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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.
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“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]
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Hu, Q., Wang, Y., Zhang, Y., Ge, Y., Yin, Y., & Zhu, H. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.747090 · Cited by 4
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“After obtaining each gene signature score, we applied a formula similar to previous studies to calculate the FA-score [18, 19]: FA-score = ∑(PC1i + PC2i), in which the level of fatty acid metabolism was determined by its related genes.”
Pan, D., Chen, H., Xu, J., Lin, X., & Li, L. (2023) · BMC Gastroenterology · doi:10.1186/s12876-023-03003-y · Cited by 3
“It is worth noting that the immuno-therapy effect mainly depends on the immune response [11], which is markedly affected by the tumor microenvironment [12].”
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“The NecropImmScore was calculated for each sample as the sum of the contributions of each gene to PC1 and PC2 using formula [26]: where i represents expression of NecropImmune-related genes.”
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Zheng, B., Cheng, F., Yao, Z., Zhang, Y., Cong, Z., Wang, J., Niu, Z., & He, W. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.818194 · Cited by 3
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“Owing to the absence of data for an HNSC cohort treated with immune-checkpoint blockade, we referred to previous research methods [36].”
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“The immune-activated related transcripts (CXCL10, CXCL9, GZMA, GZMB, PRF1, CD8A, IFNG, TBX2, and TNF), immunecheckpoint-relevant transcripts (IDO1, CD274, HAVCR2, PDCD1, CTLA4, LAG3, and PDCD1LG2), and TGF-β/EMT pathway-relevant transcripts (VIM, ACTA2, COL4A1, TGFBR2, ZEB1, CLDN3, SMAD9, TWIST1, and TGFB2) [38] were…”
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“Moreover, TME-related biological signatures (Zeng et al., 2019) had been evaluated the different clusters characteristics (Figure 4C).”
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“In conclusion, a technique resembling prior research 30,31 was utilized to establish the PPI index for every individual.”
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“Accordingly, we calculated a necroptosis score [33]: where is the expression of NRGs. AML patients were divided into a low-score group and high-score group according to the maximally selected rank statistics (maxstat) method.”
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Tian, Y., Wang, J., Wen, Q., Su, G., & Sun, Y. (2021) · Aging · doi:10.18632/aging.202941 · Cited by 3
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“In recent years, bioinformatics tools and software have been developed to quickly explore differentially expressed target genes and identify hub genes that contribute to disease progression.(10) The CIBERSORT algorithm is a newly developed tool to assess the association between immune cell landscapes in the cell micro…”
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“The differences in immune and TME scores among subtypes lead to different outcomes and immunotherapeutic responses.[29,41] Therefore, patients were divided into 2 clusters according to the expressions of these lncRNAs.”
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A Five-MicroRNA Signature Predicts the Prognosis in Nasopharyngeal Carcinoma
Wu, S., Zhang, C., Xie, J., Li, S., & Huang, S. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.723362 · Cited by 3
“The Prognostic Value of the FiveMicroRNA Signature Was Validated in Multiple Cohorts According to the cutoff value given by R package “survminer” (19), the NPC patients in all the cohorts were divided into highrisk and low-risk groups.”
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Prognostic Impact of Stromal Profiles Educated by Gastric Cancer
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“Finally, the m6Ascore was defined using a method similar to Genomic Grade Index [26, 31, 32] where i is the expression of overlapping genes with a significant prognosis of DEGs among m6Aclusters.”
Ling, F., Feng, H., Wu, S., Zhu, D., Chen, Y., Zhou, J., Lai, J., Huang, X., Hou, T., & Li, Y. (2025) · International Immunopharmacology · doi:10.1016/j.intimp.2025.114594 · Cited by 2
Zou, X., Chen, X., Yang, J., & 李彦锋 (2025) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2025.1630250 · Cited by 2
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“The correlation between AARS1/2 and immune checkpoints in BLCA was also assessed, such as ITPRIPL1, SIGLEC15, TIGIT, and CD274 [38–40].”
Yu, H., You, C., Xu, T., Zhao, Z., Wang, D., Li, H., 戴良哲, Zheng, W., Wang, L., Ji, M., et al. (2025) · Journal of Ovarian Research · doi:10.1186/s13048-025-01877-y · Cited by 3
“ITPRIPL1, SIGLEC15, TIGIT, CD274, HAVCR2, PDCD1, CTLA4, LAG3, and PDCD1LG2 are key genes associated with immune regulation [29–33].”
Sun, K., Ning, J., Jia, K., Fan, X., Li, H., Ma, C., & Wei, L. (2026) · Neoplasia · doi:10.1016/j.neo.2026.101277 · Cited by 3
Zhu, H., Lordick, F., Janjigian, Y. Y., Rha, S. Y., Nilsson, M., Shitara, K., Owen, R. P., Zhang, Z., & Smyth, E. C. (2026) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-026-01156-9 · Cited by 2
“Features of the TME that have been associated with resistance to ICIs through immunosuppression include cytokines such as IL-6, TNF and TGFβ and high densities of regulatory T cells and M2-polarized macrophages 90 .”
InfluentialXu, T., Yang, M., Huang, J., Lu, L., Deng, D., Shi, X., Sun, Y., Lovell, J. F., Lin, Z. B., Zheng, Y. B., et al. (2026) · BMC Medicine · doi:10.1186/s12916-026-05017-1 · Cited by 2
“Despite growing recognition of immune and stromal roles within the TME, comprehensive understanding of immune cell composition, states, and contributions to immune evasion in MA remains limited [42–44].”
Zhang, J., Zhang, Q., Shi, Y. X., Wang, P., Gong, Y., He, S., Li, Z., Feng, N., Wang, Y., Jiang, P., et al. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.837145 · Cited by 2
Self-citationWang, J.-B., Qiu, Q., Lian, N.-Z., Wang, H., Zheng, Q., Yang, Y., Ma, Y., Zhao, Y., Li, P., Lin, J., et al. (2022) · PubMed Central · Cited by 2
Zhang, Y., Li, L., Chu, F., Zhang, L., Zhang, L., Wu, H., & Li, K. (2022) · Aging · doi:10.18632/aging.204463 · Cited by 2
Wang, Z., Wang, T., Wu, G., Zhu, L., & Zhang, J. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12051892 · Cited by 2
“The prognostic signature developed from TME may serve as a novel predictive biomarker and improve the prognosis of BC patients receiving ICIs [23].”
BackgroundInfluentialSelf-citationDiagnosis and prognosis prediction model for digestive system tumors based on immunologic gene sets
Zhou, L., & Wang, C. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1107532 · Cited by 2
“The tumor immune microenvironment has been shown to play a key role in tumor development and influence clinical outcomes, and can serve as potential biomarkers to improve the reliability and accuracy of diagnosis and prognosis (11, 12).”
BackgroundZhang, Z., Tan, J.-L., Yu, Z., Liu, C., Wang, J., Wu, D., & Bai, X. T. (2023) · PubMed Central · doi:10.12122/j.issn.1673-4254.2023.05.01 · Cited by 2
Li, H., Jiang, Y., Chen, J., Li, Z., Zhang, R., Wei, Y., Zhao, Y., Shen, S., & Chen, F. (2023) · Molecular Omics · doi:10.1039/d3mo00171g · Cited by 2
mRNA vaccines in gastric cancer: How close are we?
Portillo‐Miño, J. D., Bettin-Gonzalez, D., & Coral, F. A. M. (2023) · Vacunas · doi:10.1016/j.vacun.2023.10.006 · Cited by 2
Clinical relevance of ORCs in predicting prognosis and immunotherapy outcomes: A pan-cancer analysis
Zhao, Y., Wang, J., Liang, X., & Wang, C. (2024) · Immunobiology · doi:10.1016/j.imbio.2024.152783 · Cited by 3
Li, P., Xiong, P., Li, X., Zhang, X., Chen, X., Zhang, W., Jia, B., & Lai, Y. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e26851 · Cited by 2
“The method used to define the m6Ascore is similar to that used for Gillette ’ s gait index (GGI) [34,35], and the specific formula is as follows: Where i represents the genes related to the m 6 A phenotype.”
MethodWang, H., Liu, Y., Tang, A., & Zhang, X. (2024) · European journal of medical research · doi:10.1186/s40001-024-01883-8 · Cited by 2
“Different molecular subtypes have different immune scores, which may result in different prognoses [56, 57].”
BackgroundPan-cancer exploration of PNO1: A prospective prognostic biomarker with ties to immune infiltration
Qin, Y., Li, Z., Zhang, X., Li, J., Teng, Y., Zhang, N., Zhao, S., Kong, L., & Niu, W. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e36819 · Cited by 2
“Another heatmap was used to show the correlation between PNO1 expression levels and TME-related scores [18].”
BackgroundMethodLi, Y., Liu, J., Qi, L., Yuan, X., Yang, K., Ren, Y., Shi, Q., Xu, G., Wang, W., Luo, C., et al. (2024) · Digestive and Liver Disease · doi:10.1016/j.dld.2024.08.003 · Cited by 2
Pan-cancer analysis of the prognostic and immunological role of hippo-YAP signaling pathway
Yang, J., Yang, C., Yang, G., Wang, R., Li, J., & Song, Y. (2024) · Discover Oncology · doi:10.1007/s12672-024-01212-9 · Cited by 2
“In addition, we used another evaluation method of the tumor microenvironment provided in previous research [45] to analyze the relationship between the Hippo-YAP pathway and DNA loss repair-related pathways, stromal/metastasis-related pathways, immune-related pathways, and immune checkpoint showed…”
MethodMao, J., Li, H., & Huang, Z. (2024) · Nucleosides Nucleotides & Nucleic Acids · doi:10.1080/15257770.2024.2410957 · Cited by 2
“[31,32] The R software program ggplot2 was used to create the box plot.”
Li, H., Niu, X., & Cheng, R. (2024) · Translational Cancer Research · doi:10.21037/tcr-24-556 · Cited by 2
“Additionally, we applied a methodology curated by Zeng (12) to investigate the correlation between the LTEMGs score and other tumor microenvironment-related biological processes.”
Host Tissue Factors Predict Immune Surveillance and Therapeutic Outcomes in Gastric Cancer
Abate, M., Stroobant, E. E., Fei, T., Lin, Y.-H., Shimada, S., Drebin, H. M., Chen, E., Tang, L. H., Shah, S. P., Wolchok, J. D., et al. (2025) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-23-0563 · Cited by 2
Predictive biomarkers for immunotherapy in gastric cancer
Li, S., Liang, H., & Li, G. (2025) · Journal of Cancer Metastasis and Treatment · doi:10.20517/2394-4722.2024.107 · Cited by 4
Zhou, S., Jiang, D., Liu, Y., Wang, Q., Hu, M., Dai, K., Chen, L., Zhang, T., Cai, C., & Wang, J. (2025) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2025.152038 · Cited by 2
Li, H., Chen, H., Zhao, T., Zhang, W., Deng, J., Xie, W., Fan, J., Lou, H., Dong, P., Han, Z., et al. (2025) · BMC Cancer · doi:10.1186/s12885-025-14248-z · Cited by 2
“TMEscore is the sum of TMEscore A and TMEscore B. TMEscore A was significantly associated with immune-relevant signatures, while TMEscore B was associated with stromal-relevant signatures [18].”
Hu, X., Ren, L., Zhu, S., Shen, H., Qian, C., Chen, G., & Chen, F. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1576696 · Cited by 2
Gao, Z., Chen, J., Tao, Y., Wang, Q., Peng, S., Yu, S., Zeng, J., Li, K., Xie, Z., & Huang, H. W. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.747985 · Cited by 2
Zhang, Y., Liang, X., Zhang, L., & Wang, D. (2021) · Journal of Oncology · doi:10.1155/2021/2408637 · Cited by 2
“2e advantage of this approach is that it concentrates the score on the largest set of highly correlated (or unrelated) gene blocks in the set while downweighting the contribution of genes that are not tracked by other set members [19, 20].”
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
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