Analysis of aberrant miRNA-mRNA interaction networks in prostate cancer to conjecture its molecular mechanisms

Article type: Research Article

Authors: Peng, Shuang^{a; 1} | Liu, Cheng^{b; 1} | Fan, Xingchen^{a; 1} | Zhu, Jingfeng^c | Zhang, Shiyu^a | Zhou, Xin^a | Wang, Tongshan^{a; *} | Gao, Feng^{d; *} | Zhu, Wei^{a; *}

Affiliations: [a] Department of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China | [b] Department of Gastroenterology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China | [c] Department of Nephrology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China | [d] Department of Osteology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China

Correspondence: [*] Corresponding authors: Tongshan Wang, Department of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China. Tel.: +86 137 05174300; E-mail: wangtongshan @jsph.org.cn. Feng Gao, Department of Osteology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China. E-mail: gaofeng@jsph.org.cn. Wei Zhu, Department of Oncolo- gy, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China. Tel.: +86 139 13894911; E-mail: zhuwei@njmu.edu.cn.

Note: [1] Shuang Peng, Cheng Liu, and Xingchen Fan have contributed equally to this work.

Abstract: BACKGROUND: MicroRNAs (miRNAs) capable of post-transcriptionally regulating mRNA expression are essential to tumor occurrence and progression. OBJECTIVE: This study aims to find negatively regulatory miRNA-mRNA pairs in prostate adenocarcinoma (PRAD). METHODS: Combining The Cancer Genome Atlas (TCGA) RNA-Seq data with Gene Expression Omnibus (GEO) mRNA/miRNA expression profiles, differently expressed miRNA/mRNA (DE-miRNAs/DE-mRNAs) were identified. MiRNA-mRNA pairs were screened by miRTarBase and TarBase, databases collecting experimentally confirmed miRNA-mRNA pairs, and verified in 30 paired prostate specimens by real-time reverse transcription polymerase chain reaction (RT-qPCR). The diagnostic values of miRNA-mRNA pairs were measured by receiver operation characteristic (ROC) curve and Decision Curve Analysis (DCA). DAVID-mirPath database and Connectivity Map were employed in GO/KEGG analysis and compounds research. Interactions between miRNA-mRNA pairs and phenotypic features were analyzed with correlation heatmap in hiplot. RESULTS: Based on TCGA RNA-Seq data, 22 miRNA and 14 mRNA GEO datasets, 67 (20 down and 47 up) miRNAs and 351 (139 up and 212 down) mRNAs were selected. After screening from 2 databases, 8 miRNA (up)-mRNA (down) and 7 miRNA (down)-mRNA (up) pairs were identified with Pearson’s correlation in TCGA. By external validation, miR-221-3p (down)/GALNT3 (up) and miR-20a-5p (up)/FRMD6 (down) were chosen. The model combing 4 signatures possessed better diagnostic value. These two miRNA-mRNA pairs were significantly connected with immune cells fraction and tumor immune microenvironment. CONCLUSIONS: The diagnostic model containing 2 negatively regulatory miRNA-mRNA pairs was established to distinguish PRADs from normal controls.

Keywords: miRNA, miRNA-mRNA networks, prostate adenocarcinoma, TCGA, PCR

DOI: 10.3233/CBM-220051

Journal: Cancer Biomarkers, vol. 35, no. 4, pp. 395-407, 2022