Utilizing an In-silico Approach to Pinpoint Potential Biomarkers for Enhanced Early Detection of Colorectal Cancer.

IF 2.5 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Cancer Informatics Pub Date : 2024-12-16 eCollection Date: 2024-01-01 DOI:10.1177/11769351241307163

Alireza Gharebaghi, Saeid Afshar, Leili Tapak, Hossein Ranjbar, Massoud Saidijam, Irina Dinu

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Abstract

Objectives: Colorectal cancer (CRC) is a prevalent disease characterized by significant dysregulation of gene expression. Non-invasive tests that utilize microRNAs (miRNAs) have shown promise for early CRC detection. This study aims to determine the association between miRNAs and key genes in CRC.

Methods: Two datasets (GSE106817 and GSE23878) were extracted from the NCBI Gene Expression Omnibus database. Penalized logistic regression (PLR) and artificial neural networks (ANN) were used to identify relevant miRNAs and evaluate the classification accuracy of the selected miRNAs. The findings were validated through bipartite miRNA-mRNA interactions.

Results: Our analysis identified 3 miRNAs: miR-1228, miR-6765-5p, and miR-6787-5p, achieving a total accuracy of over 90%. Based on the results of the mRNA-miRNA interaction network, CDK1 and MAD2L1 were identified as target genes of miR-6787-5p.

Conclusions: Our results suggest that the identified miRNAs and target genes could serve as non-invasive biomarkers for diagnosing colorectal cancer, pending laboratory confirmation.

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利用芯片方法精确定位潜在的生物标志物以增强结直肠癌的早期检测。

研究目的结肠直肠癌（CRC）是一种以基因表达严重失调为特征的流行病。利用微RNAs（miRNAs）进行的无创检测已显示出早期检测CRC的前景。本研究旨在确定 miRNA 与 CRC 关键基因之间的关联：从 NCBI 基因表达总库数据库中提取了两个数据集（GSE106817 和 GSE23878）。采用惩罚性逻辑回归（PRR）和人工神经网络（ANN）识别相关的miRNA，并评估所选miRNA的分类准确性。结果：我们的分析确定了 3 个 miRNA：miR-1228、miR-6765-5p 和 miR-6787-5p，总准确率超过 90%。根据mRNA-miRNA相互作用网络的结果，CDK1和MAD2L1被确定为miR-6787-5p的靶基因：我们的研究结果表明，所发现的 miRNA 和靶基因可作为诊断结直肠癌的非侵入性生物标志物，但尚待实验室确认。

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.