Construction of a pancreatic cancer prediction model for oxidative stress-related lncRNA.
Construction of a pancreatic cancer prediction model for oxidative stress-related lncRNA.
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DOI：
10.1007/s10142-023-01048-6
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发表时间：
2023-04-05
影响因子：
2.9
通讯作者：
Sun, Jinjin
中科院分区：
文献类型：
--
作者：
Huang, Hao;Wei, Yaqing;Yao, Hao;Chen, Ming;Sun, Jinjin
长非编码RNA(LncRNAs)可能通过改变肿瘤微环境在氧化应激中发挥作用，从而影响胰腺癌的进展。目前关于氧化应激相关的lncRNAs作为胰腺癌新的预后标记物的信息有限。胰腺癌患者的基因表达和临床数据从癌症基因组图谱(TCGA-PAAD)和国际癌症基因组联合会(ICGC-PACA)数据库下载。构建了加权基因共表达网络分析(WGCNA)，以确定正常和肿瘤样本之间差异表达的基因。以TCGA-PAAD队列为基础，采用套索回归和COX回归建立预测模型。TCGA-PAAD和ICGC-PACA队列分别用于内部和外部验证。此外，基于临床特征的诺模图被用来预测患者的死亡率。研究还探讨了风险亚组之间突变状态和肿瘤浸润性免疫细胞的差异，并分析了基于模型的lncRNAs，以寻找潜在的免疫相关治疗药物。用套索回归和Cox回归建立了6-lncRNA的预测模型。Kaplan-Meier生存曲线和受试者操作特征(ROC)曲线表明，风险评分越低的患者预后越好。结合临床特征的COX回归分析，在TCGA-PAAD和ICGC-PACA队列中，风险评分都是预测胰腺癌患者总体生存的独立因素。突变状态和免疫相关分析表明，高危人群的基因突变率明显高于高危人群，免疫逃逸的可能性较高。此外，模型基因与免疫相关的治疗药物显示出很强的相关性。建立了基于氧化应激相关的lncRNA的胰腺癌预测模型，该模型可作为胰腺癌预后相关的生物标志物来评估胰腺癌患者的预后。网上版载有补充材料，可在10.1007/s10142-023-01048-6查阅。
Long non-coding RNAs (lncRNAs) may play a role in oxidative stress by altering the tumor microenvironment, thereby affecting pancreatic cancer progression. There is currently limited information on oxidative stress-related lncRNAs as novel prognostic markers of pancreatic cancer. Gene expression and clinical data of patients with pancreatic cancer were downloaded from The Cancer Genome Atlas (TCGA-PAAD) and the International Cancer Genome Consortium (ICGC-PACA) database. A weighted gene co-expression network analysis (WGCNA) was constructed to identify genes that were differentially expressed between normal and tumor samples. Based on the TCGA-PAAD cohort, a prediction model was established using lasso regression and Cox regression. The TCGA-PAAD and ICGC-PACA cohorts were used for internal and external validation, respectively. Furthermore, a nomogram based on clinical characteristics was used to predict mortality of patients. Differences in mutational status and tumor-infiltrating immune cells between risk subgroups were also explored and model-based lncRNAs were analyzed for potential immune-related therapeutic drugs. A prediction model for 6-lncRNA was established using lasso regression and Cox regression. Kaplan–Meier survival curves and receiver operating characteristic (ROC) curves indicated that patients with lower risk scores had a better prognosis. Combined with Cox regression analysis of clinical features, risk score was an independent factor predicting overall survival of patients with pancreatic cancer in both the TCGA-PAAD and ICGC-PACA cohorts. Mutation status and immune-related analysis indicated that the high-risk group had a significantly higher gene mutation rate and a higher possibility of immune escape, respectively. Furthermore, the model genes showed a strong correlation with immune-related therapeutic drugs. A pancreatic cancer prediction model based on oxidative stress-related lncRNA was established, which may be used as a biomarker related to the prognosis of pancreatic cancer to evaluate the prognosis of pancreatic cancer patients. The online version contains supplementary material available at 10.1007/s10142-023-01048-6.
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DOI：
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发表时间：
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期刊：
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