Bioinformatics analysis based on microarray data reveals molecular crosstalk and immune relationship between sarcopenia and atherosclerosis.

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

Background: osteoporosis (OP) and aortic stenosis (AS) are common in the elderly population, conferring a heavy world burden. Several epidemiological studies show a correlation between AS and OP independent of aging. However, the molecular and cellular mechanisms underlying stenotic calcification and OP remain poorly understood. Herein, this study aimed to identify crosstalk genes between OP and AS and potential mechanism. Method: AS and OP datasets were downloaded from the GEO database and were performed Weighted Gene Co-Expression Network Analysis (WGCNA) to get common key genes, and differential expression analysis to get common differentially expressed genes (DEGs). We also analyzed the protein–protein interactions (PPIs) of the common key genes and DEGs using the NetworkAnalyst online tool to find the intersection of correlated genes in OP and AS, the top 20 genes in each algorithm were selected as the candidate genes and the genes were calculated by more than six algorithms at the same time were chosen as candidate genes. We downloaded genes related to AS in the Comparative Toxicogenomics Database (CTD) to get the intersection with candidate genes as hub genes. The hub genes were certified in another OP dataset and AS dataset downloaded from GEO respectively. Result: 665 common Genes from WGCNA, which were enriched in the myeloid leukocyte activation, secretory granule membrane, cell adhesion molecules.13 of them were screened as candidate genes by 12 prediction algorithms in the Cytoscape. 161 common DEGs were differentiated in another AS database and OP database, which were enriched in natural killer cell mediated immunity, collagen-containing extracellular.19 genes were recognized as candidate genes by Cytoscape in the same way. 15 hub genes were selected by intersecting with candidate genes and genes downloaded from CTD as hub genes, including CD226, CD247, CD38, CD4, CD96, FCGR2B, GNG2, GRB2, GAMB, HSD17B6, ITGB2, KSD17B6, ITGB2, KLRB1, NGF, PECAM1, SDC1. The transcription factors hub genes interaction network was constructed using TRRUST. Hub genes were certified in another AS and OP dataset. CD4, GZMB, SDC1 are upregulated remarkably in AS dataset, whereas SDC1 and ITGB2 are downregulated and GZMB is upregulated in OP dataset. Conclusion: GZMB, SDC1may be potential hub genes in AS and OP, through bioinformatic analysis, we identified potential biomarkers and therapeutic targets for AS and OP, providing a theoretical basis for future studies.

Atherosclerosis (AS) is the leading cause of coronary heart disease, which is the primary cause of death worldwide. Recent studies have identified disulfidptosis as a new type of cell death that may be involved in onset and development of many diseases. However, the role of disulfidptosis in AS is not clear. In this study, bioinformatics analysis and experiments in vivo and in vitro were performed to evaluate the potential relationship between disulfidptosis and AS. AS-related sequencing data were obtained from the Gene Expression Omnibus (GEO). Bioinformatics techniques were used to evaluate differentially expressed genes (DEGs) associated with disulfidptosis-related AS. Hub genes were screened using least absolute shrinkage and selection operator (LASSO) and random forests (RF) methods. In addition, we established a foam cell model in vitro and an AS mouse model in vivo to verify the expressions of hub genes. In addition, we constructed a diagnostic nomogram with hub genes to predict progression of AS. Finally, the consensus clustering method was used to establish two different subtypes, and associations between subtypes and immunity were explored. As the results, 9 disulfidptosis-related AS DEGs were identified from GSE28829 and GSE43292 datasets. Evaluation of DEGs using LASSO and RF methods resulted in identification of 4 hub genes (CAPZB, DSTN, MYL6, PDLIM1), which were analyzed for diagnostic value using ROC curve analysis and verified in vitro and in vivo. Furthermore, a nomogram including hub genes was established that accurately predicted the occurrence of AS. The consensus clustering algorithm was used to separate patients with early atherosclerotic plaques and patients with advanced atherosclerotic plaques into two disulfidptosis subtypes. Cluster B displayed higher levels of infiltrating immune cells, which indicated that patients in cluster B may have a positive immune response for progression of AS. In summary, disulfidptosis-related genes including CAPZB, DSTN, MYL6, and PDLIM1 may be diagnostic markers and therapeutic targets for AS. In addition, these genes are closely related to immune cells, which may inform immunotherapy for AS.

Fungal keratitis is a common blinding eye disease, and Fusarium is one of the main species that cause fungal keratitis. As is well known, oxidative stress plays an important role in Fusarium keratitis and it is also a significant initiating factor of ferroptosis. But the relationship between Fusarium keratitis and ferroptosis is currently unclear. This study aimed to speculate and validate potential ferroptosis-related genes in Fusarium keratitis using bioinformatics analysis, which provided ideas for further research on its specific mechanism and new targets for its treatment. The microarray expression profiling dataset (GSE58291) came from Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) were obtained by the limma package of the R software. The DEGs were performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Then, the DEGs were intersected with the genes in the ferroptosis database. The top 5 hub genes were obtained by the protein-protein interaction (PPI) network analysis and the cytoHubba plug-in of Cytoscape software. The hub genes were subjected to GSEA analysis. Then we analyzed the immune infiltration of the samples by CIBERSORT and ssGSEA algorithm. Finally, we validated the mRNA of hub genes by qPCR. A total of 1,368 DEGs were identified and 26 ferroptosis-related DEGs were obtained. At the same time, ferroptosis-related pathways were enriched by GO and KEGG using DEGs. HMOX1, CYBB, GPX2, ALOX5 and SRC were obtained by the PPI network analysis and the cytoHubba plug-in of Cytoscape software. The iron metabolism and immune response related pathways were enriched using GSEA. They included hematopoietic cell lineage, lysosome and FC gamma R mediated phagocytosis. T cells follicular helper, monocytes, macrophages and mast cells might play an important role in Fusarium keratitis using analysis of immune infiltration. Finally, qPCR confirmed that the expression of HMOX1, CYBB, ALOX5 mRNA in the DON group was significantly elevated, while the expression of GPX2 were significantly decreased. Ferroptosis may play an important role in Fusarium keratitis. HMOX1, CYBB, ALOX5 and GPX2 may be key ferroptosis-related genes in the pathogenesis of Fusarium keratitis.

Emerging evidence suggests that the onset or worsening of psoriasis can occur following COVID-19 infection or vaccination. Additionally, individuals with psoriasis may be more susceptible to COVID-19. However, the underlying mechanisms driving this phenomenon remain unclear. This study aims to explore the potential shared mechanisms and the complex interplay between psoriasis and COVID-19. Gene expression profiles for COVID-19 (GSE162183) and psoriasis (GSE54456) were obtained from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (DEGs) were identified, followed by functional annotation, protein–protein interaction (PPI) network construction, and hub gene identification. Validation of hub genes was performed using independent datasets (GSE152075 and GSE157103 for COVID-19; GSE121212 and GSE78097 for psoriasis). Receiver operating characteristic (ROC) curves were used to assess the predictive value of the hub genes. Gene set enrichment analysis (GSEA) and immune infiltration analysis were conducted, and expression patterns of the hub genes were further explored using a single-cell RNA sequencing dataset. A total of 66 common DEGs (all upregulated) were identified. The influenza A and NOD-like receptor signaling pathways were enriched in both COVID-19 and psoriasis. OAS2, MX1, IRF7, RSAD2, OASL, IFIT1, IFIT3, and ISG15 were identified as hub genes after validation, with all are under the curve (AUC) > 0.7 for COVID-19 and AUC > 0.9 for psoriasis. Upregulation of these hub genes was associated with increased infiltration of neutrophils and Th17 cells. Single-cell analysis showed that the hub genes were primarily expressed in epithelial cells in COVID-19 and keratinocytes in psoriasis. This study reveals shared pathogenic mechanisms between psoriasis and COVID-19 and provides insights into how COVID-19 may exacerbate psoriasis. The identified common pathways, hub genes, and associated cell types offer valuable guidance for future research and potential therapeutic targets.

Cardioembolic stroke (CS) is the most common type of ischemic stroke in the clinic, leading to high morbidity and mortality worldwide. Although many studies have been conducted, the molecular mechanism underlying CS has not been fully grasped. This study was aimed at exploring the molecular mechanism of CS using comprehensive bioinformatics analysis and providing new insights into the pathophysiology of CS. We downloaded the public datasets GSE58294 and GSE16561. Differentially expressed genes (DEGs) were screened via the limma package using R software. CIBERSORT was used to estimate the proportions of 22 immune cells based on the gene expression profiling of CS patients. Using weighted gene correlation network analysis (WGCNA) to cluster the genes into different modules and detect relationships between modules and immune cell types, hub genes were identified based on the intersection of the protein-protein interaction (PPI) network analysis and WGCNA, and their clinical significance was then verified using another independent dataset GSE16561. Totally, 319 genes were identified as DEGs and 5413 genes were clustered into nine modules using WGCNA. The blue module, with the highest correlation coefficient, was identified as the key module associated with stroke, neutrophils, and B cells naïve. Based on the PPI analysis and WGCNA, five genes (MCEMP1, CLEC4D, GPR97, TSPAN14, and FPR2) were identified as hub genes. Correlation analysis indicated that hub genes had general association with infiltration-related immune cells. ROC analysis also showed they had potential clinical significance. The results were verified using another dataset, which were consistent with our analysis. Five crucial genes determined using integrative bioinformatics analysis might play significant roles in the pathophysiological mechanism in CS and be potential targets for pharmaceutic therapies.

The purpose of this study was to identify the hub genes and biological pathways of nasopharyngeal carcinoma (NPC) through bioinformatics analysis and potential new therapeutic targets. In this study, three datasets were downloaded from the Gene Expression Omnibus (GEO), and differentially expressed genes (DEGs) between NPC and normal tissues were analyzed using the GEO2R online tool. Volcano and heat maps of the DEGs were visualized using the hiplot database. Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the upregulated and downregulated DEGs were performed using the DAVID database. Finally, we established a protein-protein interaction (PPI) network using the STRING database and showed the differential expression of hub genes between the normal and tumor tissues. In all, 109,371,221 upregulated DEGs and 139,226,520 downregulated DEGs were obtained in datasets GSE40290, GSE61218, and GSE53819, respectively, and 18 common differential genes, named co-DEGs, were screened in the three datasets. The most abundant biological GO terms of the co-DEGs were inflammatory response et al. The KEGG pathway enrichment analysis showed that co-DEGs mainly participated in the interleukin (IL)-17 signaling pathway et al. Finally, we identified four hub genes using PPI analysis and observed that three of them were highly expressed in tumor tissues. In this study, the hub genes of NPC, such as PTGS2, and pathways such as IL-17 signaling, were identified through bioinformatics analysis, which may be potential new therapeutic targets for NPC.

BackgroundMechanisms underlying ischemia/reperfusion injury-acute kidney injury (IRI-AKI) are not fully elucidated. We conducted an integrative analysis of IRI-AKI by bioinformatics methods.MethodsWe screened gene expression profiles of the IRI-AKI at early phase from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and enrichment pathways were conducted based on gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and Gene set enrichment analysis (GSEA). Immune cell infiltration analysis was performed to reveal the change of the microenvironment cell types. We constructed protein–protein interaction (PPI), and Cytoscape with plug-ins to find hub genes and modules. We performed robust rank aggregation (RRA) to combine DEGs and analyzed the target genes for miRNA/transcription factor (TF) and drug-gene interaction networks.ResultsA total of 239 and 384 DEGs were identified in GSE87024 and GSE34351 separately, with the 73 common DEGs. Enrichment analysis revealed that the significant pathways involve mitogen-activated protein kinase (MAPK) signaling, interleukin-17, and tumor necrosis factor (TNF) signaling pathway, etc. RRA analysis detected a total of 27 common DEGs. Immune cell infiltration analysis showed the plasma cells reduced and T cells increased in IRI-AKI. We identified JUN, ATF3, FOS, EGR1, HMOX1, DDIT3, JUNB, NFKBIZ, PPP1R15A, CXCL1, ATF4, and HSPA1B as hub genes. The target genes interacted with 23 miRNAs and 116 drugs or molecular compounds such as curcumin, staurosporine, and deferoxamine.ConclusionOur study first focused on the early IRI-AKI adopting RRA analysis to combine DEGs in different datasets. We identified significant biomarkers and crucial pathways involved in IRI-AKI and first construct the immune landscape and detected the potential therapeutic targets of the IRI-AKI by drug-gene network.

Ferroptosis is a type of cell death characterized by the accumulation of iron-dependent lethal lipid peroxides, which is associated with various pathophysiological processes. Psoriasis is a chronic autoimmune skin disease accompanied by abnormal immune cell infiltration and excessive production of lipid reactive oxygen species (ROS). Currently, its pathogenesis remains elusive, especially the potential role of ferroptosis in its pathophysiological process. The microarrays GSE13355 (58 psoriatic skin specimens versus 122 healthy skin specimens) and the ferroptosis database were employed to identify the common differentially expressed genes (DEGs) associated with psoriasis and ferroptosis. The functions of common DEGs were investigated through functional enrichment analysis and protein-protein interaction analysis. The potential diagnostic markers for psoriasis among the common DEGs were identified using four machine-learning algorithms. DGIdb was utilized to explore potential therapeutic agents for psoriasis. Additionally, CIBERSORT was employed to investigate immune infiltration in psoriasis. A total of 8 common DEGs associated with psoriasis and ferroptosis were identified, which are involved in intercellular signaling and affect pathways of cell response to stress and stimulation. Four machine-learning algorithms were employed to identify poly (ADP-ribose) polymerase 12 (PARP12), frizzled homolog 7 (FZD7), and arachidonate 15-lipoxygenase (ALOX15B) among the eight common DEGs as potential diagnostic markers for psoriasis. A total of 18 drugs targeting the five common DEGs were identified as potential candidates for treating psoriasis. Additionally, significant changes were observed in the immune microenvironment of patients with psoriasis. This study has contributed to our enhanced comprehension of ferroptosis-related genes as potential biomarkers for psoriasis diagnosis, as well as the alterations in the immune microenvironment associated with psoriasis. Our findings offer valuable insights into the diagnosis and treatment of psoriasis.

Background: Non-obstructive azoospermia (NOA) is the most severe form of male infertility. Currently, the molecular mechanisms underlying NOA pathology have not yet been elucidated. Hence, elucidation of the mechanisms of NOA and exploration of potential biomarkers are essential for accurate diagnosis and treatment of this disease. In the present study, we aimed to screen for biomarkers and pathways involved in NOA and reveal their potential molecular mechanisms using integrated bioinformatics.Methods: We downloaded two gene expression datasets from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in NOA and matched the control group tissues were identified using the limma package in R software. Subsequently, Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), protein-protein interaction (PPI) network, gene-microRNAs network, and transcription factor (TF)-hub genes regulatory network analyses were performed to identify hub genes and associated pathways. Finally, we conducted immune infiltration analysis using CIBERSORT to evaluate the relationship between the hub genes and the NOA immune infiltration levels.Results: We identified 698 common DEGs, including 87 commonly upregulated and 611 commonly downregulated genes in the two datasets. GO analysis indicated that the most significantly enriched gene was protein polyglycylation, and KEGG pathway analysis revealed that the DEGs were most significantly enriched in taste transduction and pancreatic secretion signaling pathways. GSEA showed that DEGs affected the biological functions of the ribosome, focaladhesion, and protein_expor. We further identified the top 31 hub genes from the PPI network, and friends analysis of hub genes in the PPI network showed that NR4A2 had the highest score. In addition, immune infiltration analysis found that CD8+ T cells and plasma cells were significantly correlated with ODF3 expression, whereas naive B cells, plasma cells, monocytes, M2 macrophages, and resting mast cells showed significant variation in the NR4A2 gene expression group, and there were differences in T cell regulatory immune cell infiltration in the FOS gene expression groups.Conclusion: The present study successfully constructed a regulatory network of DEGs between NOA and normal controls and screened three hub genes using integrative bioinformatics analysis. In addition, our results suggest that functional changes in several immune cells in the immune microenvironment may play an important role in spermatogenesis. Our results provide a novel understanding of the molecular mechanisms of NOA and offer potential biomarkers for its diagnosis and treatment.

Sepsis ranks among the most common health problems worldwide, characterized by organ dysfunction resulting from infection. Excessive inflammatory responses, cytokine storms, and immune-induced microthrombosis are pivotal factors influencing the progression of sepsis. Our objective was to identify novel immune-related hub genes for sepsis through bioinformatic analysis, subsequently validating their specificity and potential as diagnostic and prognostic biomarkers in an animal experiment involving a sepsis mice model. Gene expression profiles of healthy controls and patients with sepsis were obtained from the Gene Expression Omnibus (GEO) and analysis of differentially expressed genes (DEGs) was conducted. Subsequently, weighted gene co-expression network analysis (WGCNA) was used to analyze genes within crucial modules. The functional annotated DEGs which related to the immune signal pathways were used for constructing protein-protein interaction (PPI) analysis. Following this, two hub genes, FERMT3 and CD3G, were identified through correlation analyses associated with sequential organ failure assessment (SOFA) scores. These two hub genes were associated with cell adhesion, migration, thrombosis, and T-cell activation. Furthermore, immune infiltration analysis was conducted to investigate the inflammation microenvironment influenced by the hub genes. The efficacy and specificity of the two hub genes were validated through a mice sepsis model study. Concurrently, we observed a significant negative correlation between the expression of CD3G and IL-1β and GRO/KC. These findings suggest that these two genes probably play important roles in the pathogenesis and progression of sepsis, presenting the potential to serve as more stable biomarkers for sepsis diagnosis and prognosis, deserving further study.

COVID-19 increased global mortality in 2019. Cystitis became a contributing factor in SARS-CoV-2 and COVID-19 complications. The complex molecular links between cystitis and COVID-19 are unclear. This study investigates COVID-19-associated cystitis (CAC) molecular mechanisms and drug candidates using bioinformatics and systems biology. Obtain the gene expression profiles of IC (GSE11783) and COVID-19 (GSE147507) from the Gene Expression Omnibus (GEO) database. Identified the common differentially expressed genes (DEGs) in both IC and COVID-19, and extracted a number of key genes from this group. Subsequently, conduct Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the DEGs. Additionally, design a protein–protein interaction (PPI) network, a transcription factor gene regulatory network, a TF miRNA regulatory network, and a gene disease association network using the DEGs. Identify and extract hub genes from the PPI network. Then construct Nomogram diagnostic prediction models based on the hub genes. The DSigDB database was used to forecast many potential molecular medicines that are associated with common DEGs. Assess the precision of hub genes and Nomogram models in diagnosing IC and COVID-19 by employing Receiver Operating Characteristic (ROC) curves. The IC dataset (GSE57560) and the COVID-19 dataset (GSE171110) were selected to validate the models' diagnostic accuracy. A grand total of 198 DEGs that overlapped were found and chosen for further research. FCER1G, ITGAM, LCP2, LILRB2, MNDA, SPI1, and TYROBP were screened as the hub genes. The Nomogram model, built using the seven hub genes, demonstrates significant utility as a diagnostic prediction model for both IC and COVID-19. Multiple potential molecular medicines associated with common DEGs have been discovered. These pathways, hub genes, and models may provide new perspectives for future research into mechanisms and guide personalised and effective therapeutics for IC patients infected with COVID-19.

Heart failure (HF) and osteoarthritis (OA) are medical conditions that can significantly impact daily activities. Evidence has shown that HF and OA may share some pathogenic mechanisms. However, the underlying genomic mechanisms remain unclear. This study aimed to explore the underlying molecular mechanism and identify diagnostic biomarkers for HF and OA. With the cutoff criteria of fold change (FC) > 1.3 and P < .05, 920, 1500, 2195, and 2164 differentially expressed genes (DEGs) were identified in GSE57338, GSE116250, GSE114007, and GSE169077, respectively. After making the intersection of DEGs, we obtained 90 upregulated DEGs and 51 downregulated DEGs in HF datasets and 115 upregulated DEGs and 75 downregulated DEGs in OA datasets. Afterward, we conducted genome ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, protein-protein interaction (PPI) networks, and hub genes screening based on DEGs. Then, 4 common DEGs (fibroblast activation protein alpha [FAP], secreted frizzled-related protein 4 (SFRP4), Thy-1 cell surface antigen (THY1), matrix remodeling associated 5 [MXRA5]) between HF and OA were screened and validated in GSE5406 and GSE113825 datasets, based on which we established the support vector machine (SVM) models. The combined area under the receiver operating characteristic curve (AUC) of THY1, FAP, SFRP4, and MXRA5 in the HF training and test sets reached 0.949 and 0.928. While in the OA training set and test set, the combined AUC of THY1, FAP, SFRP4, and MXRA5 reached 1 and 1, respectively. The analysis of immune cells in HF revealed high levels of dendritic cell (DC), B cells, natural killer T cell (NKT), Type 1 regulatory T cell (Tr1), cytotoxic T cell (Tc), exhausted T cell (Tex), and mucosal-associated invariant T cell (MAIT), while displaying lower levels of monocytes, macrophages, NK, CD4 + T, gamma delta T (γδ T), T helper type 1 (Th1), T helper type 2 (Th2), and effector memory T cell (Tem). Moreover, the 4 common DEGs were positively correlated with DCs and B cells and negatively correlated with γδ T. In OA patients, the abundance of monocyte, macrophage, CD4 + naïve, and natural T regulatory cell (nTreg) was higher, while the infiltration of CD8 + T, γδ T, CD8 + naïve, and MAIT was lower. The expression of THY1 and FAP was significantly correlated with macrophage, CD8 + T, nTreg, and CD8 + naïve. SFRP4 was correlated with monocyte, CD8 + T, γδ T, CD4 + naïve, nTreg, CD8 + naïve and MAIT. MXRA5 was correlated with macrophage, CD8 + T, nTreg and CD8 + naïve. FAP, THY1, MXRA5, and SFRP4 may be diagnostic biomarkers for both HF and OA, and their correlation with immune cell infiltrations suggests shared immune pathogenesis.

The aim of the present study was to reveal the potential hub genes and regulatory mechanisms associated with senescence in human annulus cells by analyzing microarray data using bioinformatics. The gene expression dataset GSE17077, of senescent and non-senescent annulus cells obtained from patients with disc degenerative diseases (DDD), was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified. Functional and pathway annotations were performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes terms, respectively. Web-based Gene Set Analysis Toolkit and Chip Enrichment Analysis were used to identify key transcription factors (TFs). A protein-protein interaction (PPI) network was constructed to analyze the hub genes associated with senescence in DDD. A total of 667 DEGs were screened, including 368 up- and 299 down-regulated genes. These DEGs were enriched in phosphorylation, regulation of apoptosis and regulation of programmed cell death. In addition, DEGs were involved in axon guidance, natural killer cell-mediated cytotoxicity, purine metabolism and the mitogen-activated protein kinase (MAPK) signaling pathway. The TFs activator protein 1 (AP1), specificity protein 1 and aryl hydrocarbon receptor may serve regulatory roles in gene expression in senescent cells. Certain key target genes of TFs, including heat shock protein 90 (HSP90) and C-X-C motif chemokine 5 (CXCL5), within the DEGs were revealed to have a high connectivity degree by PPI analysis. The results of the present study indicated that the MAPK-regulated AP1 pathway may contribute to senescence-associated disc degeneration. The DEGs, including HSP90 and CXCL5, with a high degree of connectivity may be potential targets for future investigations into molecular biomarkers.

Psoriasis and vitiligo are two common autoimmune skin diseases with increased risk of comorbidities, but the common molecular mechanism about the occurrence of these two diseases is still unknown. This study aimed to identify the combined genetic profiles and evaluate the potential mechanism underlying the occurrence of this complication. The Gene Expression Omnibus (GEO) database was used to obtain the gene expression profiles of psoriasis (GSE30999) and vitiligo (GSE75819), and common differentially expressed genes (DEGs) were identified using GEO2R. DEGs were analyzed using functional enrichment analysis, protein-protein interaction (PPI) network and module construction, hub gene identification, and co-expression analysis. And hub genes were identified using Cytoscape software, and the gene expression of hub genes were validated in psoriasis (GSE13355) and vitiligo (GSE65127) datasets and immunohistochemistry at the clinical sample. A total of 164 common DEGs with the same trend (137 upregulated and 27 downregulated) were selected for subsequent analysis. Functional analysis emphasized the important roles of the cell cycle and mitotic cell division, cytoskeletal reorganization, and chromatin remodeling in the complications of these two diseases. Fourteen important hub genes were identified, including BUB1, CEP55, CDK1, TOP2A, CENPF, PBK, MELK, CCNB2, MAD2L1, NUSAP1, TTK, NEK2, CDKN3, and PTTG1. Cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) may be an important immune checkpoint in the pathogenesis of the comorbidities. Our study identified hub genes and potential mechanisms underlying psoriasis and vitiligo complications. And we proposed a new spatio-temporal theory and the probable immune checkpoint for the pathogenesis of the comorbidity which may provide new ideas for the further research.