ArticleAmerican journal of cancer research2025
Multi-omics profiling reveals PLEKHA6 as a modulator of β-catenin signaling and therapeutic vulnerability in lung adenocarcinoma.
Article in American journal of cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Multi-Omics and Single-Cell Dissection Reveals EXT1 as a Glycosylation-Linked Therapeutic Target in Cancer.Oncology research · 2026Article
- An integrated bulk and single-cell transcriptomic analysis reveals stemness-driven immune regulation and therapeutic vulnerability in colorectal cancer.Journal of Cancer · 2026Article
- Single-cell and transcriptomic profiling reveal stemness-driven immune evasion in obstructive sleep apnea (OSA) associated lung cancer.Journal of Cancer · 2026Article
- Proteasome Assembly Chaperone 3 Defines Metabolic-Immune Programs and Poor Prognosis in Breast Cancer via Multi-Omics Approaches.Journal of Cancer · 2026Article
- Integrative Multi-Omics and Single-Cell Profiling Identify Chitinase Domain Containing Protein 1 (CHID1) as a Prognostic Biomarker in Glioblastoma.Journal of Cancer · 2026Article
- Integrative Multi-Omics and Single-Cell Analysis Reveal THOC3 and THOC7 as Oncogenic RNA Processing Regulators in Lung Adenocarcinoma.International journal of medical sciences · 2026Article
- Comprehensive characterization of AP-1 adaptor complex genes in lung cancer reveals AP1AR as a novel prognostic and therapeutic biomarker.Journal of Cancer · 2026Article
- Multi-Omics and Single-Cell Dissection of Exostosin Glycosyltransferases (EXT1/EXT2) Reveals Divergent Oncogenic Roles and Therapeutic Vulnerabilities in Gliomas.Journal of Cancer · 2026Article
- Mechanistically informed machine learning links non-canonical TCA cycle activity to Warburg metabolism and hallmarks of malignancy.PLoS computational biology · 2025Article
- A machine learning framework using urinary biomarkers for pancreatic ductal adenocarcinoma prediction with post hoc validation via single-cell transcriptomics.Briefings in bioinformatics · 2025Article
- Discovery, Optimization, and Anticancer Activity of Lipid-Competitive Pleckstrin Homology Domain-Containing Family A Inhibitors.Journal of medicinal chemistry · 2025Article
- Metal ion transporter SLC39A14-mediated ferroptosis and glycosylation modulate the tumor immune microenvironment: pan-cancer multi-omics exploration of therapeutic potential.Cancer cell international · 2025Article
- Multi-level Transcriptomic and Machine-learning Analyses IdentifyCancer genomics & proteomicsArticle
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17 authors.
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Abstract
Lung adenocarcinoma (LUAD) remains the most prevalent and lethal subtype of lung cancer, largely due to late diagnosis and therapeutic resistance. In this study, we conducted a comprehensive multi-omics analysis to characterize the pleckstrin homology domain-containing (PLEKHA) family gene in LUAD. Among the eight members, PLEKHA6 was uniquely overexpressed in LUAD tissues and significantly associated with poor prognosis. Integrated bulk RNA-Seq, single-cell RNA-Seq, DNA methylation, and pharmacogenomic analyses identified PLEKHA6 as a key modulator of oncogenic processes, including Wnt/β-catenin signaling, cadherin-mediated adhesion, and cytoskeletal remodeling. Functional assays in A549 LUAD cells revealed that PLEKHA6 knockdown suppressed β-catenin and VE-cadherin expression, leading to impaired proliferation, migration, and colony formation, along with enhanced apoptosis and cell cycle arrest. Single-cell RNA sequencing demonstrated a correlation between PLEKHA6 expression and tumor-associated macrophage (TAM) infiltration, implicating PLEKHA6 in immune remodeling within the tumor microenvironment (TME). Drug sensitivity analysis and molecular docking further identified potential therapeutic drugs targeting PLEKHA6-expressing LUAD cells. Collectively, our findings establish PLEKHA6 as a novel oncogenic driver and immune modulator in LUAD, supporting its potential as both a prognostic biomarker and a therapeutic target for precision oncology.
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