He Shengquan, Li Xiaowen, Zhou Xindong, Weng Weiming, Lai Jiajun
Department of Gastrointestinal Surgery, Yuebei People's Hospital (Yuebei People's Hospital Affiliated to Shantou University Medical College), Shaoguan, China.
J Gastrointest Oncol. 2023 Apr 29;14(2):744-757. doi: 10.21037/jgo-23-49. Epub 2023 Apr 17.
Despite advances in colon cancer screening, diagnosis, chemotherapy, and targeted therapy, the prognosis remains poor once colon cancer develops distant metastasis or local recurrence. To further improve the prognosis of colon cancer patients, researchers or clinicians may need to identify new indicators for predicting the prognosis and treatment of colon cancer.
In order to discover the new mechanism of epithelial-mesenchymal transition (EMT) promoting tumor progression and to find new indicators of colon cancer diagnosis, targeted therapy and prognosis, this study conducted The Cancer Genome Atlas (TCGA) analysis, differential gene analysis, prognostic analysis, protein-protein interaction (PPI), enrichment analysis, molecular typing, and a machine algorithm were combined with data from TCGA and Gene Expression Omnibus (GEO) databases and EMT-related genes.
Our study identified 22 EMT-related genes with clinical prognostic value in colon cancer. On the basis of 22 EMT-related genes, we divided colon cancer into 2 different molecular subtypes by non-negative matrix factorization (NMF) model using 14 differentially expressed genes (DEGs), and the DEGs were enriched in multiple signaling pathways related to tumor metastasis process. Further analysis of EMT DEGs revealed that the and genes were characteristic genes for clinical prognosis of colon cancer.
In this study, 22 prognostic genes were screened out from 200 EMT-related genes, and then the and molecules were finally focused on through the combination of the NMF molecular typing model and machine learning screening feature genes, suggesting that and may have good application potential. The findings provide a theoretical basis for the next clinical transformation in the treatment of colon cancer.
尽管在结肠癌筛查、诊断、化疗和靶向治疗方面取得了进展,但一旦结肠癌发生远处转移或局部复发,其预后仍然很差。为了进一步改善结肠癌患者的预后,研究人员或临床医生可能需要确定预测结肠癌预后和治疗的新指标。
为了发现上皮-间质转化(EMT)促进肿瘤进展的新机制,并寻找结肠癌诊断、靶向治疗和预后的新指标,本研究将癌症基因组图谱(TCGA)分析、差异基因分析、预后分析、蛋白质-蛋白质相互作用(PPI)、富集分析、分子分型和机器学习算法与来自TCGA和基因表达综合数据库(GEO)的数据以及EMT相关基因相结合。
我们的研究在结肠癌中鉴定出22个具有临床预后价值的EMT相关基因。基于这22个EMT相关基因,我们使用14个差异表达基因(DEG)通过非负矩阵分解(NMF)模型将结肠癌分为2种不同的分子亚型,并且这些DEG富集于多个与肿瘤转移过程相关的信号通路中。对EMT DEG的进一步分析表明,[具体基因1]和[具体基因2]基因是结肠癌临床预后的特征基因。
本研究从200个EMT相关基因中筛选出22个预后基因,然后通过NMF分子分型模型与机器学习筛选特征基因相结合,最终聚焦于[具体分子1]和[具体分子2]分子,表明[具体分子1]和[具体分子2]可能具有良好的应用潜力。这些发现为结肠癌治疗的下一步临床转化提供了理论依据。