Validation Of Hub Genes Research Articles

Identification of oxidative stress-related diagnostic markers and immune infiltration features for idiopathic pulmonary fibrosis by bibliometrics and bioinformatics.

Idiopathic pulmonary fibrosis (IPF) garners considerable attention due to its high fatality rate and profound impact on quality of life. Our study conducts a comprehensive literature review on IPF using bibliometric analysis to explore existing hot research topics, and identifies novel diagnostic and therapeutic targets for IPF using bioinformatics analysis. Publications related to IPF from 2013 to 2023 were searched on the Web of Science Core Collection (WoSCC) database. Data analysis and visualization were conducted using CiteSpace and VOSviewer software primarily. The gene expression profiles GSE24206 and GSE53845 were employed as the training dataset. The GSE110147 dataset was employed as the validation dataset. We identified differentially expressed genes (DEGs) and differentially expressed genes related to oxidative stress (DEOSGs) between IPF and normal samples. Then, we conducted Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The hub genes were screened by protein-protein interaction (PPI) networks and machine learning algorithms. The CIBERSORT was used to analyze the immune infiltration of 22 kinds of immune cells. Finally, we conducted the expression and validation of hub genes. The diagnostic efficacy of hub genes was evaluated by employing Receiver Operating Characteristic (ROC) curves and the associations between hub genes and immune cells were analyzed. A total of 6,500 articles were identified, and the annual number of articles exhibited an upward trend. The United States emerged as the leading contributor in terms of publication count, institutional affiliations, highly cited articles, and prolific authorship. According to co-occurrence analysis, oxidative stress and inflammation are hot topics in IPF research. A total of 1,140 DEGs were identified, and 72 genes were classified as DEOSGs. By employing PPI network analysis and machine learning algorithms, PON2 and TLR4 were identified as hub genes. A total of 10 immune cells exhibited significant differences between IPF and normal samples. PON2 and TLR4, as oxidative stress-related genes, not only exhibit high diagnostic efficacy but also show close associations with immune cells. In summary, our study highlights oxidative stress and inflammation are hot topics in IPF research. Oxidative stress and immune cells play a vital role in the pathogenesis of IPF. Our findings suggest the potential of PON2 and TLR4 as novel diagnostic and therapeutic targets for IPF.

Identification of immune-related genes and small-molecule drugs in hypertension-induced left ventricular hypertrophy based on machine learning algorithms and molecular docking.

Left ventricular hypertrophy (LVH) is a common consequence of hypertension and can lead to heart failure. The immune response plays an important role in hypertensive LVH; however, there is no comprehensive method to investigate the mechanistic relationships between immune response and hypertensive LVH or to find novel therapeutic targets. This study aimed to screen hub immune-related genes involved in hypertensive LVH as well as to explore immune target-based therapeutic drugs. RNA-sequencing data from a mouse model generated by angiotensin II infusion were subjected to weighted gene co-expression network analysis (WGCNA) to identify core expression modules. Machine learning algorithms were applied to screen immune-related LVH characteristic genes. Heart structures were evaluated by echocardiography and cardiac magnetic resonance imaging (CMRI). Validation of hub genes was conducted by RT-qPCR and western blot. Using the Connectivity Map database and molecular docking, potential small-molecule drugs were explored. A total of 1215 differentially expressed genes were obtained, most of which were significantly enriched in immunoregulation and collagen synthesis. WGCNA and multiple machine learning strategies uncovered six hub immune-related genes (Ankrd1, Birc5, Nuf2, C1qtnf6, Fcgr3, and Cdca3) that may accurately predict hypertensive LVH diagnosis. Immune analysis revealed that fibroblasts and macrophages were closely correlated with hypertensive LVH, and hub gene expression was significantly associated with these immune cells. A regulatory network of transcription factor-mRNA and a ceRNA network of miRNA-lncRNA was established. Notably, six hub immune-related genes were significantly increased in the hypertensive LVH model, which were positively linked to left ventricle wall thickness. Finally, 12 small-molecule compounds with the potential to reverse the high expression of hub genes were ruled out as potential therapeutic agents for hypertensive LVH. This study identified and validated six hub immune-related genes that may play essential roles in hypertensive LVH, providing new insights into the potential pathogenesis of cardiac remodeling and novel targets for medical interventions.

Identification of potential hub genes and biological mechanism in rheumatoid arthritis and non-small cell lung cancer via integrated bioinformatics analysis

BackgroundAlthough there is evidence of the association between RA and NSCLC, little is known about their interaction mechanisms. The aim of this study is to identify potential hub genes and biological mechanism in RA and NSCLC via integrated bioinformatics analysis. MethodsThe gene expression datasets of RA and NSCLC were downloaded to discover and validate hub genes. After identifying DEGs, we performed enrichment analysis, PPI network construction and module analysis, selection and validation of hub genes. Moreover, we selected the hub gene PTPRC for expression and prognosis analysis, immune analysis, mutation and methylation analysis in NSCLC. Finally, we performed real-time PCR, colony formation assay, wound healing assay, transwell invasion assay, sphere formation assay and western blotting to validate the role of PTPRC in A549 cells. ResultsWe obtained 320 DEGs for subsequent analysis. Enrichment results showed that the DEGs were mainly involved in Th1, Th2 and Th17 cell differentiation. In addition, four hub genes, BIRC5, PTPRC, PLEK, and FYN, were identified after selection and validation. These hub genes were subsequently shown to be closely associated with immune cells and related pathways. In NSCLC, PTPRC was downregulated, positively correlated with immune infiltration and immune cells. Experiments showed that PTPRC could promote the proliferation, migration and invasion, and the ability to form spheroids of A549 cells. In addition, PTPRC could regulate the increased expression of CD45, β-catenin, c-Myc and LEF1 proteins. ConclusionsThis study explored the hub genes and related mechanisms of RA and NSCLC, demonstrated the central role of the inflammatory response and the adaptive immune system, and identified PTPRC as an immune-related biomarker and potential therapeutic target for RA and NSCLC patients. In addition, PTPRC can significantly promote the proliferation, migration and invasion of A549 cells, and its mechanism may be to promote the EMT process by regulating the Wnt signaling pathway and promote cell stemness, which in turn has a promoting effect on A549 cells.

Analysis and validation of hub genes in neutrophil extracellular traps for the long-term prognosis of myocardial infarction

IntroductionThe study focuses on the long-term prognosis of myocardial infarction (MI) influenced by neutrophil extracellular traps (NETs). It also aims to analyze and validate relative hub genes in this process, in order to further explore new therapeutic targets that can improve the prognosis of MI. Materials and methodsWe established a MI model in mice by ligating the left anterior descending branch (LAD) and conducted an 8-week continuous observation to study the dynamic changes in the structure and function of the heart in these mice. Meanwhile, we administered Apocynin, an inhibitor of NADPH Oxidase, which has also been shown to inhibit the formation of NETs, to mice undergoing MI surgery in order to compare. This study employed hematoxylin-eosin (HE) staining, echocardiography, immunofluorescence, and real-time quantitative PCR (RT-qPCR) to examine the impact of NETs on the long-term prognosis of MI. Next, datasets related to MI and NETs were downloaded from the GEO database, respectively. The Limma package of R software was used to identify differentially expressed genes (DEGs). After analyzing the “Robust Rank Aggregation (RRA)” package, we conducted a screening for robust differentially expressed genes (DEGs) and performed pathway enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to determine the functional roles of these robust DEGs. The protein–protein interaction (PPI) network was visualized and hub genes were filtered using Cytoscape. ResultsImmunofluorescence and qPCR results showed an increase in the expression of Myeloperoxidase (MPO) at week 1 and week 8 in the hearts of mice after MI. HE staining reveals a series of pathological manifestations in the heart of the MI group during 8 weeks, including enlarged size, disordered arrangement of cardiomyocytes, infiltration of inflammatory cells, and excessive deposition of collagen fibers, among others. The utilization of Apocynin could significantly improve these poor performances. The echocardiography displayed the cardiac function of the heart in mice. The MI group has a reduced range of heart movement and decreased ejection ability. Moreover, the ventricular systolic movement was found to be abnormal, and its wall thickening rate decreased over time, indicating a progressive worsening of myocardial ischemia. The Apocynin group, on the contrary, showed fewer abnormal changes in the aforementioned aspects. A total of 81 DEGs and 4 hub genes (FOS, EGR1, PTGS2, and HIST1H4H) were obtained. The results of RT-qPCR demonstrated abnormal expression of these four genes in the MI group, which could be reversed by treatment of Apocynin. ConclusionThe NETs formation could be highly related to MI and the long-term prognosis of MI can be significantly influenced by the NETs formation. Four hub genes, namely FOS, EGR1, PTGS2, and HIST1H4H, have the potential to be key genes related to this process. They could also serve as biomarkers for predicting MI prognosis and as targets for gene therapy.

Novel biomarkers related to oxidative stress and immunity in chronic kidney disease

IntroductionThe incidence of chronic kidney disease (CKD) has been increasing in recent years, gradually becoming a global health crisis. Due to limited treatment options, novel molecular pathways are urgently required to advance the treatment and diagnosis of CKD. Materials and methodsThe characteristics of differentially expressed genes (DEGs) in CKD patients were analyzed using Gene Expression Omnibus (GEO) database, and genes related to oxidative stress were retrieved from the Genecard database. Subsequently, a comprehensive approach was applied, including immune infiltration analysis, weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis, to identify hub genes among differentially expressed immune-related oxidative stress genes (DEIOSGs). Validation of hub genes was performed using an external data set, and diagnostic potential capability was evaluated through receiver operating curve (ROC) analysis. In animal experiments, the expression of hub genes in CKD was confirmed by inducing a CKD model through a 5/6 nephrectomy procedure. Finally, the relationship between these hub genes and clinical characteristics were assessed using the Nephroseq v5 database. Results29 DEIOSGs were identified by comprehensive bioinformatics analysis. PPI analysis screened the hub genes NCF2, S100A9, and SELL. ROC analysis demonstrated excellent diagnostic efficacy. Further validation from other databases and animal experiments confirmed a substantial upregulation in the expression of hub genes in CKD. Additionally, clinical correlation analysis established a clear link between hub gene expression and renal function deterioration. ConclusionsOur study confirms NCF2, S100A9, and SELL as diagnostic biomarkers associated with immune response and oxidative stress in CKD, suggesting their potential as novel targets for CKD diagnosis and treatment.

Identification of two hub genes and miRNA‑mRNA interactions in chronic obstructive pulmonary disease (COPD) plasma

Background We aimed to identify hub genes in chronic obstructive pulmonary disease (COPD) plasma through the exploration of a putative miRNA–mRNA regulatory network. Methods Three datasets (GSE24709, GSE102915, GSE136390) were utilized to discern differentially expressed miRNAs (DEMs) between COPD and normal plasma. miRNET was employed to predict the potential targets of DEMs. Subsequent GO and KEGG analyses were conducted using DAVID. For the construction of the protein-protein interaction (PPI) network and screening of hub genes, STRING and Cytoscape were employed. The expression validation was assessed through GSE56768. Results The results revealed 395 genes targeted by up-regulated DEMs and 234 genes targeted by down-regulated DEMs. The target genes exhibited significant enrichment in the PI3K-Akt signaling pathway and the p53 signaling pathway. Through the validation of hub genes’ expression, we proposed two potential miRNA-mRNA interactions: miR-126-5p/miR-495-3p/miR-193b-3p - YWHAZ and miR-937-5p/miR-183-5p/miR-34c-5p/miR-98-5p/miR-525-3p/miR-215-5p - ACTB. Conclusions In conclusion, our study posits potential miRNA-mRNA interactions in COPD by analyzing datasets from public databases, contributing valuable insights into the understanding of COPD pathogenesis and potential therapeutic avenues.

Identification of genes and key pathways underlying the pathophysiological association between sarcopenia and chronic obstructive pulmonary disease

PurposeChronic obstructive pulmonary disease (COPD) patients are likely to develop sarcopenia, while the exact mechanism underlying the association between sarcopenia and COPD is still not clear. This cohort study aims to explore the genes, signaling pathways, and transcription factors (TFs) that are related to the molecular pathogenesis of sarcopenia and COPD. MethodsAccording to the strict inclusion criteria, two gene sets (GSE8479 for sarcopenia and GSE76925 for COPD) were obtained from the Gene Expression Omnibus (GEO) platform. Overlapping differentially expressed genes (DEGs) in sarcopenia and COPD were detected, and comprehensive bioinformatics analysis was conducted, including functional annotation, enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), construction of a protein–protein interaction (PPI) network, co-expression analysis, identification and validation of hub genes, and TFs prediction and verification. ResultsIn total, 118 downregulated and 92 upregulated common DEGs were detected. Functional analysis revealed that potential pathogenesis involves oxidoreductase activity and ferroptosis. Thirty hub genes were detected, and ATP metabolic process and oxidative phosphorylation were identified to be closely related to the hub genes. Validation analysis revealed that SAA1, C3, and ACSS2 were significantly upregulated, whereas ATF4, PPARGC1A, and MCTS1 were markedly downregulated in both sarcopenia and COPD. In addition, six TFs (NFKB1, RELA, IRF7, SP1, MYC, and JUN) were identified to regulate the expression of these genes, and SAA1 was found to be coregulated by NFKB1 and RELA. ConclusionThis study uncovers potential common mechanisms of COPD complicated by sarcopenia. The hub gene SAA1 and the NF-κB signaling pathway could be involved, and oxidative phosphorylation and ferroptosis might be important contributors to this comorbidity.

Identification and validation of hub genes and molecular classifications associated with chronic myeloid leukemia.

Chronic myeloid leukemia (CML) is a kind of malignant blood tumor, which is prone to drug resistance and relapse. This study aimed to identify novel diagnostic and therapeutic targets for CML. Differentially expressed genes (DEGs) were obtained by differential analysis of the CML cohort in the GEO database. Weighted gene co-expression network analysis (WGCNA) was used to identify CML-related co-expressed genes. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to screen hub genes and construct a risk score model based on hub genes. Consensus clustering algorithm was used for the identification of molecular subtypes. Clinical samples and in vitro experiments were used to verify the expression and biological function of hub genes. A total of 378 DEGs were identified by differential analysis. 369 CML-related genes were identified by WGCNA analysis, which were mainly enriched in metabolism-related signaling pathways. In addition, CML-related genes are mainly involved in immune regulation and anti-tumor immunity, suggesting that CML has some immunodeficiency. Immune infiltration analysis confirmed the reduced infiltration of immune killer cells such as CD8+ T cells in CML samples. 6 hub genes (LINC01268, NME8, DMXL2, CXXC5, SCD and FBN1) were identified by LASSO regression analysis. The receiver operating characteristic (ROC) curve confirmed the high diagnostic value of the hub genes in the analysis and validation cohorts, and the risk score model further improved the diagnostic accuracy. hub genes were also associated with cell proliferation, cycle, and metabolic pathway activity. Two molecular subtypes, Cluster A and Cluster B, were identified based on hub gene expression. Cluster B has a lower risk score, higher levels of CD8+ T cell and activated dendritic cell infiltration, and immune checkpoint expression, and is more sensitive to commonly used tyrosine kinase inhibitors. Finally, our clinical samples validated the expression and diagnostic efficacy of hub genes, and the knockdown of LINC01268 inhibited the proliferation of CML cells, and promoted apoptosis. Through WGCNA analysis and LASSO regression analysis, our study provides a new target for CML diagnosis and treatment, and provides a basis for further CML research.

Identification and Validation of Hub Genes and Construction of miRNA-Gene and Transcription Factor-Gene Networks in Adipogenesis of Mesenchymal Stem Cells.

Adipogenic differentiation stands as a crucial pathway in the range of differentiation options for mesenchymal stem cells (MSCs), carrying significant importance in the fields of regenerative medicine and the treatment of conditions such as obesity and osteoporosis. However, the exact mechanisms that control the adipogenic differentiation of MSCs are not yet fully understood. We procured datasets, namely GSE36923, GSE80614, GSE107789, and GSE113253, from the Gene Expression Omnibus database. These datasets enabled us to perform a systematic analysis, including the identification of differentially expressed genes (DEGs) pre- and postadipogenic differentiation in MSCs. Subsequently, we conducted an exhaustive analysis of DEGs common to all four datasets. To gain further insights, we subjected these overlapped DEGs to comprehensive gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Following the construction of protein-protein interaction (PPI) networks, we meticulously identified a cohort of hub genes pivotal to the adipogenic differentiation process and validated them using real-time quantitative polymerase chain reaction. Subsequently, we ventured into the construction of miRNA-gene and TF-gene interaction networks. Our rigorous analysis revealed a total of 18 upregulated DEGs and 12 downregulated DEGs that consistently appeared across all four datasets. Notably, the peroxisome proliferator-activated receptor signaling pathway, regulation of lipolysis in adipocytes, and the adipocytokine signaling pathway emerged as the top-ranking pathways significantly implicated in the regulation of these DEGs. Subsequent to the construction of the PPI network, we identified and validated 10 key node genes, namely IL6, FABP4, ADIPOQ, LPL, PLIN1, RBP4, ACACB, NT5E, KRT19, and G0S2. Our endeavor to construct miRNA-gene interaction networks led to the discovery of the top 10 pivotal miRNAs, including hsa-mir-27a-3p, hsa-let-7b-5p, hsa-mir-1-3p, hsa-mir-124-3p, hsa-mir-155-5p, hsa-mir-16-5p, hsa-mir-101-3p, hsa-mir-21-3p, hsa-mir-146a-5p, and hsa-mir-148b-3p. Furthermore, the construction of TF-gene interaction networks revealed the top 10 critical TFs: ZNF501, ZNF512, YY1, EZH2, ZFP37, ZNF2, SOX13, MXD3, ELF3, and TFDP1. In summary, our comprehensive study has successfully unraveled the pivotal hub genes that govern the adipogenesis of MSCs. Moreover, the meticulously constructed miRNA-gene and TF-gene interaction networks are poised to significantly augment our comprehension of the intricacies underlying MSC adipogenic differentiation, thus providing a robust foundation for future advances in regenerative biology.

Identification and verification of feature biomarkers associated with immune cells in recurrent pregnancy loss.

The aim of this study was to investigate the causes, diagnostic markers, and treatment methods for recurrent pregnancy loss (RPL) using bioinformatics approaches. Bioinformatics methods were utilized to analyze gene expression databases to identify key genes and modules associated with RPL. Weighted gene co-expression network analysis (WGCNA) was employed to identify gene sets related to maternal-fetal immunity. Gene set variation analysis (GSVA) and protein-protein interaction networks were used to explore signaling pathways and molecular interactions in RPL. Immune cell infiltration was assessed using single-sample gene set enrichment analysis (ssGSEA). Thirteen genes were identified as potential diagnostic markers, some of which were involved in placental amino acid transport, glucose absorption, and reactive oxygen species production. Several gene sets related to protein transport, steroid synthesis, and glycosaminoglycan degradation were found to be associated with RPL. Immune cell infiltration analysis found that CD56bright NK cells and monocytes showed significantly increased infiltration in RPL and were associated with key hub genes. The validation of hub genes, including PCSK5, CCND2, SLC5A3, RASAL1, MYZAP, MFAP4, and P2RY14, as potential diagnostic markers, showed promising value. This study contributes to a better understanding of the etiology of RPL and potential diagnostic markers. The identified immune-related gene sets, signaling pathways, and immune cell infiltrations provide valuable insights for future research and therapeutic advancements in RPL.

Identification and verification of potential biomarkers in sertoli cell-only syndrome via bioinformatics analysis

Sertoli cell-only syndrome (SCOS), a severe testicular spermatogenic failure, is characterized by total absence of male germ cells. To better expand the understanding of the potential molecular mechanisms of SCOS, we used microarray datasets from the Gene Expression Omnibus (GEO) and ArrayExpress databases to determine the differentially expressed genes (DEGs). In addition, functional enrichment analysis including the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was performed. Protein–protein interaction (PPI) networks, modules, and miRNA-mRNA regulatory networks were constructed and analyzed and the validation of hub genes was performed. A total of 601 shared DEGs were identified, including 416 down-regulated and 185 up-regulated genes. The findings of the enrichment analysis indicated that the shared DEGs were mostly enriched in sexual reproduction, reproductive process, male gamete generation, immune response, and immunity-related pathways. In addition, six hub genes (CCNA2, CCNB2, TOP2A, CDC20, BUB1, and BUB1B) were selected from the PPI network by using the cytoHubba and MCODE plug-ins. The expression levels of the hub genes were significantly decreased in patients with SCOS compared to that in normal spermatogenesis controls as indicated by the microarray data, single-cell transcriptomic data, and clinical sample levels. Furthermore, the potential miRNAs were predicted via the miRNA-mRNA network construction. These hub genes and miRNAs can be used as potential biomarkers that may be related to SCOS. However, it has not been proven that the differential expression of these biomarkers is the molecular pathogenesis mechanisms of SCOS. Our findings suggest that these biomarkers can be serve as clinical tool for diagnosis targets and may have some impact on the spermatogenesis of SCOS from a testicular germ cell perspective.

Integrated Bioinformatics Investigation of Novel Biomarkers of Uterine Leiomyosarcoma Diagnosis and Outcome.

Uterine leiomyosarcomas (uLMS) have a poor prognosis and a high percentage of recurrent disease. Bioinformatics has become an integral element in rare cancer studies by overcoming the inability to collect a large enough study population. This study aimed to investigate and highlight crucial genes, pathways, miRNAs, and transcriptional factors (TF) on uLMS samples from five Gene Expression Omnibus datasets and The Cancer Genome Atlas Sarcoma study. Forty-one common differentially expressed genes (DEGs) were enriched and annotated by the DAVID software. With protein-protein interaction (PPI) network analysis, we selected ten hub genes that were validated with the TNMplotter web tool. We used the USCS Xena browser for survival analysis. We also predicted TF-gene and miRNA-gene regulatory networks along with potential drug molecules. TYMS and TK1 correlated with overall survival in uLMS patients. Finally, our results propose further validation of hub genes (TYMS and TK1), miR-26b-5p, and Sp1 as biomarkers of pathogenesis, prognosis, and differentiation of uLMS. Regarding the aggressive behavior and poor prognosis of uLMS, with the lack of standard therapeutic regimens, in our opinion, the results of our study provide enough evidence for further investigation of the molecular basis of uLMS occurrence and its implication in the diagnosis and therapy of this rare gynecological malignancy.

Identification and validation of hub genes and pathways associated with mitochondrial dysfunction in hypertrophy of ligamentum flavum.

Background: Lumbar spinal stenosis which can lead to irreversible neurologic damage and functional disability, is characterized by hypertrophy of ligamentum flavum (HLF). Recent studies have indicated that mitochondrial dysfunction may contribute to the development of HLF. However, the underlying mechanism is still unclear. Methods: The dataset GSE113212 was obtained from the Gene Expression Omnibus database, and the differentially expressed genes were identified. The intersection of DEGs and mitochondrial dysfunction-related genes were identified as mitochondrial dysfunction-related DEGs. Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and Gene Set Enrichment Analysis were performed. Protein-protein interaction network was constructed, and miRNAs and transcriptional factors of the hub genes were predicted via the miRNet database. Small molecule drugs targeted to these hub genes were predicted via PubChem. Immune infiltration analysis was performed to evaluate the infiltration level of immune cells and their correlation with the hub genes. In final, we measured the mitochondrial function and oxidative stress in vitro and verified the expression of hub genes by qPCR experiments. Results: In total, 43 genes were identified as MDRDEGs. These genes were mainly involved in cellular oxidation, catabolic processes, and the integrity of mitochondrial structure and function. The top hub genes were screened, including LONP1, TK2, SCO2, DBT, TFAM, MFN2. The most significant enriched pathways include cytokine-cytokine receptor interaction, focal adhesion, etc. Besides, SP1, PPARGC1A, YY1, MYC, PPARG, and STAT1 were predicted transcriptional factors of these hub genes. Additionally, increased immune infiltration was demonstrated in HLF, with a close correlation between hub genes and immune cells found. The mitochondrial dysfunction and the expression of hub genes were validated by evaluation of mitochondrial DNA, oxidative stress markers and quantitative real-time PCR. Conclusion: This study applied the integrative bioinformatics analysis and revealed the mitochondrial dysfunction-related key genes, regulatory pathways, TFs, miRNAs, and small molecules underlying the development of HLF, which improved the understanding of molecular mechanisms and the development of novel therapeutic targets for HLF.

Identification and validation of hub genes in drug induced acute kidney injury basing on integrated transcriptomic analysis.

Drug-induced acute kidney damage (DI-AKI) is a clinical phenomenon of rapid loss of kidney function over a brief period of time as a consequence of the using of medicines. The lack of a specialized treatment and the instability of traditional kidney injury markers to detect DI-AKI frequently result in the development of chronic kidney disease. Thus, it is crucial to continue screening for DI-AKI hub genes and specific biomarkers. Differentially expressed genes (DEGs) of group iohexol, cisplatin, and vancomycin's were analyzed using Limma package, and the intersection was calculated. DEGs were then put into String database to create a network of protein-protein interactions (PPI). Ten algorithms are used in the Cytohubba plugin to find the common hub genes. Three DI-AKI models' hub gene expression was verified in vivo and in vitro using PCR and western blot. To investigate the hub gene's potential as a biomarker, protein levels of mouse serum and urine were measured by ELISA kits. The UUO, IRI and aristolochic acid I-induced nephrotoxicity (AAN) datasets in the GEO database were utilized for external data verification by WGCNA and Limma package. Finally, the Elisa kit was used to identify DI-AKI patient samples. 95 up-regulated common DEGs and 32 down-regulated common DEGs were obtained using Limma package. A PPI network with 84 nodes and 24 edges was built with confidence >0.4. Four hub genes were obtained by Algorithms of Cytohubba plugin, including TLR4, AOC3, IRF4 and TNFAIP6. Then, we discovered that the protein and mRNA levels of four hub genes were significantly changed in the DI-AKI model in vivo and in vitro. External data validation revealed that only the AAN model, which also belonged to DI-AKI model, had significant difference in these hub genes, whereas IRI and UUO did not. Finally, we found that plasma TLR4 levels were higher in patients with DI-AKI, especially in vancomycin-induced AKI. The immune system and inflammation are key factors in DI-AKI. We discovered the immunological and inflammatory-related genes TLR4, AOC3, IRF4, and TNFAIP6, which may be promising specific biomarkers and essential hub genes for the prevention and identification of DI-AKI.

Identification and validation of hub genes in CD5-positive diffuse large B-cell lymphoma.

CD5+ diffuse large B-cell lymphoma (DLBCL), as a significant heterogeneity category of DLBCL, is reflected in both the molecular biological and genetic levels, which in turn induces ever-changing clinical manifestations, and what mediates tumor survival mechanisms are still unclear. This study aimed to predict the potential hub genes in CD5+ DLBCL. A total of 622 patients with DLBCL diagnosed between 2005 and 2019 were included. High expression of CD5 was correlated with IPI, LDH, and Ann Arbor stage, patients with CD5-DLBCL have longer overall survival. We identified 976 DEGs between CD5-negative and positive DLBCL patients in the GEO database and performed GO and KEGG enrichment analysis. After intersecting the genes obtained through the Cytohubba and MCODE, further external verification was performed in the TCGA database. Three hub genes were screened: VSTM2B, GRIA3, and CCND2, of which CCND2 were mainly involved in cell cycle regulation and JAK-STAT signaling pathways. Analysis of clinical samples showed that the expression of CCND2 was found to be correlated with CD5 (p = 0.001), and patients with overexpression of CCND2 in CD5+ DLBCL had poor prognosis (p = 0.0455). Cox risk regression analysis showed that, for DLBCL, CD5, and CCND2 double positive was an independent poor prognostic factor (HR: 2.545; 95% CI: 1.072-6.043; p = 0.034). These findings demonstrate that CD5 and CCND2 double-positive tumors should be stratified into specific subgroups of DLBCL with poor prognosis. CD5 may regulate CCND2 through JAK-STAT signaling pathways, mediating tumor survival. This study provides independent adverse prognostic factors for risk assessment and treatment strategies for newly diagnosed DLBCL.

Mining of Stress-related Genes in Pigmented and Non-pigmented Rice using Gene Co-expression Network and Clustering Approaches

Pigmented rice has been associated with stress-resistant traits, such as resistance to abiotic and biotic stresses, but the genes network that is responsible for such traits remains limited. Hence, this study aims to identify stress-related genes in the pigmented rice using computational approaches. The gene co-expression network was constructed using Pearson Correlation Coefficient (PCC) ≥ 0.9 among differentially expressed genes (DEGs) of trancriptomes between pigmented and non-pigmented rice. The gene co-expression network was clustered using Markov Cluster algorithm (MCL) to identify the functional modules and the hub genes for each module were determined. The functional analyses were performed to each module to determine the related gene ontology (GO) and pathway. Protein-protein interaction (PPI) from STRING database was used to validate the functional analyses. A total of 721 DEGs were used to construct the gene co-expression network of pigmented and non-pigmented rice varieties. Of these, 614 DEGs with 15,259 edges were identified by PCC. Using MCL, 10 clusters were identified in the gene co-expression network of pigmented and non-pigmented rice varieties. Three clusters were enriched with seven GO terms (i.e., response to stress, response to stimulus, transcription factor activity) that are related to stress-resistant traits, indicating the highly correlated genes were the stress-related genes. Interestingly, nine hub genes were found to be related to drought tolerance, disease resistance and hormone biosynthesis. Validation of hub genes using STRING database revealed that 48 hub genes were also connected in the PPI network, suggesting their potential as candidate proteins in stress-related traits. This study demonstrated that the molecular interaction network and the network clustering approach are efficient in identifying the stress-related genes, which could provide new insights in understanding plant responses to abiotic and biotic stresses.

Identification and validation of hub genes and potential drugs involved in osteoarthritis through bioinformatics analysis.

Purpose: Osteoarthritis (OA) is a common degenerative disease, which still lacks specific therapeutic drugs. Synovitis is one of the most important pathological process in OA. Therefore, we aim to identify and analyze the hub genes and their related networks of OA synovium with bioinformatics tools to provide theoretical basis for potential drugs. Materials and methods: Two datasets were obtained from GEO. DEGs and hub genes of OA synovial tissue were screened through Gene Ontology (GO) annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment as well as protein-protein interaction (PPI) network analysis. Subsequently, the correlation between expression of hub genes and ferroptosis or pyroptosis was analyzed. CeRNA regulatory network was constructed after predicting the upstream miRNAs and lncRNAs. The validation of hub genes was undertook through RT-qPCR and ELISA. Finally, potential drugs targeting pathways and hub genes were identified, followed by the validation of the effect of two potential drugs on OA. Results: A total of 161 commom DEGs were obtained, of which 8 genes were finally identified as hub genes through GO and KEGG enrichment analysis as well as PPI network analysis. Eight genes related to ferroptosis and pyroptosis respectively were significantly correlated to the expression of hub genes. 24 miRNAs and 69 lncRNAs were identified to construct the ceRNA regulatory network. The validation of EGR1, JUN, MYC, FOSL1, and FOSL2 met the trend of bioinformatics analysis. Etanercept and Iguratimod reduced the secretion of MMP-13 and ADAMTS5 of fibroblast-like synoviocyte. Conclusion: EGR1, JUN, MYC, FOSL1, and FOSL2 were identified as hub genes in the development of OA after series of bioinformatics analysis and validation. Etanercept and Iguratimod seemed to have opportunities to be novel drugs for OA.

Identification and Validation of Hub Genes in the Stenosis of Arteriovenous Fistula.

Arteriovenous fistula (AVF) is the most widely used hemodialysis vascular access in China. However, stenosis of the AVF limits its use. The mechanism of AVF stenosis is currently unknown. Therefore, the purpose of our study was to explore the mechanisms of AVF stenosis. In this study, we identified the differentially expressed genes (DEGs) based on the Gene Expression Omnibus (GEO) dataset (GSE39488) between venous segments of AVF and normal veins. A protein-protein interaction (PPI) network was constructed to identify hub genes of AVF stenosis. Finally, six hub genes (FOS, NR4A2, EGR2, CXCR4, ATF3, and SERPINE1) were found. Combined with the results of the PPI network analysis and literature search, FOS and NR4A2 were selected as the target genes for further investigation. We validated the bioinformatic results via reverse transcription PCR (RT-PCR) and Western blot analyses on human and rat samples. The expression levels of the mRNA and protein of FOS and NR4A2 were upregulated in both human and rat samples. In summary, we found that FOS may play an important role in AVF stenosis, which could be a potential therapeutic target of AVF stenosis.

Exploration and validation of hub genes in lung adenocarcinoma based on bioinformatics analysis.

Genomic abnormality is a crucial factor for lung cancer development. This study used bioinformatics analysis to explore the hub genes involved in lung adenocarcinoma. The GeneCards, Comparative Toxicogenomics Database (CTD), and DISEASES databases were used to screen the genes associated with lung adenocarcinoma. The hub genes were then identified using WebGestalt. The Cancer Genome Atlas (TCGA), UALCAN, and the Human Protein Atlas (HPA) were used to validate the expression of hub genes. The predictive effects of hub genes on the risk of lung adenocarcinoma were evaluated using receiver operating characteristic (ROC) curve analysis. The Tumor-Immune System Interaction Database (TISIDB) was used to estimate the correlation between hub genes and immune infiltration. A total of 21 genes were defined as common genes associated with lung adenocarcinoma, and from these, AKT1, CD44, and CDKN2A were identified as hub genes. Significant differences in the hub gene mRNA and protein expression were observed between lung adenocarcinoma samples and normal samples derived from the TCGA and UALCAN databases. The area under the ROC curve (AUC) for AKT1, CD44, and CDKN2A in predicting lung adenocarcinoma risk was 0.847, 0.880, and 0.805, respectively, with sensitivity of 89.8%, 93.2%, and 94.9%, respectively. TISIDB analysis indicated that AKT1, CD44, and CDKN2A expression had a strong relationship with immune infiltration in lung adenocarcinoma. These hub genes, AKT1, CD44, and CDKN2A, may represent tumor biomarkers that may contribute to the understanding, diagnosis, and treatment of lung adenocarcinoma.

Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis

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.