Identification of Key Genes Related to Intramuscular Fat Content of Psoas Major Muscle in Saba Pigs by Integrating Bioinformatics and Machine Learning Based on Transcriptome Data.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40282015", "kind": "science-record", "revision": 3, "content_hash": "sha256:505c5469184c9c17d02d9c6e7430a57c9f90e875ddb5e330a950dfb78907e309", "created_at": "2026-11-01T20:30:26Z", "updated_at": "2026-11-01T22:13:57Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Key Genes Related to Intramuscular Fat Content of Psoas Major Muscle in Saba Pigs by Integrating Bioinformatics and Machine Learning Based on Transcriptome Data.", "doi": "10.3390/ani15081181", "pmid": "40282015", "authors": ["Zixia Huang", "Yongli Yang", "Jinhua Lai", "Qiang Chen", "Xiaoyi Wang", "Shuyan Wang", "Mingli Li", "Shaoxiong Lu"], "journal": "Animals : an open access journal from MDPI", "year": 2025, "abstract": "The psoas major muscle (PMM) is a piece of pork with good tenderness and high value. Intramuscular fat (IMF) content, serving as a pivotal indicator of pork quality, varies greatly among pigs within the same breed. However, there is a paucity of studies focusing on investigating the molecular mechanism of PMM IMF deposition in the same pig breed. The present study aimed to identify the potential genes related to the IMF content of PMM in low- and high-IMF Saba pigs based on transcriptome data analysis. The data used in this study were the RNA sequences of PMM from 12 Saba pigs (PRJNA1223630, from our laboratory) and gene expression profiles (GSE207279) acquired from the NCBI Sequence Read Archive database and the GEO database, respectively, together with data on the fatty acid and amino acid composition of the 12 Saba pigs' PMM. It was found that the high-IMF pigs exhibited significantly elevated levels of saturated fatty acids and (mono)unsaturated fatty acids, especially C14:0, C16:0, C20:0, C16:1, C18:1n9c, and C20:2, compared with those in the low-IMF pigs (p < 0.05 or p < 0.01). A total of 370 differentially expressed genes (DEGs) (221 up- and 149 down-regulated) were identified based on PRJNA1223630. Then, 20 hub genes were identified through protein-protein interaction (PPI) network analysis. Four potential fat-deposition-related genes (DGAT2, PCK1, MELK, and FASN) were further screened via the intersection of the candidate genes identified by the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and the top five genes ranked by the Random Forest (RF) method based on the 20 hub genes and were validated in the test gene set (GSE207279). The constructed mRNA (gene)-miRNA-lncRNA network, involving miRNAs (miR-103a-3p, miR-107, and miR-485-5p), lncRNAs (XIST, NEAT1, and KCNQ1OT1), and FASN, might be crucial for IMF deposition in pigs. These findings might delineate valuable regulatory molecular mechanisms coordinating IMF deposition and could serve as a beneficial foundation for the genetic improvement of pork quality.", "claims": [{"id": "claim-method", "statement": "To assess the predictive performance of the identified genes, we analyzed their expression level differences across the low- and high-IMF pigs through an unpaired t-test, where a p-value less than 0.05 was deemed statistically significant. Subsequently, receiver operating characteristic (ROC) curves were implemented, and the area under the curve (AUC) values for the potential genes were calculated based on the validation dataset GSE207279 in order to evaluate the distinguishing efficacy of potential IMF-related genes, utilizing GraphPad Prism (version 10.2.3).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40282015", "locator": "sections[8] \"2.6. Screening of Potential IMF-Related Genes\" paragraph 2", "quote": "To assess the predictive performance of the identified genes, we analyzed their expression level differences across the low- and high-IMF pigs through an unpaired t-test, where a p-value less than 0.05 was deemed statistically significant. Subsequently, receiver operating characteristic (ROC) curves were implemented, and the area under the curve (AUC) values for the potential genes were calculated based on the validation dataset GSE207279 in order to evaluate the distinguishing efficacy of potential IMF-related genes, utilizing GraphPad Prism (version 10.2.3)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets PRJNA1223630 and GSE207279 for this study can be found in the NCBI Sequence Read Archive database and the GEO database, respectively. Furthermore, the data in this study will be made available on request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40282015", "locator": "sections[30] \"notes\" paragraph 1", "quote": "The datasets PRJNA1223630 and GSE207279 for this study can be found in the NCBI Sequence Read Archive database and the GEO database, respectively. Furthermore, the data in this study will be made available on request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The intersection of the candidate genes identified by LASSO and the top five genes ranked by the RF algorithm led to the identification of four genes, DGAT2, PCK1, MELK, and FASN, as potential feature genes associated with IMF deposition.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40282015", "locator": "sections[19] \"3.6. Identification of Potential IMF-Related Genes\" paragraph 2", "quote": "The intersection of the candidate genes identified by LASSO and the top five genes ranked by the RF algorithm led to the identification of four genes, DGAT2, PCK1, MELK, and FASN, as potential feature genes associated with IMF deposition."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The intersection of the candidate genes identified by LASSO and the top five genes ranked by the RF algorithm led to the identification of four genes, DGAT2, PCK1, MELK, and FASN, as potential feature genes associated with IMF deposition.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40282015"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE207279"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:machine_learning_random_forest", "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE207279"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40282015", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3390/ani15081181", "sha256": "sha256:7d998e65099946a7c8231e46ebb3ce83c2ea02937dac462d5f9f213542892eea", "bytes": 39475, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3390/ani15081181", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12024254", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE207279", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE207279", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE207279", "version": null, "note": "The datasets PRJNA1223630 and GSE207279 for this study can be found in the NCBI Sequence Read Archive database and the GEO database, respectively. 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