MicroRNA and transcription factor co-regulatory networks and subtype classification of seminoma and non-seminoma in testicular germ cell tumors

Guimin Qin,Christine M. Eischen,Ramkrishna Mitra,Aimin Li,Saurav Mallik,Peilin Jia,Zhongming Zhao

doi:10.1038/s41598-020-57834-w

Abstract

Recent studies have revealed that feed-forward loops (FFLs) as regulatory motifs have synergistic roles in cellular systems and their disruption may cause diseases including cancer. FFLs may include two regulators such as transcription factors (TFs) and microRNAs (miRNAs). In this study, we extensively investigated TF and miRNA regulation pairs, their FFLs, and TF-miRNA mediated regulatory networks in two major types of testicular germ cell tumors (TGCT): seminoma (SE) and non-seminoma (NSE). Specifically, we identified differentially expressed mRNA genes and miRNAs in 103 tumors using the transcriptomic data from The Cancer Genome Atlas. Next, we determined significantly correlated TF-gene/miRNA and miRNA-gene/TF pairs with regulation direction. Subsequently, we determined 288 and 664 dysregulated TF-miRNA-gene FFLs in SE and NSE, respectively. By constructing dysregulated FFL networks, we found that many hub nodes (12 out of 30 for SE and 8 out of 32 for NSE) in the top ranked FFLs could predict subtype-classification (Random Forest classifier, average accuracy ≥90%). These hub molecules were validated by an independent dataset. Our network analysis pinpointed several SE-specific dysregulated miRNAs (miR-200c-3p, miR-25-3p, and miR-302a-3p) and genes (EPHA2, JUN, KLF4, PLXDC2, RND3, SPI1, and TIMP3) and NSE-specific dysregulated miRNAs (miR-367-3p, miR-519d-3p, and miR-96-5p) and genes (NR2F1 and NR2F2). This study is the first systematic investigation of TF and miRNA regulation and their co-regulation in two major TGCT subtypes.

Highlights

Recent studies have revealed that feed-forward loops (FFLs) as regulatory motifs have synergistic roles in cellular systems and their disruption may cause diseases including cancer
By following the steps described in the Materials and methods section, we identified 2,950 highly expressed genes and 167 significantly overexpressed miRNAs in NSE tumor samples (n = 48) versus SE tumor samples (n = 55) (>2 fold-change, adjusted p-value < 0.05)
In SE, we identified that SPI1, KLF4, and JUN were in the top 3 list, which formed 59, 48, and 32 FFLs, respectively

Summary

Introduction

Recent studies have revealed that feed-forward loops (FFLs) as regulatory motifs have synergistic roles in cellular systems and their disruption may cause diseases including cancer. We extensively investigated TF and miRNA regulation pairs, their FFLs, and TF-miRNA mediated regulatory networks in two major types of testicular germ cell tumors (TGCT): seminoma (SE) and non-seminoma (NSE). While the etiology of the two TGCT subtypes is well studied, their molecular profiles, signature genetic markers, and regulatory mechanisms have not been systematically investigated, unlike other common cancers. Since miRNAs have a key role in post-transcriptional regulation of gene expression, it is important to further explore how genes are synergistically regulated in both types, leading to the elucidation of possible regulatory modules and mechanisms unique in NSE or SE. Follow-up network characteristics analysis (e.g., hub nodes for TFs, miRNAs, genes) and subtype classification analysis unveiled a subset of the FFLs that might have pathogenic potential in TGCT. The analytical approaches provided an efficient way to identify significant molecules associated with TGCT

Methods

Results

Conclusion