Construction of a TF–miRNA–gene feed-forward loop network predicts biomarkers and potential drugs for myasthenia gravis

Abstract

Myasthenia gravis (MG) is an autoimmune disease and the most common type of neuromuscular disease. Genes and miRNAs associated with MG have been widely studied; however, the molecular mechanisms of transcription factors (TFs) and the relationship among them remain unclear. A TF–miRNA–gene network (TMGN) of MG was constructed by extracting six regulatory pairs (TF–miRNA, miRNA–gene, TF–gene, miRNA–TF, gene–gene and miRNA–miRNA). Then, 3/4/5-node regulatory motifs were detected in the TMGN. Then, the motifs with the highest Z-score, occurring as 3/4/5-node composite feed-forward loops (FFLs), were selected as statistically significant motifs. By merging these motifs together, we constructed a 3/4/5-node composite FFL motif-specific subnetwork (CFMSN). Then, pathway and GO enrichment analyses were performed to further elucidate the mechanism of MG. In addition, the genes, TFs and miRNAs in the CFMSN were also utilized to identify potential drugs. Five related genes, 3 TFs and 13 miRNAs, were extracted from the CFMSN. As the most important TF in the CFMSN, MYC was inferred to play a critical role in MG. Pathway enrichment analysis showed that the genes and miRNAs in the CFMSN were mainly enriched in pathways related to cancer and infections. Furthermore, 21 drugs were identified through the CFMSN, of which estradiol, estramustine, raloxifene and tamoxifen have the potential to be novel drugs to treat MG. The present study provides MG-related TFs by constructing the CFMSN for further experimental studies and provides a novel perspective for new biomarkers and potential drugs for MG.

Identification of the hub genes and transcription factor‑miRNA axes involved in Helicobacter pylori‑associated gastric cancer

“…A total of 31 TFs were identified by a series of comprehensive analyses. Recently, numerous studies ( 38 – 40 ) have demonstrated that TFs regulate gene expression by interacting with miRNAs. Thus, the associations between the miRNAs and TFs were further investigated using the miRWalk database.…”

Identification of the regulatory role of lncRNA HCG18 in myasthenia gravis by integrated bioinformatics and experimental analyses

“…Given the complex pathogenesis of MG and the side effects of drugs, no one treatment therapy is best for all MG patients. In the past few years, we have tried to adopt several different methods, such as constructing miRNA-regulated drug-pathway network [41] and TF-miRNA-gene feed-forward loop network [42], to screen candidate treatment drugs for MG. Recently, the combination of emerging nanotechnologies and material sciences has promising applications in multiple fields, such as nanotechnology in food and agriculture [43], nano-antioxidants in environmental-related oxidative damages [44] and nanotechnology on drug delivery in autoimmune diseases [45].…”

Construction Immune Related Feed-Forward Loop Network Reveals Angiotensin II Receptor Blocker as Potential Neuroprotective Drug for Ischemic Stroke

“…Therefore, fast network motif detection (FANMOD) software was used to detect network motif types of size-3 in the original network ( Wernicke and Rasche, 2006 ). The method of generating motifs by FANMOD software can be found in our previous study ( Bo et al, 2021 ). Finally, 3-node motif types with Z-score >2.0 and p -value < 0.05 were considered to be significant in the dumping results of multiple types of motifs ( Harari and Liao, 2010 ).…”

“…In our previous study ( Bo et al, 2021 ), we mentioned two types of pivotal gene regulators: transcription factors (TFs) and miRNAs, both sharing a common regulatory logic in the co-regulation of target genes ( Hobert, 2008 ). Besides, gene regulators (miRNA and TF) and their common target gene can make up a feed-forward loop (FFL), a motif that consists of two regulators and their common target gene ( Mangan and Alon, 2003 ).…”