The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
Tumor Microenvironment Characterization in Gastric Cancer Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures
Published version of a preprint
Abstract
Tumor microenvironment (TME) cells constitute a vital element of tumor tissue. Increasing evidence has elucidated their clinicopathologic significance in predicting outcomes and therapeutic efficacy. Nonetheless, no studies have reported a systematic analysis of cellular interactions in the TME. In this study, we comprehensively estimated the TME infiltration patterns of 1,524 gastric cancer patients and systematically correlated the TME phenotypes with genomic characteristics and clinicopathologic features of gastric cancer using two proposed computational algorithms. Three TME phenotypes were defined, and the TMEscore was constructed using principal component analysis algorithms. The high TMEscore subtype was characterized by immune activation and response to virus and IFNγ. Activation of transforming growth factor β, epithelial–mesenchymal transition, and angiogenesis pathways were observed in the low TMEscore subtype, which are considered T-cell suppressive and may be responsible for significantly worse prognosis in gastric cancer [hazard ratio (HR), 0.42; 95% confidence interval (CI), 0.33–0.54; P < 0.001]. Multivariate analysis revealed that the TMEscore was an independent prognostic biomarker, and its value in predicting immunotherapeutic outcomes was also confirmed (IMvigor210 cohort: HR, 0.63; 95% CI, 0.46–0.89; P = 0.008; GSE78220 cohort: HR, 0.25; 95% CI, 0.07–0.89; P = 0.021). Depicting a comprehensive landscape of the TME characteristics of gastric cancer may, therefore, help to interpret the responses of gastric tumors to immunotherapies and provide new strategies for the treatment of cancers.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Recent studies reported that alteration of tumour immune microenvironment in gastric cancer is a pivotal strategy for predicting the immunotherapeutic responses and prognosis, which might provide new treatment strategies for patients [26, 27].”
From Phosphoserine phosphatase as a prognostic biomarker in patients with gastric cancer and its potential association with immune cells (2022) · cites this paper as [27]
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · cited 4×
“The adjusted P value for multiple testing was calculated using the Benjamini–Hochberg correction (32).”
MethodInfluentialSubramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625 · cited 1×
“Gene sets were downloaded from the MSigDB database of the Broad Institute (36), and HALLMARK gene sets were selected to perform quantification of pathway activity.”
Methodlimma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458
clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters
Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012) · OMICS: A Journal of Integrative Biology · doi:10.1089/omi.2011.0118 · Cited by 36,259 · cited 2×
“Next, the clusterProfiler R package (32) was adopted to annotate gene patterns.”
MethodAlgorithm AS 136: A K-Means Clustering Algorithm
Hartigan, J. A., & Wong, M. A. (1979) · Applied Statistics · doi:10.2307/2346830 · Cited by 14,523
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697 · cited 3×
“TMB (nonsynonymous variants), which is significantly associated with efficacy of immunotherapy, was also evaluated with ROC analysis (50) in the IMvigor210 cohort (61).”
MethodRobust enumeration of cell subsets from tissue expression profiles
Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., Hoang, C. D., Diehn, M., & Alizadeh, A. A. (2015) · Nature Methods · doi:10.1038/nmeth.3337 · Cited by 13,224
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829
Complex heatmaps reveal patterns and correlations in multidimensional genomic data
Gu, Z., Eils, R., & Schlesner, M. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw313 · Cited by 10,037 · cited 1×
“OncoPrint used to depict mutation landscape of TCGA-STAD cohort was constructed by ComplexHeatmap R package (52).”
MethodInferring tumour purity and stromal and immune cell admixture from expression data
Yoshihara, K., Shahmoradgoli, M., Martínez, E., Vegesna, R., Kim, H., Torres-Garcia, W., Treviño, V., Shen, H., Laird, P. W., Levine, D. A., et al. (2013) · Nature Communications · doi:10.1038/ncomms3612 · Cited by 9,778 · cited 1×
“Moreover, the abundance of immune cells and other cells in the TME can be estimated using computational methods (16-18).”
Cited by
Sorted by citations
Zeng, D., Ye, Z., Shen, R., Yu, G., Wu, J., Xiong, Y., Zhou, R., Qiu, W., Huang, N., Sun, L., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.687975 · Cited by 1,274
Self-citationZhang, B., Wu, Q., Li, B., Wang, D., Wang, L., & Zhou, Y. L. (2020) · Molecular Cancer · doi:10.1186/s12943-020-01170-0 · Cited by 940
Sathe, A., Grimes, S. M., Lau, B. T., Chen, J., Suarez, C. J., Huang, R. J., Poultsides, G. A., & Ji, H. P. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-3231 · Cited by 395
“This resembles findings from recent studies in gastric cancer where molecular subtypes could be differentiated by gene signatures based on a combination of immunosuppression, immune activation, and stromal activation processes rather than the leucocyte fractions within the TME (48, 49).”
ResultGambardella, V., Castillo, J., Tarazona, N., Gimeno-Valiente, F., Martínez‐Ciarpaglini, C., Cabeza-Segura, M., Roselló, S., Roda, D., Huerta, M., Cervantes, A. M. R., et al. (2020) · Cancer Treatment Reviews · doi:10.1016/j.ctrv.2020.102015 · Cited by 337
Wu, J., Li, L., Zhang, H., Zhao, Y., Zhang, H., Wu, S., & Xu, B. (2021) · Oncogene · doi:10.1038/s41388-021-01853-y · Cited by 255
Metadata
- Authors
- Zeng, D., Li, M., Zhou, R., Zhang, J., Sun, H., Shi, M., Bin, J., Liao, Y., Rao, J., & Liao, W.
- Affiliations
- Nanfang Hospital (CN)
- Southern Medical University (CN)
- ORCID
- Venue
- Cancer Immunology Research
- Published
- 2019-05-01
- Volume
- 7
- Issue
- 5
- Pages
- 737-750
- ISSN
- 2326-6066 (ISSN-L)
- 2326-6074
- OpenAlex
- W2920600229
- Semantic Scholar
- 180648d1827a7e8c086f03b00ca69831a1ca538e
- Cited by
- 986
- References
- 76
- Citations by year
- 2026: 38 · 2025: 87 · 2024: 164 · 2023: 211 · 2022: 251 · 2021: 162 · 2020: 64 · 2019: 9
- FWCI
- 53.10
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cancer Immunology Research (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (81772580)
- Topics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Health Sciences › Medicine › Oncology › Gastric Cancer Management and Outcomes
- Life Sciences › Immunology and Microbiology › Immunology › Immune cells in cancer
- Keywords
- gastric cancer, tumor microenvironment, prognostic factors, immunotherapy response, immune cell infiltration, T cell suppression, epithelial-mesenchymal transition, angiogenesis, TGF-beta signaling, IFN-gamma response, gene expression signatures, IMvigor210
- MeSH terms
- Antineoplastic Agents, Immunological, Algorithms, Humans, Immunotherapy, Phenotype, Prognosis, Stomach Neoplasms, Gene Expression Regulation, Neoplastic, Tumor Microenvironment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
