Resveratrol Modulates Lung Cancer-Associated Hub Genes Identified by Integrated Bioinformatics, Single-Cell Analysis, Molecular Docking, and Experimental Validation.

Integrated bioinformatics and molecular docking identify novel key genes, pathways, and potential candidate drugs in papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is the most prevalent form of well-differentiated cancer of the thyroid gland. In the current study, we applied integrated bioinformatics analyses to introduce key genes with diagnostic and prognostic value, and decipher the signaling pathways involved in patients with PTC. We analyzed four integrated profiles from Gene Expression Omnibus (GEO) including, GSE58545, GSE3467, GSE29265, and GSE60542. GEO2R was utilized to analyze and detect differentially expressed genes (DEGs), and then the results of four datasets were integrated. Gene ontology (GO), KEGG-related pathway, protein-protein interaction (PPI) network, survival, and immunohistochemical assessment were performed by DAVID, ShinyGO, STRING, GEPIA, and Human Protein Atlas (HPA), respectively. In addition, we performed molecular docking of hub genes with 1615 FDA approved drugs. The findings of GO enrichment indicated that the up- and down-regulated genes were mostly participating in cell adhesion, extracellular region, and bicarbonate transport, proteinaceous extracellular receptor complex, respectively. Pathway enrichment indicated that the upregulated genes were linked with ECM-receptor interaction, and downregulated genes were largely involved in tyrosine metabolism and the JAK/STAT signaling pathway. The FN1, DPP, CD36, KIT, and ITGA2 proteins and hsa-mir-124-3p were identified as druggable target genes. According to docking analysis, Olysio, Gabapentin, Naldemedine, Imatinib and Enzacamene were drugs that exhibited strong interactions with the target proteins. The findings of the current study offer that FN1, DPP4, CD36, KIT, ITGA2 and hsa-miR-124-3p may be novel biosignatures and therapeutic targets for PTC. In addition, it was demonstrated that discovered pharmaceuticals may be employed as prospective PTC treatments.

Transcriptomic network analysis and functional evidence identify a candidate EMT hub signature associated with nasopharyngeal carcinoma progression.

Investigating the mechanisms of malignant progression in colorectal cancer using weighted gene co-expression network analysis and machine learning

Integrated Bioinformatic and Experimental Analysis Reveals the Molecular Mechanisms Underlying KDM1B/LSD2 Inhibition as a Therapeutic Strategy in Human Lung Adenocarcinoma.

Lung cancer remains a major global health challenge, and the oncogenic function of KDM1B (Lysine-specific Demethylase 1B) is still poorly characterized. This study employed integrated bioinformatics and experimental approaches to investigate KDM1B's function in lung cancer. Pan-cancer analysis using databases such as TIMER revealed notably elevated KDM1B mRNA expression in LUAD datasets, suggesting its potential as a diagnostic biomarker. A strong association was also found between increased KDM1B levels and immune cell infiltration in LUAD datasets. Protein interaction networks constructed using STRING and Cytoscape revealed close associations between KDM1B and key regulatory genes in NSCLC. KEGG enrichment analysis linked KDM1B to the mTOR signaling, which is critical for cell proliferation and survival. RT-PCR and western blotting for experimental validation showed KDM1B expression was significantly increased in A549 and NCI-H460 lung cancer cells. The deletion of KDM1B inhibits cell growth, induces G0/G1 phase cell cycle arrest, and promotes apoptosis in A54 cells. Moreover, cell proliferation was significantly inhibited by the KDM1B inhibitor, tranylcypromine, and induced G0/G1 phase cell cycle arrest, increased apoptosis, ROS, and glycolytic activity in A549 cells. Collectively, these findings highlight KDM1B as a valuable therapeutic target in lung adenocarcinoma and emphasize its key role in lung cancer development.

Integrative Bioinformatics and Experimental Validation Reveal that METTL9 Drives Lung Adenocarcinoma Progression via TGF-β/Smad-Mediated EMT Activation.

BACKGROUND Methyltransferase-like 9 (METTL9) has been implicated in tumor progression, yet its clinical significance, functional roles, and immunoregulatory functions in lung adenocarcinoma (LUAD) remain unclear. This study aimed to integrate multi-omics datasets and experimental evidence to systematically evaluate the expression profile, prognostic value, molecular mechanisms, and tumor microenvironment (TME)-related functions of METTL9 in LUAD. METHODS Transcriptome and clinical data for LUAD and METTL9 protein expression were obtained from public databases. Survival analysis was performed using Kaplan-Meier analysis and Cox proportional hazards models. METTL9-related differentially expressed genes (DEGs) were screened, followed by functional annotation and pathway enrichment analysis. A protein-protein interaction (PPI) network associated with METTL9 was constructed using STRING. METTL9 gene mutations and promoter methylation levels were analyzed via cBioPortal and UALCAN. Immune infiltration was evaluated through gene set variation analysis (GSVA) and the TIMER platform. The role of METTL9 depletion in regulating transforming growth factor-β (TGF-β)/Smad signaling, epithelial-mesenchymal transition (EMT), as well as the behavior of A549 and H1975 cells was investigated. RESULTS METTL9 was markedly elevated in LUAD, which was confirmed in external datasets and immunohistochemistry. Increased METTL9 expression showed a strong association with advanced TNM stage, unfavorable clinicopathological parameters, and poor survival outcomes in univariate analysis. Receiver operating characteristic (ROC) analysis indicated that METTL9 showed moderate diagnostic utility for LUAD, with an Area Under the Curve (AUC) of 0.704 (95% Confidence Interval [CI]: 0.650-0.758) in the The Cancer Genome Atlas (TCGA) dataset and 0.815 (95% CI: 0.758-0.872) in the GSE31210 dataset. Moreover, high METTL9 expression was significantly correlated with poor survival outcomes (Hazard Ratio [HR] = 1.349, 95% CI: 1.010-1.802, p = 0.043). METTL9 interacted with multiple oncogenic proteins in the PPI network and showed significant correlations with cuproptosis-related genes. Immune infiltration analysis further demonstrated that METTL9 expression showed significantly associated with immune cell infiltration and the tumor immune microenvironment in LUAD. METTL9 knockdown suppressed LUAD cell proliferation, migration, invasion, and EMT, while promoting apoptosis and reducing the expression of B-cell lymphoma 2 (Bcl-2), phosphorylated Smad2 (p-smad2), and phosphorylated Smad3 (p-smad3). CONCLUSION METTL9 is upregulated in LUAD and correlates with an unfavorable prognosis. Our results indicate that METTL9 may contribute to LUAD progression and EMT, potentially in relation to the TGF-β/Smad signaling pathway.

Integrated Bioinformatics and RT-qPCR Validation Identify Candidate Biomarkers Associated with EGFR-TKI Resistance in EGFR-Mutant Lung Adenocarcinoma

Background: Acquired resistance to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) remains a major challenge in the treatment of EGFR-mutant lung adenocarcinoma. Identifying biomarkers associated with resistance may improve understanding of the underlying molecular mechanisms. Objective: This study aimed to identify candidate biomarkers associated with EGFR-TKI resistance using integrated bioinformatics analysis and RT-qPCR validation. Methods: Two Gene Expression Omnibus (GEO) datasets (GSE123066 and GSE178755) were analysed to identify differentially expressed genes (DEGs). Overlapping DEGs were subjected to protein–protein interaction (PPI) network construction, Gene Ontology (GO), KEGG pathway enrichment, and Kaplan–Meier survival analyses. Selected candidate genes were subsequently validated by RT-qPCR in an independent clinical cohort. Results: A total of 76 overlapping DEGs were identified, including 52 upregulated genes enriched in cell proliferation, hypoxia response, EGFR signalling, and metabolic processes. KEGG analysis identified the PI3K–Akt signalling pathway as significantly enriched. Kaplan–Meier analysis showed that higher expression of TGFA, DDIT4, and SOX9 was associated with shorter progression-free survival. RT-qPCR validation demonstrated significantly increased SOX9 expression in the resistant group, whereas TGFA and DDIT4 showed no statistically significant differences. Conclusions: Integrated bioinformatics analysis identified TGFA, DDIT4, and SOX9 as candidate genes associated with EGFR-TKI resistance in EGFR-mutant lung adenocarcinoma. These findings provide a basis for further investigation of resistance-associated biomarkers, although larger independent cohorts and functional studies are required before clinical application.