Biological Function Analysis Research Articles

Decoding the genetic comorbidity network of Alzheimer's disease

Alzheimer's disease (AD) has emerged as the most prevalent and complex neurodegenerative disorder among the elderly population. However, the genetic comorbidity etiology for AD remains poorly understood. In this study, we conducted pleiotropic analysis for 41 AD phenotypic comorbidities, identifying ten genetic comorbidities with 16 pleiotropy genes associated with AD. Through biological functional and network analysis, we elucidated the molecular and functional landscape of AD genetic comorbidities. Furthermore, leveraging the pleiotropic genes and reported biomarkers for AD genetic comorbidities, we identified 50 potential biomarkers for AD diagnosis. Our findings deepen the understanding of the occurrence of AD genetic comorbidities and provide new insights for the search for AD diagnostic markers. Graphical Study pipeline.

Mechanism of Shashen-Maidong herb pair in treating hepatocellular carcinoma using network pharmacology and experimental validation

Ethnopharmacological relevanceHepatocellular carcinoma (HCC) is among the most prevalent malignant tumors globally and represents a significant public health issue worldwide. Immune cell dysfunction is the crucial factor for the formation of immunosuppression microenvironment of HCC. Glehnia littoralis (A.Gray) F.Schmidt ex Miq. (Shashen) and Ophiopogon japonicus (Thunb.) Ker Gawl. (Maidong) are classic herb pair in traditional Chinese medicine (TCM) of nourishing Yin, and is widely applied in the treatment of HCC and possesses multiple immunomodulatory functions. However, the role of the the Shashen-Maidong herb pair (SS-MD) for the management of HCC and the potential mechanisms has not been explicated. Aim of the studyThe purpose of the research is to investigate the potential mechanism of the SS-MD herb pair for the management of HCC. Materials and methodsThe known components of the SS-MD herb pair were preliminarily identified using UPLC-Q-Orbitrap-MS/MS. The active ingredients of SS-MD herb pair in treating HCC were screened by constructing herb-component-target network, and the key therapeutic targets were explored by constructing a protein-protein interaction (PPI) network. The binding affinity of the key targets and components were validated through molecular docking and molecular dynamics simulations. GO biological function and KEGG pathway analyses were operated to elucidate the potential mechanisms of the SS-MD herb pair for the management of HCC. And the mechanism was verified in the tumor bearing mice model and cell co-culture experiments. ResultsNetwork pharmacology prediction revealed 39 active components and 138 targets of the SS-MD herb pair for the treatment of HCC. KEGG analysis mainly focused on Notch signaling pathway and Apoptosis signaling pathway. The targets were enriched in biological functions of lymphocyte effector function and lymphocyte apoptosis. In vivo and in vitro experiments proved that the SS-MD herb pair can improve the proportion of CD8+T cells in the HCC immune microenvironment, regulate its subgroup distribution. SS-MD herb pair promoted CD8+T cells to secrete IL-2, TNF-α, IFN-γ, Granzyme B and Perforin, and inhibited apoptosis by regulating Notch signaling pathway. ConclusionsThis study identified the key components, targets, and signaling pathways of the SS-MD herb pair, confirm that SS-MD herb pair play an immunomodulatory role in treating HCC, provides theoretical support for the collaborative treatment of HCC with TCM.

LC-MS/MS-based metabolomics and proteomics reveal the intervention of Kangnian decoction on the postoperative intestinal adhesion of rats.

Postoperative Intestinal Adhesions (PIAs) remain a significant complication of abdominal surgery that can cause pain, infertility, and a potentially lethal bowel obstruction. Kangnian (KN) decoction, a Traditional Chinese Medicine prescription, has been shown to be effective in treating PIAs. Nevertheless, its underlying mechanisms remain unclear. This study aims to explore the therapeutic effects of KN decoction in a PIA rat model, as well as its potential mechanisms via metabolomics and proteomics analyses. 60 rats were randomly assigned to six groups: Normal Control (NC), PIA model, Dexamethasone, KN-Low, KN-Medium, and KN-High. The PIA model was created by abdominal surgery under anesthesia. Pathological damage was evaluated through H&E staining and adhesion grading of affected tissues. The levels of serum cytokines (IL-1β, IL-6, TNF-α, and TGF-1), Connective Tissue Growth Factor (CTGF), and Motilin (MTL) in adherent intestinal tissues were detected using ELISA kits. Untargeted metabolomics was used to investigate potential metabolic pathways of the KN decoction intervention in intestinal adhesions and to screen for differential biomarkers. The label-free quantitative proteomics technique was employed to detect Differentially Expressed Proteins and for biological function and pathway enrichment analyses. In PIA rats, KN decoction significantly improved the pathological injury associated with intestinal adhesions and effectively regulated the blood inflammation indicators. Furthermore, KN presented a favorable anti-fibrotic and protective effect against abdominal adhesions, effectively modifying gastrointestinal motility disorders in PIA rats. We identified 58 variables as potential biomarkers and discovered seven main pathological pathways that may be associated with PIAs. Proteomics analysis revealed 75 DEPs that were primarily involved in Valine, leucine, and isoleucine degradation, the MAPK signaling pathway, and retrograde endocannabinoid signaling. This study proved that KN reduces intestinal mucosal injury, downregulates inflammatory factors, and alleviates intestinal adhesions, thus protecting the intestinal barrier function in PIA rats. The combination of proteomics and metabolomics provided a feasible approach for unraveling the therapeutic mechanism of KN decoction in PIAs.

New insights into potential biomarkers and their roles in biological processes associated with hepatitis C-related liver cirrhosis by hepatic RNA-seq-based transcriptome profiling

Chronic hepatitis C virus infection is a major cause of mortality due to liver cirrhosis globally. Despite the advances in recent therapeutic strategies, there is yet a high burden of HCV-related cirrhosis worldwide concerning low coverage of newly developed antiviral therapies, insufficient validity of the current diagnostic methods for cirrhosis, and incomplete understanding of the pathogenesis in this stage of liver disease. Hence we aimed to clarify the molecular events in HCV-related cirrhosis and identify a liver-specific gene signature to potentially improve diagnosis and prognosis of the disease.Through RNA-seq transcriptome profiling of liver samples of Iranian patients with HCV-related cirrhosis, the differentially expressed genes (DEGs) were identified and subjected to functional annotation including biological process (BP) and molecular function (MF) analysis and also KEGG pathway enrichment analysis. Furthermore, the validation of RNA-seq data was investigated for seven candidate genes using qRT-PCR. Moreover, the diagnostic and prognostic power of validated DEGs were analyzed in both forms of individual DEG and combined biomarkers through receiver operating characteristic (ROC) analysis. Finally, we explored the pair-wise correlation of these six validated DEGs in a new approach.We identified 838 significant DEGs (padj ˂0.05) enriching 375 and 15 significant terms subjected to BP and MF, respectively (false discovery rate ˂ 0.01) and 46 significant pathways (p-value ˂ 0.05). Most of these biological processes and pathways were related to inflammation, immune responses, and cellular processes participating somewhat in the pathogenesis of liver disease. Interestingly, some neurological-associated genes and pathways were involved in HCV cirrhosis-related neuropsychiatric disorders. Out of seven candidate genes, six DEGs, including inflammation-related genes ISLR, LTB, ZAP70, KLRB1, and neuronal-related genes MOXD1 and Slitrk3 were significantly confirmed by qRT-PCR. There was a close agreement in the expression change results between RNA-seq and qRT-PCR for our candidate genes except for SAA2-SAA4 (P= 0.8). High validity and reproducibility of six novel DEGs as diagnostic and prognostic biomarkers were observed. We also found several pair-wise correlations between validated DEGs.Our findings indicate that the six genes LTB, ZAP70, KLRB1, ISLR, MOXD1, and Slitrk3 could stand as promising biomarkers for diagnosing of HCV-related cirrhosis. However, further studies are recommended to validate the diagnostic potential of these biomarkers and evaluate their capability as targets for the prevention and treatment of cirrhosis disease.

Genome-Wide Identification of GH17s Family Genes and Biological Function Analysis of SlA6 in Tomato.

Glycoside hydrolases (GHs), enzymes that break down glycosidic bonds in carbohydrates and between carbohydrates and non-carbohydrates, are prevalent in plants, animals, microorganisms, and other organisms. The tomato is a significant crop that contains the GH17 gene family. However, its role in tomatoes has yet to be fully investigated. In this study, we identified 43 GH17 genes from the tomato genome, distributed unevenly across 12 chromosomes. We further analyzed their gene structure, phylogenetic relationships, promoter elements, and expression patterns. The promoter element analysis indicated their potential roles in response to biotic and abiotic stresses as well as phytohormone effects on growth and development. The expression studies across different tomato tissues revealed that 10 genes were specifically expressed in floral organs, with SlA6 prominently expressed early during bud formation. By using CRISPR/Cas9 gene-editing technology, SlA6 knockout plants were generated. Phenotypic characterization showed that pollen viability, pollen tube germination, fruit weight, and seed number were significantly reduced in the Sla6 mutant, but the soluble solids content (TSS) was significantly higher in the Sla6 mutant, suggesting that SlA6 affects pollen development and fruit quality.

Establish TIIC signature score based the machine learning fusion in bladder cancer

BackgroundBladder cancer is a prevalent malignant tumor with high heterogeneity. Current treatments, such as transurethral resection of bladder tumor (TURBT) and intravesical Bacillus Calmette-Guérin (BCG) therapy, still have limitations, with approximately 30% of non-muscle-invasive bladder cancer (NMIBC) progressing to muscle-invasive bladder cancer (MIBC), and a substantial number of MIBC patients experiencing recurrence after surgery. Immunotherapy has shown potential benefits, but accurate prediction of its prognostic effects remains challenging.MethodsWe analyzed bladder cancer RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and used various machine learning algorithms to screen for feature RNAs related to tumor-infiltrating immune cells (TIICs) from single-cell data. Based on these RNAs, we established a TIIC signature score and evaluated its relationship with overall survival (OS) and immunotherapy response in bladder cancer patients.ResultsThe study identified 171 TIIC-RNAs and selected 11 TIIC-RNAs with prognostic value through survival analysis. The TIIC signature score established using a machine learning fusion method was significantly associated with OS and showed good predictive performance in different datasets. Additionally, the signature score was negatively correlated with immunotherapy response, with patients with low TIIC feature scores showing better survival outcomes after immunotherapy. Further biological functional analysis revealed a close association between the TIIC signature score and immune regulation processes, cellular metabolism, and genetic variations.ConclusionThis study successfully constructed and validated an RNA signature scoring system based on tumor-infiltrating immune cell (TIIC) features, which can effectively predict OS and the effectiveness of immunotherapy in bladder cancer patients.

Single-cell RNA-sequencing Analysis Reveals the Promoting Role of the Hedgehog Pathway in Epithelial Cells during Cervical Cancer Progression.

This study explored the role of the Hedgehog pathway in epithelial cells during cervical cancer [CC] progression, providing new insights for improving current CC treatment. Abnormal activation of the Hedgehog signaling pathway is associated with the malignant transformation of CC epithelial cells. Single-cell atlas of CC and the role of Hedgehog pathway in epithelial cells during CC progression remain to be explored. To comprehensively analyze the mechanism of Hedgehog pathway activation in CC epithelial cells and its impact on tumor progression by applying single-cell RNA sequencing [scRNA-seq] analysis. The scRNA-seq data were acquired from the Gene Expression Omnibus [GEO] database and then processed with the Seurat package. FindNeighbors and Find- Clusters functions were applied to cluster the cells. The CellMarker database was used for subgroup annotation. Differentially expressed genes [DEGs] in each cell subgroup were filtered by FindAllMarkers function. Biological function analysis for the gene set of interest was performed using Clusterprofiler package. AUCell function was employed to calculate the score of the Hedgehog pathway. The differentiation trajectory in epithelial cell subtypes was generated by performing Pseudotime analysis. Finally, protein-protein network [PPI] was used to investigate the interactions between the Hedgehog pathway and other pathways enriched in the gene set of interest. A total of 9 major cell subpopulations were classified and the proportion of epithelial cells was the highest in CC samples. Further analysis revealed that the Hedgehog pathway was abnormally activated in STYK1+ and TP73+ epithelial cell subtypes. Pseudo-time trajectory analysis showed that the differentiation trajectory of STYK1+ epithelial cells gradually transformed into defense-to-virus cells or into proliferation cells, while TP73+ epithelial cells eventually differentiated into two branches of response to estrogen and virus-induced proliferation. PPI analysis showed that the Hedgehog pathway was involved in the proliferation and viral process of epithelial cells in CC. The current study comprehensively analyzed the features of CC samples and differentiation trajectories of epithelial cell subtypes, as well as the role of the Hedgehog pathway in epithelial cells during CC progression. More importantly, effective target genes were discovered for the molecular diagnosis and precise treatment of CC.

Characteristics of two different immune infiltrating pyroptosis subtypes in ischemic stroke

BackgroundIschemic stroke (IS) is a serious health hazard and identified as the second leading cause of mortality around the world. However, the role of pyroptosis in the immune microenvironment regulation in IS is still unclear. Here, our study aims to elucidate the effect of pyroptosis on immune microenvironment in IS. MethodsThe regulation mode of pyroptosis in IS was systematically evaluated, and its effects on immune microenvironment were explored, including infiltration of immune cells, immune response gene sets, and human leukocyte antigen (HLA) gene. The genes and drugs related to pyroptosis phenotype were also identified. An MCAO rat model was constructed, and the mRNA expression levels in the classifier model were validated by qRT-PCR. ResultsThe separator is composed of 11 pyroptosis genes, out of which 10 genes could distinguish between ischemic stroke and control samples. CHMP2A, CHMP4A, and NAIP genes are significantly related to immune infiltrating cells, immune response gene sets, and HLA. However, two different pyroptosis subtypes mediated by 10 pyroptosis genes were identified, which were different in immune cell abundance, HLA genes, and immune response gene sets. Furthermore, 199 genes associated with pyroptosis phenotype was identified along with the analysis of biological functions. ConclusionThese findings reveal the potential mechanism of pyroptosis in the immune microenvironment of IS, indicating that pyroptosis functions as a vital component in the complexity and diversity of the immune microenvironment in patients with IS.

Protective Effect of a Isothiazolinone Derivative on Acute Lung Injury by Regulating PI3K-AKT Signaling Pathway.

Acute lung injury (ALI) is a prevalent organ injury in sepsis, characterized by an inflammatory reactive disorder. Both the incidence and mortality rates of ALI have been steadily increasing. Isothiazolinone derivatives have displayed anti-inflammatory activity and have shown effectiveness in treating pneumonia. The objective of the study is to assess the effects and mechanisms of the isothiazolinone derivative 4-benzoyl-2-butyl-5-(ethylsulfinyl)isothiazol-3(2H)-one (C6) on sepsis-induced ALI.The analysis of biological function and signal pathway enrichment demonstrated that C6 primarily exhibited anti-inflammatory effects. Administration of different doses of C6 through intraperitoneal injection significantly improved the survival rate, body temperature, and body mass of mice with ALI induced by cecal ligation and puncture (CLP). Additionally, it mitigated lung tissue injury, pulmonary edema, lung permeability, inflammatory cell infiltration, apoptosis, and the expression of inflammatory cytokines. Network targeting analysis and experimental validation in mouse leukemia cells of monocyte macrophage (RAW264.7) cells and CLP-induced ALI mice revealed that the anti-inflammatory effect of C6 was mediated by the inhibition of the phosphatidylinositol 3 kinase -protein kinase B (PI3K-AKT) signaling pathway. The research suggest that C6 has protective effects against ALI by inhibiting the PI3K-AKT signaling pathway. This information could be valuable in developing potential treatments for ALI.

Dendrobine alleviates oleic acid-induced lipid accumulation by inhibiting FOS/METTL14 pathway.

Dendrobine (DDB), an alkaloid isolated from the Chinese herb Dendrobium, has antioxidant and anti-inflammatory effects; however, whether DDB reduces oleic acid (OA)-induced lipid accumulation remains unclear. OA-induced lipid accumulation model of HepG2 cells were treated with DDB. Cellular lipid deposition was assessed by Oil Red O (ORO) staining and triglyceride and total cholesterol detection. RNA-Sequencing (RNA-seq), biological function analysis, and transcription factor (TFs) prediction were combined to identify key TF in the DDB-treated OA model. Finally, the roles of FOS and METTL14 were examined using a DDB-induced lipid accumulation model. DDB inhibited OA-induced lipid accumulation. We identified 895 differentially expressed genes (DEGs) that were mainly enriched in various biological processes of lipid synthesis and transport. Four transcription factors (SOX9, MLXIPL, FOS, and JUN) associated with lipid metabolism and FOS levels in the OA-induced lipid accumulation model after DDB treatment had the greatest changes in expression change. Overexpression of FOS alleviates the inhibitory effect of DDB on OA-induced lipid accumulation. METTL14 is a target gene of FOS, and simultaneous interference with METTL14 in cells with high FOS expression restored the alleviating effect of DDB on lipid accumulation. DDB alleviated OA-induced lipid accumulation by inhibiting the FOS/METTL14 pathway.

Quantitative proteomic analysis and replacement therapy identifies haptoglobin as a therapeutic target in a murine model of SLE-associated diffuse alveolar hemorrhage.

Diffuse alveolar hemorrhage (DAH) is a catastrophic clinical syndrome and one of the manifestations of pulmonary involvement in systemic lupus erythematosus (SLE), which is characterized by hemoptysis, diffuse pulmonary infiltrates, and respiratory failure. However, the treatment options for DAH remain limited, and DAH-related studies are needed to explore more effective therapeutic directions for better disease management and improved prognosis. This study utilized the pristane-induced DAH murine model to mimic the pathological process of DAH in patients with SLE. Proteomic analysis was conducted to detect differentially expressed proteins (DEPs) in the plasma of surviving and non-surviving mice, followed by an analysis of biological functions and pathways. The most significant DEP was then confirmed in the plasma of SLE patients with or without DAH and DAH murine model with or without fatal outcomes. Finally, the therapeutic value of haptoglobin (Hp) replacement was validated in a DAH murine model through lung histopathology, RT-qPCR, and survival analysis. This study identified 178 DEPs, with 118 upregulated and 60 downregulated DEPs in the non-survival group. Within a set of notable Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, complement and coagulation cascades emerged as the most prominent pathway associated with the process of DAH. Later, the most significant DEP, haptoglobin (Hp), was confirmed to exhibit a significant decrease in the plasma of individuals with SLE-DAH and DAH murine model with poor outcomes by the ELISA test. Finally, compared with the control group, the severity of DAH in the Hp treatment group was alleviated significantly, as manifested by the decreased levels of pro-inflammatory cytokines (IL-6 and TNF-α), increased levels of anti-inflammatory cytokines (IL-10 and TGF-β), and decreased mortality. A reduction in plasma Hp levels was observed in SLE-DAH, and the replacement therapy with Hp could alleviate pulmonary hemorrhage and reduce mortality in DAH mice. This study identified Hp as a potential biomarker for its clinical diagnosis and a direction for treatment.

Construction of a panoramic mRNA map of adult noncystic fibrosis bronchiectasis and a preliminary study of the underlying molecular mechanisms

BackgroundThe pathogenesis of noncystic fibrosis bronchiectasis in adults is complex, and the relevant molecular mechanisms remain unclear. In this study, we constructed a panoramic map of bronchiectasis mRNA, explored the potential molecular mechanisms, and identified potential therapeutic targets, thus providing a new clinical perspective for the preventive management of bronchiectasis and its acute exacerbation.MethodsThe mRNA profiles of peripheral blood and bronchiectasis tissues were obtained through transcriptome sequencing and public databases, and bioinformatics methods were used to screen for differentially expressed genes (DEGs). The DEGs were then subjected to biological function and pathway analyses. Some DEGs were validated using a real-time quantitative polymerase chain reaction (RT-qPCR) in peripheral blood. Spearman’s correlation analysis was used to analyse the correlation between DEGs and clinical indicators.ResultsBased on transcriptome sequencing and public databases, the mRNA profile of bronchiectasis was determined. DEGs were obtained from the peripheral blood sequencing dataset (985 DEGs), tissue sequencing dataset (2919 DEGs), and GSE97258 dataset (1083 DEGs). Bioinformatics analysis showed that upregulated DEGs had enriched neutrophil-related pathways, and downregulated DEGs had enriched ribosome-related pathways. RT-qPCR testing confirmed the upregulated expression of VCAN, SESTD1, SLC12A1, CD177, IFI44L, SIGLEC1, and RSAD2 in bronchiectasis. These genes were related to many clinical parameters, such as neutrophils, C-reactive protein, and procalcitonin (P < 0.05).ConclusionsTranscriptomic methods were used to construct a panoramic map of bronchiectasis mRNA expression. The findings showed that neutrophil activation, chronic inflammation, immune regulation, impaired ribosomal function, oxidative phosphorylation, and energy metabolism disorders are important factors in the development of bronchiectasis. VCAN, SESTD1, SLC12A1, CD177, IFI44L, SIGLEC1, and RSAD2 may play important roles in the pathogenesis of bronchiectasis and are potential therapeutic targets.

LncRNA XIST/miR-381-3P/STAT1 axis as a potential biomarker for lupus nephritis.

We aim to investigate the potential roles of key genes in the development of lupus nephritis (LN), screen key biomarkers, and construct the lncRNA XIST/miR-381-3P/STAT1 axis by using bioinformatic prediction combined with clinical validation, thereby providing new targets and insights for clinical research. Gene expression microarrays GSE157293 and GSE112943 were downloaded from the GEO database to obtain differentially expressed genes (DEGs), followed by enrichment analyses on these DEGs, which were enriched and analyzed to construct a protein-protein interaction (PPI) network to screen core genes. The lncRNA-miRNA-mRNA regulatory network was predicted and constructed based on the miRNA database. 37 female patients with systemic lupus erythematosus (SLE) were recruited to validate the bioinformatics results by exploring the diagnostic value of the target ceRNA axis in LN by dual luciferase and real-time fluorescence quantitative PCR (RT-qPCR) and receiver operating characteristic (ROC). The data represented that a total of 133 differential genes were screened in the GSE157293 dataset and 2869 differential genes in the GSE112943 dataset, yielding a total of 26 differentially co-expressed genes. Six core genes (STAT1, OAS2, OAS3, IFI44, DDX60, and IFI44L) were screened. Biological functional analysis identified key relevant pathways in LN. ROC curve analysis suggested that lncRNA XIST, miR-381-3P, and STAT1 could be used as potential molecular markers to assist in the diagnosis of LN. STAT1 is a key gene in the development of LN. In conclusion, lncRNA XIST, miR-381-3P, and STAT1 can be used as new molecular markers to assist in the diagnosis of LN, and the lncRNA XIST/miR-381-3P/STAT1 axis may be a potential therapeutic target for LN.

Exploring the therapeutic efficacy and pharmacological mechanism of Guizhi Fuling Pill on ischemic stroke: a meta-analysis and network pharmacology analysis.

The role of Guizhi Fuling Pill (GZFL) in the treatment of ischemic stroke (IS) is still controversial, and its pharmacological mechanism remains unclear. To evaluate the efficacy and potential pharmacological mechanisms of GZFL on IS, a comprehensive method integrating meta-analysis, network pharmacology, and molecular docking was employed. Eight electronic databases were searched from inception to November 2023. Review Manager 5.4.1 software was used for meta-analysis. Active compounds and targets of GZFL were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database, Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine, and Encyclopaedia of Traditional Chinese Medicine. Relevant targets of IS were obtained from the DisGeNet, Genecards, and DrugBank databases. GO biological function analysis and KEGG enrichment analysis were performed in the Metascape database. AutoDock Tools and PyMOL software were employed for Molecular docking. The intervention group significantly increased the total effective rate and decreased the NIHSS score. Administration of GZFL also improved the whole blood viscosity (low and high shear rates) and levels of fibrinogen, TNF-α, and IL-6. The key active compounds included quercetin, kaempferol, catechin, and beta-sitosterol, and the core target proteins included SRC, MAPK1, TP53, JUN, RELA, AKT1, and TNF. GO analysis mainly involved inflammation response, cellular response to lipids, and regulation of ion transport. The core pathways were lipid and atherosclerosis, cAMP, calcium, IL-17, and MAPK signaling pathways. Key active compounds showed good affinity with the core targets. The underlying mechanisms of GZFL in IS treatment are primarily related to its anti-inflammatory, anti-atherosclerosis, and neuroprotective effects.

The molecular and network mechanisms of antilipidemic potential effects of Ganfule capsules in nonalcoholic fatty liver disease

BackgroundNon-alcoholic fatty liver disease (NAFLD) is a common liver disorder characterized by hepatic steatosis, inflammation and fibrosis. Ganfule (GFL), a traditional Chinese medicine, has demonstrated therapeutic potential in the treatment of NAFLD but the mechanisms involved are not fully understood.To evaluate the biochemical mechanisms of GFL in treating NAFLD by examining its effects on biological networks, key therapeutic targets, histopathological changes and clinical implications. MethodsChemical component screening, key target prediction, biological functional enrichment analysis, lipid profile localization analysis and complex network analysis were performed on GFL using multi-database mining, network analysis and molecular docking. An NAFLD rat model was then established and treated with different doses of GFL. Histopathological evaluation and western blotting were used to verify the expression levels of key target proteins in GFL-treated NAFLD rats. ResultsNetwork analysis analysis identified 12 core targets, 12 core active ingredients and 7 core Chinese medicinal herbs in GFL potentially involved in the treatment of NAFLD. Biological functional enrichment analysis revealed the involvement of lipid metabolism, apoptosis and intracellular signaling pathways. Molecular docking confirmed a strong affinity between GFL's core compounds and certain target proteins. Histopathological examination of an NAFLD rat model showed reduced hepatocellular steatosis after GFL treatment. Western blotting revealed significant downregulation of PPARA and PPARD protein expression and upregulation of PIK3CG and PRKACA protein expression in NAFLD rats treated with lower doses of GFL. ConclusionsOur results suggest that GFL modulates key proteins involved in lipid metabolism and apoptosis pathways. GFL improved the histopathological features of NAFLD rats by regulating lipid metabolism as well as reducing hepatocyte apoptosis and hepatocellular steatosis. These findings offer insights into the biochemical mechanism of action of GFL and support its use in the treatment for NAFLD.

To Establish a Nomogram Prediction of Prostate Cancer Based on Pyroptosis-Related Genes that Affect the Immune Microenvironment.

Prostate cancer is the most common tumor in men worldwide with a poor prognosis. In recent years, studies have revealed that pyroptosis can affect the tumor immune microenvironment. However, the relationship between the immune microenvironment regulated by pyroptosis-related genes and the prognosis of prostate cancer is still unclear. Thirty-three cell death-associated genes were selected from a literature review. The "DESeq2" R package was used to identify differentially expressed cell death-associated genes between normal prostate tissue (GTEx) and prostate cancer tissue (TCGA) samples. Biological functional enrichment analysis of differentially expressed cell death genes was performed using R statistical software packages, such as "clusterProfiler," "org.Hs.eg.db," "enrichplot," "ggplot2," and "GOplot." Univariate Cox and LASSO Cox regression analyses were conducted to identify prognostic genes associated with the immune microenvironment using the "survival" package. Finally, a predictive model was established based on Gleason score, T stage, and cell death-associated genes.odel was established based on Gleason score, T stage, and cell death-associated genes. Seventeen differentially expressed genes related to pyroptosis were screened out. Based on these differentially expressed genes, biological function enrichment analysis showed that they were related to pyroptosis of prostate cells. Based on univariate Cox and (LASSO) Cox regression analysis, four pyroptosis-related genes (CASP3, PLCG1, GSDMB, GPX4) were determined to be related to the prognosis of prostate cancer, and the immune correlation analysis of the four pyroptosis-related genes was performed. The expression of CASP3, PLCG1 and GSDMB was positively correlated with the proportion of immune cells, and the expression of GPX4 was negatively correlated with the proportion of immune cells. A predictive nomogram was established by combining Gleason score, T and pyroptosis genes. The nomogram was accompanied by a calibration curve and used to predict 1 -, 2 -, and 5-year survival in PAAD patients. Cell death-associated genes (CASP3, PLCG1, GSDMB, GPX4) play crucial roles in modulating the immune microenvironment and can be used to predict the prognosis of prostate cancer.

Screening of potential hub genes involved in Kidney Wilms tumor via bioinformatics analysis and experimental validation

BackgroundWilms tumor (WT) is the most common pediatric embryonal tumor. Improving patient outcomes requires advances in understanding and targeting the multiple genes and cellular control pathways, but its pathogenesis is currently not well-researched. We aimed to identify the potential molecular biological mechanism of WT and develop new prognostic markers and molecular targets by comparing gene expression profiles of Wilms tumors and fetal normal kidneys.MethodsDifferential gene expression analysis was performed on Wilms tumor transcriptomic data from the GEO and TARGET databases. For biological functional analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were utilized. Out of 24 hub genes identified, nine were found to be prognostic-related through univariate Cox regression analysis. These nine genes underwent LASSO regression analysis to enhance the predictive capability of the model. The key hub genes were validated in the GSE73209 datasets, and cell function experiments were conducted to identify the genes’ functions in WiT-49 cells.ResultsThe enrichment analysis revealed that DEGs were significantly involved in the regulation of angiogenesis and regulation of cell differentiation. 24 DEGs were identified through PPI networks and the MCODE algorithm, and 9 of 24 genes were related to WT patients’ prognosis. EMCN and CCNA1 were identified as key hub genes, and related to the progression of WT. Functionally, over-expression of EMCN and CCNA1 knockdown inhibited cell viability, proliferation, migration, and invasion of Wilms tumor cells.ConclusionsEMCN and CCNA1 were identified as key prognostic markers in Wilms tumor, suggesting their potential as therapeutic targets. Differential gene expression and enrichment analyses indicate significant roles in angiogenesis and cell differentiation.

The roles of IRF8 in nonspecific orbital inflammation: an integrated analysis by bioinformatics and machine learning

BackgroundNonspecific Orbital Inflammation (NSOI) represents a persistent and idiopathic proliferative inflammatory disorder, characterized by polymorphous lymphoid infiltration within the orbit. The transcription factor Interferon Regulatory Factor 8 (IRF8), integral to the IRF protein family, was initially identified as a pivotal element for the commitment and differentiation of myeloid cell lineage. Serving as a central regulator of innate immune receptor signaling, IRF8 orchestrates a myriad of functions in hematopoietic cell development. However, the intricate mechanisms underlying IRF8 production remain to be elucidated, and its potential role as a biomarker for NSOI is yet to be resolved.MethodsIRF8 was extracted from the intersection analysis of common DEGs of GSE58331 and GSE105149 from the GEO and immune- related gene lists in the ImmPort database using The Lasso regression and SVM-RFE analysis. We performed GSEA and GSVA with gene sets coexpressed with IRF8, and observed that gene sets positively related to IRF8 were enriched in immune-related pathways. To further explore the correlation between IRF8 and immune-related biological process, the CIBERSORT algorithm and ESTIMATE method were employed to evaluate TME characteristics of each sample and confirmed that high IRF8 expression might give rise to high immune cell infiltration. Finally, the GSE58331 was utilized to confirm the levels of expression of IRF8.ResultsAmong the 314 differentially expressed genes (DEGs), some DEGs were found to be significantly different. With LASSO and SVM-RFE algorithms, we obtained 15 hub genes. For biological function analysis in IRF8, leukocyte mediated immunity, leukocyte cell-cell adhesion, negative regulation of immune system process were emphasized. B cells naive, Macrophages M0, Macrophages M1, T cells CD4 memory activated, T cells CD4 memory resting, T cells CD4 naive, and T cells gamma delta were shown to be positively associated with IRF8. While, Mast cells resting, Monocytes, NK cells activated, Plasma cells, T cells CD8, and T cells regulatory (Tregs) were shown to be negatively linked with IRF8. The diagnostic ability of the IRF8 in differentiating NSOI exhibited a good value.ConclusionsThis study discovered IRF8 that are linked to NSOI. IRF8 shed light on potential new biomarkers for NSOI and tracking its progression.

Six-gene prognostic signature for non-alcoholic fatty liver disease susceptibility using machine learning.

nonalcoholic fatty liver disease (NAFLD) is a common liver disease affecting the global population and its impact on human health will continue to increase. Genetic susceptibility is an important factor influencing its onset and progression, and there is a lack of reliable methods to predict the susceptibility of normal populations to NAFLD using appropriate genes. RNA sequencing data relating to nonalcoholic fatty liver disease was analyzed using the "limma" package within the R software. Differentially expressed genes were obtained through preliminary intersection screening. Core genes were analyzed and obtained by establishing and comparing 4 machine learning models, then a prediction model for NAFLD was constructed. The effectiveness of the model was then evaluated, and its applicability and reliability verified. Finally, we conducted further gene correlation analysis, analysis of biological function and analysis of immune infiltration. By comparing 4 machine learning algorithms, we identified SVM as the optimal model, with the first 6 genes (CD247, S100A9, CSF3R, DIP2C, OXCT 2 and PRAMEF16) as predictive genes. The nomogram was found to have good reliability and effectiveness. Six genes' receiver operating characteristic curves (ROC) suggest an essential role in NAFLD pathogenesis, and they exhibit a high predictive value. Further analysis of immunology demonstrated that these 6 genes were closely connected to various immune cells and pathways. This study has successfully constructed an advanced and reliable prediction model based on 6 diagnostic gene markers to predict the susceptibility of normal populations to NAFLD, while also providing insights for potential targeted therapies.

Identification and analysis of immunogenicity and immunotherapy efficacy by fatty acid genes: a novel prognostic features of lumbar disc herniation and Mendelian randomization analysis

ABSTRACT Background: Sciatica is a phrase used to describe radiating leg discomfort. The most common cause is lumbar disc herniation (LDH), which is considered to start in the nucleus pulposus. Advancements in lipidomics and metabolomics have unveiled the complex role of fatty acid metabolism (FAM) in both healthy and pathological states. However, the specific roles of fatty acid metabolism-related genes (FAMGs) in shaping therapeutic approaches, especially in LDH, remain largely unexplored and are a subject of ongoing research. Methods: The junction of the weighted correlation network analysis (WGCNA) test with 6 FAMGs enabled the finding of FAMGs. Gene set variation analysis (GSVA) was used to identify the possible biological activities and pathways of FAMGs. LASSO was used to determine diagnostic effectiveness of the four FAMGs in diagnosing LDH. GSE124272, GSE147383, GSE150408, and GSE153761 were utilized to confirm the levels of expression of four FAMGs. Results: Four FAMGs were discovered [Acyl-CoA Thioesterase 4 (ACOT4), Cytochrome P450 Family 4 Subfamily A Member 11 (CYP4A11), Acyl-CoA Dehydrogenase Long Chain (ACADL), Enoyl-CoA Hydratase and 3-Hydroxyacyl CoA Dehydrogenase (EHHADH)] For biological function analysis, mhc class ib receptor activity, response to thyroxine, response to l phenylalanine derivative were emphasized. Conclusions: FAMGs can help with prognosis and immunology, and provide evidence for fatty acid metabolism-related targeted therapeutics. In LDH, FAMGs and their interactions with immune cells might be therapeutic targets.