Identification of cuproptosis-related long noncoding RNA signature for predicting prognosis and immunotherapy response in bladder cancer

Abstract

Bladder cancer (BC) is the most common malignant tumour of the urinary system and one of the leading causes of cancer-related death. Cuproptosis is a novel form of programmed cell death, and its mechanism in tumours remains unclear. This study aimed to establish the prognostic signatures of cuproptosis-related lncRNAs and determine their clinical prognostic value. RNA sequencing data from The Cancer Genome Atlas were used to detect the expression levels of cuproptosis-related genes in BC. Cuproptosis-related lncRNAs linked to survival were identified using co-expression and univariate Cox regression. Furthermore, consensus cluster analysis divided the lncRNAs into two subtypes. Subsequently, we established a signature model consisting of seven cuproptosis-related lncRNAs (AC073534.2, AC021321.1, HYI-AS1, PPP1R26-AS1, AC010328.1, AC012568.1 and MIR4435-2Hg) using least absolute shrinkage and selection operator regression. Survival analysis based on risk score showed that the overall survival and progression-free survival of patients in the high-risk group were worse than those in the low-risk group. Multivariate Cox analysis demonstrated the independent prognostic potential of this signature model for patients with BC. Moreover, age and clinical stage were also significantly correlated with prognosis. The constructed nomogram plots revealed good predictive power for the prognosis of patients with BC and were validated using calibration plots. Additionally, enrichment analysis, Single sample gene set enrichment analysis and immune infiltration abundance analysis revealed significant differences in immune infiltration between the two risk groups, with high levels of immune cell subset infiltrations observed in the high-risk group accompanied by various immune pathway activation. Moreover, almost all the immune checkpoint genes showed high expression levels in the high-risk group. Moreover, TIDE analysis suggested that the high-risk group was more responsive to immunotherapy. Finally, eight drugs with low IC50 values were screened, which may prove to be beneficial for patients in the high-risk group.

Construction and Assessment of a Prognostic Risk Model for Cervical Cancer Based on Lactate Metabolism-Related lncRNAs

“…Based on TCGA RNA sequencing data, the CIBERSORT tool was used to analyze 22 immune cell components, including B cells, T cells, natural killer cells, macrophages, and dendritic cells in medium tumor samples. 16 The p<0.05 indicated that the cell components inferred from the samples had high credibility. Pearson analysis was performed to correlate different immune cells and risk scores.…”

基于铜死亡相关的 lncRNA的膀胱癌预后模型的构建及免疫浸润分析

“…膀胱癌是起源于尿路上皮的恶性肿瘤，是我国泌尿系统肿瘤所致死亡的最常见原因 ［ 1 ］ 。膀胱癌的治疗方案包括手术切除、放化疗和免疫治疗等，尽管这些治疗方法近年来取得了显著进展，但患者治疗后复发或进展的可能性仍较高，其中约40%的患者会多次复发 ［ 2 ］ 。目前临床上膀胱癌患者的预后主要通过组织病理学诊断和肿瘤分期系统进行预测，但准确度不高。虽已有多个研究通过TCGA和GEO等公共数据库构建膀胱癌预后模型，但仍无法同时满足方便快捷和高准确率的要求 ［ 3 - 5 ］ 。因此，建立膀胱癌患者预后的高效预测模型，对于指导临床个性化治疗十分必要。…”