DOI: 10.57760/sciencedb.j00217.05213
title: Identification of biomarkers for comorbid osteoporosis in fibromyalgia syndrome based on bioinformatics and machine learning
title: 基于生物信息学及机器学习识别纤维肌痛综合征共病骨质疏松症的生物标志物

description: Background It has been found that patients with fibromyalgia syndrome (FMS) have an increased risk of developing osteoporosis (OP), but the reason is unclear. Exploring the comorbidity mechanisms of two diseases at the molecular level will be beneficial for developing targeted prevention and treatment plans. Objective Based on bioinformatics methods, potential co disease genes of FMS and OP were screened and enriched. Furthermore, machine learning algorithms were used to screen for core genes and evaluate diagnostic efficacy, in order to identify biomarkers of co disease between FMS and OP. Methods Then, weighted gene co expression network analysis (WGCNA) was used to screen for module genes that are highly correlated with FMS. Draw Venn diagrams of the above three genes and identify potential comorbid genes for parallel Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Finally, LASSO regression analysis was used to re screen the potential comorbidity genes in the FMS dataset and OP dataset, and the core comorbidity genes were obtained. Machine learning algorithms were then used to construct a diagnostic model to verify its diagnostic efficacy. Results By analyzing the FMS dataset, a total of 13041 differentially expressed genes were screened; By analyzing the data from the OP dataset, a total of 410 differentially expressed genes were screened. After conducting WGCNA analysis on the FMS dataset, 15 module genes were co classified. After selecting four module genes with high correlation with the incidence of FMS and taking the intersection of differentially expressed genes, a total of 31 potential comorbid genes were obtained. The GO functional enrichment analysis results showed that it mainly participates in purine ribonucleoside metabolism and Th cell related biological processes; The KEGG pathway enrichment analysis results showed that it mainly participates in the chemical carcinogenic reactive oxygen species and oxidative phosphorylation signaling pathways. After further LASSO regression analysis of the 31 intersecting genes in the FMS dataset, 15 genes can be obtained; After conducting LASSO regression analysis on the OP dataset, 10 genes can be obtained. The intersection of the two resulted in four co pathogenic core genes, namely, proprotein Convertase Subtilisin/Kexin Type 5 (PCSK5), recombinant Peptidylprolyl Isomerase F (PPIF), recombinant Pim-1 Oncogene (PIM1), and zinc finger protein 528 (ZNF528).Conclusion The comorbidity mechanism of FMS and OP is complex, involving multiple biological processes and metabolic pathways. PCSK5、PPIF、PIM1、 ZNF528 plays an important role in the occurrence and development of the comorbidity between FMS and OP, and has diagnostic value. It can be used as a biomarker for the comorbidity of FMS and OP.
description: Background It has been found that patients with fibromyalgia syndrome (FMS) have an increased risk of developing osteoporosis (OP), but the reason is unclear. Exploring the comorbidity mechanisms of two diseases at the molecular level will be beneficial for developing targeted prevention and treatment plans. Objective Based on bioinformatics methods, potential co disease genes of FMS and OP were screened and enriched. Furthermore, machine learning algorithms were used to screen for core genes and evaluate diagnostic efficacy, in order to identify biomarkers of co disease between FMS and OP. Methods Then, weighted gene co expression network analysis (WGCNA) was used to screen for module genes that are highly correlated with FMS. Draw Venn diagrams of the above three genes and identify potential comorbid genes for parallel Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Finally, LASSO regression analysis was used to re screen the potential comorbidity genes in the FMS dataset and OP dataset, and the core comorbidity genes were obtained. Machine learning algorithms were then used to construct a diagnostic model to verify its diagnostic efficacy. Results By analyzing the FMS dataset, a total of 13041 differentially expressed genes were screened; By analyzing the data from the OP dataset, a total of 410 differentially expressed genes were screened. After conducting WGCNA analysis on the FMS dataset, 15 module genes were co classified. After selecting four module genes with high correlation with the incidence of FMS and taking the intersection of differentially expressed genes, a total of 31 potential comorbid genes were obtained. The GO functional enrichment analysis results showed that it mainly participates in purine ribonucleoside metabolism and Th cell related biological processes; The KEGG pathway enrichment analysis results showed that it mainly participates in the chemical carcinogenic reactive oxygen species and oxidative phosphorylation signaling pathways. After further LASSO regression analysis of the 31 intersecting genes in the FMS dataset, 15 genes can be obtained; After conducting LASSO regression analysis on the OP dataset, 10 genes can be obtained. The intersection of the two resulted in four co pathogenic core genes, namely, proprotein Convertase Subtilisin/Kexin Type 5 (PCSK5), recombinant Peptidylprolyl Isomerase F (PPIF), recombinant Pim-1 Oncogene (PIM1), and zinc finger protein 528 (ZNF528).Conclusion The comorbidity mechanism of FMS and OP is complex, involving multiple biological processes and metabolic pathways. PCSK5、PPIF、PIM1、 ZNF528 plays an important role in the occurrence and development of the comorbidity between FMS and OP, and has diagnostic value. It can be used as a biomarker for the comorbidity of FMS and OP.