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.

Keywords: colon adenocarcinoma; cuproptosis; lncRNA; prognostic model.

Figure 1

The flow chart diagram of the study.

Figure 2

Screening for Prognostic lncRNA Signature. (a) Co-expression analysis of lncRNAs associated with cuproptosis-related genes in COAD. (b,c) The least absolute shrinkage and selection operator (LASSO) regression was performed with the minimum criteria. (d) Correlation analysis of 15 signature lncRNAs with 10 cuproptosis-related genes (horizontal coordinates are lncRNAs involved in model construction, blue represents negative correlation, red represents positive correlation).

Figure 3

Construction of signature models for cuproptosis-related lncRNA in the overall group, training group and validation group. (a–c) Analysis of overall survival between high- and low-risk subgroups in overall, training and validation groups (compared to low-risk subgroup, high-risk subgroup showed significantly unfavorable prognosis, p < 0.05). (d) Exploration of differences in progression-free survival between high- and low-risk subgroups based on the overall subgroup (patients in the low-risk had significantly better PFS than high-risk patients, p < 0.001). (e–g) Risk score distribution (red is risk score of high-risk patients, blue is risk score of low-risk patients). (h–j) Relationship between risk score and survival time (horizontal coordinates indicate patients’ risk score, vertical coordinates indicate patients’ survival time. The number of patients who died increased with increasing risk score). (k–m) Heatmap of the expression of 15 lncRNAs in the high- and low-risk subgroups.

Figure 4

Validation of a prognostic model of cuproptosis-related lncRNA. (a,b) Univariate and multivariate analysis of clinic pathological factors of overall survival in COAD patients. (c) Comparison of receiver operating characteristic (ROC) curves for risk score models to predict 1-, 3-, and 5-year overall survival (OS). (The horizontal coordinate is the false-positive rate and the vertical coordinate represents the true-positive rate. The larger the area under the curve of the ROC curve, the higher the accuracy of the model’s prediction). (d) Comparison of ROC curves for risk score and other clinical factors (the risk score had the largest area under the curve, indicating that the constructed model was a better predictor of prognosis than other clinical characteristics in COAD patients). (e) Nomogram with risk score model and clinicopathological features (“***” indicates p < 0.001). (f) C-index curve combining risk score, stage, age and gender (The consistency index of the risk score was much greater than other clinical factors, and the risk score was more accurate in predicting survival in patients with COAD). (g,h) Different clinical stages of COAD were introduced for model validation (significant differences between high and low risk subgroups for stages I–II and III–IV of COAD, p < 0.001). (i,j) Kaplan–Meier survival curve with gender as an indicator (female vs. male). (k,l) Kaplan–Meier survival curve with age as an indicator (>60 years old vs. ≤60 years old).

Figure 5

KEGG analysis and immune-related functional analysis. (a,b) GO enrichment analysis of risk differential genes. (c) KEGG analysis of risk differential genes. (d) Analysis of immune-related functions in high-risk and low-risk subgroups (* p < 0.05).

Figure 6

Correlations between the risk score model and somatic variants. (a) Differential analysis of tumor mutational burden (TMB) between high and low risk subgroups for colon adenocarcinoma. (b) Correlation between the prognosis and TMB in COAD patients. (c) Predictive power of risk model in high and low TMB groups. (d,e) Compare the mutation rates of reported prognosis-associated genes in low- and high-risk groups.

Figure 7

Screening for modules significantly associated with colon adenocarcinoma by WGCNA. (a) Hierarchical cluster analysis of the identified lncRNAs was performed to detect co-expression clusters with corresponding color assignments. (b) Correlation analysis of clinical features of colon adenocarcinoma with Eigengene of lncRNAs, where MEblue had the highest correlation coefficient. (c) Hierarchical clustering analysis of the identified mRNAs. (d) Correlation analysis of clinical features of colon adenocarcinoma with Eigengene of mRNAs, where MEpurple had the highest correlation coefficient.

Figure 8

Construction of lncRNA–miRNA–mRNA ceRNA network.

Figure 9

Construction of protein–protein interaction (PPI) network and gene set enrichment analysis. (a) PPI network was constructed with 325 targeting mRNAs. (b) PPI network of 13 genes were obtained by Cytoscape, among which YWHAG, KRAS, MAP2K4 were core genes. (c,d) Potential signaling pathways for targeting mRNAs revealed by GSEA analysis.