Identification of potential tissue-specific biomarkers involved in pig fat deposition through integrated bioinformatics analysis and machine learning

Abstract

Backfat thickness (BT) and intramuscular fat (IMF) content are closely appertained to meat production and quality in pig production. Deposition in subcutaneous adipose (SA) and IMF concerns different genes and regulatory mechanisms. And larger studies with rigorous design should be carried to explore the molecular regulation of fat deposition in different tissues. The purpose of this study is to gain a better understanding of the molecular mechanisms underlying differences in fat deposition among different tissues and identify tissue-specific genes involved in regulating fat deposition. The SA-associated datasets (GSE122349 and GSE145956) and IMF-associated datasets (GSE165613 and GSE207279) were downloaded from the Gene Expression Omnibus (GEO) as the BT and IMF group, respectively. Subsequently, the Robust Rank Aggregation (RRA) algorithm identified 27 down- and 29 up-regulated differentially expressed genes (DEGs) in the BT group. Based on bioinformatics and three machine learning algorithms, four SA deposition-related potential biomarkers, namely ACLY, FASN, ME1, and ARVCF were selected. FASN was evaluated as the most valuable biomarker for the SA mechanism. The 18 down- and 34 up-regulated DEGs in the IMF group were identified, and ACTA2 and HMGCL were screened as the IMF deposition-related candidate core genes, especially the ACTA2 may play the critical role in IMF deposition regulation. Moreover, based on the constructed ceRNA network, we postulated that the role of predicted ceRNA interaction network of XIST, NEAT1/miR-15a-5p, miR-16-5p, miR-424-5p, miR-497-5p/FASN were vital in the SA metabolism, XIST, NEAT1/miR-27a/b-3p, 181a/c-5p/ACTA2 might contribute to the regulation to IMF metabolism, which all gave suggestions in molecular mechanism for regulation of fat deposition. These findings may facilitate advancements in porcine quality at the genetic and molecular levels and assist with human obesity-associated diseases.

Keywords

• Subcutaneous adipose
• Intramuscular fat
• Bioinformatics analysis
• Machine learning
• Biomarkers