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EMCN is associated with vascular-immune crosstalk and represents a potential biomarker in lung adenocarcinoma

Front Mol Biosci. 2026 Aug 12;13:1752442. doi: 10.3389/fmolb.2026.1752442. eCollection 2026.

ABSTRACT

BACKGROUND: While MUC family genes have been established as prognostic biomarkers in gastric cancer, and GWAS studies link EMCN mutations to chemotherapy-induced myelosuppression in NSCLC, the systematic characterization of EMCN in lung adenocarcinoma (LUAD) remains elusive.

METHODS: This multi-omics strategy combining bulk and single-cell transcriptomics study integrated differential expression analysis, WGCNA, and machine learning algorithms (LASSO/SVM-RFE/Random Forest) to identify EMCN as a diagnostic hub gene, followed by experimental validation using immunohistochemistry Western blot and qRT-PCR.

RESULTS: EMCN (Endomucin) is a sialomucin-like glycoprotein predominantly expressed in vascular endothelial cells. Using bulk transcriptomic datasets and single-cell RNA-seq analysis, we found that EMCN expression was reduced in lung adenocarcinoma (LUAD) compared with non-tumor controls and was primarily localized to the endothelial compartment. Survival analysis using the median expression cutoff showed that high EMCN expression was associated with improved overall survival (Cox HR_high vs. low = 0.73, p = 0.04), indicating that low EMCN expression correlates with poorer prognosis. Machine learning-based feature selection (LASSO, Random Forest, and SVM) further prioritized EMCN among consensus candidate genes, supporting its potential relevance to the vascular-associated tumor microenvironment in LUAD. EMCN expression levels also showed a significant positive correlation with the degree of immune cell infiltration. Gene set enrichment analysis (GSEA) revealed that high EMCN expression in tumor tissues activates negative regulatory pathways associated with angiogenesis. Receiver operating characteristic (ROC) curve analysis highlights EMCN's excellent diagnostic potential for LUAD, with an area under the curve (AUC) of 0.963. In vitro experiments confirm the downregulation of EMCN at both protein and mRNA levels, consistent with our bioinformatics predictions.

CONCLUSION: This first comprehensive study establishes EMCN as a dual-functional regulator of vascular-immune crosstalk in LUAD, providing both a molecular diagnostic tool and therapeutic target for precision oncology.

PMID:42656419 | PMC:PMC13506425 | DOI:10.3389/fmolb.2026.1752442

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S100A6 promotes liver metastasis by activating FGFR3 signaling in <i>BAP1</i>-deficient uveal melanoma

Oncogene, Published online: 04 April 2026; doi:10.1038/s41388-026-03766-0

S100A6 promotes liver metastasis by activating FGFR3 signaling in BAP1-deficient uveal melanoma
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Survey of Computerized Adaptive Testing: A Machine Learning Perspective

arXiv:2404.00712v3 Announce Type: replace-cross Abstract: Computerized Adaptive Testing (CAT) offers an efficient and personalized method for assessing examinee proficiency by dynamically adjusting test questions based on individual performance. Compared to traditional, non-personalized testing methods, CAT requires fewer questions and provides more accurate assessments. As a result, CAT has been widely adopted across various fields, including education, healthcare, sports, sociology, and the evaluation of AI models. While traditional methods rely on psychometrics and statistics, the increasing complexity of large-scale testing has spurred the integration of machine learning techniques. This paper aims to provide a machine learning-focused survey on CAT, presenting a fresh perspective on this adaptive testing paradigm. We delve into measurement models, question selection algorithm, bank construction, and test control within CAT, exploring how machine learning can optimize these components. Through an analysis of current methods, strengths, limitations, and challenges, we strive to develop robust, fair, and efficient CAT systems. By bridging psychometric-driven CAT research with machine learning, this survey advocates for a more inclusive and interdisciplinary approach to the future of adaptive testing.
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