Mokhtaridoost Milad, Maass Philipp G, Gönen Mehmet
Genetics & Genome Biology Program, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada.
Graduate School of Sciences and Engineering, Koç University, İstanbul 34450, Turkey.
Cancers (Basel). 2022 Oct 9;14(19):4939. doi: 10.3390/cancers14194939.
MicroRNA (miRNA) alterations significantly impact the formation and progression of human cancers. miRNAs interact with messenger RNAs (mRNAs) to facilitate degradation or translational repression. Thus, identifying miRNA-mRNA regulatory modules in cohorts of primary tumor tissues are fundamental for understanding the biology of tumor heterogeneity and precise diagnosis and treatment. We established a multitask learning sparse regularized factor regression (MSRFR) method to determine key tissue- and cohort-specific miRNA-mRNA regulatory modules from expression profiles of tumors. MSRFR simultaneously models the sparse relationship between miRNAs and mRNAs and extracts tissue- and cohort-specific miRNA-mRNA regulatory modules separately. We tested the model's ability to determine cohort-specific regulatory modules of multiple cancer cohorts from the same tissue and their underlying tissue-specific regulatory modules by extracting similarities between cancer cohorts (i.e., blood, kidney, and lung). We also detected tissue-specific and cohort-specific signatures in the corresponding regulatory modules by comparing our findings from various other tissues. We show that MSRFR effectively determines cancer-related miRNAs in cohort-specific regulatory modules, distinguishes tissue- and cohort-specific regulatory modules from each other, and extracts tissue-specific information from different cohorts of disease-related tissue. Our findings indicate that the MSRFR model can support current efforts in precision medicine to define tumor-specific miRNA-mRNA signatures.
微小RNA（miRNA）改变对人类癌症的形成和进展有显著影响。miRNA与信使核糖核酸（mRNA）相互作用，促进其降解或翻译抑制。因此，在原发性肿瘤组织队列中识别miRNA-mRNA调控模块对于理解肿瘤异质性生物学以及精确诊断和治疗至关重要。我们建立了一种多任务学习稀疏正则化因子回归（MSRFR）方法，以从肿瘤表达谱中确定关键的组织和队列特异性miRNA-mRNA调控模块。MSRFR同时对miRNA和mRNA之间的稀疏关系进行建模，并分别提取组织和队列特异性的miRNA-mRNA调控模块。我们通过提取癌症队列（即血液、肾脏和肺）之间的相似性，测试了该模型从同一组织中确定多个癌症队列的队列特异性调控模块及其潜在组织特异性调控模块的能力。我们还通过比较来自其他各种组织的研究结果，在相应的调控模块中检测到组织特异性和队列特异性特征。我们表明，MSRFR有效地确定了队列特异性调控模块中与癌症相关的miRNA，区分了组织和队列特异性调控模块，并从不同疾病相关组织队列中提取了组织特异性信息。我们的研究结果表明，MSRFR模型可以支持当前精准医学中定义肿瘤特异性miRNA-mRNA特征的努力。
