Ning Biao, Huang Tianhe, Liu Yixin, Wei Yongchang
Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, 169, Donghu Road, Wuchang District, Wuhan, Hubei Province, 430071, China.
Hubei Key Laboratory of Tumor Biological Behaviors Zhongnan Hospital of Wuhan University, 169, Donghu Road, Wuchang District, Wuhan, Hubei Province, 430071, China.
Heliyon. 2024 Jul 10;10(15):e34399. doi: 10.1016/j.heliyon.2024.e34399. eCollection 2024 Aug 15.
Depression and long non-coding RNA (lncRNA) have been reported to be associated with tumor progression and prognosis in gastric cancer (GC). This study aims to build a GC risk classification and prognosis model based on depression-related lncRNAs (DRLs).
To develop a risk model, we performed univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses using RNA sequencing data of GC from The Cancer Genome Atlas (TCGA) and depression-related genes (DRGs) from previous studies. Kaplan-Meier analysis, receiver operating characteristic (ROC) curve analysis, nomogram construction, pathway enrichment analysis, assessment of immunological features, and drug sensitivity testing were conducted using a series of bioinformatics methods.
Seven DRLs were identified to build a prognostic model, whose robustness was verified in an internal cohort. Subsequent prognostic analyses found that high risk scores were associated with worse overall survival (OS). Univariate and multivariate analyses revealed that the risk score could be used as an independent prognostic factor. Furthermore, the ROC curve indicated that the risk score had higher diagnostic efficiency than traditional clinicopathological features. The calibration curve confirmed the accuracy and reliability of the nomogram. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that there were differences in digestive system and nervous system-related pathways between the high- and low-risk groups. Results of tumor mutational burden (TMB) and tumor immune dysfunction and exclusion (TIDE) analyses indicated that patients in the low-risk group had a better response rate to immunotherapy. Finally, the results of drug sensitivity analysis showed that risk score could influence sensitivity to EHT 1864 in GC.
We have successfully developed and verified a 7-DRL risk model which can assess the prognosis and immunological features and guide individualized therapy of GC patients.
抑郁症和长链非编码RNA（lncRNA）已被报道与胃癌（GC）的肿瘤进展和预后相关。本研究旨在构建基于抑郁症相关lncRNAs（DRLs）的GC风险分类和预后模型。
为了开发一个风险模型，我们使用来自癌症基因组图谱（TCGA）的GC RNA测序数据和先前研究中的抑郁症相关基因（DRGs）进行单变量Cox回归以及最小绝对收缩和选择算子（LASSO）回归分析。使用一系列生物信息学方法进行了Kaplan-Meier分析、受试者工作特征（ROC）曲线分析、列线图构建、通路富集分析、免疫特征评估和药物敏感性测试。
确定了7个DRLs来构建一个预后模型，其稳健性在内部队列中得到验证。随后的预后分析发现，高风险评分与较差的总生存期（OS）相关。单变量和多变量分析表明，风险评分可作为独立的预后因素。此外，ROC曲线表明风险评分比传统临床病理特征具有更高的诊断效率。校准曲线证实了列线图的准确性和可靠性。基因本体（GO）和京都基因与基因组百科全书（KEGG）富集分析表明，高风险组和低风险组在消化系统和神经系统相关通路方面存在差异。肿瘤突变负荷（TMB）和肿瘤免疫功能障碍与排除（TIDE）分析结果表明，低风险组患者对免疫治疗的反应率更高。最后，药物敏感性分析结果表明，风险评分可影响GC患者对EHT 1864的敏感性。
我们成功开发并验证了一个7-DRL风险模型，该模型可以评估GC患者的预后和免疫特征，并指导个体化治疗。
