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Purpose: Osteoarthritis (OA) is a prevalent cause of disability in older adults. Identifying diagnostic markers for OA is essential for elucidating its mechanisms and facilitating early diagnosis. Methods: We analyzed 53 synovial tissue samples (n = 30 for OA, n = 23 for the control group) from two datasets in the Gene Express Omnibus (GEO) database. We identified differentially expressed genes (DEGs) between the groups and applied dimensionality reduction using six machine learning algorithms to pinpoint characteristic genes (key genes). We classified the OA samples into subtypes based on these key genes and explored the differences in biological functions and immune characteristics among subtypes, as well as the roles of the key genes. Additionally, we constructed a protein-protein interaction network to predict small molecules that target these genes. Further, we accessed synovial tissue sample data from the single-cell RNA dataset GSE152805, categorized the cells into various types, and examined variations in gene expression and their correlation with OA progression. Validation of key gene expression was conducted in cellular experiments using the qPCR method. Results: Four genes AGMAT, MAP3K8, PER1, and XIST, were identified as characteristic genes of OA. All can independently predict the occurrence of OA. With these genes, the OA samples can be clustered into two subtypes, which showed significant differences in functional pathways and immune infiltration. Eight cell types were obtained by analyzing the single-cell RNA data, with synovial intimal fibroblasts (SIF) accounting for the highest proportion in each sample. The key genes were found over-expressed in SIF and significantly correlated with OA progression and the content of immune cells (ICs). We validated the relative levels of key genes in OA and normal cartilage tissue cells, which showed an expression trend consistency with the bioinformatics result except for XIST. Conclusion: Four genes, AGMAT, MAP3K8, PER1, and XIST are closely related to the progression of OA, and play as diagnostic and predictive markers in early OA.

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BACKGROUND: Our previous study demonstrated that long intergenic noncoding RNA 02454 (LINC02454) may act as an oncogene to promote the proliferation and inhibit the apoptosis of papillary thyroid cancer (PTC) cells. This study was designed to investigate the mechanisms whereby LINC02454 is related to PTC tumorigenesis. METHODS: Thyroid cancer RNA sequence data were obtained from The Cancer Genome Atlas (TCGA) database. Weighted gene coexpression network analysis (WGCNA) was applied to identify modules closely associated with PTC. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was used to identify the key pathways, and the maximal clique centrality (MCC) topological method was used to identify the hub genes. The Gene Expression Profiling Interactive Analysis (GEPIA) database was used to compare expression levels of key genes between PTC samples and normal samples and explore the prognostic value of key genes. The key genes were further validated with GEO dataset. RESULTS: The top 5000 variable genes were investigated, followed by an analysis of 8 modules, and the turquoise module was the most positively correlated with the clinical stage of PTC. KEGG pathway analysis found the top two pathways of the ECM - receptor interaction and MAPK signaling pathway. In addition, five key genes (FN1, LAMB3, ITGA3, SDC4, and IL1RAP) were identified through the MCC algorithm and KEGG analysis. The expression levels of the five key genes were significantly upregulated in thyroid cancer in both TCGA and GEO datasets, and of these five genes, FN1 and ITGA3 were associated with poor disease-free prognosis. CONCLUSIONS: Our study identified five key genes and two key pathways associated with LINC02454, which might shed light on the underlying mechanism of LINC02454 action in PTC.

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Type 2 diabetes (T2D) and Clear-cell renal cell carcinoma (ccRCC) are both complicated diseases which incidence rates gradually increasing. Population based studies show that severity of ccRCC might be associated with T2D. However, so far, no researcher yet investigated about the molecular mechanisms of their association. This study explored T2D and ccRCC causing shared key genes (sKGs) from multiple transcriptomics profiles to investigate their common pathogenetic processes and associated drug molecules. We identified 259 shared differentially expressed genes (sDEGs) that can separate both T2D and ccRCC patients from control samples. Local correlation analysis based on the expressions of sDEGs indicated significant association between T2D and ccRCC. Then ten sDEGs (CDC42, SCARB1, GOT2, CXCL8, FN1, IL1B, JUN, TLR2, TLR4, and VIM) were selected as the sKGs through the protein-protein interaction (PPI) network analysis. These sKGs were found significantly associated with different CpG sites of DNA methylation that might be the cause of ccRCC. The sKGs-set enrichment analysis with Gene Ontology (GO) terms and KEGG pathways revealed some crucial shared molecular functions, biological process, cellular components and KEGG pathways that might be associated with development of both T2D and ccRCC. The regulatory network analysis of sKGs identified six post-transcriptional regulators (hsa-mir-93-5p, hsa-mir-203a-3p, hsa-mir-204-5p, hsa-mir-335-5p, hsa-mir-26b-5p, and hsa-mir-1-3p) and five transcriptional regulators (YY1, FOXL1, FOXC1, NR2F1 and GATA2) of sKGs. Finally, sKGs-guided top-ranked three repurposable drug molecules (Digoxin, Imatinib, and Dovitinib) were recommended as the common treatment for both T2D and ccRCC by molecular docking and ADME/T analysis. Therefore, the results of this study may be useful for diagnosis and therapies of ccRCC patients who are also suffering from T2D.

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Drought is a major abiotic stress that occurs frequently due to climate change, severely hampers agricultural production, and threatens food security. In this study, the effect of drought-tolerant PGPRs, i.e., PGPR-FS2 and PGPR-VHH4, was assessed on wheat by withholding water. The results indicate that drought-stressed wheat seedlings treated with PGPRs-FS2 and PGPR-VHH4 had a significantly higher shoot and root length, number of roots, higher chlorophyll, and antioxidant enzymatic activities of guaiacol peroxidase (GPX) compared to without PGPR treatment. The expression study of wheat genes related to tryptophan auxin-responsive (TaTAR), drought-responsive (TaWRKY10, TaWRKY51, TaDREB3, and TaDREB4) and auxin-regulated gene organ size (TaARGOS-A, TaARGOS-B, and TaARGOS-D) exhibited significantly higher expression in the PGPR-FS2 and PGPR-VHH4 treated wheat under drought as compared to without PGPR treatment. The results of this study illustrate that PGPR-FS2 and PGPR-VHH4 mitigate the drought stress in wheat and pave the way for imparting drought in wheat under water deficit conditions. Among the two PGPRs, PGPR-VHH4 more efficiently altered the root architecture to withstand drought stress.

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INTRODUCTION: Hepatocellular carcinoma (HCC) is a common and fatal cancer, and its molecular mechanisms are still not fully understood. This study aimed to explore the potential molecular mechanisms and immune infiltration characteristics of celecoxib combined with sorafenib in the treatment of HCC by analyzing the differentially expressed genes (DEGs) from the GSE45340 dataset in the GEO database and identifying key genes. METHODS: The GSE45340 dataset was downloaded from the GEO database, and DEGs were screened using GEO2R, and visualization and statistical analysis were performed. Metascape was used to perform functional annotation and protein-protein interaction network analysis of DEGs. The immune infiltration was analyzed using the TIMER database, and the expression of key genes and their relationship with patient survival were analyzed and verified using the UALCAN database. RESULTS: A total of 2181 DEGs were screened through GEO2R analysis, and heat maps were drawn for the 50 genes with the highest expression. Metascape was used for enrichment analysis, and the enrichment results of KEGG and GO and the PPI network were obtained, and 44 core genes were screened. Analysis of the TIMER database found that 12 genes were closely related to tumor immune infiltration. UALCAN analysis further verified the differential expression of these genes in HCC and was closely related to the overall survival of patients. CONCLUSIONS: Through comprehensive bioinformatics analysis, this study identified a group of key genes related to the treatment of HCC with celecoxib combined with sorafenib. These genes play an important role in tumor immune infiltration and patient survival, providing important clues for further studying the molecular mechanism of HCC and developing potential therapeutic targets.

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In order to resolve the key genes for weed control by Trichoderma polysporum at the genomic level, we extracted the genomic DNA and sequenced the whole genome of T. polysporum strain HZ-31 on the Illumina Hiseq platform. The raw data was cleaned up using Trimmomatic and checked for quality using FastQC. The sequencing data was assembled using SPAdes, and GeneMark was used to perform gene prediction on the assembly results. The results showed that the genome size of T. polysporum HZ-31 was 39,325,746 bp, with 48% GC content, and the number of genes encoded was 11,998. A total of 148 tRNAs and 45 rRNAs were predicted. A total of 782 genes were annotated in the Carbohydrase Database, 757 genes were annotated to the Pathogen-Host Interaction Database, and 67 gene clusters were identified. In addition, 1023 genes were predicted to be signal peptide proteins. The annotation and functional analysis of the whole genome sequence of T. polymorpha HZ-31 provide a basis for the in-depth study of the molecular mechanism of its herbicidal action and more effective utilization for weed control.

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BACKGROUND: Given the significant role of immune-related genes in uterine corpus endometrial carcinoma (UCEC) and the long-term outcomes of patients, our objective was to develop a prognostic risk prediction model using immune-related genes to improve the accuracy of UCEC prognosis prediction. METHODS: The Limma, ESTIMATE, and CIBERSORT methods were used for cluster analysis, immune score calculation, and estimation of immune cell proportions. Univariate and multivariate analyses were utilized to develop a prognostic risk model for UCEC. Risk model scores and nomograms were used to evaluate the models. String constructs a protein-protein interaction (PPI) network of genes. The qRT-PCR, immunofluorescence, and immunohistochemistry (IHC) all confirmed the genes. RESULTS: Cluster analysis divided the immune-related genes into four subtypes. 33 immune-related genes were used to independently predict the prognosis of UCEC and construct the prognosis model and risk score. The analysis of the survival nomogram indicated that the model has excellent predictive ability and strong reliability for predicting the survival of patients with UCEC. The protein-protein interaction network analysis of key genes indicates that four genes play a pivotal role in interactions: GZMK, IL7, GIMAP, and UBD. The quantitative real-time polymerase chain reaction (qRT-PCR), immunofluorescence, and immunohistochemistry (IHC) all confirmed the expression of the aforementioned genes and their correlation with immune cell levels. This further revealed that GZMK, IL7, GIMAP, and UBD could potentially serve as biomarkers associated with immune levels in endometrial cancer. CONCLUSION: The study identified genes related to immune response in UCEC, including GZMK, IL7, GIMAP, and UBD, which may serve as new biomarkers and therapeutic targets for evaluating immune levels in the future.

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BACKGROUND: Skin cancer, a prevalent form of cancer that is on the rise worldwide, requires proactive prevention strategies to reduce the burden of screening, treatment, and mortality. The KEGG research highlighted the significant involvement of red module genes in protein digestion and absorption. These findings provide valuable insights into the underlying molecular mechanisms associated with skin cancer susceptibility, offering potential targets for further research and development of preventive strategies. MATERIALS AND METHODS: Hub genes numbered 130. "limma" in R found 600 DEGs from GSE66359 dataset. DEGs are enriched in BP: chromosome segregation, CC: chromosomal region, and MF: DNA replication origin binding, according to GO analysis. Cell cycle was enriched in DEGs by KEGG and GSEA. Finally, significant genes were COL5A1, CTHRC1, ECM1, FSTL1, KDELR3, and WIPI1. RESULTS: ECM1 and WIPI1 greatly prevented skin cancer. This study created a coexpression network using WGCNA to investigate skin cancer susceptibility modules and cardiovascular disease genes. CONCLUSION: Our study finds a module and many important genes that are essential building blocks in the etiology of skin cancer, which may help us understand the molecular mechanisms of disease prevention.

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Biological control is a scientific management method used in modern agricultural production, and microbially derived biopesticides are one effective method with which to control weeds in agricultural fields. In order to determine the key genes for weed control by Trichoderma polysporum, transcriptome sequencing was carried out by high-throughput sequencing technology, and the strains of T. polysporum HZ-31 infesting Avena fatua L. at 24, 48, and 72 h were used as the experimental group, with 0 h as the control group. A total of 690,713,176 clean reads were obtained, and the sequencing results for each experimental group and the control group (0 h) were analyzed. In total, 3464 differentially expressed genes were found after 24 h of infection with the pathogen, including 1283 down-regulated genes and 2181 up-regulated genes. After 48 h of infection, the number of differentially expressed genes was 3885, of which 2242 were up-regulated and 1643 were down-regulated. The number of differentially expressed genes after 72 h of infection was the highest among all the groups, with 4594 differentially expressed genes, of which 2648 were up-regulated and 1946 were down-regulated. The up-regulated genes were analyzed by GO and KEGG, and the results showed that the up-regulated differentially expressed genes were mainly enriched in the biosynthesis of phenylalanine, tyrosine, and tryptophan; the degradation of aromatic compounds; methane metabolism; and other pathways. Among them, the PHA2, GDH, ADH2, and AROF genes were significantly enriched in the above-mentioned pathways, so they were hypothesized to play an important role in the synthesis of the herbicidally active substances of T. polysporum HZ-31. The results of this study can provide a theoretical basis for further studies on the pathogenicity of T. polysporum to A. fatua L., and accelerate the development and utilization of new and efficient bioherbicides.

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BACKGROUND: Pulmonary fibrosis (PF) is a group of respiratory diseases that are extremely complex and challenging to treat. Due to its high mortality rate and short survival, it's often referred to as a "tumor-like disease" that poses a serious threat to human health. OBJECTIVE: We aimed validate the potential of Deapioplatycodin D (DPD) to against PF and clarify the underlying mechanism of action of DPD for the treatment of PF based on bioinformatics and experimental verification. This finding provides a basis for the development of safe and effective therapeutic PF drugs based on DPD. METHODS: We used LPS-induced early PF rats as a PF model to test the overall efficacy of DPD in vivo. Then, A variety of bioinformatics methods, such as WGCNA, LASSO algorithm and immune cell infiltration (ICI), were applied to analyze the gene microarray related to PF obtained from Gene Expression Omnibus (GEO) to obtained key targets of PF. Finally, an in vitro PF model was constructed based on BEAS-2B cells while incorporating rat lung tissues to validate the regulatory effects of DPD on critical genes. RESULTS: DPD can effectively alleviate inflammatory and fibrotic markers in rat lungs. WGCNA analysis resulted in a total of six expression modules, with the brown module having the highest correlation with PF. Subsequently, seven genes were acquired by intersecting the genes in the brown module with DEGs. Five key genes were identified as potential biomarkers of PF by LASSO algorithm and validation dataset verification analysis. In the ICI analysis, infiltration of activated B cell, immature B cell and natural killer cells were found to be more crucial in PF. Ultimately, it was observed that DPD could modulate key genes to achieve anti-PF effects. CONCLUSION: In short, these comprehensive analysis methods were employed to identify critical biomarkers closely related to PF, which helps to elucidate the pathogenesis and potential immunotherapy targets of PF. It also provides essential support for the potential of DPD against PF.

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BACKGROUND: Studies have shown that mitochondrial function and macrophages may play a role in the development of idiopathic pulmonary fibrosis (IPF). However, the understanding of the interactions and specific mechanisms between mitochondrial function and macrophages in pulmonary fibrosis is still very limited. METHODS: To construct a prognostic model for IPF based on Macrophage- related genes (MaRGs) and Mitochondria-related genes (MitoRGs), differential analysis was performed to achieve differentially expressed genes (DEGs) between IPF and Control groups in the GSE28042 dataset. Then, MitoRGs, MaRGs and DEGs were overlapped to screen out the signature genes. The univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) algorithm were implemented to achieve key genes. Furthermore, the independent prognostic analysis was employed. The ingenuity pathway analysis (IPA) was employed to further understand the molecular mechanisms of key genes.Next, the immune infiltration analysis was implemented to identify differential immune cells between two risk subgroups. RESULTS: There were 4791 DEGs between IPF and Control groups. Furthermore, 26 signature genes were achieved by the intersection processing. Three key genes including ALDH2, MCL1, and BCL2A1 were achieved, and the risk model based on the key genes was created. In addition, a nomogram for survival forecasting of IPF patients was created based on riskScore, Age, and Gender, and we found that key genes were associated with classical pathways including 'Apoptosis Signaling', 'PI3K/AKT Signaling', and so on. Next, two differential immune cells including Monocytes and CD8 T cells were identified between two risk subgroups. Moreover, we found that MIR29B2CHG and hsa-mir-1-3p could regulate the expression of ALDH2. CONCLUSION: We achieved 3 key genes including ALDH2, MCL1,, and BCL2A1 associated with IPF, providing a new theoretical basis for clinical treatment of IPF.

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SARS-CoV-2 infections, commonly referred to as COVID-19, remain a critical risk to both human life and global economies. Particularly, COVID-19 patients with weak immunity may suffer from different complications due to the bacterial co-infections/super-infections/secondary infections. Therefore, different variants of alternative antibacterial therapeutic agents are required to inhibit those infection-causing drug-resistant pathogenic bacteria. This study attempted to explore these bacterial pathogens and their inhibitors by using integrated statistical and bioinformatics approaches. By analyzing bacterial 16S rRNA sequence profiles, at first, we detected five bacterial genera and taxa (Bacteroides, Parabacteroides, Prevotella Clostridium, Atopobium, and Peptostreptococcus) based on differentially abundant bacteria between SARS-CoV-2 infection and control samples that are significantly enriched in 23 metabolic pathways. A total of 183 bacterial genes were found in the enriched pathways. Then, the top-ranked 10 bacterial genes (accB, ftsB, glyQ, hldD, lpxC, lptD, mlaA, ppsA, ppc, and tamB) were selected as the pathogenic bacterial key genes (bKGs) by their protein-protein interaction (PPI) network analysis. Then, we detected bKG-guided top-ranked eight drug molecules (Bemcentinib, Ledipasvir, Velpatasvir, Tirilazad, Acetyldigitoxin, Entreatinib, Digitoxin, and Elbasvir) by molecular docking. Finally, the binding stability of the top-ranked three drug molecules (Bemcentinib, Ledipasvir, and Velpatasvir) against three receptors (hldD, mlaA, and lptD) was investigated by computing their binding free energies with molecular dynamic (MD) simulation-based MM-PBSA techniques, respectively, and was found to be stable. Therefore, the findings of this study could be useful resources for developing a proper treatment plan against bacterial co-/super-/secondary-infection in SARS-CoV-2 infections.

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Diabetic foot ulcers (DFU) and cutaneous lupus erythematosus (CLE) are both diseases that can seriously affect a patient's quality of life and generate economic pressure in society. Symptomatically, both DLU and CLE exhibit delayed healing and excessive inflammation; however, there is little evidence to support a molecular and cellular connection between these two diseases. In this study, we investigated potential common characteristics between DFU and CLE at the molecular level to provide new insights into skin diseases and regeneration, and identify potential targets for the development of new therapies. The gene expression profiles of DFU and CLE were obtained from the Gene Expression Omnibus (GEO) database and used for analysis. A total of 41 common differentially expressed genes (DEGs), 16 upregulated genes and 25 downregulated genes, were identified between DFU and CLE. GO and KEGG analysis showed that abnormalities in epidermal cells and the activation of inflammatory factors were both involved in the occurrence and development of DFU and CLE. Protein-protein interaction network (PPI) and sub-module analysis identified enrichment in seven common key genes which is KRT16, S100A7, KRT77, OASL, S100A9, EPGN and SAMD9. Based on these seven key genes, we further identified five miRNAs(has-mir-532-5p, has-mir-324-3p,has-mir-106a-5p,has-mir-20a-5p,has-mir-93-5p) and7 transcription factors including CEBPA, CEBPB, GLI1, EP30D, JUN,SP1, NFE2L2 as potential upstream molecules. Functional immune infiltration assays showed that these genes were related to immune cells. The CIBERSORT algorithm and Pearson method were used to determine the correlations between key genes and immune cells, and reverse key gene-immune cell correlations were found between DFU and CLE. Finally, the DGIbd database demonstrated that Paquinimod and Tasquinimod could be used to target S100A9 and Ribavirin could be used to target OASL. Our findings highlight common gene expression characteristics and signaling pathways between DFU and CLE, indicating a close association between these two diseases. This provides guidance for the development of targeted therapies and mutual interactions.

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Background: Dysfunction in myocardial energy metabolism plays a vital role in the pathological process of Dilated Cardiomyopathy (DCM). However, the precise mechanisms remain unclear. This study aims to investigate the key molecular mechanisms of energy metabolism and potential therapeutic agents in the progression of dilated cardiomyopathy with heart failure. Methods: Gene expression profiles and clinical data for patients with dilated cardiomyopathy complicated by heart failure, as well as healthy controls, were sourced from the Gene Expression Omnibus (GEO) database. Gene sets associated with energy metabolism were downloaded from the Molecular Signatures Database (MSigDB) for subsequent analysis. Weighted Gene Co-expression Network Analysis (WGCNA) and differential expression analysis were employed to identify key modules and genes related to heart failure. Potential biological mechanisms were investigated through Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and the construction of a competing endogenous RNA (ceRNA) network. Molecular docking simulations were then conducted to explore the binding affinity and conformation of potential therapeutic drugs with hub genes. Results: Analysis of the left ventricular tissue expression profiles revealed that, compared to healthy controls, patients with dilated cardiomyopathy exhibited 234 differentially expressed genes and 2 genes related to myocardial energy metabolism. Additionally, Benzoylaconine may serve as a potential therapeutic agent for the treatment of dilated cardiomyopathy. Conclusion: The study findings highlight the crucial role of myocardial energy metabolism in the progression of Dilated Cardiomyopathy. Notably, Benzoylaconine emerges as a potential candidate for treating Dilated Cardiomyopathy, potentially exerting its therapeutic effects by targeted modulation of myocardial energy metabolism through NRK and NT5.

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Many studies have demonstrated the mechanisms of progression to castration-resistant prostate cancer (CRPC) and novel strategies for its treatment. Despite these advances, the molecular mechanisms underlying the progression to CRPC remain unclear, and currently, no effective treatments for CRPC are available. Here, we characterized the key genes involved in CRPC progression to gain insight into potential therapeutic targets. Bicalutamide-resistant prostate cancer cells derived from LNCaP were generated and named Bical R. RNA sequencing was used to identify differentially expressed genes (DEGs) between LNCaP and Bical R. In total, 631 DEGs (302 upregulated genes and 329 downregulated genes) were identified. The Cytohubba plug-in in Cytoscape was used to identify seven hub genes (ASNS, AGT, ATF3, ATF4, DDIT3, EFNA5, and VEGFA) associated with CRPC progression. Among these hub genes, ASNS and DDIT3 were markedly upregulated in CRPC cell lines and CRPC patient samples. The patients with high expression of ASNS and DDIT3 showed worse disease-free survival in patients with The Cancer Genome Atlas (TCGA)-prostate adenocarcinoma (PRAD) datasets. Our study revealed a potential association between ASNS and DDIT3 and the progression to CRPC. These results may contribute to the development of potential therapeutic targets and mechanisms underlying CRPC progression, aiming to improve clinical efficacy in CRPC treatment.

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Aims: Autophagy plays a significant role in the development of acute myocardial infarction (AMI), and cardiomyocyte autophagy is of major importance in maintaining cardiac function. We aimed to identify key genes associated with autophagy in AMI through bioinformatics analysis and verify them through clinical validation. Materials and Methods: We downloaded an AMI expression profile dataset GSE166780 from Gene Expression Omnibus (GEO). Autophagy-associated genes potentially differentially expressed in AMI were screened using R software. Then, to identify key autophagy-related genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, protein-protein interaction (PPI) analysis, Receiver Operating Characteristic (ROC) curve analysis, and correlation analysis were performed on the differentially expressed autophagy-related genes in AMI. Finally, we used quantificational real-time polymerase chain reaction (qRT-PCR) to verify the RNA expression of the screened key genes. Results: TSC2, HSPA8, and HIF1A were screened out as key autophagy-related genes. qRT-PCR results showed that the expression levels of HSPA8 and TSC2 in AMI blood samples were lower, while the expression level of HIF1A was higher than that in the healthy controls. Conclusions: TSC2, HSPA8, and HIF1A were identified as key autophagy-related genes in this study. They may influence the development of AMI through autophagy. These findings may help deepen our understanding of AMI and may be useful for the treatment of AMI.

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Introduction: Autophagy refers to the process of breaking down and recycling damaged or unnecessary components within a cell to maintain cellular homeostasis. Heart failure (HF) is a severe medical condition that poses a serious threat to the patient's life. Autophagy is known to play a pivotal role in the pathogenesis of HF. However, our understanding of the specific mechanisms involved remains incomplete. Here, we identify autophagy-related genes (ARGs) associated with HF, which we believe will contribute to further comprehending the pathogenesis of HF. Methods: By searching the GEO (Gene Expression Omnibus) database, we found the GSE57338 dataset, which was related to HF. ARGs were obtained from the HADb and HAMdb databases. Annotation of GO and enrichment analysis of KEGG pathway were carried out on the differentially expressed ARGs (AR-DEGs). We employed machine learning algorithms to conduct a thorough screening of significant genes and validated these genes by analyzing external dataset GSE76701 and conducting mouse models experimentation. At last, immune infiltration analysis was conducted, target drugs were screened and a TF regulatory network was constructed. Results: Through processing the dataset with R language, we obtained a total of 442 DEGs. Additionally, we retrieved 803 ARGs from the database. The intersection of these two sets resulted in 15 AR-DEGs. Upon performing functional enrichment analysis, it was discovered that these genes exhibited significant enrichment in domains related to "regulation of cell growth", "icosatetraenoic acid binding", and "IL-17 signaling pathway". After screening and verification, we ultimately identified 4 key genes. Finally, an analysis of immune infiltration illustrated significant discrepancies in 16 distinct types of immune cells between the HF and control group and up to 194 potential drugs and 16 TFs were identified based on the key genes. Discussion: In this study, TPCN1, MAP2K1, S100A9, and CD38 were considered as key autophagy-related genes in HF. With these relevant data, further exploration of the molecular mechanisms of autophagy in HF can be carried out.

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Hyperlipidemia is an independent risk factor for cardiovascular and cerebrovascular diseases. The transcriptomic data and the gene regulatory networks of hyperlipidemia are largely unclear. We analyzed the changes in liver gene expression and the serum levels of biochemical indicators in rats with hyperlipidemia induced by high-fat diet (HFD). The body weight, liver weight, and the serum levels of TG, TC, HDL-C, LDL-C, ALT, and AST were significantly higher in the hyperlipidemic rats compared to the healthy controls (P < 0.05). In addition, HFD feeding decreased the antioxidant capacity of the liver tissues and significantly increased the arteriosclerosis index (AI) (P < 0.05). There were 584 differentially expressed genes (DEGs) in the hyperlipidemia model compared to the control, with |log2FC|≥ 1 and P-adjust ≤ 0.05 as the thresholds. GO analysis of the DEGs revealed significant enrichment of 382 biological processes (BP), 18 cellular components (CC), and 40 molecular functions (MF). In addition, pathways related to bile secretion, cholesterol metabolism, and steroid hormone biosynthesis were significantly associated with hyperlipidemia. The key genes potentially involved in the blood lipid changes were Agt, Src, Gnai3, Cyp2c7, Cyp2c11, Cyp2c22, Apoa1, Apoe, and Srebf1. The genes and pathways identified in this study are potential intervention targets for hyperlipidemia and warrant further investigation.

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BACKGROUND: Nasopharyngeal carcinoma (NPC) is a highly aggressive and metastatic malignancy originating in the nasopharyngeal tissue. Pyroptosis is a relatively newly discovered, regulated form of necrotic cell death induced by inflammatory caspases that is associated with a variety of diseases. However, the role and mechanism of pyroptosis in NPC are not fully understood. METHODS: We analyzed the differential expression of pyroptosis-related genes (PRGs) between patients with and without NPC from the GSE53819 and GSE64634 datasets of the Gene Expression Omnibus (GEO) database. We mapped receptor operating characteristic profiles for these key PRGs to assess the accuracy of the genes for disease diagnosis and prediction of patient prognosis. In addition, we constructed a nomogram based on these key PRGs and carried out a decision curve analysis. The NPC patients were classified into different pyroptosis gene clusters by the consensus clustering method based on key PRGs, whereas the expression profiles of the key PRGs were analyzed by applying principal component analysis. We also analyzed the differences in key PRGs, immune cell infiltration and NPC-related genes between the clusters. Finally, we performed differential expression analysis for pyroptosis clusters and obtained differentially expressed genes (DEGs) and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. RESULTS: We obtained 14 differentially expressed PRGs from GEO database. Based on these 14 differentially expressed PRGs, we applied least absolute shrinkage and selection operator analysis and the random forest algorithm to obtain four key PRGs (CHMP7, IL1A, TP63 and GSDMB). We completely distinguished the NPC patients into two pyroptosis gene clusters (pyroptosis clusters A and B) based on four key PRGs. Furthermore, we determined the immune cell abundance of each NPC sample, estimated the association between the four PRGs and immune cells, and determined the difference in immune cell infiltration between the two pyroptosis gene clusters. Finally, we obtained and functional enrichment analyses 259 DEGs by differential expression analysis for both pyroptosis clusters. CONCLUSIONS: PRGs are critical in the development of NPC, and our research on the pyroptosis gene cluster may help direct future NPC therapeutic approaches.

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Early-onset preeclampsia (EOPE) is a complex pregnancy complication that poses significant risks to the health of both mothers and fetuses, and research on its pathogenesis and pathophysiology remains insuffcient. This study aims to explore the role of candidate genes and their potential interaction mechanisms in EOPE through bioinformatics analysis techniques. Two gene expression datasets, GSE44711 and GSE74341, were obtained from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) between EOPE and gestational age-matched preterm control samples. Functional enrichment analysis was performed utilizing the kyoto encyclopedia of genes and genomes (KEGG), gene ontology (GO), and gene set enrichment analysis (GSEA). A protein-protein interaction (PPI) network was constructed using the STRING database, and hub DEGs were identified through Cytoscape software and comparative toxicogenomics database (CTD) analysis. Furthermore, a diagnostic logistic model was established using these hub genes, which were confirmed through reverse transcription polymerase chain reaction (RT-PCR). Finally, immune cell infiltration was analyzed using CIBERSORT. In total, 807 DEGs were identified in the GSE44711 dataset (451 upregulated genes and 356 downregulated genes), and 787 DEGs were identified in the GSE74341 dataset (446 upregulated genes and 341 downregulated genes). These DEGs were significantly enriched in various molecular functions such as extracellular matrix structural constituent, receptor-ligand activity binding, cytokine activity, and platelet-derived growth factor. KEGG and GSEA annotation revealed significant enrichment in pathways related to ECM-receptor interaction, PI3K-AKT signaling, and focal adhesion. Ten hub genes were identified through the CytoHubba plugin in Cytoscape. Among these hub genes, three key DEGs (COL1A1, SPP1, and THY1) were selected using CTD analysis and various topological methods in Cytoscape. The diagnostic logistic model based on these three genes exhibited high efficiency in predicting EOPE (AUC = 0.922). RT-PCR analysis confirmed the downregulation of these genes in EOPE, and immune cell infiltration analysis suggested the significant role of M1 and M2 macrophages in EOPE. In conclusion, this study highlights the association of three key genes (COL1A1, SPP1, and THY1) with EOPE and their contribution to high diagnostic efficiency in the logistic model. Additionally, it provides new insights for future research on EOPE and emphasizes the diagnostic value of these identified genes. More research is needed to explore their functional and diagnostic significance in EOPE.