分子生物学
IVD分子诊断
细胞培养与分析
蛋白研究
细胞因子
重组蛋白
抗体
高通量测序建库
病原检测UCF系列
生物医药
工具酶
抑制剂激活剂与常用试剂
仪器
耗材

Identification of unique biomarkers in colorectal cancer based on comprehensive analysis and machine learning

Liwei Wang, Aigang Ren, Xiaolong Cui, Yuan Shen, Qingxing Huang

Journal:Frontiers in Oncology

IF:3.3

DOI:10.3389/fonc.2025.1678750

PMID:

Published:2026-01-26

research field:分子生物学干细胞生物学RNA加工遗传学发育生物学

Abstract

Introduction: Colorectal cancer (CRC) is a common malignant tumor with high incidence and poor prognosis. Identifying effective biomarkers is crucial for its diagnosis and treatment.Methods: Gene expression data were obtained from TCGA-CRC and GSE39582 datasets. After preprocessing, differentially expressed genes (DEGs) were screened using the limma package. Hub genes were identified via WGCNA, miRNA-hub/TF-hub gene network construction, and LASSO, SVM-RFE, and random forest algorithms. Subtype analysis, survival analysis, external validation, qRT-PCR, Western blot, and ferroptosis-related assays were performed.Results: Fourteen ferroptosis-mitochondria-RBP-related genes (IMRBPs) were identified, including seven hub RBP genes (APEX1, BRCA1, DNMT1, EZH2, PTTG1, SND1, UHRF1). APEX1 was downregulated in CRC, while the other six were upregulated. The diagnostic model based on these seven genes showed high AUC values (0.818-0.924) in multiple datasets. These hub genes were associated with ferroptosis suppression by regulating GSH/GSSGand Fe²⁺ levels.Discussion: The seven hub RBP genes are potential biomarkers for CRC, providing new insights and therapeutic targets. However, functional validation and larger sample sizes are needed for clinical application.

本文使用的Yeasen产品

购物车
客服
转染试用