Qiu Lupeng, Liu Yaru, Yang Zizhong, Zhao Xiao, Gong Yixin, Jiao Shunchang
Medical School of Chinese PLA, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China.
Department of Medical Oncology, The First Medical Centre, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China.
Mol Biotechnol. 2025 Jan;67(1):209-225. doi: 10.1007/s12033-023-00997-4. Epub 2023 Dec 25.
Gastric cancer (GC) is a progressive disease with high morbidity and mortality. Accumulating evidence indicated that nervous system-cancer crosstalk can affect the occurrence and progression of GC. However, the role of nerve-related lncRNAs (NRLs) in GC remains largely unexplored. In this study, a total of 441 nerve-related genes were collected from the KEGG database, and two approaches, unsupervised clustering and WGCNA, were employed to identify NRLs. Lasso regression analysis was then used to construct the nerve-related lncRNA signature (NRLS). Based on the expression profiles of 5 lncRNAs, we developed a stable NRLS to predict survival in GC patients, and survival analyses showed significantly shorter overall survival (OS) in patients with high NRLS. In addition, the NRLS was found to be positively correlated with immune characteristics, including tumor-infiltrating immune cells, immune modulators, cytokines and chemokines. We then analyzed the role of NRLS in predicting chemotherapy and immunotherapy responses, and constructed the OS nomogram combining NRLS and other clinical features. In conclusion, we constructed a robust NRLS model to stratify GC patients and predict the outcomes of chemotherapy and immunotherapy. This study can provide a new perspective for future individualized treatment of GC.
胃癌（GC）是一种发病率和死亡率都很高的进展性疾病。越来越多的证据表明，神经系统与癌症之间的相互作用会影响GC的发生和发展。然而，神经相关长链非编码RNA（NRL）在GC中的作用在很大程度上仍未被探索。在本研究中，从KEGG数据库收集了总共441个神经相关基因，并采用无监督聚类和加权基因共表达网络分析（WGCNA）两种方法来识别NRL。然后使用套索回归分析构建神经相关长链非编码RNA特征（NRLS）。基于5种长链非编码RNA的表达谱，我们开发了一种稳定的NRLS来预测GC患者的生存情况，生存分析显示NRLS高的患者总生存期（OS）显著缩短。此外，发现NRLS与免疫特征呈正相关，包括肿瘤浸润免疫细胞、免疫调节剂、细胞因子和趋化因子。然后我们分析了NRLS在预测化疗和免疫治疗反应中的作用，并构建了结合NRLS和其他临床特征的OS列线图。总之，我们构建了一个强大的NRLS模型来对GC患者进行分层，并预测化疗和免疫治疗的结果。本研究可为未来GC的个体化治疗提供新的视角。
