A novel cuproptosis-related molecular pattern and its tumor microenvironment characterization in colorectal cancer

Abstract

Cuproptosis, or copper-induced cell death, has been reported as a novel noncanonical form of cell death in recent times. However, the potential roles of cuproptosis in the alteration of tumor clinicopathological features and the formation of a tumor microenvironment (TME) remain unclear. In this study, we comprehensively analyzed the cuproptosis-related molecular patterns of 1,274 colorectal cancer samples based on 16 cuproptosis regulators. The consensus clustering algorithm was conducted to identify cuproptosis-related molecular patterns and gene signatures. The ssGSEA and ESTIMATE algorithms were used to evaluate the enrichment levels of the infiltrated immune cells and tumor immune scores, respectively. The cuproptosis score was established to assess the cuproptosis patterns of individuals with principal component analysis algorithms based on the expression of cuproptosis-related genes. Three distinct cuproptosis patterns were confirmed and demonstrated to be associated with distinguishable biological processes and clinical prognosis. Interestingly, the three cuproptosis patterns were revealed to be consistent with three immune infiltration characterizations: immune-desert, immune-inflamed, and immune-excluded. Enhanced survival, activation of immune cells, and high tumor purity were presented in patients with low cuproptosisScore, implicating the immune-inflamed phenotype. In addition, low scores were linked to high tumor mutation burden, MSI-H and high CTLA4 expression, showing a higher immune cell proportion score (IPS). Taken together, our study revealed a novel cuproptosis-related molecular pattern associated with the TME phenotype. The formation of cuproptosisScore will further strengthen our understanding of the TME feature and instruct a more personalized immunotherapy schedule in colorectal cancer.

Keywords: colorectal cancer; cuproptosis; immunotherapy; molecular subtype; tumor microenvironment.

Figure 1

Landscape of genetic variation of cuproptosis regulators in colorectal cancer. (A) The mutation frequency of 16 cuproptosis regulators in 536 samples from TCGA-COAD/READ. (B) The CNV variation frequency of 16 cuproptosis regulators in TCGA-COAD/READ. (C) The location diagram of CNV of 16 cuproptosis regulators on 23 chromosomes. (D) The expression of 16 cuproptosis regulators in cancer and normal samples of TCGA-COAD/READ. (E) Go analysis results of 16 cuproptosis regulators (F). Metascape analysis of 16 cuproptosis regulators (G). The overall landscape of the interaction between cuproptosis regulators and the prognostic significance of the regulators in colorectal cancer patients. *P <0.05; **P <0.01; ***P <0.001.

Figure 2

Identification of cuproptosis patterns and their TME characteristics in colorectal cancer. (A) Unsupervised consensus clustering of 16 cuproptosis regulators in the TCGA and GEO data. The CuproptosisCluster, gender, age, T stage, N stage, M stage, TNM stage, and survival status were used as annotations. (B) GSVA enrichment analysis showing the biological processes in CuproptosisClusters A and C. (C) The abundance analysis of each immune cell in the TME of three CuproptosisClusters. (D) The expression levels of immune activity related genes in three CuproptosisClusters. (E) The stromal score and tumor purity in three CuproptosisClusters with ESTIMATE algorithm. (F) The CMS analysis of the three CuproptosisClusters. (G) The molecular subtypes analysis of GSE39582 dataset in three CuproptosisClusters. *P <0.05; **P <0.01; ***P <0.001.

Figure 3

Generation of cuproptosis-related geneclusters. (A) The Venn diagram of DEGs between three CuproptosisClusters. (B) KEGG results of 965 DEGs. (C) PCA for three geneClusters to distinguish samples in TCGA COAD/READ and GSE39582. (D) Unsupervised clustering of 260 cuproptosis-related genes. (E) The Kaplan–Meier curves of overall survival between different geneClusters.

Figure 4

TME characteristics of three geneClusters. (A) The heatmap of GSVA results between geneCluster A and geneCluster B. (B) The heatmap of GSVA results between geneCluster A and geneCluster C. (C) The abundances of TME infiltrating immune cells in three geneClusters by ssGSVA algorithm. (D) The stromal score and tumor purity in three geneClusters by ESTIMATE algorithm. (E) The CMS analysis of the three geneClusters. (F) The molecular subtypes analysis of GSE39582 dataset in three geneClusters. (G) The expression of immune activated genes between three geneClusters. *P <0.05; **P <0.01; ***P <0.001.

Figure 5

Construction of CuproptosisScore and their TME characteristics in colorectal cancer. (A) Alluvial diagram showing the attribute changes from CuproptosisClusters to gene Clusters to CuproptosisScore to survival status. (B) The Kaplan–Meier curves of overall survival between high and low CuproptosisScore group. (C) The frequencies of alive and dead status in high and low CuproptosisScore group. (D) The CuproptosisScores of the three geneClusters. (E) The stromal score and tumor purity in high and low CuproptosisScore group. (F) The expression levels of immune activity-related genes in high and low CuproptosisScore groups. (G) The CMS analysis of the two CuproptosisScore groups. (H) The molecular subtypes analysis of GSE39582 dataset in the two CuproptosisScore groups. *P <0.05; ***P <0.001.

Figure 6

Relationship of cuproptosis patterns with tumor somatic mutation and immunotherapy. (A, B) The waterfall plot of tumor somatic mutation of low CuproptosisScore group (A) and high CuproptosisScore group (B). (C) The TMB of high and low CuproptosisScore group. (D) The Kaplan–Meier curves of overall survival in different groups of TMB combination with CuproptosisScore. (E) The CuproptosisScores of samples with MSS, MSI-L, and MSI-H. (F) CTLA4 expression of the two CuproptosisScore groups. (G) IPS of anti-CTLA4 drug in the two CuproptosisScore groups.