Cuproptosis-related long non-coding RNAs model that effectively predicts prognosis in hepatocellular carcinoma

Abstract

BACKGROUND Cuproptosis has recently been considered a novel form of programmed cell death. To date, long-chain non-coding RNAs (lncRNAs) crucial to the regulation of this process remain unelucidated. AIM To identify lncRNAs linked to cuproptosis in order to estimate patients' prognoses for hepatocellular carcinoma (HCC). METHODS Using RNA sequence data from The Cancer Genome Atlas Live Hepatocellular Carcinoma (TCGA-LIHC), a co-expression network of cuproptosis-related genes and lncRNAs was constructed. For HCC prognosis, we developed a cuproptosis-related lncRNA signature (CupRLSig) using univariate Cox, lasso, and multivariate Cox regression analyses. Kaplan-Meier analysis was used to compare overall survival among high- and low-risk groups stratified by median CupRLSig risk score. Furthermore, comparisons of functional annotation, immune infiltration, somatic mutation, tumor mutation burden (TMB), and pharmacologic options were made between high- and low-risk groups. RESULTS Three hundred and forty-three patients with complete follow-up data were recruited in the analysis. Pearson correlation analysis identified 157 cuproptosis-related lncRNAs related to 14 cuproptosis genes. Next, we divided the TCGA-LIHC sample into a training set and a validation set. In univariate Cox regression analysis, 27 LncRNAs with prognostic value were identified in the training set. After lasso regression, the multivariate Cox regression model determined the identified risk equation as follows: Risk score = (0.2659 × PICSAR expression) + (0.4374 × FOXD2-AS1 expression) + (-0.3467 × AP001065.1 expression). The CupRLSig high-risk group was associated with poor overall survival (hazard ratio = 1.162, 95%CI = 1.063-1.270; P < 0.001) after the patients were divided into two groups depending upon their median risk score. Model accuracy was further supported by receiver operating characteristic and principal component analysis as well as the validation set. The area under the curve of 0.741 was found to be a better predictor of HCC prognosis as compared to other clinicopathological variables. Mutation analysis revealed that high-risk combinations with high TMB carried worse prognoses (median survival of 30 mo vs 102 mo of low-risk combinations with low TMB group). The low-risk group had more activated natural killer cells (NK cells, P = 0.032 by Wilcoxon rank sum test) and fewer regulatory T cells (Tregs, P = 0.021) infiltration than the high-risk group. This finding could explain why the low-risk group has a better prognosis. Interestingly, when checkpoint gene expression (CD276, CTLA-4, and PDCD-1) and tumor immune dysfunction and rejection (TIDE) scores are considered, high-risk patients may respond better to immunotherapy. Finally, most drugs commonly used in preclinical and clinical systemic therapy for HCC, such as 5-fluoro...

Tumor Mutational Burden for Predicting Prognosis and Therapy Outcome of Hepatocellular Carcinoma

“…microRNA (miRNAs) and lncRNAs have recently gained attention as possible markers for prediction of HCC and therapy outcome. In the last two years, many studies have been published on this topic [ 136 , 137 , 138 , 139 , 140 ]. One of them set up a prediction model based on eight lncRNAs strongly associated with TMB and tumor immune infiltration to predict HCC patients’ prognosis [ 141 ].…”

A Novel Differentially Expressed Cuproptosis-Related lncRNAs Signature to predict the prognosis and immune characteristics of hepatocellular carcinoma

“…Chen et al [16].also reported that cuproptosis-related LncRNA signature with AUC of 0.719, 0.695, and 0.638 at 1-, 3-5-year. While the highest AUC was 0.741 in the study by Huang [17]. In our study, the highest AUC is 0.800, which indicates this DECRLs signature has strong predictive power.…”

Systematic analysis of cuproptosis abnormalities and functional significance in cancer

“…Our results elucidate the multi-omics characteristics of cuproptosis related genes in pan-cancer, establish a CPI index model that predicts the prognosis of CRC patients and evaluates the correlation between CuRGs and immune infiltration levels. In recent cuproptosis related studies, Huang et al proposed a risk model for cuproptosis related lncRNAs to predict the prognosis of hepatocellular carcinoma patients [ 23 ]. Xu et al developed a strategy to induce copper poisoning using copper-based nanomaterials to treat cancer [ 24 ].…”