CMap Database Research Articles

Mitophagy related diagnostic biomarkers for coronary in-stent restenosis identified using machine learning and bioinformatics

Percutaneous coronary intervention (PCI) combined with stent implantation is currently one of the most effective treatments for coronary artery disease (CAD). However, in-stent restenosis (ISR) significantly compromises its long-term efficacy. Mitophagy plays a crucial role in vascular homeostasis, yet its role in ISR remains unclear. This study aims to identify mitophagy-related biomarkers for ISR and explore their underlying molecular mechanisms. Through differential gene expression analysis between ISR and Control samples in the combined dataset, 169 differentially expressed genes (DEGs) were identified. Twenty-three differentially expressed mitophagy-related genes (DEMRGs) were identified by intersecting with mitophagy-related genes (MRGs) from the GeneCards, and functional enrichment analysis indicated their significant involvement in mitophagy-related biological processes. Using Weighted Gene Co-expression Network Analysis (WGCNA) and three machine learning algorithms (Logistic-LASSO, RF, and SVM-RFE), LRRK2, and ANKRD13A were identified as mitophagy-related biomarkers for ISR. The nomogram based on these two genes also exhibited promising diagnostic performance for ISR. Gene Set Enrichment Analysis (GSEA) as well as immune infiltration analyses showed that these two genes were closely associated with immune and inflammatory responses in ISR. Furthermore, potential small molecule compounds with therapeutic implications for ISR were predicted using the connectivity Map (cMAP) database. This study systematically investigated mitophagy-related biomarkers for ISR and their potential biological functions, providing new insights into early diagnosis and precision treatment strategies for ISR.

Amlexanox reduces new-onset atrial fibrillation risk in sepsis by downregulating S100A12: a Mendelian randomization study.

Sepsis is characterized by high morbidity and mortality rates, alongside limited therapeutic efficacy. Atrial fibrillation (AF), the most common arrhythmia, has been closely linked to sepsis in prior research. However, the specific mechanisms through which sepsis leads to new-onset AF remain poorly understood. This study focuses on identifying critical genes that are dysregulated in the development of new-onset AF within the context of sepsis, with the goal of uncovering new potential targets for its diagnosis and prevention. Our study began by applying Mendelian Randomization (MR) to assess the causal link between sepsis and AF. We then sourced sepsis and AF datasets from the Gene expression Omnibus (GEO) database. Using Weighted Gene Co-expression Network Analysis (WGCNA), we pinpointed key modules and genes associated with both sepsis and AF conditions. Protein-protein interaction (PPI) network was constructed. The Transcriptional Regulatory Relationships Unravelled by Sentence-based Text-mining (TRRUST) database helped build the transcription factor (TF) interaction network. Key genes were scrutinized through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) to delve into their roles in new-onset AF's pathophysiology during sepsis. We employed the CIBERSORT algorithm to evaluate immune infiltration and the association between key genes and immune cells. The Connectivity Map (CMap) database facilitated the prediction of potential small molecule compounds targeting key genes. To culminate, an acute sepsis mouse model was developed to validate the implicated mechanisms of key genes involved in new-onset AF during sepsis, and to assess the prophylactic effectiveness of identified drug candidates. MR revealed potential independent risk factors for new-onset AF in sepsis. S100A12 was identified as a core interaction gene with elevated levels in sepsis and AF, underscoring its diagnostic and predictive significance. S100A12, along with associated genes, was mainly linked to immune and inflammatory response signaling pathways, correlating with immune cell levels. Targeting S100A12 identifies five potential small molecule therapeutics: amlexanox, balsalazide, methandriol, olopatadine, and tiboloe. In animal studies, acute sepsis increased S100A12 expression in serum and atrial tissues, correlating positively with inflammatory markers (IL-1β, IL-6, TNF-α) and negatively with heart rate, indicating a predisposition to AF. Early amlexanox administration can reduced S100A12 expression, dampened inflammation, and lessened new-onset AF risk in sepsis. This study demonstrates that sepsis may independently increase the risk of new-onset AF. We identified S100A12 as a key gene influencing the new-onset AF in sepsis through immune regulation, presenting considerable diagnostic and predictive value. Notably, amlexanox, by targeting S100A12 emerges as the most clinical relevant intervention for managing new-onset AF in sepsis patients.

Integrative bioinformatics analysis for identifying the mitochondrial-related gene signature associated with immune infiltration in premature ovarian insufficiency.

Premature ovarian insufficiency (POI) is a reproductive disorder characterized by the cessation of ovarian function before the age of 40. Although mitochondrial dysfunction and immune disorders are believed to contribute to ovarian damage in POI, the interplay between these factors remains understudied. In this research, transcriptomic data related to POI were obtained from the NCBI GEO database. Hub biomarkers were identified through the construction of a protein‒protein interaction (PPI) network and further validated using RT‒qPCR and Western blot. Moreover, their expression across various cell types was elucidated via single-cell RNA sequencing analysis. A comprehensive investigation of the mitochondrial and immune profiles of POI was carried out through correlation analysis. Furthermore, potential therapeutic agents were predicted utilizing the cMap database. A total of 119 mitochondria-related differentially expressed genes (MitoDEGs) were identified and shown to be significantly enriched in metabolic pathways. Among these genes, Hadhb, Cpt1a, Mrpl12, and Mrps7 were confirmed both in a POI model and in human granulosa cells (GCs), where they were found to accumulate in GCs and theca cells. Immune analysis revealed variations in macrophages, monocytes, and 15 other immune cell types between the POI and control groups. Notably, strong correlations were observed between seven hub-MitoDEGs (Hadhb, Cpt1a, Cpt2, Mrpl12, Mrps7, Mrpl51, and Eci1) and various functions, such as mitochondrial respiratory complexes, dynamics, mitophagy, mitochondrial metabolism, immune-related genes, and immunocytes. Additionally, nine potential drugs (calyculin, amodiaquine, eudesmic acid, cefotaxime, BX-912, prostratin, SCH-79797, HU-211, and pizotifen) targeting key genes were identified. Our results highlight the crosstalk between mitochondrial function and the immune response in the development of POI. The identification of MitoDEGs could lead to reliable biomarkers for the early diagnosis, monitoring, and personalized treatment of POI.

Exploration of small molecule compounds targeting abdominal aortic aneurysm based on CMap database and molecular dynamics simulation.

The mitigation of abdominal aortic aneurysm (AAA) growth through pharmaceutical intervention offers the potential to avert the perils associated with AAA rupture and the subsequent need for surgical intervention. Nevertheless, the existing effective drugs for AAA treatment are limited, necessitating a pressing exploration for novel therapeutic medications. AAA-related transcriptome data were downloaded from GEO, and differentially expressed genes (DEGs) in AAA tissue were screened for GO and KEGG enrichment analyses. Small molecule compounds and their target proteins with negative connectivity to the AAA expression profile were predicted in the Connectivity Map (CMap) database. Molecular docking and molecular dynamics simulation were performed to predict the binding of the target protein to the small molecule compound, and the MM/GBSA method was used to calculate the binding free energy. Cluster analysis was performed using the cluster tool in the GROMACS package. An AAA cell-free model was built, and CETSA experiments were used to demonstrate the binding ability of small molecules to the target protein in cells. A total of 2244 DEGs in AAA were obtained through differential analysis, and the DEGs were mainly enriched in the tubulin binding biological function and cell cycle pathway. The CMap results showed that Apicidin had a potential therapeutic effect on AAA with a connectivity score of -97.74, and HDAC4 was the target protein of Apicidin. Based on literature, HDAC4-Apicidin was selected as the subsequent research object. The lowest affinity of Apicidin-HDAC4 molecular docking was -8.218kcal/mol. Molecular dynamics simulation results indicated that Apicidin-HDAC4 could form a stable complex. MM/GBSA analysis showed a total binding free energy of -55.40 ± 0.79kcal/mol, and cluster analysis showed that there were two main conformational clusters during the binding process, accounting for 22.4% and 57.8%, respectively. Apicidin could form hydrogen bonds with surrounding residues for stable binding. CETSA experiment proved the stable binding ability of Apicidin and HDAC4. Apicidin inhibited HDAC4 in AAA and exhibited favorable protein-ligand interactions and stability, making it a potential candidate drug for treating AAA.

Key genes and regulatory networks for diabetic retinopathy based on hypoxia-related genes: a bioinformatics analysis.

To prevent neovascularization in diabetic retinopathy (DR) patients and partially control disease progression. Hypoxia-related differentially expressed genes (DEGs) were identified from the GSE60436 and GSE102485 datasets, followed by gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Potential candidate drugs were screened using the CMap database. Subsequently, a protein-protein interaction (PPI) network was constructed to identify hypoxia-related hub genes. A nomogram was generated using the rms R package, and the correlation of hub genes was analyzed using the Hmisc R package. The clinical significance of hub genes was validated by comparing their expression levels between disease and normal groups and constructing receiver operating characteristic curve (ROC) curves. Finally, a hypoxia-related miRNA-transcription factor (TF)-Hub gene network was constructed using the NetworkAnalyst online tool. Totally 48 hypoxia-related DEGs and screened 10 potential candidate drugs with interaction relationships to upregulated hypoxia-related genes were identified, such as ruxolitinib, meprylcaine, and deferiprone. In addition, 8 hub genes were also identified: glycogen phosphorylase muscle associated (PYGM), glyceraldehyde-3-phosphate dehydrogenase spermatogenic (GAPDHS), enolase 3 (ENO3), aldolase fructose-bisphosphate C (ALDOC), phosphoglucomutase 2 (PGM2), enolase 2 (ENO2), phosphoglycerate mutase 2 (PGAM2), and 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3). Based on hub gene predictions, the miRNA-TF-Hub gene network revealed complex interactions between 163 miRNAs, 77 TFs, and hub genes. The results of ROC showed that the except for GAPDHS, the area under curve (AUC) values of the other 7 hub genes were greater than 0.758, indicating their favorable diagnostic performance. PYGM, GAPDHS, ENO3, ALDOC, PGM2, ENO2, PGAM2, and PFKFB3 are hub genes in DR, and hypoxia-related hub genes exhibited favorable diagnostic performance.

Construction of a prognostic model for colon cancer by combining endoplasmic reticulum stress responsive genes

Endoplasmic reticulum stress may affect the occurrence and development of cancer. However, its effect on the prognosis of colon cancer (CC) patients is not clear yet. Herein, based on TCGA database, we screened 15 endoplasmic reticulum stress responsive genes (ERSRGs) associated with the prognosis of CC patients by Cox regression. By LASSO and multivariate Cox regression analyses, a prognostic risk assessment model involving 12 genes (DNAJB2, EIF4A1, YPEL4, COQ10A, IRX3, ASPHD1, NTRK2, TRIM39, XBP1, GRIN2B, LRRC59, and RORC) was built. The survival curves indicated that patients in the low-risk group had good prognosis. ROC curves demonstrated a good performance of this 12-gene prognostic model, and the Riskscore could be considered as an independent prognostic factor. Patients in low-risk group benefit more from immune checkpoint inhibitor and immune checkpoint blockade (ICB) treatment. Besides, the enrichment analysis suggested a remarkable difference in Ca2+ signaling in both groups. Finally, based on the cMAP database, we identified several potential drugs that could target high-risk groups, such as Dasatinib, GNF-2, Saracatinib, and WZ-1-84. To sum up, our research constructed an ERSRGs-characteristic prognostic model. The model is a promising biomarker for prediction of clinical outcomes and immune therapy response of CC patients. SignificanceBased on the transcriptomic data of colon cancer in the TCGA database, this study screens 12 endoplasmic reticulum stress-related genes (ERSRGs), including DNAJB2, EIF4A1, YPEL4, COQ10A, IRX3, ASPHD1, NTRK2, TRIM39, XBP1, asphD1, NTRK2. GRIN2B, LRRC59, and RORC, and a prognostic model was constructed. This model can be used as a predictor of prognosis and immunotherapy response in colon cancer patients. At the same time, model-based prediction of drugs can also be a potential option for colon cancer treatment in the future.

Repositioning fluphenazine as a cuproptosis-dependent anti-breast cancer drug candidate based on TCGA database

Breast cancer is one of the most prevalent malignancies among women. Enhancing the prognosis is an effective approach to enhance the survival rate of breast cancer. Cuproptosis, a copper-dependent programmed cell death process, has been associated with patient prognosis. Inducing cuproptosis is a promising approach for therapy. However, there is currently no anti-breast cancer drug that induces cuproptosis. In this study, we repositioned the clinical drug fluphenazine as a potential agent for breast cancer treatment by inducing cuproptosis. Firstly, we utilized the Cancer Genome Atlas (TCGA) database and Connectivity Map (CMap) database to identify 22 potential compounds with anti-breast cancer activity through inducing cuproptosis. Subsequently, our findings demonstrated that fluphenazine effectively suppressed the viability of MCF-7 cells. Fluphenazine also significantly inhibited the viability of triple negative breast cancer cells MDA-MB-453 and MDA-MB-231. Furthermore, our study revealed that fluphenazine significantly down-regulated the expression of potential prognostic biomarkers associated with cuproptosis, increased copper ion levels, and reduced intracellular pyruvate accumulation. Additionally, it up-regulated the expression of FDX1 at both the mRNA and protein levels, which has been reported to play a crucial role in the induction of cuproptosis. These findings suggest that fluphenazine has the potential to be used as an anti-breast cancer drug by inducing cuproptosis. Therefore, this research provides an insight for the development of novel cuproptosis-dependent anti-cancer agents.

Exploring prognostic DNA methylation genes in bladder cancer: a comprehensive analysis

The current study aimed to investigate the status of genes with prognostic DNA methylation sites in bladder cancer (BLCA). We obtained bulk transcriptome sequencing data, methylation data, and single-cell sequencing data of BLCA from public databases. Initially, Cox survival analysis was conducted for each methylation site, and genes with more than 10 methylation sites demonstrating prognostic significance were identified to form the BLCA prognostic methylation gene set. Subsequently, the intersection of marker genes associated with epithelial cells in single-cell sequencing analysis was obtained to acquire epithelial cell prognostic methylation genes. Utilizing ten machine learning algorithms for multiple combinations, we selected key genes (METRNL, SYT8, COL18A1, TAP1, MEST, AHNAK, RPP21, AKAP13, RNH1) based on the C-index from multiple validation sets. Single-factor and multi-factor Cox analyses were conducted incorporating clinical characteristics and model genes to identify independent prognostic factors (AHNAK, RNH1, TAP1, Age, and Stage) for constructing a Nomogram model, which was validated for its good diagnostic efficacy, prognostic prediction ability, and clinical decision-making benefits. Expression patterns of model genes varied among different clinical features. Seven immune cell infiltration prediction algorithms were used to assess the correlation between immune cell scores and Nomogram scores. Finally, drug sensitivity analysis of Nomogram model genes was conducted based on the CMap database, followed by molecular docking experiments. Our research offers a reference and theoretical basis for prognostic evaluation, drug selection, and understanding the impact of DNA methylation changes on the prognosis of BLCA.

The Co-pathogenic Target Gene CNTN1 Involved in Coronary Artery Disease and Pulmonary Arterial Hypertension Has Potential for Diagnosis of Coronary Artery Disease.

We aimed to find a gene for coronary artery disease (CAD) early diagnosis by detecting co-pathogenic target gene involved in CAD and pulmonary arterial hypertension (PAH).
 
 Methods: Datasets were obtained from the Gene Expression Omnibus (GEO) database, including GSE113079, GSE113439, and GSE12288, to investigate gene expression patterns in cardiovascular diseases. Weighted Gene Co-expression Network Analysis (WGCNA) was performed to identify gene modules associated with clinical traits. Differential gene expression analysis and functional enrichment analysis were carried out. Protein-protein interaction (PPI) networks were constructed. JASPAR database and FIMO tool were utilized to predict transcription factor (TF) binding sites.
 
 Results: Fifteen key genes were identified in CAD and PAH, with CNTN1 being prioritized for further investigation due to its high connectivity degree. Upstream regulation analysis identified potential TFs (DRGX, HOXD3, and RAX) and 7 miRNAs targeting CNTN1. The expression profile of CNTN1 was significantly upregulated in CAD samples, and ROC analysis indicated potential diagnostic value for CAD. CMap database analysis predicted potential targeted drugs for CAD.
 
 Conclusion: CNTN1 was detected as a co-pathogenetic gene for CAD and PAH. It is highly expressed in CAD patients and has potential value for CAD diagnosis. CNTN1 is potentially regulated by 3 TFs and 7 miRNAs.

Identification of a prognostic gene signature in patients with cisplatin resistant squamous cell lung cancer.

In the absence of targeted mutations and immune checkpoints, platinum-based chemotherapy remains a gold standard agent in the treatment of patients with lung squamous cell carcinoma (LUSC). However, cisplatin resistance greatly limits its therapeutic efficacy and presents challenges in the treatment of lung cancer patients. Therefore, the potential clinical needs for this research focus on identifying novel molecular signatures to further elucidate the underlying mechanisms of cisplatin resistance in LUSC. A growing body of evidence indicates that alternative splicing (AS) events significantly influence the tumor progression and survival of patients with LUSC. However, there are few systematic analyses of AS reported in LUSC. This study aims to explore the role of messenger RNA (mRNA), microRNA (miRNA), and AS in predicting prognosis in patients with cisplatin-resistant LUSC and provide potential therapeutic targets and drugs. Gene expression and miRNA expression, using RNA sequencing (RNA-seq), and SpliceSeq data were downloaded from The Cancer Genome Atlas (TCGA) database. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis were used to construct predictive models. Kaplan-Meier survival analyses were used to evaluate patients' prognosis. Single-sample gene set enrichment analysis (ssGSEA) conducted via the R package "GSEAbase" was used to evaluate the immune-related characteristics. Immunohistochemistry was used to examine protein expression. The Connectivity Map (CMap) database was used to screen for potential drugs. The 3-(4,5)-dimethylthiahiazo (-z-y1)-3,5-di-phenytetrazoliumromide (MTT) assay was used to determine and calculate the half-maximal inhibitory concentration (IC50) of the drugs, sulforaphane and parthenolide. In this study, bioinformatics were used to identify mRNAs, miRNAs, and AS events related to response to cisplatin and to establish an integrated prognostic signature for 70 patients with LUSC and cisplatin resistance. The prognostic signature served as an independent prognostic factor with high accuracy [hazard ratio (HR) =2.346, 95% confidence interval (CI): 1.568-3.510; P<0.001], yielding an area under the curve (AUC) of 0.825, 0.829, and 0.877 for 1-, 3-, and 5-year survival, respectively. It also demonstrated high predictive performance in this cohort of patients with LUSC, with an AUC of 0.734, 0.767, and 0.776 for 1-, 3-, and 5-year survival, respectively. This integrated signature was also found to be an independent indicator among conventional clinical features (HR =2.288, 95% CI: 1.547-3.383; P<0.001). In addition, we analyzed the correlation of the signature with immune infiltration and identified several small-molecule drugs that had the potential to improve the survival of patients with LUSC. This study provides a framework for the mRNA-, miRNA-, and AS-based evaluation of cisplatin response and several potential therapeutic drugs for targeting cisplatin resistance in LUSC. These findings may serve as a theoretical basis for the clinical alleviation of cisplatin resistance and thus help to improve treatment responses to chemotherapy in patients with LUSC.

Identification of epithelial-related artificial neural network prognostic models for the prediction of bladder cancer prognosis through comprehensive analysis of single-cell and bulk RNA sequencing

BackgroundBladder cancer (BLCA) presents as a heterogeneous epithelial malignancy. Progress in the early detection and effective treatment of BLCA relies heavily on the identification of novel biomarkers. Therefore, the primary goal of this study is to pinpoint potential biomarkers for BLCA through the fusion of single-cell RNA sequencing and RNA sequencing assessments. Furthermore, the aim is to establish practical clinical prognostic models that can facilitate accurate categorization and individualized therapy for patients. MethodsIn this research, training sets were acquired from the TCGA database, whereas validation sets (GSE32894) and single-cell datasets (GSE135337) were extracted from the GEO database. Single-cell analysis was utilized to obtain characteristic subpopulations along with their associated marker genes. Subsequently, a novel BLCA subtype was identified within TCGA-BLCA. Furthermore, an artificial neural network prognostic model was constructed within the TCGA-BLCA cohort and subsequently verified utilizing a validation set. Two machine learning algorithms were employed to screen hub genes. QRT-qPCR was performed to detect the gene expression levels utilized in the construction of prognostic models across various cell lines. Additionally, the cMAP database and molecular docking were utilized for searching small molecule drugs. ResultsThe results of single-cell analysis revealed the presence of epithelial cells in multiple subpopulations, with 1579 marker genes selected for subsequent investigations. Subsequently, four epithelial cell subtypes were identified within the TCGA-BLCA cohort. Notably, cluster A exhibited a significant survival advantage. Concurrently, an artificial neural network prognostic model comprising 17 feature genes was constructed, accurately stratifying patient risk. Patients categorized in the low-risk group demonstrated a considerable survival advantage. The ROC analysis suggested that the model has strong prognostic ability. Furthermore, the findings of the validation group align consistently with those from the training group. Two types of machine learning algorithms screened NFIC as hub genes. Forskolin, a small molecule drug that binds to NFIC, was identified by employing a cMAP database and molecular docking. ConclusionThe analysis results supplement the research on the role of epithelial cells in BLCA. An artificial neural network prognostic model containing 17 characteristic genes demonstrates the capability to accurately stratify patient risk, thereby potentially improving clinical decision-making and optimizing personalized therapeutic approaches.

Identification of lysosome-related hub genes as potential biomarkers and immune infiltrations of moyamoya disease by multiple bioinformatics methods and machine-learning strategies

BackgroundMoyamoya disease (MMD), characterized by chronic cerebrovascular pathology, poses a rare yet significant clinical challenge, associated with elevated rates of mortality and disability. Despite intensive research endeavors, the exact biomarkers driving its pathogenesis remain enigmatic. MethodsThe expression patterns of GSE189993 and GSE141022 were retrieved from the Gene Expression Omnibus (GEO) repository to procure differentially expressed genes (DEGs) between samples afflicted with MMD and those under control conditions. The Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine with Recursive Feature Elimination (SVM-RFE), and Random Forest (RF) algorithms were employed for identifying candidate diagnostic genes associated with MMD. Subsequently, these candidate genes underwent validation in an independent cohort (GSE157628). The CMAP database was ultimately employed to forecast drugs pertinent to MMD for clinical translation. ResultsA collective of 240 DEGs were discerned. Functional enrichment scrutiny unveiled the enrichment of the cholesterol metabolism pathway, salmonella infection pathway, and allograft rejection pathway within the MMD cohort. EPDR1, DENND3, and NCSTN emerged as discerned diagnostic biomarkers for MMD. The CMAP database was ultimately employed to scrutinize the ten most auspicious pharmaceutical compounds for managing MMD. Finally, after validation through in vitro experiments, EPDR1, DENND3, and NCSTN were identified as the key genes. ConclusionEPDR1, DENND3, and NCSTN have emerged as potential novel biomarkers for MMD. The involvement of T lymphocytes, neutrophilic granulocytes, dendritic cells, natural killer cells, and plasma cells could be pivotal in the pathogenesis and advancement of MMD.

Investigating the Role of Inflammatory Response in Polycystic Ovary Syndrome Using Integrated RNA-Seq Analysis.

An important factor in the pathogenesis of polycystic ovary syndrome (PCOS) is chronic low-grade inflammation. However, the exact pathophysiology of PCOS is currently unknown, which makes clinical diagnosis and the development of effective treatments more difficult. We aimed to investigate the role of the inflammatory response in initiating and progressing PCOS. 13 control granulosa cell samples and 15 granulosa cell samples from patients with PCOS were obtained from the GSE102293, GSE34526, and GSE5850 datasets. The gene set variation analysis (GSVA) method was used to calculate the inflammatory response score. Subsequently, the genes associated with inflammation in the hub were identified using differential expression analysis and weighted gene co-expression network analysis (WGCNA). The findings were confirmed by analysis of independent datasets and examination of clinical samples by qRT-PCR analysis. A consensus cluster analysis was conducted to categorize the PCOS samples into subtypes related to inflammation. Functional enrichment and analysis of immune cell infiltration were conducted to explore the potential mechanisms involved. Additionally, the CMap database was utilized to predict potential drugs, and the results were confirmed through molecular docking. During the training cohort analysis, we identified five distinct genes (TGFBR2, ICAM3, WIPF1, SLC11A1, and NCF2) that could serve as potential diagnostic markers for PCOS. The expression levels of these genes were confirmed through validation in both the test set and clinical samples. In training cohort, two distinct inflammatory patterns (C1 and C2) were identified, and the C2 subtype exhibited activated immune- and inflammation-related pathways. Esmolol was shown to have potential as a drug to treat PCOS and it showed good results for molecular binding at TGFBR2, ICAM3, WIPF1, SLC11A1, and NCF2 proteins. Five diagnostic biomarkers and two inflammation-related molecular types associated with PCOS were identified, and esmolol was a potential drug for PCOS treatment. Our findings provided new diagnostic markers and potential small-molecule drugs for PCOS diagnosis and prevention.

Screening of potential drugs for the treatment of diabetic kidney disease using single-cell transcriptome sequencing and connectivity map data

ObjectiveTo explore the feasibility of screening potential drugs for the treatment of diabetic kidney disease (DKD) using a single-cell transcriptome sequencing dataset and Connectivity Map (CMap) database screening. MethodsA DKD single-nucleus transcriptome sequencing dataset was analyzed using Seurat 4.0 to obtain specific podocyte subclusters and differentially expressed genes (DEGs) related to DKD. These DEGs were subsequently subjected to a search against the CMap database to screen for drug candidates. Cell and animal experiments were conducted to evaluate the efficacy of the top 3 drug candidates. ResultsInitially, we analyzed the DKD single-nucleus transcriptome sequencing dataset to obtain intrinsic renal cells such as podocytes, endothelial cells, mesangial cells, proximal tubular cells, collecting duct cells and immune cells. Podocytes were further divided into four subclusters, among which the proportion of POD_1 podcytes was significantly greater in DKD kidneys than in control kidneys (34.0 % vs. 3.4 %). The CMap database was searched using the identified DEGs in the POD_1 subcluster, and the drugs, including tozasertib, paroxetine, and xylazine, were obtained. Cell-based experiments showed that tozasertib, paroxetine and xylazine had no significant podocyte toxicity in the concentration range of 0.01–50 μM. Tozasertib, paroxetine, and xylazine all reversed the advanced glycation end products (AGEs)-induced decrease in podocyte marker levels, but the effect of paroxetine was more prominent. Animal experiments showed that paroxetine decreased urine ALB/Cr levels in DKD model mice by approximately 51.5 % (115.7 mg/g vs. 238.8 mg/g, P < 0.05). Histopathological assessment revealed that paroxetine attenuated basement membrane thickening, restored the number of foot processes of podocytes, and reduced foot process fusion. In addition, paroxetine also attenuated renal tubular-interstitial fibrosis. Mechanistically, paroxetine inhibited the expression of GRK2 and NLRP3, decreased the phosphorylation level of p65, restored NRF2 expression, and relieved inflammation and oxidative stress. ConclusionThis strategy based on single-cell transcriptome sequencing and CMap data can facilitate the identification and aid the rapid development of clinical DKD drugs. Paroxetine, screened by this strategy, has excellent renoprotective effects.

Identification of mitophagy and ferroptosis-related hub genes associated with intracerebral haemorrhage through bioinformatics analysis

Background Mitophagy and ferroptosis occur in intracerebral haemorrhage (ICH) but our understanding of mitophagy and ferroptosis-related genes remains incomplete. Aim This study aims to identify shared ICH genes for both processes. Methods ICH differentially expressed mitophagy and ferroptosis-related genes (DEMFRGs) were sourced from the GEO database and literature. Enrichment analysis elucidated functions. Hub genes were selected via STRING, MCODE, and MCC algorithms in Cytoscape. miRNAs targeting hubs were predicted using miRWalk 3.0, forming a miRNA-hub gene network. Immune microenvironment variances were assessed with MCP and TIMER. Potential small molecules for ICH were forecasted via CMap database. Results 64 DEMFRGs and ten hub genes potentially involved in various processes like ferroptosis, TNF signalling pathway, MAPK signalling pathway, and NF-kappa B signalling pathway were discovered. Several miRNAs were identified as shared targets of hub genes. The ICH group showed increased infiltration of monocytic lineage and myeloid dendritic cells compared to the Healthy group. Ten potential small molecule drugs (e.g. Zebularine, TWS-119, CG-930) were predicted via CMap. Conclusion Several shared genes between mitophagy and ferroptosis potentially drive ICH progression via TNF, MAPK, and NF-kappa B pathways. These results offer valuable insights for further exploring the connection between mitophagy, ferroptosis, and ICH.

Identification and Analysis of Biomarkers Associated with Lipophagy and Therapeutic Agents for COVID-19.

Lipids, as a fundamental cell component, play an regulating role in controlling the different cellular biological processes involved in viral infections. A notable feature of coronavirus disease 2019 (COVID-19) is impaired lipid metabolism. The function of lipophagy-related genes in COVID-19 is unknown. The present study aimed to investigate biomarkers and drug targets associated with lipophagy and lipophagy-based therapeutic agents for COVID-19 through bioinformatics analysis. Lipophagy-related biomarkers for COVID-19 were identified using machine learning algorithms such as random forest, Support Vector Machine-Recursive Feature Elimination, Generalized Linear Model, and Extreme Gradient Boosting in three COVID-19-associated GEO datasets: scRNA-seq (GSE145926) and bulk RNA-seq (GSE183533 and GSE190496). The cMAP database was searched for potential COVID-19 medications. The lipophagy pathway was downregulated, and the lipid droplet formation pathway was upregulated, resulting in impaired lipid metabolism. Seven lipophagy-related genes, including ACADVL, HYOU1, DAP, AUP1, PRXAB2, LSS, and PLIN2, were used as biomarkers and drug targets for COVID-19. Moreover, lipophagy may play a role in COVID-19 pathogenesis. As prospective drugs for treating COVID-19, seven potential downregulators (phenoxybenzamine, helveticoside, lanatoside C, geldanamycin, loperamide, pioglitazone, and trichostatin A) were discovered. These medication candidates showed remarkable binding energies against the seven biomarkers. The lipophagy-related genes ACADVL, HYOU1, DAP, AUP1, PRXAB2, LSS, and PLIN2 can be used as biomarkers and drug targets for COVID-19. Seven potential downregulators of these seven biomarkers may have therapeutic effects for treating COVID-19.

Identification of potential biomarkers for sepsis based on neutrophil extracellular trap-related genes

Sepsis is a highly lethal disease that poses a serious threat to human health. Increasing evidence indicates that neutrophil extracellular traps (NETs) are key factors in the pathological progression of sepsis. This study aims to screen potential biomarkers for sepsis and delve into their regulatory function in the pathogenesis. We downloaded 6 microarray datasets from the Gene Expression Omnibus (GEO) database, with 4 as the training sets and 2 as the validation sets. NETs-related genes (NRGs) were obtained from relevant literature. Differential expression analysis was performed on four training sets separately. We intersected differentially expressed genes (DEGs) from the four training sets and NRGs, finally resulting in 19 NETs-related sepsis genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) unearthed that NETs-related sepsis genes were majorly abundant in functions and pathways such as defense response to bacterium and Neutrophil extracellular trap formation. Using the PPI network, the MCC algorithm, and the MCODE algorithm in the CytoHubba plugin, 7 sepsis hub genes (ELANE, TLR4, MPO, PADI4, CTSG, MMP9, S100A12) were identified. ROC curve for each Hub gene in the training and validation sets were plotted, which revealed that the Area Under Curve (AUC) values are all greater than 0.6, indicating good classification ability. A total of 349 miRNAs targeting Hub genes were predicted in the mirDIP database, and 620 lncRNAs targeting miRNAs were predicted in the ENCORI database. The ceRNA regulatory network was constructed using Cytoscape software. Finally, we employed the cMAP database to predict small molecular complexes as potentially effective drugs for the treatment of sepsis, such as chloroquine, harpagoside, and PD-123319. In conclusion, this project successfully identified 7 core genes, which may serve as promising candidates for novel sepsis biomarkers. Meanwhile, we constructed a related ceRNA network and predicted potential targeted drugs, providing potential therapeutic targets and treatment strategies for sepsis patients.

Targeting RIP3 inhibits osteoarthritis development by restoring anabolic-catabolic balance in the bone-cartilage unit

BackgroundOsteoarthritis (OA) is a debilitating joint disorder characterized by progressive cartilage degeneration. During OA, subchondral bone undergoes microstructural and molecular changes that precede cartilage degradation. However, specific mechanisms underlying metabolic dysregulation of the bone-cartilage unit remain unclear. This study aims to investigate the role of receptor-interacting protein kinase-3 (RIP3) in OA progression, focusing on bone-cartilage metabolic homeostasis. MethodsRIP3-mediated pathological and metabolic alterations in chondrocytes, osteoblasts, and bone marrow-derived macrophages (BMMs) were evaluated. RIP3-mediated OA manifestations in cartilage and, more importantly, subchondral bone were determined by intra-articular overexpression of RIP3 in rats. The protective effect of RIP3 deficiency on the bone-cartilage unit during OA was systematically investigated using Rip3 knockout mice. The CMap database was used to screen for compounds that abrogate RIP3-induced OA pathological changes. ResultsRIP3 was upregulated in the cartilage and subchondral bone of OA patients and post-traumatic OA mouse model. RIP3 overexpression not only inhibited extracellular matrix (ECM) anabolism in chondrocytes but also attenuated osteoblast differentiation, whereas RIP3 deficiency blunted receptor activator of NF-kappaB ligand-mediated osteoclastogenesis of BMMs. Intra-articular RIP3 overexpression induced the imbalance of SP7+ osteoblasts/tartrate-resistant acid phosphatase (TRAP)+ osteoclasts within the subchondral bone in addition to cartilage degeneration in rats, while Rip3 deletion significantly improved structural outcomes of the bone-cartilage unit, and achieved pain relief as well as functional improvement in surgery-induced and spontaneous OA mouse models. Mechanistically, RIP3 initiates OA by perturbing critical events, including cartilage metabolism, inflammatory responses, senescence, and osteoclast differentiation. Clofibrate, a hypolipidemic drug, was identified as a novel RIP3 inhibitor that reverses ECM catabolism in OA. ConclusionsRIP3 is an essential governor of whole joint metabolic homeostasis by regulating both cartilage metabolism and subchondral bone remodeling. Reconstruction of the bone-cartilage unit by targeting RIP3 might provide a two-birds-one-stone approach for the development of future OA therapies.

Integrated bioinformatics combined with machine learning to analyze shared biomarkers and pathways in psoriasis and cervical squamous cell carcinoma.

Psoriasis extends beyond its dermatological inflammatory manifestations, encompassing systemic inflammation. Existing studies have indicated a potential risk of cervical cancer among patients with psoriasis, suggesting a potential mechanism of co-morbidity. This study aims to explore the key genes, pathways, and immune cells that may link psoriasis and cervical squamous cell carcinoma (CESC). The cervical squamous cell carcinoma dataset (GSE63514) was downloaded from the Gene Expression Omnibus (GEO). Two psoriasis-related datasets (GSE13355 and GSE14905) were merged into one comprehensive dataset after removing batch effects. Differentially expressed genes were identified using Limma and co-expression network analysis (WGCNA), and machine learning random forest algorithm (RF) was used to screen the hub genes. We analyzed relevant gene enrichment pathways using GO and KEGG, and immune cell infiltration in psoriasis and CESC samples using CIBERSORT. The miRNA-mRNA and TFs-mRNA regulatory networks were then constructed using Cytoscape, and the biomarkers for psoriasis and CESC were determined. Potential drug targets were obtained from the cMAP database, and biomarker expression levels in hela and psoriatic cell models were quantified by RT-qPCR. In this study, we identified 27 key genes associated with psoriasis and cervical squamous cell carcinoma. NCAPH, UHRF1, CDCA2, CENPN and MELK were identified as hub genes using the Random Forest machine learning algorithm. Chromosome mitotic region segregation, nucleotide binding and DNA methylation are the major enrichment pathways for common DEGs in the mitotic cell cycle. Then we analyzed immune cell infiltration in psoriasis and cervical squamous cell carcinoma samples using CIBERSORT. Meanwhile, we used the cMAP database to identify ten small molecule compounds that interact with the central gene as drug candidates for treatment. By analyzing miRNA-mRNA and TFs-mRNA regulatory networks, we identified three miRNAs and nine transcription factors closely associated with five key genes and validated their expression in external validation datasets and clinical samples. Finally, we examined the diagnostic effects with ROC curves, and performed experimental validation in hela and psoriatic cell models. We identified five biomarkers, NCAPH, UHRF1, CDCA2, CENPN, and MELK, which may play important roles in the common pathogenesis of psoriasis and cervical squamous cell carcinoma, furthermore predict potential therapeutic agents. These findings open up new perspectives for the diagnosis and treatment of psoriasis and squamous cell carcinoma of the cervix.

Mechanistic study of pre-eclampsia and macrophage-associated molecular networks: bioinformatics insights from multiple datasets

BackgroundPre-eclampsia is a pregnancy-related disorder characterized by hypertension and proteinuria, severely affecting the health and quality of life of patients. However, the molecular mechanism of macrophages in pre-eclampsia is not well understood.MethodsIn this study, the key biomarkers during the development of pre-eclampsia were identified using bioinformatics analysis. The GSE75010 and GSE74341 datasets from the GEO database were obtained and merged for differential analysis. A weighted gene co-expression network analysis (WGCNA) was constructed based on macrophage content, and machine learning methods were employed to identify key genes. Immunoinfiltration analysis completed by the CIBERSORT method, R package “ClusterProfiler” to explore functional enrichment of these intersection genes, and potential drug predictions were conducted using the CMap database. Lastly, independent analysis of protein levels, localization, and quantitative analysis was performed on placental tissues collected from both preeclampsia patients and healthy control groups.ResultsWe identified 70 differentially expressed NETs genes and found 367 macrophage-related genes through WGCNA analysis. Machine learning identified three key genes: FNBP1L, NMUR1, and PP14571. These three key genes were significantly associated with immune cell content and enriched in multiple signaling pathways. Specifically, these genes were upregulated in PE patients. These findings establish the expression patterns of three key genes associated with M2 macrophage infiltration, providing potential targets for understanding the pathogenesis and treatment of PE. Additionally, CMap results suggested four potential drugs, including Ttnpb, Doxorubicin, Tyrphostin AG 825, and Tanespimycin, which may have the potential to reverse pre-eclampsia.ConclusionStudying the expression levels of three key genes in pre-eclampsia provides valuable insights into the prevention and treatment of this condition. We propose that these genes play a crucial role in regulating the maternal-fetal immune microenvironment in PE patients, and the pathways associated with these genes offer potential avenues for exploring the molecular mechanisms underlying preeclampsia and identifying therapeutic targets. Additionally, by utilizing the Connectivity Map database, we identified drug targets like Ttnpb, Doxorubicin, Tyrphostin AG 825, and Tanespimycin as potential clinical treatments for preeclampsia.