Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 16Gastric Cancer Management and OutcomesFerroptosis and cancer prognosisCancer Immunotherapy and Biomarkers

The DOI matches its Crossref record.

Development and Validation of a Robust Immune-Related Prognostic Signature for Gastric Cancer

Open access
Huo, Junyu, Wu, Liqun, Zang, Yunjin·2021·Journal of Immunology Research
DOI 10.1155/2021/5554342PMC PMC8110424
Open this paper in CoChat — free accountOpen PDF

Abstract

Background. An increasing number of reports have found that immune-related genes (IRGs) have a significant impact on the prognosis of a variety of cancers, but the prognostic value of IRGs in gastric cancer (GC) has not been fully elucidated. Methods. Univariate Cox regression analysis was adopted for the identification of prognostic IRGs in three independent cohorts (GSE62254,n=300; GSE15459,n=191; and GSE26901,n=109). After obtaining the intersecting prognostic genes, the three independent cohorts were merged into a training cohort (n=600) to establish a prognostic model. The risk score was determined using multivariate Cox and LASSO regression analyses. Patients were classified into low-risk and high-risk groups according to the median risk score. The risk score performance was validated externally in the three independent cohorts (GSE26253,n=432; GSE84437,n=431; and TCGA,n=336). Immune cell infiltration (ICI) was quantified by the CIBERSORT method. Results. A risk score comprising nine genes showed high accuracy for the prediction of the overall survival (OS) of patients with GC in the training cohort (AUC>0.7). The risk of death was found to have a positive correlation with the risk score. The univariate and multivariate Cox regression analyses revealed that the risk score was an independent indicator of the prognosis of patients with GC (p<0.001). External validation confirmed the universal applicability of the risk score. The low-risk group presented a lower infiltration level of M2 macrophages than the high-risk group (p<0.001), and the prognosis of patients with GC with a higher infiltration level of M2 macrophages was poor (p=0.011). According to clinical correlation analysis, compared with patients with the diffuse and mixed type of GC, those with the Lauren classification intestinal GC type had a significantly lower risk score (p=0.00085). The patients’ risk score increased with the progression of the clinicopathological stage. Conclusion. In this study, we constructed and validated a robust prognostic signature for GC, which may help improve the prognostic assessment system and treatment strategy for GC.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1155/2021/5554342

Cites

Statistics

Figures

Figure 1 Gene cluster analysis. (a) Venn plot of the 45 intersecting prognosis-related genes. (b) Heatmap of gene clusters. (c) Kaplan–Meier survival analysis regarding gene clusters and OS in the training cohort.
Figure 1 Gene cluster analysis. (a) Venn plot of the 45 intersecting prognosis-related genes. (b) Heatmap of gene clusters. (c) Kaplan–Meier survival analysis regarding gene clusters and OS in the training cohort.CC BY · PubMed Central
Figure 2 Construction of a nine-gene prognostic signature. (a, b) Kaplan–Meier survival analysis and time-dependent ROC analysis regarding risk score and OS in the training cohort. (c, d) Prognostic signature visualized as a nomogram and tested by the calibration curve.
Figure 2 Construction of a nine-gene prognostic signature. (a, b) Kaplan–Meier survival analysis and time-dependent ROC analysis regarding risk score and OS in the training cohort. (c, d) Prognostic signature visualized as a nomogram and tested by the calibration curve.CC BY · PubMed Central
Figure 3 Heatmap, risk score distribution, and survival status of patients in the training cohort.
Figure 3 Heatmap, risk score distribution, and survival status of patients in the training cohort.CC BY · PubMed Central
Figure 4 Internal validation of the prognostic signature. (a–c) Kaplan–Meier survival analysis and the time-dependent ROC analysis of the risk score and gene cluster for predicting OS in the GSE62254 cohort. (d–f) Kaplan–Meier survival analysis and the time-dependent ROC analysis of the risk score and gene cluster for predicting OS in the GSE15459 cohort. (g–i) Kaplan–Meier survival analysis and the time-dependent ROC analysis of the risk score and gene cluster for predicting OS in the GSE26901 cohort.
Figure 4 Internal validation of the prognostic signature. (a–c) Kaplan–Meier survival analysis and the time-dependent ROC analysis of the risk score and gene cluster for predicting OS in the GSE62254 cohort. (d–f) Kaplan–Meier survival analysis and the time-dependent ROC analysis of the risk score and gene cluster for predicting OS in the GSE15459 cohort. (g–i) Kaplan–Meier survival analysis and the time-dependent ROC analysis of the risk score and gene cluster for predicting OS in the GSE26901 cohort.CC BY · PubMed Central
Figure 5 External validation of the prognostic signature. (a–c) Kaplan–Meier survival analysis of the prognostic signature for predicting OS in the GSE84437, GSE26253, and TCGA cohorts. (d–f) The risk score distribution and the survival status of patients in the GSE84437, GSE26253, and TCGA cohorts.
Figure 5 External validation of the prognostic signature. (a–c) Kaplan–Meier survival analysis of the prognostic signature for predicting OS in the GSE84437, GSE26253, and TCGA cohorts. (d–f) The risk score distribution and the survival status of patients in the GSE84437, GSE26253, and TCGA cohorts.CC BY · PubMed Central
Figure 6 The immune cell infiltration landscape of all included samples. (a, b) Heatmap and the boxplot showing the difference in immune cell infiltration in different risk groups. (c) Kaplan–Meier survival analysis regarding immune cell infiltration and OS.
Figure 6 The immune cell infiltration landscape of all included samples. (a, b) Heatmap and the boxplot showing the difference in immune cell infiltration in different risk groups. (c) Kaplan–Meier survival analysis regarding immune cell infiltration and OS.CC BY · PubMed Central
Figure 7 The correlation analysis between the risk score and clinical characteristics. (a) The Kaplan–Meier survival analysis regarding Lauren classification and OS. (b) Barplot of proportions of different Lauren classification subtypes in high- and low-risk groups. (c) Boxplot of the risk score difference in different Lauren classification subtypes. (d) Kaplan–Meier survival analysis regarding stage and OS. (e) Barplot of proportions of different stages in high- and low-risk groups. (f) Boxplot of the risk score difference in different stages.
Figure 7 The correlation analysis between the risk score and clinical characteristics. (a) The Kaplan–Meier survival analysis regarding Lauren classification and OS. (b) Barplot of proportions of different Lauren classification subtypes in high- and low-risk groups. (c) Boxplot of the risk score difference in different Lauren classification subtypes. (d) Kaplan–Meier survival analysis regarding stage and OS. (e) Barplot of proportions of different stages in high- and low-risk groups. (f) Boxplot of the risk score difference in different stages.CC BY · PubMed Central
Figure 8 Gene set enrichment analysis for immune-related pathways in different risk groups.
Figure 8 Gene set enrichment analysis for immune-related pathways in different risk groups.CC BY · PubMed Central
Figure 9 The presentation of ESTIMATE calculation results.
Figure 9 The presentation of ESTIMATE calculation results.CC BY · PubMed Central
Figure 10 The Kaplan–Meier survival analysis of Stromal Score, Immune Score, and ESTIMATE Score in the six independent cohorts.
Figure 10 The Kaplan–Meier survival analysis of Stromal Score, Immune Score, and ESTIMATE Score in the six independent cohorts.CC BY · PubMed Central
Figure 11 The association between risk score and the Stromal Score, Immune Score, and ESTIMATE Score.
Figure 11 The association between risk score and the Stromal Score, Immune Score, and ESTIMATE Score.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 47

  1. 10.3748/wjg.v12.i3.354 · DOI
  2. 10.1016/S0140-6736(16)30354-3 · DOI
  3. 10.1016/S0140-6736(09)60617-6 · DOI
  4. 10.1016/j.cgh.2019.07.045 · DOI
  5. 10.1111/jgh.15037 · DOI
  6. R. E. Sexton, 2020, Gastric cancer: a comprehensive review of current and future treatment strategies, Cancer and Metastasis Reviews, 1
  7. 10.3390/cancers12071981 · DOI
  8. H. R. Kazmi, 2018, Epigenetic mechanisms and events in gastric cancer-emerging novel biomarkers, Pathology & Oncology Research, 24, 757, 10.1007/s12253-018-0410-z · DOI
  9. W. Yang, 2017, Molecular mechanisms and theranostic potential of miRNAs in drug resistance of gastric cancer, Expert opinion on therapeutic targets, 21, 1063, 10.1080/14728222.2017.1389900 · DOI
  10. J. Wang, 2020, A novel prognostic signature of immune‐related genes for patients with colorectal cancer, Journal of cellular and molecular medicine, 24, 8491, 10.1111/jcmm.15443 · DOI
  11. B. Li, 2017, Development and validation of an individualized immune prognostic signature in early-stage nonsquamous non–small cell lung cancer, JAMA oncology, 3, 1529, 10.1001/jamaoncol.2017.1609 · DOI · Paper page
  12. J. Huo, 2020, Development and validation of a novel immune-gene pairs prognostic model associated with CTNNB1 alteration in hepatocellular carcinoma, Medical Science Monitor, 26
  13. 10.3389/fmolb.2020.581354 · DOI
  14. J. Huo, 2021, Eight-gene metabolic signature related with tumor-associated macrophages predicting overall survival for hepatocellular carcinoma, BMC cancer, 21, 1
  15. J. Huo, 2021, Development and validation of a CTNNB1‐associated metabolic prognostic model for hepatocellular carcinoma, Journal of Cellular and Molecular Medicine, 25, 1151, 10.1111/jcmm.16181 · DOI
  16. 10.1016/j.lfs.2020.118396 · DOI
  17. P. Charoentong, 2017, Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade, Cell reports, 18, 248, 10.1016/j.celrep.2016.12.019 · DOI · Paper page
  18. A. Jemal, 2011, Global cancer statistics, CA: a cancer journal for clinicians, 61, 69
  19. A. D. Wagner, 2017, Chemotherapy for advanced gastric cancer, Cochrane database of systematic reviews, 10.1002/14651858.CD004064.pub4 · DOI
  20. T. Sano, 2017, Proposal of a new stage grouping of gastric cancer for TNM classification: International Gastric Cancer Association staging project, Gastric cancer, 20, 217, 10.1007/s10120-016-0601-9 · DOI
  21. T. L. Whiteside, 2008, The tumor microenvironment and its role in promoting tumor growth, Oncogene, 27, 5904, 10.1038/onc.2008.271 · DOI · Paper page
  22. R. Kim, 2007, Cancer immunoediting: from immune surveillance to immune escape, 9, 10.1016/B978-012372551-6/50066-3 · DOI
  23. S. Yu, 2020, Seven-gene signature based on glycolysis is closely related to the prognosis and tumor immune infiltration of patients with gastric cancer, Frontiers in oncology, 10
  24. R. Cristescu, 2015, Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes, Nature medicine, 21, 449, 10.1038/nm.3850 · DOI · Paper page
  25. S. C. Oh, 2018, Clinical and genomic landscape of gastric cancer with a mesenchymal phenotype, Nature communications, 9, 1
  26. S. Aras, 2017, TAMeless traitors: macrophages in cancer progression and metastasis, British journal of cancer, 117, 1583, 10.1038/bjc.2017.356 · DOI
  27. S. C. Ming, 1977, Gastric carcinoma: a pathobiological classification, Cancer, 39, 2475, 10.1002/1097-0142(197706)39:6<2475::AID-CNCR2820390626>3.0.CO;2-L · DOI
  28. M. Qiu, 2014, Lauren classification combined with HER2 status is a better prognostic factor in Chinese gastric cancer patients, BMC cancer, 14, 1
  29. J. Y. Lee, 2017, The characteristics and prognosis of diffuse-type early gastric cancer diagnosed during health check-ups, Gut and liver, 11, 807, 10.5009/gnl17033 · DOI
  30. H. Jung, 2011, Validation of the seventh edition of the American Joint Committee on Cancer TNM staging system for gastric cancer, Cancer, 117, 2371, 10.1002/cncr.25778 · DOI
  31. S. Zhang, 2021, Construction of an immune-related gene signature to predict survival and treatment outcome in gastric cancer, Science Progress, 104, 0036850421997286, 10.1177/0036850421997286 · DOI
  32. B. Jiang, 2019, An immune-related gene signature predicts prognosis of gastric cancer, Medicine, 98, 10.1097/MD.0000000000016273 · DOI
  33. E. Zhao, 2020, A signature of 14 immune-related gene pairs predicts overall survival in gastric cancer, Clinical and Translational Oncology, 1
  34. A. Etemadi, 2020, The global, regional, and national burden of stomach cancer in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease study 2017, The Lancet Gastroenterology & Hepatology, 5, 42, 10.1016/S2468-1253(19)30328-0 · DOI
  35. H. Osumi, 2020, Tumor cell‐derived angiopoietin‐like protein 2 establishes a preference for glycolytic metabolism in lung cancer cells, Cancer science, 111, 1241, 10.1111/cas.14337 · DOI
  36. H. Horiguchi, 2020, Stroma-derived ANGPTL2 establishes an anti-tumor microenvironment during intestinal tumorigenesis, Oncogene, 1
  37. Y. W. Shen, 2020, Targeting CTGF in cancer: an emerging therapeutic opportunity
  38. Y. Cui, 2020, Pan-cancer analysis identifies ESM1 as a novel oncogene for esophageal cancer, Esophagus, 1
  39. 10.1155/2020/6909672 · DOI
  40. Y. Chen, 2020, Sox9/INHBB axis-mediated crosstalk between the hepatoma and hepatic stellate cells promotes the metastasis of hepatocellular carcinoma, Cancer Letters, 499, 243, 10.1016/j.canlet.2020.11.025 · DOI
  41. A. Dasgupta, 2020, SIRT1–NOX4 signaling axis regulates cancer cachexia, Journal of Experimental Medicine, 217, 10.1084/jem.20190745 · DOI
  42. K. Ford, 2020, NOX4 inhibition potentiates immunotherapy by overcoming cancer-associated fibroblast-mediated CD8 T-cell exclusion from tumors, Cancer research, 80, 1846, 10.1158/0008-5472.CAN-19-3158 · DOI · Paper page
  43. A. Sharanek, 2020, OSMR controls glioma stem cell respiration and confers resistance of glioblastoma to ionizing radiation, Nature communications, 11, 1, 10.1038/s41467-020-17885-z · DOI
  44. L. Gao, 2020, The RBP1–CKAP4 axis activates oncogenic autophagy and promotes cancer progression in oral squamous cell carcinoma, Cell death & disease, 11, 1
  45. Y. Zhang, 2021, Slit2 is a potential biomarker for renal impairment in systemic lupus erythematosus, Clinical and Experimental Medicine, 21, 63, 10.1007/s10238-020-00664-x · DOI
  46. B. Tavora, 2020, Tumoural activation of TLR3–SLIT2 axis in endothelium drives metastasis, Nature, 586, 299, 10.1038/s41586-020-2774-y · DOI
  47. Y. Zhou, 2020, Single-cell multiomics sequencing reveals prevalent genomic alterations in tumor stromal cells of human colorectal cancer, Cancer Cell, 38, 818, 10.1016/j.ccell.2020.09.015 · DOI

Cited by

Metadata

Authors
Huo, J., Wu, L., & Zang, Y.
Affiliations
  • Qingdao University (CN)
  • Affiliated Hospital of Qingdao University (CN)
Corresponding author
Wu, Liqun
ORCID
Venue
Journal of Immunology Research
Publisher
Wiley
Published
2021-04-30
Volume
2021
Pages
1-24
ISSN
  • 2314-7156 (ISSN-L)
  • 2314-8861
DOI
10.1155/2021/5554342
PMC
PMC8110424
OpenAlex
W3157208801
Cited by
16
References
0
Citations by year
2026: 2 · 2025: 2 · 2024: 1 · 2023: 5 · 2022: 4 · 2021: 2
FWCI
2.60
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Immunology Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Oncology › Gastric Cancer Management and Outcomes
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Ferroptosis and cancer prognosis
  • Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
Keywords
gastric cancer, immune-related genes, prognostic gene signature, risk score, overall survival, M2 macrophage infiltration, tumor immune cell infiltration, Lauren classification, clinicopathological stage, LASSO, Cox proportional hazards model
MeSH terms
Aged, Female, Humans, Male, Middle Aged, Neoplasm Staging, Risk Factors, Stomach Neoplasms, Biomarkers, Tumor, Cohort Studies, Gene Expression Regulation, Neoplastic, Risk Assessment, Gene Expression Profiling, Protein Interaction Mapping, Nomograms, Kaplan-Meier Estimate, Gene Regulatory Networks, Protein Interaction Maps, Datasets as Topic

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

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.