Network pharmacology and experimental verification to explore the effect of Hedyotis diffusa on Alzheimer's disease.

This study aimed to explore the active components and the effect of Hedyotis diffusa (HD) against Alzheimer's disease (AD) via network pharmacology, molecular docking, and experimental evaluations. We conducted a comprehensive screening process using the TCMSP, Swiss Target Prediction, and PharmMapper databases to identify the active components and their related targets in HD. In addition, we collected potential therapeutic targets of AD from the Gene Cards, Drugbank, and OMIM databases. Afterward, we utilized Cytoscape to establish both protein-protein interaction (PPI) networks and compound-target (C-T) networks. To gain further insights into the functional aspect, we performed GO and KEGG pathway analyses using the David database. Next, we employed Autodock vina to estimate the binding force between the components and the hub genes. To validate our network pharmacology findings, we conducted relevant experiments on Caenorhabditis elegans, further confirming the reliability of our results. Then a total of six active compounds and 149 therapeutic targets were detected. Through the analysis of the association between active compounds, therapeutic targets, and signaling pathways, it was observed that the therapeutic effect of HD primarily encompassed the inhibition of Aß, suppression of AChE activity, and mitigating oxidative stress. Additionally, our investigation revealed that the key active compounds in HD primarily consisted of iridoids, which exhibited resistance against AD by acting on the Alzheimer's disease pathway and the AGE-RAGE signaling pathway in diabetic complications.

Study of hub nodes of transcription factor-target gene regulatory network and immune mechanism for type 2 diabetes based on chip analysis of GEO database.

Identification of novel therapeutic targets for type 2 diabetes is a key area of contemporary research. In this study, we screened differentially expressed genes in type 2 diabetes through the GEO database and sought to identify the key virulence factors for type 2 diabetes through a transcription factor regulatory network. Our findings may help identify new therapeutic targets for type 2 diabetes. Data pertaining to the humoral (whole blood) gene expression profile of diabetic patients were obtained from the NCBI's GEO Datasets database and gene sets with differential expression were identified. Subsequently, the TRED transcriptional regulatory element database was integrated to build a gene regulatory network for type 2 diabetes. Functional analysis (GO-Analysis) and Pathway-analysis of differentially expressed genes were performed using the DAVID database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Finally, gene-disease correlation analysis was performed using the DAVID online annotation tool. A total of 236 pathogenic genes, four transcription factors related to the pathogenic genes, and 261 corresponding target genes were identified. A transcription factor-target gene regulatory network for type 2 diabetes was constructed. Most of the key factors of the transcription factor-target gene regulatory network for type 2 diabetes were found closely related to the immune metabolic system and the functions of cell proliferation and transformation.

Identification of common mechanisms and biomarkers for dermatomyositis and atherosclerosis based on bioinformatics analysis.

BACKGROUND: Dermatomyositis (DM) manifests as an autoimmune and inflammatory condition, clinically characterized by subacute progressive proximal muscle weakness, rashes or both along with extramuscular manifestations. Literature indicates that DM shares common risk factors with atherosclerosis (AS), and they often co-occur, yet the etiology and pathogenesis remain to be fully elucidated. This investigation aims to utilize bioinformatics methods to clarify the crucial genes and pathways that influence the pathophysiology of both DM and AS. METHOD: Microarray datasets for DM (GSE128470, GSE1551, GSE143323) and AS (GSE100927, GSE28829, GSE43292) were retrieved from the Gene Expression Omnibus (GEO) database. The weighted gene co-expression network analysis (WGCNA) was used to reveal their co-expressed modules. Differentially expression genes (DEGs) were identified using the "limma" package in R software, and the functions of common DEGs were determined by functional enrichment analysis. A protein-protein interaction (PPI) network was established using the STRING database, with central genes evaluated by the cytoHubba plugin, and validated through external datasets. Immune infiltration analysis of the hub genes was conducted using the CIBERSORT method, along with Gene Set Enrichment Analysis (GSEA). Finally, the NetworkAnalyst platform was employed to examine the transcription factors (TFs) responsible for regulating pivotal crosstalk genes. RESULTS: Utilizing WGCNA analysis, a total of 271 overlapping genes were pinpointed. Subsequent DEG analysis revealed 34 genes that are commonly found in both DM and AS, including 31 upregulated genes and 3 downregulated genes. The Degree Centrality algorithm was applied separately to the WGCNA and DEG collections to select the 15 genes with the highest connectivity, and crossing the two gene sets yielded 3 hub genes (PTPRC, TYROBP, CXCR4). Validation with external datasets showed their diagnostic value for DM and AS. Analysis of immune infiltration indicates that lymphocytes and macrophages are significantly associated with the pathogenesis of DM and AS. Moreover, GSEA analysis suggested that the shared genes are enriched in various receptor interactions and multiple cytokines and receptor signaling pathways. We coupled the 3 hub genes with their respective predicted genes, identifying a potential key TF, CBFB, which interacts with all 3 hub genes. CONCLUSION: This research utilized comprehensive bioinformatics techniques to explore the shared pathogenesis of DM and AS. The three key genes, including PTPRC, TYROBP, and CXCR4, are related to the pathogenesis of DM and AS. The central genes and their correlations with immune cells may serve as potential diagnostic and therapeutic targets.

Proteo-genomic characterization of cirrhosis-associated liver cancers reveals potential subtypes and therapeutic targets.

BACKGROUND: This study aimed to identify potential subtypes of hepatocellular carcinoma (HCC) associated with cirrhosis and to investigate key markers using bioinformatic analysis of gene expression datasets-0. METHODS: Three data sets (GSE17548, GSE56140, and GSE87630) were extracted from the Gene Expression Omnibus (GEO) database and normalized using the Limma package in R. Principal component analysis (PCA) and cluster analysis was performed to examine data distribution and identify subtypes. Differential gene expression analysis was performed using the Limma software package. Protein-protein interaction analysis and functional annotation were performed using the STRING database and Cytoscape software. Important signaling pathways and processes were identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analysis. RESULTS: The analysis revealed different subtypes of HCC associated with cirrhosis and identified several key genes, including CCNB2, MCM4, and CDC20, with strong binding power and prognostic value. Functional annotation indicated involvement in cell cycle regulation and metabolic pathways. ROC analysis showed high sensitivity and specificity of these genes in predicting HCC prognosis. CONCLUSION: These results suggest that CCNB2, MCM4, and CDC20 may serve as potential biomarkers for predicting HCC prognosis in patients with cirrhosis and provide insights into the molecular mechanisms of HCC progression.

Decoding cardiovascular risks: analyzing type 2 diabetes mellitus and ASCVD gene expression.

Background: ASCVD is the primary cause of mortality in individuals with T2DM. A potential link between ASCVD and T2DM has been suggested, prompting further investigation. Methods: We utilized linear and multivariate logistic regression, Wilcoxon test, and Spearman's correlation toanalyzethe interrelation between ASCVD and T2DM in NHANES data from 2001-2018.The Gene Expression Omnibus (GEO) database and Weighted Gene Co-expression Network Analysis (WGCNA) wereconducted to identify co-expression networks between ASCVD and T2DM. Hub genes were identified using LASSO regression analysis and further validated in two additional cohorts. Bioinformatics methods were employed for gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, along with the prediction of candidate small molecules. Results: Our analysis of the NHANES dataset indicated a significant impact of blood glucose on lipid levels within diabetic cohort, suggesting that abnormal lipid metabolism is a critical factor in ASCVD development. Cross-phenotyping analysis revealed two pivotal genes, ABCC5 and WDR7, associated with both T2DM and ASCVD. Enrichment analyses demonstrated the intertwining of lipid metabolism in both conditions, encompassing adipocytokine signaling pathway, fatty acid degradation and metabolism, and the regulation of adipocyte lipolysis. Immune infiltration analysis underscored the involvement of immune processes in both diseases. Notably, RITA, ON-01910, doxercalciferol, and topiramate emerged as potential therapeutic agents for both T2DM and ASCVD, indicating their possible clinical significance. Conclusion: Our findings pinpoint ABCC5 and WDR7 as new target genes between T2DM and ASCVD, with RITA, ON-01910, doxercalciferol, and topiramate highlighted as promising therapeutic agents.

Integrative analysis of plasma and substantia nigra in Parkinson's disease: unraveling biomarkers and insights from the lncRNA-miRNA-mRNA ceRNA network.

Introduction: Parkinson's disease (PD) is a rapidly growing neurological disorder characterized by diverse movement symptoms. However, the underlying causes have not been clearly identified, and accurate diagnosis is challenging. This study aimed to identify potential biomarkers suitable for PD diagnosis and present an integrative perspective on the disease. Methods: We screened the GSE7621, GSE8397-GPL96, GSE8397-GPL97, GSE20163, and GSE20164 datasets in the NCBI GEO database to identify differentially expressed (DE) mRNAs in the substantia nigra (SN). We also screened the GSE160299 dataset from the NCBI GEO database to identify DE lncRNAs and miRNAs in plasma. We then constructed 2 lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) regulatory networks based on the ceRNA hypothesis. To understand the biological function, we performed Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology analyses for each ceRNA network. The receiver operating characteristic analyses (ROC) was used to assess ceRNA results. Results: We identified 7 upregulated and 29 downregulated mRNAs as common DE mRNAs in the 5 SN datasets. In the blood dataset, we identified 31 DE miRNAs (9 upregulated and 22 downregulated) and 332 DE lncRNAs (69 upregulated and 263 downregulated). Based on the determined interactions, 5 genes (P2RX7, HSPA1, SLCO4A1, RAD52, and SIRT4) appeared to be upregulated as a result of 10 lncRNAs sponging 4 miRNAs (miR-411, miR-1193, miR-301b, and miR-514a-2/3). Competing with 9 genes (ANK1, CBLN1, RGS4, SLC6A3, SYNGR3, VSNL1, DDC, KCNJ6, and SV2C) for miR-671, a total of 26 lncRNAs seemed to function as ceRNAs, influencing genes to be downregulated. Discussion: In this study, we successfully constructed 2 novel ceRNA regulatory networks in patients with PD, including 36 lncRNAs, 5 miRNAs, and 14 mRNAs. Our results suggest that these plasma lncRNAs are involved in the pathogenesis of PD by sponging miRNAs and regulating gene expression in the SN of the brain. We propose that the upregulated and downregulated lncRNA-mediated ceRNA networks represent mechanisms of neuroinflammation and dopamine neurotransmission, respectively. Our ceRNA network, which was associated with PD, suggests the potential use of DE miRNAs and lncRNAs as body fluid diagnostic biomarkers. These findings provide an integrated view of the mechanisms underlying gene regulation and interactions in PD.

Transcriptome and Animal Model Integration Reveals Inhibition of Calcium Homeostasis-Associated Gene ITPKB Alleviates Amyloid Plaque Deposition.

Alzheimer's disease (AD) is a severe neurological illness that causes memory loss and is a global problem. The calcium hypothesis recently steadily evolved in AD. The prospective targets for calcium homeostasis therapy, however, are limited, and gene expression-level research connected to calcium homeostasis in AD remains hazy. In this study, we analyzed the microarray dataset (GSE132903) taken from the Gene Expression Omnibus (GEO) database to investigate calcium homeostasis-related genes for AD. Using immunoblot analysis, we examined the association of ITPKB with inflammation in AD. Additionally, the immunofluorescence technique was employed to assess the impact of pharmacological inhibition of ITPKB on the amyloid-ß (Aß) plaque deposition in APP/PS1 mice. This article's further exploration of calcium homeostasis-related genes has propelled the validation of the calcium homeostasis theory in AD.

Mechanism of Xing 9 ling tablet candy for alcoholic liver disease based on network pharmacology.

Xing 9 Ling tablet candy (X9LTC) effectively treats alcoholic liver disease (ALD), but its potential mechanism and molecular targets remain unstudied. We aimed to address this gap using network pharmacology. Furthermore, high-performance liquid chromatography (HPLC) and database analysis revealed a total of 35 active ingredients and 311 corresponding potential targets of X9LTC. Protein interaction analysis revealed PTGS2, JUN, and FOS as its core targets. Enrichment analysis indicated that chemical carcinogenesis-receptor activation, IL-17 and TNF signaling pathway were enriched by multiple core targets, which might be the main pathway of action. Further molecular docking validation showed that the core targets had good binding activities with the identified compounds. Animal experiments showed that X9LTC could reduce the high expression of ALT, AST and TG in the serum of ALD mice, alleviate the lesions in liver tissues, and reverse the high expression of PTGS2, JUN, and FOS proteins in the liver tissues. In this study, we established a method for the determination of X9LTC content for the first time, and predicted its active ingredient and mechanism of action in treating ALD, providing theoretical basis for further research.

Mechanism of Astragalus mongholicus Bunge ameliorating cerebral ischemia-reperfusion injury: Based on network pharmacology analysis and experimental verification.

ETHNOPHARMACOLOGICAL RELEVANCE: Astragalus mongholicus Bunge (AMB) is a herb with wide application in traditional Chinese medicine, exerting a wealth of pharmacological effects. AMB has been proven to have an evident therapeutic effect on ischemic cerebrovascular diseases, including cerebral ischemia-reperfusion injury (CIRI). However, the specific mechanism underlying AMB in CIRI remains unclear. AIM OF THE STUDY: This study aimed to investigate the potential role of AMB in CIRI through a comprehensive approach of network pharmacology and in vivo experimental research. METHODS: The intersection genes of drugs and diseases were obtained through analysis of the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and Gene Expression Omnibus (GEO) database. The protein-protein interaction (PPI) network was created through the string website. Meanwhile, the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was carried out using R studio, and thereafter the key genes were screened. Then, the molecular docking prediction was made between the main active ingredients and target genes, and hub genes with high binding energy were obtained. In addition, molecular dynamic (MD) simulation was used to validate the result of molecular docking. Based on the results of network pharmacology, we used animal experiments to verify the predicted hub genes. First, the rat middle cerebral artery occlusion and reperfusion (MACO/R) model was established and the effective dose of AMB in CIRI was determined by behavioral detection and 2,3,5-Triphenyltetrazolium chloride (TTC) staining. Then the target proteins corresponding to the hub genes were measured by Western blot. Moreover, the level of neuronal death was measured using hematoxylin and eosin (HE) and Nissl staining. RESULTS: Based on the analysis of the TCMSP database and GEO database, a total of 62 intersection target genes of diseases and drugs were obtained. The KEGG enrichment analysis showed that the therapeutic effect of AMB on CIRI might be realized through the advanced glycation endproduct-the receptor of advanced glycation endproduct (AGE-RAGE) signaling pathway in diabetic complications, nuclear factor kappa-B (NF-κB) signaling pathway and other pathways. Molecular docking results showed that the active ingredients of AMB had good binding potential with hub genes that included Prkcb, Ikbkb, Gsk3b, Fos and Rela. Animal experiments showed that AWE (60 g/kg) could alleviate CIRI by regulating the phosphorylation of PKCß, IKKß, GSK3ß, c-Fos and NF-κB p65 proteins. CONCLUSION: AMB exerts multi-target and multi-pathway effects against CIRI, and the underlying mechanism may be related to anti-apoptosis, anti-inflammation, anti-oxidative stress and inhibiting calcium overload.

Analysis of Key Genes and miRNA-mRNA Networks Associated with Glucocorticoids Treatment in Chronic Obstructive Pulmonary Disease.

Background: Some patients with chronic obstructive pulmonary disease (COPD) benefit from glucocorticoid (GC) treatment, but its mechanism is unclear. Objective: With the help of the Gene Expression Omnibus (GEO) database, the key genes and miRNA-mRNA related to the treatment of COPD by GCs were discussed, and the potential mechanism was explained. Methods: The miRNA microarray dataset (GSE76774) and mRNA microarray dataset (GSE36221) were downloaded, and differential expression analysis were performed. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the differentially expressed genes (DEGs). The protein interaction network of the DEGs in the regulatory network was constructed with the STRING database, and the key genes were screened through Cytoscape. Potential downstream target genes regulated by differentially expressed miRNAs (DEMs) were predicted by the miRWalk3.0 database, and miRNA-mRNA regulatory networks were constructed. Finally, some research results were validated. Results: ① Four DEMs and 83 DEGs were screened; ② GO and KEGG enrichment analysis mainly focused on the PI3K/Akt signalling pathway, ECM receptor interaction, etc.; ③ CD2, SLAMF7, etc. may be the key targets of GC in the treatment of COPD; ④ 18 intersection genes were predicted by the mirwalk 3.0 database, and 9 pairs of miRNA-mRNA regulatory networks were identified; ⑤ The expression of miR-320d-2 and TFCP2L1 were upregulated by dexamethasone in the COPD cell model, while the expression of miR-181a-2-3p and SLAMF7 were downregulated. Conclusion: In COPD, GC may mediate the expression of the PI3K/Akt signalling pathway through miR-181a-2-3p, miR-320d-2, miR-650, and miR-155-5p, targeting its downstream signal factors. The research results provide new ideas for RNA therapy strategies of COPD, and also lay a foundation for further research.

Necroptosis pathway emerged as potential diagnosis markers in spinal cord injury.

The present research focused on identifying necroptosis-related differentially expressed genes (NRDEGs) in spinal cord injury (SCI) to highlight potential therapeutic and prognostic target genes in clinical SCI. Three SCI-related datasets were downloaded, including GSE151371, GSE5296 and GSE47681. MSigDB and KEGG datasets were searched for necroptosis-related genes (NRGs). Differentially expressed genes (DEGs) and NRGs were intersected to obtain NRDEGs. The MCC algorithm was employed to select the first 10 genes as hub genes. A protein-protein interaction (PPI) network related to NRDEGs was developed utilizing STRING. Several databases were searched to predict interactions between hub genes and miRNAs, transcription factors, potential drugs, and small molecules. Immunoassays were performed to identify DEGs using CIBERSORTx. Additionally, qRT-PCR was carried out to verify NRDEGs in an animal model of SCI. Combined analysis of all datasets identified 15 co-expressed DEGs and NRGs. GO and KEGG pathway analyses highlighted DEGs mostly belonged to pathways associated with necroptosis and apoptosis. Hub gene expression analysis showed high accuracy in SCI diagnosis was associated with the expression of CHMP7 and FADD. A total of two hub genes, i.e. CHMP7, FADD, were considered potential targets for SCI therapy.

Elucidating the role of TWIST1 in ulcerative colitis: a comprehensive bioinformatics and machine learning approach.

Background: Ulcerative colitis (UC) is a common and progressive inflammatory bowel disease primarily affecting the colon and rectum. Prolonged inflammation can lead to colitis-associated colorectal cancer (CAC). While the exact cause of UC remains unknown, this study aims to investigate the role of the TWIST1 gene in UC. Methods: Second-generation sequencing data from adult UC patients were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, and characteristic genes were selected using machine learning and Lasso regression. The Receiver Operating Characteristic (ROC) curve assessed TWIST1's potential as a diagnostic factor (AUC score). Enriched pathways were analyzed, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Variation Analysis (GSVA). Functional mechanisms of marker genes were predicted, considering immune cell infiltration and the competing endogenous RNA (ceRNA) network. Results: We found 530 DEGs, with 341 upregulated and 189 downregulated genes. TWIST1 emerged as one of four potential UC biomarkers via machine learning. TWIST1 expression significantly differed in two datasets, GSE193677 and GSE83687, suggesting its diagnostic potential (AUC = 0.717 in GSE193677, AUC = 0.897 in GSE83687). Enrichment analysis indicated DEGs associated with TWIST1 were involved in processes like leukocyte migration, humoral immune response, and cell chemotaxis. Immune cell infiltration analysis revealed higher rates of M0 macrophages and resting NK cells in the high TWIST1 expression group, while TWIST1 expression correlated positively with M2 macrophages and resting NK cell infiltration. We constructed a ceRNA regulatory network involving 1 mRNA, 7 miRNAs, and 32 long non-coding RNAs (lncRNAs) to explore TWIST1's regulatory mechanism. Conclusion: TWIST1 plays a significant role in UC and has potential as a diagnostic marker. This study sheds light on UC's molecular mechanisms and underscores TWIST1's importance in its progression. Further research is needed to validate these findings in diverse populations and investigate TWIST1 as a therapeutic target in UC.

Comprehensive analysis of DNA methylation gene expression profiles in GEO dataset reveals biomarkers related to malignant transformation of sinonasal inverted papilloma.

BACKGROUND: DNA methylation may be involved in the regulation of malignant transformation from sinonasal inverted papilloma (SNIP) to squamous cell carcinoma (SCC). The study of gene methylation changes and screening of differentially methylated loci (DMLs) are helpful to predict the possible key genes in the malignant transformation of SNIP-SCC. MATERIALS AND METHODS: Microarray dataset GSE125399 was downloaded from the Gene Expression Omnibus (GEO) database and differentially methylated loci (DMLs) were analyzed using R language (Limma package). ClusterProfiler R package was used to perform Gene Ontology (GO) analysis on up-methylated genes and draw bubble maps. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and its visualization analysis were analyzed to speculate the possible key Genes in SNIP-SCC malignant transformation. Subsequently, SNIP cases archived in our department were collected, tissue microarray was made, and immunohistochemical staining was performed to analyze the expression levels of UCKL1, GSTT1, HLA-G, MAML2 and NRGN in different grades of sinonasal papilloma tissues. RESULTS: Analysis of dataset GSE125399 identified 56 DMLs, including 49 upregulated DMLs and 7 downregulated DMLs. Thirty-one genes containing upregulated DNA methylation loci and three genes containing downregulated DNA methylation loci were obtained by methylation microarray annotation analysis. In addition, KEGG pathway visualization analysis of 31 up-methylated genes showed that there were four significantly up-methylated genes including UCKL1, GSTT1, HLA-G and MAML2, and one significantly down-methylated gene NRGN. Subsequently, compared with non-neoplasia nasal epithelial tissues, the expression of HLA-G and NRGN was upregulated in grade I, II, III and IV tissues, while the expression of MAML2 was lost. The protein expression changes of MAML2 and NRGN were significantly negatively correlated with their gene methylation levels. CONCLUSIONS: By analyzing the methylation dataset, we obtained four up-regulated methylation genes UCKL1, GSTT1, HLA-G and MAML2 and one down-regulated gene NRGN. MAML2, a tumor suppressor gene with high methylation modification but loss of protein expression, and NRGN, a tumor gene with low methylation modification but upregulated protein expression, can be used as biological indicators to judge the malignant transformation of SNIP-SCC.

Hmgcs2 is the hub gene in diabetic cardiomyopathy and is negatively regulated by Hmgcs2, promoting high glucose-induced cardiomyocyte injury.

BACKGROUND: Diabetic cardiomyopathy (DCM) represents a major cause of heart failure and a large medical burden worldwide. This study screened the potentially regulatory targets of DCM and analyzed their roles in high glucose (HG)-induced cardiomyocyte injury. METHODS: Through GEO database, we obtained rat DCM expression chips and screened differentially expressed genes. Rat cardiomyocytes (H9C2) were induced with HG. 3-hydroxy-3-methylglutarylcoenzyme A synthase 2 (Hmgcs2) and microRNA (miR)-363-5p expression patterns in cells were measured by real-time quantitative polymerase chain reaction or Western blot assay, with the dual-luciferase assay to analyze their binding relationship. Then, 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide assay, lactate dehydrogenase assay, terminal deoxynucleotidyl transferase dUTP nick end labeling assay, enzyme-linked immunosorbent assay, and various assay kits were applied to evaluate cell viability, cytotoxicity, apoptosis, inflammation responses, and oxidative burden. RESULTS: Hmgcs2 was the vital hub gene in DCM. Hmgcs2 was upregulated in HG-induced cardiomyocytes. Hmgcs2 downregulation increased cell viability, decreased TUNEL-positive cell number, reduced HG-induced inflammation and oxidative stress. miR-363-5p is the upstream miRNA of Hmgcs2. miR-363-5p overexpression attenuated HG-induced cell injury. CONCLUSIONS: Hmgcs2 had the most critical regulatory role in DCM. We for the first time reported that miR-363-5p inhibited Hmgcs2 expression, thereby alleviating HG-induced cardiomyocyte injury.

Identification and Analysis of Gene Biomarkers for Ovarian Cancer.

Objective: To identify potential diagnostic markers for ovarian cancer (OC) and explore the contribution of immune cells infiltration to the pathogenesis of OC. Methods: As the study cohort, two gene expression datasets of human OC (GSE27651 and GSE26712, taken as the metadata) taken from the Gene Expression Omnibus (GEO) database were combined, comprising 228 OC and 16 control samples. Analysis was performed to identify the differentially expressed genes between the OC and control samples, while support vector machine analysis using the recursive feature elimination algorithm and least absolute shrinkage and selection operator regression were performed to identify candidate biomarkers that could discriminate OC. In addition, immunohistochemistry staining was performed to verify the diagnostic value and protein expression levels of the candidate biomarkers. The GSE146553 dataset (OC n = 40, control n = 3) was used to further validate the diagnostic values of those biomarkers. Further, the proportions of various immune cells infiltration in the OC and control samples were evaluated using the CIBERSORT algorithm. Results: CLEC4M, PFKP, and SCRIB were identified as potential diagnostic markers for OC in both the metadata (area under the receiver operating characteristic curve [AUC] = 0.996, AUC = 1.000, AUC = 1.000) and GSE146553 dataset (AUC = 0.983, AUC = 0.975, AUC = 0.892). Regarding immune cell infiltration, there was an increase in the infiltration of follicular helper dendritic cells, and a decrease in the infiltration of M2 macrophages and neutrophils, as well as activated natural killer (NK) cells and T cells in OC. CLEC4M showed a significantly positive correlation with neutrophils (r = 0.57, p < 0.001) and resting NK cells (r = 0.42, p = 0.0047), but a negative correlation with activated dendritic cells (r = -0.33, p = 0.032). PFKP displayed a significantly positive correlation with activated NK cells (r = 0.36, p = 0.016) and follicular helper T cells (r = 0.32, p = 0.035), but a negative correlation with the naive B cells (r = -0.3, p = 0.049) and resting NK cells (r = -0.41, p = 0.007). SCRIB demonstrated a significantly positive correlation with plasma cells (r = 0.39, p = 0.01), memory B cells (r = 0.34, p = 0.025), and follicular helper T cells (r = 0.31, p = 0.04), but a negative correlation with neutrophils (r = -0.46, p = 0.002) and naive B cells (r = -0.48, p = 0.0012). Conclusion: CLEC4M, PFKP, and SCRIB were identified and verified as potential diagnostic biomarkers for OC. This work and identification of the three biomarkers may provide guidance for future studies into the mechanism and treatment of OC.

Diagnostic potential of NRG1 in benign nerve sheath tumors and its influence on the PI3K-Akt signaling and tumor immunity.

OBJECTIVE: Benign nerve sheath tumors (BNSTs) present diagnostic challenges due to their heterogeneous nature. This study aimed to determine the significance of NRG1 as a novel diagnostic biomarker in BNST, emphasizing its involvement in the PI3K-Akt pathway and tumor immune regulation. METHODS: Differential genes related to BNST were identified from the GEO database. Gene co-expression networks, protein-protein interaction networks, and LASSO regression were utilized to pinpoint key genes. The CIBERSORT algorithm assessed immune cell infiltration differences, and functional enrichment analyses explored BNST signaling pathways. Clinical samples helped establish PDX models, and in vitro cell lines to validate NRG1's role via the PI3K-Akt pathway. RESULTS: Nine hundred eighty-two genes were upregulated, and 375 downregulated in BNST samples. WGCNA revealed the brown module with the most significant difference. Top hub genes included NRG1, which was also determined as a pivotal gene in disease characterization. Immune infiltration showed significant variances in neutrophils and M2 macrophages, with NRG1 playing a central role. Functional analyses confirmed NRG1's involvement in key pathways. Validation experiments using PDX models and cell lines further solidified NRG1's role in BNST. CONCLUSION: NRG1 emerges as a potential diagnostic biomarker for BNST, influencing the PI3K-Akt pathway, and shaping the tumor immune microenvironment.

Expression and Clinical Significance of MIS18BP1 in Bladder Cancer Tissues / 肿瘤防治研究

Objective To explore the expression, correlation with clinicopathologic parameters, and clinical significance of MIS18 binding protein 1 (MIS18BP1) in bladder cancer. Methods TCGA and GEO databases were used to analyze the mRNA expression of MIS18BP1 in tumors and controls, and the results were verified via qRT-PCR. UALCAN online database was utilized in the analysis of the expression of MIS18BP1 and its correlation with clinicopathological parameters and the degree of immune cell infiltration. Immunohistochemistry was employed to analyze the expression of MIS18BP1 in bladder cancer and its relationship with clinicopathological features. The ROC curve was applied to evaluate the diagnostic value of MIS18BP1 mRNA in bladder cancer. Results Bioinformatics analysis and qRT-PCR results revealed the increased expression of MIS18BP1 mRNA in bladder cancer compared with that in the control group (P<0.05). Immunohistochemistry unveiled the significantly high positive rate of MIS18BP1 protein in bladder cancer (P<0.05) and its correlation with the clinical stage of tumors, depth of invasion, and lymph node metastasis (P<0.05). The immune infiltration analysis showed the association of MIS18BP1 with immune cell infiltration in bladder cancer. Conclusion The increased expression level of MIS18BP1 gene and protein in bladder cancer may regulate the development of bladder cancer by influencing immune cell infiltration.

Identification of key genes and regulatory mechanisms in adult degenerative scoliosis.

BACKGROUND: Adult degenerative scoliosis (ADS) is a spinal disorder, but its pathogenesis remain unclear. Therefore, in this study, we utilized data from the GEO database and explored the key genes and regulatory mechanisms involved in ADS. METHODS: We performed bioinformatics analysis on the GSE209825 dataset of GEO database. Weighted gene co-expression network analysis (WGCNA) was used to identify ADS-related gene modules, and we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. We constructed a protein-protein interaction (PPI) network using the STRING database. We validated the specificity of hub genes in ADS using the GSE34095 dataset and plotted ROC curves for the identification of different degenerative spinal diseases based on the hub genes expression RESULTS: We identified 113 differentially expressed lncRNAs. WGCNA identified the MEblack module had the strongest correlation to ADS. GO and KEGG analyses of target genes in lncRNAs revealed their involvement in immune responses, inflammation, cellular processes, and metabolic pathways. Through PPI and ROC analysis, 10 hub genes linked to ADS diseases with certain specificity were found: ELANE, LTF, DEFA1B, SLC2A4, DEFA1, FAXDC2, LCN2, CTSB, FDFT1, and AURKA. CONCLUSIONS: We identified 10 potential hub genes associated with ADS and constructed a transcription factors (TFs)-lncRNAs-hub genes regulatory network. These findings provide a new direction and research basis for the targeted treatment and mechanism research of ADS.

Ferroptosis-related genes as diagnostic markers for major depressive disorder and their correlations with immune infiltration.

Background: Major depression disorder (MDD) is a devastating neuropsychiatric disease, and one of the leading causes of suicide. Ferroptosis, an iron-dependent form of regulated cell death, plays a pivotal role in numerous diseases. The study aimed to construct and validate a gene signature for diagnosing MDD based on ferroptosis-related genes (FRGs) and further explore the biological functions of these genes in MDD. Methods: The datasets were downloaded from the Gene Expression Omnibus (GEO) database and FRGs were obtained from the FerrDb database and other literatures. Least absolute shrinkage and selection operator (LASSO) regression and stepwise logistic regression were performed to develop a gene signature. Receiver operating characteristic (ROC) curves were utilized to assess the diagnostic power of the signature. Gene ontology (GO) enrichment analysis was used to explore the biological roles of these diagnostic genes, and single sample gene set enrichment analysis (ssGSEA) algorithm was used to evaluate immune infiltration in MDD. Animal model of depression was constructed to validate the expression of the key genes. Results: Eleven differentially expressed FRGs were identified in MDD patients compared with healthy controls. A signature of three FRGs (ALOX15B, RPLP0, and HP) was constructed for diagnosis of MDD. Afterwards, ROC analysis confirmed the signature's discriminative capacity (AUC = 0.783, 95% CI = 0.719-0.848). GO enrichment analysis revealed that the differentially expressed genes (DEGs) related to these three FRGs were mainly involved in immune response. Furthermore, spearman correlation analysis demonstrated that these three FRGs were associated with infiltrating immune cells. ALOX15B and HP were significantly upregulated and RPLP0 was significantly downregulated in peripheral blood of the lipopolysaccharide (LPS)-induced depressive model. Conclusion: Our results suggest that the novel FRG signature had a good diagnostic performance for MDD, and these three FRGs correlated with immune infiltration in MDD.