A machine learning-based prognostic model integrating mRNA stemness index, hypoxia, and glycolysis‑related biomarkers for colorectal cancer.

IF 1.6 4区医学 Q2 MEDICINE, GENERAL & INTERNAL

Open Medicine Pub Date : 2025-09-15 eCollection Date: 2025-01-01 DOI:10.1515/med-2025-1247

Dan Liu, MingLong Zhang, Ying Nie, XingNan Li, WanQuan Liu, LiLing Yue, XianDong Meng, PengHui Li, LuLu Wang, QingBu Mei

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引用次数: 0

Abstract

Background: Cancer stemness, hypoxia, and glycolysis collectively influence colorectal cancer (CRC) progression. However, the intricate relationships among these factors remain incompletely understood.

Methods: This study (1) explored hypoxia and glycolysis-related genes (HGRGs) in CRC by mRNA stemness index (mRNAsi), analyzed the gene expression profiles from Gene Expression Omnibus and The Cancer Genome Atlas (TCGA) databases, (2) established a Cox-prognostic model based on single-sample gene set enrichment analysis, differentially expressed gene analysis, weighted gene co-expression network analysis, and Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression analyses, and (3) assessed the predictive accuracy of the model. Decision curve analysis (DCA) was employed to determine the clinical utility of the model.

Results: Ten HGRGs were selected based on mRNAsi to create the LASSO model. High-risk CRC patients in the TCGA dataset displayed unfavorable clinical outcomes and responses to immunotherapy. Consensus cluster analysis revealed two distinct colon adenocarcinoma/rectal adenocarcinoma clusters, with patients in cluster 2 having a worse prognosis compared to patients in cluster 1. A five-gene prognostic nomogram was developed through univariate and multivariate Cox regression analyses, with DCA confirming its accuracy.

Conclusions: This innovative prognostic model, incorporating ALDOB, AQP1, IL1A, PHGDH, and PTGIS, is highly accurate in predicting patient survival.

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基于机器学习的预后模型，整合mRNA干性指数、缺氧和结直肠癌糖酵解相关生物标志物。

背景：肿瘤干性、缺氧和糖酵解共同影响结直肠癌（CRC）的进展。然而，这些因素之间错综复杂的关系仍然不完全清楚。方法:本研究(1)通过mRNA干性指数（mRNAsi）探索CRC中缺氧和糖溶酶相关基因（HGRGs），分析来自gene expression Omnibus和the Cancer Genome Atlas （TCGA）数据库的基因表达谱；(2)建立基于单样本基因集富集分析、差异表达基因分析、加权基因共表达网络分析、最小绝对收缩和选择算子（LASSO）和Cox回归分析的CRC预后模型。(3)评估模型的预测精度。采用决策曲线分析（Decision curve analysis， DCA）确定模型的临床应用价值。结果：基于mRNAsi选取10个hgrg建立LASSO模型。TCGA数据集中的高危CRC患者表现出不利的临床结果和对免疫治疗的反应。一致聚类分析显示两种不同的结肠腺癌/直肠腺癌聚类，聚类2患者的预后比聚类1患者差。通过单因素和多因素Cox回归分析，建立了一个五基因预后nomogram， DCA证实了其准确性。结论：这一包含ALDOB、AQP1、IL1A、PHGDH和PTGIS的创新预后模型在预测患者生存方面具有很高的准确性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Open Medicine Medicine-General Medicine

CiteScore

3.00

自引率

0.00%

发文量

153

审稿时长

20 weeks

期刊介绍： Open Medicine is an open access journal that provides users with free, instant, and continued access to all content worldwide. The primary goal of the journal has always been a focus on maintaining the high quality of its published content. Its mission is to facilitate the exchange of ideas between medical science researchers from different countries. Papers connected to all fields of medicine and public health are welcomed. Open Medicine accepts submissions of research articles, reviews, case reports, letters to editor and book reviews.