Genome-wide analysis of dysregulated RNA-binding proteins and alternative splicing genes in keloid.

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Methods: In this study, we analyzed RNA-Seq data (GSE113619) of the local skin tissue of 8 keloid-prone individuals (KPI) and 6 healthy controls (HC) before and 42 days after trauma from the gene expression omnibus (GEO) database. The differential alternative splicing (AS) events associated with trauma healing between KPIs and HCs were identifified, and their functional differences were analyzed by gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) pathways. The co-expression relationship of differentially alternative splicing genes and differentially expressed RNA binding proteins (RBPs) was established subsequently. Results: A total of 674 differential AS events between the KD42 and the KD0 and 378 differential AS events between the HD42 and the HD0 were discovered. Notably, most of the differential genes related to keloids are enriched in actin, microtubule cells, and cortical actin cytoskeletal tissue pathway. We observed a signifificant association between AS genes (EPB41, TPM1, NF2, PARD3) and trauma healing in KPIs and HCs. We also found that the differential expression of healthy controls-specifific trauma healing-related RBPs (TKT, FDPS, SAMHD1) may affect the response of HCs to trauma healing by regulating the AS of downstream trauma healing-related genes such as DCN and DST. In contrast, KPIs also has specifific differential expression of trauma healing related RBPs (S100A9, HspB1, LIMA1, FBL), which may affect the healing response of KPIs to trauma by regulating the AS of downstream trauma healing-related genes such as FN1 and TPM1. Discussion: Our results were innovative in revealing early wound healing-related genes (EPB41, TPM1, NF2, PARD3) in KPI from the perspective of AS regulated by RBPs.", "claims": [{"id": "claim-method", "statement": "When we do gene differential expression analysis, we choose the software DEseq2 (Love et al., 2014) and use the volcano plot for visualization.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36777722", "locator": "sections[4] \"2.2 Reads alignment and differentially expressed gene analysis\" paragraph 2", "quote": "When we do gene differential expression analysis, we choose the software DEseq2 (Love et al., 2014) and use the volcano plot for visualization."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36777722", "locator": "sections[18] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 674 differential AS events between the KD42 and the KD0 and 378 differential AS events between the HD42 and the HD0 were discovered.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36777722", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A total of 674 differential AS events between the KD42 and the KD0 and 378 differential AS events between the HD42 and the HD0 were discovered."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of 674 differential AS events between the KD42 and the KD0 and 378 differential AS events between the HD42 and the HD0 were discovered.", "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:36777722"], "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. 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Methods: In this study, we analyzed RNA-Seq data (GSE113619) of the local skin tissue of", "nalysis of differentially expressed genes In transcriptomic data based on skin tissue samples from 8 KPI", "HD0 and KD0 were fused, indicating that the transcriptomic genes of HC and KPI were very similar before"], "order_basis": "paper_text_order:150", "id": "assay:1"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["When we do gene differential expression analysis, we choose the software DEseq2 (Love et al., 2014) and use the volcano plot for visualization."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}