In Silico Identification and Validation of Cuproptosis-Related LncRNA Signature as a Novel Prognostic Model and Immune Function Analysis in Colon Adenocarcinoma

Abstract

Background: Colon adenocarcinoma (COAD) is the most common subtype of colon cancer, and cuproptosis is a recently newly defined form of cell death that plays an important role in the development of several malignant cancers. However, studies of cuproptosis-related lncRNAs (CRLs) involved in regulating colon adenocarcinoma are limited. The purpose of this study is to develop a new prognostic CRLs signature of colon adenocarcinoma and explore its underlying biological mechanism. Methods: In this study, we downloaded RNA-seq profiles, clinical data and tumor mutational burden (TMB) data from the TCGA database, identified cuproptosis-associated lncRNAs using univariate Cox, lasso regression analysis and multivariate Cox analysis, and constructed a prognostic model with risk score based on these lncRNAs. COAD patients were divided into high- and low-risk subgroups based on the risk score. Cox regression was also used to test whether they were independent prognostic factors. The accuracy of this prognostic model was further validated by receiver operating characteristic curve (ROC), C-index and Nomogram. In addition, the lncRNA/miRNA/mRNA competing endogenous RNA (ceRNA) network and protein–protein interaction (PPI) network were constructed based on the weighted gene co-expression network analysis (WGCNA). Results: We constructed a prognostic model based on 15 cuproptosis-associated lncRNAs. The validation results showed that the risk score of the model (HR = 1.003, 95% CI = 1.001–1.004; p < 0.001) could serve as an independent prognostic factor with accurate and credible predictive power. The risk score had the highest AUC (0.793) among various factors such as risk score, stage, gender and age, also indicating that the model we constructed to predict patient survival was better than other clinical characteristics. Meanwhile, the possible biological mechanisms of colon adenocarcinoma were explored based on the lncRNA/miRNA/mRNA ceRNA network and PPI network constructed by WGCNA. Conclusion: The prognostic model based on 15 cuproptosis-related lncRNAs has accurate and reliable predictive power to effectively predict clinical outcomes in colon adenocarcinoma patients.

Identification of an Eight-Cuproptosis-related lncRNA Signature as a Novel Prognostic Model and Prediction of Immunotherapy Response in Ovarian Cancer

“…MUC16 and TP53 mutations are common among other Cuproptosis-related lncRNA signatures. For example, in the TMB analysis of Cuproptosis-related lncRNA signatures in colorectal cancer, the mutation frequency of MUC16 in the high-risk group was lower than that of the low-risk group (24% vs. 35%), and the mutation frequency of TP53 in the high-risk group was higher than that of the low-risk group (64% vs. 49%) (19). The frequency of MUC16 mutations was higher in the low-risk group, suggesting that these patients responded well to immunotherapy.…”

“…Cuproptosis-related lncRNAs have been identi ed and constructed as new prognostic markers in colorectal cancer (19), head and neck squamous cell carcinoma (20), hepatocellular carcinoma (21), and gastric adenocarcinoma (22), providing a new perspective for its clinical treatment, especially immunotherapy. However, the role of Cuproptosis-related lncRNAs in OC remains unclear.…”

Identification of seven cuproptosis-related lncRNAs signature and establishment of a prognostic nomogram predicting overall survival in patients with endometrial cancer

“…CRLs can be used to predict the prognosis and immune microenvironment of patients suffering from bladder cancer, which high risk patients were enriched in several immune-related pathways such as cell proliferation, nucleotide metabolism, and in ammatory immune response [19]. The cuproptosis is closely related to immune system [20]. In cancer cells, copper supplements increase the function and level of PD-L1 at mRNA and protein levels.…”

Machine learning and bioinformatics-based insights into the potential targets of saponins in Paris polyphylla smith against non-small cell lung cancer

“…Hs.eg.db, enrichplot, GSEABase, and DOSE packages to explore in which diseases differential genes were significantly enriched. GO, KEGG and DO enrichment analyses were performed with p < 0.05 as cutoff values ( Wang et al, 2022 ).…”