Gene Set Enrichment Research Articles

Key genes and immune pathways in T-cell mediated rejection post-liver transplantation identified via integrated RNA-seq and machine learning

Liver transplantation is the definitive treatment for end-stage liver disease, yet T-cell mediated rejection (TCMR) remains a major challenge. This study aims to identify key genes associated with TCMR and their potential biological processes and mechanisms. The GSE145780 dataset was subjected to differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms to pinpoint key genes associated with TCMR. Gene Set Enrichment Analysis (GSEA), immune infiltration analysis, and regulatory networks were constructed to ascertain the biological relevance of these genes. Expression validation was performed using single-cell RNA-seq (scRNA-seq) data and liver biopsy tissues from patients. We identified 5 key genes (ITGB2, FCER1G, IL-18, GBP1, and CD53) that are associated with immunological functions, such as chemotactic activity, antigen processing, and T cell differentiation. GSEA highlighted enrichment in chemokine signaling and antigen presentation pathways. A lncRNA-miRNA-mRNA network was delineated, and drug target prediction yielded 26 potential drugs. Evaluation of expression levels in non-rejection (NR) and TCMR groups exhibited significant disparities in T cells and myeloid cells. Tissue analyses from patients corroborated the upregulation of GBP1, IL-18, CD53, and FCER1G in TCMR cases. Through comprehensive analysis, this research has identified 4 genes intimately connected with TCMR following liver transplantation, shedding light on the underlying immune activation pathways and suggesting putative targets for therapeutic intervention.

THE EPIGENETIC REGULATORY PROTEIN CBX2 AS A NOVEL THERAPEUTIC TARGET FOR GLIOBLASTOMA

Abstract AIMS Glioblastoma (GBM) is the deadliest and most common form of brain tumour. The average length of survival for GBM patients remains low, in part, due to a lack of targeted therapeutics. Identification and validation of novel therapeutic targets is therefore crucial. Chomobox 2 (CBX2; a component of polycomb repressive complex 1 (PRC1)), represses gene expression and has a pro-oncogenic role in several aggressive cancer subtypes. CBX2 expression is elevated in GBM, however, very little is known about its role in this deadly disease. We aim to assess the phenotypic and gene expression effects of preventing CBX2-chromatin interaction in patient-derived GBM cell models, and to determine the functional mechanisms by which CBX2 promotes GBM progression; thereby determining its potential as a novel therapeutic target. METHOD Crucial to impactful target validation is the use of translationally relevant models, this study utilises a 3D spheroid model, using patient-derived cells, which more closely recapitulates the in vivo tumour microenvironment. GBM cell monolayers and spheroids were treated with SW2_152F, a selective CBX2 inhibitor. The phenotypic effects of CBX2 inhibition were assessed by cell count, CellTiter-Glo viability assays, fluorescence microscopy, and flow cytometry. Genes and biological pathways differentially regulated following CBX2 inhibition were assessed by RNA-sequencing and Gene Set Enrichment Analysis (GSEA). RESULTS Inhibition of CBX2 induced cell death and reduced cell viability in both 2D and 3D GBM cultures. RNA-seq and GSEA of SW2_152F-treated cells identified dysregulation of gene signatures involved in pro-oncogenic MYC and E2F signalling pathways, and dysregulation of genes associated with G2/M cell cycle progression. CONCLUSION Preventing CBX2-chromatin interaction induces GBM cell death. Further research will identify CBX2- chromatin binding sites to characterise the activity and direct regulatory role of CBX2 in GBM. Furthermore, we will assess the phenotypic and gene expression effects of CBX2 inhibition and depletion on ex vivo maintained GBM tissue biopsies.

ARGININE DEPRIVATION SENSITISES GBM CELLS TO RADIATION THROUGH DOWNREGULATION OF DNA REPAIR PATHWAYS

Abstract AIMS Glioblastoma (GBM) is the most aggressive form of brain cancer, and therapy hasn’t changed for almost 20 years. We have previously shown that GBM tumours lack expression of argininosuccinate synthase 1 (ASS1), a key enzyme involved in arginine biosynthesis, due to promoter methylation. This renders them sensitive to systemic arginine depletion using agents such as pegylated arginine deiminase (ADI), allowing us to circumvent the issue of blood-brain-barrier (BBB) penetration. Furthermore, ADI appears to synergise with the DNA damaging agent temozolomide (TMZ), which is routinely used to treat patients with GBM. Here, we aimed to explore whether ADI can synergise with radiotherapy, and determine the mechanisms by which ADI is able to sensitise cells to DNA damaging agents. METHOD Primary GBM cell lines were treated with ADI in combination with radiation and underwent analysis using an RNA microarray, Qiagen DNA repair RT2 array, qPCR, western blotting, and proliferation assays in both 2D (SRB) and 3D (spheroid, CCK8) format. We performed orthotopic injection of primary GBM cells into the striatum of athymic nude mice, and randomised them to ADI, radiation, or the combination, plus controls. Tissue was analysed using IHC staining. RESULTS In ASS1 methylated primary GBM, treatment with ADI led to a significant change in the expression of ~500 genes with a fold change >2. Gene set enrichment analysis indicated that these genes were predominantly involved in cell cycle regulation, which is closely linked to the DNA damage and repair machinery. In another ASS1 methylated primary GBM cell line, an RT2 array revealed specific downregulation of genes involved in DNA repair. qPCR and western blot validation of these changes showed a significant decrease in the expression and protein levels of PARP1 and LIG1 with ADI treatment. CONCLUSION Arginine deprivation using ADI leads to a downregulation of PARP1 in GBM cells, sensitising them to radiotherapy.

Retinol-binding protein type 1 expression predicts poor prognosis in head and neck squamous cell carcinoma

BackgroundHead and neck squamous cell carcinoma (HNSCC) is the sixth most prevalent malignancy worldwide, with high incidence and poor survival rates. RBP1 is highly expressed in several kinds of cancer and plays a potential prognostic factor. However, the relationship between RBP1 and HNSCC were analyzed based on The Cancer Genome Atlas (TCGA) database.Materials and methodsRBP1 expression and clinical information were obtained from the Cancer Genome Atlas (TCGA) database. Tumor tissue and adjacent normal tissue of 6 HNSCC patients were collected to analyze the RBP1 mRNA expression level by quantitative PCR. Cox regression analysis was used to evaluate the prognostic values of RBP1 and clinical data in HNSCC. A nomogram was also established to predict the impact of RBP1 on prognosis based on Cox multivariate results. The methylation level of RBP1 in HNSC and its prognosis were analyzed in UALACN and MethSurv. Finally, the potential biological functions of RBP1 were investigated using gene set enrichment analysis (GSEA) and single sample GSEA (ssGSEA).ResultsThe mRNA expression levels of RBP1 were highly expressed in HNSCC tissue. The Cox analyses demonstrate that highly-expressed RBP1 is an independent prognosis marker(P < 0.05). ROC curve analysis showed that performances of RBP1 (area under the ROC curve: 0.887, sensitivity: 84.1%, specificity: 79.9%). The methylation was increased in HNSCC patients compared with normal subjects(P < 0.05) and was associated with better prognosis at sites cg06208339, cg12298268, cg12497564, cg15288618, cg20532370, cg23448348. Additionally, RBP1 expression is mildly associated with immune cell infiltration and immunological checkpoints.ConclusionRBP1 is overexpressed and associated with poor patient prognosis in head and neck squamous cell carcinoma.

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

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

Establishment of the Prognostic Signature with Genes Related to G2/M Checkpoint in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer mortality in the world. Prognostic indicators such as clinicopathological characteristics and single molecular signature are far from satisfactory in clinical practice. More and more researches have suggested that polygenic prognostic features could predict the prognosis of cancer more precisely than single genes nowadays. In this study, we performed gene set enrichment analysis (GSEA) to identify the sets of TCGA hallmark genes. Univariate Cox regression analysis was used to select preliminary genes, and then multivariate Cox regression analysis was used to identify genes associated with overall survival (OS). We also used Kaplan-Meier analysis and receiver operating characteristic (ROC) analysis to validate the prognostic gene signature. Lastly, qRT-PCR was used to evaluate the expression of these genes in clinical samples, and immunohistochemical staining was performed to confirm the signature. A 12-gene signature was finally built and the risk score was significantly associated with the survival of the patients. Subsequent validation by qRT-PCR and immunohistochemical staining in clinical specimens confirmed the value of the risk score in predicting the prognosis. We developed a 12-gene signature that could predict the prognosis of HCC patients. This signature is of high precision and would help identifying subgroups of HCC patients with high or low risk of unfavorable survival.

Identification of immune feature genes and intercellular profiles in diabetic cardiomyopathy

BACKGROUND Diabetic cardiomyopathy (DCM) is a multifaceted cardiovascular disorder in which immune dysregulation plays a pivotal role. The immunological molecular mechanisms underlying DCM are poorly understood. AIM To examine the immunological molecular mechanisms of DCM and construct diagnostic and prognostic models of DCM based on immune feature genes (IFGs). METHODS Weighted gene co-expression network analysis along with machine learning methods were employed to pinpoint IFGs within bulk RNA sequencing (RNA-seq) datasets. Single-sample gene set enrichment analysis (ssGSEA) facilitated the analysis of immune cell infiltration. Diagnostic and prognostic models for these IFGs were developed and assessed in a validation cohort. Gene expression in the DCM cell model was confirmed through real time-quantitative polymerase chain reaction and western blotting techniques. Additionally, single-cell RNA-seq data provided deeper insights into cellular profiles and interactions. RESULTS The overlap between 69 differentially expressed genes in the DCM-associated module and 2483 immune genes yielded 7 differentially expressed immune-related genes. Four IFGs showed good diagnostic and prognostic values in the validation cohort: Proenkephalin (Penk) and retinol binding protein 7 (Rbp7), which were highly expressed, and glucagon receptor and inhibin subunit alpha, which were expressed at low levels in DCM patients (all area under the curves &gt; 0.9). SsGSEA revealed that IFG-related immune cell infiltration primarily involved type 2 T helper cells. High expression of Penk (P &lt; 0.0001) and Rbp7 (P = 0.001) was detected in cardiomyocytes and interstitial cells and further confirmed in a DCM cell model in vitro . Intercellular events and communication analysis revealed abnormal cellular phenotype transformation and signaling communication in DCM, especially between mesenchymal cells and macrophages. CONCLUSION The present study identified Penk and Rbp7 as potential DCM biomarkers, and aberrant mesenchymal-immune cell phenotype communication may be an important aspect of DCM pathogenesis.

Pancancer analysis of NDUFA4L2 with focused role in tumor progression and metastasis of colon adenocarcinoma.

Colon adenocarcinoma (COAD) is a prevalent gastrointestinal malignant disease with a high mortality rate, and identification of novel prognostic biomarkers and therapeutic targets is urgently needed. Although NDUFA4L2 has high expressions in various tumors and affects tumor progression, its role in COAD remains unclear. The role of NDUFA4L2 in COAD was analyzed utilizing datasets available from public databases including The Cancer Genome Atlas, The Genotype-Tissue Expression (GTEx), Gene Expression Omnibus, Alabama Cancer Database (UALCAN), and The Human Protein Atlas databases. The prognostic value of NDUFA4L2 was determined using Kaplan-Meier analysis and Cox regression analysis. To investigate the possible mechanism underlying the role of NDUFA4L2 in COAD, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were employed. The correlation between NDUFA4L2 expression and immune cell infiltration levels was examined through single-sample gene set enrichment analysis (ssGSEA). The NDUFA4L2 expression levels in COAD patients and cell lines were validated through immunohistochemistry, immunofluorescence, qRT-PCR, and Western blot. Wound healing assay was also performed to evaluate the effect of NDUFA4L2 on COAD metastasis. Furthermore, the NDUFA4L2 mediated competing endogenous RNA (ceRNA) regulatory network was predicted and constructed through a variety of databases. The comprehensive pan-cancer analysis showed that NDUFA4L2 possesses diagnostic and prognostic value in many cancers, especially in COAD. GO-KEGG and GSEA analyses indicated that NDUFA4L2 was associated with multiple biological functions including epithelial-mesenchymal transition and adaptation to hypoxia. The ssGSEA analysis showed that NDUFA4L2 expression was associated with immune infiltration. In vitro experiments confirmed upregulation of NDUFA4L2 in COAD tissues and cell lines, and NDUFA4L2 overexpression significantly promoted migration of COAD cells. In addition, the C9orf139 /miR-194-3p axis was speculated as the possible upstream regulators of NDUFA4L2 in COAD. This study demonstrated that NDUFA4L2 upregulation was correlated with tumor progression, relapsed prognosis and aggressive migration of COAD, suggesting that NDUFA4L2 can act as an effective prognostic biomarker and a promising therapeutic target for COAD treatment.

HIF-1α regulates mitochondrial function in bone marrow-derived macrophages, but not in tissue-resident alveolar macrophages.

HIF-1α plays a critical role in shaping macrophage phenotype and effector function. We have previously shown that tissue-resident alveolar macrophages (TR-AMs) have extremely low glycolytic capacity at steady-state, but can shift toward glycolysis under hypoxic conditions. Here, using inducible HIF-1α knockout ( Hif1a -/- ) TR-AMs and bone marrow-derived macrophages (BMDMs) and show that TR-AM HIF-1α is required for the glycolytic shift under prolyl hydroxylase inhibition, but is dispensable at steady-state for inflammatory effector function. In contrast, HIF-1α deletion in BMDMs led to diminished glycolytic capacity at steady-state and reduced inflammatory capacity, but higher mitochondrial function. Gene set enrichment analysis revealed enhanced c-Myc transcriptional activity in Hif1a -/- BMDMs, and upregulation of gene pathways related to ribosomal biogenesis and cellular proliferation. The findings highlight the heterogeneity of HIF-1α function in distinct macrophage populations and provide new insight into how HIF-1α regulates gene expression, inflammation, and metabolism in macrophages.

Decoding visceral adipose tissue molecular signatures in obesity and insulin resistance: a multi-omics approach.

Obesity-associated insulin resistance (IR) is responsible for considerable morbidity and mortality globally. Despite vast genomic data, many areas, from pathogenesis to management, still have significant knowledge gaps. We aimed to characterize visceral adipose tissue (VAT) in obesity and IR through a multi-omics approach. We procured data on VAT samples from the Gene Expression Omnibus (GEO) for the following two groups: 1) populations with obesity (n = 34) versus those without (n = 26); and 2) populations with obesity and IR (n = 15) versus those with obesity but without IR (n = 15). Gene set enrichment, protein-protein interaction network construction, hub gene identification, and drug-gene interactions were performed, followed by regulatory network prediction involving transcription factors (TFs) and microRNAs (miRNAs). Interleukin signaling pathways, cellular differentiation, and regulation of immune response revealed a significant cross talk between VAT and the immune system. Other findings include cancer pathways, neurotrophin signaling, and aging. A total of 10 hub genes, i.e., STAT1, KLF4, DUSP1, EGR1, FOS, JUN, IL2, IL6, MMP9, and FGF9, 24 TFs, and approved hub gene-targeting drugs were obtained. A total of 10 targeting miRNAs (e.g., hsa-miR-155-5p, hsa-miR-34a-5p) were associated with obesity and IR-related pathways. Our multi-omics integration method revealed hub genes, TFs, and miRNAs that can be potential targets for investigation in VAT-relatedinflammatory processes and IR, therapeutic management, and risk stratifications.

Autophagy and inflammasome activation are associated with poor response to FLT3 inhibitors in patients with FLT3-ITD acute myeloid leukemia

Beyond its clinical diversity and severity, acute myeloid leukemia (AML) is known for its complex molecular background and for rewiring biological processes to aid disease onset and maintenance. FLT3 mutations are among the most recurring molecular entities that cooperatively drive AML, and their inhibition is a critical molecularly oriented therapeutic strategy. Despite being a promising avenue, it still faces challenges such as intrinsic and acquired drug resistance, which led us to investigate whether and how autophagy and inflammasome interact and whether this interaction could be leveraged to enhance FLT3 inhibition as a therapeutic strategy. We observed a strong and positive correlation between the expression of key genes associated with autophagy and the inflammasome. Gene set enrichment analysis of the FLT3-ITD samples and their ex vivo response to five different FLT3 inhibitors revealed a common molecular signature compatible with autophagy and inflammasome activation across all poor responders. Inflammasome activation was also shown to strongly increase the likelihood of a poor ex vivo response to the FLT3 inhibitors quizartinib and sorafenib. These findings reveal a distinct molecular pattern within FLT3-ITD AML samples that underscores the necessity for further exploration into how approaching these supportive parallel yet altered pathways could improve therapeutic strategies.

Deciphering a hydrogen sulfide-related signature to supervise prognosis and therapeutic response in colon adenocarcinoma.

Hydrogen sulfide (H2S) is a critical molecule that participates in various molecular, physiological, and pathophysiological processes in biological systems. Emerging evidence has revealed that H2S is implicated in the progression of colon cancer and immune escape. Against this backdrop, the present study aimed to construct a prognostic risk feature for colon adenocarcinoma (COAD) by leveraging hydrogen sulfide-related genes (HSRG). Transcriptomic data and corresponding clinical-pathological information of colon cancer were obtained from The Cancer Genome Atlas and gene expression omnibus databases. Univariate Cox regression analysis was employed to assess the prognostic relevance of HSRG. Consensus clustering was utilized to perform molecular subtyping of COAD, followed by comparison of immune cell infiltration, drug sensitivity, and immune therapy response between subtypes. Differential expression gene and gene set enrichment analyses were conducted between subtypes. Univariate, lasso, and multivariate Cox regression analyses were applied to construct a prognostic model derived from HSRG. A nomogram model for predicting COAD prognosis was constructed and evaluated. In this study, we identified 12 HSRGs that were associated with COAD prognosis. Consensus clustering analysis revealed 3 COAD molecular subtypes that exhibited significant differences in terms of prognosis, tumor immune cell infiltration, drug sensitivity, and immune therapy response. Gene set enrichment analysis demonstrated that immunoregulatory processes were significantly suppressed in the poor-prognosis subtype while Wnt-related pathways and processes were significantly upregulated. Based on the differentially expressed genes between subtypes, we constructed a risk model comprising 11 genes that effectively distinguished high-risk patients from low-risk patients with significant associations with patient survival outcomes, drug treatment, pathological staging, and T staging. The HSRG-derived risk feature was an independent prognostic factor for COAD in drug treatment and pathological staging and could be integrated into a nomogram for prognosis prediction. Calibration curve, receiver operating characteristic curve, and decision curve analysis demonstrated excellent performance of the nomogram in evaluating COAD prognosis. Our study systematically assessed the prognostic significance of HSRG in COAD, identified HSRG-based molecular subtypes and risk features, and highlighted their potential utility in predicting prognosis and treatment response.

Construction and validation of a prognostic model of angiogenesis-related genes in multiple myeloma

BackgroundAngiogenesis is associated with tumour growth, infiltration, and metastasis. This study aimed to detect the mechanisms of angiogenesis-related genes (ARGs) in multiple myeloma (MM) and to construct a new prognostic model.MethodsMM research foundation (MMRF)-CoMMpass cohort, GSE47552, GSE57317, and ARGs were sourced from public databases. Differentially expressed genes (DEGs) in the tumour and control cohorts in GSE47552 were determined through differential expression analysis and were enriched with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Weighted gene coexpression network analysis (WGCNA) was applied to derive modules linked to the ARG scores and obtain module genes in GSE47552. Differentially expressed ARGs (DE-ARGs) were selected for subsequent analyses by overlapping DEGs and module genes. Furthermore, prognostic genes were selected using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. Depending on the prognostic genes, a risk model was constructed, and risk scores were determined. Moreover, MM samples from MMRF-CoMMpass were sorted into high- and low-risk teams on account of the median risk score. Additionally, correlations among clinical characteristics, gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), immune analysis, immunotherapy predictions and the mRNA‒miRNA‒lncRNA network were carried out.ResultsA total of 898 DEGs, 211 module genes, 24 DE-ARGs and three prognostic genes (AKAP12, C11orf80 and EMP1) were selected for this study. Enrichment analysis revealed that the DEGs were related to 86 GO terms, such as ‘cytoplasmic translation’, and 41 KEGG pathways, such as ‘small cell lung cancer’. A prognostic gene-based risk model was created in MMRF-CoMMpass and confirmed with the GSE57317 dataset. Moreover, a nomogram was established on the basis of independent prognostic factors that have proven to be good predictors. In addition, the immune cell infiltration results suggested that memory B cells were enriched in the high-risk group and that immature B cells were enriched in the low-risk group. Finally, the mRNA‒miRNA‒lncRNA network demonstrated that hsa-miR-508-5p was tightly associated with EMP1 and AKAP12. RT‒qPCR was used to validate the expression of the genes associated with prognosis.ConclusionA new prognostic model of MM associated with ARGs was created and validated, providing a new perspective for exploring the connection between ARGs and MM.

Investigating PPT2’s role in ovarian cancer prognosis and immunotherapy outcomes

Ovarian cancer (OC) remains the primary cause of mortality among gynecological malignancies, and the identification of reliable molecular biomarkers to prognosticate OC outcomes is yet to be achieved. The gene palmitoyl protein thioesterase 2 (PPT2), which has been sparsely studied in OC, was closely associated with metabolism. This study aimed to determine the association between PPT2 expression, prognosis, immune infiltration, and potential molecular mechanisms in OC. We obtained the RNA-seq and clinical data from The Cancer Genome Atlas (TCGA), The Genotype-Tissue Expression (GTEx) and Gene Expression Omnibus (GEO) databases, then Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, nomogram, and calibration were conducted to assess and verify the role of PPT2. Gene set enrichment analysis (GSEA) was used to figure out the closely correlated pathways with PPT2. Overexpression experiment was performed to explore the function of PPT2. Our findings showed that PPT2 mRNA expression was apparent down-regulation in OC tissue compared to normal ovarian tissues in TCGA, GTEx datasets, and GEO datasets. This differential expression was also confirmed in our in-house datasets at both the mRNA and protein levels. Decreased PPT2 expression correlated with lower survival rates in TCGA, several GEO datasets, and our in-house datasets. Multivariate analysis revealed that PPT2 was an independent factor in predicting better outcomes for OC patients in TCGA and GEO. A negative correlation was revealed between immune infiltration and PPT2 expression through Single-sample GSEA (ssGSEA). Additionally, PPT2 was negatively correlated with an up-regulated immune score, stromal score, and estimate score, suggesting that patients with low PPT2 expression might benefit more from immunotherapy. Numerous chemical agents showed lower IC50 in patients with high PPT2 expression. In single-cell RNA sequencing (scRNA-seq) analysis of several OC datasets, we found PPT2 was mainly expressed in endothelial cells. Furthermore, we found that PPT2 inhibited OC cell proliferation in vitro. Our results demonstrated that PPT2 was considered a favorable prognostic biomarker for OC and may be vital in predicting response to immunotherapy and chemotherapy. Further research was needed to fully understand the relationship between PPT2 and immunotherapy efficacy in OC patients.

Identification of neutrophil extracellular traps genes as potential biomarkers in psoriasis based on bioinformatics analysis

The epidermal infiltration of neutrophils is a hallmark of psoriasis (PSO) and its activation leads to the release of neutrophil extracellular traps (NETs). However, the molecular mechanism of NETs-related genes (NETRGs) has not been extensively studied in PSO. To define NETs-related-biomarkers for PSO. The GSE13355 and GSE78097 datasets, and NETRGs gene set were included in this study. The datasets used in this study were all microarray data. The weighted gene co-expression network analysis (WGCNA) and machine learning algorithms were used to mine key genes. Later on, single-gene gene set enrichment analysis (GSEA) and immune infiltration analysis were implemented. Finally, the expression of key genes was verified using quantitative real-time fluorescence PCR (qRT-PCR). A total of 3 key genes (S100A9, CLEC7A, and CXCR4) were derived, and they all had excellent diagnostic performance. The single-gene GSEA enrichment results indicated that the key genes were mainly enriched in the chemokine signaling pathway and humoral immune response in the high-expression group, while focal adhesion was enriched in the low-expression group. The correlation analysis indicated that all key genes were strongly negatively correlated with resting mast cells and TGF-β family member receptor, while they were strongly positively correlated with activated CD4 memory T cells and antigen processing and presentation. Lastly, the experimental results showed that the expression trends of key genes were consistent with public database. In this study, we successfully screened three potential PSO diagnostic genes (S100A9, CLEC7A and CXCR4) that were closely related to NETs, and these findings not only provided new molecular marker candidates for the precise diagnosis of PSO patients, but also revealed possible future therapeutic targets. However, further in-depth research and validation were necessary.

Integrating anoikis and ErbB signaling insights with machine learning and single-cell analysis for predicting prognosis and immune-targeted therapy outcomes in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) poses a significant global health challenge due to its poor prognosis and limited therapeutic modalities. Anoikis and ErbB signaling pathways are pivotal in cancer cell proliferation and metastasis, but their relevance in HCC remains insufficiently explored. This study evaluates the prognostic significance of anoikis and ErbB signaling pathways in HCC by utilizing data from The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), three additional independent validation cohorts, and an in-house cohort. Advanced bioinformatics analyses and 167 machine learning models based on leave-one-out cross-validation (LOOCV) were used to predict HCC prognosis and assess outcomes of immune-targeted therapies. Additionally, key biological processes of the anoikis and ErbB signaling pathways in HCC were further investigated. The single sample Gene Set Enrichment Analysis revealed a strong correlation between upregulated ErbB signaling in high anoikis-expressing tumors and poor clinical outcomes. The development of the Anoikis-ErbB Related Signature (AERS) using the LASSO + RSF model demonstrated robust predictive capabilities, as validated across multiple patient cohorts, and proved effective in predicting responses to immune-targeted therapies. Further investigation highlighted activated NOTCH signaling pathways and decreased macrophage infiltration was associated with resistance to sorafenib and immune checkpoint inhibitors, as evidenced by bulk and single-cell RNA sequencing (scRNA-seq). AERS provides a novel tool for clinical prognosis and paves the way for immune-targeted therapeutic approaches, underscoring the potential of integrated molecular profiling in enhancing treatment strategies for HCC.

Mechanism of action of "cistanche deserticola-Polygala" in treating Alzheimer's disease based on network pharmacology methods and molecular docking analysis.

This article used network pharmacology, molecular docking, GEO analysis, and Gene Set Enrichment Analysis to obtain 38 main chemical components and 66 corresponding targets involved in Alzheimer's disease (AD) treatment in "Cistanche deserticola-Polygala". Through further Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes enrichment analysis, we obtained AD signalling pathways, calcium signalling pathways, and other signalling pathways related to the treatment of AD with "Cistanche deserticola-Polygala". Molecular docking showed that most of the core chemical components had good binding ability with the core targets. This article aims to reveal the mechanism of "Cistanche deserticola-Polygala" in treating AD and provide a basis for the treatment of AD with "Cistanche deserticola-Polygala".

Serum liver enzymes and risk of stroke: Systematic review with meta-analyses and Mendelian randomization studies.

Previous observational studies have identified correlations between liver enzyme levels and stroke risk. However, the strength and consistency of these associations vary. To comprehensively evaluate the relationship between liver enzymes and stroke risk, we conducted meta-analyses complemented by Mendelian randomization (MR) analyses. Following the PRISMA guidelines, we performed meta-analyses of prospective studies and conducted subgroup analyses stratified by sex and stroke subtype. Subsequently, adhering to the STROBE-MR guidelines, we performed two-sample bidirectional univariable MR (UVMR) and multivariable MR (MVMR) analyses using the largest genome-wide association studies summary data. Finally, the single-nucleotide polymorphisms associated with liver enzymes on sex differences underwent gene annotation, gene set enrichment, and tissue enrichment analyses. In the meta-analyses of 17 prospective studies, we found the relative risks for serum γ-glutamyl transferase (GGT) and alkaline phosphatase (ALP) were 1.23 (95% CI: 1.16-1.31) and 1.3 (95% CI: 1.19-1.43), respectively. Subgroup analyses revealed sex and stroke subtype differences in liver enzyme-related stroke risk. Bidirectional UVMR analyses confirmed that elevated GGT, alanine aminotransferase, and aspartate aminotransferase levels were associated with increased stroke occurrence. The primary results from the MVMR analyses revealed that higher ALP levels significantly increased the risk of stroke and ischemic stroke. Gene set and tissue enrichment analyses supported genetic differences in liver enzymes across sexes. Our study provides evidence linking liver enzyme levels to stroke risk, suggesting liver enzymes as potential biomarkers for early identification of high-risk individuals. Personalized, sex-specific interventions targeting liver enzymes could offer new strategies for stroke prevention.

Microarray analysis points to LMNB1 and JUN as potential target genes for predicting metastasis promotion by etoposide in colorectal cancer

Etoposide is a second-line chemotherapy agent widely used for metastatic colorectal cancer. However, we discovered that etoposide treatment induced greater motility potential in four colorectal cancer cell lines. Therefore, we used microarrays to test the mRNA of these cancer cell lines to investigate the mechanisms of etoposide promoting colorectal cancer metastasis. Differentially expressed genes (DEGs) were identified by comparing the gene expression profiles in samples from etoposide-treated cells and untreated cells in all four colorectal cancer cell lines. Next, these genes went through the Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway analysis. Among the top 10 genes including the upregulated and downregulated, eight genes had close interaction according to the STRING database: FAS, HMMR, JUN, LMNB1, MLL3, PLK2, STAG1 and TBL1X. After etoposide treatment, the cell cycle, metabolism-related and senescence signaling pathways in the colorectal cancer cell lines were significantly downregulated, whereas necroptosis and oncogene pathways were significantly upregulated. We suggest that the differentially expressed genes LMNB1 and JUN are potential targets for predicting colorectal cancer metastasis. These results provide clinical guidance in chemotherapy, and offer direction for further research in the mechanism of colorectal cancer metastasis.

Changes and associations between synovial fluid and magnetic resonance imaging markers of osteoarthritis after high tibial osteotomy

BackgroundMechanobiological mechanisms of osteoarthritis (OA) are unclear. Our objectives were to explore: 1) changes in knee joint physiology using a large panel of synovial fluid biomarkers from before to one year after high tibial osteotomy (HTO) surgery, and 2) the association of changes in the synovial fluid biomarkers with the changes in MRI measures of knee effusion-synovitis and articular cartilage composition.MethodsTwenty-six patients with symptomatic knee OA and varus alignment underwent synovial fluid aspirations and 3 T MRI before and one year after medial opening wedge HTO. Cytokine and growth factor levels in synovial fluid were measured with multiplex assays. Ontology and pathway enrichment was assessed using data protein sets with gene set enrichment analysis (GSEA), and analyzed using linear mixed effects models. MRIs were analyzed for effusion-synovitis and T2 cartilage relaxation time using manual segmentations. Changes in biomarker concentrations were correlated to changes in MRI effusion-synovitis volume and articular cartilage T2 relaxation times.ResultsDecreased enrichment in Toll-like receptor and TNF-α signalling was detected one year after HTO. The leading contributors to this reduction included IL-6, TNF-α and IL-1β, whereas the highest contributors to positive enrichment were EGF, PDGF-BB and FGF-2. Effusion-synovitis volume decreased (mean [95%CI]) one year after HTO (-2811.58 [-5094.40, -528.76mm3]). Effusion-synovitis volume was moderately correlated (r [95% CI]) with decreased MMP-1 (0.44 [0.05; 0.71]), IL-7 (0.41 [0.00; 0.69]) and IL-1β (0.59 [0.25; 0.80]) and increased MIP-1β (0.47 [0.10; 0.73]). Medial tibiofemoral articular cartilage T2 relaxation time decreased (mean [95% CI]) one year after HTO (-0.33 [-2.69; 2.05]ms). Decreased T2 relaxation time was moderately correlated to decreased Flt-3L (0.61 [0.28; 0.81]), IL-10 (0.47 [0.09; 0.73]), IP-10 (0.42; 0.03–0.70) and increased MMP-9 (-0.41 [-0.7; -0.03]) and IL-18 (-0.48 [-0.73; -0.10]).ConclusionsDecreased aberrant knee mechanical loading in patients with OA is associated with decreased biological and imaging measures of inflammation (measured in synovial fluid and on MRI) and increased anabolic processes. These exploratory findings suggest that improvement in knee loading can produce long-term (one year) improvement in joint physiology.