DNA Methylation Biomarker Discovery for Colorectal Cancer Diagnosis Assistance Through Integrated Analysis.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Cancer Informatics Pub Date : 2025-04-15 eCollection Date: 2025-01-01 DOI:10.1177/11769351251324545

Yi-Hsuan Tsai, Yi-Husan Lai, Shu-Jen Chen, Yi-Chiao Cheng, Tun-Wen Pai

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引用次数: 0

Abstract

Objective: This study aimed to identify biomarkers for colorectal cancer (CRC) with representative gene functions and high classification accuracy in tissue and blood samples.

Methods: We integrated CRC DNA methylation profiles from The Cancer Genome Atlas and comorbidity patterns of CRC to select biomarker candidates. We clustered these candidates near the promoter regions into multiple functional groups based on their functional annotations. To validate the selected biomarkers, we applied 3 machine learning techniques to construct models and compare their prediction performances.

Results: The 10 screened genes showed significant methylation differences in both tissue and blood samples. Our test results showed that 3-gene combinations achieved outstanding classification performance. Selecting 3 representative biomarkers from different genetic functional clusters, the combination of ADHFE1, ADAMTS5, and MIR129-2 exhibited the best performance across the 3 prediction models, achieving a Matthews correlation coefficient > .85 and an F1-score of .9.

Conclusions: Using integrated DNA methylation analysis, we identified 3 CRC-related biomarkers with remarkable classification performance. These biomarkers can be used to design a practical clinical toolkit for CRC diagnosis assistance and may also serve as candidate biomarkers for further clinical experiments through liquid biopsies.

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通过综合分析发现DNA甲基化生物标志物有助于结直肠癌的诊断。

目的：本研究旨在鉴定组织和血液样本中具有代表性基因功能且分类准确率高的结直肠癌（CRC）生物标志物。方法：我们整合了来自癌症基因组图谱的CRC DNA甲基化图谱和CRC的合并症模式，以选择候选的生物标志物。我们根据它们的功能注释将这些靠近启动子区域的候选基因聚类成多个功能组。为了验证所选择的生物标志物，我们应用了3种机器学习技术来构建模型并比较它们的预测性能。结果：筛选的10个基因在组织和血液样本中都显示出显著的甲基化差异。我们的测试结果表明，3-基因组合具有出色的分类性能。从不同的遗传功能聚类中选择3个具有代表性的生物标志物，ADHFE1、ADAMTS5和MIR129-2组合在3个预测模型中表现最佳，马修斯相关系数为>.85，f1得分为0.9。结论：通过综合DNA甲基化分析，我们确定了3个具有显著分类性能的crc相关生物标志物。这些生物标志物可以用来设计一个实用的临床工具包，以帮助结直肠癌诊断，也可以作为候选生物标志物，通过液体活检进行进一步的临床实验。

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

Cancer Informatics Medicine-Oncology

CiteScore

3.00

自引率

5.00%

发文量

审稿时长

8 weeks

期刊介绍： The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.