A comparative cross-platform analysis of cuproptosis-related genes in human nonobstructive azoospermia: An observational study.

This study aimed to identify novel biomarkers associated with cuproptosis in human nonobstructive azoospermia (NOA). We obtained 4 NOA microarray datasets (GSE145467, GSE9210, GSE108886, and GSE45885) from the NCBI Gene Expression Omnibus database and merged them into training set. Another NOA dataset (GSE45887) was used as validation set. Differentially expressed cuproptosis-related genes were identified from training set. Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway analyses were conducted. Least absolute shrinkage and selection operator regression and support vector machine-recursive feature elimination were used to identify hub cuproptosis-related genes. We calculated the expression of the hub cuproptosis-related genes in both validation set and patients with NOA. Gene set variation analysis was used to explore their potential biological functions. The risk prediction model was built by logistic regression analysis and was evaluated in the validation set. Finally, we constructed a competing endogenous RNA network. The training set included 29 patents in the control group and 92 in the NOA group, and 10 cuproptosis-related differentially expressed genes were identified. Subsequently, we screened 6 hub cuproptosis-related genes (DBT, GCSH, NFE2L2, NLRP3, PDHA1, and SLC31A1) by least absolute shrinkage and selection operator regression and support vector machine-recursive feature elimination. GCSH, NFE2L2, NLRP3, and SLC31A1 expressed higher in NOA group than in control group (P < .05) in the validation set (4 patients in control and 16 in NOA groups), while the expression levels of GCSH, NFE2L2, NLRP3, PDHA1, and SLC31A1 were higher in NOA group than in control group (P < .05) in our patients (3 patients in control and 4 in NOA groups). The model based on the 6-gene signature showed superior performance with an AUC value of 0.970 in training set, while 1.0 in validation set. Gene set variation analysis revealed a higher enrichment score of "homologous recombination" in the high expression groups of the 6 hub genes. Finally, we constructed a competing endogenous RNA network and found hsa-miR-335-3p and hsa-miR-1-3p were the most frequently related to the 6 hub genes. DBT, GCSH, NFE2L2, NLRP3, PDHA1, and SLC31A1 may serve as predictors of cuproptosis and play important roles in the NOA pathogenesis.

Identification and verification of immune-related genes for diagnosing the progression of atherosclerosis and metabolic syndrome.

BACKGROUND: Atherosclerosis and metabolic syndrome are the main causes of cardiovascular events, but their underlying mechanisms are not clear. In this study, we focused on identifying genes associated with diagnostic biomarkers and effective therapeutic targets associated with these two diseases. METHODS: Transcriptional data sets of atherosclerosis and metabolic syndrome were obtained from GEO database. The differentially expressed genes were analyzed by RStudio software, and the function-rich and protein-protein interactions of the common differentially expressed genes were analyzed.Furthermore, the hub gene was screened by Cytoscape software, and the immune infiltration of hub gens was analyzed. Finally, relevant clinical blood samples were collected for qRT-PCR verification of the three most important hub genes. RESULTS: A total of 1242 differential genes (778 up-regulated genes and 464 down-regulated genes) were screened from GSE28829 data set. A total of 1021 differential genes (492 up-regulated genes and 529 down-regulated genes) were screened from the data set GSE98895. Then 23 up-regulated genes and 11 down-regulated genes were screened by venn diagram. Functional enrichment analysis showed that cytokines and immune activation were involved in the occurrence and development of these two diseases. Through the construction of the Protein-Protein Interaction(PPI) network and Cytoscape software analysis, we finally screened 10 hub genes. The immune infiltration analysis was further improved. The results showed that the infiltration scores of 7 kinds of immune cells in GSE28829 were significantly different among groups (Wilcoxon Test < 0.05), while in GSE98895, the infiltration scores of 4 kinds of immune cells were significantly different between groups (Wilcoxon Test < 0.05). Spearman method was used to analyze the correlation between the expression of 10 key genes and 22 kinds of immune cell infiltration scores in two data sets. The results showed that there were 42 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE28829 (|Cor| > 0.3 & P < 0.05). There were 41 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE98895 (|Cor| > 0.3 & P < 0.05). Finally, our results identified 10 small molecules with the highest absolute enrichment value, and the three most significant key genes (CX3CR1, TLR5, IL32) were further verified in the data expression matrix and clinical blood samples. CONCLUSION: We have established a co-expression network between atherosclerotic progression and metabolic syndrome, and identified key genes between the two diseases. Through the method of bioinformatics, we finally obtained 10 hub genes in As and MS, and selected 3 of the most significant genes (CX3CR1, IL32, TLR5) for blood PCR verification. This may be helpful to provide new research ideas for the diagnosis and treatment of AS complicated with MS.

Integrating bioinformatics and machine learning methods to analyze diagnostic biomarkers for HBV-induced hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a malignant tumor. It is estimated that approximately 50-80% of HCC cases worldwide are caused by hepatitis b virus (HBV) infection, and other pathogenic factors have been shown to promote the development of HCC when coexisting with HBV. Understanding the molecular mechanisms of HBV-induced hepatocellular carcinoma (HBV-HCC) is crucial for the prevention, diagnosis, and treatment of the disease. In this study, we analyzed the molecular mechanisms of HBV-induced HCC by combining bioinformatics and deep learning methods. Firstly, we collected a gene set related to HBV-HCC from the GEO database, performed differential analysis and WGCNA analysis to identify genes with abnormal expression in tumors and high relevance to tumors. We used three deep learning methods, Lasso, random forest, and SVM, to identify key genes RACGAP1, ECT2, and NDC80. By establishing a diagnostic model, we determined the accuracy of key genes in diagnosing HBV-HCC. In the training set, RACGAP1(AUC:0.976), ECT2(AUC:0.969), and NDC80 (AUC: 0.976) showed high accuracy. They also exhibited good accuracy in the validation set: RACGAP1(AUC:0.878), ECT2(AUC:0.731), and NDC80(AUC:0.915). The key genes were found to be highly expressed in liver cancer tissues compared to normal liver tissues, and survival analysis indicated that high expression of key genes was associated with poor prognosis in liver cancer patients. This suggests a close relationship between key genes RACGAP1, ECT2, and NDC80 and the occurrence and progression of HBV-HCC. Molecular docking results showed that the key genes could spontaneously bind to the anti-hepatocellular carcinoma drugs Lenvatinib, Regorafenib, and Sorafenib with strong binding activity. Therefore, ECT2, NDC80, and RACGAP1 may serve as potential biomarkers for the diagnosis of HBV-HCC and as targets for the development of targeted therapeutic drugs.

Ant colony optimization for the identification of dysregulated gene subnetworks from expression data.

BACKGROUND: High-throughput experimental technologies can provide deeper insights into pathway perturbations in biomedical studies. Accordingly, their usage is central to the identification of molecular targets and the subsequent development of suitable treatments for various diseases. Classical interpretations of generated data, such as differential gene expression and pathway analyses, disregard interconnections between studied genes when looking for gene-disease associations. Given that these interconnections are central to cellular processes, there has been a recent interest in incorporating them in such studies. The latter allows the detection of gene modules that underlie complex phenotypes in gene interaction networks. Existing methods either impose radius-based restrictions or freely grow modules at the expense of a statistical bias towards large modules. We propose a heuristic method, inspired by Ant Colony Optimization, to apply gene-level scoring and module identification with distance-based search constraints and penalties, rather than radius-based constraints. RESULTS: We test and compare our results to other approaches using three datasets of different neurodegenerative diseases, namely Alzheimer's, Parkinson's, and Huntington's, over three independent experiments. We report the outcomes of enrichment analyses and concordance of gene-level scores for each disease. Results indicate that the proposed approach generally shows superior stability in comparison to existing methods. It produces stable and meaningful enrichment results in all three datasets which have different case to control proportions and sample sizes. CONCLUSION: The presented network-based gene expression analysis approach successfully identifies dysregulated gene modules associated with a certain disease. Using a heuristic based on Ant Colony Optimization, we perform a distance-based search with no radius constraints. Experimental results support the effectiveness and stability of our method in prioritizing modules of high relevance. Our tool is publicly available at github.com/GhadiElHasbani/ACOxGS.git.

Identification of potential biomarkers for atrial fibrillation and stable coronary artery disease based on WGCNA and machine algorithms.

BACKGROUND: Patients with atrial fibrillation (AF) often have coronary artery disease (CAD), but the biological link between them remains unclear. This study aims to explore the common pathogenesis of AF and CAD and identify common biomarkers. METHODS: Gene expression profiles for AF and stable CAD were downloaded from the Gene Expression Omnibus database. Overlapping genes related to both diseases were identified using weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. Hub genes were then identified using the machine learning algorithm. Immune cell infiltration and correlations with hub genes were explored, followed by drug predictions. Hub gene expression in AF and CAD patients was validated by real-time qPCR. RESULTS: We obtained 28 common overlapping genes in AF and stable CAD, mainly enriched in the PI3K-Akt, ECM-receptor interaction, and relaxin signaling pathway. Two hub genes, COL6A3 and FKBP10, were positively correlated with the abundance of MDSC, plasmacytoid dendritic cells, and regulatory T cells in AF and negatively correlated with the abundance of CD56dim natural killer cells in CAD. The AUCs of COL6A3 and FKBP10 were all above or close to 0.7. Drug prediction suggested that collagenase clostridium histolyticum and ocriplasmin, which target COL6A3, may be potential drugs for AF and stable CAD. Additionally, COL6A3 and FKBP10 were upregulated in patients with AF and CAD. CONCLUSION: COL6A3 and FKBP10 may be key biomarkers for AF and CAD, providing new insights into the diagnosis and treatment of this disease.

Identifying the key hub genes linked with lung squamous cell carcinoma by examining the differentially expressed and survival genes.

Lung Squamous Cell Carcinoma is characterised by significant alterations in RNA expression patterns, and a lack of early symptoms and diagnosis results in poor survival rates. Our study aimed to identify the hub genes involved in LUSC by differential expression analysis and their influence on overall survival rates in patients. Thus, identifying genes with the potential to serve as biomarkers and therapeutic targets. RNA sequence data for LUSC was obtained from TCGA and analysed using R Studio. Survival analysis was performed on DE genes. PPI network and hub gene analysis was performed on survival-relevant genes. Enrichment analysis was conducted on the PPI network to elucidate the functional roles of hub genes. Our analysis identified 2774 DEGs in LUSC patient datasets. Survival analysis revealed 511 genes with a significant impact on patient survival. Among these, 20 hub genes-FN1, ACTB, HGF, PDGFRB, PTEN, SNAI1, TGFBR1, ESR1, SERPINE1, THBS1, PDGFRA, VWF, BMP2, LEP, VTN, PXN, ABL1, ITGA3 and ANXA5-were found to have lower expression levels associated with better patient survival, whereas high expression of SOX2 correlated with longer survival. Enrichment analysis indicated that these hub genes are involved in critical cellular and cancer-related pathways. Our study has identified six key hub genes that are differentially expressed and exhibit significant influence over LUSC patient survival outcomes. Further, in vitro and in vivo studies must be conducted on the key genes for their utilisation as therapeutic targets and biomarkers in LUSC.

Systems biology-based analysis exploring shared biomarkers and pathogenesis of myocardial infarction combined with osteoarthritis.

Background: More and more evidence supports the association between myocardial infarction (MI) and osteoarthritis (OA). The purpose of this study is to explore the shared biomarkers and pathogenesis of MI complicated with OA by systems biology. Methods: Gene expression profiles of MI and OA were downloaded from the Gene Expression Omnibus (GEO) database. The Weighted Gene Co-Expression Network Analysis (WGCNA) and differentially expressed genes (DEGs) analysis were used to identify the common DEGs. The shared genes related to diseases were screened by three public databases, and the protein-protein interaction (PPI) network was built. GO and KEGG enrichment analyses were performed on the two parts of the genes respectively. The hub genes were intersected and verified by Least absolute shrinkage and selection operator (LASSO) analysis, receiver operating characteristic (ROC) curves, and single-cell RNA sequencing analysis. Finally, the hub genes differentially expressed in primary cardiomyocytes and chondrocytes were verified by RT-qPCR. The immune cell infiltration analysis, subtypes analysis, and transcription factors (TFs) prediction were carried out. Results: In this study, 23 common DEGs were obtained by WGCNA and DEGs analysis. In addition, 199 common genes were acquired from three public databases by PPI. Inflammation and immunity may be the common pathogenic mechanisms, and the MAPK signaling pathway may play a key role in both disorders. DUSP1, FOS, and THBS1 were identified as shared biomarkers, which is entirely consistent with the results of single-cell RNA sequencing analysis, and furher confirmed by RT-qPCR. Immune infiltration analysis illustrated that many types of immune cells were closely associated with MI and OA. Two potential subtypes were identified in both datasets. Furthermore, FOXC1 may be the crucial TF, and the relationship of TFs-hub genes-immune cells was visualized by the Sankey diagram, which could help discover the pathogenesis between MI and OA. Conclusion: In summary, this study first revealed 3 (DUSP1, FOS, and THBS1) novel shared biomarkers and signaling pathways underlying both MI and OA. Additionally, immune cells and key TFs related to 3 hub genes were examined to further clarify the regulation mechanism. Our study provides new insights into shared molecular mechanisms between MI and OA.

Identification of differentially expressed genes, pathways, and immune infiltration in diabetes.

This study aimed to perform exhaustive bioinformatic analysis by using GSE29221 micro-array maps obtained from healthy controls and Type 2 Diabetes (T2DM) patients. Raw data are downloaded from the Gene Expression Omnibus database and processed by the limma package in R software to identify Differentially Expressed Genes (DEGs). Gene ontology functional analysis and Kyoto Gene Encyclopedia and Genome Pathway analysis are performed to determine the biological functions and pathways of DEGs. A protein interaction network is constructed using the STRING database and Cytoscape software to identify key genes. Finally, immune infiltration analysis is performed using the Cibersort method. This study has implications for understanding the underlying molecular mechanism of T2DM and provides potential targets for further research.

Pan-cancer analysis of TMEM45A and exploration of its prognostic value and mechanism in gastric cancer.

Cancer is a major category of diseases that need to be addressed urgently, bringing a huge burden to the world. Gastric cancer (GC) is a frequent malignant tumor of the digestive system with the highest incidence and mortality rate among all tumors. The purpose of this study was to explore the mechanism of action of TMEM45A in pan-cancer and gastric cancer. First, GEO and TCGA database were employed to analyze the expression of TMEM45A in GC patients. Then, we determined the association between TMEM45A expression and survival of GC patients using the Kaplan-Meier Plotter database and TCGA database and verified the accuracy of TMEM45A in predicting prognosis. Next, we analyzed the effect of CTHRC expression on TIICs in GC tissues. A prognostic model was constructed using immunomodulatory genes associated with TMEM45A. The specificity and accuracy of the model were verified. TMEM45A expression was markedly higher in GC tissue than in normal tissue. GC patients with TMEM45A overexpression had a poor prognosis. The AUC value of 5-year survival on the ROC curve was 0.705, indicating that TMEM45A is a reliable prognostic factor and can be used as a clinicopathological indicator alone to predict patient prognosis. Three high-risk immunomodulatory genes (CXCR4 and TGFB1) and one low-risk immunomodulatory gene (PDCD1) were obtained using both univariate and multivariate COX methods. These three immunomodulatory molecules were used to construct prognostic models. GC patients with TMEM45A overexpression have a poor prognosis and are associated with immune cell infiltration. Hence, TMEM45A is a fairly reliable independent prognostic marker.

Molecular Mechanisms Linking Genes and Vitamins of the Complex B Related to One-Carbon Metabolism in Breast Cancer: An In Silico Functional Database Study.

Carcinogenesis is closely related to the expression, maintenance, and stability of DNA. These processes are regulated by one-carbon metabolism (1CM), which involves several vitamins of the complex B (folate, B2, B6, and B12), whereas alcohol disrupts the cycle due to the inhibition of folate activity. The relationship between nutrients related to 1CM (all aforementioned vitamins and alcohol) in breast cancer has been reviewed. The interplay of genes related to 1CM was also analyzed. Single nucleotide polymorphisms located in those genes were selected by considering the minor allele frequency in the Caucasian population and the linkage disequilibrium. These genes were used to perform several in silico functional analyses (considering corrected p-values < 0.05 as statistically significant) using various tools (FUMA, ShinyGO, and REVIGO) and databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) and GeneOntology (GO). The results of this study showed that intake of 1CM-related B-complex vitamins is key to preventing breast cancer development and survival. Also, the genes involved in 1CM are overexpressed in mammary breast tissue and participate in a wide variety of biological phenomena related to cancer. Moreover, these genes are involved in alterations that give rise to several types of neoplasms, including breast cancer. Thus, this study supports the role of one-carbon metabolism B-complex vitamins and genes in breast cancer; the interaction between both should be addressed in future studies.

Integrated Bioinformatics-Based Identification and Validation of Neuroinflammation-Related Hub Genes in Primary Open-Angle Glaucoma.

Glaucoma is a leading cause of permanent blindness, affecting 80 million people worldwide. Recent studies have emphasized the importance of neuroinflammation in the early stages of glaucoma, involving immune and glial cells. To investigate this further, we used the GSE27276 dataset from the GEO (Gene Expression Omnibus) database and neuroinflammation genes from the GeneCards database to identify differentially expressed neuroinflammation-related genes associated with primary open-angle glaucoma (POAG). Subsequently, these genes were submitted to Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes for pathway enrichment analyses. Hub genes were picked out through protein-protein interaction networks and further validated using the external datasets (GSE13534 and GSE9944) and real-time PCR analysis. The gene-miRNA regulatory network, receiver operating characteristic (ROC) curve, genome-wide association study (GWAS), and regional expression analysis were performed to further validate the involvement of hub genes in glaucoma. A total of 179 differentially expressed genes were identified, comprising 60 upregulated and 119 downregulated genes. Among them, 18 differentially expressed neuroinflammation-related genes were found to overlap between the differentially expressed genes and neuroinflammation-related genes, with six genes (SERPINA3, LCN2, MMP3, S100A9, IL1RN, and HP) identified as potential hub genes. These genes were related to the IL-17 signaling pathway and tyrosine metabolism. The gene-miRNA regulatory network showed that these hub genes were regulated by 118 miRNAs. Notably, GWAS data analysis successfully identified significant single nucleotide polymorphisms (SNPs) corresponding to these six hub genes. ROC curve analysis indicated that our genes showed significant accuracy in POAG. The expression of these genes was further confirmed in microglia, Müller cells, astrocytes, and retinal ganglion cells in the Spectacle database. Moreover, three hub genes, SERPINA3, IL1R1, and LCN2, were validated as potential diagnostic biomarkers for high-risk glaucoma patients, showing increased expression in the OGD/R-induced glaucoma model. This study suggests that the identified hub genes may influence the development of POAG by regulation of neuroinflammation, and it may offer novel insights into the management of POAG.

Deciphering the Molecular Complexity of Hepatocellular Carcinoma: Unveiling Novel Biomarkers and Therapeutic Targets Through Advanced Bioinformatics Analysis.

BACKGROUND: Hepatocellular carcinoma (HCC) represents a primary liver tumor characterized by a bleak prognosis and elevated mortality rates, yet its precise molecular mechanisms have not been fully elucidated. This study uses advanced bioinformatics techniques to discern differentially expressed genes (DEGs) implicated in the pathogenesis of HCC. The primary objective is to discover novel biomarkers and potential therapeutic targets that can contribute to the advancement of HCC research. METHODS: The bioinformatics analysis in this study primarily utilized the Gene Expression Omnibus (GEO) database as data source. Initially, the Transcriptome analysis console (TAC) screened for DEGs. Subsequently, we constructed a protein-protein interaction (PPI) network of the proteins associated to the identified DEGs with the STRING database. We obtained our hub genes using Cytoscape and confirmed the results through the GEPIA database. Furthermore, we assessed the prognostic significance of the identified hub genes using the GEPIA database. To explore the regulatory interactions, a miRNA-gene interaction network was also constructed, incorporating information from the miRDB database. For predicting the impact of gene overexpression on drug effects, we utilized CANCER DP. RESULTS: A comprehensive analysis of HCC gene expression profiles revealed a total of 4716 DEGs, consisting of 2430 upregulated genes and 2313 downregulated genes in HCC sample compared to healthy control group. These DEGs exhibited significant enrichment in key pathways such as the PI3K-Akt signaling pathway, nuclear receptors meta-pathway, and various metabolism-related pathways. Further exploration of the PPI network unveiled the P53 signaling pathway and pyrimidine metabolism as the most prominent pathways. We identified 10 hub genes (ASPM, RRM2, CCNB1, KIF14, MKI67, SHCBP1, CENPF, ANLN, HMMR, and EZH2) that exhibited significant upregulation in HCC samples compared to healthy control group. Survival analysis indicated that elevated expression levels of these genes were strongly associated with changes in overall survival in HCC patients. Lastly, we identified specific miRNAs that were found to influence the expression of these genes, providing valuable insights into potential regulatory mechanisms underlying HCC progression. CONCLUSION: The findings of this study have successfully identified pivotal genes and pathways implicated in the pathogenesis of HCC. These novel discoveries have the potential to significantly enhance our understanding of HCC at the molecular level, opening new ways for the development of targeted therapies and improved prognosis evaluation.

Key genes and immune infiltration patterns and the clinical implications in psoriasis patients.

BACKGROUND: Psoriasis is an immune-mediated skin disease, closely related to immune regulation. The aim was to understand the pathogenesis of psoriasis further, reveal potential therapeutic targets, and provide new clues for its diagnosis, treatment, and prevention. MATERIALS AND METHODS: Expression profiling data were obtained from the Gene Expression Omnibus (GEO) database for skin tissues from healthy population and psoriasis patients. Differentially expressed genes (DEGs) were selected for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) analysis separately. Machine learning algorithms were used to obtain characteristic genes closely associated with psoriasis. Receiver operating characteristic (ROC) curve was used to assess the diagnostic value of the characteristic genes for psoriasis. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to calculate the proportion of immune cell infiltration. Correlation analysis was used to characterize the connection between gene expression and immune cell, Psoriasis Area and Severity Index (PASI). RESULTS: A total of 254 DEGs were identified in the psoriasis group, including 185 upregulated and 69 downregulated genes. GO was mainly enriched in cytokine-mediated signaling pathway, response to virus, and cytokine activity. KEGG was mainly focused on cytokine-cytokine receptor interaction and IL-17 signaling pathway. GSEA was mainly in chemokine signaling pathway and cytokine-cytokine receptor interaction. The machine learning algorithm screened nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9. In the validation set, the expressions of these nine genes increased in the psoriasis group, and the AUC values were all > 0.9, consistent with those of the training set. The immune infiltration results showed increased proportions of macrophages, T cells, and neutrophils in the psoriasis group. The characteristic genes were positively or negatively correlated to varying degrees with T cells and macrophages. Nine characteristic genes were highly expressed in the moderate to severe psoriasis group and positively correlated with PASI scores. CONCLUSION: High levels of nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9 were risk factors for psoriasis, the differential expression of which was related to the regulation of immune system activity and PASI scores, affecting the proportions of different immune cells and promoting the occurrence and development of psoriasis.

Identification of TFRC as a biomarker for pulmonary arterial hypertension based on bioinformatics and experimental verification.

BACKGROUND: Pulmonary arterial hypertension (PAH) is a life-threatening chronic cardiopulmonary disease. However, there is a paucity of studies that reflect the available biomarkers from separate gene expression profiles in PAH. METHODS: The GSE131793 and GSE113439 datasets were combined for subsequent analyses, and batch effects were removed. Bioinformatic analysis was then performed to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) and a protein-protein interaction (PPI) network analysis were then used to further filter the hub genes. Functional enrichment analysis of the intersection genes was performed using Gene Ontology (GO), Disease Ontology (DO), Kyoto encyclopedia of genes and genomes (KEGG) and gene set enrichment analysis (GSEA). The expression level and diagnostic value of hub gene expression in pulmonary arterial hypertension (PAH) patients were also analyzed in the validation datasets GSE53408 and GSE22356. In addition, target gene expression was validated in the lungs of a monocrotaline (MCT)-induced pulmonary hypertension (PH) rat model and in the serum of PAH patients. RESULTS: A total of 914 differentially expressed genes (DEGs) were identified, with 722 upregulated and 192 downregulated genes. The key module relevant to PAH was selected using WGCNA. By combining the DEGs and the key module of WGCNA, 807 genes were selected. Furthermore, protein-protein interaction (PPI) network analysis identified HSP90AA1, CD8A, HIF1A, CXCL8, EPRS1, POLR2B, TFRC, and PTGS2 as hub genes. The GSE53408 and GSE22356 datasets were used to evaluate the expression of TFRC, which also showed robust diagnostic value. According to GSEA enrichment analysis, PAH-relevant biological functions and pathways were enriched in patients with high TFRC levels. Furthermore, TFRC expression was found to be upregulated in the lung tissues of our experimental PH rat model compared to those of the controls, and the same conclusion was reached in the serum of the PAH patients. CONCLUSIONS: According to our bioinformatics analysis, the observed increase of TFRC in the lung tissue of human PAH patients, as indicated by transcriptomic data, is consistent with the alterations observed in PAH patients and rodent models. These data suggest that TFRC may serve as a potential biomarker for PAH.

Database size positively correlates with the loss of species-level taxonomic resolution for the 16S rRNA and other prokaryotic marker genes.

For decades, the 16S rRNA gene has been used to taxonomically classify prokaryotic species and to taxonomically profile microbial communities. However, the 16S rRNA gene has been criticized for being too conserved to differentiate between distinct species. We argue that the inability to differentiate between species is not a unique feature of the 16S rRNA gene. Rather, we observe the gradual loss of species-level resolution for other nearly-universal prokaryotic marker genes as the number of gene sequences increases in reference databases. This trend was strongly correlated with how represented a taxonomic group was in the database and indicates that, at the gene-level, the boundaries between many species might be fuzzy. Through our study, we argue that any approach that relies on a single marker to distinguish bacterial taxa is fraught even if some markers appear to be discriminative in current databases.

Effects of HOX family regulator-mediated modification patterns and immunity characteristics on tumor-associated cell type in endometrial cancer.

Endometrial cancer (UCEC) is one of three major malignant tumors in women. The HOX gene regulates tumor development. However, the potential roles of HOX in the expression mechanism of multiple cell types and in the development and progression of tumor microenvironment (TME) cell infiltration in UCEC remain unknown. In this study, we utilized both the The Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) database to analyze transcriptome data of 529 patients with UCEC based on 39 HOX genes, combing clinical information, we discovered HOX gene were a pivotal factor in the development and progression of UCEC and in the formation of TME diversity and complexity. Here, a new scoring system was developed to quantify individual HOX patterns in UCEC. Our study found that patients in the low HOX score group had abundant anti-tumor immune cell infiltration, good tumor differentiation, and better prognoses. In contrast, a high HOX score was associated with blockade of immune checkpoints, which enhances the response to immunotherapy. The Real-Time quantitative PCR (RT-qPCR) and Immunohistochemistry (IHC) exhibited a higher expression of the HOX gene in the tumor patients. We revealed that the significant upregulation of the HOX gene in the epithelial cells can activate signaling pathway associated with tumour invasion and metastasis through single-cell RNA sequencing (scRNA-seq), such as nucleotide metabolic proce and so on. Finally, a risk prognostic model established by the positive relationship between HOX scores and cancer-associated fibroblasts (CAFs) can predict the prognosis of individual patients by scRNA-seq and transcriptome data sets. In sum, HOX gene may serve as a potential biomarker for the diagnosis and prediction of UCEC and to develop more effective therapeutic strategies.

Spock2 Functions as a Key Time-Series Gene of Endothelial Cells in Sepsis-Induced Cardiomyopathy.

ABSTRACT: The study aimed to investigate the pathogenesis of sepsis-induced cardiomyopathy, a leading cause of mortality in septic patients. Transcriptome data from cecal ligation and puncture-induced septic mice were analyzed at different time points (24, 48, and 72 hours) using GSE171546 data. Through weighted gene co-expression network analysis, time series, and differential expression analyses, key time-series differentially expressed genes were identified. In addition, single-cell sequencing data (GSE207363) were used for both differential and pseudotime analyses to pinpoint differentially expressed genes specific to endothelial cells. The study highlighted Spock2, S100a9, S100a8, and Xdh as differential genes specific to endothelial cells in a time-dependent manner. Immunofluorescence validation confirmed the increased expression of SPOCK2 in the endothelial cells of cecal ligation and puncture-induced septic mice. Furthermore, in vitrostudies showed that deletion of Spock2 significantly increased LPS-induced apoptosis and necrosis in human umbilical vein endothelial cells. In conclusion, SPOCK2 expression was increased in septic cardiac endothelial cells and LPS-induced human umbilical vein endothelial cells and may play a protective role.

SoDCoD: a comprehensive database of Cu/Zn superoxide dismutase conformational diversity caused by ALS-linked gene mutations and other perturbations.

A structural alteration in copper/zinc superoxide dismutase (SOD1) is one of the common features caused by amyotrophic lateral sclerosis (ALS)-linked mutations. Although a large number of SOD1 variants have been reported in ALS patients, the detailed structural properties of each variant are not well summarized. We present SoDCoD, a database of superoxide dismutase conformational diversity, collecting our comprehensive biochemical analyses of the structural changes in SOD1 caused by ALS-linked gene mutations and other perturbations. SoDCoD version 1.0 contains information about the properties of 188 types of SOD1 mutants, including structural changes and their binding to Derlin-1, as well as a set of genes contributing to the proteostasis of mutant-like wild-type SOD1. This database provides valuable insights into the diagnosis and treatment of ALS, particularly by targeting conformational alterations in SOD1. Database URL: https://fujisawagroup.github.io/SoDCoDweb/.

ncStem: a comprehensive resource of curated and predicted ncRNAs in cancer stemness.

Cancer stemness plays an important role in cancer initiation and progression, and is the major cause of tumor invasion, metastasis, recurrence, and poor prognosis. Non-coding RNAs (ncRNAs) are a class of RNA transcripts that generally cannot encode proteins and have been demonstrated to play a critical role in regulating cancer stemness. Here, we developed the ncStem database to record manually curated and predicted ncRNAs associated with cancer stemness. In total, ncStem contains 645 experimentally verified entries, including 159 long non-coding RNAs (lncRNAs), 254 microRNAs (miRNAs), 39 circular RNAs (circRNAs), and 5 other ncRNAs. The detailed information of each entry includes the ncRNA name, ncRNA identifier, disease, reference, expression direction, tissue, species, and so on. In addition, ncStem also provides computationally predicted cancer stemness-associated ncRNAs for 33 TCGA cancers, which were prioritized using the random walk with restart (RWR) algorithm based on regulatory and co-expression networks. The total predicted cancer stemness-associated ncRNAs included 11 132 lncRNAs and 972 miRNAs. Moreover, ncStem provides tools for functional enrichment analysis, survival analysis, and cell location interrogation for cancer stemness-associated ncRNAs. In summary, ncStem provides a platform to retrieve cancer stemness-associated ncRNAs, which may facilitate research on cancer stemness and offer potential targets for cancer treatment. Database URL: http://www.nidmarker-db.cn/ncStem/index.html.

Identification and Validation of Aging Related Genes Signature in Chronic Obstructive Pulmonary Disease.

PURPOSE: Chronic Obstructive Pulmonary Disease (COPD) is regarded as an accelerated aging disease. Aging-related genes in COPD are still poorly understood. METHOD: Data set GSE76925 was obtained from the Gene Expression Omnibus (GEO) database. The "limma" package identified the differentially expressed genes. The weighted gene co-expression network analysis (WGCNA) constructes co-expression modules and detect COPD-related modules. The least absolute shrinkage and selection operator (LASSO) and the support vector machine recursive feature elimination (SVM-RFE) algorithms were chosen to identify the hub genes and the diagnostic ability. Three external datasets were used to identify differences in the expression of hub genes. Real-time reverse transcription polymerase chain reaction (RT-qPCR) was used to verify the expression of hub genes. RESULT: We identified 15 differentially expressed genes associated with aging (ARDEGs). The SVM-RFE and LASSO algorithms pinpointed four potential diagnostic biomarkers. Analysis of external datasets confirmed significant differences in PIK3R1 expression. RT-qPCR results indicated decreased expression of hub genes. The ROC curve demonstrated that PIK3R1 exhibited strong diagnostic capability for COPD. CONCLUSION: We identified 15 differentially expressed genes associated with aging. Among them, PIK3R1 showed differences in external data sets and RT-qPCR results. Therefore, PIK3R1 may play an essential role in regulating aging involved in COPD.