Protein-protein Interaction Network Construction Research Articles

Identification of key genes and signalling pathways in clear cell renal cell carcinoma: An integrated bioinformatics approach.

Clear cell Renal Cell Carcinoma (ccRCC) is one of the most prevalent types of kidney cancer. Unravelling the genes responsible for driving cellular changes and the transformation of cells in ccRCC pathogenesis is a complex process. In this study, twelve microarray ccRCC datasets were chosen from the gene expression omnibus (GEO) database and subjected to integrated analysis. Through GEO2R analysis, 179 common differentially expressed genes (DEGs) were identified among the datasets. The common DEGs were subjected to functional enrichment analysis using ToppFun followed by construction of protein-protein interaction network (PPIN) using Cytoscape. Clusters within the DEGs PPIN were identified using the Molecular Complex Detection (MCODE) Cytoscape plugin. To identify the hub genes, the centrality parameters degree, betweenness, and closeness scores were calculated for each DEGs in the PPIN. Additionally, Gene Expression Profiling Interactive Analysis (GEPIA) was utilized to validate the relative expression levels of hub genes in the normal and ccRCC tissues. The common DEGs were highly enriched in Hypoxia-inducible factor (HIF) signalling and metabolic reprogramming pathways. VEGFA, CAV1, LOX, CCND1, PLG, EGF, SLC2A1, and ENO2 were identified as hub genes. Among 8 hub genes, only the expression levels of VEGFA, LOX, CCND1, and EGF showed a unique expression pattern exclusively in ccRCC on compared to other type of cancers.

Genetic associations in ankylosing spondylitis: circulating proteins as drug targets and biomarkers.

Ankylosing spondylitis (AS) is a complex condition with a significant genetic component. This study explored circulating proteins as potential genetic drug targets or biomarkers to prevent AS, addressing the need for innovative and safe treatments. We analyzed extensive data from protein quantitative trait loci (pQTLs) with up to 1,949 instrumental variables (IVs) and selected the top single-nucleotide polymorphism (SNP) associated with AS risk. Utilizing a two-sample Mendelian randomization (MR) approach, we assessed the causal relationships between identified proteins and AS risk. Colocalization analysis, functional enrichment, and construction of protein-protein interaction networks further supported these findings. We utilized phenome-wide MR (phenMR) analysis for broader validation and repurposing of drugs targeting these proteins. The Drug-Gene Interaction database (DGIdb) was employed to corroborate drug associations with potential therapeutic targets. Additionally, molecular docking (MD) techniques were applied to evaluate the interaction between target protein and four potential AS drugs identified from the DGIdb. Our analysis identified 1,654 plasma proteins linked to AS, with 868 up-regulated and 786 down-regulated. 18 proteins (AGER, AIF1, ATF6B, C4A, CFB, CLIC1, COL11A2, ERAP1, HLA-DQA2, HSPA1L, IL23R, LILRB3, MAPK14, MICA, MICB, MPIG6B, TNXB, and VARS1) that show promise as therapeutic targets for AS or biomarkers, especially MAPK14, supported by evidence of colocalization. PhenMR analysis linked these proteins to AS and other diseases, while DGIdb analysis identified potential drugs related to MAPK14. MD analysis indicated strong binding affinities between MAPK14 and four potential AS drugs, suggesting effective target-drug interactions. This study underscores the utility of MR analysis in AS research for identifying biomarkers and therapeutic drug targets. The involvement of Th17 cell differentiation-related proteins in AS pathogenesis is particularly notable. Clinical validation and further investigation are essential for future applications.

Exploring the Molecular Mechanisms of Huaier on Modulating Metabolic Reprogramming of Hepatocellular Carcinoma: A Study based on Network Pharmacology, Molecular Docking and Bioinformatics.

Huaier (Trametes robiniophila Murr), a traditional Chinese medicine, is widely used in China as a complementary and alternative therapy to treat hepatocellular carcinoma (HCC). Past studies have shown that Huaier can arrest the cell cycle, promote apoptosis and inhibit the proliferation of cancer cells. However, how it regulates the metabolism of HCC is still unclear. This study explores the metabolic-related function of Huaier in treating HCC with an in-silico approach. A network pharmacology and bioinformatics-based approach was employed to investigate the molecular pathogenesis of metabolic reprogramming in HCC with Huaier. The compounds of Huaier were obtained from public databases. Oral bioavailability and drug likeness were screened using the TCMSP platform. The differential gene expressions between HCC and non-tumor tissue were calculated and used to find the overlap from the targets of Huaier. The enrichment analysis of the overlapped targets by Metascape helped filter out the metabolism-related targets of Huaier in treating HCC. Protein-protein interaction (PPI) network construction and topological screening revealed the hub nodes. The prognosis and clinical correlation of these targets were validated from the cancer genome atlas (TCGA) database, and the interactions between the hub nodes and active ingredients were validated by molecular docking. The results showed that Peroxyergosterol, Daucosterol, and Kaempferol were the primary active compounds of Huaier involved in the metabolic reprogramming of HCC. The top 6 metabolic targets included AKR1C3, CYP1A1, CYP3A4, CYP1A2, CYP17A1, and HSD11B1. The decreased expression of CYP3A4 and increased expression of AKR1C3 were related to the poor overall survival of HCC patients. The molecular docking validated that Peroxyergosterol and Kaempferol exhibited the potential to modulate CYP3A4 and AKR1C3 from a computational perspective. This study provided a workflow for understanding the mechanism of Huaier in regulating the metabolic reprogramming of HCC.

Identification of bioactive ingredients and potential mechanisms of flowers of Hosta Plantaginea in treating pneumonia based on network pharmacology and experimental validation

Flowers of Hosta plantaginea (H. plantaginea), a widely utilized medicinal herb in Mongolian medicine, holds a significant historical background in terms of its medicinal applications. This herb is renowned for its ability to clear heat and detoxify the body, alleviate cough, and provide relief to the throat. However, the active ingredients and the potential mechanism of action remain ambiguity. The objective of this study was to conduct a comprehensive analysis of the efficacy of H. plantaginea in treating pneumonia, identify its active ingredients and unveil the pharmacological mechanism in the treatment of pneumonia. In vivo experiments demonstrate the plant's anti-pneumonia properties, while mass spectrometry analysis identifies 62 chemicals, with 14 absorbed into the bloodstream. Network pharmacology and Venn analysis reveal 195 targets of 52 active ingredients, with 49 gene targets common to H. plantaginea and pneumonia. Protein-protein interaction (PPI) network construction and enrichment analysis highlight the key targets and pathways such as AKT, EGFR, IL-17. Western blotting confirms downregulation of these pathways, indicating the anti-inflammatory effects of H. plantaginea in treating acute lung injury. Our findings showed that the treatment of ALI with the H. plantaginea exerts its anti-inflammatory effects through multiple components, targets, and pathways. This study established a solid experimental foundation for investigating the various components, targets, and pathways involved in the treatment of pneumonia using H. plantaginea. It offers valuable insights from multiple perspectives and can serve as a reference for the clinical application of this plant in pneumonia treatment.

Identification of potential hub genes and regulatory networks of smoking-related endothelial dysfunction in atherosclerosis using bioinformatics analysis.

Endothelial dysfunction, the earliest stage of atherosclerosis, can be caused by smoking, but its molecular mechanism requires further investigation. This study aimed to use bioinformatics analysis to identify potential mechanisms involved in smoking-related atherosclerotic endothelial dysfunction. The transcriptome data used for this bioinformatics analysis were obtained from the Gene Expression Omnibus (GEO) database. The GSE137578 and GSE141136 datasets were used to identify common differentially expressed genes (co-DEGs) in endothelial cells treated with oxidized low-density lipoprotein (ox-LDL) and tobacco. The co-DEGs were annotated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG) databases. Additionally, a protein-protein interaction (PPI) network was constructed to visualize their interactions and screen for hub genes. GSE120521 dataset was used to verify the expression of hub genes in unstable plaques. The miRNA expression profile GSE137580 and online databases (starBase 2.0, TargetScan 8.0 and DGIdb v4.2.0) were used to predict the related non-coding RNAs and drugs. A total of 232 co-DEGs were identified, including 113 up-regulated genes and 119 down-regulated genes. These DEGs were primarily enriched in detrimental autophagy, cell death, transcription factors, and cytokines, and were implicated in ferroptosis, abnormal lipid metabolism, inflammation, and oxidative stress pathways. Ten hub genes were screened from the constructed PPI network, including up-regulated genes such as FOS, HMOX1, SQSTM1, PTGS2, ATF3, DDIT3, and down-regulated genes MCM4, KIF15, UHRF1, and CCL2. Importantly, HMOX1 was further up-regulated in unstable plaques (p= 0.034). Finally, a regulatory network involving lncRNA/circRNA-miRNA-hub genes and drug-hub genes was established. Atherosclerotic endothelial dysfunction is associated with smoking-induced injury. Through bioinformatics analysis, we identified potential mechanisms and provided potential therapeutic targets.

Evaluation of the Expression Level of Some Coding and Non-coding Genes in Breast Cancer Samples Based on Bioinformatics and Laboratory Studies

Background: Breast cancer (BC) is the leading cause of cancer-associated mortality in women worldwide. However, the molecular mechanism underlying the process is still unclear. In this regard, bioinformatics studies play a decisive role in facilitating the path of biological investigations and can ultimately lead to the identification of better molecular candidates for further study. Objectives: Due to the abnormal expression of many coding and non-coding genes in all types of cancers and their relationship with various mechanisms of carcinogenesis, this study aimed at evaluating the expression levels of certain coding and non-coding genes involved in BC based on bioinformatics findings and laboratory investigations. Methods: Gene expression dataset, module extraction, functional enrichment analysis, protein-protein interaction network construction, and RT-qPCR were performed based on bioinformatics methods and laboratory investigations. Additionally, the promoter region mutations of these genes were investigated, using sequencing of extracted DNAs from formalin-fixed paraffin-embedded (FFPE) tumor tissues. Results: A module was selected as a candidate for further investigation. Estrogen receptor 1 (ESR1) and forkhead box A1 (FOXA1) showed the highest degrees in the PPI network with 9 and 7 links, respectively. Furthermore, the expression levels of the FOXA1 gene, RNA component of mitochondrial RNA processing endoribonuclease (RMRP), and nuclear enriched abundant transcript 1 (NEAT1) were significantly upregulated in the tumor group compared to the control group (in order, P = 0.044, P = 0.014, and P = 0.0004). The tumors of patients with positive metastasis displayed significantly higher levels of NEAT1 and RMRP expression compared to those of negative metastasis samples (P < 0.05). Moreover, the expression level of RMRP dramatically decreased in HER2-positive patients compared to negative samples (P = 0.011). Finally, no mutations were observed in the promoter sequencing of positive metastasis samples compared to normal samples. Conclusions: The upregulation levels of all three examined genes may correlate with BC progression. Therefore, they could potentially be used as biomarkers for detecting BC development.

Construction of protein-protein interaction network in sulfate-reducing bacteria: Unveiling of global response to Hg

Sulfate-reducing bacteria (SRB) play pivotal roles in the biotransformation of mercury (Hg). However, unrevealed global responses of SRB to Hg have restricted our understanding of details of Hg biotransformation processes. The absence of protein-protein interaction (PPI) network under Hg stimuli has been a bottleneck of proteomic analysis for molecular mechanisms of Hg transformation. This study constructed the first comprehensive PPI network of SRB in response to Hg, encompassing 67 connected nodes, 26 independent nodes, and 121 edges, covering 93% of differentially expressed proteins from both previous studies and this study. The network suggested that proteomic changes of SRB in response to Hg occurred globally, including microbial metabolism in diverse environments, carbon metabolism, nucleic acid metabolism and translation, nucleic acid repair, transport systems, nitrogen metabolism, and methyltransferase activity, partial of which could cover the known knowledge. Antibiotic resistance was the original response revealed by this network, providing insights into of Hg biotransformation mechanisms. This study firstly provided the foundational network for a comprehensive understanding of SRB's responses to Hg, convenient for exploration of potential targets for Hg biotransformation. Furthermore, the network indicated that Hg enhances the metabolic activities and modification pathways of SRB to maintain cellular activities, shedding light on the influences of Hg on the carbon, nitrogen, and sulfur cycles at the cellular level.

Role of CENPF and NDC80 in the rehabilitation nursing of hepatocellular carcinoma and cirrhosis: An observational study.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors globally and often develops on the foundation of chronic liver disease or cirrhosis. Cirrhosis is a clinically prevalent chronic progressive liver disease characterized by diffuse liver damage resulting from long-term or repeated actions of 1 or more etiological factors. However, the impact of CENPF and nuclear division cycle 80 (NDC80) genes on rehabilitation nursing of HCC and cirrhosis remains unclear. HCC and cirrhosis datasets GSE63898 and GSE89377 profile files were downloaded from the gene expression omnibus database generated on platforms GPL13667 and GPL6947, respectively. Differentially expressed genes (DEGs) screening, weighted gene co-expression network analysis (WGCNA), construction and analysis of protein-protein interaction (PPI) networks, functional enrichment analysis, gene set enrichment analysis (GSEA), survival analysis, immune infiltration analysis, and comparative toxicogenomics database (CTD) analysis were conducted. Gene expression heatmaps were plotted. miRNAs regulating central DEGs were selected through TargetScan. A total of 626 DEGs were identified. According to gene ontology (GO) analysis, they were primarily enriched in small molecule metabolic processes, drug metabolic processes, binding of identical proteins, and lipid metabolic processes. Kyoto Encyclopedia of Gene and Genome (KEGG) analysis results indicated that the target genes were mainly enriched in metabolic pathways, phagosomes, glycine, serine, and threonine metabolism. The construction and analysis of the PPI network revealed 3 core genes (NDC80, CENPF, RRM2). Gene expression heatmaps showed that core genes (CENPF, NDC80) were highly expressed in HCC and cirrhosis samples. CTD analysis found that 2 genes (CENPF and NDC80) were associated with liver, jaundice, ascites, fever, dyspepsia, and hepatic encephalopathy. CENPF and NDC80 are highly expressed in HCC and cirrhosis, and CENPF and NDC80 might be the biomarkers of rehabilitation nursing of HCC and cirrhosis.

Transcriptomic analysis revealed potential regulatory biomarkers and repurposable drugs for breast cancer treatment.

Breast cancer (BC) is the most widespread cancer worldwide. Over 2 million new cases of BC were identified in 2020 alone. Despite previous studies, the lack of specific biomarkers and signaling pathways implicated in BC impedes the development of potential therapeutic strategies. We employed several RNAseq datasets to extract differentially expressed genes (DEGs) based on the intersection of all datasets, followed by protein-protein interaction network construction. Using the shared DEGs, we also identified significant gene ontology (GO) and KEGG pathways to understand the signaling pathways involved in BC development. A molecular docking simulation was performed to explore potential interactions between proteins and drugs. The intersection of the four datasets resulted in 146 DEGs common, including AURKB, PLK1, TTK, UBE2C, CDCA8, KIF15, and CDC45 that are significant hub-proteins associated with breastcancer development. These genes are crucial in complement activation, mitotic cytokinesis, aging, and cancer development. We identified key microRNAs (i.e., hsa-miR-16-5p, hsa-miR-1-3p, hsa-miR-147a, hsa-miR-195-5p, and hsa-miR-155-5p) that are associated with aggressive tumor behavior and poor clinical outcomes in BC. Notable transcription factors (TFs) were FOXC1, GATA2, FOXL1, ZNF24 and NR2F6. These biomarkers are involved in regulating cancer cell proliferation, invasion, and migration. Finally, molecular docking suggested Hesperidin, 2-amino-isoxazolopyridines, and NMS-P715 as potential lead compounds against BC progression. We believe that these findings will provide important insight into the BC progression as well as potential biomarkers and drug candidates for therapeutic development.

Identification of Diagnostic Genes of Aortic Stenosis That Progresses from Aortic Valve Sclerosis.

Aortic valve sclerosis (AVS) is a pathological state that can progress to aortic stenosis (AS), which is a high-mortality valvular disease. However, effective medical therapies are not available to prevent this progression. This study aimed to explore potential biomarkers of AVS-AS advancement. A microarray dataset and an RNA-sequencing dataset were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened from AS and AVS samples. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning model construction were conducted to identify diagnostic genes. A receiver operating characteristic (ROC) curve was generated to evaluate diagnostic value. Immune cell infiltration was then used to analyze differences in immune cell proportion between tissues. Finally, immunohistochemistry was applied to further verify protein concentration of diagnostic factors. A total of 330 DEGs were identified, including 92 downregulated and 238 upregulated genes. The top 5% of DEGs (n = 17) were screened following construction of a PPI network. IL-7 and VCAM-1 were identified as the most significant candidate genes via least absolute shrinkage and selection operator (LASSO) regression. The diagnostic value of the model and each gene were above 0.75. Proportion of anti-inflammatory M2 macrophages was lower, but the fraction of pro-inflammatory gamma-delta T cells was elevated in AS samples. Finally, levels of IL-7 and VCAM-1 were validated to be higher in AS tissue than in AVS tissue using immunohistochemistry. IL-7 and VCAM-1 were identified as biomarkers during the disease progression. This is the first study to analyze gene expression differences between AVS and AS and could open novel sights for future studies on alleviating or preventing the disease progression.

​Comprehensive mendelian randomization analysis of plasma proteomics to identify new therapeutic targets for the treatment of coronary heart disease and myocardial infarction.

Ischemic heart disease is one of the leading causes of mortality worldwide, and thus calls for development of more effective therapeutic strategies. This study aimed to identify potential therapeutic targets for coronary heart disease (CHD) and myocardial infarction (MI) by investigating the causal relationship between plasma proteins and these conditions. A two-sample Mendelian randomization (MR) study was performed to evaluate more than 1600 plasma proteins for their causal associations with CHD and MI. The MR findings were further confirmed through Bayesian colocalization, Summary-data-based Mendelian Randomization (SMR), and Transcriptome-Wide Association Studies (TWAS) analyses. Further analyses, including enrichment analysis, single-cell analysis, MR analysis of cardiovascular risk factors, phenome-wide Mendelian Randomization (Phe-MR), and protein-protein interaction (PPI) network construction were conducted to verify the roles of selected causal proteins. Thirteen proteins were causally associated with CHD, seven of which were also causal for MI. Among them, FES and PCSK9 were causal proteins for both diseases as determined by several analytical methods. PCSK9 was a risk factor of CHD (OR = 1.25, 95% CI: 1.13-1.38, P = 7.47E-06) and MI (OR = 1.36, 95% CI: 1.21-1.54, P = 2.30E-07), whereas FES was protective against CHD (OR = 0.68, 95% CI: 0.59-0.79, P = 6.40E-07) and MI (OR = 0.65, 95% CI: 0.54-0.77, P = 5.38E-07). Further validation through enrichment and single-cell analysis confirmed the causal effects of these proteins. Moreover, MR analysis of cardiovascular risk factors, Phe-MR, and PPI network provided insights into the potential drug development based on the proteins. This study investigated the causal pathways associated with CHD and MI, highlighting the protective and risk roles of FES and PCSK9, respectively. FES. Specifically, the results showed that these proteins are promising therapeutic targets for future drug development.

High expression of CASP1 induces atherosclerosis.

Atherosclerosis is a chronic, progressive vascular disease. The relationship between CASP1 gene expression and atherosclerosis remains unclear. The atherosclerosis dataset GSE132651 and GSE202625 profiles were downloaded from gene expression omnibus. Differentially expressed genes (DEGs) were screened. The construction and analysis of protein-protein interaction network, functional enrichment analysis, gene set enrichment analysis, and Comparative Toxicogenomics Database analysis were performed. Gene expression heatmap was drawn. TargetScan was used to screen miRNAs that regulate central DEG. 47 DEGs were identified. According to gene ontology analysis, they were mainly enriched in the regulation of stimulus response, response to organic matter, extracellular region, extracellular region, and the same protein binding. Kyoto Encyclopedia of Gene and Genome analysis results showed that the target cells were mainly enriched in the PI3K-Akt signaling pathway, Ras signaling pathway, and PPAR signaling pathway. In the enrichment project of Metascape, vascular development, regulation of body fluid levels, and positive regulation of cell motility can be seen in the gene ontology enrichment project. Eleven core genes (CASP1, NLRP3, MRC1, IRS1, PPARG, APOE, IL13, FGF2, CCR2, ICAM1, HIF1A) were obtained. IRS1, PPARG, APOE, FGF2, CCR2, and HIF1A genes are identified as core genes. Gene expression heatmap showed that CASP1 was highly expressed in atherosclerosis samples and low expressed in normal samples. NLRP3, MRC1, IRS1, PPARG, APOE, IL13, FGF2, CCR2, ICAM1, HIF1A were low expressed in atherosclerosis samples. CTD analysis showed that 5 genes (CASP1, NLRP3, CCR2, ICAM1, HIF1A) were found to be associated with pneumonia, inflammation, cardiac enlargement, and tumor invasiveness. CASP1 gene is highly expressed in atherosclerosis. The higher the CASP1 gene, the worse the prognosis.

CDK1 and CCNA2 play important roles in oral squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC) is a malignant tumor that occurs in oral cavity and is dominated by squamous cells. The relationship between CDK1, CCNA2, and OSCC is still unclear. The OSCC datasets GSE74530 and GSE85195 configuration files were downloaded from the Gene Expression Omnibus (GEO) database and were derived from platforms GPL570 and GPL6480. Differentially expressed genes (DEGs) were screened. The weighted gene co-expression network analysis, functional enrichment analysis, gene set enrichment analysis, construction and analysis of protein-protein interaction (PPI) network, Comparative Toxicogenomics Database analysis were performed. Gene expression heatmap was drawn. TargetScan was used to screen miRNAs that regulate central DEGs. A total of 1756 DEGs were identified. According to Gene Ontology (GO) analysis, they were predominantly enriched in processes related to organic acid catabolic metabolism, centromeric, and chromosomal region condensation, and oxidoreductase activity. In Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, the DEGs were mainly concentrated in metabolic pathways, P53 signaling pathway, and PPAR signaling pathway. Weighted gene co-expression network analysis was performed with a soft-thresholding power set at 9, leading to the identification of 6 core genes (BUB1B, CCNB1, KIF20A, CCNA2, CDCA8, CDK1). The gene expression heatmap revealed that core genes (CDK1, CCNA2) were highly expressed in OSCC samples. Comparative Toxicogenomics Database analysis demonstrated associations between the 6 genes (BUB1B, CCNB1, KIF20A, CCNA2, CDCA8, CDK1) and oral tumors, precancerous lesions, inflammation, immune system disorders, and tongue tumors. The associated miRNAs for CDK1 gene were hsa-miR-203a-3p.2, while for CCNA2 gene, they were hsa-miR-6766-3p, hsa-miR-4782-3p, and hsa-miR-219a-5p. CDK1 and CCNA2 are highly expressed in OSCC. The higher the expression of CDK1 and CCNA2, the worse the prognosis.

Integrative analysis based on the cell cycle-related genes identifies TPX2 as a novel prognostic biomarker associated with tumor immunity in breast cancer.

This study aims to identify the essential cell cycle-related genes associated with prognosis in breast cancer (BRCA), and to verify the relationship between the central gene and immune infiltration, so as to provide detailed and comprehensive information for the treatment of BRCA. Gene expression profiles (GSE10780, GSE21422, GSE61304) and the Cancer Genome Atlas (TCGA) BRCA data were used to identify differentially expressed genes (DEGs) and further functional enrichment analysis. STRING and Cytoscape were employed for the protein-protein interaction (PPI) network construction. TPX2 was viewed as the crucial prognostic gene by the Survival and Cox analysis. Furthermore, the connection between TPX2 expression and immune infiltrating cells and immune checkpoints in BRCA was also performed by the TIMER online database and R software. A total of 18 cell cycle-related DEGs were identified in this study. Subsequently, an intersection analysis based on TCGA-BRCA prognostic genes and the above DEGs identified three genes (TPX2, UBE2C, CCNE2) as crucial prognostic candidate biomarkers. Moreover, we also demonstrated that TPX2 is closely associated with immune infiltration in BRCA and a positive relation between TPX2 and PD-L1 expression was firstly detected. These results revealed that TPX2 is a potential prognostic biomarker and closely correlated with immune infiltration in BRCA, which could provide powerful and efficient strategies for breast cancer immunotherapy.

ITGAM is a critical gene in ischemic stroke.

Globally, ischemic stroke (IS) is ranked as the second most prevailing cause of mortality and is considered lethal to human health. This study aimed to identify genes and pathways involved in the onset and progression of IS. GSE16561 and GSE22255 were downloaded from the Gene Expression Omnibus (GEO) database, merged, and subjected to batch effect removal using the ComBat method. The limma package was employed to identify the differentially expressed genes (DEGs), followed by enrichment analysis and protein-protein interaction (PPI) network construction. Afterward, the cytoHubba plugin was utilized to screen the hub genes. Finally, a ROC curve was generated to investigate the diagnostic value of hub genes. Validation analysis through a series of experiments including qPCR, Western blotting, TUNEL, and flow cytometry was performed. The analysis incorporated 59 IS samples and 44 control samples, revealing 226 DEGs, of which 152 were up-regulated and 74 were down-regulated. These DEGs were revealed to be linked with the inflammatory and immune responses through enrichment analyses. Overall, the ROC analysis revealed the remarkable diagnostic potential of ITGAM and MMP9 for IS. Quantitative assessment of these genes showed significant overexpression in IS patients. ITGAM modulation influenced the secretion of critical inflammatory cytokines, such as IL-1β, IL-6, and TNF-α, and had a distinct impact on neuronal apoptosis. The inflammation and immune response were identified as potential pathological mechanisms of IS by bioinformatics and experiments. In addition, ITGAM may be considered a potential therapeutic target for IS.

Mining and exploration of rehabilitation nursing targets for colorectal cancer.

There are often subtle early symptoms of colorectal cancer, a common malignancy of the intestinal tract. However, it is not yet clear how MYC and NCAPG2 are involved in colorectal cancer. We obtained colorectal cancer datasets GSE32323 and GSE113513 from the Gene Expression Omnibus (GEO). After downloading, we identified differentially expressed genes (DEGs) and performed Weighted Gene Co-expression Network Analysis (WGCNA). We then undertook functional enrichment assay, gene set enrichment assay (GSEA) and immune infiltration assay. Protein-protein interaction (PPI) network construction and analysis were undertaken. Survival analysis and Comparative Toxicogenomics Database (CTD) analysis were conducted. A gene expression heat map was generated. We used TargetScan to identify miRNAs that are regulators of DEGs. 1117 DEGs were identified. Their predominant enrichment in activities like the cellular phase of the cell cycle, in cell proliferation, in nuclear and cytoplasmic localisation and in binding to protein-containing complexes was revealed by Gene Ontology (GO). When the enrichment data from GSE32323 and GSE113513 colon cancer datasets were merged, the primary enriched DEGs were linked to the cell cycle, protein complex, cell cycle control, calcium signalling and P53 signalling pathways. In particular, MYC, MAD2L1, CENPF, UBE2C, NUF2 and NCAPG2 were identified as highly expressed in colorectal cancer samples. Comparative Toxicogenomics Database (CTD) demonstrated that the core genes were implicated in the following processes: colorectal neoplasia, tumour cell transformation, inflammation and necrosis. High MYC and NCAPG2 expression has been observed in colorectal cancer, and increased MYC and NCAPG2 expression correlates with worse prognosis.

Novel insights into immune-related genes associated with type 2 diabetes mellitus-related cognitive impairment

BACKGROUND The cognitive impairment in type 2 diabetes mellitus (T2DM) is a multifaceted and advancing state that requires further exploration to fully comprehend. Neuroinflammation is considered to be one of the main mechanisms and the immune system has played a vital role in the progression of the disease. AIM To identify and validate the immune-related genes in the hippocampus associated with T2DM-related cognitive impairment. METHODS To identify differentially expressed genes (DEGs) between T2DM and controls, we used data from the Gene Expression Omnibus database GSE125387. To identify T2DM module genes, we used Weighted Gene Co-Expression Network Analysis. All the genes were subject to Gene Set Enrichment Analysis. Protein-protein interaction network construction and machine learning were utilized to identify three hub genes. Immune cell infiltration analysis was performed. The three hub genes were validated in GSE152539 via receiver operating characteristic curve analysis. Validation experiments including reverse transcription quantitative real-time PCR, Western blotting and immunohistochemistry were conducted both in vivo and in vitro . To identify potential drugs associated with hub genes, we used the Comparative Toxicogenomics Database (CTD). RESULTS A total of 576 DEGs were identified using GSE125387. By taking the intersection of DEGs, T2DM module genes, and immune-related genes, a total of 59 genes associated with the immune system were identified. Afterward, machine learning was utilized to identify three hub genes (H2-T24 , Rac3 , and Tfrc ). The hub genes were associated with a variety of immune cells. The three hub genes were validated in GSE152539. Validation experiments were conducted at the mRNA and protein levels both in vivo and in vitro , consistent with the bioinformatics analysis. Additionally, 11 potential drugs associated with RAC3 and TFRC were identified based on the CTD. CONCLUSION Immune-related genes that differ in expression in the hippocampus are closely linked to microglia. We validated the expression of three hub genes both in vivo and in vitro , consistent with our bioinformatics results. We discovered 11 compounds associated with RAC3 and TFRC . These findings suggest that they are co-regulatory molecules of immunometabolism in diabetic cognitive impairment.

Exploring the roles and potential therapeutic strategies of inflammation and metabolism in the pathogenesis of vitiligo: a mendelian randomization and bioinformatics-based investigation.

Vitiligo, a common autoimmune acquired pigmentary skin disorder, poses challenges due to its unclear pathogenesis. Evidence suggests inflammation and metabolism's pivotal roles in its onset and progression. This study aims to elucidate the causal relationships between vitiligo and inflammatory proteins, immune cells, and metabolites, exploring bidirectional associations and potential drug targets. Mendelian Randomization (MR) analysis encompassed 4,907 plasma proteins, 91 inflammatory proteins, 731 immune cell features, and 1400 metabolites. Bioinformatics analysis included Protein-Protein Interaction (PPI) network construction, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Subnetwork discovery and hub protein identification utilized the Molecular Complex Detection (MCODE) plugin. Colocalization analysis and drug target exploration, including molecular docking validation, were performed. MR analysis identified 49 proteins, 39 immune cell features, and 59 metabolites causally related to vitiligo. Bioinformatics analysis revealed significant involvement in PPI, GO enrichment, and KEGG pathways. Subnetwork analysis identified six central proteins, with Interferon Regulatory Factor 3 (IRF3) exhibiting strong colocalization evidence. Molecular docking validated Piceatannol's binding to IRF3, indicating a stable interaction. This study comprehensively elucidates inflammation, immune response, and metabolism's intricate involvement in vitiligo pathogenesis. Identified proteins and pathways offer potential therapeutic targets, with IRF3 emerging as a promising candidate. These findings deepen our understanding of vitiligo's etiology, informing future research and drug development endeavors.

Identification of the Hub Genes Involved in Chikungunya Viral Infection.

Background Chikungunya virus (CHIKV) infection poses a significant global health threat, necessitating a deeper understanding of its molecular mechanisms for effective management and treatment. This study aimed to understand the molecular and geneticmechanisms of CHIKV infection by analyzing microarray expression data. Methodology National Center for Biotechnology Information (NCBI) GEO2R with an adjusted p-value cut-off of <0.05 and |log2FC ≥ 1.5| was used to identify the differentially expressed genes involved in CHIKV infection using microarray data from the Gene Expression Omnibus (GEO) database, followed by enrichment analysis, protein-protein interaction (PPI) network construction, and, finally, hub gene identification. Results Analysis of the microarray dataset revealed 25 highly significant differentially expressed genes (DEGs), including 21 upregulated and four downregulated genes. PPI network analysis elucidated interactions among these DEGs, with hub genes such as ACTB and CTNNB1 exhibiting central roles. Enrichment analysis identified crucial pathways, including leukocyte transendothelial migration, regulation of actin cytoskeleton, and thyroid hormone signaling, implicating their involvement in CHIKV infection. Furthermore, the study highlights potential therapeutic targets such as ACTB and CTNNB1, which showed significant upregulation in infected cells. Conclusions These findings underscore the complex interplay between viral infection and host cellular processes, shedding light on novel avenues for diagnostic marker discovery and advancing antiviral strategies. In this study, we shed light on the molecular and genetic mechanisms of CHIKV infection and the potential role of ACTB and CTNNB1 genes.

The Molecular Landscape of Lung Metastasis in Primary Head and Neck Squamous Cell Carcinomas.

Background Lung metastasis in head and neck cancer (HNC) patients is a critical concern, often indicating an advanced disease stage and a poor prognosis. This study explores the molecular complexities of such metastases, identifying specific genes and pathways that may serve as valuable targets for diagnosis and treatment. The findings underscore the potential for significantly improved patient outcomes through targeted therapeutic strategies. Methodology In this research, we systematically collected raw gene expression data from head and neck squamous cell carcinoma (HNSCC) and lung squamous cell carcinoma (LSCC). By comparing tumorous and normal gene expression profiles from paired patient samples, we identified differentially expressed genes (DEGs). Network analysis helped visualize protein interactions and pinpoint crucial hub genes. Through validation and comparison across several datasets, we identified common DEGs. Additionally, we employed Kaplan-Meier analysis and log-rank testing to examine the relationship between gene expression patterns and patient survival. Result The study identified 145 overlapping DEGs in both HNSCC and LSCC, which are crucial for cancer progression and linked to lung metastasis, offering vital targets for personalized therapy by identifying key genes affecting disease development and patient survival. Pathway analyses linked these to lung metastasis, while protein-protein interaction network construction and hub gene identification highlighted genes crucial for development and patient survival, offering targets for personalized therapy. Conclusion Identifying key genes and pathways in lung metastasis from HNC, this study highlights potential targets for enhanced diagnosis and therapy. It underscores the crucial role of molecular insights in driving forward personalized treatment approaches and improving patient outcomes.