Exploring Biomarkers Related to Cell Adhesion in Polycystic Ovary Syndrome Using Bioinformatics and Conducting Experimental Validation

Jiani Zhu,1,2,* Xinyue Qi,3,4,* Zhenyu Zhang,1,2,* Qun Zhou,5 Ran Gu,1,2 Xiaorong Wu,6,7 Lanping Zhong6,7 1Department of Clinical Nutrition, The First People’s Hospital of Yunnan Province, Kunming, Yunnan, People’s Republic of China; 2Department of Clinical Nutrition, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, People’s Republic of China; 3Department of Preventive Health Care, The First People’s Hospital of Yunnan Province, Kunming, Yunnan, People’s Republic of China; 4Department of Preventive Health Care, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, People’s Republic of China; 5Department of Internal Medicine No.3, Shilin Yi Autonomous County People’s Hospital, Kunming, Yunnan, People’s Republic of China; 6Department of Reproductive Medicine, The First People’s Hospital of Yunnan Province, Kunming, Yunnan, People’s Republic of China; 7Department of Reproductive Medicine, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiaorong Wu; Lanping Zhong, The First People’s Hospital of Yunnan Province (The Affiliated Hospital of Kunming University of Science and Technology), No. 157 Jinbi Road, Xishan District, Kunming City, Yunnan Province, People’s Republic of China, Tel +8615087160943 ; +8618064820074, Fax +86-0871-63637735 ; +86-0871-63637735, Email [email protected]; [email protected]: Related studies have pointed out that cell adhesion may play an important role for treating Polycystic Ovary Syndrome (PCOS). This study aimed to identify and analyze the biomarkers associated with cell adhesion-related genes (CRGs) for treating PCOS and their biological mechanisms.Patients and Methods: In this study, GSE80432 was used to identify differentially expressed genes (DEGs) (PCOS vs control group) through differential expression analysis. Then, the DEGs were overlapped with 1531 CRGs to obtain the cross - genes. Subsequently, the Support Vector Machine-Recursive Feature Elimination combined with the least absolute shrinkage and selection operator was utilized to obtain candidate genes, and the genes with AUC greater than 0.7 and consistent expression trends in the two datasets were defined as biomarkers. Finally, a nomogram was constructed, and enrichment analysis, regulatory network, drug prediction, the association between biomarkers and PCOS, and reverse transcription quantitative PCR (RT-qPCR) were carried out respectively.Results: A total of 10 cross-genes were identified, and 2 biomarkers (DSG2 and TH11) were screened out from them. RT-qPCR analysis showed that the expression of THBS1 was increased in PCOS samples, while there was no significant difference in DSG2. In addition, enrichment analysis indicated that both DSG2 and THBS1 were enriched in the B-cell receptor signaling pathway. Then, based on these two biomarkers, lncRNA-miRNA-mRNA (81 nodes and 135 edges) and TFs biomarker networks (38 nodes and 38 edges), such as MIR17HG′-has-miR-7-5p′-THBS1, TFDP1-DSG2, were constructed respectively. By predicting drugs targeting biomarkers, 61 drugs were predicted to target DSG2, while 133 drugs were predicted to target THBS1. Moreover, a stronger association between THBS1 and PCOS was detected (inference score = 27.15).Conclusion: In this study, 2 biomarkers (DSG2 and THBS1) were identified, providing a potential theoretical basis for PCOS treatment. Keywords: polycystic ovary syndrome, cell adhesion, nomogram, DSG2, THBS1