumab (CheckMate025 cohort [15]) and patients with advanced melanoma treated with various types of immunotherapy (TCGA-SKCM) were downloaded and analyzed to determine the response to immunotherapy and the predictive value of the risk score [16].”
Pan-Cancer Analysis of P3H1 and Experimental Validation in Renal Clear Cell Carcinoma
Li, Y., Wang, T., & Jiang, F. (2024) · Applied Biochemistry and Biotechnology · doi:10.1007/s12010-023-04845-8 · Cited by 4
“High and low P3H1 gene expression groups were visually distinguished through one box plot per tumor was generated to visually distinguish between, and a heat map was used to illustrate the relationship between gene and pathway scores as part of a TME study [24].”
Ferroptosis, M6A and immune checkpoint-related gene expression in the middle temporal gyrus of the Alzheimer's disease brain
Liu, Q., Yang, F., Wu, S., Yuan, K., Huang, L., & Cai, S. (2024) · Meta-Radiology · doi:10.1016/j.metrad.2024.100048 · Cited by 4

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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