■可变剪接(AS)发生在基因转录后过程中,这对蛋白质的正确合成和功能非常重要。AS模式的改变可能导致肺癌相关基因的表达水平或功能改变,进而影响肺癌的发生和发展。特定的AS模式可能被用作预测框架中的癌症的早期预警和预后评估的生物标志物,预防性,和个性化医疗(PPPM;3PM)。免疫相关基因(IRGs)的AS事件与肿瘤进展和免疫治疗密切相关。我们假设IRG-AS事件在肺腺癌(LUADs)与对照或肺鳞状细胞癌(LUSC)与controls.鉴定IRG-AS改变谱以构建IRG差异表达的AS(IRG-DEAS)特征模型。研究肺癌患者特异性IRGs的选择性AS事件对进一步探讨肺癌的发病机制具有重要意义。实现肺癌的早期发现和有效监测,寻找新的治疗靶点,克服耐药性,制定更有效的治疗策略,更好地用于预测,诊断,预防,和肺癌的个性化医疗。
■转录组,临床,从TCGA及其SpliceSeq数据库下载LUAD和LUSC的AS数据。在LUAD和LUSC中发现了IRG-DEAS事件,其次是它们的功能特征,和总生存期(OS)分析。用Lasso回归建立LUAD和LUSC的OS相关IRG-DEAS预后模型,用于将LUADs和LUSCs分为低危和高危评分组。此外,免疫细胞分布,免疫相关评分,药物敏感性,突变状态,和GSEA/GSVA状态在低和高风险组之间进行分析。此外,在LUAD和LUSC中分析了低和高免疫簇和AS因子(SF)-OS相关的AS共表达网络以及CELF6细胞功能的验证。
■转录组的综合分析,临床,LUAD和LUSC的AS数据确定了LUAD(n=1607)和LUSC(n=1656)中的IRG-AS事件,包括LUAD(n=127)和LUSC(n=105)中与OS相关的IRG-AS事件。与对照相比,在LUAD中鉴定出总共66个IRG-DEAS事件和在LUSC中鉴定出89个IRG-DEAS事件。IRG-DEAS和与OS相关的IRG-AS事件之间的重叠分析显示,LUAD有14个与OS相关的IRG-DEAS事件,LUSC有16个与OS相关的IRG-DEAS事件,用于识别和优化LUAD的12-OS相关IRG-DEAS特征预后模型和LUSC的11-OS相关IRG-DEAS特征预后模型。这两个预后模型有效地将LUAD或LUSC样本分为与OS密切相关的低和高风险评分组。临床特征,和肿瘤免疫微环境,在两组中丰富了显著的基因集和通路。此外,加权基因共表达网络(WGCNA)和非负矩阵分解方法(NMF)分析确定了LUAD的四个OS相关亚型和LUSC的六个OS相关亚型,ssGSEA确定了LUAD的5种免疫相关亚型和LUSC的5种免疫相关亚型。有趣的是,剪接因子-OS相关-AS网络显示hub分子CELF6与肺癌细胞的恶性表型显著相关。
■这项研究建立了两个可靠的IRG-DEAS签名预后模型,并在LUAD和LUSC中构建了有趣的剪接因子-剪接事件网络,可用于构建临床相关的免疫亚型,患者分层,预后预测,PPPM实践中的个性化医疗服务。
■在线版本包含补充材料,可在10.1007/s13167-024-00366-4获得。
UNASSIGNED: Alternative splicing (AS) occurs in the process of gene post-transcriptional process, which is very important for the correct synthesis and function of protein. The change of AS pattern may lead to the change of expression level or function of lung cancer-related genes, and then affect the occurrence and development of lung cancers. The specific AS pattern might be used as a biomarker for early warning and prognostic assessment of a cancer in the framework of predictive, preventive, and personalized medicine (PPPM; 3PM). AS events of immune-related genes (IRGs) were closely associated with tumor progression and immunotherapy. We hypothesize that IRG-AS events are significantly different in lung adenocarcinomas (LUADs) vs. controls or in lung squamous cell carcinomas (LUSCs) vs. controls. IRG-AS alteration profiling was identified to construct IRG-differentially expressed AS (IRG-DEAS) signature models. Study on the selective AS events of specific IRGs in lung cancer patients might be of great significance for further exploring the pathogenesis of lung cancer, realizing early detection and effective monitoring of lung cancer, finding new therapeutic targets, overcoming drug resistance, and developing more effective therapeutic strategies, and better used for the prediction, diagnosis, prevention, and personalized medicine of lung cancer.
UNASSIGNED: The transcriptomic, clinical, and AS data of LUADs and LUSCs were downloaded from TCGA and its SpliceSeq databases. IRG-DEAS events were identified in LUAD and LUSC, followed by their functional characteristics, and overall survival (OS) analyses. OS-related IRG-DEAS prognostic models were constructed for LUAD and LUSC with Lasso regression, which were used to classify LUADs and LUSCs into low- and high-risk score groups. Furthermore, the immune cell distribution, immune-related scores, drug sensitivity, mutation status, and GSEA/GSVA status were analyzed between low- and high-risk score groups. Also, low- and high-immunity clusters and AS factor (SF)-OS-related-AS co-expression network and verification of cell function of CELF6 were analyzed in LUAD and LUSC.
UNASSIGNED: Comprehensive analysis of transcriptomic, clinical, and AS data of LUADs and LUSCs identified IRG-AS events in LUAD (n = 1607) and LUSC (n = 1656), including OS-related IRG-AS events in LUAD (n = 127) and LUSC (n = 105). A total of 66 IRG-DEAS events in LUAD and 89 IRG-DEAS events in LUSC were identified compared to controls. The overlapping analysis between IRG-DEASs and OS-related IRG-AS events revealed 14 OS-related IRG-DEAS events for LUAD and 16 OS-related IRG-DEAS events for LUSC, which were used to identify and optimize a 12-OS-related-IRG-DEAS signature prognostic model for LUAD and an 11-OS-related-IRG-DEAS signature prognostic model for LUSC. These two prognostic models effectively divided LUAD or LUSC samples into low- and high-risk score groups that were closely associated with OS, clinical characteristics, and tumor immune microenvironment, with significant gene sets and pathways enriched in the two groups. Moreover, weighted gene co-expression network (WGCNA) and nonnegative matrix factorization method (NMF) analyses identified four OS-relevant subtypes of LUAD and six OS-relevant subtypes of LUSC, and ssGSEA identified five immunity-relevant subtypes of LUAD and five immunity-relevant subtypes of LUSC. Interestingly, splicing factors-OS-related-AS network revealed hub molecule CELF6 was significantly related to the malignant phenotype in lung cancer cells.
UNASSIGNED: This study established two reliable IRG-DEAS signature prognostic models and constructed interesting splicing factor-splicing event networks in LUAD and LUSC, which can be used to construct clinically relevant immune subtypes, patient stratification, prognostic prediction, and personalized medical services in the PPPM practice.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s13167-024-00366-4.