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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BackgroundSelf-citation
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Method
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“Based on this, the PCA algorithm further calculated the score of genes with the highest correlation with the m6A cluster, while decreasing the contributions from other factors (16, 34).”
Method
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Result
Role and value of the tumor microenvironment in the progression and treatment resistance of gastric cancer (Review)
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“The PCA algorithm has been used to establish model signatures in our previous work [22, 23].”
BackgroundMethodSelf-citation
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Liu, X., Zheng, Y., Xiao, M., Chen, X., Zhu, Y., Xu, C., Wang, F., Liu, Z., & Cao, K. (2022) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-022-02558-0 · Cited by 30
“0 according to a reported protocol [22].”
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“This approach has the advantage of focusing the score on the set with the largest block of well-correlated (or anti-correlated) genes in the set, while down-weighing the contributions from genes that do not track with other set members (30, 32).”
BackgroundSelf-citation
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“Finally, the m6Ascore was defined by a method similar to Gene-gene interaction (GGI) [16,17]: m6Ascore ¼ X PC1i þ PC2ið Þ where, i stands for the expression of a gene associated to m6A.”
Method
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“Finally, we defined the immunotherapeutic benefit (ITBscore) of each patient using the method adapted from GGI and the TMEscore (25, 26):”
Method
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Background
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NOD2 deficiency confers a pro‐tumorigenic macrophage phenotype to promote lung adenocarcinoma progression
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“The TME has been found to be a key determining factor for diagnostic and therapeutic reactions in tumor patients (10, 11).”
Background
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Ning, Z., Hu, C., Huang, C. B., Liu, J., Zhou, T., & Zong, Z. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2020.626912 · Cited by 28
Pretreatment tumour immune microenvironment predicts clinical response and prognosis of muscle-invasive bladder cancer in the neoadjuvant chemotherapy setting
Ikarashi, D., Kitano, S., Tsuyukubo, T., Takenouchi, K., Nakayama, T., Onagi, H., Sakaguchi, A., Yamashita, M., Mizugaki, H., Maekawa, S., et al. (2021) · British Journal of Cancer · doi:10.1038/s41416-021-01628-y · Cited by 28
“The context of TME determined at diagnosis reflects the immune response [12, 13] and the effect of chemotherapy [14], and changes in the number of various immune cells infiltrating the TME are associated with clinical outcomes [15, 16].”
Background
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Zhang, X., Wei, X., Wang, Y., Wang, S., Ji, C., Yao, L., & Song, N. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2021.774279 · Cited by 28
“We at that point characterize the index employing a strategy comparable to GGI (12, 13): Index =o(PC1i + PC2i) Where i represents the expression of PRRs.”
Method
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Ren, C., Li, J., Zhou, Y., Zhang, S., & Wang, Q. (2022) · Translational Oncology · doi:10.1016/j.tranon.2022.101367 · Cited by 28
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“The studies revealed that different TME subtypes displayed different advantages in tumor immunotherapy [46,47].”
Background
Visual analysis of global research on immunotherapy for gastric cancer: A literature mining from 2012 to 2022
Chen, Y., Chen, Y., Tan, S., Zheng, Y., Liu, S., Zheng, T., Mi, Y., Lin, S., Yang, C., & Li, W. (2023) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2023.2186684 · Cited by 28
“The clinicopathological significance of TME in predicting outcomes and therapeutic effects has been demonstrated.(53) Therefore, exploring the characteristics of TME may help explain the efficacy gap of immunotherapy in different GC populations.”
Background
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Lü, Y., Wu, X., Zhang, L., Yang, M. Y., Wang, X., Huang, W., Pan, H., Wu, Y., Huang, J., Liang, W., et al. (2021) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-021-02995-4 · Cited by 27
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“Sanguini diagram was drawn via the ‘ggalluval’ package (30) for displaying the distribution of the gene expression in survival status, ages, genders, stages, and other clinical characteristics for ACC.”
Method
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Immune Landscape of Gastric Carcinoma Tumor Microenvironment Identifies a Peritoneal Relapse Relevant Immune Signature
Zhang, C., Li, D., Yu, R., Li, C., Song, Y., Chen, X., Fan, Y., Liu, Y., & Qu, X. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.651033 · Cited by 26
“the key factors affecting OS of patients with GC, several predictive models based on tumor- infiltration immune cells scores have been established to quantify the immune contexture and provide a statistical parameter for the prognosis of GC patients (16, 18, 19, 50).”
Background
A Ferroptosis-Related lncRNAs Signature Predicts Prognosis and Therapeutic Response of Gastric Cancer
Xiao, S., Liu, X., Yuan, L., & Wang, F. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.736682 · Cited by 26
“…the cytokines and chemokines characterizing different risk groups were extracted from the published literature (Barbie et al., 2009; Zeng et al., 2019), for example, PDGFRA, TGFB2, SMAD9, TWIST1, CLDN3, TGFBR2, ACTA2, COL4A1, ZEB1, and VIM are correlated with the transcripts of…”
Method
Mapk14 is a Prognostic Biomarker and Correlates with the Clinicopathological Features and Immune Infiltration of Colorectal Cancer
Wang, D., Peng, L., Hua, L., Li, J., Liu, Y., & Zhou, Y. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.817800 · Cited by 26
“In addition, we also analyzed immune checkpoint-related genes by the Wilcoxon test (Supplementary Tables S7–S8) (Ravi et al., 2018; Wang et al., 2019; Zeng et al., 2019; Yi et al., 2020).”
Method
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“Tumor Microenvironment Assessment The TMEscore was estimated using principal component analysis (PCA) based on transcriptome data of genes associated with different TME phenotypes and prognoses in GC, as previously described (11), using the R package available in GitHub (https:// github.”
BackgroundMethodSelf-citation
Identification of CXCL10-Relevant Tumor Microenvironment Characterization and Clinical Outcome in Ovarian Cancer
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“After that, we evaluated the TME score by 1https://www.ncbi.nlm.nih.gov/geo/ 2https://portal.gdc.cancer.gov/ using a method similar to GGI (Sotiriou et al., 2006; Zeng et al., 2019).”
MethodResult
Identification of novel antioxidant gene signature to predict the prognosis of patients with gastric cancer
Wu, J., Wang, X., Wang, N., Ma, L., Xie, X., Zhang, H., Kang, H., & Zhou, Z. (2021) · World Journal of Surgical Oncology · doi:10.1186/s12957-021-02328-w · Cited by 25
“In addition, more and more studies have tried to establish molecular signatures or combine multiple biomarkers to present a more convincing prediction of GC prognosis [6, 7].”
Background
Identification of copy number variation-driven molecular subtypes informative for prognosis and treatment in pancreatic adenocarcinoma of a Chinese cohort
Zhan, Q., Wen, C., Zhao, Y., Fang, L., Jin, Y., Zhang, Z., Zou, S., Li, F., Yang, Y., Wu, L., et al. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2021.103716 · Cited by 25
“Following the approach [24], we constructed the following fomula to calculate the CNV score: CNV score 1⁄4 XPC 1i XPC 1j”
MethodSelf-citation
Pan-cancer analysis identifies RNA helicase DDX1 as a prognostic marker
Gao, B., Li, X., Li, S., Wang, S., Wu, J., & Li, J. (2022) · Phenomics · doi:10.1007/s43657-021-00034-x · Cited by 25
Tumor Immunophenotyping‐Derived Signature Identifies Prognosis and Neoadjuvant Immunotherapeutic Responsiveness in Gastric Cancer
Wang, J., Qiu, Q., Zheng, Q., Zhao, Y., Xu, Y., Zhang, T., Wang, S., Wang, Q., Jin, Q., Ye, Y., et al. (2023) · Advanced Science · doi:10.1002/advs.202207417 · Cited by 24
The Potential of Five Immune-Related Prognostic Genes to Predict Survival and Response to Immune Checkpoint Inhibitors for Soft Tissue Sarcomas Based on Multi-Omic Study
Gu, H.-Y., Lin, L., Zhang, C., Yang, M., Zhong, H.-C., & Wei, R. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.01317 · Cited by 24
“Therefore, studies investigating the immune microenvironment or immune gene-related prognostic biomarkers, which have been identified in cervical cancer (15), lung adenocarcinoma (16), and cancers of the digestive system (17) are warranted.”
Background
Development and validation of metabolism-related gene signature in prognostic prediction of gastric cancer
Luo, T., Li, Y., Nie, R., Liang, C., Liu, Z., Xue, Z., Chen, G., Jiang, K., Liu, Z., Lin, H., et al. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.09.037 · Cited by 24
“Zeng et al. revealed that inﬁltrating immune cell was an independent prognostic biomarker in GC, and estimated its value in predicting chemotherapeutic and immunotherapeutic outcomes [32,33].”
Background
The distinct roles of exosomes in tumor-stroma crosstalk within gastric tumor microenvironment
Zhang, H., Yang, M., Wu, X., Li, Q., Li, X., Zhao, Y., Du, F., Chen, Y., Wu, Z., Xiao, Z., et al. (2021) · Pharmacological Research · doi:10.1016/j.phrs.2021.105785 · Cited by 23
Self-citation
GARP Correlates With Tumor-Infiltrating T-Cells and Predicts the Outcome of Gastric Cancer
Jiang, S., Zhang, Y., Zhang, X., Lu, B., Sun, P., Wu, Q., Ding, X., & Huang, J. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.660397 · Cited by 25
“The variations in the phenotype of tumor-infiltrating immune cells and their relationship with prognosis highlight the clinical significance of the crosstalk between tumor cells and TME (34).”
Background
Characterization of gastric cancer stem-like molecular features, immune and pharmacogenomic landscapes
Wei, C., Chen, M., Deng, W., Bie, L., Ma, Y., Zhang, C., Liu, K., Shen, W., Wang, S., Yang, C., et al. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab386 · Cited by 24
Glutamine Metabolism Scoring Predicts Prognosis and Therapeutic Resistance in Hepatocellular Carcinoma
Ying, L., Cheng, M., Lü, Y., Tao, Q., Chen, X., Shen, B., Xiong, F., Hu, Z., Wang, D., & Li, X. (2021) · Pathology & Oncology Research · doi:10.3389/pore.2021.1610075 · Cited by 25
“Genetic expression data presented as the fragments per kilobase per million (FPKM) values were transformed into transcripts per kilobase million (TPM) values to improve comparability between samples [8].”
Method
DNA Methylation Modification Map to Predict Tumor Molecular Subtypes and Efficacy of Immunotherapy in Bladder Cancer
Ye, F., Liang, Y., Hu, J., Hu, Y. Y., Liu, Y., Zhang, C., Ou, Y., Xu, C., & Jiang, H. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.760369 · Cited by 25
“Besides, HAVCR2, CD80, LAG3, CD86, TIGIT, PDCD1, TNFRSF9, PD-L1, IDO1, CTLA4, and PD-L2 are associated with the transcripts of immune checkpoints, and CXCL10, PRF1, CD8A, CXCL9, GZMB, GZMA, TNF, IFNG, and TBX2 are associated with immune-activated transcripts (Sotiriou et al., 2006; Barbie et al., 2009; Ritchie et al.,…”
Tumor Microenvironment Characterization for Assessment of Recurrence and Survival Outcome in Gastric Cancer to Predict Chemotherapy and Immunotherapy Response
Chen, Y., Sun, Z., Wan, L., Chen, H., Xi, T., & Jiang, Y. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.890922 · Cited by 24
“Subsequent studies defined other classification models based on tumor-associated infiltrating cells or mesenchymal–epithelial phenotypes (8, 24).”
Background
Tumor Microenvironment Evaluation for Gastrointestinal Cancer in the Era of Immunotherapy and Machine Learning
Ye, Z., Zeng, D., Zhou, R., Shi, M., & Liao, W. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.819807 · Cited by 25
“With the revolutionary breakthroughs in cancer immunotherapy including immune checkpoint blockades (ICBs), the evergrowing investigation into the host immune response has shed new light on the TME (10 – 15).”
BackgroundInfluentialSelf-citation
Polyamine metabolism patterns characterized tumor microenvironment, prognosis, and response to immunotherapy in colorectal cancer
Zhang, E., Ding, C., Li, S., Aikemu, B., Zhou, X., Fan, X., Sun, J. P., Yang, X. Y., & Zheng, M. (2023) · Cancer Cell International · doi:10.1186/s12935-023-02892-z · Cited by 26
Perineural Invasion and Postoperative Adjuvant Chemotherapy Efficacy in Patients With Gastric Cancer
Qing, T., Zhu, W., Zhao, X., Li, M., Shu, Y., Wang, D., & Li, X. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.00530 · Cited by 24
Self-citation
Current therapies and progress in the treatment of advanced gastric cancer
Li, H., Shen, M., & Wang, S. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1327055 · Cited by 23
Uncovering the Association Between m5C Regulator-Mediated Methylation Modification Patterns and Tumour Microenvironment Infiltration Characteristics in Hepatocellular Carcinoma
Gu, X., Zhou, H., Chu, Q., Zheng, Q., Wang, J., & Zhu, H. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.727935 · Cited by 23
“Tumour necrosis factor, interferon, CD8A, CXCL9, CXCL10, GZMA, GZMB, PRF1, and TBX2 were associated with immune activation transcription (Barbie et al., 2009; Zeng et al., 2019).”
Background
m6A regulator-mediated methylation modification patterns and tumor microenvironment infiltration characterization in hepatocellular carcinoma
Huang, X., Qiu, Z., Li, L., Chen, B., & Huang, P. (2021) · Aging · doi:10.18632/aging.203456 · Cited by 23
“Then, based on previous research [15, 16], the m6A score was calculated using the formula:”
Method
Comprehensive analysis of cuproptosis-related long noncoding RNA immune infiltration and prediction of prognosis in patients with bladder cancer
Zhang, Y., Li, X., Li, X., Zhao, Y.-G., Zhou, T., Jiang, X., Wen, Y., Meng, W., & Li, S. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.990326 · Cited by 23
“Subtypes vary with immune and TME scores, which in turn lead to different prognosis and immunotherapeutic responses (Zeng et al., 2019; DeBerardinis, 2020).”
Background
ENO2 affects the EMT process of renal cell carcinoma and participates in the regulation of the immune microenvironment
Chen, W., Yang, W., Gong, M., He, Y., Xu, D., Chen, J., Chen, W., Li, W., Wang, Y., Dong, K., et al. (2022) · Oncology Reports · doi:10.3892/or.2022.8470 · Cited by 24
Biological knowledge graph-guided investigation of immune therapy response in cancer with graph neural network
Zhao, L., Qi, X., Chen, Y., Qiao, Y., Bu, D., Wu, Y., Luo, Y., Wang, S., Zhang, R., & Zhao, Y. C. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad023 · Cited by 24
Identifying ITGB2 as a Potential Prognostic Biomarker in Ovarian Cancer
Li, C., Deng, T., Cao, J., Zhou, Y., Luo, X., Feng, Y., Huang, H., & Liu, J. (2023) · Diagnostics · doi:10.3390/diagnostics13061169 · Cited by 23
The Role of LMP1 in Epstein-Barr Virus-associated Gastric Cancer
Huang, X., Zhang, M., & Zhang, Z. (2023) · Current Cancer Drug Targets · doi:10.2174/1568009623666230512153741 · Cited by 23
An Integrated Analysis Identified TAGLN2 As an Oncogene Indicator Related to Prognosis and Immunity in Pan-Cancer
Pan, T., Wang, S., & Wang, Z. (2023) · Journal of Cancer · doi:10.7150/jca.84454 · Cited by 23
“Therefore, TAGLN2 expression and TME-related gene signature scores were examined using R packages according to a previously reported method[14].”
Method
Distribution pattern of tumor infiltrating lymphocytes and tumor microenvironment composition as prognostic indicators in anorectal malignant melanoma
Kim, S., Kim, Y. I., Mustafa, B., Kim, M., Jeong, G., Ahn, S.-M., Lim, S., Yu, C. S., Kim, J. C., Hong, S., et al. (2020) · Modern Pathology · doi:10.1038/s41379-020-0633-x · Cited by 23
The Study of Sarcoma Microenvironment Heterogeneity Associated With Prognosis Based on an Immunogenomic Landscape Analysis
Deng, J., Zeng, W., Kong, W., Shi, Y., & Mou, X. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.01003 · Cited by 23
C3, C3AR1, HLA-DRA, and HLA-E as potential prognostic biomarkers for renal clear cell carcinoma
Chu, G., Jiao, W., Yang, X., Liang, Y., Li, Z., & Niu, H. (2020) · Translational Andrology and Urology · doi:10.21037/tau-20-699 · Cited by 22
“TPM data can make samples more comparable and improve the credibility of the analysis results (9-11).”
OGDHL closely associates with tumor microenvironment and can serve as a prognostic biomarker for papillary thyroid cancer
Mao, M., Huang, R., Zheng, J., Hai-qi, L., Huang, W., Liu, J., & Li, J. (2021) · Cancer Medicine · doi:10.1002/cam4.3640 · Cited by 22
USH2A Mutation is Associated With Tumor Mutation Burden and Antitumor Immunity in Patients With Colon Adenocarcinoma
Sun, Y., Li, L., Yao, W., Liu, X., Yang, Y., Ma, B., & Xue, D. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.762160 · Cited by 21
“In cases with a USH2A mutation, among the common immune checkpoint genes (PDL1, CTLA4, LAG3, SIGLEC15, HAVCR2, PDCD1LG2, PD1, and TIGIT) (Wang et al., 2019a; Zeng et al., 2019), the expression levels of PDL1, CTLA4, LAG3, HAVCR2, PD1, LG2, and TIGIT were significantly increased (Figure 5E).”
Background
Unraveling the Expression Patterns of Immune Checkpoints Identifies New Subtypes and Emerging Therapeutic Indicators in Lung Adenocarcinoma
Zhang, J., Han, X., Lin, L., Chen, J., Wang, F., Ding, Q., Li, H., Wang, L., Wei, J., Wang, Y., et al. (2022) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2022/3583985 · Cited by 21
“The formula we de ﬁ ned was similar to previous studies [28, 29]: ICGscore = ∑ ð PC1 i + PC2 i Þ , where i represents the expression of ICG-related genes.”
Self-citation
A novel prognostic index of stomach adenocarcinoma based on immunogenomic landscape analysis and immunotherapy options
Xue, W., Dong, B., Wang, Y., Xie, Y., Xue, Q., Gong, Z., Zhu, C., & Niu, Z. (2022) · Experimental and Molecular Pathology · doi:10.1016/j.yexmp.2022.104832 · Cited by 22
Mechanism underlying circRNA dysregulation in the TME of digestive system cancer
Wu, Z., Yu, X., Zhang, S., He, Y., & Guo, W. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.951561 · Cited by 22
Sirtuins (SIRTs) As a Novel Target in Gastric Cancer
Poniewierska-Baran, A., Warias, P., & Zgutka, K. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232315119 · Cited by 22
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Background
Integrative analysis of cancer-associated fibroblast signature in gastric cancer
Zhao, Z., Mak, T. K., Shi, Y., Li, K., Huo, M., & Zhang, C. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e19217 · Cited by 22
Tumor microenvironment characterization identifies two lung adenocarcinoma subtypes with specific immune and metabolic state
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Identification and Validation of the Immune Subtypes of Lung Adenocarcinoma: Implications for Immunotherapy
Song, Y., Yan, S., Fan, W., Zhang, M., Liu, W., Lu, H., Cao, M., Hao, C., Chen, L., Tian, F., et al. (2020) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.00550 · Cited by 23
Survival-associated alternative splicing events interact with the immune microenvironment in stomach adenocarcinoma
Ye, Z., Zheng, M., Liu, Q., Zeng, Y., Wei, S., Wang, Y., Lin, Z., Chen, S., Zheng, Q., & Chen, L. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i21.2871 · Cited by 22
Comprehensive Analysis of the Tumor Immune Microenvironment Landscape in Glioblastoma Reveals Tumor Heterogeneity and Implications for Prognosis and Immunotherapy
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The Characteristics of Tumor Microenvironment Predict Survival and Response to Immunotherapy in Adrenocortical Carcinomas
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Chimeric cytokine receptor TGF-β RⅡ/IL-21R improves CAR-NK cell function by reversing the immunosuppressive tumor microenvironment of gastric cancer
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Self-citation
Construction of a Necroptosis-Associated Long Non-Coding RNA Signature to Predict Prognosis and Immune Response in Hepatocellular Carcinoma
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Background
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PLCG2 as a potential indicator of tumor microenvironment remodeling in soft tissue sarcoma
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RANBP2 Activates O-GlcNAcylation through Inducing CEBPα-Dependent OGA Downregulation to Promote Hepatocellular Carcinoma Malignant Phenotypes
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“0 according to a reported protocol [41].”

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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