Shared senescence-associated gene networks in PCOS and T2DM: biomarker identification and functional validation

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.

Investigating the molecular mechanisms between type 1 diabetes and mild cognitive impairment using bioinformatics analysis, with a focus on immune response

The present study was designed to detect possible biomarkers associated with Type 1 diabetes mellitus (T1DM) incidence in an effort to develop novel treatments for this condition. Three mRNA expression datasets of peripheral blood mononuclear cells (PBMCs) were obtained from the GEO database. Differentially expressed genes (DEGs) between T1DM patients and healthy controls were identified by Limma package in R, and using the DEGs to conduct GO and DO pathway enrichment. The LASSO-SVM were used to screen the hub genes. We performed immune correlation analysis of hub genes and established a T1DM prognosis model. CIBERSORT algorithm was used to identify the different immune cells in distribution between T1DM and normal samples. The correlation of the hub genes and immune cells was analyzed by Spearman. ROC curves were used to assess the diagnostic value of genes in T1DM. A total of 60 immune related DEGs were obtained from the T1DM and normal samples. Then, DEGs were further screened to obtain 3 hub genes, ANP32A-IT1, ESCO2 and NBPF1. CIBERSORT analysis revealed the percentage of immune cells in each sample, indicating that there was significant difference in monocytes, T cells CD8+, gamma delta T cells, naive CD4+ T cells and activated memory CD4+ T cells between T1DM and normal samples. The area under curve (AUC) of ESCO2, ANP32A-IT1 and NBPF1 were all greater than 0.8, indicating that these three genes have high diagnostic value for T1DM. Together, the findings of these bioinformatics analyses thus identified key hub genes associated with T1DM development.

Thoracic aortic aneurysm and dissection (TAAD) is a cardiovascular disease with a high mortality rate. Aging is an important risk factor for TAAD. This study explored the relationship between aging and TAAD and investigated the underlying mechanisms, which may contribute to the diagnosis and treatment of TAAD. Human aging genes were obtained from the Aging Atlas official website. Various datasets were downloaded from the GEO database:the human TAAD dataset GSE52093 were used for screening differentially expressed genes (DEGs); GSE137869, GSE102397 and GSE153434 were used as validation sets, and GSE9106 was used for diagnostic prediction of receiver operating characteristic (ROC) curves. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and protein-protein interaction (PPI) network analysis were used to screen differentially co-expressed genes from human aging genes and TAAD. Using five methods of the cytoHubba plugin in Cytoscape software (Degree, Closeness, EPC, MNC, Radiality), hub genes were identified from the differentially co-expressed genes. Single-cell RNA sequencing was used to verify the expression levels of hubgenes in different cell types of aortic tissue. ROC curves were used to further screen for diagnostic genes. A total of 70 differentially co-expressed genes were screened from human aging genes and DEGs in human TAAD dataset GSE52093. GO enrichment analysis revealed that the DEGs played a major role in regulating DNA metabolism and damaged DNA binding. KEGG enrichment analysis revealed enrichment in the longevity regulating pathway, cellular senescence, and HIF-1 signaling pathway. GSEA indicated that the DEGs were concentrated in the cell cycle and aging-related p53 signaling pathway. The five identified hubgenes were MYC, IL6, HIF1A, ESR1, and PTGS2. Single-cell sequencing of the aging rat aorta showed that hubgenes were expressed differently in different types of cells in aortic tissue. Among these five hubgenes, HIF1A and PTGS2 were validated in the aging dataset GSE102397; MYC, HIF1A and ESR1 were validated in the TAAD dataset GSE153434. The combined area under the diagnostic ROC curve (AUC) values for the five hub genes were >0.7 in the testing and training sets of the dataset GSE9106. The combined AUC values of MYC and ESR1 were equal to the combin ed AUC values of the five hub genes. The HIF-1 signaling pathway may play an important role in TAAD and aging. MYC and ESR1 may have diagnostic value for aging-related TAAD.

This study aims to identify differentially expressed genes (DEGs) in neuroblastoma (NB) through comprehensive bioinformatics analysis and machine learning techniques. We seek to elucidate these DEGs' biological functions and associated signaling pathways. Furthermore, our objective extends to predicting upstream microRNAs (miRNAs) and relevant transcription factors of pivotal genes, with the ultimate goal of guiding clinical diagnostics and informing future treatment strategies for Neuroblastoma. In this study, we sourced datasets GSE49710 and TARGET from the GEO and UCSC-XENA databases, respectively. Differentially expressed genes (DEGs) were identified using the R language "limma" package. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of these DEGs were conducted using the "clusterProfiler" package. We employed Weighted Gene Co-expression Network Analysis (WGCNA) to isolate the most significant modules associated with death and MYCN amplification, specifically MEpink and MEbrown modules. These modules were then cross-referenced with the DEGs for further GO and KEGG pathway analyses. LASSO regression analysis, facilitated by the "glmnet" package, was utilized to pinpoint three hub genes. We performed differential analysis on these genes and constructed Receiver Operating Characteristic (ROC) curves for disease diagnosis purposes. Immune infiltration analysis was conducted using the "GSVA" package's ssGSEA function. Additionally, single-gene Gene Set Enrichment Analysis (GSEA) on the hub gene was carried out based on Reactome and KEGG databases. Upstream miRNA and transcription factors associated with the hub gene were predicted using RegNetwork, with visual representations created in Cytoscape. Furthermore, to validate the three identified markers in neuroblastoma tissues, quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) analysis was conducted. We identified 483 differentially expressed genes (DEGs) in neuroblastoma. These genes predominantly function in protein translation, membrane composition, and RNA transcription regulation, and are implicated in multiple signaling pathways relevant to neurodegenerative diseases. Utilizing LASSO regression analysis, we pinpointed three hub genes: VGF, DGKD, and C19orf52. The Receiver Operating Characteristic (ROC) curve analysis yielded Area Under Curve (AUC) values of 0.751 and 0.722 for VGF, 0.79 and 0.656 for DGKD, and 0.8 and 0.753 for C19orf52, respectively. Our immune infiltration analysis revealed significant correlations among monocytes, follicular helper T cells, and CD4+ T cells. Notably, in the death group, we observed heightened infiltration levels of activated CD4+ T cells, macrophages, and Th2 cells. C19orf52 exhibited a close association with the infiltration of monocytes, CD4+ T cells, and Th2 cells, with P-values less than 0.05. Furthermore, qRT-PCR analysis corroborated the upregulation of VGF in neuroblastoma tissues, further validating our findings. The hub genes (VGF, DGKD, and C19orf52) of neuroblastoma are screened. VGF, one of the hub genes, may have a high diagnostic value and is involved in the immune cell infiltration in neuroblastoma tissue, which may be used as a biomarker for the diagnosis of neuroblastoma and provides a new direction for clinical prognosis prediction and management improvement.

Identification and verification of key genes related to oxidative stress in type 2 diabetes and screening of candidate drugs from Traditional Chinese Medicine.

Identification of critical genes and pathways associated with hepatocellular carcinoma and type 2 diabetes mellitus using integrated bioinformatics analysis

Background The onset and progression of type 1 diabetes mellitus (T1DM) is closely related to autoimmunity. Effective monitoring of the immune system and developing targeted therapies are frontier fields in T1DM treatment. Currently, the most available tissue that reflects the immune system is peripheral blood mononuclear cells (PBMCs). Thus, the aim of this study was to identify key PBMC biomarkers of T1DM.Methods Common differentially expressed genes (DEGs) were screened from the Gene Expression Omnibus (GEO) datasets GSE9006, GSE72377, and GSE55098, and PBMC mRNA expression in T1DM patients was compared with that in healthy participants by GEO2R. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interaction (PPI) network analyses of DEGs were performed using the Cytoscape, DAVID, and STRING databases. The vital hub genes were validated by reverse transcription-polymerase chain reaction using clinical samples. The disease-gene-drug interaction network was built using the Comparative Toxicogenomics Database (CTD) and Drug Gene Interaction Database (DGIdb).Results We found that various biological functions or pathways related to the immune system and glucose metabolism changed in PBMCs from T1DM patients. In the PPI network, the DEGs of module 1 were significantly enriched in processes including inflammatory and immune responses and in pathways of proteoglycans in cancer. Moreover, we focused on four vital hub genes, namely, chitinase-3-like protein 1 (CHI3L1), C-X-C motif chemokine ligand 1 (CXCL1), matrix metallopeptidase 9 (MMP9), and granzyme B (GZMB), and confirmed them in clinical PBMC samples. Furthermore, the disease-gene-drug interaction network revealed the potential of key genes as reference markers in T1DM.Conclusion These results provide new insight into T1DM pathogenesis and novel biomarkers that could be widely representative reference indicators or potential therapeutic targets for clinical applications.

BackgroundVascular calcification (VC) is the most widespread pathological change in diseases of the vascular system. However, we know poorly about the molecular mechanisms and effective therapeutic approaches of VC.MethodsThe VC dataset, GSE146638, was downloaded from the Gene Expression Omnibus (GEO) database. Using the edgeR package to screen Differentially expressed genes (DEGs). Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to find pathways affecting VC. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed on the DEGs. Meanwhile, using the String database and Cytoscape software to construct protein-protein interaction (PPI) networks and identify hub genes with the highest module scores. Correlation analysis was performed for hub genes. Receiver operating characteristic (ROC) curves, expression level analysis, GSEA, and subcellular localization were performed for each hub gene. Expression of hub genes in normal and calcified vascular tissues was verified by quantitative reverse transcription PCR (RT-qPCR) and immunohistochemistry (IHC) experiments. The hub gene-related miRNA-mRNA and TF-mRNA networks were constructed and functionally enriched for analysis. Finally, the DGIdb database was utilized to search for alternative drugs targeting VC hub genes.ResultsBy comparing the genes with normal vessels, there were 64 DEGs in mildly calcified vessels and 650 DEGs in severely calcified vessels. Spp1, Sost, Col1a1, Fn1, and Ibsp were central in the progression of the entire VC by the MCODE plug-in. These hub genes are primarily enriched in ossification, extracellular matrix, and ECM-receptor interactions. Expression level results showed that Spp1, Sost, Ibsp, and Fn1 were significantly highly expressed in VC, and Col1a1 was incredibly low. RT-qPCR and IHC validation results were consistent with bioinformatic analysis. We found multiple pathways of hub genes acting in VC and identified 16 targeting drugs.ConclusionsThis study perfected the molecular regulatory mechanism of VC. Our results indicated that Spp1, Sost, Col1a1, Fn1, and Ibsp could be potential novel biomarkers for VC and promising therapeutic targets.

Polycystic ovary syndrome (PCOS) is associated with an increased risk of type 2 diabetes mellitus (T2DM), and the risk of PCOS increases in patients with T2DM of reproductive age. The bidirectional link between PCOS and T2DM has been confirmed through experimental and epidemiological evidence; however, the genetic factors that contribute to deeper insights into the shared pathogenesis of these two diseases remain unclear. We aimed to identify shared immune- and inflammation-related genes and pathways in PCOS and T2DM, further explore the molecular mechanisms in developing this comorbidity, and predict drugs with potential effects to develop novel therapeutic strategies. We obtained microarray expression profiling datasets (GSE34526 and GSE25724) of PCOS and T2DM from the Gene Expression Omnibus (GEO) database. The differential expression genes (DEGs) between disease and control groups were identified and analyzed via the R package "limma" following data preprocessing. The R package "clusterProfiler" was applied to conduct Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analyses. Hub genes were identified from the protein-protein interaction (PPI) network using the Molecular Complex Detection (MCODE) and cytoHubba plug-ins of Cytoscape. Transcription factor (TF)-hub and miRNA-hub gene regulatory networks were constructed and visualized using Cytoscape. The Drug-Gene Interaction Database (DGIdb) was used to predict prospective drugs targeting hub genes. In addition, hub genes were verified by RT-qPCR. A total of 239 common DEGs, including 140 upregulated genes and 99 downregulated genes, were discovered. These common DEGs were primarily associated with immune regulation and inflammatory processes. Moreover, ITGAM, ITGB2, SPI1, C1QB, CCR5, C3AR1, LY86, AIF1, and IRF8 were identified as hub genes and the RT-qPCR results showed significant differences. These hub genes were predominantly related to the regulation of neutrophil degranulation (ITGAM, ITGB2, and SPI1), dendritic cell chemotaxis (CCR5 and SPI1), follicular B cell differentiation (SPI1 and IRF8), synapse pruning (ITGAM and C1QB), integrin αM-β2 complex (ITGAM and ITGB2), the regulation of prostaglandin-E synthase activity (ITGAM and ITGB2), Staphylococcus aureus infection (ITGAM, ITGB2, C1QB, and C3AR1) and pertussis (IRF8). Finally, we predicted 19 TFs, 170 miRNAs, and 40 potential therapeutic drugs interacting with hub genes. We identified nine hub genes and related gene regulatory networks and discussed novel perspectives on the roles of immunity and inflammation in patients with PCOS and T2DM. Moreover, maraviroc, cenicriviroc, PF-04634817 (targeting CCR5), butein (targeting ITGB2), dimethyl sulfoxide (targeting ITGAM), and rovelizumab (targeting both ITGB2 and ITGAM) are potential therapeutic drugs. However, these findings require validation through further clinical and experimental studies.

Type 2 diabetes mellitus (T2DM) and cancer are highly prevalent diseases imposing major health burden globally. Several epidemiological studies indicate increased susceptibility to cancer in T2DM patients. However, genetic factors linking T2DM with cancer have been poorly studied. In this study, we followed computational approaches using the raw gene expression data of peripheral blood mononuclear cells of T2DM and cancer patients available in the gene expression omnibus (GEO) database. Our analysis identified shared differentially expressed genes (DEGs) in T2DM and three common cancer types, namely, pancreatic cancer (PC), liver cancer (LC), and breast cancer (BC). The functional and pathway enrichment analysis of identified common DEGs highlighted the involvement of critical biological pathways, including cell cycle events, immune system processes, cell morphogenesis, gene expression, and metabolism. We retrieved the protein–protein interaction network for the top DEGs to deduce molecular-level interactions. The network analysis found 7, 6, and 5 common hub genes in T2DM vs. PC, T2DM vs. LC, and T2DM vs. BC comparisons, respectively. Overall, our analysis identified important genetic markers potentially able to predict the chances of PC, LC, and BC onset in T2DM patients.

Type 2 diabetes mellitus (T2DM) and cancer are highly prevalent diseases imposing major health burden globally. Several epidemiological studies indicate increased susceptibility to cancer in T2DM patients. However, genetic factors linking T2DM with cancer have been poorly studied. In this study, we followed computational approaches using the raw gene expression data of peripheral blood mononuclear cells of T2DM and cancer patients available in the gene expression omnibus (GEO) database. Our analysis identified shared differentially expressed genes (DEGs) in T2DM and three common cancer types, namely, pancreatic cancer (PC), liver cancer (LC), and breast cancer (BC). The functional and pathway enrichment analysis of identified common DEGs highlighted the involvement of critical biological pathways, including cell cycle events, immune system processes, cell morphogenesis, gene expression, and metabolism. We retrieved the protein–protein interaction network for the top DEGs to deduce molecular-level interactions. The network analysis found 7, 6, and 5 common hub genes in T2DM vs. PC, T2DM vs. LC, and T2DM vs. BC comparisons, respectively. Overall, our analysis identified important genetic markers potentially able to predict the chances of PC, LC, and BC onset in T2DM patients.

Severe neurotoxicity after chimeric antigen receptor T cell (CAR-T) therapy can be a crucial lifethreatening event in diffuse large B-cell lymphoma (DLBCL), and management of those toxicities is still a serious clinical challenge. The underlying mechanisms of CAR-T cell-mediated neurotoxicity remain poorly elucidated because very few studies examine the intact tumor microenvironment before CAR-T cell infusion. Herein, we pur-posed to identify differentially expressed genes (DEGs) related to CAR-T cell-mediated neurotoxicity in the DLBCL microenvironment before CAR-T cell infusion and reveal their potential mechanisms. The mRNA expression profile data of GSE153438 were obtained from the GEO database. The GSE153438 dataset includes 26 samples with non-severe neurotoxicity (grade 0 - 2) and 10 samples with severe neurotoxicity (grade 3 or higher). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) patway enrichment assessment was carried out. We screened the hub gene by protein-protein interaction (PPI) network analysis and Cytoscape software. Gene set enrichment analysis (GSEA) was also analyzed with the GSEA software. Moreover, the predictive value of the hub gene for severe neurotoxicity was evaluated via receiver operating characteristic (ROC) curve analysis. We identified a total of 25 up-regulated DEGs and 26 downregulated DEGs associated with CAR-T cell-mediated neurotoxicity in the DLBCL microenvironment before CAR-T cell infusion. Results of GO analysis showed that DEGs were mainly enriched in T cell activation, leukocyte cell-cell adhesion, and positive regulation of cell adhesion. The KEGG analysis revealed that DEGs were significantly enriched in T cell receptor signaling pathway, cell adhesion molecules, and Epstein-Barr virus infection. GSEA revealed that the glycolysis pathway was significantly associated with severe neurotoxicity. The top centrality hub gene GZMB was identified from the PPI network. ROC curve analysis showed that GZMB had a potential predictive value for severe neurotoxicity. In DLBCL microenvironment before CAR-T cell infusion, we identified T cell activation and glycolysis pathways significantly associated with CAR-T cell-mediated severe neurotoxicity. GZMB might be used as a predictive and therapeutic molecular marker for neurotoxicity. The study suggested that the tumor microenviron-ment before CAR-T cell infusion plays an essential role in the early prediction of neurotoxicity.

Type 1 diabetes mellitus (T1DM) is a metabolic disorder for which the underlying molecular mechanisms remain largely unclear. This investigation aimed to elucidate essential candidate genes and pathways in T1DM by integrated bioinformatics analysis. In this study, differentially expressed genes (DEGs) were analyzed using DESeq2 of R package from GSE162689 of the Gene Expression Omnibus (GEO). Gene ontology (GO) enrichment analysis, REACTOME pathway enrichment analysis, and construction and analysis of protein–protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network, and validation of hub genes were performed. A total of 952 DEGs (477 up regulated and 475 down regulated genes) were identified in T1DM. GO and REACTOME enrichment result results showed that DEGs mainly enriched in multicellular organism development, detection of stimulus, diseases of signal transduction by growth factor receptors and second messengers, and olfactory signaling pathway. The top hub genes such as MYC, EGFR, LNX1, YBX1, HSP90AA1, ESR1, FN1, TK1, ANLN and SMAD9 were screened out as the critical genes among the DEGs from the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Receiver operating characteristic curve (ROC) analysis confirmed that these genes were significantly associated with T1DM. In conclusion, the identified DEGs, particularly the hub genes, strengthen the understanding of the advancement and progression of T1DM, and certain genes might be used as candidate target molecules to diagnose, monitor and treat T1DM.

We aimed to determine the cis-expression Quantitative Trait Loci (cis-eQTL) and trans-eQTL of differentially expressed genes (DEGs) in insulin resistance (IR) related pathways. The expression profile data for insulin sensitivity (IS) and IR in the adipose tissue of patients with type 2 diabetes mellitus (T2DM) were acquired from the Gene Expression Omnibus databases. Then, the Gene set enrichment analysis (GSEA) and Gene set variation analysis (GSVA) methods were performed to identify the significant enrichment of potential Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways between IS and IR groups, and the Wilcoxon rank sum test was carried out to identify the DEGs related to KEGG pathways. Finally, the cis-eQTLs and trans-eQTLs that can affect the expression of DEGs were screened from the eQTLGen database. The GSEA and GSVA analysis indicated that the mTOR signaling pathway, insulin signaling pathway and T2DM had a strong correlation with the pathological process of T2DM. Furthermore, six genes (ACACA, GYS2, PCK1, PRKAR1A, SLC2A4, and VEGFA) were found to be significantly differentially expressed in IR-related pathways. Finally, we have identified a total of 1073 cis-eQTLs and 24 trans-eQTLs. We screened out six genes that were significantly differentially expressed in IR-related pathways, including ACACA, GYS2, PCK1, PRKAR1A, SLC2A4, and VEGFA. Moreover, we discovered that these six genes were affected by 1073 cis-eQTLs and 24 trans-eQTLs.