Cuproptosis-Associated lncRNA Establishes New Prognostic Profile and Predicts Immunotherapy Response in Clear Cell Renal Cell Carcinoma

Abstract

Background: Clear cell renal cell carcinoma (ccRCC) accounts for 80% of all kidney cancers and has a poor prognosis. Recent studies have shown that copper-dependent, regulated cell death differs from previously known death mechanisms (apoptosis, ferroptosis, and necroptosis) and is dependent on mitochondrial respiration (Tsvetkov et al., Science, 2022, 375 (6586), 1254-1261). Studies also suggested that targeting cuproptosis may be a novel therapeutic strategy for cancer therapy. In ccRCC, both cuproptosis and lncRNA were critical, but the mechanisms were not fully understood. The aim of our study was to construct a prognostic profile based on cuproptosis-associated lncRNAs to predict the prognosis of ccRCC and to study the immune profile of clear cell renal cell carcinoma (ccRCC). Methods: We downloaded the transcriptional profile and clinical information of ccRCC from The Cancer Genome Atlas (TCGA). Co-expression network analysis, Cox regression method, and least absolute shrinkage and selection operator (LASSO) method were used to identify cuproptosis-associated lncRNAs and to construct a risk prognostic model. In addition, the predictive performance of the model was validated and recognized by an integrated approach. We then also constructed a nomogram to predict the prognosis of ccRCC patients. Differences in biological function were investigated by GO, KEGG, and immunoassay. Immunotherapy response was measured using tumor mutational burden (TMB) and tumor immune dysfunction and rejection (TIDE) scores. Results: We constructed a panel of 10 cuproptosis-associated lncRNAs (HHLA3, H1-10-AS1, PICSAR, LINC02027, SNHG15, SNHG8, LINC00471, EIF1B-AS1, LINC02154, and MINCR) to construct a prognostic prediction model. The Kaplan-Meier and ROC curves showed that the feature had acceptable predictive validity in the TCGA training, test, and complete groups. The cuproptosis-associated lncRNA model had higher diagnostic efficiency compared to other clinical features. The analysis of Immune cell infiltration and ssGSEA further confirmed that predictive features were significantly associated with the immune status of ccRCC patients. Notably, the superimposed effect of patients in the high-risk group and high TMB resulted in shorter survival. In addition, the higher TIDE scores in the high-risk group suggested a poorer outcome for immune checkpoint blockade response in these patients. Conclusion: The ten cuproptosis-related risk profiles for lncRNA may help assess the prognosis and molecular profile of ccRCC patients and improve treatment options, which can be further applied in the clinic.

Keywords: bioinformatics; ccRCC; cuproptosis; lncRNA; prognostic model.

FIGURE 1

Identification of Cuproptosis-associated lncRNA prognostic features in ccRCC. The forest plot shows prognosis-related genes for cuproptosis-associated lncRNAs (A). Sankey relationship diagram of cuproptosis genes and cuproptosis-associated lncRNAs (B). Differential expression of 81 cuproptosis-associated lncRNAs associated with survival between ccRCC and normal samples (C). Distribution of the LASSO coefficients of cuproptosis-associated lncRNAs (D). The 10-fold cross-validation of variable selection in the least absolute shrinkage and selection operator (LASSO) algorithm (E). Correlation of lncRNAs with cuproptosis-related genes in risk models (F).

FIGURE 2

Prognosis of the risk model in different groups. The distribution of overall survival risk scores (A–C), survival time and survival status (D–F), heat maps of 10 lncRNA expressions (G–I), Kaplan–Meier survival curves of overall survival of ccRCC patients (J–L), and Kaplan–Meier survival curves of progression-free survival of ccRCC patients (M–O) between low- and high-risk groups in the train, test, and entire sets, respectively.

FIGURE 3

Kaplan–Meier survival curves for low- and high-risk populations by different clinical variables. Age (A,B), sex (C,D), stage (E,F), T stage (G,H), N stage (I,J), and M stage (K,L).

FIGURE 4

Accuracy of the risk characteristic based on a whole-group prediction of 1-, 3-, and 5-years receiver operating characteristic curves (A). Predictive accuracy of the risk model compared with clinicopathologic characteristics such as age, sex, and stage (B). C-index curve of the risk model (C).

FIGURE 5

Construction and validation of the nomogram. A nomogram combining clinicopathological variables and risk scores predicts 1-, 3-, and 5-years overall survival in patients with ccRCC (A). Calibration curves test the agreement between actual and predicted outcomes at 1, 3, and 5 years (B).

FIGURE 6

PCA in both groups of patients. PCA of all genes (A). PCA of cuproptosis genes (B). PCA of cuproptosis-related lncRNAs (C). PCA of risk lncRNAs (D).

FIGURE 7

GO and KEGG analysis. Gene Ontology (GO) analysis demonstrated the richness of molecular biological processes (BP), cellular components (CC), and molecular functions (MF) (A,B). KEGG pathway analysis showed the significantly enriched pathways (C,D).

FIGURE 8

Differences in the tumor immune microenvironment between the low- and high-risk groups. Immune cell bubble of risk groups (A). Differences in expression of common immune checkpoints in the risk groups (B). ssGSEA scores of immune cells and immune function in the risk group (C). Box plots comparing StromalScore, ImmuneScore and ESTIMATEScore between the low- and high-risk groups, respectively (D–F). *p < 0.05, **p < 0.01, and ***p < 0.001.

FIGURE 9

TMB, TIDE, and Chemotherapeutic Sensitivity. Waterfall plots of somatic mutation characteristics in the two groups (A-B). TMB between the low-risk and high-risk groups (C). K–M survival curves between the high- and low-TMB groups (D). K–M survival curves between the four groups (E). TIDE scores between the two groups (F).

FIGURE 10

Drug sensitivity. Sorafenib was more effective in the high-risk group (A). Pazopanib was more effective in the low-risk group (B).

FIGURE 11

External validation of cuproptosis-associated lncRNAs as potential biomarkers. OS analysis of SNHG15 and LINC00471 in the Kaplan–Meier Plotter datasets (A and B).

FIGURE 12

Expression levels of cuproptosis-associated lncRNAs in paired tumor tissues. RT-qPCR was used to measure the expression of SNHG15 and LINC00471 in paired tumor tissues (A,B).