Wu Wen-Jing, Wang Kan, Yang Yang Vivian, Yang Xiaoning
State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, China.
Department of Gastroenterology, The National Key Clinical Specialty, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Transl Cancer Res. 2025 May 30;14(5):2737-2757. doi: 10.21037/tcr-24-1988. Epub 2025 May 27.
Nervous system-cancer interactions can regulate tumorigenesis, invasion, and metastasis. However, specific biomarkers for targeting neuron synapse in colorectal cancer (CRC) remain unexplored. This study aims to develop a neuronal synapse-related signature (NSRS) to predict survival in CRC patients, identify potential therapeutic drugs, and explore its clinical applications.
We collected neuronal synapse genes (NSGs) from the Molecular Signatures Database (MSigDB) and published mass spectrometry data. Using weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator Cox regression (LASSO-Cox), we identified prognostic NSGs and constructed a NSRS through multivariate Cox regression. Functional enrichment analysis revealed the molecular characteristics of NSRS subgroups. Additionally, xCell and ESTIMATE algorithms quantified the abundance of 54 cell subtypes and assessed the tumor immune microenvironment (TIME) of the two NSRS subgroups. Finally, drug prediction and molecular docking identified candidate drugs with therapeutic potential.
Seven key prognostic NSGs were identified, and an independent, stable NSRS model was constructed. Kaplan-Meier survival curves indicated that the high NSRS group had poorer outcomes (log-rank test, P<0.05). Functional enrichment analysis revealed significant enrichment of epithelial-mesenchymal transition, hypoxia, and inflammation features in the high NSRS group. xCell and ESTIMATE analyses showed a more complex TIME and lower tumor purity in the high NSRS group, highlighting the role of neuro-tumor interactions in CRC. Drug prediction and molecular docking suggested alprostadil, dihydroergocristine, and nocodazole as candidate drugs for CRC treatment.
This is the first study to develop neuron synapse-related biomarkers from the perspective of neuron-cancer interactions using machine learning. We constructed a robust NSRS model and identified candidate drugs targeting prognostic NSGs, providing new insights into CRC prognosis and treatment.
神经系统与癌症的相互作用可调节肿瘤发生、侵袭和转移。然而，针对结直肠癌（CRC）中神经元突触的特异性生物标志物仍未得到探索。本研究旨在开发一种神经元突触相关特征（NSRS），以预测CRC患者的生存情况，识别潜在的治疗药物，并探索其临床应用。
我们从分子特征数据库（MSigDB）收集神经元突触基因（NSGs）并公布质谱数据。使用加权基因共表达网络分析（WGCNA）和最小绝对收缩和选择算子Cox回归（LASSO-Cox），我们鉴定了预后NSGs，并通过多变量Cox回归构建了NSRS。功能富集分析揭示了NSRS亚组的分子特征。此外，xCell和ESTIMATE算法量化了54种细胞亚型的丰度，并评估了两个NSRS亚组的肿瘤免疫微环境（TIME）。最后，药物预测和分子对接确定了具有治疗潜力的候选药物。
鉴定出七个关键的预后NSGs，并构建了一个独立、稳定的NSRS模型。Kaplan-Meier生存曲线表明，高NSRS组的预后较差（对数秩检验，P<0.05）。功能富集分析显示，高NSRS组中上皮-间质转化、缺氧和炎症特征显著富集。xCell和ESTIMATE分析表明，高NSRS组的TIME更复杂，肿瘤纯度更低，突出了神经-肿瘤相互作用在CRC中的作用。药物预测和分子对接表明，前列地尔、双氢麦角隐亭和诺考达唑是CRC治疗的候选药物。
这是第一项从神经元-癌症相互作用的角度利用机器学习开发神经元突触相关生物标志物的研究。我们构建了一个强大的NSRS模型，并确定了针对预后NSGs的候选药物，为CRC的预后和治疗提供了新的见解。
