AUTHOR=Wu Ming , Zhang Yan 

TITLE=Integrated bioinformatics, network pharmacology, and artificial intelligence to predict the mechanism of celastrol against muscle atrophy caused by colorectal cancer

JOURNAL=Frontiers in Genetics

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.1012932

DOI=10.3389/fgene.2022.1012932

ISSN=1664-8021

ABSTRACT=Muscle atrophy due to colorectal cancer severely reduces the quality and survival time of patients. However, the underlying causative mechanisms and therapeutic agents are not well understood. The aim of this study was to screen and identify the miRNA-mRNA regulatory network and therapeutic targets of celastrol in colorectal cancer causing muscle atrophy via blood exosomes.Datasets were downloaded from the GEO online database. Differential analysis was first performed by blood exosome dataset GSE39833 from colorectal cancer and normal human to identify differential miRNAs (DEmiRNAs), and then transcriptional enrichment analysis was done to identify important enriched genes. Gene ontology (GO), Kyoto Gene and Genome Encyclopedia (KEGG) pathway enrichment was performed by funrich software. Using the muscle atrophy sample GSE34111, the differential mRNAs（DEmRNAs） in the muscle atrophy sample were analysed, a regulatory network map was established based on miRNA-mRNA regulatory mechanisms, further GO and KEGG enrichment analysis was performed for the differential genes in muscle atrophy via Cytoscapo's ClueGo plug-in, and the network pharmacology pharmacophore analysis method was used to analyse the celastrol therapeutic targets, taking intersections to find the therapeutic targets of celastrol , using the artificial intelligence AlphaFold2 to predict the protein structure of the key targets, and finally using molecular docking to verify whether celastrol  and the target proteins can be successfully docked. Firstly, 82 DEmiRNAs were obtained, and then the top 10 enriched target genes were demonstrated. The enrichment of 82 miRNAs showed close correlation with muscle atrophy, and 332 DEmRNAs were found by differential analysis in muscle atrophy samples, among which the mRNA genes involved in miRNA-mRNA were 44. The differential genes in muscle atrophy were enriched for 30 signalling pathways, and 228 target genes were annotated after pharmacophore target analysis. The NR1D2 gene, the target of treatment, was found by taking intersections, and the protein structure of this target was predicted by AlphaFold2, which was successfully docked and validated using molecular docking. In our present study, colorectal cancer likely enters muscle from blood exosomes and regulates skeletal muscle atrophy through miRNA-mRNA regulatory network mechanisms, and celastrol treats through NR1D2 in the miRNA-mRNA regulatory network.