Immune Cell Infiltration Analysis Research Articles

Bioinformatics analysis of therapeutic targets for idiopathic pulmonary fibrosis and exploration of immune cell infiltration patterns

Idiopathic pulmonary fibrosis (IPF) is a severe progressive lung disease characterized by fibrotic changes in lung tissue, with limited treatment options. This study analyzed gene expression data from three gene expression omnibus datasets (GSE2052, GSE53845, and GSE110147) using R and LIMMA to identify differentially expressed genes (DEGs) in IPF samples. We identified 215 DEGs, comprising 106 upregulated and 109 downregulated genes. Weighted gene coexpression network analysis revealed five gene modules, with the module eigengene yellow showing the strongest correlation with IPF. Functional enrichment analysis of 40 consensus genes in this module indicated their significant involvement in extracellular matrix (ECM) organization. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed pathways related to protein digestion, cell adhesion molecules, and the advanced glycation end product–receptor for advanced glycation end product signaling pathway. Based on protein–protein interaction network analysis, collagen type XV alpha 1 chain (COL15A1) and collagen type VI alpha 3 chain (COL6A3) were identified as upregulated hub genes in IPF, which were regulated by microRNAs and transcription factors. Immune cell infiltration analysis showed significant changes in immune cell populations in IPF samples, with increases in memory B cells, plasma cells, and M0 macrophages and decreases in CD8 T cells, and resting natural killer cells. Potential drugs targeting COL15A1 and COL6A3 were predicted using multiple databases, revealing compounds such as (+)-JQ1, aristolochic acid I, and dexamethasone with promising binding potential. These findings suggest that COL15A1 and COL6A3 are central hub genes in IPF, are associated with ECM organization and immune response, and serve as therapeutic targets for IPF.

Identification and Verification of Potential Markers Related to Myocardial Fibrosis by Bioinformatics Analysis.

Mounting evidence indicates that myocardial fibrosis (MF) is frequently intertwined with immune and metabolic disorders. This comprehensive review aims to delve deeply into the crucial role of immune-related signature genes in the pathogenesis and progression of MF. This exploration holds significant importance as understanding the underlying mechanisms of MF is essential for developing effective diagnostic and therapeutic strategies. The dataset GSE9735 about myocardial fibrosis and non-fibrosis was downloaded from GEO database. Differentially expressed genes (DEGs) were identified by 'limma' package in R software. Then, the biological function of DEG was determined by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. XCell was used to estimate the composition pattern of matrix and immune cells. Protein-protein interaction (PPI) network was constructed based on STRING analysis software, and Hub genes were screened and functional modules were analyzed. The correlation between hub genes and immune cell subtypes was analyzed. Hub genes with |correlation coefficient|> 0.45 and p-value < 0.05 were used as characteristic biomarkers. Finally, the logistic regression model is used to verify the three markers in the training set and verification set (GSE97358 and GSE225336). A total of 635 DEGs were identified. Functional enrichment analysis shows that inflammation and immune response, extracellular matrix and structural remodeling play an important role in the pathological mechanism of MF. Immune cell infiltration analysis showed that immune cells (Plasma cells, Eosinophils, Chondrocytes and Th2 cells) significantly changed in MF pathological conditions. In PPI network analysis, IL1β, TTN, PTPRC, IGF1, ALDH1A1, CYP26A1, ALDH1A3, MYH11, CSF1R and CD80 were identified as hub genes, among which IL1β, CYP26A1 and GNG2 were regarded as immune-related characteristic markers. The AUC scores of the three biomarkers are all above 0.65, which proves that they have a good discrimination effect in MF. In this study, three immune-related genes were identified as diagnostic biomarkers of MF, which provided a new perspective for exploring the molecular mechanism of MF. This study takes a comprehensive approach to understanding the intricate relationship between myocardial fibrosis and immune metabolism. By identifying key immune-related biomarkers, this study not only reveals the molecular basis of myocardial fibrosis but also paves the way for the development of novel diagnostic tools and therapeutic strategies. These findings are critical for improving patient prognosis and may have broader implications for studying and treating other cardiovascular diseases associated with immune dysregulation.

Significance of pyroptosis-related genes in the diagnosis and classification of diabetic kidney disease

Objective This study aimed to identify the potential biomarkers associated with pyroptosis in diabetic kidney disease (DKD). Methods Three datasets from the Gene Expression Omnibus (GEO) were downloaded and merged into an integrated dataset. Differentially expressed genes (DEGs) were filtered and intersected with pyroptosis-related genes (PRGs). Pyroptosis-related DEGs (PRDEGs) were obtained and analyzed using functional enrichment analysis. Random forest, Least Absolute Shrinkage and Selection Operator, and logistic regression analyses were used to select the features of PRDEGs. These feature genes were used to build a diagnostic prediction model, identify the subtypes of the disease, and analyze their interactions with transcription factors (TFs)/miRNAs/drugs and small molecules. We conducted a comparative analysis of immune cell infiltration at different risk levels of pyroptosis. qRT-PCR was used to validate the expression of the feature genes. Results A total of 25 PRDEGs were obtained. These genes were coenriched in biological processes and pathways, such as the regulation of inflammatory responses. Five key genes (CASP1, CITED2, HTRA1, PTGS2, S100A12) were identified and verified using qRT-PCR. The diagnostic model based on key genes has a good diagnostic prediction ability. Five key genes interacted with TFs and miRNAs in 67 and 80 pairs, respectively, and interacted with 113 types of drugs or molecules. Immune infiltration of samples with different pyroptosis risk levels showed significant differences. Thus, CASP1, CITED2, HTRA1, PTGS2 and S100A12 are potential DKD biomarkers. Conclusion Genes that regulate pyroptosis can be used as predictors of DKD. Early diagnosis of DKD can aid in its effective treatment.

Bioinformatics analysis of effective biomarkers and immune infiltration in type 2 diabetes with cognitive impairment and aging

With the increasing prevalence of diabetes mellitus worldwide, type 2 diabetes mellitus (T2D) combined with cognitive impairment and aging has become one of the common and important complications of diabetes mellitus, which seriously affects the quality of life of the patients, and imposes a heavy burden on the patients’ families and the society. Currently, there are no special measures for the treatment of cognitive impairment and aging in type 2 diabetes mellitus. Therefore, the search for potential biological markers of type 2 diabetes mellitus combined with cognitive impairment and aging is of great significance for future precisive treatment. We downloaded three gene expression datasets from the GEO database: GSE161355 (related to T2D with cognitive impairment and aging), GSE122063, and GSE5281 (related to Alzheimer’s disease). Differentially expressed genes (DEGs) were identified, followed by gene set enrichment analysis (GSEA). A protein-protein interaction (PPI) network was constructed using the STRING database, and the top 15 hub genes were identified using the CytoHubba plugin in Cytoscape. Core genes were ultimately determined using three machine learning methods: LASSO regression, Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Linear Discriminant Analysis (LDA). The diagnostic performance of these genes was assessed using ROC curve analysis and validated in an independent dataset (GSE5281). Regulatory genes related to ferroptosis were screened from the FerrDb database, and their biological functions were further explored through GO and KEGG enrichment analyses. Finally, the CIBERSORT algorithm was used to analyze immune cell infiltration, and the correlation between core genes and immune cell infiltration levels was calculated, leading to the construction of an mRNA-miRNA regulatory network. In the GSE161355 and GSE122063 datasets, 217 common DEGs were identified. GSEA analysis revealed their enrichment in the PI3K-PLC-TRK signaling pathway, TP53 regulation of metabolic genes pathway, Notch signaling pathway, among others. PPI network analysis identified 15 candidate core genes, and further selection using LASSO, LDA, and SVM-RFE machine learning algorithms resulted in 6 core genes: BCL6, TP53, HSP90AA1, CRYAB, IL1B, and DNAJB1. ROC curve analysis indicated that these genes had good diagnostic performance in the GSE161355 dataset, with TP53 and IL1B achieving an AUC of 0.9, indicating the highest predictive accuracy. BCL6, HSP90AA1, CRYAB, and DNAJB1 also had AUCs greater than 0.8, demonstrating moderate predictive accuracy. Validation in the independent dataset GSE5281 showed that these core genes also had good diagnostic performance in Alzheimer’s disease samples (AUC > 0.6). Ferroptosis-related analysis revealed that IL1B and TP53 play significant roles in apoptosis and immune response. Immune cell infiltration analysis showed that IL1B is significantly positively correlated with infiltration levels of monocytes and NK cells, while TP53 is significantly negatively correlated with infiltration levels of follicular helper T cells. The construction of the miRNA-mRNA regulatory network suggested that miR-150a-5p might play a key role in the regulation of T2D-associated cognitive impairment and aging by TP53. This study, by integrating bioinformatics and machine learning methods, identified BCL6, TP53, HSP90AA1, CRYAB, IL1B, and DNAJB1 as potential diagnostic biomarkers for T2D with cognitive impairment and aging, with a particular emphasis on the significance of TP53 and IL1B in immune cell infiltration. These findings not only enhance our understanding of the molecular mechanisms linking type 2 diabetes to cognitive impairment and aging, providing new targets for early diagnosis and treatment, but also offer new directions and targets for basic research.

Identification of novel hub genes and immune infiltration in atopic dermatitis using integrated bioinformatics analysis

The aim of this study was to identify key genes and investigate the immunological mechanisms of atopic dermatitis (AD) at the molecular level via bioinformatics analysis. Gene expression profiles (GSE32924, GSE107361, GSE121212, and GSE230200) were obtained for screening common differentially expressed genes (co-DEGs) from the gene expression omnibus database. Functional enrichment analysis, protein–protein interaction network and module construction, and identification of common hub genes were performed. Hub genes were validated using receiver operating characteristic curve analysis based on GSE130588 and GSE16161. NetworkAnalyst was used to detect microRNAs (miRNAs) and transcription factors (TFs) associated with the hub genes. The immune cell infiltration was analyzed using the CIBERSORT algorithm to further analyze the correlation between hub genes and immune cells. A total of 146 co-DEGs were obtained, showing significant enrichment in cytokine–cytokine receptor interaction and JAK-STAT signaling pathway. Seven hub genes were identified by Cytoscape and validated with external datasets. Subsequent prediction of miRNAs and TFs targeting these hub genes revealed their regulatory roles. Analysis of immune cell infiltration and correlation revealed a significant positive correlation between CCL22 expression and the number of dendritic cells activated. The identified hub genes represent potential diagnostic and therapeutic targets in the immunological pathogenesis of AD.

IGFBP7+ subpopulation and IGFBP7 risk score in astrocytoma: insights from scRNA-Seq and bulk RNA-Seq.

Glioma is the predominant malignant brain tumor that lacks effective treatment options due to its shielding by the blood-brain barrier (BBB). Astrocytes play a role in the development of glioma, yet the diverse cellular composition of astrocytoma has not been thoroughly researched. We examined the internal diversity of seven distinct astrocytoma subgroups through single-cell RNA sequencing (scRNA-seq), pinpointed crucial subgroups using CytoTRACE, monocle2 pseudotime analysis, and slingshot pseudotime analysis, employed various techniques to identify critical subgroups, and delved into cellular communication analysis. Then, we combined the clinical information of GBM patients and used bulk RNA sequencing (bulk RNA-seq) to analyze the prognostic impact of the relevant molecules on GBM patients, and we performed in vitro experiments for validation. The analysis of the current study revealed that C0 IGFBP7+ Glioma cells were a noteworthy subpopulation of astrocytoma, influencing the differentiation and progression of astrocytoma. A predictive model was developed to categorize patients into high- and low-scoring groups based on the IGFBP7 Risk Score (IGRS), with survival analysis revealing a poorer prognosis for the high-IGRS group. Analysis of immune cell infiltration, identification of genes with differential expression, various enrichment analyses, assessment of copy number variations, and evaluation of drug susceptibility were conducted, all of which highlighted their significant influence on the prognosis of astrocytoma. This research enhances comprehension of the diverse cell composition of astrocytoma, delves into the various factors impacting the prognosis of astrocytoma, and offers fresh perspectives on treating glioma.

Identification of biological significance of different stages of varicose vein development based on mRNA sequencing.

Normal veins could develop to varicose vein (VV) by some risk factors, and might further progress to shallow vein thrombosis (SVT). However, the molecular mechanism of key genes associated with the progression and regression of VV are still not thorough enough. In this study, the healthy control (HC), VV, and SVT vascular samples were collected for transcriptome sequencing. The differentially expressed genes (DEGs) were screened by "DESeq2", including DEGs1 (HC vs. VV), DEGs2 (HC vs. SVT) and DEGs3 (VV vs. SVT). And their functional enrichment analyses were conducted by "ClusterProfiler". The receiver operating characteristic (ROC) curve was used to obtain the key genes (KGs) of the pathogenesis of VV and SVT. The qRT-PCR assay was performed to validate the expressions of KGs. Immune cell infiltration analyses were conducted based on ssGSEA method. The competitive endogenous RNAs (ceRNAs) regulatory network was constructed. The target drugs of KGs were predicted using DrugBank database. The biofunctions of DACT3 were further investigated through a series of experiments in vitro. All of these DEGs were associated with inflammation and immunity related functions. Immune cell infiltration was significantly different between VV and SVT. Six key genes including PLP2, DACT3, LRRC25, PILRA, MSX1 and APOD that were associated with the progression and regression of VV were screened. The expression of LRRC25 and PILRA was significantly negatively associated with central memory T cell, and significantly positively associated with B cell. Besides, XIST was the critical regulator of multiple KGs. Cimetidine was potential drug for VV and SVT therapy. Overexpression of DACT3 significantly inhibited the proliferation and migration of vascular smooth muscle cells (VSMCs), and affected their cell cycle and phenotypic transition. This study identified six key genes associated with the progression and regression of VV. Among them, DACT3 was proved to hinder VV progression. These findings may help to deepen understanding its underlying mechanisms.

Identification of ferroptosis biomarkers and immune infiltration landscapes in atrial fibrillation: A bioinformatics analysis.

Ferroptosis has been recognized as a critical factor in the development of atrial fibrillation (AF), but its precise mechanisms remain unclear. We downloaded the GSE115574 dataset from the gene expression omnibus database to analyze the expression levels of ferroptosis-related genes (FRGs) and identify differentially expressed genes (DEGs). Least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) machine learning techniques were employed to identify key genes associated with AF. The diagnostic performance of these genes was evaluated using Receiver operating characteristic curves (ROC) and validated in an independent AF dataset. miRNA and lncRNA predictions for potential binding to these key genes were conducted using miRBase, miRDB, and TargetScan. Furthermore, gene set enrichment analysis (GSEA) enrichment analysis, immune cell infiltration analysis, and targeted drug prediction were performed. The intersection of LASSO regression and SVM-RFE analyses identified 7 DEGs significantly associated with AF. Validation through ROC and an additional dataset confirmed the importance of MAPK14, CAV1, and ADAM23. Significant infiltration of memory B cells, regulatory T cells, and monocytes was observed in atrial tissues. Seventy-two miRNAs were predicted to potentially target MAPK14, and 2 drugs were identified as targeting CAV1. This study underscores the involvement of FRGs in AF through machine learning and validation approaches. The observed immune cell infiltration suggests a potential link between immune response and AF. The predicted ceRNA network offers new insights into gene regulation, presenting potential biomarkers and therapeutic targets for AF.

Establish a novel immune-related gene prognostic risk index (IRGPRI) associated with CD8+ cytotoxic T lymphocytes in non-small-cell lung cancer (NSCLC)

BackgroundThe aim of this study is to create an index called IRGPRI (immune-related gene prognostic risk index) that can be utilized for predicting the prognosis and assessing the efficacy of immune checkpoint inhibitors (ICIs) therapy in patients with non-small-cell lung cancer (NSCLC). MethodsDistinguishing gene expression patterns (DEGs) were detected in CD8+ cytotoxic T lymphocytes (CTLs) compared to other cellular types such as CD4 T cells, B cells, plasma cells, and CD8 Tex using the advanced technology of Single-cell RNA Sequencing (scRNA-seq). The construction of IRGPRI was accomplished by employing LASSO Cox regression analysis. We conducted a comparative analysis on clinical characteristics and molecular features, such as pathway enrichment and gene mutation, among the distinct subgroups of IRGPRI. Furthermore, we explored the correlation between immunological characteristics and IRGPRI subgroups to comprehensively assess the effectiveness of ICIs in NSCLC patients. ResultsA total of 109 genes were identified by intersecting immune-related genes with DEGs obtained from single-cell RNA sequencing data (GSE131907), specifically comparing CTLs to other cell types. From these, we selected 7 prognosis-related genes, namely TRBC1, HLA-DMA, CTSH, RAC1, CTSL, ANXA2, and CEBPB. These genes were used to construct the IRGPRI. The prognosis of patients diagnosed with NSCLC was found to be significantly better in the low-risk group compared to the high-risk group, as demonstrated by Kaplan-Meier (K-M) survival analysis. This observation was further confirmed through the utilization of data from the GEO cohort. The low-risk group demonstrated an increase in pathways linked with immune response, whereas the high-risk group exhibited a higher prevalence of pathways related to cancer. Furthermore, it was noted in the TCGA cohort that there existed a significant rise in the mutation frequency of every gene within the high-risk group as opposed to the low-risk group. Missense variation emerged as the most prevalent form of mutation. According to the analysis of immune cell infiltration and function, the comprehensive findings suggest that the group with a low risk is characterized by an increased presence of plasma cells, CTLs, T cells follicular helper, Tregs, and Dendritic cell resting. Additionally, they exhibit a higher score in terms of immune function for B cells, CD8+ T cells, checkpoint activity, T cell inhibition and stimulation. Moreover, this low-risk group demonstrates greater efficacy when treated with ICIs therapy compared to the high-risk group. ConclusionsOur research effectively developed and verified a unique IRGPRI, showcasing its association with immune-related characteristics. As a result, the potential of IRGPRI as a valuable biomarker for predicting prognosis and evaluating the effectiveness of ICIs treatment in cancer is evident.

Innovative biomarkers TCN2 and LY6E can significantly inhibit respiratory syncytial virus infection

BackgroundRespiratory syncytial virus (RSV) is a prominent etiological agent of lower respiratory tract infections in children, responsible for approximately 80% of cases of pediatric bronchiolitis and 50% of cases of infant pneumonia. Despite notable progress in the diagnosis and management of pediatric RSV infection, the current biomarkers for early-stage detection remain insufficient to meet clinical needs. Therefore, the development of more effective biomarkers for early-stage pediatric respiratory syncytial virus infection (EPR) is imperative.MethodsThe datasets used in this study were derived from the Gene Expression Omnibus (GEO) database. We used GSE188427 dataset as the training set to screen for biomarkers. Biomarkers of EPR were screened by Weighted Gene Co-expression Network Analysis (WGCNA), three machine-learning algorithms (LASSO regression, Random Forest, XGBoost), and other comprehensive bioinformatics analysis techniques. To evaluate the diagnostic value of these biomarkers, multiple external and internal datasets were employed as validation sets. Next, an examination was performed to investigate the relationship between the screened biomarkers and the infiltration of immune cells. Furthermore, an investigation was carried out to identify potential small molecule compounds that interact with selected diagnostic markers. Finally, we confirmed that the expression levels of the selected biomarkers exhibited a significant increase following RSV infection, and they were further identified as having antiviral properties.ResultsThe study found that lymphocyte antigen 6E (LY6E) and Transcobalamin-2 (TCN2) are two biomarkers with diagnostic significance in EPR. Analysis of immune cell infiltration showed that they were associated with activation of multiple immune cells. Furthermore, our analysis demonstrated that small molecules, 3ʹ-azido-3ʹ-deoxythymine, methotrexate, and theophylline, have the potential to bind to TCN2 and exhibit antiviral properties. These compounds may serve as promising therapeutic agents for the management of pediatric RSV infections. Additionally, our data revealed an upregulation of LY6E and TCN2 expression in PBMCs from patients with RSV infection. ROC analysis indicated that LY6E and TCN2 possessed diagnostic value for RSV infection. Finally, we confirmed that LY6E and TCN2 expression increased after RSV infection and further inhibited RSV infection in A549 and BEAS-2B cell lines. Importantly, based on TCN2, our findings revealed the antiviral properties of a potentially efficacious compound, vitamin B12.ConclusionLY6E and TCN2 are potential peripheral blood diagnostic biomarkers for pediatric RSV infection. LY6E and TCN2 inhibit RSV infection, indicating that LY6E and TCN2 are potential therapeutic target for RSV infection.

Elucidating the molecular interactions and immune modulation of bisphenols exposure in the pathogenesis of polycystic ovary syndrome

Bisphenols (BPs) are known endocrine disruptors potentially contributing to the pathogenesis of Polycystic Ovary Syndrome (PCOS). This study aims to elucidate the molecular interactions between BPs and PCOS-related genes and their combined effects on PCOS development. We identified common genes associated with BPs and PCOS using the CTD. Differential expression analysis was performed on three GEO datasets, leading to the identification of differentially expressed genes (DEGs). Protein-Protein Interaction (PPI) network construction, enrichment analysis, single-gene Gene Set Enrichment Analysis (GSEA), and immune cell infiltration analysis were carried out. A nomogram was developed for PCOS risk prediction, and molecular docking studies were performed using AutoDock Vina, with interaction visualizations via PyMOL. We identified 139 common genes between BPs exposure and PCOS, enrichment analysis highlighted pathways related to hormone metabolism, ovarian steroidogenesis, and p53 signaling. Four hub DEGs (PBK, CCNE2, LPCAT2, S100P) were identified, and a nomogram incorporating these genes demonstrated excellent predictive accuracy. GSEA revealed roles in cell adhesion, immune response, and metabolism. ssGSEA analysis showed significant differences in immune cell infiltration between PCOS and control groups, with notable correlations between hub DEGs and immune cells. Molecular docking indicated strong binding affinities between the hub DEGs and BPAF, suggesting potential disruptions induced by BPs. BPs exposure is associated with significant molecular and immunological changes in PCOS, impacting genes involved in hormone regulation, immune response, and metabolic pathways. The strong binding affinities between BPs and key PCOS-related genes reveal their potential role in exacerbating PCOS, providing insights for targeted therapeutic strategies.

Identifying signature genes and their associations with immune cell infiltration in spinal cord injury

BackgroundEarly detection of spinal cord injury (SCI) is conducive to improving patient outcomes. In addition, many studies have revealed the role of immune cells in the progression or treatment of SCI. The objective of this study was to identify the early signature genes and clarify how they are related to immune cell infiltration in SCI. MethodsWe analysed and identified early signature genes associated with SCI via bioinformatics analysis of the GSE151371 dataset from the GEO database. These genes were subsequently verified in the GSE33886 dataset and qRTPCR. Finally, the CIBERSORT algorithm was used to examine the immune cell infiltration in SCI and its relationship with signature genes. ResultsSeven SCI-related signature genes, including ARG1, RETN, BPI, GGH, CCNB1, HIST1H2AC, and HIST1H2BJ, were identified, and their expression was verified via an external validation cohort and qRTPCR. Moreover, the ROC curves revealed the diagnostic value of these genes. In addition, on the basis of immune cell infiltration analysis, plasma cells, M0 macrophages, activated CD4+ memory T cells, γδ T cells, naive CD4+ T cells, and resting CD4+ memory T cells may participate in the progression of SCI. ConclusionThis study identified seven early signature genes of SCI that may serve as biomarkers for the early diagnosis of SCI and contribute to our understanding of immune changes during the pathology of SCI.

Exploring the impact of inflammatory cytokines on alcoholic liver disease: a Mendelian randomization study with bioinformatics insights into potential biological mechanisms

ABSTRACT Background: Alcoholic liver disease (ALD) significantly contributes to global morbidity and mortality. The role of inflammatory cytokines in alcohol-induced liver injury is pivotal yet not fully elucidated. Objectives: To establish a causal link between inflammatory cytokines and ALD using a Mendelian Randomization (MR) framework. Methods: This MR study utilized genome-wide significant variants as instrumental variables (IVs) for assessing the relationship between inflammatory cytokines and ALD risk, focusing on individuals of European descent. The approach was supported by comprehensive sensitivity analyses and augmented by bioinformatics tools including differential gene expression, protein-protein interactions (PPI), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and analysis of immune cell infiltration. Results: Our findings reveal that increased levels of stem cell growth factor beta (SCGF-β, beta = 0.141, p = .032) and interleukin-7 (IL-7, beta = 0.311, p = .002) are associated with heightened ALD risk, whereas higher levels of macrophage inflammatory protein-1α (MIP-1α, beta = -0.396, p = .004) and basic fibroblast growth factor (bFGF, beta = -0.628, p = .008) are linked to reduced risk. The sensitivity analyses support these robust causal relationships. Bioinformatics analyses around inflammatory cytokine-associated SNP loci suggest multiple pathways through which cytokines influence ALD. Conclusion: The genetic evidence from this study convincingly demonstrates that certain inflammatory cytokines play directional roles in ALD pathogenesis. These findings provide insights into the complex biological pathways involved and underscore the potential for developing targeted therapies that modulate these inflammatory responses, ultimately improving clinical outcomes for ALD patients.

Anoikis-related genes in breast cancer patients: reliable biomarker of prognosis

BackgroundBreast cancer (BC) is the most common cancer in women, and its progression is closely related to the phenomenon of anoikis. Anoikis, the specific programmed death resulting from a lack of contact between cells and the extracellular matrix, has recently been recognized as playing a critical role in tumor initiation, maintenance, and treatment. The ability of cancer cells to resist anoikis leads to cancer progression and metastatic colonization. However, the impact of anoikis on the prognosis of BC patients remains unclear.MethodThis study utilized data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to collect transcriptome and clinical data of BC patients. Anoikis-related genes (ARGs) were classified into subtypes A and B through consensus clustering. Subsequently, survival prognosis analysis, immune cell infiltration analysis, and functional enrichment analysis were performed for both subtypes. Using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a set of 10 ARGs related to prognosis was identified. Immune cell infiltration and tumor microenvironment analyses were conducted on these 10 ARGs to develop a prognostic model. Furthermore, single-cell data analysis and real-time polymerase chain reaction (RT-PCR) analysis were employed to study the expression of the 10 identified prognostic ARGs in BC cells.ResultsOne hundred thirty-five ARGs were identified as differentially expressed genes in the TCGA and GEO databases, with 42 of them associated with the survival prognosis of BC patients. Analyses involving Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) revealed distinct expression patterns of ARGs between types A and B. Patients in type A exhibited worse survival prognosis and lower immune cell infiltration compared to type B. Subsequent analyses identified 10 key ARGs (YAP1, PIK3R1, BAK1, PHLDA2, EDA2R, LAMB3, CD24, SLC2A1, CDC25C, and SLC39A6) relevant to BC prognosis. Kaplan–Meier analysis indicated that high-risk patients based on these ARGs had a poorer BC prognosis. Additionally, Cox regression analysis established gender, age, T (tumor), N (nodes), and risk score as predictive factors in a nomogram model for BC. The model demonstrated diagnostic value for BC patients at 1, 3, and 5 years. Decision curve analysis (DCA) verified the risk score as a reliable predictor of BC patient survival rates. Moreover, RT-PCR results confirmed differential expressions of YAP1, PIK3R1, BAK1, PHLDA2, CD24, SLC2A1, and CDC25C in BC cells, with SLC39A6, EDA2R, and LAMB3 showing low expression levels.ConclusionARGs markers can be used as BC biomarkers for risk stratification and survival prediction in BC patients. Besides, ARGs can be used as stratification factors for individualized and precise treatment of BC patients.

Screening and identification of the hub genes in severe acute pancreatitis and sepsis.

Severe acute pancreatitis (SAP) is accompanied with acute onset, rapid progression, and complicated condition. Sepsis is a common complication of SAP with a high mortality rate. This research aimed to identify the shared hub genes and key pathways of SAP and sepsis, and to explore their functions, molecular mechanism, and clinical value. We obtained SAP and sepsis datasets from the Gene Expression Omnibus (GEO) database and employed differential expression analysis and weighted gene co-expression network analysis (WGCNA) to identify the shared differentially expressed genes (DEGs). Functional enrichment analysis and protein-protein interaction (PPI) was used on shared DEGs to reveal underlying mechanisms in SAP-associated sepsis. Machine learning methods including random forest (RF), least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) were adopted for screening hub genes. Then, receiver operating characteristic (ROC) curve and nomogram were applied to evaluate the diagnostic performance. Finally, immune cell infiltration analysis was conducted to go deeply into the immunological landscape of sepsis. We obtained a total of 123 DEGs through cross analysis between Differential expression analysis and WGCNA important module. The Gene Ontology (GO) analysis uncovered the shared genes exhibited a significant enrichment in regulation of inflammatory response. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the shared genes were primarily involved in immunoregulation by conducting NOD-like receptor (NLR) signaling pathway. Three machine learning results revealed that two overlapping genes (ARG1, HP) were identified as shared hub genes for SAP and sepsis. The immune infiltration results showed that immune cells played crucial part in the pathogenesis of sepsis and the two hub genes were substantially associated with immune cells, which may be a therapy target. ARG1 and HP may affect SAP and sepsis by regulating inflammation and immune responses, shedding light on potential future diagnostic and therapeutic approaches for SAP-associated sepsis.

Identification of novel biomarkers, shared molecular signatures and immune cell infiltration in heart and kidney failure by transcriptomics.

Heart failure (HF) and kidney failure (KF) are closely related conditions that often coexist, posing a complex clinical challenge. Understanding the shared mechanisms between these two conditions is crucial for developing effective therapies. This study employed transcriptomic analysis to unveil molecular signatures and novel biomarkers for both HF and KF. A total of 2869 shared differentially expressed genes (DEGs) were identified in patients with HF and KF compared to healthy controls. Functional enrichment analysis was performed to explore the common mechanisms underlying these conditions. A protein-protein interaction (PPI) network was constructed, and machine learning algorithms, including Random Forest (RF), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to identify key signature genes. These genes were further analyzed using Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA), with their diagnostic values validated in both training and validation sets. Molecular docking studies were conducted. Additionally, immune cell infiltration and correlation analyses were performed to assess the relationship between immune responses and the identified biomarkers. The functional enrichment analysis indicated that the common mechanisms are associated with cellular homeostasis, cell communication, cellular replication, inflammation, and extracellular matrix (ECM) production, with the PI3K-Akt signaling pathway being notably enriched. The PPI network revealed two key protein clusters related to the cell cycle and inflammation. CDK2 and CCND1 were identified as signature genes for both HF and KF. Their diagnostic value was validated in both training and validation sets. Additionally, docking studies with CDK2 and CCND1 were performed to evaluate potential drug candidates. Immune cell infiltration and correlation analyses highlighted the immune microenvironment, and that CDK2 and CCND1 are associated with immune responses in HF and KF. This study identifies CDK2 and CCND1 as novel biomarkers linking cell cycle regulation and inflammation in heart and kidney failure. These findings offer new insights into the molecular mechanisms of HF and KF and present potential targets for diagnosis and therapy.

Bioinformatical and experimental validation on EFNA5 biomarker for pancreatic cancer

Pancreatic cancer is one of the most aggressive malignancies in the digestive system, characterized by vague early symptoms and rapid disease progression. Currently, treatment options for pancreatic cancer are limited, with unsatisfactory outcomes and poor prognosis, as the overall 5-year survival rate is less than 10%. Therefore, it is crucial to identify new biomarkers for pancreatic cancer.We analyzed the differential expression of EFNA5 at the mRNA level using data from the TCGA, GTEX, and GEO databases, followed by validation of EFNA5 protein expression differences through immunohistochemistry. Kaplan-Meier survival analysis and Cox regression analysis were used to explore the prognostic value of EFNA5. KEGG, GO enrichment analyses, and GSEA were employed to predict the biological functions of EFNA5. Bioinformatics algorithms were utilized to analyze the impact of EFNA5 on drug sensitivity, immune cell infiltration, and tumor mutational burden. Finally, we conducted cell phenotype experiments to investigate the effects of EFNA5 on the proliferation, migration, and invasion of pancreatic cancer cells. Bioinformatics data showed that the mRNA level of EFNA5 was higher in pancreatic cancer tissues compared to normal tissues. Immunohistochemistry experiments confirmed that the protein expression level of EFNA5 was significantly higher in pancreatic cancer tissues (n = 19) than in adjacent normal tissues (n = 19). Survival analysis indicated that the overall survival (OS) and progression-free survival (PFS) were worse in the high EFNA5 expression group compared to the low expression group. Univariate and multivariate Cox regression analyses demonstrated that EFNA5 is a risk factor affecting the prognosis of pancreatic cancer patients. KEGG pathway enrichment analysis revealed that differentially expressed genes (DEGs) between high and low EFNA5 expression groups were primarily enriched in pathways such as PI3K/Akt, axon guidance, and focal adhesion. GO enrichment analysis showed that DEGs were mainly associated with functions like epidermis development, cell adhesion, and intercellular junctions. Immune cell infiltration analysis found that EFNA5 expression was correlated with the infiltration levels of various immune cells. The tumor mutational burden was higher in the high EFNA5 expression group compared to the low expression group. Drug sensitivity analysis indicated that the high EFNA5 expression group had lower sensitivity to several commonly used chemotherapy drugs for pancreatic cancer. Cellular experiments demonstrated that knocking down EFNA5 inhibited the proliferation, invasion, and migration of pancreatic cancer cells. EFNA5 is overexpressed in pancreatic cancer tissues, and its high expression is associated with poor prognosis in pancreatic cancer patients. Downregulation of EFNA5 reduces the proliferation, migration, and invasion capabilities of the pancreatic cancer cell line BxPC-3. EFNA5 is closely related to the occurrence, development, prognosis, immune infiltration, and drug sensitivity of pancreatic cancer. Our study suggests that EFNA5 is a potential biomarker and therapeutic target.

Integrated transcriptomic analysis and machine learning for characterizing diagnostic biomarkers and immune cell infiltration in fetal growth restriction.

Fetal growth restriction (FGR) occurs in 10% of pregnancies worldwide. Placenta dysfunction, as one of the most common causes of FGR, is associated with various poor perinatal outcomes. The main objectives of this study were to screen potential diagnostic biomarkers for FGR and to evaluate the function of immune cell infiltration in the process of FGR. Firstly, differential expression genes (DEGs) were identified in two Gene Expression Omnibus (GEO) datasets, and gene set enrichment analysis was performed. Diagnosis-related key genes were identified by using three machine learning algorithms (least absolute shrinkage and selection operator, random forest, and support vector machine model), and the nomogram was then developed. The receiver operating characteristic curve, calibration curve, and decision curve analysis curve were used to verify the validity of the diagnostic model. Using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), the characteristics of immune cell infiltration in placental tissue of FGR were evaluated and the candidate key immune cells of FGR were screened. In addition, this study also validated the diagnostic efficacy of TREM1 in the real world and explored associations between TREM1 and various clinical features. By overlapping the genes selected by three machine learning algorithms, four key genes were identified from 290 DEGs, and the diagnostic model based on the key genes showed good predictive performance (AUC = 0.971). The analysis of immune cell infiltration indicated that a variety of immune cells may be involved in the development of FGR, and nine candidate key immune cells of FGR were screened. Results from real-world data further validated TREM1 as an effective diagnostic biomarker (AUC = 0.894) and TREM1 expression was associated with increased uterine artery PI (UtA-PI) (p-value = 0.029). Four candidate hub genes (SCD, SPINK1, TREM1, and HIST1H2BB) were identified, and the nomogram was constructed for FGR diagnosis. TREM1 was not only associated with a variety of key immune cells but also correlated with increased UtA-PI. The results of this study could provide some new clues for future research on the prediction and treatment of FGR.

Exploring the transcriptomic landscape of moyamoya disease and systemic lupus erythematosus: insights into crosstalk genes and immune relationships.

Systemic Lupus Erythematosus (SLE) is acknowledged for its significant influence on systemic health. This study sought to explore potential crosstalk genes, pathways, and immune cells in the relationship between SLE and moyamoya disease (MMD). We obtained data on SLE and MMD from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were conducted to identify common genes. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on these shared genes. Hub genes were further selected through the least absolute shrinkage and selection operator (LASSO) regression, and a receiver operating characteristic (ROC) curve was generated based on the results of this selection. Finally, single-sample Gene Set Enrichment Analysis (ssGSEA) was utilized to assess the infiltration levels of 28 immune cells in the expression profile and their association with the identified hub genes. By intersecting the important module genes from WGCNA with the DEGs, the study highlighted CAMP, CFD, MYO1F, CTSS, DEFA3, NLRP12, MAN2B1, NMI, QPCT, KCNJ2, JAML, MPZL3, NDC80, FRAT2, THEMIS2, CCL4, FCER1A, EVI2B, CD74, HLA-DRB5, TOR4A, GAPT, CXCR1, LAG3, CD68, NCKAP1L, TMEM33, and S100P as key crosstalk genes linking SLE and MMD. GO analysis indicated that these shared genes were predominantly enriched in immune system process and immune response. LASSO analysis identified MPZL3 as the optimal shared diagnostic biomarkers for both SLE and MMD. Additionally, the analysis of immune cell infiltration revealed the significant involvement of activation of T and monocytes cells in the pathogenesis of SLE and MMD. This study is pioneering in its use of bioinformatics tools to explore the close genetic relationship between MMD and SLE. The genes CAMP, CFD, MYO1F, CTSS, DEFA3, NLRP12, MAN2B1, NMI, QPCT, KCNJ2, JAML, MPZL3, NDC80, FRAT2, THEMIS2, CCL4, FCER1A, EVI2B, CD74, HLA-DRB5, TOR4A, GAPT, CXCR1, LAG3, CD68, NCKAP1L, TMEM33, and S100P have been identified as key crosstalk genes that connect MMD and SLE. Activation of T and monocytes cells-mediated immune responses are proposed to play a significant role in the association between MMD and SLE.

Exploring resveratrol against Alzheimer's disease and Parkinson's disease through integrating network pharmacology, bioinformatics, and experimental validation strategy in vitro

BackgroundThe study aims to investigate the pharmacological basis and molecular mechanisms of resveratrol in the treatment of Alzheimer's disease (AD) and Parkinson's disease (PD) through the approach of treating different diseases with the same method, guided by traditional Chinese medicine theory. Utilizing network pharmacology and bioinformatics methods, this research aims to provide modern medical evidence for the theory of treating different diseases with the same method in traditional Chinese medicine. MethodsOmnibus from Swiss Target Prediction, TCMSP, SuperPred, SEA, HIT, CTD, TCMIP and Gene Expression Disease datasets for resveratrol related genes, Alzheimer's disease, and Parkinson's disease were obtained from the GEO database. Core targets were identified by weighted gene coexpression network analysis (WGCNA) and minimum absolute contraction and selection operator (LASSO). The expression of core targets was verified in AD and PD cell models. The immune characteristics of AD and PD were analyzed by CIBERSORT algorithm. Finally, the potential mechanism of resveratrol intervention on the core target was studied by molecular docking technique. ResultsThe results of network pharmacological analysis showed that resveratrol acted on 85 common targets such as STAT3 and CASP3, affected AGE-RAGE signaling pathway and PI3K-Akt signaling pathway, and showed the effect of "same disease and different treatment" for AD and PD. Three core targets associated with AD and PD (PLK4, FCGRT, and PRKAR2A) were finally identified through comprehensive transcriptome analysis, and experimentally verified in cell models of AD and PD. At the same time, the analysis of immune cell infiltration suggested that AD and PD had dysregulation of inflammation, and the core target was significantly related to M2 macrophages. ConclusionResveratrol may play a potential mechanism of "treating the same disease with different diseases" and target three core targets (PLK4, FCGRT and PRKAR2A) to improve the disease process of AD and PD by participating in the regulation of immune and inflammatory pathways. These findings have potential implications for clinical practice and future research.