Screening of Potential Biomarkers in the Peripheral Serum for Steroid-Induced Osteonecrosis of the Femoral Head Based on WGCNA and Machine Learning Algorithms

Identification of potential biomarkers in the peripheral blood of neonates with bronchopulmonary dysplasia using WGCNA and machine learning algorithms

Bronchopulmonary dysplasia (BPD) is often seen as a pulmonary complication of extreme preterm birth, resulting in persistent respiratory symptoms and diminished lung function. Unfortunately, current diagnostic and treatment options for this condition are insufficient. Hence, this study aimed to identify potential biomarkers in the peripheral blood of neonates affected by BPD. The Gene Expression Omnibus provided the expression dataset GSE32472 for BPD. Initially, using this database, we identified differentially expressed genes (DEGs) in GSE32472. Subsequently, we conducted gene set enrichment analysis on the DEGs and employed weighted gene co-expression network analysis (WGCNA) to screen the most relevant modules for BPD. We then mapped the DEGs to the WGCNA module genes, resulting in a gene intersection. We conducted detailed functional enrichment analyses on these overlapping genes. To identify hub genes, we used 3 machine learning algorithms, including SVM-RFE, LASSO, and Random Forest. We constructed a diagnostic nomogram model for predicting BPD based on the hub genes. Additionally, we carried out transcription factor analysis to predict the regulatory mechanisms and identify drugs associated with these biomarkers. We used differential analysis to obtain 470 DEGs and conducted WGCNA analysis to identify 1351 significant genes. The intersection of these 2 approaches yielded 273 common genes. Using machine learning algorithms, we identified CYYR1, GALNT14, and OLAH as potential biomarkers for BPD. Moreover, we predicted flunisolide, budesonide, and beclomethasone as potential anti-BPD drugs. The genes CYYR1, GALNT14, and OLAH have the potential to serve as diagnostic biomarkers for BPD. This may prove beneficial in clinical diagnosis and prevention of BPD.

A Network Pharmacology Approach and Validation Experiments to Investigate the Mechanism of Wen-Dan Decoction in the Treatment of SINFH

INTRODUCTION

Steroid-induced necrosis of the femoral head (SINFH) is a femoral head necrotic disease caused by prolonged use of hormones. Wen-Dan decoction is used in Chinese clinical practice for the treatment of steroid-induced necrosis of the femoral head (SINFH). However, the mechanism and active compounds of Wen-Dan decoction used to treat SINFH are not well understood.

OBJECTIVES

We studied the mechanism of action of Wen-Dan decoction in treating steroidinduced necrosis of the femoral head (SINFH) via network pharmacology and in vivo experiments.

METHODS

The active compounds of Wen-Dan decoction and SINFH-related target genes were identified through public databases. Then, network pharmacological analysis was conducted to explore the potential key active compounds, core targets and biological processes of Wen-Dan decoction in SINFH. The potential mechanisms of Wen-Dan decoction in SINFH obtained by network pharmacology were validated through in vivo experiments.

RESULTS

We identified 608 DEGs (differentially expressed genes) (230 upregulated, 378 downregulated) in SINFH. GO analysis revealed that the SINFH-related genes were mainly involved in neutrophil activation and the immune response. KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis showed that the SINFH-related genes were mainly associated with cytokine receptor interactions, lipids, atherosclerosis, and tuberculosis. We identified 147 active ingredients of Wen-Dan decoction; the core ingredient was quercetin, and licorice was an active ingredient. Moreover, 277 target genes in the treatment of SINFH with Wen-Dan decoction were identified, and NCF1, PTGS2, and RUNX2 were selected as core target genes. QRT-PCR of peripheral blood from SINFH patients showed higher levels of PGTS2 and NCF1 and showed lower levels of RUNX2 compared to controls. QRT-PCR analysis of peripheral blood and femoral bone tissue from a mouse model of SINFH showed higher levels of PGTS2 and NCF1 and lower levels of RUNX2 in the experimental animals than the controls, which was consistent with the bioinformatics results. HE, immunohistochemistry, and TUNEL staining confirmed a significant reduction in hormone-induced femoral head necrosis in the quercetintreated mice. HE, immunohistochemistry, and TUNEL staining confirmed significant improvement in hormone-induced femoral head necrosis in the quercetin-treated mice.

CONCLUSION

We provide new insights into the genes and related pathways involved in SINFH and report that PTGS2, RUNX2, and NCF1 are potential drug targets. Quercetin improved SINFH by promoting osteogenesis and inhibiting apoptosis.

Screening of potential biomarkers of system lupus erythematosus based on WGCNA and machine learning algorithms

Systemic lupus erythematosus (SLE) is an autoimmune disease involving multiple systems. Its recurrent episodes and fluctuating disease courses have a severe impact on patients. Biomarkers to predict disease prognosis and remission are still lacking in SLE. We downloaded the GSE50772 dataset from the Gene Expression Omnibus database and identified differentially expressed genes (DEGs) between SLE and healthy controls. Weighted gene co-expression network analysis was used to identify key gene modules and corresponding genes in SLE. The overlapped genes in DEGs and key modules are used as key genes for subsequent analysis. These key genes were analyzed using 3 machine learning algorithms, including the least absolute shrinkage and selection operator, support vector machine recursive elimination, and random forest algorithms. The overlapped genes were obtained as potential biomarkers for further analysis, investigating and validating the potential biomarkers' possible functions, regulatory mechanisms, diagnostic value, and expression levels. And finally studied the differences between groups in level of immune cell infiltration and explored the relationship between potential biomarkers and immunity. A total of 234 overlapped genes in DEGs and key modules are used as key genes for subsequent analysis. After taking the intersection of the key genes obtained by 3 algorithms, we got 4 potential biomarkers (ARID2, CYSTM1, DDIT3, and RNASE1) with high diagnostic values. Finally, further immune infiltration analysis showed differences in various immune cells in the SLE and healthy control samples. ARID2, CYSTM1, DDIT3, and RNASE1 can affect the immune function of SLE patients. ARID2, CYSTM1, DDIT3, and RNASE1 could be used as immune-related potential biomarkers and therapeutic or diagnostic targets for further research.

Identifying potential biomarkers for non-obstructive azoospermia using WGCNA and machine learning algorithms

Objective

The cause and mechanism of non-obstructive azoospermia (NOA) is complicated; therefore, an effective therapy strategy is yet to be developed. This study aimed to analyse the pathogenesis of NOA at the molecular biological level and to identify the core regulatory genes, which could be utilised as potential biomarkers.

Methods

Three NOA microarray datasets (GSE45885, GSE108886, and GSE145467) were collected from the GEO database and merged into training sets; a further dataset (GSE45887) was then defined as the validation set. Differential gene analysis, consensus cluster analysis, and WGCNA were used to identify preliminary signature genes; then, enrichment analysis was applied to these previously screened signature genes. Next, 4 machine learning algorithms (RF, SVM, GLM, and XGB) were used to detect potential biomarkers that are most closely associated with NOA. Finally, a diagnostic model was constructed from these potential biomarkers and visualised as a nomogram. The differential expression and predictive reliability of the biomarkers were confirmed using the validation set. Furthermore, the competing endogenous RNA network was constructed to identify the regulatory mechanisms of potential biomarkers; further, the CIBERSORT algorithm was used to calculate immune infiltration status among the samples.

Results

A total of 215 differentially expressed genes (DEGs) were identified between NOA and control groups (27 upregulated and 188 downregulated genes). The WGCNA results identified 1123 genes in the MEblue module as target genes that are highly correlated with NOA positivity. The NOA samples were divided into 2 clusters using consensus clustering; further, 1027 genes in the MEblue module, which were screened by WGCNA, were considered to be target genes that are highly correlated with NOA classification. The 129 overlapping genes were then established as signature genes. The XGB algorithm that had the maximum AUC value (AUC=0.946) and the minimum residual value was used to further screen the signature genes. IL20RB, C9orf117, HILS1, PAOX, and DZIP1 were identified as potential NOA biomarkers. This 5 biomarker model had the highest AUC value, of up to 0.982, compared to other single biomarker models; additionally, the results of this biomarker model were verified in the validation set.

Conclusions

As IL20RB, C9orf117, HILS1, PAOX, and DZIP1 have been determined to possess the strongest association with NOA, these five genes could be used as potential therapeutic targets for NOA patients. Furthermore, the model constructed using these five genes, which possessed the highest diagnostic accuracy, may be an effective biomarker model that warrants further experimental validation.

Identifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms

Background

COVID-19, a serious respiratory disease that has the potential to affect numerous organs, is a serious threat to the health of people around the world. The objective of this article is to investigate the potential biological targets and mechanisms by which SARS-CoV-2 affects benign prostatic hyperplasia (BPH) and related symptoms.

Methods

We downloaded the COVID-19 datasets (GSE157103 and GSE166253) and the BPH datasets (GSE7307 and GSE132714) from the Gene Expression Omnibus (GEO) database. In GSE157103 and GSE7307, differentially expressed genes (DEGs) were found using the "Limma" package, and the intersection was utilized to obtain common DEGs. Further analyses followed, including those using Protein-Protein Interaction (PPI), Gene Ontology (GO) function enrichment analysis, and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Potential hub genes were screened using three machine learning methods, and they were later verified using GSE132714 and GSE166253. The CIBERSORT analysis and the identification of transcription factors, miRNAs, and drugs as candidates were among the subsequent analyses.

Results

We identified 97 common DEGs from GSE157103 and GSE7307. According to the GO and KEGG analyses, the primary gene enrichment pathways were immune-related pathways. Machine learning methods were used to identify five hub genes (BIRC5, DNAJC4, DTL, LILRB2, and NDC80). They had good diagnostic properties in the training sets and were validated in the validation sets. According to CIBERSORT analysis, hub genes were closely related to CD4 memory activated of T cells, T cells regulatory and NK cells activated. The top 10 drug candidates (lucanthone, phytoestrogens, etoposide, dasatinib, piroxicam, pyrvinium, rapamycin, niclosamide, genistein, and testosterone) will also be evaluated by the P value, which is expected to be helpful for the treatment of COVID-19-infected patients with BPH.

Conclusion

Our findings reveal common signaling pathways, possible biological targets, and promising small molecule drugs for BPH and COVID-19. This is crucial to understand the potential common pathogenic and susceptibility pathways between them.

Transcriptomics Based Network Analyses and Molecular Docking Highlighted Potentially Therapeutic Biomarkers for Colon Cancer

In this study, machine learning-based multiple bioinformatics analysis was carried out for the purpose of the deep and efficient mining of high-throughput transcriptomics data from the TCGA database. Compared with normal colon tissue, 2469 genes were significantly differentially expressed in colon cancer tissue. Gene functional annotation and pathway analysis suggested that most DEGs were functionally related to the cell cycle and metabolism. Weighted gene co-expression network analysis revealed a significant module and the enriched genes that were closely related to fatty acid degradation and metabolism. Based on colon cancer progression, the trend analysis highlighted that several gene sets were significantly correlated with disease development. At the same time, the most specific genes were functionally related to cancer cell features such as the high performance of DNA replication and cell division. Moreover, survival analysis and target drug prediction were performed to prioritize reliable biomarkers and potential drugs. In consideration of a combination of different evidence, four genes (ACOX1, CPT2, CDC25C and PKMYT1) were suggested as novel biomarkers in colon cancer. The potential biomarkers and target drugs identified in our study may provide new ideas for colonic-related prevention, diagnosis, and treatment; therefore, our results have high clinical value for colon cancer.

LTF induces senescence and degeneration in the meniscus via the NF-κB signaling pathway: A study based on integrated bioinformatics analysis and experimental validation

Background: The functional integrity of the meniscus continually decreases with age, leading to meniscal degeneration and gradually developing into osteoarthritis (OA). In this study, we identified diagnostic markers and potential mechanisms of action in aging-related meniscal degeneration through bioinformatics and experimental verification. Methods: Based on the GSE98918 dataset, common differentially expressed genes (co-DEGs) were screened using differential expression analysis and the WGCNA algorithm, and enrichment analyses based on Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were further performed. Next, the co-DEGs were imported into the STRING database and Cytoscape to construct a protein‒protein interaction (PPI) network and further validated by three algorithms in cytoHubba, receiver operating characteristic (ROC) curve analysis and the external GSE45233 dataset. Moreover, the diagnostic marker lactotransferrin (LTF) was verified in rat models of senescence and replicative cellular senescence via RT‒qPCR, WB, immunohistochemistry and immunofluorescence, and then the potential molecular mechanism was explored by loss of function and overexpression of LTF. Results: According to the analysis of the GSE98918 dataset, we identified 52 co-DEGs (42 upregulated genes and 10 downregulated genes) in the OA meniscus. LTF, screened out by Cytoscape, ROC curve analysis in the GSE98918 dataset and another external GSE45233 dataset, might have good predictive power in meniscal degeneration. Our experimental results showed that LTF expression was statistically increased in the meniscal tissue of aged rats (24 months) and senescent passage 5th (P5) meniscal cells. In P5 meniscal cells, LTF knockdown inhibited the NF-κB signaling pathway and alleviated senescence. LTF overexpression in passage 0 (P0) meniscal cells increased the expression of senescence-associated secretory phenotype (SASP) and induced senescence by activating the NF-κB signaling pathway. However, the senescence phenomenon caused by LTF overexpression could be reversed by the NF-κB inhibitor pyrrolidine dithiocarbamate (PDTC). Conclusion: For the first time, we found that increased expression of LTF was observed in the aging meniscus and could induce meniscal senescence and degeneration by activating the NF-κB signaling pathway. These results revealed that LTF could be a potential diagnostic marker and therapeutic target for age-related meniscal degeneration.

The role of immune cells in modulating chronic inflammation and osteonecrosis

Osteonecrosis occurs when, under continuous stimulation by adverse factors such as glucocorticoids or alcohol, the death of local bone and marrow cells leads to abnormal osteoimmune function. This creates a chronic inflammatory microenvironment, which interferes with bone regeneration and repair. In a variety of bone tissue diseases, innate immune cells and adaptive immune cells interact with bone cells, and their effects on bone metabolic homeostasis have attracted more and more attention, thus developing into a new discipline - osteoimmunology. Immune cells are the most important regulator of inflammation, and osteoimmune disorder may be an important cause of osteonecrosis. Elucidating the chronic inflammatory microenvironment regulated by abnormal osteoimmune may help develop potential treatments for osteonecrosis. This review summarizes the inflammatory regulation of bone immunity in osteonecrosis, explains the pathophysiological mechanism of osteonecrosis from the perspective of osteoimmunology, and provides new ideas for the treatment of osteonecrosis.

Identification of hub genes of Parkinson's disease through bioinformatics analysis

Parkinson's disease (PD) is a common neurodegenerative disease, and there is still a lack of effective diagnostic and treatment methods. This study aimed to search for hub genes that might serve as diagnostic or therapeutic targets for PD. All the analysis was performed in R software. The expression profile data of PD (number: GSE7621) was acquired from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) associated with PD were screened by the “Limma” package of the R software. Key genes associated with PD were screened by the “WGCNA” package of the R software. Target genes were screened by merging the results of “Limma” and “WGCNA.” Enrichment analysis of target genes was performed by Gene Ontology (GO), Disease Ontology (DO), and Kyoto Enrichment of Genes and Genomes (KEGG). Machine learning algorithms were employed to screen for hub genes. Nomogram was constructed using the “rms” package. And the receiver operating characteristic curve (ROC) was plotted to detect and validate our prediction model sensitivity and specificity. Additional expression profile data of PD (number: GSE20141) was acquired from the GEO database to validate the nomogram. GSEA was used to determine the biological functions of the hub genes. Finally, RPL3L, PLEK2, PYCRL, CD99P1, LOC100133130, MELK, LINC01101, and DLG3-AS1 were identified as hub genes of PD. These findings can provide a new direction for the diagnosis and treatment of PD.

Integration of single-cell RNA sequencing and bulk RNA sequencing to reveal an immunogenic cell death-related 5-gene panel as a prognostic model for osteosarcoma

Background

Despite the comparatively low prevalence of osteosarcoma (OS) compared to other cancer types, metastatic OS has a poor overall survival rate of fewer than 30%. Accumulating data has shown the crucial functions of immunogenic cell death (ICD) in various cancers; nevertheless, the relationship between ICD and OS was not previously well understood. This research aims to determine the function of ICD in OS and construct an ICD-based prognostic panel.

Methods

Single cell RNA sequencing data from GSE162454 dataset distinguished malignant cells from normal cells in OS. The discrepancy in ICD scores and corresponding gene expression was intensively explored between malignant cells and normal cells. Using the RNA sequencing data of the TARGET-OS, GSE16091, GSE21257, and GSE39058 datasets, the molecular subtype of OS was determined by clustering seventeen ICD-related genes obtained from the literature. Differentially expressed genes (DEGs) between different molecular subtypes were identified to develop a novel ICD-associated prognostic panel.

Results

The malignant cells had a remarkable decrease in the ICD scores and corresponding gene expression compared with normal cells. A total of 212 OS patients were successfully stratified into two subtypes: C1 and C2. C1-like OS patients were characterized by better prognostic outcomes, overexpression of ICD genes, activation of the ICD pathway, high inflitration abundance of immunocytes, and low expression levels of immune checkpoint genes (ICGs); however, the reverse is true in C2-like OS patients. Utilizing the limma programme in R, the DEGs between two subtypes were determined, and a 5-gene risk panel consisting of BAMBI, TMCC2, NOX4, DKK1, and CBS was developed through LASSO-Cox regression analysis. The internal- and external-verification cohorts were employed to verify the efficacy and precision of the risk panel. The AUC values of ROC curves indicated excellent prognostic prediction values of our risk panel.

Conclusions

Overall, ICD represented a protective factor against OS, and our 5-gene risk panel serving as a biomarker could effectively evaluate the prognostic risk in patients with OS.

Screening of potential biomarkers in peripheral blood of patients with depression based on weighted gene co-expression network analysis and machine learning algorithms

BACKGROUND

The prevalence of depression has been increasing worldwide in recent years, posing a heavy burden on patients and society. However, the diagnostic and therapeutic tools available for this disease are inadequate. Therefore, this research focused on the identification of potential biomarkers in the peripheral blood of patients with depression.

METHODS

The expression dataset GSE98793 of depression was provided by the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/gds). Initially, differentially expressed genes (DEGs) were detected in GSE98793. Subsequently, the most relevant modules for depression were screened according to weighted gene co-expression network analysis (WGCNA). Finally, the identified DEGs were mapped to the WGCNA module genes to obtain the intersection genes. In addition, Gene Ontology (GO), Disease Ontology (DO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were conducted on these genes. Moreover, biomarker screening was carried out by protein-protein interaction (PPI) network construction of intersection genes on the basis of various machine learning algorithms. Furthermore, the gene set enrichment analysis (GSEA), immune function analysis, transcription factor (TF) analysis, and the prediction of the regulatory mechanism were collectively performed on the identified biomarkers. In addition, we also estimated the clinical diagnostic ability of the obtained biomarkers, and performed Mfuzz expression pattern clustering and functional enrichment of the most potential biomarkers to explore their regulatory mechanisms. Finally, we also perform biomarker-related drug prediction.

RESULTS

Differential analysis was used for obtaining a total of 550 DEGs and WGCNA for obtaining 1,194 significant genes. Intersection analysis of the two yielded 140 intersection genes. Biological functional analysis indicated that these genes had a major role in inflammation-related bacterial infection pathways and cardiovascular diseases such as atherosclerosis. Subsequently, the genes S100A12, SERPINB2, TIGIT, GRB10, and LHFPL2 in peripheral serum were identified as depression biomarkers by using machine learning algorithms. Among them, S100A12 is the most valuable biomarker for clinical diagnosis. Finally, antidepressants, including disodium selenite and eplerenone, were predicted.

CONCLUSION

The genes S100A12, TIGIT, SERPINB2, GRB10, and LHFPL2 in peripheral serum are viable diagnostic biomarkers for depression. and contribute to the diagnosis and prevention of depression in clinical practice.