结直肠癌的神经-免疫-基质环境：通过机器学习预测生存、复发和治疗反应的肠胶质细胞驱动的预后模型。

IF 3.5 3区生物学 Q3 CELL BIOLOGY

Experimental cell research Pub Date : 2025-09-01 DOI:10.1016/j.yexcr.2025.114733

Quan Wang , Jinsheng Huang , Shichao Wu , Jintian Wang , Tingting Yu , Wei Wei , Tao Yang , Xuelei Wu , Jianning Zhai , Xiaopeng Zhang

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引用次数: 0

摘要

背景：肠胶质细胞（EGCs）与结直肠癌（CRC）的进展有关。本研究旨在建立并验证一种整合EGC和crc相关基因表达的预后模型，以预测患者的生存、复发、转移和治疗反应。方法：对大量和单细胞RNA测序数据进行分析，采用RSF随机森林算法构建基于机器学习的模型。该模型的预后价值通过生存分析、途径富集、免疫谱分析和治疗反应预测来评估。结果：该模型有效地将患者分为高风险和低风险组，高风险患者的总生存期（OS）明显较差，复发和转移的可能性增加。基因集富集分析（GSEA）确定了与肿瘤进展、免疫调节和微环境相互作用相关的关键途径。该模型与免疫细胞浸润和趋化因子信号通路显著相关。高危患者表现出较低的免疫治疗效果和不同的药物敏感性，这表明它有可能指导个性化治疗策略。结论：该模型为结直肠癌的预后和治疗分层提供了有价值的工具，具有潜在的临床应用前景，有待进一步验证。

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Neuro-immuno-stromal context in colorectal cancer: An enteric glial cell-driven prognostic model via machine learning predicts survival, recurrence, and therapy response

Background

Enteric glial cells (EGCs) have been implicated in colorectal cancer (CRC) progression. This study aimed to develop and validate a prognostic model integrating EGC- and CRC-associated gene expression to predict patient survival, recurrence, metastasis, and therapy response.

Methods

Bulk and single-cell RNA sequencing data were analyzed, and a machine learning-based model was constructed using the RSF random forest algorithm. The model's prognostic value was evaluated through survival analysis, pathway enrichment, immune profiling, and therapy response predictions.

Results

The model effectively stratified patients into high- and low-risk groups, with high-risk patients exhibiting significantly worse overall survival (OS) and an increased likelihood of recurrence and metastasis. Gene Set Enrichment Analysis (GSEA) identified key pathways associated with tumor progression, immune regulation, and microenvironmental interactions. The model was significantly correlated with immune cell infiltration and chemokine signaling. High-risk patients exhibited reduced immune therapy efficacy and distinct drug sensitivity profiles, suggesting its potential to guide personalized treatment strategies.

Conclusion

This model serves as a valuable tool for CRC prognosis and treatment stratification, with potential clinical applications pending further validation.

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来源期刊

Experimental cell research 医学-细胞生物学

CiteScore

7.20

自引率

0.00%

发文量

295

审稿时长

30 days

期刊介绍： Our scope includes but is not limited to areas such as: Chromosome biology; Chromatin and epigenetics; DNA repair; Gene regulation; Nuclear import-export; RNA processing; Non-coding RNAs; Organelle biology; The cytoskeleton; Intracellular trafficking; Cell-cell and cell-matrix interactions; Cell motility and migration; Cell proliferation; Cellular differentiation; Signal transduction; Programmed cell death.