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Cited by 16Gastric Cancer Management and OutcomesFerroptosis and cancer prognosisCancer Immunotherapy and Biomarkers

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

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Huo, Junyu, Wu, Liqun, Zang, Yunjin·2021·Journal of Immunology Research
DOI 10.1155/2021/5554342PMC PMC8110424
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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.

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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

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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

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Huo, J., Wu, L., & Zang, Y. (2021). Development and Validation of a Robust Immune-Related Prognostic Signature for Gastric Cancer. Journal of Immunology Research. https://doi.org/10.1155/2021/5554342

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