JOURNAL ARTICLE

A novel cuproptosis-related lncRNAs signature predicts prognosis in bladder cancer

Lingfeng WuWei ChenYifang CaoBin ChenYi HeXueping Wang

Year: 2023 Journal:   Aging Vol: 15 (13)Pages: 6445-6466   Publisher: Impact Journals LLC

Abstract

This study constructed a novel cuproptosis-related lncRNAs signature to predict the prognosis of BLCA patients. The Cancer Genome Atlas (TCGA) database was used to retrieve the RNA-seq data together with the relevant clinical information. The cuproptosis-related genes were first discovered. The cuproptosis-related lncRNAs were then acquired by univariate, the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis to create a predictive signature. An eight cuproptosis-related lncRNAs (AC005261.1, AC008074.2, AC021321.1, AL024508.2, AL354919.2, ARHGAP5-AS1, LINC01106, LINC02446) predictive signature was created. Compared with the low-risk group, the prognosis was poorer for the high-risk group. The signature served as an independent overall survival (OS) predictor. Receiver operating characteristic (ROC) curve indicated that the signature demonstrated superior predictive ability, as evidenced by the area under the curve (AUC) of 0.782 than the clinicopathological variables. When we performed a subgroup analysis of the different variables, the high-risk group's OS for BLCA patients was lower than that of the low-risk group's patients. Gene Set Enrichment Analysis (GSEA) showed that high-risk groups were clearly enriched in many immune-related biological processes and tumor-related signaling pathways. Single sample gene set enrichment analysis (ssGSEA) revealed that the immune infiltration level was different between the two groups. Finally, quantitative RT-PCR showed that AC005261.1, AC021321.1, AL024508.2, LINC02446 and LINC01106 were lowly expressed in tumor cells, while ARHGAP5-AS1 showed the opposite trend. In summary, the predictive signature can independently predict the prognosis and provide clinical treatment guidance for BLCA patients.

Keywords:
Receiver operating characteristic Proportional hazards model Univariate Oncology Gene signature Lasso (programming language) Multivariate statistics Bladder cancer Multivariate analysis Survival analysis Internal medicine Biology Framingham Risk Score Gene Computational biology Medicine Cancer Gene expression Disease Computer science Statistics Genetics Mathematics

Metrics

5
Cited By
1.11
FWCI (Field Weighted Citation Impact)
53
Refs
0.82
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Cancer-related molecular mechanisms research
Life Sciences →  Biochemistry, Genetics and Molecular Biology →  Cancer Research
Ferroptosis and cancer prognosis
Health Sciences →  Medicine →  Pulmonary and Respiratory Medicine
Cancer, Lipids, and Metabolism
Life Sciences →  Biochemistry, Genetics and Molecular Biology →  Cancer Research
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