Lu Jing, Zhang Haotian, Gu Xiaoyu, Liu Yonghui, Zhao Chengwen, Wang Xudong
Medical School of Nantong University, Nantong, China.
Department of Laboratory Medicine, Affiliated Hospital of Nantong University, Nantong, China.
Medicine (Baltimore). 2025 May 30;104(22):e40910. doi: 10.1097/MD.0000000000040910.
Cancer stem cells (CSCs), distinguished by their abilities to differentiate and self-renew, play a pivotal role in the progression of colorectal cancer (CRC). However, the mechanisms that sustain CSCs in CRC remain unclear. This study aimed to identify and characterize gene expressions associated with CRC stemness. We applied a 1-class logistic regression machine learning model to calculate the mRNA expression-based stemness index (mRNAsi) for CRC samples from The Cancer Genome Atlas and cBioPortal databases, adjusting the mRNAsi by tumor purity. Clinical features of CRC were considered in assessing both mRNAsi and adjusted mRNAsi levels. Using DESeq2, we screened differentially expressed genes between high and low mRNAsi groups. Enrichment analysis provided functional annotation for these differentially expressed genes. Key genes linked to mRNAsi were identified using the Kaplan-Meier plotter and Cytoscape software, followed by an evaluation of their prognostic significance. Potential small-molecule compounds targeting the CRC stemness signature were explored via L1000FWD, DGIdb, and CMap databases. CRC samples with higher mRNAsi or adjusted mRNAsi values showed improved disease-free survival (DSS) and progression-free survival (PFS). Strong correlation between clinical characteristics of CSCs and mRNAsi was observed; CMS4 subtype CRC patients had lower mRNAsi with worse DSS and PFS. Ten key genes associated with mRNAsi were identified: collagen type I alpha 1, fibrillin 1, matrix metalloproteinase 9, SPP1, BGN, COL5A1, FN1, elastin, matrix metalloproteinase 2, collagen type I alpha 2. Lower expression of these genes correlated with better PFS and DSS. High correlation among these genes was confirmed in the protein-protein interaction network. This study identifies potential small-molecule drugs targeting stemness in CRC and highlights the prognostic value of the 10 key genes, offering insights into therapeutic targets for CRC treatment.
癌症干细胞(CSCs)以其分化和自我更新能力为特征,在结直肠癌(CRC)的进展中起着关键作用。然而,CRC中维持CSCs的机制仍不清楚。本研究旨在鉴定和表征与CRC干性相关的基因表达。我们应用单类逻辑回归机器学习模型来计算来自癌症基因组图谱和cBioPortal数据库的CRC样本基于mRNA表达的干性指数(mRNAsi),并通过肿瘤纯度对mRNAsi进行调整。在评估mRNAsi和调整后的mRNAsi水平时考虑了CRC的临床特征。使用DESeq2,我们筛选了高mRNAsi组和低mRNAsi组之间差异表达的基因。富集分析为这些差异表达的基因提供了功能注释。使用Kaplan-Meier绘图仪和Cytoscape软件鉴定了与mRNAsi相关的关键基因,随后评估了它们的预后意义。通过L1000FWD、DGIdb和CMap数据库探索了靶向CRC干性特征的潜在小分子化合物。具有较高mRNAsi或调整后mRNAsi值的CRC样本显示出无病生存期(DSS)和无进展生存期(PFS)改善。观察到CSCs的临床特征与mRNAsi之间存在强相关性;CMS4亚型CRC患者的mRNAsi较低,DSS和PFS较差。鉴定了10个与mRNAsi相关的关键基因:I型胶原蛋白α1、原纤蛋白1、基质金属蛋白酶9、SPP1、BGN、COL5A1、FN1、弹性蛋白、基质金属蛋白酶2、I型胶原蛋白α2。这些基因的低表达与更好的PFS和DSS相关。在蛋白质-蛋白质相互作用网络中证实了这些基因之间的高度相关性。本研究鉴定了靶向CRC干性的潜在小分子药物,并突出了这10个关键基因的预后价值,为CRC治疗的治疗靶点提供了见解。