Integrative bioinformatics analysis for identifying the mitochondrial-related gene signature associated with immune infiltration in premature ovarian insufficiency.

BACKGROUND: Premature ovarian insufficiency (POI) is a reproductive disorder characterized by the cessation of ovarian function before the age of 40. Although mitochondrial dysfunction and immune disorders are believed to contribute to ovarian damage in POI, the interplay between these factors remains understudied. METHODS: In this research, transcriptomic data related to POI were obtained from the NCBI GEO database. Hub biomarkers were identified through the construction of a protein‒protein interaction (PPI) network and further validated using RT‒qPCR and Western blot. Moreover, their expression across various cell types was elucidated via single-cell RNA sequencing analysis. A comprehensive investigation of the mitochondrial and immune profiles of POI was carried out through correlation analysis. Furthermore, potential therapeutic agents were predicted utilizing the cMap database. RESULTS: A total of 119 mitochondria-related differentially expressed genes (MitoDEGs) were identified and shown to be significantly enriched in metabolic pathways. Among these genes, Hadhb, Cpt1a, Mrpl12, and Mrps7 were confirmed both in a POI model and in human granulosa cells (GCs), where they were found to accumulate in GCs and theca cells. Immune analysis revealed variations in macrophages, monocytes, and 15 other immune cell types between the POI and control groups. Notably, strong correlations were observed between seven hub-MitoDEGs (Hadhb, Cpt1a, Cpt2, Mrpl12, Mrps7, Mrpl51, and Eci1) and various functions, such as mitochondrial respiratory complexes, dynamics, mitophagy, mitochondrial metabolism, immune-related genes, and immunocytes. Additionally, nine potential drugs (calyculin, amodiaquine, eudesmic acid, cefotaxime, BX-912, prostratin, SCH-79797, HU-211, and pizotifen) targeting key genes were identified. CONCLUSIONS: Our results highlight the crosstalk between mitochondrial function and the immune response in the development of POI. The identification of MitoDEGs could lead to reliable biomarkers for the early diagnosis, monitoring, and personalized treatment of POI.

Exploring the significance and potential mechanisms of hippo pathway-associated genes in prognosis of glioma patients.

PURPOSE: Despite the efforts of countless researchers to develop glioma treatment strategies, the current therapeutic effect of glioma is still not ideal, and it is necessary to further explore the mechanism to guide treatment. Thus, this study aims to introduce a novel approach for predicting patient prognosis and guiding further treatment interventions. METHODS: Initially, we conducted a differential gene expression analysis to identify Hippo pathway-associated genes overexpressed in tumors and determined genes correlated with prognosis. Subsequently, employing cluster analysis, we categorized samples into two groups and performed further analyses including prediction, immune cell infiltration abundance, and drug response rates. We utilized weighted gene co-expression analysis to reveal gene sets with high co-variation, delineate inter-sample gene correlation patterns, and conduct enrichment analysis. Prognostic models were built using ten machine learning algorithms combined in 101 different combinations, followed by evaluation and validation. Immune infiltration analysis, differential expression analysis of depleted T cell-related markers, drug sensitivity analysis, and exploration of pathway dysregulation were performed for different risk groups. Quality control and batch integration were performed, and single-cell data were analyzed using dimensionality reduction clustering algorithms and annotation tools to evaluate the activity of the prognostic model in malignant cells. RESULTS: We conducted data filtering to identify genes overexpressed in tumors, intersecting these genes with Hippo pathway-related genes, identifying 62 genes correlated with prognosis, and performing cluster analysis to divide tumor tissues into two groups. Cluster 2 exhibited a poorer prognosis and demonstrated differences in immune cell infiltration. Utilizing weighted gene co-expression analysis on Cluster 2, we identified gene modules, conducted functional enrichment analysis, and delineated pathways. Employing a combined model based on ten machine learning algorithm combinations, we selected the optimal prognostic model system and validated the model's predictive ability within the dataset. Through immune-related analysis and drug sensitivity analysis, we uncovered differences in immune infiltration and varying sensitivities to chemotherapy drugs. Additionally, the enrichment analysis of gene set revealed discrepancies in upregulation within relevant pathways between the high and low-risk groups. Finally, annotation and evaluation of malignant cells via single-cell analysis showed increased activity of the prognostic model and variations in distribution across different prognostic levels in malignant cells. CONCLUSION: This study introduces a novel approach utilizing the Hippo pathway and associated genes for glioma prognosis research, demonstrating the potential and significance of this method in evaluating the outcome for patients with glioma. These findings hold substantial clinical significance in guiding therapy and predicting outcomes for individuals diagnosed with glioma, offering significant clinical utility.

Identification and validation of ferroptosis-related biomarkers in intervertebral disc degeneration.

Introduction: Ferroptosis plays a significant role in intervertebral disc degeneration (IDD). Understanding the key genes regulating ferroptosis in IDD could reveal fundamental mechanisms of the disease, potentially leading to new diagnostic and therapeutic targets. Methods: Public datasets (GSE23130 and GSE70362) and the FerrDb database were analyzed to identify ferroptosis-related genes (DE-FRGs) involved in IDD. Single-cell RNA sequencing data (GSE199866) was used to validate the specific roles and expression patterns of these genes. Immunohistochemistry and Western blot analyses were subsequently conducted in both clinical samples and mouse models to assess protein expression levels across different tissues. Results: The analysis identified seven DE-FRGs, including MT1G, CA9, AKR1C1, AKR1C2, DUSP1, CIRBP, and KLHL24, with their expression patterns confirmed by single-cell RNA sequencing. Immunohistochemistry and Western blot analysis further revealed that MT1G, CA9, AKR1C1, AKR1C2, DUSP1, and KLHL24 exhibited differential expression during the progression of IDD. Additionally, the study highlighted the potential immune-modulatory functions of these genes within the IDD microenvironment. Discussion: Our study elucidates the critical role of ferroptosis in IDD and identifies specific genes, such as MT1G and CA9, as potential targets for diagnosis and therapy. These findings offer new insights into the molecular mechanisms underlying IDD and present promising avenues for future research and clinical applications.

Screening of genes co-associated with osteoporosis and chronic HBV infection based on bioinformatics analysis and machine learning.

Objective: To identify HBV-related genes (HRGs) implicated in osteoporosis (OP) pathogenesis and develop a diagnostic model for early OP detection in chronic HBV infection (CBI) patients. Methods: Five public sequencing datasets were collected from the GEO database. Gene differential expression and LASSO analyses identified genes linked to OP and CBI. Machine learning algorithms (random forests, support vector machines, and gradient boosting machines) further filtered these genes. The best diagnostic model was chosen based on accuracy and Kappa values. A nomogram model based on HRGs was constructed and assessed for reliability. OP patients were divided into two chronic HBV-related clusters using non-negative matrix factorization. Differential gene expression analysis, Gene Ontology, and KEGG enrichment analyses explored the roles of these genes in OP progression, using ssGSEA and GSVA. Differences in immune cell infiltration between clusters and the correlation between HRGs and immune cells were examined using ssGSEA and the Pearson method. Results: Differential gene expression analysis of CBI and combined OP dataset identified 822 and 776 differentially expressed genes, respectively, with 43 genes intersecting. Following LASSO analysis and various machine learning recursive feature elimination algorithms, 16 HRGs were identified. The support vector machine emerged as the best predictive model based on accuracy and Kappa values, with AUC values of 0.92, 0.83, 0.74, and 0.7 for the training set, validation set, GSE7429, and GSE7158, respectively. The nomogram model exhibited AUC values of 0.91, 0.79, and 0.68 in the training set, GSE7429, and GSE7158, respectively. Non-negative matrix factorization divided OP patients into two clusters, revealing statistically significant differences in 11 types of immune cell infiltration between clusters. Finally, intersecting the HRGs obtained from LASSO analysis with the HRGs identified three genes. Conclusion: This study successfully identified HRGs and developed an efficient diagnostic model based on HRGs, demonstrating high accuracy and strong predictive performance across multiple datasets. This research not only offers new insights into the complex relationship between OP and CBI but also establishes a foundation for the development of early diagnostic and personalized treatment strategies for chronic HBV-related OP.

Exploring the diagnostic and immune infiltration roles of disulfidptosis related genes in pulmonary hypertension.

BACKGROUND: Pulmonary hypertension (PH) is marked by elevated pulmonary artery pressures due to various causes, impacting right heart function and survival. Disulfidptosis, a newly recognized cell death mechanism, may play a role in PH, but its associated genes (DiGs) are not well understood in this context. This study aims to define the diagnostic relevance of DiGs in PH. METHODS: Using GSE11726 data, we analyzed DiGs and their immune characteristics to identify core genes influencing PH progression. Various machine learning models, including RF, SVM, GLM, and XGB, were compared to determine the most effective diagnostic model. Validation used datasets GSE57345 and GSE48166. Additionally, a CeRNA network was established, and a hypoxia-induced PH rat model was used for experimental validation with Western blot analysis. RESULTS: 12 DiGs significantly associated with PH were identified. The XGB model excelled in diagnostic accuracy (AUC = 0.958), identifying core genes DSTN, NDUFS1, RPN1, TLN1, and MYH10. Validation datasets confirmed the model's effectiveness. A CeRNA network involving these genes, 40 miRNAs, and 115 lncRNAs was constructed. Drug prediction suggested therapeutic potential for folic acid, supported by strong molecular docking results. Experimental validation in a rat model aligned with these findings. CONCLUSION: We uncovered the distinct expression patterns of DiGs in PH, identified core genes utilizing an XGB machine-learning model, and established a CeRNA network. Drugs targeting the core genes were predicted and subjected to molecular docking. Experimental validation was also conducted for these core genes.

Deciphering MOSPD1's impact on breast cancer progression and therapeutic response.

BACKGROUND: Motile Sperm Domain-Containing Protein 1 (MOSPD1) has been implicated in breast cancer (BC) pathophysiology, but its exact role remains unclear. This study aimed to assess MOSPD1 expression levels in BC versus normal tissues and investigate its diagnostic potential. METHODS: MOSPD1 expression was analyzed in BC and normal tissues, with Receiver Operating Characteristic analysis for diagnostic evaluation. Validation was performed using immunohistochemistry. Functional studies included tumor growth assays, MOSPD1 suppression and overexpression experiments, and testing BC cell responses to anti-PD-L1 therapy. RESULTS: MOSPD1 expression was significantly higher in BC samples than normal tissues, correlating with poor clinical outcomes in BC patients. MOSPD1 suppression inhibited tumor growth, while overexpression accelerated it. Silencing MOSPD1 enhanced BC cell sensitivity to anti-PD-L1 therapy and decreased Th2 cell activity. In vivo experiments supported these findings, showing the impact of MOSPD1 on tumor growth and response to therapy. CONCLUSIONS: Elevated MOSPD1 levels in BC suggest its potential as a biomarker for adverse outcomes. Targeting MOSPD1, particularly with anti-PD-L1 therapy, may effectively inhibit BC tumor growth and modulate immune responses. This study emphasizes the significance of MOSPD1 in BC pathophysiology and highlights its promise as a therapeutic target.

Comprehensive Analysis of the Significance of Breast Cancer Gene 1 (BRCA-1) in Bladder Cancer.

Background: Bladder carcinoma (BLCA) is characterized by high morbidity, mortality, and treatment costs. Breast cancer gene 1 (BRCA1), a tumor suppressor gene, inhibits the development of malignant tumors. However, research on the significance of BRCA1 in BLCA is limited. This study aims to explore the importance of BRCA1 in BLCA using bioinformatic methods and immunohistochemistry. Methods: Gene expression, clinical, and survival data were collected from the TCGA databases through the UCSC Xena platform (http://xena.ucsc.edu/). The TPM data from the TCGA and GETEx databases were integrated using the GEPIA database (http://GEPIA.cancer-pku.cn). The study then explored the differential expression, survival prognosis, functional enrichment, and immune cell infiltration analyses of BRCA1 in BLCA. A PPI network of BRCA1 was constructed using the STRING database, and a BRCA1-associated gene-gene interaction network was generated using the GeneMANIA database. Immunohistochemistry (IHC) assays were performed to verify the expression levels of BRCA1 in bladder tumour tissues and adjacent normal tissues. Results: BRCA1 is associated with BLCA. Differential analysis indicated that BRCA1 acts as a risk factor for BLCA but does not show significant expression differences across genders, stages, tumor stages, lymph node stages, or metastasis stages. Additionally, staging was based on the eighth edition of the American Joint Committee on Cancer (AJCC) for BLCA. Co-expression network and Gene Set Enrichment Analysis (GESA) confirmed that BRCA1 is involved in various BLCA pathways. Furthermore, BRCA1 expression was also linked to immune cell infiltration. However, survival prognosis analysis revealed no significant correlation between the prognosis of BLCA and BRCA1. Conclusion: We demonstrated that BRCA1 is a prospective predicted and immunological biomarker in BLCA, offering new avenues for potential therapies.

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

Background: 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. Methods: 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. Result: 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. Conclusion: 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 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.

Predicting gene signature in breast cancer patients with multiple machine learning models.

AIMS: The aim of this study was to predict gene signatures in breast cancer patients using multiple machine learning models. METHODS: In this study, we first collated and merged the datasets GSE54002 and GSE22820, obtaining a gene expression matrix comprising 16,820 genes (including 593 breast cancer (BC) samples and 26 normal control (NC) samples). Subsequently, we performed enrichment analyses using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO). RESULTS: We identified 177 differentially expressed genes (DEGs), including 40 up-regulated and 137 down-regulated genes, through differential expression analysis. The GO enrichment results indicated that these genes are primarily involved in extracellular matrix organization, positive regulation of nervous system development, collagen-containing extracellular matrix, heparin binding, glycosaminoglycan binding, and Wnt protein binding, among others. KEGG enrichment analysis revealed that the DEGs were primarily associated with pathways such as focal adhesion, the PI3K-Akt signaling pathway, and human papillomavirus infection. DO enrichment analysis showed that the DEGs play a significant role in regulating diseases such as intestinal disorders, nephritis, and dermatitis. Further, through LASSO regression analysis and SVM-RFE algorithm analysis, we identified 9 key feature DEGs (CF-DEGs): ANGPTL7, TSHZ2, SDPR, CLCA4, PAMR1, MME, CXCL2, ADAMTS5, and KIT. Additionally, ROC curve analysis demonstrated that these CF-DEGs serve as a reliable diagnostic index. Finally, using the CIBERSORT algorithm, we analyzed the infiltration of immune cells and the associations between CF-DEGs and immune cell infiltration across all samples. CONCLUSIONS: Our findings provide new insights into the molecular functions and metabolic pathways involved in breast cancer, potentially aiding in the discovery of new diagnostic and immunotherapeutic biomarkers.

ERBB2 is a potential diagnostic and prognostic biomarker in renal clear cell carcinoma.

Renal clear cell carcinoma (ccRCC) is a common parenchymal tumor of the kidney, and the discovery of biomarkers for early and effective diagnosis of ccRCC can improve the early diagnosis of patients and thus improve long-term survival. Erb-b2 receptor tyrosine kinase 2 (ERBB2) mediates the processes of cell proliferation, differentiation, and apoptosis inhibition. The purpose of this study was to investigate the diagnostic and prognostic role of ERBB2 in ccRCC. We analyzed the expression levels of ERBB2 in various cancers from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. RNA-seq data were analyzed using R packages to identify differentially expressed genes between the high and low ERBB2 expression groups in the TCGA-KIRC dataset. Spearman correlation analysis was performed to determine the correlation between ERBB2 expression and immune cell infiltration, immune checkpoint expression, and PTEN expression. DNA methylation changes and genetic alterations in ERBB2 were assessed using the MethSurv and cBioPortal databases. Logistic regression analysis was performed to determine the correlation between ERBB2 expression and the clinicopathological characteristics of ccRCC patients. The diagnostic and prognostic value of ERBB2 was assessed using Kaplan‒Meier (K‒M) survival curves, diagnostic ROC curves, time-dependent ROC curves, nomogram models, and Cox regression models. The expression level of ERBB2 is lower in tumor tissues of ccRCC patients than in the corresponding control tissues. Differentially expressed genes associated with ERBB2 were significantly enriched in the pathways "BMP2WNT4FOXO1 pathway in primary endometrial stromal cell differentiation" and "AMAN pathway". In ccRCC tissues, ERBB2 expression levels were associated with immune cell infiltration, immune checkpoints, and PTEN. The DNA methylation status of 10 CpG islands in the ERBB2 gene was associated with the prognosis of ccRCC. ERBB2 expression levels in ccRCC tissues were associated with race, sex, T stage, M stage, histological grade, and pathological stage. Cox regression analysis showed that ERBB2 was a potential independent predictor of overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in ccRCC patients. ROC curve analysis showed that the expression level of ERBB2 could accurately distinguish between ccRCC tissue and adjacent normal renal tissue. Our study showed that ERBB2 expression in ccRCC tissues can be of clinical importance as a potential diagnostic and prognostic biomarker.

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.

A novel tumor-derived exosomal gene signature predicts prognosis in patients with pancreatic cancer.

Background: Pancreatic cancer is a devastating disease with poor prognosis. Accumulating evidence has shown that exosomes and their cargo have the potential to mediate the progression of pancreatic cancer and are promising non-invasive biomarkers for the early detection and prognosis of this malignancy. This study aimed to construct a gene signature from tumor-derived exosomes with high prognostic capacity for pancreatic cancer using bioinformatics analysis. Methods: Gene expression data of solid pancreatic cancer tumors and blood-derived exosome tissues were downloaded from The Cancer Genome Atlas (TCGA) and ExoRBase 2.0. Overlapping differentially expressed genes (DEGs) in the two datasets were analyzed, followed by functional enrichment analysis, protein-protein interaction networks, and weighted gene co-expression network analysis (WGCNA). Using the least absolute shrinkage and selection operator (LASSO) regression of prognosis-related exosomal DEGs, a tumor-derived exosomal gene signature was constructed based on the TCGA dataset, which was validated by an external validation dataset, GSE62452. The prognostic power of this gene signature and its relationship with various pathways and immune cell infiltration were analyzed. Results: A total of 166 overlapping DEGs were identified from the two datasets, which were markedly enriched in functions and pathways associated with the cell cycle. Two key modules and corresponding 70 exosomal DEGs were identified using WGCNA. Using LASSO Cox regression of prognosis-related exosomal DEGs, a tumor-derived exosomal gene signature was built using six exosomal DEGs (ARNTL2, FHL2, KRT19, MMP1, CDCA5, and KIF11), which showed high predictive performance for prognosis in both the training and validation datasets. In addition, this prognostic signature is associated with the differential activation of several pathways, such as the cell cycle, and the infiltration of some immune cells, such as Tregs and CD8+ T cells. Conclusions: This study established a six-exosome gene signature that can accurately predict the prognosis of pancreatic cancer.

Identification of novel mitophagy-related biomarkers for Kawasaki disease by integrated bioinformatics and machine-learning algorithms.

Background: Kawasaki disease (KD) is a systemic vasculitis primarily affecting the coronary arteries in children. Despite growing attention to its symptoms and pathogenesis, the exact mechanisms of KD remain unclear. Mitophagy plays a critical role in inflammation regulation, however, its significance in KD has only been minimally explored. This study sought to identify crucial mitophagy-related biomarkers and their mechanisms in KD, focusing on their association with immune cells in peripheral blood. Methods: This research used four datasets from the Gene Expression Omnibus (GEO) database that were categorized as the merged and validation datasets. Screening for differentially expressed mitophagy-related genes (DE-MRGs) was conducted, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A weighted gene co-expression network analysis (WGCNA) identified the hub module, while machine-learning algorithms [random forest-recursive feature elimination (RF-RFE) and support vector machine-recursive feature elimination (SVM-RFE)] pinpointed the hub genes. Receiver operating characteristic (ROC) curves were generated for these genes. Additionally, the CIBERSORT algorithm was used to assess the infiltration of 22 immune cell types to explore their correlations with hub genes. Interactions between transcription factors (TFs), genes, and Gene-microRNAs (miRNAs) of hub genes were mapped using the NetworkAnalyst platform. The expression difference of the hub genes was validated using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). Results: Initially, 306 DE-MRGs were identified between the KD patients and healthy controls. The enrichment analysis linked these MRGs to autophagy, mitochondrial function, and inflammation. The WGCNA revealed a hub module of 47 KD-associated DE-MRGs. The machine-learning algorithms identified cytoskeleton-associated protein 4 (CKAP4) and serine-arginine protein kinase 1 (SRPK1) as critical hub genes. In the merged dataset, the area under the curve (AUC) values for CKAP4 and SRPK1 were 0.933 [95% confidence interval (CI): 0.901 to 0.964] and 0.936 (95% CI: 0.906 to 0.966), respectively, indicating high diagnostic potential. The validation dataset results corroborated these findings with AUC values of 0.872 (95% CI: 0.741 to 1.000) for CKAP4 and 0.878 (95% CI: 0.750 to 1.000) for SRPK1. The CIBERSORT analysis connected CKAP4 and SRPK1 with specific immune cells, including activated cluster of differentiation 4 (CD4) memory T cells. TFs such as MAZ, SAP30, PHF8, KDM5B, miRNAs like hsa-mir-7-5p play essential roles in regulating these hub genes. The qRT-PCR results confirmed the differential expression of these genes between the KD patients and healthy controls. Conclusions: CKAP4 and SRPK1 emerged as promising diagnostic biomarkers for KD. These genes potentially influence the progression of KD through mitophagy regulation.

Anoikis-related subtype and prognosis analyses based on bioinformatics, and an expression verification of ANGPTL4 based on experiments of lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is one of the most common malignant tumors with high mortality. Anoikis resistance is an important mechanism of tumor cell proliferation and migration. Our research is devoted to exploring the role of anoikis in the diagnosis, classification, and prognosis of LUAD. Methods: We downloaded the expression profile, mutation, and clinical data of LUAD from The Cancer Genome Atlas (TCGA) database. The "ConsensusClusterPlus" package was then used for the cluster analysis, and least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were used to establish the prognostic model. We verified the reliability of the model using a Gene Expression Omnibus (GEO) data set. A gene set variation analysis (GSVA) was conducted to investigate the functional enrichment differences in the different clusters and risk groups. The CIBERSORT algorithm and a single-sample gene set enrichment analysis (ssGSEA) were used to analyze immune cell infiltration. The tumor mutation burden (TMB) and Tumor Immune Dysfunction and Exclusion (TIDE) scores were used to evaluate the patients' sensitivity to immunotherapy. Immunohistochemical staining of tissue microarrays was used to verify the correlation between ANGPTL4 expression and the clinicopathological characteristics and prognosis of LUAD patients. Results: First, we screened 135 differentially expressed anoikis-related genes (ARGs) and 23 prognosis-related ARGs from TCGA-LUAD data set. Next, 494 LUAD samples were allocated to cluster A and cluster B based on the 23 prognosis-related ARGs. The Kaplan-Meier (K-M) analysis showed the overall survival (OS) of cluster B was better than that of cluster A. The clinicopathological characteristics and functional enrichment analyses revealed significant differences between clusters A and B. The tumor microenvironment (TME) analysis showed that cluster B had more immune cell infiltration and a higher TME score than cluster A. Subsequently, a LASSO Cox regression model of LUAD was constructed with ten ARGs. The K-M analysis showed that the low-risk patients had longer OS than the high-risk patients. The receiver operating characteristic curve, nomogram, and GEO data set verification results showed that the model had high accuracy and reliability. The level of immune cell infiltration and TME score were higher in the low-risk group than the high-risk group. The high-risk group had stronger sensitivity to immune checkpoint block therapy and weaker sensitivity to chemotherapy drugs than the low-risk group. ANGPTL4 expression was correlated with stage, tumor differentiation, tumor size, lymph node metastasis, and OS. Conclusions: We discovered novel molecular subtypes and constructed a novel prognostic model of LUAD. Our findings provide important insights into subtype classification and the accurate survival prediction of LUAD. We also identified ANGPTL4 as a prognostic indicator of LUAD.

Pan-Cancer Analysis Confirms the Prognostic and Immunological Implications of the 1,25-Dihydroxy Vitamin D3 Receptor in Cervical Squamous Cell Carcinoma.

Vitamin D receptor (VDR), specifically the 1,25-dihydroxy form, holds significant importance in various types of cancer, including cervical squamous cell carcinoma (CESC), which poses a significant public health challenge. A pan-cancer analysis was conducted on VDR in CESC, with a focus on its expression and relationship with immune infiltration and genetic alterations. Bioinformatics databases, including TIMER, GEPIA, UALCAN, cBioportal, and Kaplan-Meier Plotter, have been utilized. VDR expression in CESC has been validated using publicly available data. Results were significantly upregulated (P=0.05) in THCA, BRCA, KICH, LUAD, LIHC, STAD, UCEC, CESC, CHOL, ESCA, and HNSC samples. We analyzed the correlation between VDR expression and various clinicopathological factors such as age, race, and cancer stage. VDR expression was significantly upregulated across all age groups, with the highest levels observed in older adults followed by young and middle-aged adults. VDR gene expression was significantly elevated across all races, including Caucasians, African-Americans, and Asians, compared to that in the normal group. Furthermore, VDR expression was significantly upregulated in cancer stages 1, 2, 3, and 4, with the highest increase observed in stage 3 compared to that in normal individuals. We analyzed the expression of the VDR in relation to immune cell type and tumor cell purity in CESC. Our results indicated that VDR expression was positively correlated with neutrophils and dendritic cells and negatively correlated with tumor cell purity in CESC patients. There was no significant correlation between VDR expression and the abundance of B cells, CD8+ T cells, CD4+ T cells, and macrophages. Our study found no significant effect of VDR expression on patient prognosis, although it was positively correlated with CD4+ T cells. The Cox proportional hazards model indicated that age and immune cells did not significantly affect prognosis. Most VDR mutations are concentrated in diffuse large B-cell lymphoma, with an amplification frequency of 4% and a deep deletion frequency of 2.2%. GEO confirmed VDR expression in CESC, identifying 1515 upregulated and 1877 downregulated genes, with volcano plots showing CESC downregulation in patients.

Key Disease-Related Genes and Immune Cell Infiltration Landscape in Inflammatory Bowel Disease: A Bioinformatics Investigation.

Inflammatory Bowel Diseases (IBD), which encompass ulcerative colitis (UC) and Crohn's disease (CD), are characterized by chronic inflammation and tissue damage of the gastrointestinal tract. This study aimed to uncover novel disease-gene signatures, dysregulated pathways, and the immune cell infiltration landscape of inflamed tissues. Eight publicly available transcriptomic datasets, including inflamed and non-inflamed tissues from CD and UC patients were analyzed. Common differentially expressed genes (DEGs) were identified through meta-analysis, revealing 180 DEGs. DEGs were implicated in leukocyte transendothelial migration, PI3K-Akt, chemokine, NOD-like receptors, TNF signaling pathways, and pathways in cancer. Protein-protein interaction network and cluster analysis identified 14 central IBD players, which were validated using eight external datasets. Disease module construction using the NeDRex platform identified nine out of 14 disease-associated genes (CYBB, RAC2, GNAI2, ITGA4, CYBA, NCF4, CPT1A, NCF2, and PCK1). Immune infiltration profile assessment revealed a significantly higher degree of infiltration of neutrophils, activated dendritic cells, plasma cells, mast cells (resting/activated), B cells (memory/naïve), regulatory T cells, and M0 and M1 macrophages in inflamed IBD tissue. Collectively, this study identified the immune infiltration profile and nine disease-associated genes as potential modulators of IBD pathogenesis, offering insights into disease molecular mechanisms, and highlighting potential disease modulators and immune cell dynamics.

Analyzing the role of TM4SF1 expression in pancreatic adenocarcinoma: understanding prognostic implications and therapeutic opportunities.

Background: Pancreatic adenocarcinoma (PAAD) is a highly lethal malignancy characterized by aggressive growth and poor prognosis. Understanding the molecular mechanisms underlying PAAD is crucial for developing effective therapies. This study aimed to explore the role of TM4SF1 and other key genes in PAAD progression, their prognostic implications, and therapeutic opportunities. Methods: Differential gene expression analysis was performed using PAAD and normal tissue samples to identify upregulated genes, with TM4SF1 emerging as significantly elevated in PAAD. Functional enrichment analysis elucidated associated signaling pathways. A prognostic model comprising BPIFB4, PLEKHN1, CPTP, DVL1, and DDR1 was developed using least absolute shrinkage and selection operator (LASSO) regression and validated in an independent cohort. Genetic mutation analysis provided insights into the functional significance of identified genes. Pharmacogenomic analysis examined associations between gene expression and drug sensitivity. Experimental validation included quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Western blot analyses to confirm gene expression patterns and protein levels. Results: Lower TM4SF1 expression correlated with enhanced anti-tumor immune activity in PAAD, suggesting a complex interplay between genetic expression and immune response. The prognostic model showed robust associations with patient survival outcomes, validated across diverse patient cohorts. Genetic mutation analysis highlighted potential therapeutic targets. Pharmacogenomic analysis revealed correlations between gene expression profiles and drug responsiveness, suggesting personalized treatment strategies. Experimental validation confirmed elevated TM4SF1 levels in tumor tissues and demonstrated its role in promoting cancer cell proliferation and colony formation. Conclusions: This study advances understanding of the molecular landscape of PAAD, emphasizing TM4SF1 as a key regulator and potential therapeutic target. The integration of genetic expression, immune response dynamics, and pharmacogenomics offers a multifaceted approach to personalized treatment strategies for PAAD, paving the way for improved patient outcomes and novel therapeutic interventions. Further research is warranted to elucidate the clinical utility of targeting TM4SF1 and other identified genes in PAAD management.

Single-Cell WGCNA Combined with Transcriptome Sequencing to Study the Molecular Mechanisms of Inflammation-Related Ferroptosis in Myocardial Ischemia-Reperfusion Injury.

Purpose: Myocardial ischemia-reperfusion injury (MIRI) is characterized by inflammation and ferroptosis, but the precise mechanisms remain unknown. This study used single-cell transcriptomics technology to investigate the changes in various cell subtypes during MIRI and the regulatory network of ferroptosis-related genes and immune infiltration. Methods: Datasets GSE146285, GSE83472, GSE61592, and GSE160516 were obtained from Gene Expression Omnibus. Each cell subtype in the tissue samples was documented. The Seurat package was used for data preprocessing, standardization, and clustering. Cellphonedb was used to investigate the ligand-receptor interactions between cells. The hdWGCNA analysis was used to create a gene co-expression network. GSVA and GSEA were combined to perform functional enrichment and pathway analysis on the gene set. Furthermore, characteristic genes of the disease were identified using Lasso regression and SVM algorithms. Immune cell infiltration analysis was also performed. MIRI rat models were created, and samples were taken for RT-qPCR and Western blot validation. Results: The proportion of MIRI samples in the C2, C6, and C11 subtypes was significantly higher than that of control samples. Three genes associated with ferroptosis (CD44, Cfl1, and Zfp36) were identified as MIRI core genes. The expression of these core genes was significantly correlated with mast cells and monocyte immune infiltrating cells. The experimental validation confirmed the upregulation of Cd44 and Zfp36 expression levels in MIRI, consistent with current study trends. Conclusion: This study used single-cell transcriptomics technology to investigate the molecular mechanisms underpinning MIRI. Numerous important cell subtypes, gene regulatory networks, and disease-associated immune infiltration were also discovered. These findings provide new information and potential therapeutic targets for MIRI diagnosis and treatment.

Investigation of TMEM41A's function in breast cancer prognosis and its connection to immune cell infiltration.

BACKGROUND: Globally, breast cancer is the most common type of malignant tumor. It has been demonstrated that TMEM41A is abnormally expressed in a number of cancers and is linked to a dismal prognosis. TMEM41A's involvement in breast cancer remains unknown, though. METHODS: Data from databases such as TCGA were used in this study. Expression differences were compared using non-parametric tests. Cox regression analysis was employed, and analyses such as Nomogram were used to assess the significance of TMEM41A in predicting the prognosis of breast cancer. Lastly, it was looked into how immune cell infiltration in breast cancer is related to TMEM41A expression levels. RESULTS: The results suggest that TMEM41A is overexpressed in breast cancer and correlates with poor prognosis (P = 0.01), particularly in early-stage and ductal A breast cancer (P < 0.01). Breast cancer patients' expression of TMEM41A was found to be an independent risk factor (HR = 1.132, 95% CI 1.036-1.237) by multifactorial Cox regression analysis. The Nomogram prediction model's c-index was 0.736 (95% CI 0.684-0.787). The results of GSEA biofunctional enrichment analysis included the B cell receptor signaling pathway (P < 0.05). Ultimately, there was a significant correlation (P < 0.05) between TMEM41A expression in breast cancer and an infiltration of twenty immune cells. CONCLUSIONS: Breast cancer tissues overexpress TMEM41A, which is linked to immune cell infiltration and prognosis (particularly in early stage and luminal A breast cancer). Overexpression of TMEM41A is anticipated to serve as a novel prognostic indicator and therapeutic target for breast cancer.