Lin Yuxin, Miao Zhijun, Zhang Xuefeng, Wei Xuedong, Hou Jianquan, Huang Yuhua, Shen Bairong
Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Department of Urology, Suzhou Dushuhu Public Hospital, Suzhou, China.
Front Genet. 2021 Jan 15;11:596826. doi: 10.3389/fgene.2020.596826. eCollection 2020.
Prostate cancer (PCa) is occurred with increasing incidence and heterogeneous pathogenesis. Although clinical strategies are accumulated for PCa prevention, there is still a lack of sensitive biomarkers for the holistic management in PCa occurrence and progression. Based on systems biology and artificial intelligence, translational informatics provides new perspectives for PCa biomarker prioritization and carcinogenic survey. In this study, gene expression and miRNA-mRNA association data were integrated to construct conditional networks specific to PCa occurrence and progression, respectively. Based on network modeling, hub miRNAs with significantly strong single-line regulatory power were topologically identified and those shared by the condition-specific network systems were chosen as candidate biomarkers for computational validation and functional enrichment analysis. Nine miRNAs, i.e., , and , were prioritized as key players for PCa management. Most of these miRNAs achieved high AUC values (AUC > 0.70) in differentiating different prostate samples. Among them, seven of the miRNAs have been previously reported as PCa biomarkers, which indicated the performance of the proposed model. The remaining and could serve as novel candidates for PCa predicting and monitoring. In particular, key miRNA-mRNA regulations were extracted for pathogenetic understanding. Here was selected as the case and and axis were found to be putative mechanisms during PCa evolution. In addition, signaling, prostate cancer, microRNAs in cancer etc. were significantly enriched by the identified miRNAs-mRNAs, demonstrating the functional role of the identified miRNAs in PCa genesis. Biomarker miRNAs together with the associated miRNA-mRNA relations were computationally identified and analyzed for PCa management and carcinogenic deciphering. Further experimental and clinical validations using low-throughput techniques and human samples are expected for future translational studies.
前列腺癌（PCa）的发病率呈上升趋势，其发病机制具有异质性。尽管针对PCa预防已积累了临床策略，但在PCa发生和进展的整体管理方面仍缺乏敏感的生物标志物。基于系统生物学和人工智能，转化信息学为PCa生物标志物的优先级排序和致癌研究提供了新的视角。在本研究中，整合了基因表达和miRNA-mRNA关联数据，分别构建了特定于PCa发生和进展的条件网络。基于网络建模，从拓扑结构上识别出具有显著强大单线调控能力的枢纽miRNA，并选择条件特异性网络系统共有的那些miRNA作为候选生物标志物，进行计算验证和功能富集分析。9种miRNA，即……被优先列为PCa管理的关键因素。这些miRNA中的大多数在区分不同前列腺样本时获得了较高的AUC值（AUC>0.70）。其中，有7种miRNA先前已被报道为PCa生物标志物，这表明了所提出模型的性能。其余的……可作为PCa预测和监测的新候选物。特别是，提取了关键的miRNA-mRNA调控关系以了解发病机制。这里选择……作为案例，发现……轴是PCa演变过程中的假定机制。此外，所鉴定的miRNA-mRNA显著富集了……信号通路、前列腺癌、癌症中的microRNA等，证明了所鉴定的miRNA在PCa发生中的功能作用。通过计算识别并分析了用于PCa管理和致癌解读的生物标志物miRNA及其相关的miRNA-mRNA关系。未来的转化研究有望使用低通量技术和人类样本进行进一步的实验和临床验证。
