Results Of Gene Set Enrichment Analysis Research Articles

Identification of potential diagnostic genes for atherosclerosis in women with polycystic ovary syndrome

Polycystic ovary syndrome (PCOS), which is the most prevalent endocrine disorder among women in their reproductive years, is linked to a higher occurrence and severity of atherosclerosis (AS). Nevertheless, the precise manner in which PCOS impacts the cardiovascular well-being of women remains ambiguous. The Gene Expression Omnibus database provided four PCOS datasets and two AS datasets for this study. Through the examination of genes originating from differentially expressed (DEGs) and critical modules utilizing functional enrichment analyses, weighted gene co-expression network (WGCNA), and machine learning algorithm, the research attempted to discover potential diagnostic genes. Additionally, the study investigated immune infiltration and conducted gene set enrichment analysis (GSEA) to examine the potential mechanism of the simultaneous occurrence of PCOS and AS. Two verification datasets and cell experiments were performed to assess biomarkers' reliability. The PCOS group identified 53 genes and AS group identified 175 genes by intersecting DEGs and key modules of WGCNA. Then, 18 genes from two groups were analyzed by machine learning algorithm. Death Associated Protein Kinase 1 (DAPK1) was recognized as an essential gene. Immune infiltration and single-gene GSEA results suggest that DAPK1 is associated with T cell-mediated immune responses. The mRNA expression of DAPK1 was upregulated in ox-LDL stimulated RAW264.7 cells and in granulosa cells. Our research discovered the close association between AS and PCOS, and identified DAPK1 as a crucial diagnostic biomarker for AS in PCOS.

Integrative analysis reveals the potential prognostic roles and immunological values of unc-5 netrin receptor A (UNC5A) in glioma

BackgroundUNC5A had been reported to play crucial roles in multiple cancers. However, little was known about the associations among UNC5A and glioma. Therefore, we first combined scRNA-seq, proteomics, as well as bulk RNA-seq in order to investigate UNC5A’s functions in gliomas.MethodsOnline databases provided scRNA-seq, proteomics, as well as bulk RNA-seq data on UNC5A in gliomas. The following procedures were conducted in order: QRT-PCR, Norman chart, gene set enrichment analysis (GSEA), and univariate/multifactor Cox regression analyses. We further explored the associations among UNC5A and tumor immunity.ResultsBy comparing gliomas with normal tissues, the TCGA dataset showed a significantly reduced expression of UNC5A, which was also confirmed by GSE50161, GSE4290, and QRT-PCR findings (p < 0.05). In both the TCGA and CGGA datasets, gliomas patients with low-UNC5A expression would have poorer overall survival (OS) prognoses (p < 0.05). ScRNA-seq analysis by the CancerSEA online website presented that UNC5A had a low expression in various glioma clusters and significantly associated with six functional states. Moreover, UNC5A might be a reliable independent biomarker of OS in gliomas patients (p < 0.05). Based on the results of GSEA, UNC5A might be connected to three significant pathways in gliomas. We also successfully created a Norman chart to assess the OS prognoses of these patients. Additionally, in aspects of tumor immunity, the infiltration levels of immune cells in LGG, the immune cell pathways, tumor immune microenvironment, as well as immune checkpoints in both LGG and GBM were revealed to be significantly influenced by UNC5A (p < 0.05).ConclusionsUNC5A was found to have prognostic and immunological significance in gliomas, offering patients with gliomas new treatment options.

Identification of Hub genes with prognostic values in colorectal cancer by integrated bioinformatics analysis.

Our study aimed to investigate the Hub genes and their prognostic value in colorectal cancer (CRC) via bioinformatics analysis. The data set of colorectal cancer was downloaded from the GEO database (GSE21510, GSE110224 and GSE74602) for differential expression analysis using the GEO2R tool. Hub genes were screened by protein-protein interaction (PPI) comprehensive analysis. GEPIA was used to verify the expression of Hub genes and evaluate its prognostic value. The protein expression of Hub gene in CRC was analyzed using the Human Protein Atlas database. The cBioPortal was used to analyze the type and frequency of Hub gene mutations, and the effects of mutation on the patients' prognosis. The TIMER database was used to study the correlation between Hub genes and immune infiltration in CRC. Gene set enrichment analysis (GSEA) was used to explore the biological function and signal pathway of the Hub genes and corresponding co-expressed genes. We identified 346 differentially expressed genes (DEGs), including 117 upregulated and 229 downregulated. Four Hub genes (AURKA, CCNB1, EXO1 and CCNA2) were selected by survival analysis and differential expression validation. The protein and mRNA expression levels of AURKA, CCNB1, EXO1 and CCNA2 were higher in CRC tissues than in adjacent tissues. There were varying degrees of immune cell infiltration and gene mutation of Hub genes, especially B cells and CD8+ T cells. The results of GSEA showed that Hub genes and their co-expressed genes mainly participated in chromosome segregation, DNA replication, translational elongation and cell cycle. Overexpression of AURKA, CCNB1, CCNA2 and EXO1 had a better prognosis for CRC and this effect was correlation with gene mutation and infiltration of immune cells.

BootGSEA: a bootstrap and rank aggregation pipeline for multi-study and multi-omics enrichment analyses.

Introduction: Gene set enrichment analysis (GSEA) subsequent to differential expression analysis is a standard step in transcriptomics and proteomics data analysis. Although many tools for this step are available, the results are often difficult to reproduce because set annotations can change in the databases, that is, new features can be added or existing features can be removed. Finally, such changes in set compositions can have an impact on biological interpretation. Methods: We present bootGSEA, a novel computational pipeline, to study the robustness of GSEA. By repeating GSEA based on bootstrap samples, the variability and robustness of results can be studied. In our pipeline, not all genes or proteins are involved in the different bootstrap replicates of the analyses. Finally, we aggregate the ranks from the bootstrap replicates to obtain a score per gene set that shows whether it gains or loses evidence compared to the ranking of the standard GSEA. Rank aggregation is also used to combine GSEA results from different omics levels or from multiple independent studies at the same omics level. Results: By applying our approach to six independent cancer transcriptomics datasets, we showed that bootstrap GSEA can aid in the selection of more robust enriched gene sets. Additionally, we applied our approach to paired transcriptomics and proteomics data obtained from a mouse model of spinal muscular atrophy (SMA), a neurodegenerative and neurodevelopmental disease associated with multi-system involvement. After obtaining a robust ranking at both omics levels, both ranking lists were combined to aggregate the findings from the transcriptomics and proteomics results. Furthermore, we constructed the new R-package "bootGSEA," which implements the proposed methods and provides graphical views of the findings. Bootstrap-based GSEA was able in the example datasets to identify gene or protein sets that were less robust when the set composition changed during bootstrap analysis. Discussion: The rank aggregation step was useful for combining bootstrap results and making them comparable to the original findings on the single-omics level or for combining findings from multiple different omics levels.

Downregulation of NAT1 Expression is Associated with Poor Prognosis and Immune Infiltration in COAD.

An increasing corpus of evidence has identified the involvement of N-acetyltransferase 1 (NAT1), a member of the NAT family, in the progression of various cancers. However, the specific function of NAT1 in colon cancer (COAD) remains elusive. This study aims to decip her the role of NAT1 in COAD and its associated mechanisms. The Tumor Immunity Evaluation Resource (TIMER), The Cancer Genome Atlas (TCGA), and the Gene Expression Omnibus (GEO) databases were employed to assess the NAT1 expression level in COAD. The differential expression between COAD and normal colon tissue was further validated using quantitative real-time reverse-transcription PCR (RT-qPCR) and Western blot (WB) analyses. Additionally, survival analysis of NAT1 in COAD was carried out using the PrognoScan database and TCGA dataset. The functions of NAT1 were explored through gene set enrichment analysis (GSEA) and immuno-infiltration analysis. There was a significant reduction in NAT1 expression in COAD samples compared to normal tissue. Notably, low NAT1 expression in COAD correlated significantly with various clinical parameters such as tumor stage (T stage, N stage, M stage, pathologic stage), primary therapy outcome, carcinoembryonic antigen (CEA) level, and lymphatic invasion. The downregulation of NAT1 was also strongly linked with poor outcomes in overall survival (OS), progression-free interval (PFI), and disease-specific survival (DSS). Cox regression analysis highlighted NAT1 as an independent prognostic indicator for overall survival in COAD patients. GSEA results revealed NAT1's involvement in multiple pathways, including the neuroactive ligand-receptor interaction, olfactory transduction, olfactory signaling, extracellular matrix receptor interaction, calcium signaling, and focal adhesion pathways. Furthermore, NAT1 expression was found to significantly correlate with infiltration levels of various immune cells. The findings reveal NAT1's potential as a valuable prognostic biomarker for COAD. Moreover, its associated mechanisms offer insights that might pave the way for therapeutic interventions for COAD patients.

Screening for immune-related biomarkers associated with myasthenia gravis and dilated cardiomyopathy based on bioinformatics analysis and machine learning

BackgroundWe aim to investigate genes associated with myasthenia gravis (MG), specifically those potentially implicated in the pathogenesis of dilated cardiomyopathy (DCM). Additionally, we seek to identify potential biomarkers for diagnosing myasthenia gravis co-occurring with DCM. MethodsWe obtained two expression profiling datasets related to DCM and MG from the Gene Expression Omnibus (GEO). Subsequently, we conducted differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) on these datasets. The genes exhibiting differential expression common to both DCM and MG were employed for protein-protein interaction (PPI), Gene Ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Additionally, machine learning techniques were employed to identify potential biomarkers and develop a diagnostic nomogram for predicting MG-associated DCM. Subsequently, the machine learning results underwent validation using an external dataset. Finally, gene set enrichment analysis (GSEA) and machine algorithm analysis were conducted on pivotal model genes to further elucidate their potential mechanisms in MG-associated DCM. ResultsIn our analysis of both DCM and MG datasets, we identified 2641 critical module genes and 11 differentially expressed genes shared between the two conditions. Enrichment analysis disclosed that these 11 genes primarily pertain to inflammation and immune regulation. Connectivity map (CMAP) analysis pinpointed SB-216763 as a potential drug for DCM treatment. The results from machine learning indicated the substantial diagnostic value of midline 1 interacting protein1 (MID1IP1) and PI3K-interacting protein 1 (PIK3IP1) in MG-associated DCM. These two hub genes were chosen as candidate biomarkers and employed to formulate a diagnostic nomogram with optimal diagnostic performance through machine learning. Simultaneously, single-gene GSEA results and immune cell infiltration analysis unveiled immune dysregulation in both DCM and MG, with MID1IP1 and PIK3IP1 showing significant associations with invasive immune cells. ConclusionWe have elucidated the inflammatory and immune pathways associated with MG-related DCM and formulated a diagnostic nomogram for DCM utilizing MID1IP1/PIK3IP1. This contribution offers novel insights for prospective diagnostic approaches and therapeutic interventions in the context of MG coexisting with DCM.

Development of an immune-related diagnostic predictive model for oral lichen planus.

Oral lichen planus (OLP) was a chronic inflammatory disease of unknown etiology with a 1.4% chance of progressing to malignancy. However, it has been suggested in several studies that immune system disorders played a dominant role in the onset and progression of OLP. Therefore, this experiment aimed to develop a diagnostic prediction model for OLP based on immunopathogenesis to achieve early diagnosis and treatment and prevent cancer. In this study, 2 publicly available OLP datasets from the gene expression omnibus database were filtered. In the experimental group (GSE52130), the level of immune cell infiltration was assessed using MCPcounter and ssGSEA algorithms. Subsequently, differential expression analysis and gene set enrichment analysis were performed between the OLP and control groups. The resulting differentially expressed genes were intersected with immunologically relevant genes provided on the immunology database and analysis portal database (ImmPort) website to obtain differentially expressed immunologically relevant genes (DEIRGs). Furthermore, the gene ontology and kyoto encyclopedia of genes and genomes analyses were carried out. Finally, protein-protein interaction network and least absolute shrinkage and selection operator regression analyses constructed a model for OLP. Receiver operating characteristic curves for the experimental and validation datasets (GSE38616) were plotted separately to validate the model's credibility. In addition, real-time quantitative PCR experiment was performed to verify the expression level of the diagnostic genes. Immune cell infiltration analysis revealed a more significant degree of inflammatory infiltration in the OLP group compared to the control group. In addition, the gene set enrichment analysis results were mainly associated with keratinization, antibacterial and immune responses, etc. A total of 774 differentially expressed genes was obtained according to the screening criteria, of which 65 were differentially expressed immunologically relevant genes. Ultimately, an immune-related diagnostic prediction model for OLP, which was composed of 5 hub genes (BST2, RNASEL, PI3, DEFB4A, CX3CL1), was identified. The verification results showed that the model has good diagnostic ability. There was a significant correlation between the 5 hub diagnostic biomarkers and immune infiltrating cells. The development of this model gave a novel insight into the early diagnosis of OLP.

Actin filament-associated protein 1-antisense RNA1 promotes the development and invasion of tongue squamous cell carcinoma via the AFAP1-AS1/miR-133a-5p/ZIC2 axis.

The present study aimed to explore the biological role and underlying mechanism of the long non-coding RNA actin filament-associated protein 1-antisense RNA1 (lncRNA AFAP1-AS1) in the progression of tongue squamous cell carcinoma (TSCC). A quantitative reverse transcriptase-PCR (RT-qPCR) was conducted to assess relative levels of the miR-133a-5p, lncRNAs AFAP1-AS1 and zinc finger family member 2 (ZIC2) in TSCC cell lines and specimens, whereas ZIC2 protein levels were measured using western blotting. After modifying the levels of expression of lncRNA AFP1-AS1, miR-133a-5p and ZIC2 using lentivirus or plasmid transfection, we examined AKT/epithelial-mesenchymal transition signaling pathway alterations, in vivo carcinogenesis of TSCC in nude mice and in vitro malignant phenotypes. A dual-luciferase reporter assay was conducted to confirm the targeting relationship between ZIC2 and miR-133a-5p, as well as between miR-133a-5p and lncRNA AFAP1-AS1. Based on The Cancer Genome Atlas (TCGA) database, we additionally validated AFP1-AS1. The potential biological pathway for AFP1-AS1 was investigated using gene set enrichment analysis (GSEA). We also evaluated the clinical diagnostic capacities of AFP1-AS1 and clustered the most potential biomarkers with the Mfuzz expression pattern. Finally, we also made relevant drug predictions for AFP1-AS1. In TSCC cell lines and specimens, lncRNA AFAP1-AS1 was upregulated. ZIC2 was upregulated in TSCC cells as a result of lncRNA AFAP1-AS1 overexpression, which also promoted TSCC cell migration, invasion, viability, and proliferation. Via the microRNA sponge effect, it was found that lncRNA AFAP1-AS1 could upregulate ZIC2 by competitively inhibiting miR-133a-5p. Interestingly, knockdown of ZIC2 reversed the biological roles of lncRNA AFAP1-AS1 with respect to inducing malignant phenotypes in TSCC cells. In addition, in vivo overexpression of lncRNA AFAP1-AS1 triggered subcutaneous tumor growth in nude mice implanted with TSCC cells and upregulated ZIC2 in the tumors. The TCGA database findings revealed that AFAP1-AS1 was significantly upregulated in TSCC specimens and had good clinical diagnostic value. The results of GSEA showed that peroxisome proliferator-activated receptor signaling pathway was significantly correlated with low expression of AFP1-AS1. Finally, the results of drug prediction indicated that the group with high AFAP1-AS1 expression was more sensitive to docetaxel, AZD4547, AZD7762 and nilotinib. The upregulation of lncRNA AFAP1-AS1, which increases TSCC cell viability, migration, proliferation and invasion via the AFAP1-AS1/miR-133a-5p/ZIC2 axis, aids in the progression of TSCC.

Identification and subtype analysis of biomarkers associated with the solute carrier family in acute myocardial infarction.

The dysregulation of some solute carrier (SLC) proteins has been linked to a variety of diseases, including diabetes and chronic kidney disease. However, SLC-related genes (SLCs) has not been extensively studied in acute myocardial infarction (AMI). The GSE66360 and GSE60993 datasets, and SLCs geneset were enrolled in this study. Differentially expressed SLCs (DE-SLCs) were screened by overlapping DEGs between the AMI and control groups and SLCs. Next, functional enrichment analysis was carried out to research the function of DE-SLCs. Consistent clustering of samples from the GSE66360 dataset was accomplished based on DE-SLCs selected. Next, the gene set enrichment analysis (GSEA) was performed on the DEGs-cluster (cluster 1 vs cluster 2). Three machine learning models were performed to obtain key genes. Subsequently, biomarkers were obtained through receiver operating characteristic (ROC) curves and expression analysis. Then, the immune infiltration analysis was performed. Afterwards, single-gene GSEA was carried out, and the biomarker-drug network was established. Finally, quantitative real-time fluorescence PCR (qRT-PCR) was performed to verify the expression levels of biomarkers. In this study, 13 DE-SLCs were filtered by overlapping 366 SLCs and 448 DEGs. The functional enrichment results indicated that the genes were implicated with amino acid transport and TNF signaling pathway. After the consistency clustering analysis, the samples were classified into cluster 1 and cluster 2 subtypes. The functional enrichment results showed that DEGs-cluster were implicated with chemokine signaling pathway and so on. Further, SLC11A1 and SLC2A3 were identified as SLC-related biomarkers, which had the strongest negative relationship with resting memory CD4 T cells and the strongest positive association with activated mast cells. In addition, the single-gene GSEA results showed that cytosolic ribosome was enriched by the biomarkers. Five drugs targeting SLC2A3 were predicted as well. Lastly, the experimental results showed that the biomarkers expression trends were consistent with public database. In this study, 2 SLC-related biomarkers (SLC11A1 and SLC2A3) were screened and drug predictions were carried out to explore the prediction and treatment of AMI.

Perspectives for next generation sequencing in patients with follicular lymphoma

Aim. To study the prognostic significance of gene mutations and intracellular signaling pathways involved in lymphomagenesis in patients with follicular lymphoma using next generation sequencing (NGS).Materials and methods. The prospective study included 26 patients with a median age of 51.5 years. Mutational screening was performed for cohort using custom NGS Panel of 118 genes. Gene set enrichment analysis (GSEA) was performed using Metascape. The data was analyzed in SPSS Statistics 26 and R 4.2.2.Results. The highest mutation frequency was noted in the genes: KMT2C – 50 %, KMT2D – 50 %, CREBBP – 31 %, NOTCH2 – 31 %, GNAS – 23 %. Missense mutations occurred with a frequency of 84.3 %. ARID1A gene mutation is an unfavorable prognostic factor according to progressive-free (p = 0.014) and event-free (p = 0.029) survival analysis. Tumor mutation burden (TMB) was defined as the number of mutations per megabase (Mb) of the coding sequence, the median TMB was 5.0 (3.3–8.3) mutations/Mb. The TMB threshold of 6 mutations/Mb divided patients into groups with high (44 %) and low (56 %) TMB. In the high TMB group, 2-year event-free survival was 27.3 % (95 % confidence interval 6.0–61.0), which was significantly lower than in low TMB group – 72.7 % (95 % confidence interval 41.9–91.6; p = 0.037). The most enriched cellular pathways according to GSEA results were regulation of cell activation (–log10(q-value) = 6.357), chromatin remodeling (–log10(q-value) = 5.707), histone modification (–log10(q-value) = 4.569). We have also demonstrated other possibilities of GSEA using follicular lymphoma as an example.Conclusion. TMB is a significant prognostic factor in patients with follicular lymphoma. We have shown that mutations in the MYC, CREBBP, EZH2, KMT2D genes lead to dysregulation in several intracellular processes, mediating complex molecular changes. The most enriched intracellular pathways in follicular lymphoma are those of chromatin remodeling, regulation of cell activation and histone modification.

Overexpression of PSAT1 is Correlated with Poor Prognosis and Immune Infiltration in Non-Small Cell Lung Cancer.

Current evidence suggests that phosphoserine aminotransferase 1 (PSAT1) is overexpressed in various tumors. Herein, we investigate the significance of PSAT1 in non-small cell lung cancer (NSCLC) and its correlation with immune infiltration. The expression profile of PSAT1 in NSCLC patients and related clinical information was obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA-NSCLC) databases. In silico and experimental validation were conducted to assess the role of PSAT1 in NSCLC. Gene set enrichment analysis (GSEA) was performed to investigate the disparities in biological functions between groups with high and low PSAT1 expression. Additionally, the biological characteristics and immune cell infiltration were compared between these two groups. We also assessed whether PSAT1 expression could predict the sensitivity of NSCLC patients to immunotherapy using the immunophenotype score (IPS) and an anti-PD-L1 immunotherapy cohort (IMvig-or210). Furthermore, the difference in drug sensitivity between PSAT1-high and PSAT1-low expression cell lines was investigated. Analysis of transcriptional expression profiles using TCGA data revealed overexpression of PSAT1 in NSCLC tissues correlated with poor overall survival (OS). GSEA results showed enrichment of DNA recombination and repair, nucleotide biosynthesis, and the P53 signaling pathway in the PSAT1-high group. Experimental validation demonstrated that the knockdown of PSAT1 suppressed cell proliferation, migration, and invasion of NSCLC. Immune cell infiltration analysis revealed an immune-activated tumor microenvironment in the PSAT1-low group. It was also observed that PSAT1-low cell lines were more likely to benefit from immunotherapy and several chemotherapy drugs. PSAT1 has enormous potential for applications in the prediction of NSCLC patient outcomes and provides the foothold for more precise individualized treatment of this patient population.

COL3A1, COL5A1 and COL6A2 serve as potential molecular biomarkers for osteoarthritis based on weighted gene co‑expression network analysis bioinformatics analysis.

Osteoarthritis (OA) is a non-inflammatory degenerative joint disease, characterized by joint pain and stiffness. The prevalence of OA increases with age. However, the relationship between biomarkers [collagen type III α1 (COL3A1), COL5A1, COL6A2, COL12A1] and OA remains unclear. The OA subchondral bone dataset GSE51588 was downloaded from the GEO database, and the differentially expressed genes (DEGs) were screened. Weighted gene co-expression network analysis was performed, and a protein-protein interaction network was constructed and further analyzed using Cytoscape and STRING. Functional enrichment analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and then Gene Set Enrichment Analysis (GSEA) was used to formulate the molecular functions and pathways based on the results of GO and KEGG analyses. Comparative Toxicogenomics Database and TargetScan were used to identify the hub-gene-related diseases and the microRNAs that regulated the central hub genes. Immunohistochemical staining was performed to confirm the expression of related proteins in OA and non-OA tissue samples. A total of 1,679 DEGs were identified. GO analysis showed that the DEGs were primarily enriched in the process of 'immune system', 'extracellular region', 'secretory granule', 'collagen-containing extracellular matrix', 'ECM-receptor, glycosaminoglycan binding' and 'systemic lupus erythematosus'. The results of GSEA were similar to those of GO and KEGG enrichment terms for DEGs. A total of 25 important modules were generated, and two core gene clusters and seven core genes were obtained (COL6A2, COL5A2, COL12A1, COL5A1, COL6A1, LUM and COL3A1). Core genes were expressed differentially between OA subchondral bone and normal tissue samples. The expression levels of COL3A1, COL5A1 and COL6A2 in OA subchondral bone tissue were higher compared with those in normal tissues, but COL12A1 expression was not significantly increased; all stained markers were highly expressed in surrounding tissues of immunohistochemical staining. In conclusion, COL3A1, COL5A1 and COL6A2 may be potential molecular biomarkers for OA.

Revealing mechanism of Methazolamide for treatment of ankylosing spondylitis based on network pharmacology and GSEA

Methazolamide is a carbonic anhydrase (CA) inhibitor with satisfactory safety. Our previous studies have demonstrated the elevation of CA1 expression and the therapeutic effect of Methazolamide in Ankylosing spondylitis (AS). In this study, we explored the pathogenic role of CA1 and the pharmacological mechanism of Methazolamide in AS through Gene Set Enrichment Analysis (GSEA) and network pharmacology. Seven out of twelve CA1 related gene sets were enriched in AS group. CA1 was core enriched in above seven gene sets involving zinc ion binding, arylesterase activity and one carbon metabolic process. Functional analysis of the candidate target genes obtained from the intersection of AS associated genes and Methazolamide target genes indicated that Methazolamide exerts therapeutic effects on AS mainly through inflammatory pathways which regulate the production of tumor necrosis factor, IL-6 and nitric oxide. PTGS2, ESR1, GSK3β, JAK2, NOS2 and CA1 were selected as therapeutic targets of Methazolamide in AS. Molecular docking and molecular dynamics simulations were performed successfully. In addition, we innovatively obtained the intersection of Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and GSEA results, and found that 18 GO terms and 5 KEGG terms were indicated in the pharmacological mechanism of Methazolamide in AS, involving bone mineralization, angiogenesis, inflammation, and chemokine signaling pathways. Nevertheless, validation for these mechanisms is needed in vivo/vitro experiments.

Downregulated APOD and FCGR2A correlates with immune infiltration and lipid-induced symptoms of irritable bowel syndrome

Fat intake is among the most significant triggers for symptom development in patients with irritable bowel syndrome (IBS). Nevertheless, long-term restriction in fatty foods ingestion may lead to nutritional inadequacies. This study aimed to identify the crucial genes involved in lipid-induced gastrointestinal symptoms, contributing to helping IBS patients regulate fat. The clinical characteristics of the subjects were collected by questionnaire investigation and analyzed using multivariate logistic regression. Differentially expressed genes (DEG) and signaling pathways were analyzed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. ImmuInfiltration and CIBERSORT packages evaluated small intestine immune cell infiltration. Random forest and SVM-RFE algorithms were used to select hub genes. A receiver operating characteristic curve was used to access the diagnostic significance of each hub gene. Gene Set Enrichment Analysis (GSEA) was performed to identify hub genes’ molecular processes in IBS development after lipid infusion. IBS patients’ risk, severity, and quality of life increased with fat intake. In total, 116 robust DEGs were identified in IBS patients after lipid infusion using the GSE166869 dataset and were mainly clustered in the immune and inflammatory pathways. IBS patients had greater Neutrophils, CD4+ T cells, and M1 Macrophages than healthy controls. Furthermore, infiltration levels of Neutrophils and resting memory CD4+ T cells were inversely related to the expression of hub genes (IGKV1D-43, IGKV1-12, APOD, FCGR2A and IGKV2-29). After lipid infusion, GSEA results of each hub gene indicated the relevance of proinflammatory pathways in IBS pathogenesis. After verification, only APOD and FCGR2A were stably downregulated in small intestinal mucosa and plasma of IBS patients. The area under the curve of APOD combined with FCGR2A expression was 0.9. APOD and FCGR2A may be promising biomarkers for IBS diagnosis and lipid-sensitive IBS patients. Their potential roles in the immune microenvironment of the small intestinal mucosa may provide a vital clue to IBS precision therapy.

Decreased Expression of GLYATL1 Predicts Poor Prognosis in Patients with Clear Cell Renal Cell Carcinoma.

GLYATL1 is a member of the glycine-N-acyltransferase family, which catalyses acyl group transfer. The role of GLYATL1 in cancer is largely unknown; therefore, the potential value of GLYATL1 in clear cell renal cell carcinoma (ccRCC) was explored. The ccRCC gene expression profiles and clinical data were obtained from the University of California Santa Cruz Xena platform. Differential expression and survival analysis were performed using R software. Samples from the TIMER public database and real-world were used for validation. The potential molecular mechanism of GLYATL1 in ccRCC was explored using gene set enrichment analysis (GSEA). GLYATL1 was downregulated, indicating a poor prognosis in ccRCC. Low expression of GLYATL1 was significantly associated with advanced stage and higher histological grade ccRCC. The differential expression of GLYATL1 was validated at the protein level using clinical samples and tissue microarray. The results of GSEA showed that multiple crucial signalling pathways including fatty acid metabolism, adipogenesis, oxidative phosphorylation and epithelial-mesenchymal transition were enriched. This study demonstrated that GLYATL1 downregulation has an unfavourable impact on the survival of patients with ccRCC. The resulting data indicated that GLYATL1 could be a potential new target for ccRCC therapy, which may be helpful for the personalized treatment of such individuals.

A longitudinal genome-wide association study of bone mineral density mean and variability in the UK Biobank.

This study aimed to identify the genetic architecture and potential biomarkers of BMD. Our analysis included 141,261 white participants from the UK Biobank with heel BMD phenotype data. We used a genome-wide trajectory analysis tool, TrajGWAS, to conduct a genome-wide association study (GWAS) of BMD. Then, we validated our findings in previously reported BMD genetic associations and performed replication analysis in the Asian participants. Finally, gene-set enrichment analysis (GSEA) of the identified candidate genes was conducted using the FUMA platform. A total of 52 genes associated with BMD trajectory mean were identified, of which the top three significant genes were WNT16 (P = 1.31 × 10-126), FAM3C (P = 4.18 × 10-108), and CPED1 (P = 8.48 × 10-106). In addition, 114 genes associated with BMD within-subject variability were also identified, such as AC092079.1 (P = 2.72 × 10-13) and RGS7 (P = 4.72 × 10-10). The associations for these candidate genes were confirmed in the previous GWASs and replicated successfully in the Asian participants. GSEA results of BMD change identified multiple GO terms related to skeletal development, such as SKELETAL SYSTEM DEVELOPMENT (Padjusted = 2.45 × 10-3) and REGULATION OF OSSIFICATION (Padjusted = 2.45 × 10-3). KEGG enrichment analysis showed that these genes were mainly enriched in WNT SIGNALING PATHWAY. Our findings indicated that the CPED1-WNT16-FAM3C locus plays a significant role in BMD mean trajectories and identified several novel candidate genes contributing to BMD within-subject variability, facilitating the understanding of the genetic architecture of BMD.

A novel tumor microenvironment-related gene signature with immune features for prognosis of lung squamous cell carcinoma.

Lung squamous cell carcinoma (LUSC) is an aggressive subset of non-small-cell lung cancer (NSCLC). The tumor microenvironment (TME) plays an important role in the development of LUSC. We aim to identify potential therapeutic targets and a TME-related prognostic signature and for LUSC. TME-related genes were obtained from TCGA-LUSC dataset. LUSC samples were clustered by the non-negative matrix clustering algorithm (NMF). The prognostic signature was constructed through univariate Cox regression, multivariate Cox regression, and the least absolute shrinkage and selection operator (LASSO) analyses. Gene set enrichment analysis (GSEA) was carried out to explore the enrichment pathways. This study constructed a prognostic signature which contained 12 genes: HHIPL2, PLK4, SLC6A4, LSM1, TSLP, P4HA1, AMH, CLDN5, NRTN, CDH2, PTGIS, and STX1A. Patients were classified into high-risk and low-risk groups according to the median risk score of this signature. Compared with low-risk group patients, patients in high-risk group patients had poorer overall survival, which demonstrated this signature was an independent prognostic factor. Besides, correlation analysis and GSEA results revealed that genes of this signature were correlated with immune cells and drug response. Our novel signature based on 12 TME-related genes might be applied as an independent prognostic indicator. Importantly, the signature could be a promising biomarker and accurately predict the prognosis of LUSC patients.

Hub genes, diagnostic model, and predicted drugs in systemic sclerosis by integrated bioinformatics analysis.

Background: Systemic sclerosis (scleroderma; SSc), a rare and heterogeneous connective tissue disease, remains unclear in terms of its underlying causative genes and effective therapeutic approaches. The purpose of the present study was to identify hub genes, diagnostic markers and explore potential small-molecule drugs of SSc. Methods: The cohorts of data used in this study were downloaded from the Gene Expression Complex (GEO) database. Integrated bioinformatic tools were utilized for exploration, including Weighted Gene Co-Expression Network Analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) regression, gene set enrichment analysis (GSEA), Connectivity Map (CMap) analysis, molecular docking, and pharmacokinetic/toxicity properties exploration. Results: Seven hub genes (THY1, SULF1, PRSS23, COL5A2, NNMT, SLCO2B1, and TIMP1) were obtained in the merged gene expression profiles of GSE45485 and GSE76885. GSEA results have shown that they are associated with autoimmune diseases, microorganism infections, inflammatory related pathways, immune responses, and fibrosis process. Among them, THY1 and SULF1 were identified as diagnostic markers and validated in skin samples from GSE32413, GSE95065, GSE58095 and GSE125362. Finally, ten small-molecule drugs with potential therapeutic effects were identified, mainly including phosphodiesterase (PDE) inhibitors (BRL-50481, dipyridamole), TGF-β receptor inhibitor (SB-525334), and so on. Conclusion: This study provides new sights into a deeper understanding the molecular mechanisms in the pathogenesis of SSc. More importantly, the results may offer promising clues for further experimental studies and novel treatment strategies.

Novel cell subtypes of SPP1 + S100P+, MS4A1-SPP1 + S100P+ were key subpopulations in intrahepatic cholangiocarcinoma

BackgroundIn this study, we integrated single-cell RNA sequencing (scRNA-seq) data to investigate cell heterogeneity and utilized MSigDB and CIBERSORTx to explore the pathways of major cell types and the relationships between different cell subtypes. Subsequently, we explored the correlation of cell subtypes with survival and used Gene Set Enrichment Analysis (GSEA) analyses to assess the pathways associated with the infiltration of specific cell subtypes. Finally, multiplex immunohistochemistry in tissue microarray cohort were performed to validate differences in protein level and their correlation with survival. ResultsiCCA presented a unique immune ecosystem, with increased proportions of Epi (epithelial)-SPP1–2, Epi-S100P-1, Epi-DN (double negative for SPP1 and S100P expression)-1, Epi-DN-2, Epi-DP (double positive for SPP1 and S100P expression)-1, Plasma B-3, Plasma B-2, B-HSPA1A-1, B-HSPA1A-2 cells, and decreased proportions of B-MS4A1. High level of Epi-DN-2, Epi-SPP1–1, Epi-SPP1–2, B-MS4A1, and low level of Epi-DB-1, Epi-S100P-1, and Epi-S100P-2 was significantly associated with longer overall survival (OS), and high level of B-MS4A1_Low_Epi-DN-2_Low was associated with the shortest OS. Moreover, the results of MsigDB and GSEA suggest that bile acid metabolism is a crucial process in iCCA. Finally, we found that S100P+, SPP1+, SPP1 + S100P+, and MS4A1-SPP1 + S100P+ were highly expressed, whereas MS4A1 was lowly expressed in iCCA, and patients with high level of S100P+, SPP1 + S100P+, and MS4A1-SPP1 + S100P+ exhibited shorter survival. ConclusionsWe identified the cell heterogeneity of iCCA, found that iCCA is a unique immune ecosystem with many cell subtypes, and showed that the novel cell subtypes of SPP1 + S100P+ and MS4A1-SPP1 + S100P+ were key subpopulations in iCCA.

Identification and validation of a novel prognostic model for gastric cancer based on m7G-related genes.

The role of N7-methyladenosine (m7G)-related genes in the progression and prognosis of gastric cancer (GC) remains unclear. This study aimed to explore prognostic biomarkers for GC based on m7G methylation regulators and to construct a prognostic risk model. RNA sequencing profiles with corresponding clinicopathological information associated with GC of which the histological type was stomach adenocarcinoma (STAD) were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), respectively. A total of 29 m7G regulators were extracted from previous studies. According to the expression similarity of m7G regulators, the GC samples obtained from TCGA were further classified into 2 clusters demonstrating different overall survival (OS) rates and genetic heterogeneity, and the differentially expressed genes (DEGs) between these 2 clusters were defined as m7G-related genes. Univariate regression analysis and regression analysis were then used to obtain the prognostic m7G-related genes. The samples in TCGA and Genotype-Tissue Expression (GTEx) were used to verify the differential expression and prognostic value of these m7G-related genes contained in the prognostic model. Subsequently, the risk score was combined with other prognostic factors to develop a nomogram. The predictive ability of the nomogram was evaluated by the standard receiver operating characteristic (ROC) curve. Gene set enrichment analysis (GSEA) was used to identify activation pathways in both groups. Finally, the association between the prognostic model and the immune characteristics of GC were appraised. A prognostic model consisting of 11 m7G-related genes was constructed. GC patients in the high-risk group were shown to have a poor prognosis and this result was further demonstrated in each group. The risk model can be applied for patients with different clinical features. The results of GSEA showed that cell adhesion, cell junction, and focal adhesion were highly enriched in the high-risk group. In addition, we found that the expression of programmed cell death ligand 1 (PD-L1) was significantly elevated in the low-risk group, whereas programmed cell death ligand 2 (PD-L2) and tumor necrosis factor receptor superfamily member 4 (TNFRSF4) were overexpressed in the high-risk group. We successfully built and verified a m7G relevant prognostic model for predicting prognosis and providing a new train of thought for improving the treatment of GC.