Aims Epidemiological studies indicate a heightened risk of pancreatic adenocarcinoma (PAAD) in patients with type 2 diabetes mellitus (T2DM). This study investigates the molecular mechanisms underlying their comorbidity. Materials and methods Common DEGs between T2DM and PAAD were identified from GEO datasets. Functional and pathway enrichment analyses were performed via PPI, GO, and KEGG. Core genes were screened and their diagnostic value was validated by ROC curves. Immune infiltration and TF–mRNA–miRNA regulatory networks were constructed to explore disease mechanisms. Core gene expression and prognostic significance in PAAD were assessed using GEPIA2 and HPA. Potential therapeutics targeting core genes were predicted via the Therapeutic Target Database. Results A total of 35 DEGs were identified. GO analysis linked these genes to cell adhesion and extracellular matrix (ECM) components. KEGG enrichment highlighted ECM-receptor interaction as the top pathway. Key ECM-related molecules—ITGA3, FN1, LAMB3, ITGA2, and LAMC2—were upregulated in both T2DM and PAAD. Six potential therapeutic agents targeting ITGA2, LAMB3, and FN1 were identified. Conclusion Three genes and associated known drugs identified in this study may serve as potential targets for treating the coexistence of the two diseases.

Purpose: To identify significant genes responsible for altering immune response in viral infections, including SARS, H1N1, Influenza, and Rhinovirus, as there are no previous studies that have analyzed these viral infections together. Methods: Viral infection datasets pertaining to SARS, H1N1, Influenza, and Rhinovirus were obtained from the NCBI Gene Expression Omnibus. We have used three GEO datasets with accession numbers: GSE47962, GSE48466, and GSE71766. The Differentially Expressed Genes (DEG’s) were identified from each of the datasets, and then common DEGs were extracted. Protein-ProteinInteraction (PPI) network was constructed for the common DEGs obtained in all the virus datasets. Finally, we analyzed the PPI network to identify the hub genes that have high interconnectivity with other genes. The significantly enriched pathways are reported. Results: By performing the comparative analysis, we identified 463 common DEG’s among the viral infection datasets under study. The highly interconnected PPI network constructed from these genes contained 3396 edges with an average node degree of 14.7 and an average local clustering coefficient of 0.406. There were 51 nodes with degree>50. The highest interconnected node, STAT1 had degree 113. Conclusion: STAT 1 gene is identified as the most significant hub gene related to the immune response in all four viral infections, including SARS, H1N1, Influenza, and Rhinovirus. Its trivial role is already known in different viral infections, but being most significant in the four viruses together is a novel finding. It is thus identified as a central gene that is a potential therapeutic drug and vaccine target for viral infections.

BackgroundLate‐onset Alzheimer's disease (LOAD) patients often have comorbid neuropsychiatric symptoms (NPS) with depression and anxiety being most prevalent. Neuropsychiatric disorders including major depressive disorder (MDD) confer risk for LOAD later in life. Previously we identified shared genetic risk loci between LOAD and MDD. Here, we aimed to investigate in‐depth and compare the brain transcriptomic landscapes of LOAD and MDD at a cell‐type specific precision. Since MDD often develops early in life in contrast to LOAD, shared genetic etiology may reflect some of the earliest pathophysiological changes common to both diseases.MethodSingle nucleus (sn)RNA‐seq data from two studies: ROSMAP for LOAD and the Douglas Research Center for MDD was analyzed, specifically, dorsolateral pre‐frontal cortex datasets for 157 LOAD cases and 143 controls vs 37 MDD cases and 34 controls. Differential gene expression (DEG) was performed using Nebula with case/control status based on clinical diagnosis of LOAD or MDD. Shared and district LOAD and MDD DEGs were catalogued. Intercellular communication networks were analyzed using CellChat.ResultFDR‐significant DEGs were found for multiple cell types. Common DEGs were found for GABAergic neurons (n = 101), Glutamatergic (n = 163) neurons and Interneurons (n = 15). Two DEGs were found for Microglia (PARD3B, S100Z). The genes in these cell types were downregulated in both LOAD and MDD cases. Pathway analysis for the DEGs, across these cell subtypes showed significant enrichment for terms related to oxidative phosphorylation, mitochondrial electron transport and synaptic signaling, suggesting mitochondrial dysfunction as a common biological process. In MDD and LOAD there was loss in cell‐cell communication (CCC) networks between astrocytes and other cell subtypes while in LOAD there was an overall gain in CCC between several of the neuronal subtypes.ConclusionOur single‐cell analysis provides mechanistic insights into the shared and divergent molecular etiologies, dysregulated pathways, and impaired cellular communications between LOAD and MDD. This knowledge has a translational impact toward the identification of actionable targets as novel therapies to treat depression symptoms earlier in disease stages as a mean to delay or alleviate depression onset in LOAD.

Identification of hub genes as potential diagnostic biomarkers for cervical cancer: A bioinformatic approach.

Cold resistance identification and transcriptomic responses of rapeseed (Brassica napus L.) seedlings under cold stress.

Sepsis-associated encephalopathy (SAE) is a serious sepsis complication with high mortality. Animal models, including cecal ligation and puncture (CLP), lipopolysaccharide (LPS) injection, and peritoneal contamination and infection (PCI), are known to trigger distinct inflammatory responses with differential hippocampal impact. This study aimed to comprehensively compare the hippocampal transcriptomic profiles and validate key findings through independent experimentation. Transcriptomic datasets GSE253309 (CLP), GSE226120 (LPS), and GSE167610 (PCI) were retrieved from the GEO database. Bioinformatics analyses were employed to identify DEGs and enriched pathways. WGCNA pinpointed characteristic modules, and PPI networks were constructed and analyzed. Critically, an independent CLP-induced SAE mouse model was established, and hippocampal RNA sequencing was performed for confirmation. DEG analysis revealed 381, 533, and 85 significant DEGs in the CLP, LPS, and PCI datasets, respectively. CLP and LPS models shared a robust signature of neuroinflammation, significantly enriching GO terms related to immune response and inflammatory response, and KEGG pathways such as TNF, NF-κB, IL-17. In stark contrast, the PCI model was predominantly associated with cell migration, aldarate metabolism, and enriched in metabolic pathways, including bile secretion, ascorbate and aldarate metabolism. Cross-dataset analysis identified 29 common DEGs, from which a PPI network of 16 hub genes was constructed. Importantly, independent validation confirmed a strong concordance (r = 0.576) between the CLP-seq discovery cohort and the experimental CLP-seq data. Lcn2, S100a8, S100a9, Lrg1 and the TNF/IL-17 signaling pathways were robustly verified. CLP and LPS models demonstrate convergent hippocampal transcriptomic profiles distinct from PCI. Lcn2, S100a8, S100a9, Lrg1 and the TNF and IL-17 signaling pathways are highly reliable core features in SAE.

Osteoarthritis (OA) is a multifactorial joint disease involving both cartilage and subchondral bone. The molecular mechanism of subchondral bone in OA is still unclear. This study aimed to identify key molecular targets and transcription factors (TFs) in subchondral bone that drive OA progression, with a focus on their diagnostic and therapeutic potential. Differentially expressed genes (DEGs) from human OA patients and rat OA models were analyzed using RNA-seq data from GEO database. Then functional enrichment analysis, including GSEA, GO and KEGG revealed high conservation. The iRegulon analysis was applied to identify common TFs. Bingo analysis linked TF target genes to OA-relevant biological functions, and single-cell RNA sequencing (sc-RNA seq) identified the primary cell types expressing these TFs. Finally, the expression of identified TFs was validated in human OA and animal OA samples using immunofluorescence (IF) and qPCR. We identified 77 common DEGs in OA patients and rat models, including genes associated with bone development or remodeling. Functional enrichment analysis identified common biological processes, such as skeletal system development, and shared pathways, including calcium signaling, across both species. Moreover, iRegulon analysis identified three conserved TFs-Transcription Factor 12 (TCF12), E2F Transcription Factor 1 (E2F1), and TEA domain transcription factor 4 (TEAD4) in both humans and rats. Single-cell analysis revealed that TFs mainly originate from bone-related cell populations. Finally, experimental validation confirmed that TCF12 was up-regulated, while E2F1 and TEAD4 were down-regulated in human OA subchondral bone and mouse DMM model. This study highlights TCF12, E2F1, and TEAD4 as key regulators of subchondral bone remodeling in OA. Their conserved expression patterns across species and strong correlation with OA progression suggest their potential as novel diagnostic markers and therapeutic targets for OA intervention.

Fuchs Endothelial Corneal Dystrophy (FECD) is an age-related disorder that affects about 4% of the population over the age of 40 years and is genetically associated with CTG repeat expansion in Transcription factor-4 (TCF4) gene. Although both genetic variants as well as environmental exposures like ultraviolent-A light (UVA) have been reported to cause FECD, there are no pharmacological treatments due to the lack of understanding of disease pathogenesis. To characterize the corneal endothelial (CE)cells in FECD individuals with or without CTG repeat expansions in TCF4, we performed RNA-sequencing (RNAseq) of three normal CE cell lines and seven FECD cell lines, including 3 generated from donors with TCF4 repeat expansions (FECD-R) and 4 from non-expanded donors (FECD-NR). Analysis of transcriptomic profiles in all 7 FECD cell lines compared to normal, revealed 214 differentially expressed genes or DEGs with 193 upregulated and 21 downregulated genes. Ingenuity Pathway Analysis (IPA) detected hepatic fibrosis and endothelial-mesenchymal transition pathways as top canonical pathways, which is consistent with extracellular matrix (ECM) deposition in the form of guttae seen in FECD. We identified and further validated transcriptional regulator genes like MAFB, TFAP2B, and POU6F2 to be differentially downregulated in FECD. Furthermore, 48 common DEGs were detected in both FECD-R and FECD-NR, and their upstream regulators included β-estradiol, TGF-β1, Aryl hydrocarbon receptor (AHR), and transcription regulators like CEBPA, CEBPB and SMARCA4. There were 29 genes identified to be differentially regulated only in FECD-R, compared to normal cells. In addition, other canonical pathways like tryptophan and melatonin degradation, Wnt signaling, AHR signaling, mitochondrial dysfunction, and estrogen receptor signaling were also highly enriched in FECD. Our findings support the previously proposed underlying mechanisms of disease progression, including role of transcriptional regulation of endothelial cells, mitochondrial dysfunction, Wnt- and estrogen receptor signaling, as well as the phenotypic clinical FECD hallmark of ECM deposits. Further investigation focusing on differentially expressed genes related to these pathways may be beneficial for elucidating disease-causing mechanisms and developing novel therapies for FECD.

Helicobacter pylori (H. pylori) infection is a major etiological factor for stomach adenocarcinoma (STAD), yet the key molecular drivers linking infection to tumor progression remain insufficiently defined. This study aimed to identify H. pylori-related hub genes in STAD and validate their functional relevance using integrated bioinformatics and experimental approaches. Differentially expressed genes (DEGs) were identified from two microarray datasets (GSE13911 and GSE54129) comparing H. pylori-positive STAD samples with controls. Common DEGs were used to construct a protein-protein interaction (PPI) network via STRING and Cytoscape, and hub genes were ranked using CytoHubba. Transcriptomic validation was conducted using TCGA-STAD data, followed by analyses of enrichment pathways, promoter methylation, somatic mutations, CNVs, immune subtype associations, and drug sensitivity using GSCA, UALCAN, cBioPortal, and CTRP datasets. miRNA-mRNA regulatory interactions were predicted using miRNet and validated in vitro. Experimental validation included RT-qPCR, Western blotting, CCK-8 proliferation assays, colony formation, and wound-healing assays in MKN45 and AGS cells following siRNA-mediated knockdown of key hub genes. Additionally, AGS cells were infected with live H. pylori to directly assess infection-induced changes in gene expression and malignant phenotypes. Four hub genes (THBS2, CTNNB1, COL4A1, and E2F3) were identified as commonly upregulated in H. pylori-positive STAD samples and were further validated as highly expressed in STAD tissues and cell lines. Promoter hypomethylation and CNV gains contributed to their overexpression. Pathway analyses linked the hub genes to EMT, cell cycle progression, immune suppression, and oncogenic signaling. miRNA profiling identified hsa-miR-9-3p and hsa-miR-9-5p as common regulators with diagnostic potential. Importantly, H. pylori infection of AGS cells induced strong upregulation of COL4A1 and CTNNB1 and significantly increased proliferation, clonogenicity, and migration, demonstrating a direct infection-driven oncogenic response. Conversely, siRNA-mediated silencing of COL4A1 or CTNNB1 markedly reduced proliferation, colony formation, and wound closure, confirming their functional roles in STAD progression. Immune correlation and drug sensitivity analyses further linked high hub-gene expression to immunosuppressive microenvironments and resistance to multiple therapeutic agents. This study identifies THBS2, CTNNB1, COL4A1, and E2F3 as key H. pylori-associated oncogenic drivers in STAD. Functional assays demonstrate that H. pylori enhance malignant phenotypes through COL4A1 and CTNNB1, while gene silencing reverses these effects. These findings highlight the hub genes and their regulatory miRNAs as promising diagnostic biomarkers and potential therapeutic targets in H. pylori-related gastric cancer.

Introduction: Infantile-onset Pompe disease, which presents with a broad spectrum of nonspecific findings in newborns and lacks a clearly defined clinical picture, is a significant factor that delays patients’ access to diagnosis and treatment. In this disease, insufficient diagnosis rates, low levels of clinical suspicion, and delays in diagnosis are the main problems that hinder early and accurate diagnosis. This study aims to address diagnostic and therapeutic challenges by elucidating the functional roles and associations of microRNAs in the pathogenesis of infantile-onset Pompe disease. As a result of comparative data analysis, an inventory of known and novel microRNA sequences predicted to target pathogenic pathways associated with infantile-onset Pompe disease was established. Methods: In this study, IOPD and control samples from GSE38680 data were normalized on the Affymetrix platform. Differential gene expression analysis was performed using the limma package, and common DEGs were identified. Subsequently, significant signaling pathways were identified using WebGestalt and Reactome/KEGG/GO databases, and FDR correction was applied. Finally, miRNA expression analysis and miRNA interactions associated with IOPD genes were examined using R packages such as DESeq2, miRNATap, and multiMiR. Results: In this study, 1967 differentially expressed genes (1108 upregulated, 859 downregulated) were identified in the GSE38680 data. GO and KEGG analyses revealed biological processes associated with IOPD, particularly muscle function and the lysosome pathway. The miRNA analysis identified 54 candidate miRNAs, of which only hsa-miR-4749-3p was found to bind directly to the 3′-UTR region of the GAA gene. Conclusion: Early diagnosis is critical to prevent or mitigate irreversible organ damage associated with the progression of IOPD, and circulating miRNAs may serve as additional biomarkers for diagnosis, disease severity, and treatment response. This study used high-throughput technology to identify potential miRNAs for IOPD.

Non-small cell lung cancer (NSCLC) is the most common lung cancer that has shown resistance to multiple treatments. This article aims to screen out reliable core genes and explore their underlying mechanisms in limited-stage NSCLC. Three datasets (GSE19188, GSE27262, and GSE33532) were selected from the gene expression omnibus (GEO). Venn diagram software, omicshare tools, and gene ontology (GO) were used to screen identified differentially expressed genes (DEGs) and potential pathways. A protein-protein interaction (PPI) network was drawn using Search Tool for the Retrieval of Interacting Genes (STING), and Cytoscape was applied for module analysis. Prognostic information in NSCLC clinical samples was also reconfirmed by Gene Expression Profiling Interactive Analysis (GEPIA) and the Kaplan Meier-plotter program. The filtered core genes were analyzed through the Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment. 235 common DEGs (Differentially Expressed Genes) were obtained. 32 genes were screened out through the PPI network and MCODE. 9 core genes (CCNB1, CCNB2, MAD2L1, BUB1, TTK, CDC20, AURKA, RRM2, GTSE1) were obtained through the KEGG pathway enrichment, which mainly enriched in 4 pathways, namely, Cell cycle, Oocyte meiosis, p53 signaling pathway, and Progesterone-mediated oocyte maturation, respectively. Nine critical dependable DEGs were identified in limited-stage NSCLC using integrated bioinformatical approaches, and these core genes show great potential as prospective biomarkers and therapeutic targets in the progression of limited-stage NSCLC. non-small cell lung cancer, biomarkers, therapeutic targets, p53 signaling pathway, bioinformatics analysis.

Frozen shoulder (FS) and osteoporosis (OP) are common age-related degenerative diseases, occurring more frequently in females, which suggests potential molecular links between them. This study aimed to identify shared genetic features and pathways of OP and FS using bioinformatics and machine learning approaches. Gene expression data for OP and FS were obtained from the Gene Expression Omnibus database. Common differentially expressed genes (DEGs) were identified. Functional enrichment analysis, protein-protein interaction (PPI) networks construction, and machine learning algorithms were applied to screen key genes. Diagnostic value was evaluated by receiver operating characteristic (ROC) curve analysis. Immune infiltration and regulatory networks involving transcription factors and miRNAs were explored. Potential therapeutic compounds were also predicted. A total of 111 common DEGs were identified, enriched in pathways related to neurological development, cellular signaling, and immune regulation. PPI analysis revealed 14 hub genes, with SDC1 and ELN identified as key diagnostic markers by machine learning. ROC curves confirmed their diagnostic efficacy for both OP and FS. Immune infiltration analysis revealed distinct immune cell patterns in OP, correlating with the expression of key gene. Regulatory network analysis demonstrated complex transcriptional regulation of SDC1 and ELN. Drug prediction identified five candidate small molecules targeting these genes. This study uncovered shared genetic features of FS and OP through comprehensive bioinformatics analysis, enhancing understanding of their co-morbidity mechanisms. These findings provide a theoretical basis for identifying novel diagnostic biomarkers and therapeutic targets, facilitating the development of precise diagnostic strategies for OP with FS.

Periodontitis (PD) and obstructive sleep apnea (OSA) are widespread conditions with profound health consequences. Increasing evidence suggests shared pathophysiological mechanisms between PD and OSA, prompting this study to explore their genetic connections using advanced transcriptomic approaches. Gene expression data was obtained from GEO, integrating bulk and single-cell RNA sequencing (scRNA-seq). Differentially expressed genes (DEGs) were identified, and common DEGs were analyzed via protein-protein interaction (PPI) networks and functional enrichment. Machine learning algorithms, including LASSO, SVM-RFE, and Boruta, were used to screen out hub genes. Expression patterns, diagnostic accuracy, and immune infiltration were assessed. Then, the single-cell analysis was utilized to evaluate cell-specific expression and effects of virtual hub gene knockouts. Drug candidates were predicted using the DSigDB database. In total, 37 common DEGs were identified, in which PECAM1, FCER1G, and THY1 were designated as hub genes. The hub genes were significantly upregulated in disease states, achieving high diagnostic accuracy (AUC > 0.85). Immune infiltration profiles showed differences between the disease and control groups, with hub gene expression positively correlated to plasma cells and M0 macrophages abundance. Single-cell annotation mapped hub gene expression to distinct cell types. Virtual hub gene knockouts highlighted disrupted pathways including oxygen transport and DNA double-strand break repair. Candidate drugs, including pergolide and aspirin, were proposed. This study investigates genetic links between PD and OSA, identifying PECAM1, FCER1G, and THY1 as important diagnostic and therapeutic targets. Integrating multi-omics and machine learning provides a comprehensive approach to unravelling disease interplay and advancing treatment strategies.

Abstract Objectives Hepatocellular carcinoma (HCC) and sepsis are significant global health challenges, both involving complex molecular mechanisms that may overlap. Identifying shared differentially expressed genes (DEGs) between these conditions could provide novel insights into disease progression and therapeutic targets. This study aimed to determine common DEGs between HCC and sepsis using microarray datasets and to explore their biological implications through bioinformatics analyses. Methods Publicly available microarray datasets for HCC and sepsis were retrieved from gene expression repositories. After preprocessing and normalization, DEGs were identified using statistical approaches, and overlapping genes were determined through comparative analysis. Functional enrichment analysis was performed with the DAVID platform to assess associated biological processes and pathways. A protein–protein interaction (PPI) network was then constructed to identify hub genes, and transcription factor (TF)–gene interaction analysis was carried out to evaluate potential regulatory mechanisms shared between the two conditions. Results A total of 379 common DEGs were identified between HCC and sepsis. Functional enrichment analysis indicated that these DEGs were mainly related to immune response, cell cycle regulation, and antigen presentation pathways. PPI network analysis revealed hub genes including CCNA2, NUSAP1, TOP2A, and CDK1, all of which were significantly upregulated in both diseases. TF–gene interaction analysis highlighted convergent transcriptional regulatory mechanisms linking immune dysregulation in sepsis with tumorigenesis in HCC. Conclusions This study demonstrates molecular similarities between HCC and sepsis, emphasizing shared DEGs and regulatory networks. The identification of hub genes and enriched pathways provides potential diagnostic markers and therapeutic targets, underscoring the importance of transcriptional dysregulation in both cancer development and sepsis pathophysiology.

Introduction: The impact of global ischemia and reperfusion on the brain has been extensively studied in animal models of cardiac arrest (CA) and resuscitation. Although therapeutic hypothermia has shown efficacy following the restoration of circulation, its underlying mechanisms remain incompletely understood. To gain insights into molecular pathways potentially effected by therapeutic hypothermia, we performed whole transcriptomic analysis using RNA sequencing to investigate differential gene expression in the hippocampus. Methods: Male Sprague Dawley rats (Charles River Laboratories) with a weight between 400-800g were subjected to 8 minutes of ventricular fibrillation cardiac arrest (VFCA) and resuscitated with extracorporeal cardiopulmonary resuscitation (eCPR) (n=10). After return of spontaneous circulation, five animals were treated with hypothermic temperature control (33 ±0,5°C) for 12 hours (HT), and five animals where kept normothermic (NT). Sham animals (n=5) were subjected to the surgical procedures but underwent no cardiac arrest or eCPR. After 24 hours of survival, the hippocampus of one hemisphere was extracted, homogenized, and preserved in Trizol. Subsequently RNA libraries were prepared and analysed with Next Generation Sequencing. After preprocessing of reads and alignment to the rat genome, we applied DESeq to identify differentially expressed genes (DEGs) between groups. We considered DEGs with an adjusted p-value of <0.05 and log2 fold change > ± 1 as significantly deregulated. Results: We found the largest number of DEGs between NT and sham (590 genes). In contrast, the HT vs sham group yielded 318 DEGs. Notably, only 62 DEGS were detected between NT and HT. Subsequent Gene Ontology (GO) analysis revealed similarly enriched pathways in both the HT vs sham and NT vs sham comparisons. Overlay analysis of common and unique DEGs identified 88 genes that were exclusively deregulated in response to HT, while 230 DEGs were commonly affected in both NT and HT conditions. Pathway analysis suggested that immune and apoptotic pathways were largely preserved among the 230 shared DEGs, whereas the HT-exclusive gene set showed upregulation of well-known neuroprotective genes. Conclusion: Our findings suggest that HT does not alter the core pathological response to CA but selectively induces neuroprotective pathway, which may contribute to improved clinical outcome.

PurposeThis study investigated shared molecular pathways linking systemic lupus erythematosus (SLE) and coronary artery disease (CAD) to uncover mechanisms of coronary injury in SLE.Patients and MethodsBulk transcriptomic datasets (GSE45291 for SLE, GSE61145 for CAD) were analyzed to identify differentially expressed genes (DEGs), immune cell infiltration patterns, and co-expression networks. A diagnostic model was constructed and validated using external cohorts (GSE49454 for SLE, GSE179789 for CAD). Machine learning prioritized core genes, validated in both external cohorts and in SLE patients with coronary injury (GSE264125). Cellular localization and intercellular communication were explored by analyzing single-cell RNA-seq data (GSE135779). qPCR was used to validate the gene expression in peripheral blood mononuclear cells (PBMCs) from patients.ResultsWe identified 146 common DEGs enriched in immune pathways related to cell toxicity, and found shared dysregulation in cytotoxic lymphocytes such as natural killer (NK) cells and CD8+ T cells. Through co-expression analysis and DEG intersection, we pinpointed 11 hub genes (eg, GZMK, KLRK1, GNLY). A diagnostic model based on these genes showed strong performance (SLE: AUC 0.881 training, 0.666 validation; CAD: AUC 0.897 training, 0.781 validation). Machine learning highlighted GZMK and KLRK1 as core genes, which were further validated for their combined diagnostic utility (AUC: 0.782–1.000) in SLE-related coronary injury. Single-cell analysis revealed that these genes are primarily active in cytotoxic CD8+ T cells and NK cells, with GZMK linked to CLEC-mediated signaling and KLRK1 to HLA activation. Finally, we confirmed higher expression of these genes in blood cells from SLE patients with coronary artery disease using qPCR.ConclusionSLE and CAD share a cytotoxic lymphocyte-driven molecular axis, with GZMK/KLRK1-mediated immune dysregulation as a key contributor to coronary injury in SLE. GZMK and KLRK1 may represent promising biomarkers for early detection and risk stratification of SLE-associated coronary complications. Notably, the discrimination (AUC=1.000) was observed in a limited subgroup of SLE patients with coronary microvascular dysfunction (n=4), warranting further validation in expanded cohorts.

Osteoporosis and osteosarcoma share certain molecular pathways, and identifying common hub genes could provide new insights into their pathogenesis. This study aimed to identify and validate hub genes involved in both osteoporosis and osteosarcoma and explore their potential as biomarkers and therapeutic targets. We retrieved three publicly available datasets: GSE13850 (osteoporosis), GSE16088, and GSE12865 (osteosarcoma). Differentially expressed genes (DEGs) were identified using the limma package in R, and common DEGs were determined through Venn analysis. The hub genes were identified using the STRING database and Cytoscape software. Reverse transcription quantitative PCR was used to validate gene expression in osteosarcoma cell lines and normal bone tissue cell lines. Functional assays including cell proliferation, colony formation, and wound healing were conducted after CLTA and SMAD3 knockdown via small interfering RNA. Gene set enrichment, promoter methylation analysis, and genetic alteration studies were conducted using publicly available databases. The hub genes identified, CLTA, MSR1, MTF2, and SMAD3, were highly expressed in osteosarcoma compared to normal controls, and their expression was validated across osteosarcoma tissue samples. Survival analysis showed that high expression of these genes correlated with poor prognosis in osteosarcoma patients. Gene enrichment analysis suggested that these genes play crucial roles in osteosarcoma development through various signaling pathways. CLTA and SMAD3 silencing in MG63 and U2OS cells led to significant reductions in cell proliferation, colony formation, and migration. This study identified CLTA, MSR1, MTF2, and SMAD3 as potential biomarkers and therapeutic targets for osteoporosis and osteosarcoma. However, further studies are needed before determining clinical implications.

Papillary thyroid carcinoma (PTC), the most common thyroid malignancy, presents with multiple variants. This study aimed to identify potential biomarkers and therapeutic candidates for PTC through computational analyses and molecular docking. Gene expression data related to PTC were obtained from the TCGA-THCA and GEO datasets (GSE35570 and GSE33630) to identify differentially expressed genes (DEGs). Functional enrichment analysis was performed on the DEGs, followed by construction of a protein-protein interaction (PPI) network. Hub genes were identified using recursive feature elimination (RFE) and LASSO regression analyses. A nomogram incorporating these hub genes was developed, and its diagnostic performance was evaluated using receiver operating characteristic (ROC) curves. Furthermore, the relationship between hub genes and immune cell infiltration was investigated. Potential drug candidates targeting the hub genes were predicted and validated through molecular docking. Common DEGs across the three datasets were enriched in pathways such as ECM-receptor interaction, proteoglycans in cancer, and cell adhesion molecules. Significantly enriched GO terms included 'binding,' 'receptor activity,' 'integral component of membrane,' 'cytoplasm,' 'cell adhesion,' and 'immune response.' A PPI network was constructed by intersecting the common DEGs with PTC-related targets. Machine learning algorithms identified three hub genes: SRY-box transcription factor 4 (SOX4), cyclin D1 (CCND1), and lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1). These hub genes exhibited differential expression in PTC and were used to construct a reliable diagnostic model. Furthermore, molecular docking revealed stable binding between CCND1 and Tipifarnib, suggesting potential therapeutic relevance. While previous studies have applied bioinformatics and molecular docking in PTC research, this study uniquely integrates both approaches to identify the hub gene CCND1 and its potential targeting drug, Tipifarnib, as promising molecular markers and therapeutic candidates for PTC. The hub gene CCND1 and its targeting drug candidate Tipifarnib may contribute to PTC treatment.

Endocannabinoid signaling exerts a neurodevelopmental regulatory role via CB1 cannabinoid receptors (CB1Rs), which control pyramidal neuron differentiation, migration, and axonal guidance. Here, we investigated the long-lasting consequences of transient prenatal CB1R downregulation within the mouse prefrontal cortex by assessing its impact on gene expression, neuronal electrophysiological properties, and animal behavioral traits. Transient loss of CB1Rs as induced by in-utero small-interference RNA electroporation at Embryonic Day 14.5, when upper-layer neurons are generated, arrested cell migration leading to ectopic neurons that populated deep layers. Whole-cell current-clamp recordings showed that ectopic neurons are less excitable (increased afterhyperpolarization amplitude, decreased sag, lower firing frequency) than deep-layer-native pyramidal neurons. Differentially expressed genes (DEGs), identified by microarray characterization of FACS-sorted electroporated neurons, were significantly enriched in pathways related to cortical development, regulation of cell migration, neurotransmitter secretion, and cytoskeletal organization. Gene set enrichment analysis also supported enrichment in pathways associated with neurodegenerative disorders and synaptic function. The gene expression profile of siCB1R-derived neurons showed DEGs that had been previously associated with intellectual disability, schizophrenia, and autism. Venn diagrams unveiled one common DEG for neuropsychiatric risk databases and CB1R expression manipulation, namely, the transcription factor ZBTB20. Prenatal knockdown of CB1Rs induced long-lasting behavioral alterations in the adult offspring of either sex, with an impairment of social interaction and motor behavior in siCB1R-derived adult mice. Taken together, these findings highlight the role of CB1Rs in controlling the development of pyramidal neurons in the prefrontal cortex and support the contribution of altered endocannabinoid signaling to neuropsychiatric vulnerability.

Neoadjuvant chemotherapy (NAC) is a critical component of breast cancer treatment, but the molecular mechanisms underlying resistance remain poorly understood. This study aimed to identify transcriptomic changes associated with NAC resistance across four breast cancer subtypes: Luminal A, Luminal B/HER2-positive, Luminal B/HER2-negative, and Triple-Negative Breast Cancer (TNBC). RNA-seq analysis was performed on paired pre- and post-NAC breast cancer samples from 32 non-responders. Differentially expressed genes (DEGs) were identified, and functional enrichment analyses were conducted. Protein-protein interaction (PPI) networks were constructed to identify hub genes. Tumor microenvironment (TME) infiltration was estimated using deconvolution algorithms. The results revealed distinct gene expression profiles between pre- and post-NAC samples, with FOS and NR4A1 being common DEGs across all subtypes. Enriched pathways varied among subtypes, including signal transduction, estrogen biosynthesis, extracellular matrix organization, dendritic cell activation, and B cell activation. TME analysis showed increased infiltration of specific immune cell populations after NAC, including CD4 memory T cells, regulatory T cells, neutrophils, macrophages, and mast cells, varying by subtype. These findings suggest that NAC modulates gene expression, cellular activity, and TME interactions, potentially contributing to treatment resistance. Understanding the molecular determinants of NAC resistance is crucial for developing targeted therapeutic strategies and improving outcomes for breast cancer patients.