通过机器学习和系统生物学识别出浆液性卵巢癌的 19 个基因特征:诊断和个性化医疗的前景》。

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
ACS Applied Bio Materials Pub Date : 2024-02-01 Epub Date: 2024-01-29 DOI:10.1089/omi.2023.0273
Medi Kori, Talip Yasir Demirtas, Betul Comertpay, Kazim Yalcin Arga, Raghu Sinha, Esra Gov
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引用次数: 0

摘要

卵巢癌是女性癌症死亡的主要原因。早期诊断和精准/个性化医疗对于降低卵巢癌的死亡率和发病率至关重要,而新的分子靶点也能加速药物的发现。我们在此报告一种基于差异共表达分析的综合系统生物学和机器学习(ML)方法,以确定浆液性卵巢癌的候选系统生物标志物(即基因模块)。因此,对四个独立的转录组数据集进行了独立的统计分析,并确定了常见的差异表达基因(DEGs)。利用这些 DEGs,揭示了共表达基因对。随后,重建了共表达基因对之间的差异共表达网络,从而确定了差异共表达基因模块。根据既定标准,"SOV-模块 "被确定为重要模块,由 19 个基因组成。利用独立的数据集,采用主成分分析(PCA)和 ML 技术评估了 SOV 模块的诊断能力。主成分分析的灵敏度和特异度分别为96.7%和100%,ML分析显示,在本研究样本中,区分表型的准确率高达100%。我们使用生存分析和 ML 分析评估了 SOV 模块的预后能力。我们发现,SOV 模块在预后方面的表现非常显著(p 值 = 1.36 × 10-4),使用 ML 技术区分存活和死亡的准确率为 63%。总之,所报告的候选基因组系统生物标志物为浆液性卵巢癌诊断和预后的个性化医疗提供了希望,值得进一步开展实验和临床转化研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A 19-Gene Signature of Serous Ovarian Cancer Identified by Machine Learning and Systems Biology: Prospects for Diagnostics and Personalized Medicine.

Ovarian cancer is a major cause of cancer deaths among women. Early diagnosis and precision/personalized medicine are essential to reduce mortality and morbidity of ovarian cancer, as with new molecular targets to accelerate drug discovery. We report here an integrated systems biology and machine learning (ML) approach based on the differential coexpression analysis to identify candidate systems biomarkers (i.e., gene modules) for serous ovarian cancer. Accordingly, four independent transcriptome datasets were statistically analyzed independently and common differentially expressed genes (DEGs) were identified. Using these DEGs, coexpressed gene pairs were unraveled. Subsequently, differential coexpression networks between the coexpressed gene pairs were reconstructed so as to identify the differentially coexpressed gene modules. Based on the established criteria, "SOV-module" was identified as being significant, consisting of 19 genes. Using independent datasets, the diagnostic capacity of the SOV-module was evaluated using principal component analysis (PCA) and ML techniques. PCA showed a sensitivity and specificity of 96.7% and 100%, respectively, and ML analysis showed an accuracy of up to 100% in distinguishing phenotypes in the present study sample. The prognostic capacity of the SOV-module was evaluated using survival and ML analyses. We found that the SOV-module's performance for prognostics was significant (p-value = 1.36 × 10-4) with an accuracy of 63% in discriminating between survival and death using ML techniques. In summary, the reported genomic systems biomarker candidate offers promise for personalized medicine in diagnosis and prognosis of serous ovarian cancer and warrants further experimental and translational clinical studies.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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