Nazari Elham, Khalili-Tanha Ghazaleh, Pourali Ghazaleh, Khojasteh-Leylakoohi Fatemeh, Azari Hanieh, Dashtiahangar Mohammad, Fiuji Hamid, Yousefli Zahra, Asadnia Alireza, Maftooh Mina, Akbarzade Hamed, Nassiri Mohammadreza, Hassanian Seyed Mahdi, Ferns Gordon A, Peters Godefridus J, Giovannetti Elisa, Batra Jyotsna, Khazaei Majid, Avan Amir
Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Bioimpacts. 2024 Nov 5;15:30566. doi: 10.34172/bi.30566. eCollection 2025.
Colorectal cancer (CRC) is among the lethal cancers, indicating the need for the identification of novel biomarkers for the detection of patients in earlier stages. RNA and microRNA sequencing were analyzed using bioinformatics and machine learning algorithms to identify differentially expressed genes (DEGs), followed by validation in CRC patients.
The genome-wide RNA sequencing of 631 samples, comprising 398 patients and 233 normal cases was extracted from the Cancer Genome Atlas (TCGA). The DEGs were identified using DESeq package in R. Survival analysis was evaluated using Kaplan-Meier analysis to identify prognostic biomarkers. Predictive biomarkers were determined by machine learning algorithms such as Deep learning, Decision Tree, and Support Vector Machine. The biological pathways, protein-protein interaction (PPI), the co-expression of DEGs, and the correlation between DEGs and clinical data were evaluated. Additionally, the diagnostic markers were assessed with a combioROC package. Finally, the candidate tope score gene was validated by Real-time PCR in CRC patients.
The survival analysis revealed five novel prognostic genes, including , , , , and . Thirty-nine upregulated, 40 downregulated genes, and 20 miRNAs were detected by SVM with high accuracy and AUC. The upregulation of and genes and the downregulation of and genes had the highest coefficient in the advanced stage. Furthermore, our findings showed that three miRNAs (, and ) upregulated in the advanced stage. , as a novel gene, was validated using RT-PCR in CRC patients. The combineROC curve analysis indicated that the combination of can be considered as diagnostic markers with sensitivity, specificity, and AUC values of 0.90, 0.94, and 0.92, respectively.
Machine learning algorithms can be used to Identify key dysregulated genes/miRNAs involved in the pathogenesis of diseases, leading to the detection of patients in earlier stages. Our data also demonstrated the prognostic value of in colorectal cancer.
结直肠癌（CRC）是致命癌症之一，这表明需要鉴定新的生物标志物以在早期阶段检测患者。使用生物信息学和机器学习算法分析RNA和微小RNA测序，以鉴定差异表达基因（DEG），随后在CRC患者中进行验证。
从癌症基因组图谱（TCGA）中提取了631个样本的全基因组RNA测序数据，包括398例患者和233例正常病例。使用R中的DESeq软件包鉴定DEG。使用Kaplan-Meier分析评估生存分析以鉴定预后生物标志物。通过深度学习、决策树和支持向量机等机器学习算法确定预测性生物标志物。评估生物途径、蛋白质-蛋白质相互作用（PPI）、DEG的共表达以及DEG与临床数据之间的相关性。此外，使用combioROC软件包评估诊断标志物。最后，通过实时PCR在CRC患者中验证候选顶分基因。
生存分析揭示了五个新的预后基因，包括 、 、 、 和 。通过支持向量机以高精度和AUC检测到39个上调基因、40个下调基因和20个微小RNA。 和 基因的上调以及 和 基因的下调在晚期阶段具有最高系数。此外，我们的研究结果表明，三个微小RNA（ 、 和 ）在晚期上调。作为一个新基因， 在CRC患者中通过RT-PCR进行了验证。联合ROC曲线分析表明， 的组合可被视为诊断标志物，敏感性、特异性和AUC值分别为0.90、0.94和0.92。
机器学习算法可用于识别参与疾病发病机制的关键失调基因/微小RNA，从而在早期阶段检测患者。我们的数据还证明了 在结直肠癌中的预后价值。
