Luo Jian, Peng Jiayu, Xiao Wanying, Huang Shujing, Cao Yanqing, Wang Ting, Wang Xicheng
Department of Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangdong Pharmaceutical University, Guangzhou, China.
Department of Radiation, Sun Yat-sen University Cancer Center, Guangzhou, China.
Front Genet. 2022 Aug 25;13:984696. doi: 10.3389/fgene.2022.984696. eCollection 2022.
Numerous lncRNAs have been shown to affect colon cancer (CC) progression, and tumor necroptosis is regulated by several of them. However, the prognostic value of necroptosis-related lncRNA in CC has rarely been reported. In this study, a necroptosis-related lncRNA prognostic model was constructed, which can provide a reference for clinical diagnosis and treatment. The Cancer Genome Atlas (TCGA) database provided gene expression and lncRNA sequencing data for CC patients, and GSEA provided necroptosis gene data. Differentially expressed necroptosis-related lncRNAs related to prognosis were identified by differential expression analysis, Pearson correlation analysis, and least absolute shrinkage and selection operator (LASSO) regression. Based on the results of the multivariate COX regression analysis, a risk scoring model was constructed, A Kaplan-Meier analysis was performed to compare overall survival (OS) between low-risk and high-risk groups. A nomogram was then developed and validated based on the clinical data and risk scores of CC patients. In addition, Gene Set Enrichment Analysis (GSEA) and immune correlation analysis were conducted to explore the possible pathways and immune regulatory effects of these necroptosis-related lncRNAs. In total, we identified 326 differentially expressed necroptosis-related lncRNAs in the TCGA database. Survival analysis showed that the OS of patients in the low-risk group was significantly better than that in the high-risk group ( < 0.05). Finally, 10 prognostic necroptosis-related lncRNAs were used to construct the nomogram. The composite nomogram prediction model evaluated and validated with good prediction performance (3-year AUC = 0.85, 5-years AUC = 0.82, C-index = 0.78). The GSEA and immune correlation analyses indicated that these lncRNAs may participate in multiple pathways involved in CC pathogenesis and progression. We established a novel necroptosis-related lncRNA CC prognosis prediction model, which can provide a reference for clinicians to formulate personalized treatment and review plans for CC patients. In addition, we also found that these necroptosis-related lncRNAs may affect the pathogenesis and progression of colon cancer through multiple pathways, including altering the activity of various immune cells.
大量长链非编码RNA(lncRNA)已被证明会影响结肠癌(CC)的进展,其中一些还调控肿瘤坏死性凋亡。然而,坏死性凋亡相关lncRNA在CC中的预后价值鲜有报道。本研究构建了一种坏死性凋亡相关lncRNA预后模型,可为临床诊断和治疗提供参考。癌症基因组图谱(TCGA)数据库提供了CC患者的基因表达和lncRNA测序数据,基因集富集分析(GSEA)提供了坏死性凋亡基因数据。通过差异表达分析、Pearson相关性分析和最小绝对收缩和选择算子(LASSO)回归,鉴定出与预后相关的差异表达坏死性凋亡相关lncRNA。基于多变量COX回归分析结果,构建了风险评分模型,进行Kaplan-Meier分析以比较低风险组和高风险组之间的总生存期(OS)。然后根据CC患者的临床数据和风险评分开发并验证了列线图。此外,进行了基因集富集分析(GSEA)和免疫相关性分析,以探索这些坏死性凋亡相关lncRNA的可能途径和免疫调节作用。我们总共在TCGA数据库中鉴定出326个差异表达的坏死性凋亡相关lncRNA。生存分析表明低风险组患者的OS明显优于高风险组(<0.05)。最后,使用10个与预后相关的坏死性凋亡lncRNA构建列线图。综合列线图预测模型经评估和验证具有良好的预测性能(3年AUC = 0.85,5年AUC = 0.82,C指数 = 0.78)。GSEA和免疫相关性分析表明,这些lncRNA可能参与CC发病机制和进展的多种途径。我们建立了一种新型的坏死性凋亡相关lncRNA CC预后预测模型,可为临床医生为CC患者制定个性化治疗和复查计划提供参考。此外,我们还发现这些坏死性凋亡相关lncRNA可能通过多种途径影响结肠癌的发病机制和进展,包括改变各种免疫细胞的活性。