Evidence map›Paper›PMID 41664333›Full record

ArticleCurrent medicinal chemistry2026

Identification of Small Extracellular Vesicle (sEV)-Related Genes and the Establishment of Diagnostic Models for Esophageal Cancer.

Chenyang Wang, Yichen Sun, Wanli Yang, Liu Hong, Yabo Zhao

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Article in Current medicinal chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Chenyang WangDepartment of Digestive Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China.
Yichen SunDepartment of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi'an, China.
Wanli YangDepartment of Digestive Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China.
Liu HongDepartment of Digestive Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China.
Yabo ZhaoDepartment of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi'an, China.

Funding

Independent Funds of the Key Laboratory CBSKL2022ZZ37National Natural Science Foundation of China 82303427, 82372693Shaanxi Provincial Outstanding Youth Fund 2024JC-JCQN-77
6 · The paper itself

Abstract

introductionEsophageal cancer (EC) remains a global health challenge with high incidence and mortality. While sEV-related genes (ERGs) are emerging as key players in the tumor microenvironment, their diagnostic and pathogenic roles in EC are still poorly characterized.

methodsThis study employed integrated bioinformatics approaches to identify ERGbased diagnostic biomarkers and molecular mechanisms in EC. Four GEO datasets were analyzed to identify differentially expressed genes (DEGs), which were cross-referenced with ERGs from GeneCards and the literature. Functional enrichment analyses (GO/KEGG), Cytoscape network analysis, and machine learning algorithms (LASSO, SVM, random forest) were used to prioritize diagnostic markers. Immune infiltration patterns were assessed via ssGSEA, and key genes were validated by qRT‒PCR in EC and normal cells.

resultsWe identified 456 DEGs, including 34 sEV-related ERDEGs enriched in bacterial defense response, secretory granule lumen, calcium-dependent protein binding, and pathways such as proteoglycans in cancer and IL-17 signaling. GSEA highlighted cell cycle, ECM-receptor interaction, and DNA replication as central mechanisms. DISCUSSION: Machine learning pinpointed EpCAM and RUVBL1 as robust diagnostic biomarkers, validated by their elevated expression in EC cell lines. Immune infiltration analysis revealed correlations between these genes and 20 immune cell types, underscoring their relevance to the tumor microenvironment.

conclusionEpCAM and RUVBL1 are promising diagnostic biomarkers for EC, linking sEV-mediated pathways to disease progression. These findings provide insights into EC pathogenesis and highlight potential therapeutic targets.

Indexed as

Biomarkers, TumorEsophageal NeoplasmsExtracellular VesiclesComputational BiologyGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMachine LearningTumor MicroenvironmentBiomarkers, Tumordiagnostic markerEsophageal cancerGEOImmune cellimmune microenvironmentsmall extracellular vesicles

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.