Functional Enrichment Analysis Research Articles

Diagnostic and prognostic significance of tetraspanin 6 and its role in facilitating glioma progression

BackgroundSeveral tetraspanin (TSPAN) proteins have been implicated in tumorigenesis and disease progression. However, the precise function of tetraspanin 6 (TSPAN6) in glioma remains unclear.MethodsIntegrated raw data from the Tumor Genome Atlas (TCGA) and Gene Expression Profiling (GEO) databases were processed using R4.2.1. Bioinformatic methods were used to analyze the gene expression levels of TSPAN6 in both glioma and normal brain tissues, correlating them with clinical characteristics. Additionally, the predictive value of TSPAN6 in relation to the immune checkpoint blockade (ICB) therapy response was assessed. In vitro experiments were conducted to investigate the effects of TSPAN6 knockdown on glioma cell proliferation, migration, cell cycle regulation, and macrophage recruitment.ResultsTSPAN6 expression was significantly upregulated in glioma tissues compared to normal tissues. Elevated TSPAN6 expression is strongly correlated with unfavorable clinical characteristics in gliomas. Bioinformatic analyses revealed a significant correlation between elevated TSPAN6 expression and reduced overall survival. Additionally, TSPAN6 was co-expressed with several immune checkpoint genes and revealed a prognostic value in the context of ICB therapy. Functional enrichment analysis revealed the involvement of TSPAN6 in cell-cycle regulation. Furthermore, TSPAN6 expression positively correlated with macrophage and neutrophil infiltration. In vitro experiments confirmed that the downregulation of TSPAN6 inhibited U251 cell proliferation, disrupted the cell cycle, diminished migratory capabilities, and reduced the recruitment of macrophages.ConclusionOur findings emphasize its potential as both a diagnostic and therapeutic target as well as a predictor of immune therapy response in gliomas.

Landscape of intrinsically disordered proteins in mental disorder diseases

Disrupted genes linked to mental disorders sometimes exhibit characteristics of Intrinsically Disordered Proteins (IDPs). However, few studies have comprehensively explored the functional associations between protein disorder properties and different psychiatric disorders. In this study, we collected disrupted proteins for seven mental diseases (MDD, SCZ, BP, ID, AD, ADHD, ASD) and a control dataset from normal brains. After calculating the disorder scores for each protein, we thoroughly compared the proportions and functions of IDPs between differentially expressed proteins in each disease and healthy controls.Our findings revealed that disrupted proteins, particularly in ASD and ADHD, contain more IDPs than controls from normal brains. Distinct patterns in disorder properties were observed among different mental disorders. Functional enrichment analysis indicated that IDPs in mental disorders were associated with neurodevelopment, synaptic signaling, and gene expression regulatory pathways. In addition, we analyzed the proportion and function of liquid-phase-separated proteins (LLPS) in psychiatric disorders, finding that LLPS proteins are mainly enriched in pathways related to neurodevelopment and inter-synaptic signaling. Furthermore, to validate our findings, we conducted an analysis of differentially expressed genes in an ASD cohort, revealing that the encoded proteins also exhibit a higher proportion of IDPs. Notably, these IDPs were particularly enriched in pathways related to neurodevelopment, including head development, a process known to be disrupted in ASD.Our study sheds light on the crucial role of IDPs in psychiatric disorders, enhancing our understanding of their molecular mechanisms.

Effects of empagliflozin on myocardial transcriptome in rats with aortic stenosis-induced heart failure

Abstract Introduction Aortic stenosis (AS) represents one of the most prevalent valvular heart diseases and an important cause of heart failure. Currently, no medical therapies influence the natural history of aortic stenosis. Although sodium glucose co-transporter 2 inhibitors (SGLT2i) have improved cardiovascular outcomes in heart failure patients with both reduced and preserved ejection fraction, their action mechanisms are not clear. Transcriptome analysis present great potential to identify biomarkers of disease and to gain insight into disease pathophysiology. In this study, we analyzed myocardial mRNA profile in aortic stenosis rats treated with empagliflozin. Methods Eighteen weeks after supra-aortic banding surgery, male Wistar rats were divided into 3 groups: Sham (n=15); aortic stenosis (AS, n=27); AS+empagliflozin (AS-EMP, n=27). Empagliflozin (10 mg/kg/day) was added to rat chow for 8 weeks. Transcriptome of left ventricle (LV) myocardium was studied by RNA-sequencing (sample size: 5 per group). Differential gene expression was analyzed in R environment using the DESeq2 package. Genes were considered differentially expressed (DEG) when they exhibited a log2 fold change (log2FC) higher than 1 (upregulated) or lower than -1 (downregulated), coupled with an adjusted p-value below 0.05. Functional enrichment analysis using gene ontology was performed by the online tool Enrich. Statistical analysis: ANOVA and Tukey. Results AS-EMP had lower LV diastolic diameter (Sham 8.39±0.57; AS 9.80±0.53*; AS-EMP 9.08±0.87# mm; p<0.05: * vs Sham; # vs AS), left atrium diameter (Sham 5.68±0.40; AS 9.67±1.00*; AS-EMP 8.97±1.36*# mm; p<0.05: * vs Sham; # vs AS), and LV mass (Sham 0.85±0.11; AS 2.19±0.27*; AS-EMP 1.76±0.34*# g; p<0.05: * vs Sham; # vs AS) than AS. A total of 1246 differentially expressed genes were identified, comprising 663 upregulated genes (70 genes in AS vs SHAM; 593 in AS-EMP vs AS) and 583 downregulated genes (35 genes in AS vs SHAM; 548 in AS-EMP vs AS). Most of the differentially expressed genes were enriched in processes such as extracellular matrix and structure organization, signal transduction regulation, and substrate-dependent cell migration (AS vs SHAM), as well as myocardial contraction, negative regulation of programmed cell death, and mitochondrial membrane organization (AS-EMP vs AS). The main upregulated genes were Loxl1, Mfap4, Postn, Col8a1, Pi16, and Nppa in AS vs SHAM; and Mxra8, Zfta, Pnisr, Thoc1, and Ralbp1 in AS-EMP vs AS comparison. The main downregulated genes were Mxra8, Ralbp1, and Tubgcp6 in AS vs SHAM; and Itgb5, Ltbp2, Tmem109, Plec, and Rorc in AS-EMP vs AS comparison. Conclusion We identified myocardial genes and biological processes that are potentially involved in aortic stenosis-induced heart failure. Empagliflozin attenuates cardiac remodeling and modulates myocardial genes involved with myocyte contraction, apoptosis, and mitochondrial organization in aortic stenosis rats.

Molecular landscape of the overlap between Alzheimer’s disease and somatic insulin-related diseases

Alzheimer’s disease (AD) is a multifactorial disease with both genetic and environmental factors contributing to its etiology. Previous evidence has implicated disturbed insulin signaling as a key mechanism that plays a role in both neurodegenerative diseases such as AD and comorbid somatic diseases such as diabetes mellitus type 2 (DM2). In this study, we analysed available genome-wide association studies (GWASs) of AD and somatic insulin-related diseases and conditions (SID), i.e., DM2, metabolic syndrome and obesity, to identify genes associated with both AD and SID that could increase our insights into their molecular underpinnings. We then performed functional enrichment analyses of these genes. Subsequently, using (additional) GWAS data, we conducted shared genetic etiology analyses between AD and SID, on the one hand, and blood and cerebrospinal fluid (CSF) metabolite levels on the other hand. Further, integrating all these analysis results with elaborate literature searches, we built a molecular landscape of the overlap between AD and SID. From the landscape, multiple functional themes emerged, including insulin signaling, estrogen signaling, synaptic transmission, lipid metabolism and tau signaling. We also found shared genetic etiologies between AD/SID and the blood/CSF levels of multiple metabolites, pointing towards “energy metabolism” as a key metabolic pathway that is affected in both AD and SID. Lastly, the landscape provided leads for putative novel drug targets for AD (including MARK4, TMEM219, FKBP5, NDUFS3 and IL34) that could be further developed into new AD treatments.

Mechanistic exploration of bioactive constituents in Gnetum gnemon for GPCR-related cancer treatment through network pharmacology and molecular docking

G Protein-Coupled Receptors (GPCRs) are integral membrane proteins that have gained considerable attention as drug targets, particularly in cancer treatment. In this study, we explored the capacity of bioactive compounds derived from Gnetum gnemon (GG) for the development of of pharmaceuticals targeting GPCRs within the context of cancer therapy. Integrated approach combined network pharmacology and molecular docking to identify and validate the underlying pharmacological mechanisms. We retrieved targets for GG-derived compounds and GPCRs-related cancer from databases. Subsequently, we established a protein-protein interaction (PPI) network by mapping the shared targets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were employed to predict the mechanism of action of these targets. Molecular docking was conducted to validate our findings. We identified a total of 265 targets associated with GG-derived bioactive compounds for the treatment of GPCRs-related cancer. Functional enrichment analysis revealed the promising therapeutic effects of these targets on GPCRs-related cancer pathways. The PPI network analysis identified hub targets, including MAPK3, SRC, EGFR, STAT3, ESR1, MTOR, CCND1, and PPARG, which demonstrate as treatment targets for GPCRs-related cancer using GG-derived compounds. Additionally, molecular docking experiments demonstrated the strong binding affinity of gnetin A, gnetin C, (-)-viniferin, and resveratrol dimer, thus inhibiting MAPK3, SRC, EGFR, and MTOR. Survival analysis established the clinical prognostic relevance of identified hub genes in cancer. This study presents a novel approach for comprehending the therapeutic mechanisms of GG-derived active compounds and thereby paving the way for their prospective clinical applications in the field of cancer treatment.

Lactuca super-pangenome reduces bias towards reference genes in lettuce research

BackgroundBreeding of lettuce (Lactuca sativa L.), the most important leafy vegetable worldwide, for enhanced disease resistance and resilience relies on multiple wild relatives to provide the necessary genetic diversity. In this study, we constructed a super-pangenome based on four Lactuca species (representing the primary, secondary and tertiary gene pools) and comprising 474 accessions. We include 68 newly sequenced accessions to improve cultivar coverage and add important foundational breeding lines.ResultsWith the super-pangenome we find substantial presence/absence variation (PAV) and copy-number variation (CNV). Functional enrichment analyses of core and variable genes show that transcriptional regulators are conserved whereas disease resistance genes are variable. PAV-genome-wide association studies (GWAS) and CNV-GWAS are largely congruent with single-nucleotide polymorphism (SNP)-GWAS. Importantly, they also identify several major novel quantitative trait loci (QTL) for resistance against Bremia lactucae in variable regions not present in the reference lettuce genome. The usability of the super-pangenome is demonstrated by identifying the likely origin of non-reference resistance loci from the wild relatives Lactuca serriola, Lactuca saligna and Lactuca virosa.ConclusionsThe super-pangenome offers a broader view on the gene repertoire of lettuce, revealing relevant loci that are not in the reference genome(s). The provided methodology and data provide a strong basis for research into PAVs, CNVs and other variation underlying important biological traits of lettuce and other crops.

The role of moderate to vigorous physical activity level and number of treatments/medications in mediating the effect of body mass index on diabetic nephropathy: a Mendelian randomization study

Background Body mass index (BMI) is associated with diabetic nephropathy (DN). However, the mediator factors in the BMI-DN effects remain unclear. Methods Univariate and multivariate Mendelian randomization (MR) analysis were performed to estimate the association between six lifestyles (moderate to vigorous physical activity levels, years of schooling, BMI, nap during day, number of treatments/medications taken and coffee intake) and DN. MR Egger, Weighted median, Simple mode, and Weighted mode was supplemental methods to Inverse variance weighted. Sensitivity analysis included heterogeneity test, horizontal pleiotropy test, and Leave-One-Out. Additionally, mediation MR was conducted to evaluate the mediating role of lifestyles between BMI and DN. Finally, functional enrichment analysis based on the mediation MR results was performed. Results univariate and multivariate Mendelian randomization (MR) analysis were performed to estimate the association between six lifestyles (moderate to vigorous physical activity levels, years of schooling, BMI, nap during day, number of treatments/medications taken and coffee intake) and DN. MR Egger, Weighted median, Simple mode, and Weighted mode was supplemental methods to Inverse variance weighted. Sensitivity analysis included heterogeneity test, horizontal pleiotropy test, and Leave-One-Out. Additionally, mediation MR was conducted to evaluate the mediating role of lifestyles between BMI and DN. Finally, functional enrichment analysis based on the mediation MR results was performed. Conclusion our results supported mediation role of vigorous physical activity level and number of treatments/medications in BMI-DN effects.

Proteomic profiling of extracellular vesicles in takotsubo syndrome

Abstract Background/Purpose Takotsubo syndrome (TS) is an acute form of heart failure characterized by transient regional wall motion abnormalities triggered by a stressful event. The mechanisms underlying its development and recovery are poorly understood, resulting in a lack of disease-specific treatments and diagnostic markers. Extracellular vesicles (EVs) play a significant role in cellular communication and have been shown to be important in various diseases. Their involvement in cardiac conditions like TS is an emerging area of research. The primary objective of this study is to isolate and characterize EVs, as well as profile their proteomic profile, from the heart tissue of rats induced with TS. Methods Rats underwent TS induction through the infusion of 1 mg/kg isoprenaline over 15 minutes (TS24h), or were given saline (control). High-resolution echocardiography verified apical ballooning at 6 hours. After 24 hours, heart tissues from apical and basal segments were collected and processed. EV isolation involved tissue slicing, enzymatic digestion, and differential centrifugation steps, including a final iodixanol cushion separation for large and small EVs (Figure 1). Tandem mass tags and mass spectrometry were utilized for global proteomics and any differentially expressed proteins (DEPs) were identified. Analytical techniques such as volcano plots, heatmaps, enrichment analysis, and protein clustering/networks were employed. The strongest clusters (lowest False Discovery Rate and highest intensity) were selected, and their Gene Ontology (GO) terms/pathways were identified. Results Pure, cup-shaped, vesicles ranging from 50-800nm were successfully isolated (figure 1). EVs from apex of control and TS24h hearts had lower protein concentrations compared to their corresponding basal segments. Global proteomic analysis revealed a distinct protein profile in EVs from the apical segment of TS hearts at 24 hours. A total of 256 (TS24h-apex vs control-apex) and 561 proteins (TS24h-apex vs TS24h-base) were differentially expressed. No DEPs were observed when comparing the basal segments of TS24h and control hearts. Functional enrichment analysis of the DEPs showed an abundant change of biological processes related to metabolic lipid processes, inflammatory response, complement activation, reorganization of extracellular matrix. A downregulation was seen for biological processes related to mitochondrial ATP synthesis coupled electron transport and muscle contraction. Conclusion This study demonstrates a distinct EV protein profile in the apical segments of TS hearts, with marked changes in proteins related to metabolic lipid processes, inflammation, and mitochondrial activity. This extensive catalogue of novel proteins sets the stage for future research aimed at identifying potential therapeutic targets and diagnostic biomarkers, and enabling proof-of-concept studies in TS.

Impact of Physical Activity, Sedentary Behavior, and Basal Metabolic Rate on PTSD, Depression, and Emotional Instability

Objective: This study aimed to investigate the potential associations between physical activity, sedentary behavior, and the basal metabolic rate (BMR) with post-traumatic stress disorder (PTSD), major depressive disorder (MDD), and emotional instability (EI) using bidirectional Mendelian randomization (MR). Additionally, it sought to identify key molecular mechanisms underlying emotional instability through a comprehensive bioinformatic analysis. Methods: MR analyses utilizing genome-wide association study (GWAS) data were conducted to estimate the effects of physical activity, sedentary behavior, and the BMR on PTSD, MDD, and EI. Sensitivity analyses were performed to assess the robustness of the findings. Concurrently, a bioinformatic analysis was executed on gene expression datasets (GSE53987 and GSE21138) derived from emotionally unstable patients. This analysis encompassed the differential gene expression analysis to identify differentially expressed genes (DEGs), followed by an functional enrichment analysis to uncover key molecular pathways associated with EI. Results: The MR analysis revealed that increased physical activity may have a protective effect against PTSD, albeit with inconsistent effects on MDD and EI. Sedentary behavior and the BMR demonstrated minimal associations with PTSD, MDD, or EI. The bioinformatic analysis identified 114 DEGs associated with EI, with YWHAB, SRRM2, MST1, HDAC10, and HSPA1A highlighted as significant genes. The functional enrichment analysis of these DEGs unveiled key pathways potentially involved in the molecular pathology of emotional instability. Conclusions: Physical activity appears to protect against PTSD, whereas its effects on MDD and EI are less definitive. The bioinformatic findings offer a deeper molecular understanding of EI, pinpointing specific genes and pathways that may serve as potential therapeutic targets. Further research is warranted to elucidate these intricate interactions and the identified molecular mechanisms.

Consensus gene co-expression analysis across multiple intestinal tissues to identify key genes and pathways associated with abdominal fat deposition in broilers

ABSTRACT 1. Abdominal fat deposition (AFD) is regulated by multiple intestinal tissues, and changes in the function of intestinal tissues are associated with AFD. Currently, integration of transcriptomic data across multiple intestinal tissues to explore excessive AFD has rarely been reported in broilers. 2. In this study, a consensus gene co-expression network across the duodenum, jejunum, ileum and caecum of high- and low-abdominal fat broiler lines (HL and LL) was constructed using a publicly available transcriptomic data set. Combining the results of functional enrichment analyses and differential gene expression analyses, this investigated the genes and biological pathways across the four intestinal tissues that might influence AFD. 3. In one expression module, NDUFA5, NDUFS6, NDUFA4, NDUFS4, ATP5H, ATP5J and ATP5C1 were significantly enriched in the oxidative phosphorylation pathway, with GPX2 and GSR significantly enriched in the glutathione metabolism pathway. These genes were significantly downregulated in the four intestinal tissues of the HL compared to LL chickens, which may be associated with AFD by increasing intestinal permeability. 4. Lipid metabolism relevant genes were identified in other modules (ALDH7A1, ACSBG1, THEM4 and DECR1), which may be linked to AFD through regulation of lipid metabolism. Interestingly, in the first module, 12 genes were significantly enriched in the proteasome pathway and significantly downregulated in the four intestinal tissues in HL birds compared to LL birds, indicating a link between the proteasome and AFD.

Identification and validation of key extracellular proteins as the potential biomarkers in diabetic nephropathy

ObjectiveAccumulation of extracellular matrix (ECM) proteins in the glomerular mesangial region is a typical hallmark of diabetic nephropathy (DN). However, the molecular mechanism underlying ECM accumulation in the mesangium of DN patients remains unclear. The present study aims to establish a connection between extracellular proteins and DN with the goal of identifying potential biomarkers for this condition.MethodsDifferentially expressed genes (DEGs) between DN kidney tissue and healthy kidney tissue were analyzed using the public data GSE166239. Two gene lists encoding extracellular proteins were then utilized to identify extracellular protein-differentially expressed genes (EP-DEGs). Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, were performed on these EP-DEGs. A protein–protein interaction (PPI) network was established to identify key EP-DEGs. Furthermore, the diagnostic ability, immune cell infiltration, and clinical relevance of these EP-DEGs were investigated. Immunohistochemistry (IHC) staining of paraffin-embedded renal tissues was performed to validate the accuracy of the bioinformatic results.ResultsA total of 1204 DEGs were identified, from which 162 EP-DEGs were further characterized by overlapping with extracellular protein gene lists. From the PPI network analysis, five EP-DEGs (e.g., TNF, COL1A1, FN1, MMP9, and TGFB1) were identified as candidate biomarkers. TNF, COL1A1, and MMP9 had a high diagnostic accuracy for DN. Assessment of immune cell infiltration revealed that the expression of TNF was positively associated with resting dendritic cells (DCs) (r = 0.85, P < 0.001) and M1 macrophages (r = 0.62, P < 0.05), whereas negatively associated with regulatory T cells (r = − 0.62, P < 0.05). Nephroseq v5 analysis demonstrated a negative correlation between the estimated glomerular filtration rate (eGFR) and TNF expression (r = − 0.730, P = 0.025). Gene set enrichment analysis (GSEA) revealed significant enrichment of glycosaminoglycan (GAG) degradation in the high-TNF subgroup. IHC staining of renal tissues confirmed significantly elevated TNF-a expression and decreased hyaluronic acid (HA) levels in the DN group compared to controls (both P < 0.05), with a negative correlation observed between TNF-a and HA (r = − 0.691, P = 0.026).ConclusionOur findings suggest that TNF may play a pivotal role in the progress of DN by driving ECM accumulation, and this process might involve GAG degradation pathway activation.

The prognostic significance of twist in pancreatic cancer and its role in cancer promotion through the regulation of the immune microenvironment and EMT mechanisms

ObjectivePancreatic cancer has a poor prognosis due to its high malignancy and rapid progression. The limited immunogenicity of pancreatic cancer (PAAD) contributes to its low responsiveness to immunotherapy, yet its underlying mechanism remains poorly understood. As a cancer promoting gene, Twist participates in EMT in various tumors and promotes tumor progression. The interplay between EMT and the tumor microenvironment (TME) emerges as a pivotal factor influencing tumor immunity and response to immunotherapy. Twist therefore has potential as a biomarker for gauging the outcome of tumour immunotherapy.This research aimed to assess the Twist's prognostic significance in PAAD and its relationship to immunotherapy response.MethodsIn this research, transcriptional data and epigenetic alterations of Twist in pancreatic cancer, along with their impact on the prognosis of PAAD patients, were analyzed using databases. Functional enrichment analysis elucidated the biological role of Twist in PAAD. Subsequently, databases including CIBERSORT and TIDE were employed to investigate the association between Twist expression and immune cell infiltration, immune checkpoint genes, and immunotherapy sensitivity within the pancreatic cancer immune microenvironment.Paraffinized specimens from patients with pancreatic ductal adenocarcinoma confirmed by postoperative pathology were selected for Twist expression verification, and the difference was analyzed by Chi-square test; uncontaminated pancreatic cancer cell lines were used for Twist expression verification, and the differences were analyzed by Student t-test.ResultsTwist mRNA expression was notably upregulated in PAAD, positively correlating with gene methylation levels. Analyses of Kaplan–Meier and Cox regression showed a correlation between better overall survival and lower Twist expression. Functional annotation indicated that Twist-associated differentially expressed genes (DEGs) were involved in EMT regulation and acute inflammation. High expression of Twist leads to a significant reduction in the infiltration of anti-tumor immune cells such as Monocytes, NKcellsactivated, and TcellsCD8, further supporting its creation of a typical immunosuppressive microenvironment in pancreatic cancer. Twist expression is positively correlated with the expression of HARVCR2, LAIR1, LGALS3 and other genes, which may be related to the treatment response to immune checkpoint inhibitors (ICIs). TIDE analysis predicts that patients with high expression of Twist will be insensitive to immunotherapy. Twist is significantly over-expressed in pancreatic cancer cell lines and tissues, and is negatively correlated with E-cadhrin expression, but positively correlated with N-cadherin,Snail, and ZEB1.ConclusionHigh Twist expression in PAAD signifies a grim prognosis. Its elevated levels not only contribute to tumor progression through EMT induction but also exert regulatory control over the immune microenvironment, leading to immunosuppression and diminished effectiveness of immunotherapy.

Bioinformatics and network pharmacology discover the molecular mechanism of Liuwei Dihuang pills in treating cerebral palsy.

A collection of chronic central motor, postural, and activity restriction symptoms are referred to as cerebral palsy (CP). Previous research suggests that a number of perinatal variables, including hypoxia, may be linked to CP. And the pathophysiological process that causes brain injury in growing fetuses is mostly caused by amniotic fluid infection and intra-amniotic inflammation. Still, there is still much to learn about the molecular mechanism of CP. The goal of this study was to identify the molecular mechanism of Liuwei Dihuang pill (LWDHP) in the treatment of CP using network pharmacology and bioinformatics. The Chinese medicine database provided the LWDHP components and targets, the CP illness gene data set was gathered from a disease, and the expression profile of children with CP was chosen from anther database. Using the Kyoto Encyclopedia of Genes and Genomes and gene ontology databases, a network of interactions between proteins was created, and functional enrichment analysis was carried out. Analysis of traditional Chinese medicine found that the key active ingredients of LWDHP are quercetin, Stigmasterol and kaempferol. Through enrichment analysis, it was found that the hub genes for LWDHP treatment of CP are CXCL8, MMP9, EGF, PTGS2, SPP1, BCL2L1, MMP1, and AR. K EGG analysis found that LWDHP treatment of CP mainly regulates PI3K-Akt signaling pathway, IL-17 signaling pathway, Jak-STAT signaling pathway, NF-kappa B signaling pathway, etc. To summarize, LWDHP regulates immunological and inflammatory variables through a variety of components, targets, and signaling pathways, which plays a significant role in the development and management of CP.

Comparison of triclosan and triclocarban in triggering immunotoxicity in larval zebrafish

As two efficient broad-spectrum sterilizing agents, triclosan (TCS) and triclocarban (TCC) are widely used, especially during the COVID-19 pandemic. The health risks caused by secondary pollution of TCS and TCC have aroused wide concern. Because of the similar mother nucleus structure and high lipophilicity, it remains unknown about the differences in the effect and mechanism of the toxicity (especially immunotoxicity) between TCS and TCC in organisms in the environment. In this study, we used zebrafish as a model to compare the immunotoxicity and mechanisms between the two pollutants at the same exposure concentration (0.6 µmol/L). The results showed that both TCS and TCC led to a hatching rate below 60% at the time point of 72 hours post fertilization (hpf) and the mortality rates of 40% and 50% at 120 hpf in larval zebrafish, respectively. The zebrafish exposed to TCS and TCC displayed malformations, such as shortened body, swimming sac closure, pericardial edema, yolk cyst deposition, and absorption disorder. Moreover, the developmental abnormalities caused by TCC were significantly severer than those caused by TCS. TCS exposure increased the proliferation rate of innate immune cells to 20% and decreased the number of mature T cells by 35%, while TCC exposure inhibited the differentiation of both innate immune cells and T cells, with the inhibition rates of 25% and 60%, respectively. The results of real-time quantitative PCR (RT-qPCR) and ELISA showed that TCS and TCC exposure up-regulated the expression levels of il-1β, il-6, and tnf-α, while il-10 and IgM exhibited opposite expression patterns. Additionally, both compounds slightly decreased C3 expression. The Pearson correlation analysis showed that the developmental toxicity induced by TCS and TCC had positive and negative correlations with the differentiation of immune cells, respectively. However, the toxicity induced by either TCS or TCC was positively correlated with the expression of pro-inflammatory cytokines. GO function and KEGG pathway enrichment analyses demonstrated that the target molecules of TCS and TCC were enriched in different signaling pathways, and the key network hub genes and the enriched regulatory pathways differed between TCS and TCC. The findings provide compelling evidence that TCS and TCC adopt different mechanisms in triggering immunotoxicity and offer a theoretical reference for the recognition, warning, and management of TCS and TCC-induced health risks.

Machine learning-based new classification for immune infiltration of gliomas.

Glioma is a highly heterogeneous and poorly immunogenic malignant tumor, with limited efficacy of immunotherapy. The characteristics of the immunosuppressive tumor microenvironment (TME) are one of the important factors hindering the effectiveness of immunotherapy. Therefore, this study aims to reveal the immune microenvironment (IME) characteristics of glioma and predict different immune subtypes using machine learning methods, providing guidance for immune therapy in glioma. We first performed unsupervised cluster analysis on the genes and arrays of 693 gliomas in CGGA database and 702 gliomas in TCGA database. Then establish and verify the classification model through Machine Learning (ML). Then, use DAVID to perform functional enrichment analysis for different immune subtypes. Next step, analyze the immune cell distribution, stemness maintenance, mesenchymal phenotype, neuronal phenotype, tumorigenic cytokines, molecular and clinical characteristics of different immune subtypes of gliomas. Firstly, we divide the IME of gliomas in the CGGA database into four different subtypes, namely IM1, IM2, IM3, and IM4; similarly, the IME of gliomas in the TCGA database can also be divided into four different subtypes (IMA, IMB, IMC, and IMD). Next, based on ML, we developed a highly reliable model for predicting different immune subtypes of glioma. Then, we found that Monocytic lineage, Myeloid dendritic cells, NK cells and CD8 T cells had the highest enrichment in the IM1/IMD subtypes. Cytotoxic lymphocytes were highest expressed in the IM4/IMA subtypes. Next step, Enrichment analysis revealed that the IM1-IMD subtypes were mainly closely related to the production and secretion of IL-8 and TNF signaling pathway. The IM2-IMB subtypes were strongly associated with leukocyte activation and NK cell mediated cytotoxicity. The IM3-IMC subtypes were closely related to mitotic nuclear division and mitotic cell cycle process. The IM4-IMA subtypes were strongly associated with Central Nervous System (CNS) development and striated muscle tissue development. Afterwards, Single sample gene set enrichment analysis (ssGSEA) showed that stemness maintenance phenotypes were mainly enriched in the IM4/IMA subtypes; Neuronal phenotypes were closely associated with the IM2/IMB subtypes; and mesenchymal phenotypes and tumorigenic cytokines were highly correlated with the IM2 /IMB subtypes. Finally, we found that compared with patients in the IM2/IMB and IM4/IMA subtypes, the IM1/IMD and IM3/IMC subtypes have the highest proportion of GBM patients, the shortest average overall survival of patients and the lowest proportion of patients with IDH mutation and 1p36/19q13 co-deletion. We developed a highly reliable model for predicting different immune subtypes of glioma by ML. Then, we comprehensively analyzed the immune infiltration, molecular and clinical features of different immune subtypes of gliomas and defined gliomas into four subtypes: immunogenic subtype, adaptive immune resistance subtype, mesenchymal subtype, and immune tolerance subtype, which represent different TMEs and different stages of tumor development.

Integrative analysis of bulk and single-cell transcriptomic data reveals novel insights into lipid metabolism and prognostic factors in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is associated with high mortality rate. This study investigated the status of lipid metabolism-related genes in HCC. Bulk transcriptomic and single-cell sequencing data for HCC were retrieved from public databases. The single-cell sequencing data was subjected to dimensionality reduction, which facilitated the annotation of distinct cell subpopulations and marker gene expression analysis within each subpopulation. Genes associated with lipid metabolism in liver cells were identified, and a machine-learning model was developed using the bulk transcriptomic data randomly partitioned into training and validation sets. The efficacy of the model was validated using these two sets. A multifactorial Cox analysis on the model genes combined with clinical features, led to the identification of age, HMGCS2, HNRNPU, and RAN as independent prognostic factors, which were included in the nomogram model construction and validation. A weighted gene co-expression analysis of all genes of the bulk transcriptome samples revealed the correlation between gene modules and risk score. Genes with cor > 0.4 in the highest-expressing module were selected for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analysis. Immune-related analysis was conducted based on seven algorithms for immune cell infiltration prediction. For the genes in the nomogram model, the expression in clinical pathological factors was also analyzed. The drug sensitivity analysis offered a reference for the selection of targeting drugs. This investigation provides novel insights and a theoretical basis for the prognosis, treatment, and pharmaceutical advancements for patients diagnosed with HCC.

SASS6 promotes tumor proliferation and is associated with TP53 and immune infiltration in lung adenocarcinoma

The most common type of non-small cell lung cancer is lung adenocarcinoma (LUAD), which is characterized by high morbidity and poor survival. Up-regulation of SASS6 expression can lead to the progression of various malignant tumors. However, there are no relevant studies on the role of SASS6 in LUAD. SASS6 was highly expressed in most tumors, reflecting a good diagnostic value, and its overexpression in LUAD indicated discouraging overall prognosis. Functional enrichment analysis suggested that SASS6 was associated with cell cycle in LUAD. In addition, patients with high SASS6 expression had worse immune infiltration, but higher TMB and immune checkpoint, and higher sensitivity to multiple targeted drugs such as osimertinib. Cell experiments confirmed that knockdown of SASS6 could inhibit the viability of tumor cells.SASS6 has important value in the diagnosis of cancer. In particular, SASS6 is a crucial factor in the progression of LUAD, and has important clinical value, especially in the diagnosis, prognosis and treatment

Deciphering the role of lipid metabolism-related genes in Alzheimer's disease: a machine learning approach integrating Traditional Chinese Medicine.

Alzheimer's disease (AD) represents a progressive neurodegenerative disorder characterized by the accumulation of misfolded amyloid beta protein, leading to the formation of amyloid plaques and the aggregation of tau protein into neurofibrillary tangles within the cerebral cortex. The role of carbohydrates, particularly apolipoprotein E (ApoE), is pivotal in AD pathogenesis due to its involvement in lipid and cholesterol metabolism, and its status as a genetic predisposition factor for the disease. Despite its significance, the mechanistic contributions of Lipid Metabolism-related Genes (LMGs) to AD remain inadequately elucidated. This research endeavor seeks to bridge this gap by pinpointing biomarkers indicative of early-stage AD, with an emphasis on those linked to immune cell infiltration. To this end, advanced machine-learning algorithms and data derived from the Gene Expression Omnibus (GEO) database have been employed to facilitate the identification of these biomarkers. Differentially expressed genes (DEGs) were identified by comparing gene expression profiles between healthy individuals and Alzheimer's disease (AD) patients, using data from two Gene Expression Omnibus (GEO) datasets: GSE5281 and GSE138260. Functional enrichment analysis was conducted to elucidate the biological relevance of the DEGs. To ensure the reliability of the results, samples were randomly divided into training and validation sets. The analysis focused on lipid metabolism-related DEGs (LMDEGs) to explore potential biomarkers for AD. Machine learning algorithms, including Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model, were applied to identify a key gene biomarker. Additionally, immune cell infiltration and its relationship with the gene biomarker were assessed using the CIBERSORT algorithm. The Integrated Traditional Chinese Medicine (ITCM) database was also referenced to identify Chinese medicines related to lipid metabolism and their possible connection to AD. This comprehensive strategy aims to integrate modern computational methods with traditional medicine to deepen our understanding of AD and its underlying mechanisms. The study identified 137 genes from a pool of 751 lipid metabolism-related genes (LMGs) significantly associated with autophagy and immune response mechanisms. Through the application of LASSO and SVM-RFE machine-learning techniques, four genes-choline acetyl transferase (CHAT), member RAS oncogene family (RAB4A), acyl-CoA binding domain-containing protein 6 (ACBD6), and alpha-galactosidase A (GLA)-emerged as potential biomarkers for Alzheimer's disease (AD). These genes demonstrated strong therapeutic potential due to their involvement in critical biological pathways. Notably, nine Chinese medicine compounds were identified to target these marker genes, offering a novel treatment approach for AD. Further, ceRNA network analysis revealed complex regulatory interactions involving these genes, underscoring their importance in AD pathology. CIBERSORT analysis highlighted a potential link between changes in the immune microenvironment and CHAT expression levels in AD patients, providing new insights into the immunological dimensions of the disease. The discovery of these gene markers offers substantial promise for the diagnosis and understanding of Alzheimer's disease (AD). However, further investigation is necessary to validate their clinical utility. This study illuminates the role of Lipid Metabolism-related Genes (LMGs) in AD pathogenesis, offering potential targets for therapeutic intervention. It enhances our grasp of AD's complex mechanisms and paves the way for future research aimed at refining diagnostic and treatment strategies.

Single-cell omics and machine learning integration to develop a polyamine metabolism-based risk score model in breast cancer patients

BackgroundBreast cancer remains the leading malignant neoplasm among women globally, posing significant challenges in terms of treatment and prognostic evaluation. The metabolic pathway of polyamines is crucial in breast cancer progression, with a strong association to the increased capabilities of tumor cells for proliferation, invasion, and metastasis.MethodsWe used a multi-omics approach combining bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) to study polyamine metabolism. Data from The Cancer Genome Atlas, Gene Expression Omnibus, and Genotype-Tissue Expression identified 286 differentially expressed genes linked to polyamine pathways in breast cancer. These genes were analyzed using univariate COX and machine learning algorithms to develop a prognostic scoring algorithm. Single-cell RNA sequencing validated the model by examining gene expression heterogeneity at the cellular level.ResultsOur single-cell analyses revealed distinct subpopulations with different expressions of genes related to polyamine metabolism, highlighting the heterogeneity of the tumor microenvironment. The SuperPC model (a constructed risk score) demonstrated high accuracy when predicting patient outcomes. The immune profiling and functional enrichment analyses revealed that the genes identified play a crucial role in cell cycle control and immune modulation. Single-cell validation confirmed that polyamine metabolism genes were present in specific cell clusters. This highlights their potential as therapeutic targets.ConclusionsThis study integrates single cell omics with machine-learning to develop a robust scoring model for breast cancer based on polyamine metabolic pathways. Our findings offer new insights into tumor heterogeneity, and a novel framework to personalize prognosis. Single-cell technologies are being used in this context to enhance our understanding of the complex molecular terrain of breast cancer and support more effective clinical management.

Application of Network Pharmacology, Molecular Docking, and In Vitro Experimental Evaluation to Decipher the Anti-Inflammatory Mechanisms of Cirsium japonicum

Cirsium japonicum, a traditional herb, exhibits significant anti-inflammatory activity. However, the main components and potential mechanisms of C. japonicum remain unclear. The aim of this study is to investigate the anti-inflammatory mechanism of Cirsium japonicum through network pharmacology and cellular experiments. The effective components of and potential targets for the anti-inflammatory activity of C. japonicum were identified using a traditional Chinese medicine systematic pharmacology database, the TCMSP analysis platform, and the GeneCards database. The drug–component–target–disease network diagram was constructed using Cytoscape 3.8.0 software, while the protein interaction network diagram was created using the STRING database and Cytoscape 3.8.0 software. Gene ontology (GO) enrichment and KEGG pathway enrichment analysis were carried out using the DAVID database. Molecular docking between key targets and active components was constructed with AutoDock 4.2.6 software to determine the best binding target. The results revealed that 14 active components of C. japonicum targeted 171 anti-inflammatory proteins. GO function enrichment analysis yielded 173 items, while KEGG pathway enrichment analysis identified 48 signaling pathways related to inflammation regulation. Molecular docking showed a strong affinity of sitosterol, stigmasterol, and other components with key targets such as peroxisome proliferator-activated receptor α recombinant protein (PPARA) and cyclooxygenase-2 (PTGS2). Vanillin, one active ingredient of C. japonicum, inhibited the release of lipopolysaccharide (LPS)-induced inflammatory factors in RAW264.7 cells. These findings suggest that C. japonicum may exert its anti-inflammatory effects by modulating the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) signal pathway (PI3K-Akt) and apoptin signal pathway, highlighting the multi-component, multi-target, and multi-channel molecular mechanism underlying its anti-inflammatory properties. Finally, the anti-inflammatory effect of vanillin, an effective component of C. japonicum, was verified by cell experiments. This study provides a new understanding of the pharmacological mechanisms of C. japonicum in the treatment of inflammatory conditions.