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PubMed2026

KLF5 promotes proliferation, migration, and autophagy-/EMT‑associated molecular changes in lens epithelial cells via transcriptional activation of THBS1 in traumatic cataract.

PURPOSE: Traumatic cataract is a common blinding eye disease after ocular trauma, and its pathogenesis is closely related to lens epithelial cell dysfunction, while the definite molecular regulatory mechanism between upstream transcription factor and downstream target gene remains poorly clarified. This study aimed to clarify the role and molecular mechanism of the krüppel-like factor 5 (KLF5)/ thrombosponin 1 (THBS1) axis in regulating proliferation, migration, epithelial-mesenchymal transition and autophagy of lens epithelial cells in traumatic cataract, and to explore its potential clinical therapeutic value. METHODS: The GSE295383 dataset in the gene expression omnibus (GEO) database was downloaded, and the differentially expressed genes (DEGs) were screened by linear models for microarray data (limma) package of R language. Combined with Weighted gene co-expression network analysis (WGCNA), the gene co-expression network was constructed and the key modules were screened. Gene ontology (GO), kyoto encyclopedia of genes and genomes (KEGG) and gene set enrichment analysis (GSEA) combined with human transcription factor target (hTFtarget) and JASPAR databases were used to predict the upstream transcription factors of THBS1. Subsequently, SRA01/04 cells were induced with transforming growth factor-beta 2 (TGF-β2) to construct a cataract cell model. RESULTS: THBS1 and KLF5 were highly expressed in LECs exposed to TGF-β2. KLF5 could activate THBS1 transcription by binding to THBS1 promoter - 174 to -165 sites. Knockdown of THBS1 inhibited TGF-β2-induced viability, proliferation, migration, and altered the expression of epithelial-mesenchymal transition (EMT)- and autophagy-related markers in LECs. Knockdown of KLF5 downregulated THBS1 expression and produced a similar inhibitory effect, while overexpression of THBS1 reversed the effect of KLF5 knockdown. CONCLUSIONS: This study demonstrated that KLF5 promoted the proliferation, migration, and EMT‑associated molecular changes of LECs in traumatic cataract through transcriptional activation of THBS1, and regulated the expression of autophagy‑related markers in LECs, suggesting that KLF5/THBS1 axis might be a potential target for the treatment of traumatic cataract.

Cataract↗

PubMed2006

Mining microarray data at NCBI's Gene Expression Omnibus (GEO)*.

The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) has emerged as the leading fully public repository for gene expression data. This chapter describes how to use Web-based interfaces, applications, and graphics to effectively explore, visualize, and interpret the hundreds of microarray studies and millions of gene expression patterns stored in GEO. Data can be examined from both experiment-centric and gene-centric perspectives using user-friendly tools that do not require specialized expertise in microarray analysis or time-consuming download of massive data sets. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.

Algorithms↗

PubMed2026

Predicting diagnostic gene biomarkers associated with immune infiltration in patients with diabetes.

Diabetes is a global public health problem with various complications, which can lead to disability and mortality. This study identified potential diagnostic markers for diabetes and explored the immunometabolic mechanisms in the pathological process. The gene expression of 17 diabetes cases and 16 normal controls were obtained from the Gene Expression Omnibus (GEO) database. The "limma" package was employed for screening differentially expressed genes (DEGs). Gene functions and enriched pathways of DEGs were analyzed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Candidate key genes were screened using the least absolute shrinkage and selection operator (LASSO) regression model and support vector machine recursive feature elimination (SVM-RFE) analysis. The diagnostic effectiveness of identified markers was further verified via the receiver operating characteristic (ROC) curve. The compositional patterns of immune cell infiltration and signaling pathway enrichment associated with key genes were explored via single sample Gene Set Enrichment Analysis (ssGSEA) and GSEA analysis, respectively. Possible miRNAs interacting with key genes were predicted via miRcode database. B2M, FTL, SH3BGRL3, and SOD2 were recognized as diagnostic markers for diabetes based on LASSO regression and the support vector machine recursive feature elimination (SVM-RFE) feature selection algorithm. Analysis of immune cell infiltration demonstrated that the four key genes were related to B cells, neutrophils, macrophages, and CD8+ T cells. The diagnostic value of B2M, FTL, and SOD2 for diabetes was higher than that of SH3BGRL3 according to the ROC curve. Validation experiments indicated that the mRNA expression of B2M and FTL was increased in liver tissues of diabetic mice. B2M and FTL can act as diagnostic markers for diabetes and contribute to new understandings of the disease's molecular mechanisms.

Humans↗

PubMed2026

Furmonertinib inhibits non-small cell lung cancer progression through ANGPT1-mediated regulation of cell migration and apoptosis.

BACKGROUND: Lung cancer remains one of the leading causes of cancer-related mortality worldwide, highlighting the urgent need for effective therapeutic agents. This study investigates the antitumor effects and underlying mechanisms of furmonertinib (FUR) in lung cancer cells. METHODS: Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were utilized to identify FUR target genes, followed by functional enrichment, survival, and protein-protein interaction (PPI) network analyses. Human lung cancer cell line A549 was treated with FUR and/or ANGPT1-specific siRNA. Cell migration, apoptosis, and protein expression were assessed by wound healing, flow cytometry, and Western blotting. Cellular thermal shift assay (CETSA) and drug affinity response target stability (DARTS) assays were used to assess FUR-induced stabilization of angiopoietin-1 (ANGPT1) protein. RESULTS: FUR treatment significantly inhibited cell migration and increased apoptosis in NSCLC cells. Bioinformatics analysis revealed 12 overlapping target genes of FUR from the PharmMapper and SwissTargetPrediction databases, with ANGPT1 emerging as a key candidate. ANGPT1 expression was downregulated in tumor tissues and positively correlated with patient survival. Western blotting confirmed that FUR upregulated ANGPT1 protein levels in a dose-dependent manner. Knockdown of ANGPT1 enhanced migration and suppressed apoptosis, while FUR reversed these effects. FUR treatment enhanced the stability of ANGPT1 under high-temperature conditions while reducing its sensitivity to protease. ANGPT1 may have affected tumor cell migration through cell adhesion and extracellular matrix (ECM) pathways. CONCLUSIONS: FUR suppresses lung cancer progression by upregulating ANGPT1, thereby inhibiting cell migration and promoting apoptosis. ANGPT1 is a potential therapeutic target and provides new insights into the anti-tumor mechanism of FUR in lung cancer.

Lung cancer↗

PubMed2026

Integrated transcriptome analysis and machine learning to construct a homeostatic model of acetylation for bladder cancer and validate the key gene CES1.

BACKGROUND: Bladder cancer (BLCA) is one of the most common malignant tumors of the urinary system. Protein acetylation (PA) plays a critical role in regulating multiple biological processes (BPs), cellular homeostasis, and cancer-related signaling pathways. This study aimed to construct a homeostatic model of acetylation for BLCA using integrated transcriptome analysis and machine learning and to validate the key gene CES1. METHODS: RNA sequencing (RNA-seq) and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Acetylation-related differentially expressed genes (DEGs) in BLCA were screened using differential expression analysis (DEA). An acetylation homeostatic model was constructed via univariate, machine learning-based least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses, followed by validation in multiple cohorts. Single-cell RNA-seq analysis was used to explore gene expression patterns in diverse cell types. Enrichment analysis (EA), immune infiltration, and drug sensitivity analysis (DSA) were performed to characterize molecular features of different risk groups. Finally, the biological function of CES1 as the key gene was verified by in vitro knockdown experiments. RESULTS: We established a robust acetylation homeostatic model consisting of five genes, which effectively predicted overall survival (OS) and served as an independent prognostic factor in BLCA. High-risk patients showed significantly poorer prognosis, distinct immune infiltration profiles, and differential drug sensitivity. CES1 was identified and validated as the key gene in this model, which was highly expressed in BLCA and associated with poor prognosis. Knockdown of CES1 markedly suppressed cell proliferation, invasion, and migration, and reduced intracellular coenzyme A (CoA) levels, thereby regulating PA homeostasis. CONCLUSIONS: We developed and validated a novel acetylation homeostatic model for survival stratification and personalized treatment guidance in BLCA, based on integrated transcriptome analysis and machine learning. CES1 is closely associated with intracellular CoA levels and the malignant progression of BLCA. Its potential association with PA homeostasis requires further mechanistic validation, and it may act as a candidate therapeutic biomarker for BLCA.

Bladder cancer (BLCA)↗

PubMed2026

Integrative analysis of single-cell sequencing identifies CD8+ TIM3+ CD101+ T cell-associated genes as prognostic biomarkers in breast cancer.

BACKGROUND: Breast cancer is a prevalent and deadly malignancy that significantly impacts women's quality of life and imposes financial burdens. Despite therapeutic advancements, tumour heterogeneity and frequent relapses remain major challenges. Accordingly, this study aimed to characterize immune features associated with CD8+ TIM3+ CD101+ T cells and develop a prognostic signature for breast cancer. METHODS: This study integrated single-cell and bulk transcriptomic datasets to characterize CD8+ TIM3+ CD101+ T cell (CCT)-related immune features and construct a prognostic signature in breast cancer. Single-cell RNA-seq data were sourced from the Gene Expression Omnibus (GEO) repository, and bulk transcriptomic data were from The Cancer Genome Atlas (TCGA) and GEO databases. Analytical methods included pseudo-time trajectory reconstruction (Monocle2), intercellular signalling analysis (CellChat), functional enrichment (ClusterProfiler), and immune profiling (ssGSEA). Prognostic modeling was conducted using least absolute shrinkage and selection operator (LASSO) Cox regression, with validation via Kaplan-Meier and time-dependent receiver operating characteristic (ROC) analyses. RESULTS: Single-cell analysis identified 17 clusters spanning seven cell types, including T cells, myeloid cells, and epithelial cells. T-cell sub-clustering revealed four subtypes. Pseudotime analysis suggested a potential state-transition relationship between CD8+ CD101- TIM3+ and CD8+ CD101+ TIM3+ T-cell states. A total of 121 differentially expressed genes were enriched in vital biological processes. An 11-gene prognostic model showed strong predictive power across cohorts. Single-cell T-cell reclustering identified a CD8+ CD101+ TIM3+ T-cell subpopulation, which was primarily characterized by the expression of markers such as CD101 and HAVCR2/TIM3. CONCLUSIONS: This study maps cellular heterogeneity and molecular networks in breast cancer, offering insights for targeted therapy and improved prognosis.

Breast invasive carcinoma↗

PubMed2026

Identification of a novel signature for prognostic stratification and integrative analyses in lung adenocarcinoma.

BACKGROUND: Recently, research has revealed that the Golgi apparatus is involved in the development process of cancer; however, the specific effect of Golgi apparatus-related genes (GAGs) in lung adenocarcinoma (LUAD) remains unclear. This study aims to construct a more concise and practical risk model in LUAD using GAG. METHODS: The gene expression profiles of patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and GAGs were downloaded from the Gene Set Enrichment Analysis (GSEA) database. Univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were performed to identify the prognostic GAG signature. Kaplan-Meier and receiver operating characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signatures. The correlation between the risk model and the immune landscape was examined using CIBERSORT and TIDE analyses. Also, the genes in the signature were assessed by single-cell RNA sequencing (scRNA-seq). RESULTS: A prognostic signature comprising 5 GAG genes (GNPNAT1, RGS20, CAV3, NTSR1, and FURIN) was established after LASSO and multi-Cox analyses. Both the Kaplan-Meier analysis and the ROC curves supported the strong predictive utility of the risk model. Specifically, the former yielded significant stratification in all three validation datasets (P=1.2001e-05, P=0.006, and P=0.04), while the latter provided further evidence of its predictive precision through the area under the curve. In addition, we found that the low-risk group responded better to immunotherapy than the high-risk group (P<0.0001). scRNA-seq analysis revealed the distribution patterns of the 5 GAG genes in cells. Finally, we assessed the situation of tumor mutation burden (TMB) and performed functional analysis based on the risk model of GAGs. CONCLUSIONS: The risk model based on GAGs can effectively stratify the prognosis of patients and predict immunotherapy responses in LUAD.

Golgi apparatus↗

PubMed2026

Identification of JAML as an Immune-Associated Prognostic Marker in Non-Small Cell Lung Cancer.

INTRODUCTION: Non-small cell lung cancer (NSCLC) remains a major cause of cancer-related mortality worldwide, and the identification of novel prognostic biomarkers associated with tumor immunity is urgently needed. Junctional adhesion molecule-like (JAML), a member of the junctional adhesion molecule family, participates in leukocyte adhesion, migration, and T-cell activation. Although JAML has been implicated in immune regulation and tumor progression in other cancers, its expression pattern, prognostic significance, and association with the immune microenvironment in NSCLC remain unclear. This study aimed to investigate the clinical and immunological significance of JAML in NSCLC. METHODS: Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were analyzed to evaluate JAML expression patterns in NSCLC subtypes. The prognostic value of JAML was assessed using Kaplan-Meier survival analysis and Cox regression models. The association between JAML expression and immune cell infiltration was investigated using TIMER2.0, CIBERSORT, and TISIDB analyses. Functional enrichment analyses were performed to explore potential biological pathways associated with JAML expression. In addition, JAML expression was validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR) in paired NSCLC and adjacent normal tissues. RESULTS: JAML expression was significantly decreased in NSCLC tissues compared with normal tissues (P < 0.005), with the lowest expression observed in lung squamous cell carcinoma (LUSC) and reduced expression in lung adenocarcinoma (LUAD). Survival analysis demonstrated that patients with high JAML expression had significantly improved overall survival compared with those with low expression (univariate HR = 0.68, 95% CI: 0.54-0.86, P = 0.001; multivariate HR = 0.76, 95% CI: 0.57-1.00, P = 0.049). Immune infiltration analysis revealed that JAML expression was significantly associated with multiple immune cell populations, including CD8+ T cells (r = 0.42, P < 0.001), suggesting a close relationship between JAML expression and the tumor immune microenvironment. qRT-PCR validation confirmed that JAML expression was approximately 2.3-fold higher in adjacent normal tissues than in NSCLC tissues (P < 0.05). CONCLUSION: JAML is downregulated in NSCLC and its high expression is associated with favorable overall survival and distinct immune infiltration patterns. These findings indicate that JAML may serve as a potential prognostic biomarker and provide insights into the relationship between JAML expression and the tumor immune microenvironment in NSCLC.

JAML protein↗

PubMed2026

Prognostic Role of Global DNA Methylation in Renal Cancer Reveals Decitabine Treatment Benefit.

BACKGROUND: Renal cancer presents a significant global health challenge due to its rising incidence and mortality rates. Often undetected in early stages, it complicates diagnosis and treatment. Current therapies face resistance and limited effectiveness, especially in advanced stages. The diverse subtypes of renal cancer highlight the need for new biomarkers and risk assessment tools for targeted treatments. OBJECTIVE: This study aims to assess the prognostic significance of global DNA methylation (GM) levels in renal cancer, identify new biomarkers, and evaluate the therapeutic potential of the DNA methyltransferase inhibitor decitabine. METHODS: Data on RNA sequencing, gene mutations, DNA methylation, and clinical outcomes were collected from TCGA and GEO databases. We calculated global DNA methylation scores (GMS) and categorized patients into high, intermediate, and low GMS groups. Survival analysis and genomic analyses were conducted to explore the relationships between GMS, clinical outcomes, and tumor characteristics. RESULTS: Higher GMS was identified as an independent prognostic factor associated with worse outcomes in renal cancer. Patients with elevated GMS showed increased mutations, copy number variations, and a more aggressive tumor phenotype. Treatment with decitabine was observed to reduce tumor hypermethylation and downregulate cell cycle pathway activity, indicating potential therapeutic benefits. CONCLUSION: Global DNA methylation plays a significant role in renal cancer prognosis. GMS may serve as valuable biomarkers for prognosis and personalized treatment strategies. Decitabine shows potential efficacy for high GMS patients, particularly through its impact on cell cycle regulation, underscoring the importance of personalized approaches in cancer treatment.

Humans↗

PubMed2026

Identification of Critical Genes Related to Breast Cancer with Brain Metastasis Through Bioinformatics Analysis.

INTRODUCTION: Distant metastasis accounts for the majority of Breast Cancer (BC)-related mortality. The brain is one of the most common regions of metastasis. However, the underlying molecular mechanisms remain uncertain. METHODS: In this study, gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Datasets GSE100534 and GSE52604, containing 16 primary brain tumor samples and 38 breast cancer brain metastasis samples, were used to identify the Differentially Expressed Genes (DEGs). The Metascape database was used to analyze enriched Gene Ontology (GO) entries and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway entries in DEGs. The STRING database was then used to construct a Protein-Protein Interaction (PPI) network, and the Cytoscape platform was employed to visualize the network. Furthermore, the Kaplan-Meier curve was used to analyze the Relapse-Free Survival (RFS) among the hub genes. Finally, the iRegulon plugin was used to construct a regulatory network to find the transcription factors (TFs) that regulate the expression of the hub genes. RESULTS: A total of 344 DEGs, including 182 up-regulated and 162 down-regulated genes, were identified by using the limma package in R. A module with 18 nodes and 9 hub genes was selected from the PPI network by using the plugins MCODE and Cyto- Hubba, respectively. KEGG pathway analysis demonstrated that brain metastasis in BC was closely related to the oocyte cell cycle. The Kaplan-Meier curve showed that high expression of these 9 hub genes was associated with poor RFS in BC patients. TFs' analysis showed that E2F4, SIN3A, FOXM1, and TFDP1 interacted with these hub genes. DISCUSSION: This study revealed that Breast Cancer Brain Metastasis (BCBM) may have a promoting effect on the cell cycle of oocytes and affect the maturation and division of oocytes through the KEGG and GO analyses of 344 DEGs. The selected 9 hub genes (ASPM, BUB1, BUB1B, CCNA2, CCNB1, CDK1, NDC80, NCAPG, and TOP2A) and 4 transcription factors (E2F4, SIN3A, FOXM1, TFDP1) may play a critical role in brain metastasis of BC. CONCLUSION: The results of this study may aid in the early diagnosis and suggest potential targets for the treatment of BCBM.

Brain Neoplasms↗

PubMed2026

KLK6 is Associated with a Neutrophil-Dominant Immunosuppressive Microenvironment and Epigenetic Deregulation in Lung Adenocarcinoma.

INTRODUCTION: Lung adenocarcinoma (LUAD) is the most prevalent histological subtype of lung cancer and is associated with poor survival despite advances in targeted therapies. Kallikrein-related peptidase 6 (KLK6) has been implicated in several malignancies, but its expression pattern, clinical relevance, and biological function in LUAD remain incompletely characterized. This study aimed to evaluate KLK6 expression and its associations with prognosis, epigenetic regulation, immune infiltration, and migratory phenotypes in LUAD. METHODS: RNA-seq expression and clinical data were obtained from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases. KLK6 expression was analyzed in relation to clinicopathological parameters, survival outcomes, promoter methylation status (via UALCAN), and tumor-infiltrating immune cell abundance (via TIMER2.0). In vitro, KLK6 was knocked down using shRNA in A549 and H1299 LUAD cell lines. Cell migration was assessed by transwell assays, and the expression of Epithelial-Mesenchymal Transition (EMT)- and Wnt signaling-related markers was examined by qRT-PCR and Western blotting. RESULTS: KLK6 expression was significantly upregulated in LUAD tissues compared with normal lung tissues. High KLK6 expression was associated with poorer overall survival (HR = 1.52, P = 0.009) and disease-specific survival (HR = 1.55, P = 0.03). ROC analysis showed that KLK6 discriminated stage I LUAD from normal tissues with an AUC of 0.73. Promoter hypomethylation was observed in LUAD tumors and correlated with increased KLK6 expression. Immune infiltration analysis revealed that KLK6-high tumors exhibited reduced B-cell infiltration and increased neutrophil infiltration. Functional experiments demonstrated that KLK6 knockdown significantly suppressed cell migration, accompanied by increased E-cadherin and decreased N-cadherin, Vimentin, Wnt5a, and β-catenin expression. DISCUSSION: These findings suggest that KLK6 overexpression in LUAD is driven in part by promoter hypomethylation and is closely linked to a neutrophil-dominant immunosuppressive microenvironment. Furthermore, KLK6 appears to promote LUAD cell migration through EMT- and Wnt-related signaling pathways. Collectively, these multi-layered data position KLK6 as a potential driver of aggressive tumor behavior and a candidate biomarker for risk stratification. CONCLUSION: KLK6 is aberrantly overexpressed in LUAD and is associated with poor prognosis and enhanced migratory capacity. It may serve as a promising prognostic biomarker and a potential therapeutic target for LUAD.

KLK6↗

PubMed2026

Identification of Drug-resistant Cell Subpopulations in Colorectal Cancer Through Single-cell Analysis and Exploration of Potential Therapeutic Strategies.

INTRODUCTION: The therapeutic efficacy of Colorectal Cancer (CRC) is often compromised by resistance to the standard chemotherapy agent oxaliplatin. METHODS: This study obtained single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database. Differentially Expressed Genes (DEGs) between resistant and sensitive epithelial subpopulations were identified, followed by enrichment analysis. Pseudotemporal trajectory and cell-cell communication were analyzed using Monocle2 and CellChat, respectively. The candidate drug was predicted by Connectivity Map (cMAP) analysis. External validation included assessment of the EpC2 signature in an oxaliplatin-resistant cell line dataset (GSE76092), survival analysis using The Cancer Genome Atlas (TCGA) cohorts, and re-analysis of the GSE179784 dataset to assess the reproducibility of EpC2-like subpopulations and their DNA Damage Repair (DDR) scores. RESULTS: Cell subpopulations were divided into 10 clusters. Among them, epithelial cells comprised 5 subpopulations, with EPC2 identified as a potential oxaliplatin-resistant subset. DEGs were enriched in the TNF and IL-17 pathways. External validation confirmed the enrichment of EpC2 in resistant cell lines and its association with poor survival. Pseudotemporal trajectory revealed that epithelial cells underwent state transitions, forming two distinct branches. The resistant group exhibited enrichment in RNA splicing and NF-κB pathways. Cell-cell communication analysis revealed interactions involving MDK- NCL and PPIA-BSG. Dasatinib was predicted as a candidate drug. DISCUSSION: We identified an oxaliplatin-resistant subpopulation of Epithelial Cells (EpC2) in CRC, elucidated its multi-layered resistance mechanisms, and integrated multi- omics and cMAP database analyses to predict a potential intervention drug. CONCLUSION: This study provided potential therapeutic possibilities for oxaliplatin resistance, contributing to CRC treatment.

Humans↗

PubMed2026

Integrated Bioinformatics Analysis Revealing that the NSDHL Gene Might Be Associated with the Progression of Western HFD/SW-Induced Hepatocellular Carcinoma.

BACKGROUND AND OBJECTIVE: Hepatocellular carcinoma (HCC) remains a significant global health concern. However, the etiology and pathogenesis of HCC have yet to be fully elucidated. Previous studies have indicated a close association between obesity and the occurrence and progression of HCC. The objective of this study was to employ bioinformatics strategies in order to explore key genes associated with the clinical diagnosis and prognosis of HCC induced by a Western high-fat diet and sugar water (HFD/SW). MATERIALS AND METHODS: We obtained the expression profile chip data GSE197884 from the Gene Expression Omnibus (GEO) database. Subsequently, “DESeq” and “Limma” R packages were employed to identify differentially expressed genes (DEGs) while constructing a co-expressed gene network using weighted gene co-expression analysis (WGCNA). Functional enrichment analyses were then carried out, followed by the construction of a protein-protein interaction (PPI) network to uncover core genes. The core genes were confirmed through data retrieved from The Cancer Genome Atlas (TCGA) database in order to determine their status as hub genes. Finally, survival and tumor immune infiltration analyses were performed to unveil the prognostic significance of these hub genes. RESULTS: In total, 126 intersection targets were retrieved through the Venn diagram. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that the DEGs were primarily related to the proliferation and apoptosis of HCC cells, the digestion and metabolism of liver cells, the HCC tumor microenvironment, and immune response. The PPI network analysis identified 11 core targets, among which seven hub genes, including NSDHL, MVK, SQLW, GCAT, ALAS2, GLDC, and AGXT, were obtained after TCGA database validation. Furthermore, it was found that NSDHL was closely associated with the clinical diagnosis and prognosis of HCC induced by HFD/SW and also affected the cellular immune infiltration in the HCC tumor microenvironment. CONCLUSION: The present study demonstrated a significantly elevated expression of NSDHL in HCC tissues, suggesting its potential as a specific biomarker for precise clinical diagnosis and prognosis assessment of HCC induced by HFD/SW.

Computational Biology↗

PubMed2026

Identification of Critical Genes for Recurrent Aphthous Ulcer by Transcriptome Data Analysis and Mendelian Randomization.

PURPOSE: Recurrent aphthous ulcer (RAU) is a common oral mucosal disorder with a poorly understood etiology, significantly affecting patients' quality of life. This study aims to investigate critical genes linked to RAU and explore their biological mechanisms using transcriptomic data and Mendelian randomization (MR) analysis. MATERIALS AND METHODS: RAU-related gene expression data from the GEO database (GSE37265) were analyzed to identify differentially expressed genes (DEGs). A two-sample MR approach was used to assess the causal impact of expression quantitative trait loci (eQTL) on RAU. Critical genes were identified by intersecting DEGs with significant MR findings. GO and KEGG pathway enrichment analyses were performed, along with GSEA and immune cell infiltration analysis, to investigate the functions and mechanisms of these genes in RAU. RESULTS: A total of 184 differentially expressed genes (DEGs) were identified, while 339 RAU-associated genes were screened through MR analysis. Cross-validation further identified 7 critical genes. Among these, CCR1, ERP27, HCK, MICB, and SLC2A3 showed protective associations with RAU risk, whereas CD177 and IFITM1 were positively associated with increased risk. Enrichment analysis revealed that these genes are involved in specific biological processes, including cell migration, immune response, and metabolic regulation, which are closely linked to RAU pathogenesis. CONCLUSION: This systematic study comprehensively investigates the critical causative genes underlying RAU, emphasizing the intricate relationships between immune regulation and metabolic disturbances in its pathology. These findings lay a solid foundation for the development of novel biomarkers and may inform future research on targeted therapeutic strategies for RAU.

Stomatitis, Aphthous↗

PubMed2026

Gut microbiota-derived metabolites target C5AR1/KDM2A/HCAR3 axis in inflammatory bowel disease: a multi-machine learning algorithms and molecular docking study.

BACKGROUND: Inflammatory bowel disease (IBD) is a chronic recurrent disorder. Gut microbiota-derived metabolites regulate intestinal homeostasis, but their molecular mechanisms in IBD remain unclear. Current studies lack systematic "microbiota-metabolite-target" network mining with multi-method validation. This study integrates network pharmacology, three machine learning algorithms, and molecular docking to construct this regulatory network in IBD. METHODS: Transcriptome data were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using limma (p < 0.05, |log2FC| > 0.5). Weighted gene co-expression network analysis (WGCNA) with an optimal soft threshold of β = 7 was performed to identify key module genes. Candidate genes were obtained by intersecting DEGs, gut microbiota-associated genes from the gutMGene database, and WGCNA module genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to explore the functional roles of candidate genes. Core genes were identified using three machine learning algorithms (LASSO, Boruta, and SVM-RFE), followed by protein-protein interaction (PPI) network analysis. Molecular docking was performed to assess the binding affinities between hub proteins and gut microbiota-derived metabolites. RESULTS: A total of 885 DEGs were identified between the IBD and control groups, including 463 upregulated and 422 downregulated genes. WGCNA identified 280 key module genes from the purple and yellow modules. The intersection of DEGs, gut microbiota-associated genes, and WGCNA module genes yielded 19 core candidate genes. PPI network analysis combined with three machine learning algorithms jointly identified C5AR1, KDM2A, and HCAR3 as core hub genes. ROC curve analysis demonstrated that all three hub genes achieved AUC values greater than 0.7 in both the training and validation sets, indicating excellent diagnostic performance for IBD. Enrichment analysis revealed significant associations with the TNF, NF-κB, and IL-17 signaling pathways. Molecular docking confirmed stable binding of C5AR1 with 1,3-Diphenylpropan-2-Ol (-7.87 ± 0.83 kcal·mol-¹) and HCAR3 with 3-Indolepropionic Acid (-6.35 ± 0.70 kcal·mol-¹), both below -5.0 kcal·mol-¹. CONCLUSION: This study first constructs a "gut microbiota-metabolite-hub gene" axis in IBD, providing a computational framework for microbiota-targeted precision therapy, and identifying C5AR1/KDM2A/HCAR3 as computationally predicted diagnostic biomarkers and 1,3-Diphenylpropan-2-Ol/3-Indolepropionic Acid as candidate intervention molecules that warrant further experimental validation.

Molecular Docking Simulation↗

PubMed2005

NCBI GEO: mining millions of expression profiles--database and tools.

The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) is the largest fully public repository for high-throughput molecular abundance data, primarily gene expression data. The database has a flexible and open design that allows the submission, storage and retrieval of many data types. These data include microarray-based experiments measuring the abundance of mRNA, genomic DNA and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. GEO currently holds over 30,000 submissions representing approximately half a billion individual molecular abundance measurements, for over 100 organisms. Here, we describe recent database developments that facilitate effective mining and visualization of these data. Features are provided to examine data from both experiment- and gene-centric perspectives using user-friendly Web-based interfaces accessible to those without computational or microarray-related analytical expertise. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.

Animals↗

PubMed2006

NCBI GEO: mining tens of millions of expression profiles--database and tools update.

The Gene Expression Omnibus (GEO) repository at the National Center for Biotechnology Information (NCBI) archives and freely disseminates microarray and other forms of high-throughput data generated by the scientific community. The database has a minimum information about a microarray experiment (MIAME)-compliant infrastructure that captures fully annotated raw and processed data. Several data deposit options and formats are supported, including web forms, spreadsheets, XML and Simple Omnibus Format in Text (SOFT). In addition to data storage, a collection of user-friendly web-based interfaces and applications are available to help users effectively explore, visualize and download the thousands of experiments and tens of millions of gene expression patterns stored in GEO. This paper provides a summary of the GEO database structure and user facilities, and describes recent enhancements to database design, performance, submission format options, data query and retrieval utilities. GEO is accessible at http://www.ncbi.nlm.nih.gov/geo/

Computer Graphics↗

PubMed2006

Development of a GIS-based, real-time Internet mapping tool for rabies surveillance.

BACKGROUND: Oral rabies vaccination programs have been implemented to control the spread of wildlife rabies in the United States. However, current surveillance systems are inadequate for the efficient management and evaluation of these large scale vaccine baiting programs. With this in mind, a GIS-based rabies surveillance database and Internet mapping application was created. This surveillance system, RabID, provides a new resource for the rapid mapping and dissemination of data on animal rabies cases in relation to unaffected, enzootic, and baited areas where current interventions are underway. RESULTS: RabID is a centralized database for diagnostic and demographic information collected by local, state, and federal agencies involved in rabies surveillance. The geo-referenced database remits data to an Internet-accessible mapping application that displays rabies surveillance data in relation to environmental and geographic features. CONCLUSION: RabID provides a pioneering example of the power of geographically based Internet-accessible, infectious disease surveillance. This surveillance system was developed from existing technology and is readily adaptable to other infectious diseases and may be particularly useful for zoonoses. The development and application of public health informatics technology may enhance the effectiveness of public health interventions and allow better evaluation of public health interventions.

Animals↗