Inhibiting VEGFC - mediated hepatocyte - macrophage regulatory axis contributes to protective effects of naringin against high - fat diet - induced hepatic fibrosis.

Cisplatin remains a standard first-line therapy for epithelial ovarian cancer; however, chemoresistance leads to poor prognosis and high recurrence. Analysis of The Cancer Genome Atlas confirmed improved overall survival in cisplatin-sensitive tumors, underscoring the need for strategies to overcome resistance in clinical settings. Integrative bioinformatics of cisplatin-treated ovarian cancer datasets from the Gene Expression Omnibus (n=255) identified six molecular drivers of resistance: Kaiso (ZBTB33), pregnane X receptor (PXR), NF-κB, HER2 (ERBB2), P-glycoprotein (P-gp/ABCB1), and HIF1A. These targets were validated in ovarian tumor specimens via immunohistochemistry, confirming elevated expression in chemo-resistant disease. Additionally, the quantitative real-time PCR analysis confirms the transcriptional upregulation of the six resistance-associated genes in cisplatin-resistant SKOV3 and OVCAR-5 ovarian cancer cells, consistent with the immunohistochemistry findings. The average fold change in mRNA transcripts ranged from 2.4 for P-glycoprotein to 5 for both NF-kB and Kaiso. Although less well studied in ovarian cancer, Kaiso is known to regulate EMT and tumor invasion in other solid tumors. Functional studies using SKOV3 and OVCAR-5 cell lines demonstrated that knockdown of Kaiso via RNA interference significantly increased cisplatin-induced cell death, indicating a direct role in therapeutic resistance. Furthermore, we investigated the synergistic effects of combining stearidonic acid (SDA), a plant-based omega-3 fatty acid known to inhibit NF-κB, with cisplatin on cell death in SKOV3 and OVCAR-5 cell lines, and compared the results with those of each compound used individually. Interestingly, co-treatment with stearidonic acid (SDA) synergistically enhanced the cytotoxicity of cisplatin at a lower dose in both cell models. These findings reveal a clinically relevant resistance signature and highlight the therapeutic potential of combinatorial strategies that target both transcriptional regulators (e.g., Kaiso) and inflammatory signaling (e.g., NF-κB). Dual targeting of these pathways may resensitize tumors to cisplatin and improve outcomes for patients with advanced ovarian cancer.

Dilated Cardiomyopathy (DCM) is a debilitating cardiovascular disorder that challenges current therapeutic strategies. The exploration of novel drug repositioning opportunities through gene expression analysis offers a promising avenue for discovering effective treatments. This study aims to identify potential drug repositioning opportunities and lead compounds for DCM treatment by optimizing gene expression characteristics using published data. Our approach involved analyzing DCM expression profiles from the Gene Expression Omnibus database and identifying differentially expressed genes with GEO2R. A protein interaction network was constructed using the STRING database and visualized with Cytoscape. Enrichment analyses were conducted on these genes through the Omicshare platform, followed by the identification of candidate compounds via the Connectivity Map (CMAP) and validation through molecular docking. The Coremine Medical database was utilized to predict potential herbal medicines. We identified 29 differentially expressed genes, highlighting MYH6, NPPA, and NPPB as central to DCM pathology. Enrichment analyses indicated significant impacts on biological processes, such as organ morphogenesis and inflammatory responses. The AGE-RAGE signaling pathway was notably affected. From over 6,100 compounds analyzed, tenoxicam emerged as a promising candidate, with Radix Salviae Miltiorrhizae (Danshen) being suggested as a potential herbal treatment. This study underscores the utility of bioinformatics in uncovering new therapeutic candidates for DCM, offering a foundational step towards novel drug development.

Background Although most cases of endometrial cancer (EC) are diagnosed at an early stage with favourable outcomes, the prognosis for advanced or recurrent disease remains poor, highlighting the need for novel therapeutic targets. This study aimed to examine the correlation between Cyclin B1 (CCNB1) and Cyclin B2 (CCNB2) expression and disease severity in EC through bioinformatics analysis. Methods Common differentially expressed genes were identified in two EC cohorts from the Gene Expression Omnibus. A protein-protein interaction (PPI) network was constructed to identify hub genes. Aberrant expression of the hub genes was validated in external datasets. Their prognostic values were evaluated in a cohort from The Cancer Genome Atlas (TCGA). Knockdown of the hub genes was conducted to explore their functions in the malignant behaviour of EC cells in vitro. Results CCNB1 and CCNB2 were identified as the top 2 hub genes in the PPI network. High CCNB1/CCNB2 expression was significantly associated with shorter survival in EC patients. Overexpression of CCNB1/CCNB2 in endometrial tumour tissue was validated in public datasets. In TCGA cohort, high expression of CCNB1/CCNB2 correlated with greater disease severity and predicted poor prognosis. In addition, high expression of CCNB1/CCNB2 was strongly associated with immune cell infiltration, as well as increased expression of immune checkpoint genes and mismatch repair genes. Furthermore, knockdown of CCNB1/CCNB2 significantly suppressed the proliferation, migration, and invasion of HEC-1 and Ishikawa cells in vitro. Conclusions CCNB1 and CCNB2 may serve as potential prognostic markers and therapeutic targets for the management of EC.

Objective Fetal growth restriction (FGR) is a serious pathological complication associated with perinatal death and adverse pregnancy outcomes, which is largely related to placental dysfunction. This study aimed to identify FGR-related placenta transcriptomic features and provide insight into the pathologic mechanisms of FGR. Methods In this research, transcriptomic data were obtained by RNA sequencing on placenta samples from eight FGR patients and eight normal term-pregnant women. Differential expression analysis was used to identify the differentially expressed genes (DEGs) between groups, then function and pathway enrichment analysis by DEGs was performed. After correlation analysis between DEGs and clinical indicators of FGR, validation of DEGs by Gene Expression Omnibus (GEO) dataset was carried out. Receiver operating characteristic (ROC) analysis was used to calculate the diagnostic ability of DEG biomarkers. Results We identified seven candidate DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed candidate DEGs primarily involved in chemokine activities and lymphocyte chemotaxis. Correlation analysis found that the expression level of DPPA4 and CTAG2 gene were significantly positively correlated with neonatal birth weight (p < 0.05). The expression level of IGSF21 was significantly negatively correlated with placenta weight, neonatal birth weight and neonatal birth weight percentile (p < 0.05). The expression level of CXCL11 showed a negative correlation with neonatal birth weight and neonatal birth weight percentile (p < 0.05). Additionally, the expression level of CXCL10 exhibited a negative correlation to neonatal birth weight percentile (p < 0.05). The expression level of HLA-DQA1, CXCL10 and CXCL11 in the GEO dataset were in good agreement with our results. We conducted the ROC analysis and found the area under the curve (AUC) values based on each DEG alone were 80% for HLA-DQA1, 79%f or CXCL11 and 76% for CXCL10, suggesting that the above three DEGs had potential significance for FGR diagnosis. Conclusion This study gives an insight into transcriptional features in FGR mechanism and discovers novel genes that may provide evidence for predicting and managing FGR.

Focal segmental glomerulosclerosis (FSGS) and minimal change disease (MCD) are major causes of primary nephrotic syndrome. However, the mechanisms of liquid-liquid phase separation (LLPS)-related genes in the development of FSGS and MCD remain unclear. We retrieved the Gene Expression Omnibus (GEO) database for the expression profiles of FSGS and MCD and identified the differentially expressed genes (DEGs) related to LLPS. Algorithms including Vector Machine-Recursive Feature Elimination (SVM-RFE) and Random Forest (RF) were adopted to identify the candidate genes. Functional enrichment analysis, nomogram, immune infiltration analysis, the receiver operating characteristic (ROC) curve analysis, and external validation were performed. The research identified 59 and 40 DEGs related to LLPS for FSGS and MCD, respectively. Two candidate genes [PDS5 cohesin-associated factor A (PDS5A) and exosome component 2 (EXOSC2)] were obtained for FSGS, and three candidate genes [PDS5A, KH RNA-binding domain-containing signal transduction-associated 1 (KHDRBS1), and JunD proto-oncogene (JUND)] were obtained for MCD. Both the nomogram and ROC analysis indicated that the candidate genes had good predictive performance. For FSGS, the area under the curve (AUC) values of PDS5A and EXOSC2 were 0.994 and 0.806, respectively. For MCD, the AUC values of PDS5A, KHDRBS1, and JUND were 1, 0.896, and 0.958, respectively. Immune infiltration analysis revealed that FSGS patients had higher levels of naive B cells while MCD patients had higher levels of M2 macrophages and resting mast cells.PDS5A may represent a potential diagnostic biomarker, and targeting its aberrant phase separation may offer a therapeutic strategy for podocytopathies.

Acute myocardial infarction (AMI) stands as a major contributor to mortality and disability worldwide, with obesity playing a significant role in its development. This study aims to investigate potential biomarkers associated with obesity-related genes (ORGs) in patients with AMI. We downloaded four training gene expression datasets and one validation dataset from the Gene Expression Omnibus (GEO) database, and extracted ORGs from the GeneCards database. Feature genes were identified using machine learning techniques, and their diagnostic potential was evaluated through receiver operating characteristic (ROC) curves. To assess the identified critical pathways, Gene Set Enrichment Analysis (GSEA) was performed. Levels of immune infiltration were analyzed using the CIBERSORT algorithm and single-sample Gene Set Enrichment Analysis (ssGSEA). A signature comprising five genes (IL1RN, TLR2, NFKBIA, MMP9, and ITLN1) was established as a diagnostic biomarker for AMI, achieving an area under the curve (AUC) of 0.924, which was confirmed in the GSE59876 dataset (AUC = 0.825). According to the diagnostic model, comparisons between high- and low-risk groups revealed six distinct immune cell types and thirteen differing immune functions. Validation through reverse transcription quantitative polymerase chain reaction (RT-qPCR) reaffirmed the differential expression of the signature genes in AMI and control samples. Our findings provide crucial insights into the roles of ORGs in AMI and may facilitate the identification of valuable biomarkers for AMI diagnosis.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-28461-0.

BackgroundAlzheimer's disease (AD) is the most common type of dementia and accounts for around 70% of reported cases. MicroRNAs are small non‐coding RNAs with highly conserved sequences, involved in gene modulation and several molecular mechanisms. miRNAs can be found in cerebrospinal fluid (CSF) and serve as potential biomarkers for AD diagnosis. This study aimed to perform an in silico analysis of miRNA expression in the CSF of AD patients compared to cognitively healthy controls and to conduct an analysis of the biological pathways regulated by these miRNAs.MethodEmploying a machine learning approach, we identified the most differentially expressed miRNAs in AD, using datasets deposited in the Gene Expression Omnibus (GEO). These findings were further validated through a systematic literature review. Pathway enrichment analysis was conducted to identify the key biological pathways that exhibited greater significance after random pathway analysis.ResultThrough the LightGBM machine learning algorithm, we selected miRNAs from GEO data with the best predictive values and matched them with the miRNAs found in the systematic review. The systematic review identified 24 studies that met the inclusion criteria. The miRNAs common to both approaches were 30a‐3p, 193a‐5p, 143‐3p and 145‐5p, with reduced expression in AD patients compared to controls. The key biological pathways involving the genes regulated by these miRNAs included the TGF‐beta, ERBB signaling, MAPK pathways, and AP‐1 pathway.ConclusionOur results suggest that miRNAs 30a‐3p, 193a‐5p, 143‐3p and 145‐5p are involved in biological pathways related to AD and represent potential biomarkers. TGF‐beta, ERBB and MAPK are pathways already related to AD in the literature. The novelty is the AP‐1 pathway, which regulates cell death and apoptotic factors, its activation decondenses genomic regions and activates cell death pathways in neurons. Furthermore, the increase in Aβ1‐42 elevates AP‐1 mRNA levels in astrocytes, inducing apoptosis. These findings underscore the importance of the miRNAs in AD and offer potential advancements in the understanding of its pathophysiology, mainly involving the AP‐1 pathway.Keywords: Alzheimer's disease, miRNAs, cerebrospinal fluid.Acknowledgments: CNPq e CAPES.

This study aims to investigate the potential roles and mechanisms of palmitoylation-related key enzymes in atrial fibrillation (AF). AF gene expression data were obtained from the Gene Expression Omnibus database, and differentially expressed genes were screened. Thirty palmitoylation-related enzymes were selected, and differentially expressed palmitoylation-related genes (DPRGs) were further screened. Random Forest, Support Vector Machine-Recursive Feature Elimination, and Least Absolute Shrinkage and Selection Operator regression were used to screen DPRGs, and the intersection was taken as key palmitoylation-related genes (KPRGs). Their diagnostic performance was evaluated. Gene set enrichment analysis and gene set variation analysis were utilized to analyze the correlation between KPRGs and pathways, as well as their correlation with immune cells. Ten DPRGs were screened from the dataset (6 upregulated, 4 downregulated). Three algorithms yielded 6 KPRGs. Receiver operating characteristic curve analysis showed that the combined area under the curve value of the 6 KPRGs was 0.827, indicating good diagnostic performance. Gene set enrichment analysis revealed that KPRGs were related to pathways such as extracellular matrix-receptor interaction, while gene set variation analysis showed positive and negative correlations with multiple pathways. Immune cell infiltration analysis demonstrated significant differences in the infiltration of 5 immune cell types in AF myocardial tissue, which were correlated with KPRGs expression. This study, for the first time, used bioinformatics techniques to screen and identify 6 key palmitoylation enzymes closely related to AF. These genes exhibit good diagnostic performance and are significantly correlated with multiple pathways and immune cell infiltration, providing clues for the study of AF mechanisms and the identification of therapeutic targets.

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma (NHL). Targeting fatty acid metabolism pathway represents a promising therapeutic strategy, particularly for patients with refractory or relapsed DLBCL. In this study, fatty-acid metabolism index (FMI) signature including 20 genes was constructed by univariate Cox and LASSO-Cox regression analysis using transcriptomic data from Gene Expression Omnibus database to predict overall survival of DLBCL patients. The FMI signature exhibited a negative association with anti-tumor immune response, with FMI-high patients had decreased immune cell infiltration and downregulated immune-associated signaling pathways. Based on the FMI signature, FASN was identified as the most essential gene that negatively regulated the tumor microenvironment of DLBCL. High FASN expression was associated with reduced anti-tumor immune activity, including decreased immune score, lower T-cell inflamed score, and downregulated expression of IFN molecules. Infiltration of immune cells, including CD4+T cells, CD8+T cells, dendritic cells, and macrophages were also significantly decreased in FASN-high than FASN-low patients. Accordingly, key signal molecules and chemokines for immune cells exhibited negative correlations with FASN expression. Through FASN knockdown in DB cell line, we further validated that FASN significantly suppressed chemokine secretion and promoted DLBCL proliferation in vitro. The FMI signature can effectively distinguish the prognostic stratification of DLBCL patients, further suggesting that FA metabolism imbalance may play an important role in DLBCL heterogeneity and treatment resistance. FASN is a potential negative regulator of immune microenvironment, providing novel insights into the metabolic-immune crosstalk in DLBCL treatment.

Osteoporosis (OP) is a prevalent progressive metabolic bone disease in the middle-aged and elderly population. Previous research has indicated that physiological or pathological hypoxia may disrupt bone metabolism, ultimately contributing to OP development. However, the specific pathophysiological mechanisms remain unknown. Ferroptosis is a novel iron-dependent non-apoptotic cell death pathway. The objective of this investigation was to use bioinformatics analysis to identify key hub genes related to ferroptosis that may be linked to hypoxia and osteoporosis. Subsequently, we utilized experimental methods to validate these findings. Differentially expressed genes (DEGs) in bone marrow mesenchymal stem cells (BMSCs) from primary osteoporosis patients and hypoxia-treated BMSCs were screened from the Gene Expression Omnibus (GEO) database. These DEGs were then intersected with the ferroptosis dataset (FerrDb), resulting in FerrDEGs. The potential functional roles of FerrDEGs were investigated through gene ontology (GO) and pathway enrichment analysis (e.g., KEGG, WikiPathways and Reactome). The STRING database was used to analyze protein-protein interaction networks, pinpointing hub genes within FerrDEGs. To further assess the predictive value of these hub genes, we conducted ROC analysis using the data from GSE230665 to identify key hub genes. Subsequently, NetworkAnalyst was employed to establish networks between target microRNAs(miRNAs) and key hub genes. Finally, we validated the key hub genes and miRNAs in an OVX rat model for further confirmation. A total of 50 FerrDEGs were identified in the overlap of hypoxia, OP, and ferroptosis pathways. These FerrDEGs were primarily associated with biological processes related to cellular response to chemical stress and oxidative stress. Pathway enrichment analysis revealed that these FerrDEGs played significant roles in processes such as viral carcinogenesis and epstein-barr virus infection. Multiple algorithms (CytoHubba, CytoNCA and Analyze Network) were employed to identify eight hub genes within FerrDEGs, including TP53, JUN​, SQSTM1, STAT3​, CDKN1A​​​, CAV1, CD44 and TGFBR1. Five key hub genes (JUN, SQSTM1, STAT3, CD44 and TGFBR1) were further screened by ROC analysis. Subsequently, hsa-miR-20a-5p was identified as a potential key microRNA through the construction of the miRNA-gene network. The five key hub genes and rno-miR-20a-5p were further validated in the OVX rat model. In summary, we first screened ferroptosis-related key hub genes linking hypoxia and osteoporosis. The findings suggest that JUN, SQSTM1​, STAT3​ ​, CD44​​ and TGFBR1 are significantly associated with OP and hypoxia, potentially serving as biomarkers for diseases linked to ferroptosis. Additionally, hsa-miR-20a-5p was identified as a crucial upstream regulator likely involved in the regulation of these genes simultaneously.

BackgroundTriple-negative breast cancer (TNBC) is highly aggressive tumor with limited therapeutic options. Studying the molecular mechanisms underlying TNBC is necessary to address the unmet need in novel therapeutic targets. TNBC is demonstrated to have robust fatty acid (FA) metabolism activity, and recent studies proposed the linkage of FA metabolism with ferroptosis sensitivity. Hence, this study aimed to explore the targets that may regulate FA metabolism to sensitize TNBC cells to ferroptosis.MethodsRNA-sequencing data in The Cancer Genome Atlas (TCGA) and four microarray datasets in Gene Expression Omnibus (GEO) database were analyzed to identify key target RACGAP1, followed by a series of functional experiments to explore the exact role of RACGAP1 in two TNBC cell lines (human MDA-MB-231 and mouse 4T1) and Xenograft tumor model. Dual-luciferase and chromatin immunoprecipitation (ChIP) assay was utilized to verify the binding of RACGAP1 and MAZ. RNA sequencing on 4T1 cells transfecting with sh-NC and sh-RACGAP1 was performed to validate the actions of RACGAP1.ResultsRACGAP1 was highly expressed in breast cancer, and associated with poor prognosis and ferroptosis activity. RACGAP1 silencing could inhibit tumor cells survival and promote ferroptosis, and such anti-tumor activity could be blocked by ferroptosis inhibitors. RNA-sequencing analysis suggested that RACGAP1 silencing could inhibit FA metabolism activity, which was further confirmed by metabolic analysis and the reduced level of ATP, triglyceride and FA oxidation. CPT1A overexpression reversed such changes, indicating that the regulation of RACGAP1 on FA metabolism was CPT1A-dependent. Activation of FA metabolism activity or CPT1A overexpression blocked the ferroptosis sensitivity induced by RACGAP1 silencing. Transcription factor MAZ was identified to directly up-regulate the expression of RACGAP1.ConclusionInhibition of RACGAP1 sensitized TNBC cells to ferroptosis by inhibiting CPT1A-mediated FA metabolism. Targeting RACGAP1 might be feasible strategy for TNBC management.Graphical Supplementary InformationThe online version contains supplementary material available at 10.1186/s13046-025-03568-4.

Osteoarthritis (OA) is a prevalent degenerative joint disease characterized by cartilage destruction and synovial inflammation, with macrophage-mediated immune dysregulation playing a pivotal role. Photobiomodulation (PBM), a non-invasive light therapy, has shown promise in alleviating OA by modulating macrophage polarization. However, the precise underlying molecular mechanisms remain unclear. We integrated single-cell and bulk RNA sequencing data from the Gene Expression Omnibus (GEO) database to identify key cellular players and pathways in OA. The functional effects of PBM were assessed in vitro using ATDC5 chondrogenic cell and macrophage co-cultures, and in vivo using the DMM-induced OA murine model with macrophage depletion and PBM intervention. Analysis of single-cell and bulk RNA sequencing data revealed a strong correlation between macrophage and OA, highlighting the importance of the IL-6/JAK/STAT signalling pathway in the regulation of synovial macrophage and OA progression. In vitro experiments demonstrated that PBM intervention enhanced chondrocyte proliferation while suppressing catabolic activity and inhibiting macrophage polarization towards a pro-inflammatory phenotype through the IL-6/JAK/STAT pathway. In vivo experiments further confirmed that PBM intervention attenuated articular cartilage degradation and synovial hyperplasia via IL-6/JAK/STAT-dependent modulation of macrophage polarization. Our study demonstrated that PBM intervention mitigated OA progression by regulating macrophage polarization through the IL-6/JAK/STAT pathway, emphasizing the therapeutic potential of immune modulation in OA management. These findings provide a mechanistic basis for PBM intervention as a nonpharmacological strategy for joint degeneration.

Background/Objectives: The pathogenesis of ulcerative colitis (UC) is complex, and there is an urgent need for effective therapeutic agents with low side effects. Recent studies highlight the critical roles of abnormal bile acid (BA) metabolism and gut microbiota dysbiosis in UC progression. However, there is a significant knowledge gap about the relation between BA and gut microbiota. The BA derivative T3K exerts good anti-UC effect, and its mechanism is still unknown. In this study, we investigate how its anti-UC mechanism is involved in the modulation of the gut microbiota-BA axis and BA metabolism. Methods: Gene expression microarray GSE92415 of UC from the Gene Expression Omnibus was used to analyze BA metabolism. DSS-induced colitis mouse model, Caco-2 and IEC6 cells were used to confirm the anti-UC of T3K using intestinal permeability assay with FITC, Western-blot, immunohistochemical staining, immunofluorescenc and so on in vitro and in vivo. The changes in bile acid and microbiota were measured by 16S rRNA sequencing and bile acid analysis combined with pseudo-germ-free (PGF) models and fecal microbiota transplantation (FMT). Results: T3K demonstrated strong therapeutic effects, including reduced weight loss, lower disease activity index (DAI), and increased colon length. T3K also enhanced the expression of Occludin and Mucin2, and restored gut barrier integrity. Furthermore, T3K improved intestinal dysbiosis and abnormal BA metabolism in colitis mice. Through PGF models and FMT, we confirmed that T3K modulates BA metabolism via the gut microbiota. T3K specifically promotes the growth of beneficial bacteria, such as Akkermansia muciniphila, increases levels of hydrophilic BAs like muricholic acid (MCA), lithocholic acid (LCA) and its derivatives isoLCA and then repairs damaged intestinal mucosa. Conclusions: Bile acid derivative T3K, as a potential anti-UC candidate, effectively restores gut barrier integrity and then ameliorates colitis by improving gut microbiota composition and regulating BA metabolism, including increasing hydrophilic BAs.

BackgroundCuproptosis, or copper-induced programmed cell death, shows promise in cancer therapy. However, the role of the copper-related gene dihydrofatty acyl dehydrogenase (DLD) in the prognosis of renal clear cell carcinoma (KIRC) remains unclear.MethodsRaw data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed using R. DLD expression in cancer was evaluated through these databases, and its correlation with tumor immunological features was assessed using the TIMER and TISIDB databases.ResultThe results demonstrated that DLD is downregulated in KIRC, providing diagnostic and prognostic value. Cox regression identified DLD expression as a protective factor. GO and KEGG analyses revealed DLD-associated gene pathways, while its expression correlated with immune cell infiltration and marker expression in KIRC.ConclusionOur findings highlight DLD’s predictive value in KIRC and its role in the tumor microenvironment. As a diagnostic and prognostic marker, DLD offers potential for identifying therapeutic targets and enhancing KIRC immunotherapy.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12672-025-04311-3.

To establish a molecular classification framework for Sjögren syndrome (SS) by stratifying patients into distinct subtypes through unsupervised clustering of B cell single-cell RNA sequencing (scRNA-seq). This study characterizes subtype-specific gene signatures to construct protein-protein interaction (PPI) networks, thereby elucidating core regulatory mechanisms and potential therapeutic targets. Concurrently, it defines the clinical heterogeneity of SS by profiling autoantibodies and B-cell subset distributions across subtypes. The scRNA-seq data from 24 SS patients and 4 healthy controls were obtained from the Gene Expression Omnibus (GEO) database. We constructed a B cell atlas and identified differential gene expression profiles between SS and healthy controls B cells. Unsupervised clustering was applied to stratify SS patients into different molecular subtypes. Functional enrichment analysis of subtype-specific gene signatures was performed to infer associated biological processes/pathways. PPI networks were constructed using the STRING database and Cytoscape software to identify core functions and potential therapeutic targets for subtype-specific genes. The prevalence of autoantibodies and proportions of B cell subsets were statistically analyzed across subtypes. The B cells were classified into eight subsets: transitional B cell, naïve B cell, memory B cell, double negative 1 (DN1) B cell, double negative 2 (DN2) B cell, VAV3+IRF1+ B cell, GP9+ B cell, and plasma cell. The FindAllMarkers function identified 792 differentially expressed genes (DEGs) between the SS patients and healthy controls. Unsupervised clustering stratified patients into three subtypes: (1) Inter-feron-dominant subtype characterized by enrichment in type Ⅰ/Ⅱ interferon and non-canonical nuclear factor kappa-B (NF-κB) signaling pathways. This subtype showed the highest proportions of naïve B cells and transitional B cells, along with the highest anti-Sjögren syndrome antigen A (SSA)/Sjögren syndrome antigen B (SSB) positivity. (2) B cell activation subtype characterized by enrichment in Fc receptor and B cell receptor signaling pathways. This subtype exhibited the highest proportions of memory B cells and DN1 B cells. (3) Endoplasmic reticulum stress subtype characterized by enrichment in protein folding and endoplasmic reticulum-associated degradation pathways. This subtype was marked by the highest proportion of VAV3+IRF1+ B cells. PPI networks identified subtype-specific hub genes regulating these core functions. Stratification of SS patients through clustering of B cell DEGs successfully defined three molecular subtypes (interferon-dominant, B cell activation, and endoplasmic reticulum stress subtypes). Each subtype exhibits distinct autoantibody profiles and B cell subset distributions. This molecular typing framework advances our understanding of SS heterogeneity and provides actionable insights for targeted therapy development.

Introduction: Ovarian cancer (OC) ranks as the fifth most common gynecologic malignancy among women worldwide. Objective: The present study evaluates the diagnostic potential of hematological biomarkers for the early detection and differential diagnosis of OC. Methods: A bioinformatic analysis was performed to compare immune cell profiles in blood and tissue samples from patients with OC using data from The Cancer Genome Atlas and Gene Expression Omnibus databases. Subsequently, a retrospective clinical study was conducted at Yichang Central People&amp;rsquo;s Hospital between January 2015 and January 2021, including three cohorts: (i) Patients with benign ovarian tumors (n = 70), (ii) Patients with OC (n = 70), and (iii) Healthy controls (n = 60). A comprehensive analysis of routine blood parameters and the tumor marker cancer antigen 125 (CA125) was performed. Results: The findings revealed that peripheral blood immune markers exhibited superior diagnostic utility compared with tissue-based indicators. The combination of CA125 with erythrocyte sedimentation rate (ESR) and neutrophil-to-lymphocyte ratio showed high accuracy in differentiating benign ovarian tumors from OC (area under the curve [AUC]: 0.87). Furthermore, a panel combining CA125 and platelet-to-neutrophil ratio showed enhanced diagnostic performance in distinguishing early-stage from advanced epithelial OC (sensitivity: 81.3%; specificity: 96.6%). Notably, the triad of CA125, ESR, and white blood cell count demonstrated strong screening performance for detecting epithelial OC (AUC: 0.941; p&lt;0.001). Conclusion: These results suggest that integrating CA125 with routine hematological parameters significantly enhances the diagnostic accuracy and early detection of epithelial OC compared to CA125 alone, providing a practical and cost-effective screening strategy for clinical implementation.

Lung adenocarcinoma (LUAD) is the most prevalent subtype of nonsmall cell lung cancer. Cigarette smoking, the primary etiological factor, introduces mutagenic and epigenetic changes that promote tumorigenesis, with nicotine acting as a key bioactive component modulating cellular signaling rather than directly causing mutations. In this study, differential transcriptomic profiling of smoker and nonsmoker LUAD samples from the PanCancer Atlas identified neurotensin (NTS) and calcitonin-related polypeptide alpha (CALCA) as the most significantly upregulated genes in smokers. The analysis included 495 LUAD tumor samples with annotated smoking history, comprising 209 never smokers and 286 current smokers. A dataset from the NCBI Gene Expression Omnibus (GSE10072) was used to validate the results. Only samples from current smokers and never smokers were considered to unravel direct molecular impact of active smoking, and the analysis confirmed the observed differential expression patterns of key genes, including NTS and CALCA, between smoker- and nonsmoker-derived LUAD samples. Pathway enrichment analysis revealed G protein-coupled receptor-mediated neuroendocrine signaling activation, suggesting a nicotine-driven reprogramming of tumor cells toward a secretory phenotype. Molecular docking simulations demonstrated stable interactions of (S)-nicotine with NTS and CALCA, suggesting these proteins as potential mediators of nicotine-induced oncogenic signaling. Kaplan-Meier analysis indicated that high expression of NTS and CALCA was associated with poorer overall survival, warranting further investigation in independent cohorts. Collectively, this integrative bioinformatics and structural study informs the molecular consequences of smoking in LUAD, identifies nicotine-responsive neuropeptides as potential signatures of tumor aggressiveness, and can provide a foundation for drug repurposing strategies to mitigate smoking-associated malignancy.

Prostate cancer (PCa) poses a serious threat to the health of older men, with incidence rates steadily increasing worldwide. Antiandrogen drugs can effectively prolong survival in patients with PCa; however, resistance often develops after prolonged treatment and the mechanisms underlying this resistance remain to be elucidated. In the present study, genes that may serve key roles in antiandrogen drug resistance in PCa were investigated. Using the GSE211781 dataset from the Gene Expression Omnibus database, the present study analyzed RNA-sequencing data from lymph node carcinoma of the prostate (LNCaP) cell lines resistant to three antiandrogen drugs: Bicalutamide, enzalutamide and apalutamide. The present study identified 54 differentially expressed genes common to all three resistant lines, of which nine hub genes were confirmed using protein-protein interaction network analysis. Among these, myosin heavy chain 11 (MYH11) emerged as a key gene associated with both PCa progression and patient prognosis. Functional assays in C4-2 and LNCaP cells further indicated that MYH11 modulates sensitivity to bicalutamide and enzalutamide. Collectively, the present study findings suggest that MYH11 may serve as a potential predictive biomarker of PCa development and antiandrogen drug resistance in the future.

Sarcopenia is a common comorbidity in end-stage renal disease (ESRD) and is associated with increased risk of adverse clinical outcomes. The genetic mechanisms underlying sarcopenia in ESRD remain largely unclear. This study employed multi-omics bioinformatics approaches to elucidate potential genetic determinants. Gene expression datasets GSE1428 and GSE142135 were retrieved from the Gene Expression Omnibus (GEO) database to identify shared differentially expressed genes (DEGs). Machine learning approaches were applied to pinpoint hub genes, followed by Mendelian Randomization (MR) analyses to validate their associations with ESRD. Candidate therapeutic agents were subsequently predicted based on these hub genes. ADAM7 emerged as the principal hub gene, with MR analyses suggesting a protective role against ESRD and sarcopenia. Predicted therapeutics included arbutin, metribolone, and phenethyl isothiocyanate. Molecular docking studies revealed favorable interactions, with binding free energies consistently below 5.0 kcal/mol between ADAM7 and these compounds. Our findings identify ADAM7 as a potential biomarker and therapeutic target for ESRD-associated sarcopenia, offering insights for future intervention strategies.