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. 2023 Jul;149(8):5453-5468.
doi: 10.1007/s00432-022-04474-4. Epub 2022 Dec 3.

Comprehensive analysis of cuproptosis-related genes in prognosis, tumor microenvironment infiltration, and immunotherapy response in gastric cancer

Haihang Nie #  1   2 , Haizhou Wang #  1   2 , Meng Zhang #  1   2 , Yumei Ning  1   2 , Xiaojia Chen  1   2 , Zhang Zhang  1   2 , Xinyi Hu  1   2 , Qiu Zhao  1   2 , Pengfei Chen  3 , Jun Fang  4   5   6 , Fan Wang  7   8
Affiliations

V体育官网 - Comprehensive analysis of cuproptosis-related genes in prognosis, tumor microenvironment infiltration, and immunotherapy response in gastric cancer

Haihang Nie et al. J Cancer Res Clin Oncol. 2023 Jul.

V体育平台登录 - Abstract

Backgrounds: Cuproptosis is the most recently identified copper-dependent cell death form that influences tricarboxylic acid (TCA) cycle. However, the relationship between cuproptosis and clinical prognosis, tumor microenvironment infiltration (TME), and response to immunotherapy remains unclear VSports手机版. .

Methods: Single-sample gene-set enrichment analysis (ssGSEA) was employed to construct cuproptosisScore (cpS) and 1378 gastric cancer (GC) patients from five independent public datasets were classified into high- or low-cpS groups according to the median of cpS. Then the impacts of cuproptosis on tumor microenvironment infiltration (TME), biological function, response to immunotherapy, and clinical prognosis of GC were evaluated. RiskScore and nomogram were constructed using Lasso Cox regression algorithm to validate its predictive capability in GC patients V体育安卓版. .

Results: Compared to patients with high cpS, patients with low cpS exhibited poorer prognosis, higher TNM stage, and stronger stromal activation. Meanwhile, the analysis of response to immunotherapy confirmed patients with high cpS could better benefit from immunotherapy and had a better susceptibility to chemotherapeutic drugs. Then, 9 prognosis-related signatures were collected based on differentially expressed genes (DEGs) of cpS groups. Finally, a riskScore model was constructed using the multivariate Cox (multi-Cox) regression coefficients of prognosis-related signatures and had an excellent capability of predicting 1-, 3-, and 5-year survival in GC patients V体育ios版. .

Conclusions: This study revealed the role of curproptosis in TME, response to immunotherapy, and clinical prognosis in GC, which highlighted the significant clinical implications of curproptosis and provided novel ideas for the therapeutic application of cuproptosis in GC. VSports最新版本.

Keywords: Cuproptosis; Gastric cancer; Immunotherapy; Prognosis; Tumor microenvironment. V体育平台登录.

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"V体育ios版" Conflict of interest statement

The authors have no relevant financial or non-financial interests to disclose.

Figures

Fig. 1
Fig. 1
Genetic and expression alterations of CRGs in GC. A Mutation frequencies of 12 CRGs in 433 patients with gastric cancer from TCGA-STAD cohort. B The CNV variation frequency of CRGs in TCGA-STAD cohort. C Locations of CNV alterations in CRGs on 23 chromosomes in TCGA-STAD cohort. D Analysis of correlation for the expression of 12 CRGs. E PCA for the expression profiles of 12 CRGs to distinguish tumors from normal samples in a pooled cohort consisted of TCGA-STAD and GTEx. *p < 0.05, **p < 0.01, ***p < 0.001. F The expression of 12 CRGs between normal and tumor samples. CRGs, cuproptosis-related genes; GC, gastric cancer; CNV, copy number variations; PCA, principal component analysis; TCGA-STAD, The Cancer Genome Atlas-Stomach Adenocarcinoma. GTEx, Genotype-Tissue Expression Project
Fig. 2
Fig. 2
Clinicopathological characteristics of cpS groups in GC. A PCA for the expression profiles of two cpS groups constructed based on five cohorts (GSE62254, GSE57303, GSE84437, GSE15459, and TCGA-STAD). B The difference of expression levels of CRGs between two cpS groups. C Distribution of cpS and survival status of GC patients. D KM survival analysis showed OS time of two cpS groups. E The prognostic analyses for 12 CRGs in the five GC cohorts using uni-Cox analysis. Hazard ratio > 1 represented risk factors for survival and hazard ratio < 1 represented protective factors for survival. F Differences in clinicopathological features of CRGs between the two cpS groups from GSE62284. Red represented HCSG and blue represented LCSG. CRGs, cuproptosis-related genes; GC, gastric cancer; PCA, principal component analysis; cpS, cuproptosisScore; uni-Cox, univariate Cox regression; KM, Kaplan–Meier. OS, overall survival; HCSG, high-cuprotosisScore group; LCSG, low-cuprotosisScore group
Fig. 3
Fig. 3
Functional annotation and characteristics of the TME in two cpS groups. A GSVA showing the activation status of biological pathways in two cpS groups. The heatmap was used to visualize these biological pathways, and yellow represented activated pathways and blue represented inhibited pathways. The gastric cancer cohorts were used as sample annotations. B The comparison of stromal, immune, and ESTIMATE score between two cpS groups. *p < 0.05, **p < 0.01, ***p < 0.001. C The fold change of stromal activation pathways and immune activation pathways in LCSG vs HCSG. Red represented stromal activation pathways and blue represented immune activation pathways. D The difference of infiltration levels of 29 immune cells between two cpS groups. TME, tumor microenvironment infiltration; GSVA, gene-set variation analysis; cpS, cuproptosisScore; HCSG, high-cuprotosisScore group; LCSG, low-cuprotosisScore group
Fig. 4
Fig. 4
The comparison of response to immunotherapy and drug susceptibility between two cpS groups. A–D The upper part showed the difference of proportion of patients who respond to immunotherapy in two cpS groups. The lower part showed the survival analysis of two cpS groups. GSE78220 is anti-PD-L1 cohort. GSE100797 is ACT cohort. Nathanson2017 pre and Nathanson2017 post are anti- CTLA4 cohorts. E The prediction of drug susceptibility in cpS groups from five GC cohorts (GSE62254, GSE57303, GSE84437, GSE15459, and TCGA-STAD). ACT, adoptive T-cell therapy; cpS, cuproptosisScore; GC, gastric cancer
Fig. 5
Fig. 5
Construction and validation of the cuproptosis DEGs. A 300 patients of ACRG cohort was classified into high- and low-geneScore groups using ssGSEA. The heatmap showed the differences in clinicopathological features and expression levels of DEGs between two geneScore groups. B The expression of 12 CRGs between two geneScore groups. *p < 0.05, **p < 0.01, ***p < 0.001, ns (p > 0.05) C Survival analysis showed OS time of two geneScore groups. D GSEA showing the activation status of biological pathways in two cpS groups. E The fold change of stromal activation pathways and immune activation pathways in LGSG vs HGSG. Red represented stromal activation pathways and blue represented immune activation pathways. F Alluvial diagram showed the changes of ACRG molecular subtypes, cpS, geneScore, and survival outcomes. DEGs, differentially expressed genes; ssGSEA, single-sample gene-set enrichment analysis; OS, overall survival; GSEA, gene-set enrichment analysis; cpS, cuproptosisScore; HGSG, high-geneScore group; LGSG, low-geneScore group
Fig. 6
Fig. 6
Construction of prognosis-related signatures and riskScore model. A The LASSO coefficient profile of nine prognosis-related signatures. B Cross-validation for turning parameter selection through minimum criteria in the LASSO model. C Distribution of riskScore and survival status of GC patients. D Survival analysis showed OS time of two riskScore groups. E The 1-, 3-, and 5-year ROC curves of ACRG cohort. F–J Validation of the riskScore model in fifth external independent sets and a meta-cohort consisted of five GC cohorts (GSE62254, GSE57303, GSE84437, GSE15459, and TCGA-STAD). K Differences in riskScore among two cpS groups in ACRG cohort. L Differences in riskScore among two geneScore groups in ACRG cohort. GC, gastric cancer; OS, overall survival; ROC, receiver operating characteristic; cpS, cuproptosisScore
Fig. 7
Fig. 7
Nomogram development and estimation of riskScore. A Uni-Cox analyses of clinical factors and riskScore with OS. B Multi-Cox analyses of clinical factors and riskScore with OS. Hazard ratio > 1 represented risk factors for survival and hazard ratio < 1 represented protective factors for survival. C. Nomogram for predicting the 1-, 3-, and 5-year OS of GC patients in the ACRG cohort. D–G Calibration curves of the nomogram for predicting of 1-, 3-, and 5-year OS in GSE62254, GSE15459, TCGA-STAD, and GSE84437. Uni-Cox, univariate Cox regression; Multi-Cox, multivariate Cox regression; OS, overall survival; GC, gastric cancer
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