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Background: Inflammatory bowel disease (IBD) is a chronic, inflammatory, and autoimmune disorder, and its incidence of comorbid with major depressive disorder (MDD) is significantly higher than the general population. However, many patients lack proper recognition and necessary psychological health treatments. We aimed to identify potential biomarkers and mechanisms involved in the development of IBD comorbid with MDD (IBD-MDD). Methods: We utilized IBD and MDD-related datasets from the GEO database for differential gene expression analysis, protein-protein interaction (PPI) and pathway enrichment analysis, random forest algorithm, LASSO regression analysis, and construction of a disease prediction model. We assessed the accuracy of the model using ROC curve, explored potential mechanisms through immune infiltration analysis, and validated candidate biomarkers using peripheral blood samples from patients in our center's cohort. Results: We identified 484 IBD-related secreted proteins and 142 key module genes associated with MDD. PPI analysis revealed two crucial modules primarily involved in inflammation and immune regulation. We identified four diagnostic genes (HGF, SPARC, ADAM12, and MMP8) from the 21 shared genes between IBD-related secreted proteins and MDD key module genes, constructed a nomogram model and confirmed its accuracy using ROC curve from an external independent dataset. Immune infiltration analysis revealed significant associations between the four diagnostic genes, and cellular immune dysregulation in MDD. Finally, we validated the expression patterns of the four diagnostic genes in our cohort. Conclusions: Our study discovered four candidate biomarkers for IBD-MDD, providing new insights for the diagnosis and therapeutic intervention of serum-based IBD comorbid with MDD. [ABSTRACT FROM AUTHOR]

Introduction: Acute myeloid leukemia(AML) is a diverse malignancy with a prognosis that varies, being especially unfavorable in older patients and those with high-risk characteristics. Metabolic reprogramming has become a significant factor in AML development, presenting new opportunities for prognostic assessment and therapeutic intervention. Methods: Metabolism-related differentially expressed genes (mDEGs) were identified by integrating KEGG metabolic gene lists with AML gene expression data from GSE63270. Using TCGA data, we performed consensus clustering and survival analysis to investigate the prognostic significance of mDEGs. A metabolic risk model was constructed using LASSO Cox reg ression and enhanced by a nomogram incorporated clinical characteristics. The model was validated through receiver operating characteristic (ROC) curves and survival statistics. Gene network analysis was conducted to identify critical prognostic factors. The tumor immune microenvironment was evaluated using CIBERSORT and ESTIMATE algorithms, followed by correlation analysis between immune checkpoint gene expression and risk scores. Drug sensitivity predictions and in vitro assays were performed to explore the effects of mDEGs on cell proliferation and chemoresistance. Results: An 11-gene metabolic prognostic model was established and validated. High-risk patients had worse overall survival in both training and validation cohorts (p < 0.05). The risk score was an independent prognostic factor. High-risk patients showed increased immune cell infiltration and potential response to checkpoint inhibitors but decreased drug sensitivity. The model correlated with sensitivity to drugs such as venetoclax. Carbonic anhydrase 13 (CA13) was identified as a key gene related to prognosis and doxorubicin resistance. Knocking down CA13 reduced proliferation and increased cell death with doxorubicin treatment. Conclusion: A novel metabolic gene signature was developed to stratify risk and predict prognosis in AML, serving as an independent prognostic factor. CA13 was identified as a potential therapeutic target. This study provides new insights into the prognostic and therapeutic implications of metabolic genes in AML. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer ranks as the second most frequently diagnosed cancer in men worldwide. Recent research highlights the crucial roles IL6ST-mediated signaling pathways play in the development and progression of various cancers, particularly through hyperactivated STAT3 signaling. However, the molecular programs mediated by IL6ST/STAT3 in prostate cancer are poorly understood. Methods: To investigate the role of IL6ST signaling, we constitutively activated IL6ST signaling in the prostate epithelium of a Pten-deficient prostate cancer mouse model in vivo and examined IL6ST expression in large cohorts of prostate cancer patients. We complemented these data with in-depth transcriptomic and multiplex histopathological analyses. Results: Genetic cell-autonomous activation of the IL6ST receptor in prostate epithelial cells triggers active STAT3 signaling and significantly reduces tumor growth in vivo. Mechanistically, genetic activation of IL6ST signaling mediates senescence via the STAT3/ARF/p53 axis and recruitment of cytotoxic T-cells, ultimately impeding tumor progression. In prostate cancer patients, high IL6ST mRNA expression levels correlate with better recurrence-free survival, increased senescence signals and a transition from an immune-cold to an immune-hot tumor. Conclusions: Our findings demonstrate a context-dependent role of IL6ST/STAT3 in carcinogenesis and a tumor-suppressive function in prostate cancer development by inducing senescence and immune cell attraction. We challenge the prevailing concept of blocking IL6ST/STAT3 signaling as a functional prostate cancer treatment and instead propose cell-autonomous IL6ST activation as a novel therapeutic strategy. [ABSTRACT FROM AUTHOR]

Background: Solute Carrier Family 4 Member 4 (SLC4A4) is a membrane protein‐coding gene for a Na+/HCO3− cotransporter and plays a crucial role in regulating pH, bicarbonate secretion and homeostasis. However, the prognostic and immunological role of SLC4A4 in colon cancer remains unknown. Method: In this study, expression profiles of SLC4A4 were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, to which a variety of bioinformatic analyses were performed. Sangerbox, Xiantao, ESTIMATE and TIMER online tools were used to delve into the relationship between SLC4A4 expression and immune cell infiltration. The role of SLC4A4 in the proliferation and migration of colon cancer cells was verified by CCK8, EdU and wound healing assays. The related molecules and pathways that SLC4A4 may affect were validated by bioinformatic prediction and western blotting analysis. Results: The expression levels of SLC4A4 were significantly lower in colon cancer tissues than in normal tissues and its low expression was positively correlated with poor prognosis. TIMER and ESTIMATE showed that SLC4A4 broadly influenced immune cell infiltration. Experiments in vitro demonstrated that SLC4A4 inhibited partial epithelial-mesenchymal transition (EMT) phenotypes. Conclusions: To conclude, our study revealed that SLC4A4 is lowly expressed in colon cancer tissues, and SLC4A4 may inhibit the progression of colon cancer via regulating partial EMT phenotypes and immune cell infiltration, which may provide new perspectives for the development of more precise and personalized immune anti-tumor therapies. [ABSTRACT FROM AUTHOR]

Background: Insomnia (ISM) is one of the non-traditional drivers of atherosclerosis (AS) and an important risk factor for AS-related cardiovascular disease. Our study aimed to explore the shared pathways and diagnostic biomarkers of ISM-related AS using integrated bioinformatics analysis. Methods: We download the datasets from the Gene Expression Omnibus database and the GeneCards database. Weighted gene co-expression network analysis and gene differential expression analysis were applied to screen the ASrelated gene set. The shared genes of ISM and AS were obtained by intersecting with ISM-related genes. Subsequently, candidate diagnostic biomarkers were identified by constructing protein-protein interaction networks and machine learning algorithms, and a nomogram was constructed. Moreover, to explore potential mechanisms, a comprehensive analysis of shared genes was carried out, including enrichment analysis, protein interactions, immune cell infiltration, and single-cell sequencing analysis. Results: We successfully screened 61 genes shared by ISM and AS, of which 3 genes (IL10RA, CCR1, and SPI1) were identified as diagnostic biomarkers. A nomogram with excellent predictive value was constructed (the area under curve of the model constructed by the biomarkers was 0.931, and the validation set was 0.745). In addition, the shared genes were mainly enriched in immune and inflammatory response regulation pathways. The biomarkers were associated with a variety of immune cells, especially myeloid immune cells. Conclusion: We constructed a diagnostic nomogram based on IL10RA, CCR1, and SPI1 and explored the inflammatory-immune mechanisms, which indicated new insights for early diagnosis and treatment of ISM-related AS. [ABSTRACT FROM AUTHOR]

AbstractIntroductionMethodsResultsConclusionThe enhancer of rudimentary homolog (ERH) is significant in cancers, but its role in lung cancer is understudied.We divided lung cancer patients into high and low ERH expression groups based on tumour tissue levels. Using the log-rank test, we analysed the correlation between ERH expression and patient prognosis. The effects of high ERH expression on lung cancer cell proliferation, migration, and invasion were assessed using CCK8, EDU, transwell, and wound healing assays.ERH expression was significantly higher in cancerous versus normal lung tissue (p < 0.05), including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Patients with high ERH expression had worse overall survival (HR = 1.37, p = 2.5 × 1 0 −7) and first progression survival (HR = 1.38, p = 0.00065) in lung cancer. However, while high ERH expression predicts an unfavourable prognosis in LUAD, it does not hold true for LUSC. Furthermore, knockdown of ERH inhibited lung cancer cell proliferation, migration, and invasion. ERH expression was linked to immune cell infiltration. High ERH expression in LUAD and LUSC samples correlated with higher CD8 T cell, T cells CD4 memory activated, and M1 macrophages abundance, while low ERH expression correlated with higher T cells CD4 memory resting abundance.Upregulation of ERH in lung cancer tissue is associated with poor prognosis and immune cell infiltration. [ABSTRACT FROM AUTHOR]

Background: Myelodysplastic syndromes (MDS) are heterogeneous and clonal hematological disorders. The role and mechanism of necroptosis in MDS remain poorly understood. Methods: mRNA expression profiles and single-cell RNA-sequencing (scRNA-seq) data were sourced from the GEO database. ScRNA-seq data were processed using the "Seurat" package. After cell annotation, necroptosis-related scores (NRscores) for each cell were calculated using the "UCell" package. Differentially expressed genes (DEGs) and their associated biological functions in NRscore-related cell populations were identified. Additionally, DEGs and necroptosis-related genes (DE-NRGs) between MDS patients and healthy controls were identified. Consensus clustering was employed to classify MDS patients into distinct subclusters based on DE-NRGs. The biological functions and immune characteristics of these classifications were analyzed. Prognostic gene signatures were determined using LASSO and SVM-RFE analyses, and a nomogram was constructed based on the prognostic gene signature. Results: A total of 12 cell types were identified in MDS and healthy controls. NRscore was found to be elevated in monocytes and common lymphoid precursors (CLPs). Enrichment analysis revealed that monocytes and CLPs with high NRscore were associated with mitochondria-related and immune-related pathways. Eleven DEGs in monocytes and CLPs between MDS patients and healthy controls were identified. Additionally, 13 DE-NRGs were identified from 951 DEGs between MDS and healthy controls. MDS patients were classified into two distinct subclusters based on these 13 DE-NRGs, revealing several immune-related processes and signaling pathways. Differences in immune subpopulations between the two subclusters were observed. A necroptosis-related diagnostic gene signature (IRF9, PLA2G4A, MLKL, BAX, JAK2, and STAT3) was identified as predictive of MDS prevalence. Conclusion: Necroptosis plays a role in MDS progression by inducing inflammation. A novel necroptotic gene signature has been developed to distinguish and diagnose MDS at early stages of the disease. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Adenomatous polyposis coli (APC) gene, an oncogene, has been implicated in stomach adenocarcinoma (STAD), which is a common type of gastric cancer (GC). Although the relationship between APC gene mutations and gastric adenocarcinoma has been comprehensively studied, the potential role of these mutations in the prognosis and targeted therapy remains known. Methods: We utilized The Cancer Genome Atlas (TCGA) database to obtain gene expression matrices, clinical information, and mutation data from patients with STAD. The mutation status of the APC gene was analyzed, and its correlation with tumor mutational burden (TMB), microsatellite instability (MSI), and clinical prognosis in STAD was investigated. Gene set enrichment analysis (GSEA) was conducted to explore the pathological role of APC gene mutations in STAD metabolic pathways. Drug sensitivity analysis was conducted to identify potential targeted antitumor drugs for patients with APC gene mutations in gastric adenocarcinoma. Results: The results revealed that 88% (46/52) of STAD samples had nonsynonymous mutations. The mutation group exhibited a significantly higher TMB than the wild‐type group (p < 0.001), and the percentage of high MSI (MSI‐H) was significantly higher in the mutation group than in the wild‐type group (p < 0.001). Patients with APC mutations had a worse prognosis than those with APC wild‐type (p = 0.009). The APC gene mutation group displayed significant enrichment in amino acids, RNA, and several pathways (|NES| > 1 and nominal p value < 0.01). Compared to the wild‐type group, the mutation group exhibited a higher infiltration proportion of natural killer (NK) cells resting and eosinophils, whereas a lower infiltration proportion of monocytes and resting mast cells (p value < 0.05). AZD5991 exhibited significant sensitivity in patients with STAD carrying APC mutations (p = 0.028). Conclusion:APC gene mutations play a crucial role in the prognosis, molecular characteristics, and potential therapeutic strategies for gastric adenocarcinoma. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a neurodegenerative disorder with early autophagy deficits. Our study probed the role of lysosomal-related genes (LRGs) in AD. Using the Gene Expression Omnibus (GEO) database, we analyzed differentially expressed genes (DEGs) in AD. AD-related genes and lysosomal-related genes (LRGs) were extracted from public databases. Leveraging the UpSetR package, we identified differentially expressed LRGs (DE-LRGs). Subsequently, consensus cluster analysis was used to stratify AD patients into distinct molecular subtypes based on DE-LRGs. Immune cell patterns were studied via Single-Sample Gene Set Enrichment Analysis (ssGSEA). Molecular pathways were assessed through Gene Set Variation Analysis (GSVA), while Mendelian Randomization (MR) discerned potential gene-AD causations. To reinforce our bioinformatics findings, we conducted in vitro experiments. In total, 52 DE-LRGs were identified, with LAMP1, VAMP2, and CTSB as standout hub genes. Leveraging the 52 DE-LRGs, AD patients were categorized into three distinct molecular subtypes. Interestingly, the three aforementioned hub genes exhibited significant predictive accuracy for AD differentiation across the subtypes. The ssGSEA further illuminated correlations between LAMP1, VAMP2, and CTSB with plasma cells, fibroblasts, eosinophils, and endothelial cells. GSVA analysis underscored significant associations of LAMP1, VAMP2, and CTSB with NOTCH, TGFβ, and P53 pathways. Compellingly, MR findings indicated a potential causative relationship between LAMP1, CTSB, and AD. Augmenting our bioinformatics conclusions, in vitro tests revealed that LAMP1 potentially alleviates AD progression by amplifying autophagic processes. LAMP1 and CTSB emerge as potential AD biomarkers, paving the way for innovative therapeutic interventions [ABSTRACT FROM AUTHOR]

Glioma, a type of brain tumor, poses significant challenges due to its heterogeneous nature and limited treatment options. Interferon-related genes (IRGs) have emerged as potential players in glioma pathogenesis, yet their expression patterns and clinical implications remain to be fully elucidated. We conducted a comprehensive analysis to investigate the expression patterns and functional enrichment of IRGs in glioma. This involved constructing protein-protein interaction networks, heatmap analysis, survival curve plotting, diagnostic and prognostic assessments, differential expression analysis across glioma subgroups, GSVA, immune infiltration analysis, and drug sensitivity analysis. Our analysis revealed distinct expression patterns and functional enrichment of IRGs in glioma. Notably, IFNW1 and IFNA21 were markedly downregulated in glioma tissues compared to normal tissues, and higher expression levels were associated with improved overall survival and disease-specific survival. Furthermore, these genes showed diagnostic capabilities in distinguishing glioma tissues from normal tissues and were significantly downregulated in higher-grade and more aggressive gliomas. Differential expression analysis across glioma subgroups highlighted the association of IFNW1 and IFNA21 expression with key pathways and biological processes, including metabolic reprogramming and immune regulation. Immune infiltration analysis revealed their influence on immune cell composition in the tumor microenvironment. Additionally, elevated expression levels were associated with increased resistance to chemotherapeutic agents. Our findings underscore the potential of IFNW1 and IFNA21 as diagnostic biomarkers and prognostic indicators in glioma. Their roles in modulating glioma progression, immune response, and drug sensitivity highlight their significance as potential therapeutic targets. These results contribute to a deeper understanding of glioma biology and may inform the development of personalized treatment strategies for glioma patients. [ABSTRACT FROM AUTHOR]

Background: N6-methyladenosine (m6A), is the most common modification found in mRNA and lncRNA in higher organisms and plays an important role in physiology and pathology. However, its role in pan-cancer has not been explored. Results: A total of 31 m6A modification regulators, including 12 writers, 2 erasers, and 17 readers are identified in the current study. The functional analysis of the regulators results in the enrichment of processes, primarily related to RNA modification and metabolism, and the PPI network reveals multiple interactions among the regulators. The mRNA expression analysis reveals a high expression for most of the regulators in pan-cancer. Most of the m6A regulators are found to be mutated across the cancers, with ZC3H13, VIRMA, and PRRC2A having a higher frequency rate. Significant correlations of the regulators with clinicopathological parameters, such as age, gender, tumor stage, and grade are identified in pan-cancer. The m6A regulators' expression is found to have significant positive correlations with the miRNAs in pan-cancer. The expression pattern of the m6A regulators is able to classify the tumors into different subclusters as well as into high- and low-risk groups. These tumor groups show differential patterns in terms of their immune cell infiltration, tumor stemness score, genomic heterogeneity score, expression of immune regulatory/checkpoint genes, and correlations between the regulators and the drugs. Conclusions: Our study provide a comprehensive overview of the functional roles, genetic and epigenetic alterations, and prognostic value of the RNA m6A regulators in pan-cancer. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 nonnegative 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. [ABSTRACT FROM AUTHOR]

The Hippo-Yes-associated protein (Hippo-YAP) signaling pathway, a conserved pathway that regulates organ size, participates in tumor progression. However, there are few comprehensive analyses of tumor prognosis and immunity. In the present study, TCGA, GTEx, GEO, TIMER2, STRING, GSCA, ImmuCellAI, and other bioinformatics tools were used to reveal the involvement of the Hippo-YAP signaling pathway in the prognosis and immunity of pan-cancers. The obtained results showed that mRNA expression differences of Hippo-YAP pathway genes between normal samples and tumor samples in pan-cancers and some genes (such as TEAD4, MAP4K4, and STK3) might affect the prognosis of patients with skin cutaneous melanoma (SKCM) and pancreatic adenocarcinoma (PAAD). Furthermore, mutation and methylation of the Hippo-YAP signaling pathway genes in normal and primary tumor tissues differ in various cancers (KIRP, BRCA). Additionally, the relationship between the tumor microenvironment, molecular pathways, and the Hippo-YAP pathway indicated that it might lead to a suppressive immune microenvironment that affects the efficacy of immunotherapy. This is a pan-cancer overview of the Hippo-YAP signaling pathway genes, which explores the aberrant expression or mutation of this pathway that regulates the tumor microenvironment and immunotherapy. [ABSTRACT FROM AUTHOR]

Background: 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. Methods: 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. Results: 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) (pvalue = 0.029). Conclusion: 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. [ABSTRACT FROM AUTHOR]

Background: Idiopathic pulmonary fibrosis (IPF) is a severe lung condition, and finding better ways to diagnose and treat the disease is crucial for improving patient outcomes. Our study sought to develop an artificial neural network (ANN) model for IPF and determine the immune cell types that differed between the IPF and control groups. Methods: From the Gene Expression Omnibus (GEO) database, we first obtained IPF microarray datasets. To conduct protein-protein interaction (PPI) networks and enrichment analyses, differentially expressed genes (DEGs) were screened between tissues of patients with IPF and tissues of controls. Afterward, we identified the important feature genes associated with IPF using random forest (RF) analysis, and then constructed and validated a prediction ANN mode. In addition, the proportions of immune cells were quantified using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) analysis, which was performed on microarray datasets based on gene expression profiling. Results: A total of 11 downregulated and 36 upregulated DEGs were identified. PPI networks and enrichment analyses were carried out; the immune system and extracellular matrix were the subjects of the enrichments. Using RF analysis, the significant feature genes LRRC17, COMP, ASPN, CRTAC1, POSTN, COL3A1, PEBP4, IL13RA2, and CA4 were identified. The nine feature gene scores were integrated into the ANN to develop a diagnostic prediction model. The receiver operating characteristic (ROC) curves demonstrated the strong diagnostic ability of the ANN in predicting IPF in the training and testing sets. An analysis of IPF tissues in comparison to normal tissues revealed a reduction in the infiltration of natural killer cells resting, monocytes, macrophages M0, and neutrophils; conversely, the infiltration of T cells CD4 memory resting, mast cells, and macrophages M0 increased. Conclusion: LRRC17, COMP, ASPN, CRTAC1, POSTN, COL3A1, PEBP4, IL13RA2, and CA4 were determined as key feature genes for IPF. The nine feature genes in the ANN model will be extremely important for diagnosing IPF. It may be possible to use differentiated immune cells from IPF samples in comparison to normal samples as targets for immunotherapy in patients with IPF. [ABSTRACT FROM AUTHOR]

Objectives: Aim of this study was to explore the immune-related lncRNAs having prognostic role and establishing risk score model for better prognosis and immunotherapeutic coherence for esophageal cancer (EC) patients. Methods: To determine the role of immune-related lncRNAs in EC, we analyzed the RNA-seq expression data of 162 EC patients and 11 non-cancerous individuals and their clinically relevant information from the cancer genome atlas (TCGA) database. Bioinformatic and statistical analysis such as Differential expression analysis, co-expression analysis, Kaplan Meier survival analysis, Cox proportional hazards model, ROC analysis of risk model was employed. Results: Utilizing a cutoff criterion (log2FC > 1 + log2FC < −1 and FDR < 0.01), we identified 3737 RNAs were significantly differentially expressed in EC patients. Among these, 2222 genes were classified as significantly differentially expressed mRNAs (demRNAs), and 966 were significantly differentially expressed lncRNAs (delncRNA). Through Pearson correlation analysis between differentially expressed lncRNAs and immune related-mRNAs, we identified 12 immune-related lncRNAs as prognostic signatures for EC. Notably, through Kaplan-Meier analysis on these lncRNAs, we found the low-risk group patients showed significantly improved survival compared to the high-risk group. Moreover, this prognostic signature has consistent performance across training, testing and entire validation cohort sets. Using ESTIMATE and CIBERSORT algorithm we further observed significant enriched infiltration of naive B cells, regulatory T cells resting CD4+ memory T cells, and, plasma cells in the low-risk group compared to high-risk EC patients group. On the contrary, tumor-associated M2 macrophages were highly enriched in high-risk patients. Additionally, we confirmed immune-related biological functions and pathways such as inflammatory, cytokines, chemokines response and natural killer cell-mediated cytotoxicity, toll-like receptor signaling pathways, JAK-STAT signaling pathways, chemokine signaling pathways significantly associated with identified IRlncRNA signature and their co-expressed immune genes. Furthermore, we assessed the predictive potential of the lncRNA signature in immune checkpoint inhibitors; we found that programed cell death ligand 1 (PD-L1; P-value =.048), programed cell death ligand 2 (PD-L2; P-value =.002), and T cell immunoglobulin and mucin-domain containing-3 (TIM-3; P-value =.045) expression levels were significantly higher in low-risk patients compared to high-risk patients. Conclusion: We believe this study will contribute to better prognosis prediction and targeted treatment of EC in the future. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: The present study was performed to identify key biomarkers associated with immune cell infiltration in peri‐implantitis through bioinformatic analyses. Methods: Six peri‐implantitis soft tissue samples and six healthy gingiva samples were obtained from GSE106090, and were used to identify immune‐associated differentially expressed genes (DEGs) in peri‐implantitis. The candidate biomarkers associated with immune cell infiltration were examined by immunohistochemical staining. Results: We identified 2089 upregulated and 2173 downregulated genes. Upregulated DEGs were significantly associated with immune response. Ten key candidate biomarkers were identified in the PPI network, including IL1B, TLR2, TLR4, CCL4, CXCL8, IL10, IL6, CD4, CCL3, and PTPRC. The expression level of the 10 genes increased in peri‐implantitis soft tissue samples compared with healthy gingiva samples. The proportion of CD4+ T cells, iTreg, and Tfh in infiltration immune cells increased in peri‐implantitis soft tissue samples and were positively correlated with the expression level of candidate biomarkers TLR4, CCL3, CXCL8, and IL1B. Immunohistochemistry showed that there were more lymphocytes in peri‐implantitis soft tissue samples, with an increased expression level of TLR4, CCL3, CXCL8, and IL1B. Conclusion: Identification of four novel diagnostic biomarkers was helpful for revealing the molecular mechanisms and could serve as a risk predictor for the immune microenvironment in peri‐implantitis. [ABSTRACT FROM AUTHOR]

Background: This study aims to identify a novel gene signature for coronary artery disease (CAD), explore the role of immune cell infiltration in CAD pathogenesis, and assess the cell function of mast cell-expressed membrane protein 1 (MCEMP1) in human umbilical vein endothelial cells (HUVECs) treated with oxidized low-density lipoprotein (ox-LDL). Methods: To identify differentially expressed genes (DEGs) of CAD, datasets GSE24519 and GSE61145 were downloaded from the Gene Expression Omnibus (GEO) database using the R "limma" package with p < 0.05 and |log2 FC| > 1. Gene ontology (GO) and pathway analyses were conducted to determine the biological functions of DEGs. Hub genes were identified using support vector machine-recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO). The expression levels of these hub genes in CAD were validated using the GSE113079 dataset. CIBERSORT program was used to quantify the proportion of immune cell infiltration. Western blot assay and qRT‐PCR were used to detect the expression of hub genes in ox-LDL-treated HUVECs to validate the bioinformatics results. Knockdown interference sequences for MCEMP1 were synthesized, and cell proliferation and apoptosis were examined using a CCK8 kit and Muse® Cell Analyzer, respectively. The concentrations of IL-1β, IL-6, and TNF-α were measured with respective enzyme-linked immunosorbent assay (ELISA) kits. Results: A total of 73 DEGs (four down-regulated genes and 69 up-regulated genes) were identified in the metadata (GSE24519 and GSE61145) cohort. GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis results indicated that these DEGs might be associated with the regulation of platelet aggregation, defense response or response to bacterium, NF-kappa B signaling pathway, and lipid and atherosclerosis. Using SVM-RFE and LASSO, seven hub genes were obtained from the metadata. The upregulated expression of DIRC2 and MCEMP1 in CAD was confirmed in the GSE113079 dataset and in ox-LDL-treated HUVECs. The associations between the two hub genes (DIRC2 and MCEMP1) and the 22 types of immune cell infiltrates in CAD were found. MCEMP1 knockdown accelerated cell proliferation and suppressed cell apoptosis for ox-LDL-treated HUVECs. Additionally, MCEMP1 knockdown appeared to decrease the expression of inflammatory factors IL-1β, IL-6, and TNF-α. Conclusions: The results of this study indicate that MCEMP1 may play an important role in CAD pathophysiology. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

To screen programmed cell death (PCD)-related genes in esophageal squamous cell carcinoma (ESCC) based on transcriptomic data and to explore its clinical value. Differentially expressed PCD genes (DEPCDGs) were screened from ESCC transcriptome and clinical data in TCGA database. Univariate COX and LASSO COX were performed on prognostically DEPCDGs in ESCC to develop prognostic model. Differences in immune cell infiltration in different RiskScore groups were determined by ssGSEA and CIBERSORT. The role of RiskScore in immunotherapy response was explored using Tumor Immune Dysfunction and Exclusion (TIDE) and IMvigor210 cohorts. Fourteen DEPCDGs associated with prognosis were tapped in ESCC. These DEPCDGs form a RiskScore with good predictive performance for prognosis. RiskScore demonstrated excellent prediction accuracy in three data sets. The abundance of M2 macrophages and Tregs was higher in the high RiskScore group, and the abundance of M1 macrophages was higher in the low RiskScore group. The RiskScore also showed good immunotherapy sensitivity. RT-qPCR analysis showed that AUP1, BCAP31, DYRK2, TAF9 and UBQLN2 were higher expression in KYSE-150 cells. Knockdown BCAP31 inhibited migration and invasion. A prognostic risk model can predict prognosis of ESCC and may be a useful biomarker for risk stratification and immunotherapy assessment. [ABSTRACT FROM AUTHOR]

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Abstract Background Solute Carrier Family 4 Member 4 (SLC4A4) is a membrane protein‐coding gene for a Na+/HCO3 − cotransporter and plays a crucial role in regulating pH, bicarbonate secretion and homeostasis. However, the prognostic and immunological role of SLC4A4 in colon cancer remains unknown. Method In this study, expression profiles of SLC4A4 were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, to which a variety of bioinformatic analyses were performed. Sangerbox, Xiantao, ESTIMATE and TIMER online tools were used to delve into the relationship between SLC4A4 expression and immune cell infiltration. The role of SLC4A4 in the proliferation and migration of colon cancer cells was verified by CCK8, EdU and wound healing assays. The related molecules and pathways that SLC4A4 may affect were validated by bioinformatic prediction and western blotting analysis. Results The expression levels of SLC4A4 were significantly lower in colon cancer tissues than in normal tissues and its low expression was positively correlated with poor prognosis. TIMER and ESTIMATE showed that SLC4A4 broadly influenced immune cell infiltration. Experiments in vitro demonstrated that SLC4A4 inhibited partial epithelial-mesenchymal transition (EMT) phenotypes. Conclusions To conclude, our study revealed that SLC4A4 is lowly expressed in colon cancer tissues, and SLC4A4 may inhibit the progression of colon cancer via regulating partial EMT phenotypes and immune cell infiltration, which may provide new perspectives for the development of more precise and personalized immune anti-tumor therapies.

Lingling Ding,1,&ast; Guowan Zheng,1– 3,&ast; Aoni Zhou,4 Fahuan Song,1– 3 Lei Zhu,5 Yefeng Cai,6 Yehao Guo,1 Tebo Hua,7 Yunye Liu,1 Wenli Ma,1 Yiqun Hu,1– 3 Yawen Guo,1– 3 Chuanming Zheng1– 3 1Otolaryngology & Head and Neck Center, Cancer Center, Department of Head and Neck Surgery, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, 310000, People’s Republic of China; 2Zhejiang Provincial Clinical Research Center for Malignant Tumor, Hangzhou, Zhejiang, 310000, People’s Republic of China; 3Zhejiang Key Laboratory of Precision Medicine Research on Head & Neck Cancer, Hangzhou, Zhejiang, 310000, People’s Republic of China; 4Hangzhou Normal University, Hangzhou, Zhejiang, 311121, People’s Republic of China; 5Department of Thyroid Surgery, The Fifth Hospital Affiliated to Wenzhou Medical University, Lishui Central Hospital, Lishui, Zhejiang, 323000, People’s Republic of China; 6Department of Thyroid Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, People’s Republic of China; 7Department of Thyroid Surgery, Ningbo Medical Centre Lihuili Hospital, Ningbo, Zhejiang, 315000, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yawen Guo; Chuanming Zheng, Department of Head and Neck Surgery, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, No. 158, Shangtang Road, Hangzhou, Zhejiang, People’s Republic of China, Email mingdoc@163.com; gyw20072644@126.comBackground: The incidence of papillary thyroid cancer (PTC) has been increasing annually; however, early diagnosis can improve patient outcomes. Pyroptosis is a programmed cell death modality that has received considerable attention recently. However, no studies have reported using pyroptosis-related genes in PTC diagnosis.Methods: Analyzed 33 pyroptosis-related genes in PTC transcriptome data from the Gene Expression Omnibus database. Subsequently, used the Least Absolute Shrinkage and Selection Operator (LASSO) model to construct a PTC molecular diagnostic model. Furthermore, confirmed differences in the expression of five genes between PTC and non-tumor tissues using immunohistochemistry. Collected 338 PTC and control samples to construct a five-gene PTC diagnostic model, which was then validated using a training set and underwent correlation analysis with immune cell infiltration. Additionally, validated the biological functions of the core gene NOD1 in vitro.Results: The five-gene PTC diagnostic model demonstrated good diagnostic value for PTC. Moreover, identified three reliable subtypes of pyroptosis and found that NOD1 is involved in tumor-suppressive microenvironment formation. Notably, patients with high NOD1 expression had lower Progression-Free Survival (PFS). Additionally, NOD1 expression was positively correlated with immune markers such as CD47, CD68, CD3, and CD8. Lastly, inhibiting NOD1 showed significant anti-PTC activity in vitro.Conclusion: Our results suggest that pyroptosis-related genes can be used for PTC diagnosis, and NOD1 could be a promising therapeutic target.Keywords: pyroptosis-related genes, papillary thyroid cancer, PTC, immune cell infiltration, bioinformatic, diagnosis model

Huikang Wang,1– 3,&ast; Xinjun Xu,1– 3,&ast; Haoran Lu,1– 3 Yang Zheng,1– 3 Liting Shao,1– 3 Zhaoyang Lu,2– 4 Yu Zhang,2,3 Xicheng Song2,3 1Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, QingdaoUniversity, Yantai, People’s Republic of China; 2Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai Yuhuangding Hospital, Yantai, People’s Republic of China; 3Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai Yuhuangding Hospital, Yantai, People’s Republic of China; 4Second Clinical Medicine College, Binzhou Medical University, Yantai, Shandong, 264003, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xicheng Song; Yu Zhang, Department of Otolaryngology, Head and Neck Surgery. Yantai Yuhuangding Hospital, Qingdao University, Yantai, 264000, People’s Republic of China, Tel +86535 6691999, Fax +86535 6240341, Email drxchsong@163.com; superzhang013@163.comPurpose: The pathogenesis of CRSwNP is complex and not yet fully explored, so we aimed to identify the pivotal hub genes and associated pathways of CRSwNP, to facilitate the detection of novel diagnostic or therapeutic targets.Methods: Utilizing two CRSwNP sequencing datasets from GEO, differential expression gene analysis, WGCNA, and three machine learning methods (LASSO, RF and SVM-RFE) were applied to screen for hub genes. A diagnostic model was then formulated utilizing hub genes, and the AUC was generated to evaluate the performance of the prognostic model and candidate genes. Hub genes were validated through the validation set and qPCR performed on normal mice and CRSwNP mouse model. Lastly, the ssGSEA algorithm was employed to assess the differences in immune infiltration levels.Results: A total of 239 DEGs were identified, with 170 upregulated and 69 downregulated in CRSwNP. Enrichment analysis revealed that these DEGs were primarily enriched in pathways related to nucleocytoplasmic transport and HIF-1 signaling pathway. Data yielded by WGCNA analysis contained 183 DEGs. The application of three machine learning algorithms identified 11 hub genes. Following concurrent validation analysis with the validation set and qPCR performed after establishing the mouse model confirmed the overexpression of BTBD10, ERAP1, GIPC1, and PEX6 in CRSwNP. The examination of immune cell infiltration suggested that the infiltration rate of type 2 T helper cell and memory B cell experienced a decline in the CRSwNP group. Conversely, the infiltration rates of Immature dendritic cell and Effector memory CD8 T cell were positive correlation.Conclusion: This study successfully identified and validated BTBD10, ERAP1, GIPC1, and PEX6 as potential novel diagnostic or therapeutic targets for CRSwNP, which offers a fresh perspective and a theoretical foundation for the diagnostic prediction and therapeutic approach to CRSwNP.Keywords: chronic rhinosinusitis with nasal polyposis, key genes, machine learning, immune cell infiltration

Abstract Background Myelodysplastic syndromes (MDS) are heterogeneous and clonal hematological disorders. The role and mechanism of necroptosis in MDS remain poorly understood. Methods mRNA expression profiles and single-cell RNA-sequencing (scRNA-seq) data were sourced from the GEO database. ScRNA-seq data were processed using the “Seurat” package. After cell annotation, necroptosis-related scores (NRscores) for each cell were calculated using the “UCell” package. Differentially expressed genes (DEGs) and their associated biological functions in NRscore-related cell populations were identified. Additionally, DEGs and necroptosis-related genes (DE-NRGs) between MDS patients and healthy controls were identified. Consensus clustering was employed to classify MDS patients into distinct subclusters based on DE-NRGs. The biological functions and immune characteristics of these classifications were analyzed. Prognostic gene signatures were determined using LASSO and SVM-RFE analyses, and a nomogram was constructed based on the prognostic gene signature. Results A total of 12 cell types were identified in MDS and healthy controls. NRscore was found to be elevated in monocytes and common lymphoid precursors (CLPs). Enrichment analysis revealed that monocytes and CLPs with high NRscore were associated with mitochondria-related and immune-related pathways. Eleven DEGs in monocytes and CLPs between MDS patients and healthy controls were identified. Additionally, 13 DE-NRGs were identified from 951 DEGs between MDS and healthy controls. MDS patients were classified into two distinct subclusters based on these 13 DE-NRGs, revealing several immune-related processes and signaling pathways. Differences in immune subpopulations between the two subclusters were observed. A necroptosis-related diagnostic gene signature (IRF9, PLA2G4A, MLKL, BAX, JAK2, and STAT3) was identified as predictive of MDS prevalence. Conclusion Necroptosis plays a role in MDS progression by inducing inflammation. A novel necroptotic gene signature has been developed to distinguish and diagnose MDS at early stages of the disease.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract Background Idiopathic pulmonary fibrosis (IPF) is a severe lung condition, and finding better ways to diagnose and treat the disease is crucial for improving patient outcomes. Our study sought to develop an artificial neural network (ANN) model for IPF and determine the immune cell types that differed between the IPF and control groups. Methods From the Gene Expression Omnibus (GEO) database, we first obtained IPF microarray datasets. To conduct protein-protein interaction (PPI) networks and enrichment analyses, differentially expressed genes (DEGs) were screened between tissues of patients with IPF and tissues of controls. Afterward, we identified the important feature genes associated with IPF using random forest (RF) analysis, and then constructed and validated a prediction ANN mode. In addition, the proportions of immune cells were quantified using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) analysis, which was performed on microarray datasets based on gene expression profiling. Results A total of 11 downregulated and 36 upregulated DEGs were identified. PPI networks and enrichment analyses were carried out; the immune system and extracellular matrix were the subjects of the enrichments. Using RF analysis, the significant feature genes LRRC17, COMP, ASPN, CRTAC1, POSTN, COL3A1, PEBP4, IL13RA2, and CA4 were identified. The nine feature gene scores were integrated into the ANN to develop a diagnostic prediction model. The receiver operating characteristic (ROC) curves demonstrated the strong diagnostic ability of the ANN in predicting IPF in the training and testing sets. An analysis of IPF tissues in comparison to normal tissues revealed a reduction in the infiltration of natural killer cells resting, monocytes, macrophages M0, and neutrophils; conversely, the infiltration of T cells CD4 memory resting, mast cells, and macrophages M0 increased. Conclusion LRRC17, COMP, ASPN, CRTAC1, POSTN, COL3A1, PEBP4, IL13RA2, and CA4 were determined as key feature genes for IPF. The nine feature genes in the ANN model will be extremely important for diagnosing IPF. It may be possible to use differentiated immune cells from IPF samples in comparison to normal samples as targets for immunotherapy in patients with IPF.

Abstract The Hippo-Yes-associated protein (Hippo-YAP) signaling pathway, a conserved pathway that regulates organ size, participates in tumor progression. However, there are few comprehensive analyses of tumor prognosis and immunity. In the present study, TCGA, GTEx, GEO, TIMER2, STRING, GSCA, ImmuCellAI, and other bioinformatics tools were used to reveal the involvement of the Hippo-YAP signaling pathway in the prognosis and immunity of pan-cancers. The obtained results showed that mRNA expression differences of Hippo-YAP pathway genes between normal samples and tumor samples in pan-cancers and some genes (such as TEAD4, MAP4K4, and STK3) might affect the prognosis of patients with skin cutaneous melanoma (SKCM) and pancreatic adenocarcinoma (PAAD). Furthermore, mutation and methylation of the Hippo-YAP signaling pathway genes in normal and primary tumor tissues differ in various cancers (KIRP, BRCA). Additionally, the relationship between the tumor microenvironment, molecular pathways, and the Hippo-YAP pathway indicated that it might lead to a suppressive immune microenvironment that affects the efficacy of immunotherapy. This is a pan-cancer overview of the Hippo-YAP signaling pathway genes, which explores the aberrant expression or mutation of this pathway that regulates the tumor microenvironment and immunotherapy.

Background: Early 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. Methods: We 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 qRTPCR. Finally, the CIBERSORT algorithm was used to examine the immune cell infiltration in SCI and its relationship with signature genes. Results: Seven 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 qRTPCR. 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. Conclusion: This 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.

Abstract Neutrophil infiltration plays a key role in the pathogenesis of chronic rhinosinusitis with nasal polyps (CRSwNP). However, pertinent mechanisms remain poorly elucidated. Here, we obtained the data from gene expression omnibus (GEO) and gene set enrichment analysis (GSEA) to identify and validate neutrophil-associated hub genes in CRSwNP. We found that four neutrophil-associated hub genes, namely ICAM1, IL-1β, TYROBP, and BCL2A1, were markedly upregulated and positively correlated with neutrophil infiltration levels in patients with CRSwNP. Subsequently, this was confirmed by real-time quantitative PCR. In conclusion, we identified the role of neutrophil infiltration in the pathophysiology of CRSwNP, which may be the potential targets for the diagnosis and treatment of CRSwNP.

Bowen Wei,1,2,&ast; Qingyun Yue,1,2,&ast; Yuxiu Ka,1,2,&ast; Chenyang Sun,1,2 Yuxing Zhao,1,2 Xiaomei Ning,1,2 Yue Jin,1,2 Jingyue Gao,1,2 Yuanhao Wu,1,2 Wei Liu1,2 1Department of Rheumatism and Immunity, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China; 2National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Wei Liu, Email fengshiliuwei@163.comBackground: Primary Sjögren’s syndrome (pSS) is an autoimmune condition marked by lymphocyte infiltration in the exocrine glands. Our study aimed to identify a novel biomarker for pSS to improve its diagnosis and treatment.Methods: The gene expression profiles of pSS were obtained from the Gene Expression Omnibus (GEO) database. The specific differentially expressed genes (DEGs) were screened by the Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Recursive Feature Elimination with Support Vector Machines (SVM-RFE). A biomarker was picked out based on correlation and diagnostic performance, the connection between the biomarker and clinical traits and immune infiltrating cells was explored, and the biomarker’s protein expression level in the serum of pSS patients was detected by enzyme-linked immunosorbent assay (ELISA). The competitive endogenous RNA (ceRNA) network regulated by the biomarker was predicted to verify the reliability of the biomarker in diagnosing pSS.Results: IFI44, XAF1, GBP1, EIF2AK2, IFI27, and IFI6 showed prominent diagnostic ability, with the high accuracy (AUC = 0.859) and significance (R ≥ 0.8) of IFI44 within the training dataset. IFI44 strongly exhibited a negative correlation with resting NK cells, macrophages M0, and eosinophils, and a positive correlation with activated dendritic cells, naive B cells, and activated CD4 memory T cells. Furthermore, IFI44 was significantly positively correlated with clinical traits such as IgG, SSA, SSB, ANA, and ESSDAI, with its protein expression level in the serum of pSS patients being notably elevated compared to controls (p < 0.001). Finally, the ceRNA regulatory network showed that hsa-miR-944, hsa-miR-9-5p, hsa-miR-126-5p, and hsa-miR-335-3p were significantly targeted IFI44, suggesting that IFI44 may serve as a dependable biomarker for pSS.Conclusion: In this study, we dug out IFI44 as a biomarker for pSS, systematically studied the potential regulatory mechanism of IFI44, and verified its reliability as a biomarker for pSS.Keywords: primary Sjögren’s syndrome, machine learning, immune cell infiltration, biomarker, IFI44

This study leverages the GSE4386 dataset, obtained from atrial tissue samples post-coronary artery bypass graft (CABG) surgery, to investigate the impact of anesthetic agents (sevoflurane and propofol) on gene expression and immune cell infiltration.

Abstract Background Gastric cancer (GC) is a major contributor to cancer-related mortality. Glycolysis plays a pivotal role in tumor microenvironment (TME) reprogramming. In this research, the functions of glycolysis-associated genes (GRGs) were evaluated to predict the outcome and reveal the characteristics of the immune microenvironment in individuals with stomach cancer. Methods The Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) cohort provided gene expression and clinical data for gastric cancer (GC) patients, which were further authenticated using datasets sourced from the Gene Expression Omnibus (GEO). By referencing the Molecular Signatures Database (MSigDB), a total of 326 GRGs were pinpointed. The various subtypes of GC were outlined through consensus clustering, derived from the expression patterns of these GRGs. Utilizing multivariate Cox regression analysis, a multigene risk score model was formulated. Both the CIBERSORT and ESTIMATE algorithms played a pivotal role in assessing the immune microenvironment. To delve into the biological functions of the key genes, wound healing, transwell invasion, and MTT assays were conducted. Results Based on the expression patterns of GRGs, patients were categorized into two distinct groups: the metabolic subtype, designated as cluster A, and the immune subtype, labeled as cluster B. Patients belonging to cluster B exhibited a poorer prognosis. A prognostic risk score model, formulated upon the expression levels of six key GRGs — ME1, PLOD2, NUP50, CXCR4, SLC35A3, and SRD35A3 — emerged as a viable tool for predicting patient outcomes. The downregulation of CXCR4 notably diminished the glycolytic capacity of gastric cancer (GC) cells, alongside their migratory, invasive, and proliferative capabilities. Intriguingly, despite the adverse prognostic implications associated with both the immune subtype (cluster B) and the high-risk cohort, these groups exhibited a favorable immune microenvironment coupled with elevated expression of immune checkpoint genes. Our investigations revealed a positive correlation between high CXCR4 expression and low ME1 expression with the infiltration of CD8+ T cells, as well as an enhanced responsiveness to treatment with an anti-PD-1 immune checkpoint inhibitor. Conclusions In this study, we discovered that the expression profiles of GRGs hold the potential to forecast the prognosis of gastric cancer (GC) patients, thereby possibly aiding in clinical treatment decision-making.

Na Wang,1,2 Guofeng Wang,2 Xiuli Feng,2 Teng Yang2 1Department of Endocrinology, Lianyungang Clinical College of Nanjing Medical University, Lianyungang City, Jiangsu Province, 222000, People’s Republic of China; 2Department of Endocrinology, Jinzhou Medical University(The First People’s Hospital of Lianyungang), Lianyungang City, Jiangsu Province, 222000, People’s Republic of ChinaCorrespondence: Guofeng Wang, Department of Endocrinology, Lianyungang Clinical College of Nanjing Medical University (The First People’s hospital of Lianyungang), No. 6 Zhenhua East Road, Haizhou District, Lianyungang City, Jiangsu Province, 222000, People’s Republic of China, Email wangguofengo@21cn.comPurpose: This study aimed to investigate the abnormal infiltration of immune cells in type 1 diabetes mellitus (T1D) and elucidate their regulatory mechanisms.Methods: Public T1D-related gene expression data were obtained from the Gene Expression Omnibus database.The GSE123658 dataset analyzed whole blood RNA-seq data from type 1 diabetic patients and healthy volunteers. The GSE110914 dataset analyzed neutrophils purified from peripheral blood of patients with symptomatic and pre-symptomatic type 1 diabetes (T1D), at risk of T1D, and healthy controls. Immune cell infiltration analysis was performed to identify abnormally infiltrating immune cells. Differentially expressed immune genes (DEIGs) in T1D samples were identified, followed by the construction of an immune gene signature (IGS) using a protein-protein interaction (PPI) network and Least absolute shrinkage and selection operator Cox regression analyses (LASSO Cox regression analyses). The regulatory mechanisms underlying IGS were explored using gene set enrichment analysis. Furthermore, expression validation, diagnostic efficacy evaluation, and upstream miRNA prediction of hub signature genes were performed. We verified the miRNA expression of the key gene colony stimulating factor 1 (CSF1) and microRNA-326 (miR-326) by reverse transcription-quantitative PCR (RT‒qPCR).Results: The proportion of infiltrating T and natural killer (NK) cells differed between the T1D and control samples, and 207 immune genes (IGs) related to these immune cells were extracted. After differential expression, PPI, and LASSO Cox regression analyses, four signature DEIGs were identified for IGS construction: notch receptor 1 (NOTCH1), Janus kinase 3 (JAK3), tumor necrosis factor receptor superfamily member 4(TNFRSF4), and CSF1. Key pathways such as the Toll-like receptor signaling pathway were significantly activated in the high-risk group. Moreover, the upregulation of CSF1 in T1D samples was confirmed using a validation dataset, and CSF1 showed high diagnostic efficacy for T1D. Furthermore, CSF1 was targeted by miR-326.We used validated key genes in T1D patients, several of which were confirmed by RT‒qPCR.Conclusion: In conclusion, the identified key IGs may play an important role in T1D. CSF1 can be developed as a novel diagnostic biomarker for T1D.Keywords: Type 1 diabetes, immune cell infiltration, immune gene signature, diagnosis, CSF1

Abstract A macroscopic perspective is indispensable for understanding the intricate relationship between deubiquitinases and tumorigenesis. Proteomics has been proposed as a viable approach for elucidating the complex role of deubiquitylation in cellular progression. Instead of studying the function of a single ubiquitinase, research on a deubiquitinase family with similar catalytic core(s) may provide a new perspective for the pathological understanding of cancer. The Ubiquitin C-terminal hydrolase L (UCHL) family consists of four members: UCHL1, UCHL3, UCHL5, and BRAC1 associated protein-1 (BAP1), and they have been implicated in tumorigenesis and metastasis. Some members are considered hallmarks of intracranial lesions, colon cancer, chromatin remodeling, and histone stability. The present study uncovered an unknown correlation between the UCHL family and renal cancer. We discovered that UCHLs exhibit diverse regulatory effects in renal cancer, establishing connections between the renal cancer and truncated gene mutations, mitochondrial energetic metastasis, immune cell infiltration, and chromosomal stability of UCHLs family. Notably, we found that the increase of UCHL5 expression in renal cancer cells decreases the antigen processing and presentation of RCC tumor-infiltrating B cells. Further research identified that the expression of UCHL5 in RCC tumors is correlated with transport proteins, which led us to find that the abundance of UCHL5 in the blood of late-stage renal cell cancer patients is upregulated from 18 ng/L to 500 ng/L. Therefore, we propose that the abundance of UCHL5 in patients' blood can be a possible indicator of poor prognosis for renal cell cancer.

Abstract The study explored the role of circadian rhythm genes (CRGs) in lower grade glioma (LGG) development and found that certain genes, such as CRY1, NPAS2, and RORB, were associated with increased or decreased risk of LGG. The study also investigated the correlation between CRGs and immune cell infiltration, revealing a negative association with macrophage infiltration and a positive correlation with B cell and CD8 + T cell infiltration. Additionally, the study identified major mutated CRGs, including PER2, BMAL1, CLOCK, and BMAL2, and their potential interaction with other CNS-associated genes. The study suggests that CRGs play a crucial role in immune response and tumorigenesis in LGG patients and warrants further investigation.