Liu Gao-Lei, Luo Hao, Liang Dan-Dan, Zhong Li, Dai Nan, Lan Wei-Hua
Department of Urology, Army Medical Center, Chongqing, 400042, China.
Department of Oncology, Army Medical Center, Chongqing, 400042, China.
Recent Pat Anticancer Drug Discov. 2025;20(2):185-199. doi: 10.2174/0115748928329276241020184935.
Bladder cancer exhibits substantial heterogeneity encompassing genetic expressions and histological features. This heterogeneity is predominantly attributed to alternative splicing (AS) and AS-regulated splicing factors (SFs), which, in turn, influence bladder cancer development, progression, and response to treatment.
This study aimed to explore the immune landscape of aberrant AS in bladder cancer and establish the prognostic signatures for survival prediction.
Bladder cancer-related RNA-Seq, transcriptome, and corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA). Gene set enrichment analysis (GSEA) was used to identify significantly enriched pathways of cancer-related AS events. The underlying interactions among differentially expressed genes (DEGs) and cancer-related AS events were assessed by a protein-protein interaction network. Univariate and multivariate Cox regression analyses were performed to identify crucial prognostic DEGs that co-occurred with cancer-related AS events (DEGAS) for overall survival. The area under the curve (AUC) of receiver operating characteristic (ROC) curves was used to assess the efficiency of the prognostic signatures. The CIBERSORT algorithm was used to explore the abundance of immune infiltrating cells.
A total of 3755 cancer-related AS events and 3110 DEGs in bladder cancer were identified. Among them, 379 DEGs co-occurred with cancer-related AS events (DEGAS), of which 102 DEGAS were associated with 14 dysregulated SFs. GSEA and KEGG analysis showed that cancer-related AS events were predominantly enriched in pathways related to immunity, tumorigenesis, and treatment difficulties of bladder cancer. Multivariate Cox regression analysis identified 8 DEGAS (CABP1, KCNN2, TNFRSF13B, PCDH7, SNRPA1, APOLD1, CX3CL1, and DENND5A) significantly associated with OS, and they were further integrated into the prediction model with good AUCs at 3-year, 5-year and 7-year ROC curves (all>0.7). Immune infiltration analysis revealed the significant enrichment of three immune cell types (B cells naïve, dendritic cells resting, and dendritic cell activated) in high-risk bladder cancer patients.
This study not only unveiled comprehensive prognostic signatures of AS events in bladder cancer but also established a robust prognostic model based on survival-related DEGAS. These aberrant AS events, dysregulated SFs, and the identified 8 DEGAS may have significant clinical potential as therapeutic targets for bladder cancer.
膀胱癌表现出显著的异质性，包括基因表达和组织学特征。这种异质性主要归因于可变剪接（AS）和AS调节的剪接因子（SFs），反过来，它们又影响膀胱癌的发生、发展和对治疗的反应。
本研究旨在探索膀胱癌中异常AS的免疫格局，并建立用于生存预测的预后特征。
从癌症基因组图谱（TCGA）下载膀胱癌相关的RNA测序、转录组和相应的临床信息。基因集富集分析（GSEA）用于识别癌症相关AS事件显著富集的途径。通过蛋白质-蛋白质相互作用网络评估差异表达基因（DEGs）与癌症相关AS事件之间的潜在相互作用。进行单变量和多变量Cox回归分析，以识别与癌症相关AS事件（DEGAS）共同出现的关键预后DEGs，用于总体生存。采用受试者工作特征（ROC）曲线下面积（AUC）评估预后特征的效率。使用CIBERSORT算法探索免疫浸润细胞的丰度。
共鉴定出膀胱癌中3755个癌症相关AS事件和3110个DEGs。其中，379个DEGs与癌症相关AS事件（DEGAS）共同出现，其中102个DEGAS与14个失调的SFs相关。GSEA和KEGG分析表明，癌症相关AS事件主要富集在与膀胱癌免疫、肿瘤发生和治疗困难相关的途径中。多变量Cox回归分析确定了8个与总生存期显著相关的DEGAS（CABP1、KCNN2、TNFRSF13B、PCDH7、SNRPA1、APOLD1、CX3CL1和DENND5A），并将它们进一步整合到预测模型中，在3年、5年和7年ROC曲线下具有良好的AUC（均>0.7）。免疫浸润分析显示，高危膀胱癌患者中三种免疫细胞类型（幼稚B细胞、静息树突状细胞和活化树突状细胞）显著富集。
本研究不仅揭示了膀胱癌中AS事件的综合预后特征，还基于与生存相关的DEGAS建立了一个强大的预后模型。这些异常的AS事件、失调的SFs以及鉴定出的8个DEGAS作为膀胱癌的治疗靶点可能具有显著的临床潜力。
