Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning

Identifying Key Genes and Functionally Enriched Pathways in Osteoporotic Patients by Weighted Gene Co-Expression Network Analysis

Osteoporosis is a systemic bone disease characterized by low bone mineral density and bone microstructure damage, resulting in increased bone fragility and fracture risk. The present study aimed to identify key genes and functionally enriched pathways in osteoporotic patients. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to microarray datasets of blood samples of osteoporotic patients from the Sao Paulo Ageing & Health [SPAH] study (26 osteoporotic samples and 31 normal samples) to construct co-expression networks and identify hub gene. The results showed that HDGF, AP2M1, DNAJC6, TMEM183B, MFSD2B, IGKV1-5, IGKV1-8, IGKV3-7, IGKV3D-11, and IGKV1D-42 are genes which were associated with the disease status of osteoporosis. Differentially expressed genes are enriched in proteasomal protein catabolic process, ubiquitin ligase complex, and ubiquitin-like protein transferase activity. Functional enrichment analysis demonstrated that genes in the tan module were enriched in immune-related functions, indicating that the immune system plays a critical role in osteoporosis. Validation assay demonstrated that the HDGF, AP2M1, TMEM183B, and MFSD2B levels were decreased in osteoporosis samples compared with healthy controls, while the levels of IGKV1-5, IGKV1-8, and IGKV1D-42 were increased in osteoporosis samples compared with healthy controls. In conclusion, our data identified and validated the association of HDGF, AP2M1, TMEM183B, MFSD2B, IGKV1-5, IGKV1-8, and IGKV1D-42 with osteoporosis in elderly women. These results suggest that these transcripts have potential clinical significance and may help to explain the molecular mechanisms and biological functions of osteoporosis.

Using machine learning to identify patients at high risk of developing low bone density or osteoporosis after gastrectomy: a 10-year multicenter retrospective analysis

INTRODUCTION

Osteoporosis that emerges subsequent to gastrectomy poses a significant threat to the long-term health of patients. The primary objective of this investigation was to formulate a machine learning algorithm capable of identifying substantial preoperative, intraoperative, and postoperative risk factors. This algorithm, in turn, would enable the anticipation of osteoporosis occurrence after gastrectomy.

METHODS

This research encompassed a cohort of 1125 patients diagnosed with gastric cancer, including 108 individuals with low bone density or osteoporosis. A total of 40 distinct variables were collected, comprising patient demographics, pertinent medical history, medication records, preoperative examination attributes, surgical procedure specifics, and intraoperative details. Four distinct machine learning algorithms-extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor algorithm (KNN)-were employed to establish the predictive model. Evaluation of the models involved receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Shapley additive explanation (SHAP) was employed for visualization and analysis.

RESULTS

Among the four prediction models employed, the XGBoost algorithm demonstrated exceptional performance. The ROC analysis yielded excellent predictive accuracy, showcasing area under the curve (AUC) values of 0.957 and 0.896 for training and validation sets, respectively. The calibration curve further confirmed the robust predictive capacity of the XGBoost model. The DCA demonstrated a notably higher benefit rate for patients undergoing intervention based on the XGBoost model. Moreover, the AUC value of 0.73 for the external validation set indicated favorable extrapolation of the XGBoost prediction model. SHAP analysis outcomes unveiled numerous high-risk factors for osteoporosis development after gastrectomy, including a history of chronic obstructive pulmonary disease (COPD), inflammatory bowel disease (IBD), hypoproteinemia, postoperative neutrophil-to-lymphocyte ratio (NLR) exceeding 3, steroid usage history, advanced age, and absence of calcitonin use.

CONCLUSION

The osteoporosis prediction model derived through the XGBoost machine learning algorithm in this study displays remarkable predictive precision and carries significant clinical applicability.

Exploring the shared gene signatures of smoking-related osteoporosis and chronic obstructive pulmonary disease using machine learning algorithms

Objectives: Cigarette smoking has been recognized as a predisposing factor for both osteoporosis (OP) and chronic obstructive pulmonary disease (COPD). This study aimed to investigate the shared gene signatures affected by cigarette smoking in OP and COPD through gene expression profiling. Materials and methods: Microarray datasets (GSE11784, GSE13850, GSE10006, and GSE103174) were obtained from Gene Expression Omnibus (GEO) and analyzed for differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA). Least absolute shrinkage and selection operator (LASSO) regression method and a random forest (RF) machine learning algorithm were used to identify candidate biomarkers. The diagnostic value of the method was assessed using logistic regression and receiver operating characteristic (ROC) curve analysis. Finally, immune cell infiltration was analyzed to identify dysregulated immune cells in cigarette smoking-induced COPD. Results: In the smoking-related OP and COPD datasets, 2858 and 280 DEGs were identified, respectively. WGCNA revealed 982 genes strongly correlated with smoking-related OP, of which 32 overlapped with the hub genes of COPD. Gene Ontology (GO) enrichment analysis showed that the overlapping genes were enriched in the immune system category. Using LASSO regression and RF machine learning, six candidate genes were identified, and a logistic regression model was constructed, which had high diagnostic values for both the training set and external validation datasets. The area under the curves (AUCs) were 0.83 and 0.99, respectively. Immune cell infiltration analysis revealed dysregulation in several immune cells, and six immune-associated genes were identified for smoking-related OP and COPD, namely, mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1), tissue-type plasminogen activator (PLAT), sodium channel 1 subunit alpha (SCNN1A), sine oculis homeobox 3 (SIX3), sperm-associated antigen 9 (SPAG9), and vacuolar protein sorting 35 (VPS35). Conclusion: The findings suggest that immune cell infiltration profiles play a significant role in the shared pathogenesis of smoking-related OP and COPD. The results could provide valuable insights for developing novel therapeutic strategies for managing these disorders, as well as shedding light on their pathogenesis.

The Hub Genes Related to Osteoporosis Were Identified by Bioinformatics Analysis

Osteoporosis (OP) is commonly encountered, which is a kind of systemic injury of bone mass and microstructure, leading to brittle fractures. With the aging of the population, this disease will pose a more serious impact on medical, social, and economic aspects, especially postmenopausal osteoporosis (PMOP). This study is aimed at figuring out potential therapeutic targets and new biomarkers in OP via bioinformatics tools. After differentially expressed gene (DEG) analysis, we successfully identified 97 upregulated and 172 downregulated DEGs. They are mainly concentrated in actin binding, regulation of cytokine production, muscle cell promotion, chemokine signaling pathway, and cytokine-cytokine receiver interaction. According to the diagram of protein-protein interaction (PPI), we obtained 10 hub genes: CCL5, CXCL10, EGFR, HMOX1, IL12B, CCL7, TBX21, XCL1, PGR, and ITGA1. Expression analysis showed that Egfr, Hmox1, and Pgr were significantly upregulated in estrogen-treated osteoporotic patients, while Ccl5, Cxcl10, Il12b, Ccl7, Tbx21, Xcl1, and Itga1 were significantly downregulated. In addition, the analysis results of Pearson's correlation revealed that CCL7 has a strong positive association with IL12b, TBX21, and CCL5 and so was CCL5 with IL12b. Conversely, EGFR has a strong negative association with XCL1 and CXCL10. In conclusion, this study screened 10 hub genes related to OP based on the GEO database, laying a biological foundation for further research on new biomarkers and potential therapeutic targets in OP.

Identification of key ferroptosis genes in diabetic retinopathy based on bioinformatics analysis

OBJECTIVES

Diabetic retinopathy (DR) is a retinal microvascular disease associated with diabetes. Ferroptosis is a new type of programmed cell death that may participate in the occurrence and development of DR. Therefore, this study aimed to identify the DR ferroptosis-related genes by bioinformatics methods.

METHODS

The RNAseq data of DR and healthy control retinas were downloaded from the gene expression synthesis (GEO) database and analyzed using the R package DESeq2. The key modules were obtained using the WGCNA algorithm, and their genes were intersected with ferroptosis-related genes in the FerrDb database to obtain differentially expressed ferroptosis-related genes (DE-FRGs). Enrichment analysis was conducted to understand the function and enrichment pathways of ferroptosis genes in DR, and hub genes were identified by protein-protein interaction (PPI) analysis. The diagnostic accuracy of hub genes for DR was evaluated according to the area under the ROC curve. The TRRUST database was then used to predict the regulatory relationship between transcription factors and target genes, with the mirDIP, ENCORI, RNAnter, RNA22, miRWalk and miRDB databases used to predict the regulatory relationship between miRNAs and target genes. Finally, another data set was used to verify the hub genes.

RESULTS

In total, 52 ferroptosis-related DEGs (43 up-regulated and 9 down-regulated) were identified using 15 DR samples and 3 control samples and were shown to be significantly enriched in the intrinsic apoptotic signaling pathway, autophagosome, iron ion binding and p53 signaling pathway. Seven hub genes of DR ferroptosis were identified through PPI network analysis, but only HMOX1 and PTGS2 were differentially expressed in another data set. The miRNAs prediction showed that hsa-miR-873-5p was the key miRNA regulating HMOX1, while hsa-miR-624-5p and hsa-miR-542-3p were the key miRNAs regulating PTGS2. Furthermore, HMOX1 and PTGS2 were regulated by 13 and 20 transcription factors, respectively.

CONCLUSION

The hub genes HMOX1 and PTGS2, and their associated transcription factors and miRNAs, may be involved in ferroptosis in diabetic retinopathy. Therefore, the specific mechanism is worthy of further investigation.

Consensus gene modules related to levels of bone mineral density (BMD) among smokers and nonsmokers

Osteoporosis, as a common metabolic disorder characterized by the decrease of bone mass, can cause fractures, thereby threatening the life quality of females, especially postmenopausal women. Thus, it is necessary to reveal the genes involved in osteoporosis and explore biomarkers for osteoporosis. In this study, two groups, smokers and nonsmokers with different bone mineral density (BMD) levels, were collected from the Gene Expression Omnibus (GEO) database GSE13850. Consensus modules of the two groups were identified; the variety of gene modules between smokers and nonsmokers with different BMD levels was observed; and a consensus module, including 390 genes significantly correlated with different BMD levels, was identified. Function analysis revealed the significantly enriched osteoporosis-related pathways, such as the PI3K-Akt signaling pathway. Hub genes analysis revealed the critical role of CXCL12 and CHRM2 in modules related to BMD levels. Based on the support vector machine recursive feature elimination (SVM-RFE) analysis, the model containing 10 genes (TNS4, IRF2, BSG, GZMM, ARRB2, COX15, RALY, TP53, RPS6KA3, and SYNPO) with good performance in identifying people with different BMD levels was constructed. Among them, the roles of RALY and SYNPO in the osteogenic differentiation of hBMSCs were verified experimentally. Overall, this study provides a strategy to explore the biomarkers for osteoporosis through analysis of consensus modules.