{"title":"Secreted proteins encoded by super enhancer-driven genes could be promising biomarkers for early detection of esophageal squamous cell carcinoma","authors":"","doi":"10.1016/j.bj.2023.100662","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Early detection of cancer remains an unmet need in clinical practice, and high diagnostic sensitivity and specificity biomarkers are urgently required. Here, we attempted to identify secreted proteins encoded by super-enhancer (SE)-driven genes as diagnostic biomarkers for esophageal squamous cell carcinoma (ESCC).</p></div><div><h3>Methods</h3><p>We conducted an integrative analysis of multiple data sets including ChIP-seq data, secretome data, CCLE data and GEO data to screen secreted proteins encoded by SE-driven genes. Using ELISA, we further identified up-regulated secreted proteins through a small size of clinical samples and verified in a multi-centre validation stage (345 in test cohort and 231 in validation cohort). Receiver operating characteristic curves were used to calculate diagnostic accuracy. Artificial intelligence (AI) method named gradient boosting machine (GBM) were applied for model construction to enhance diagnostic accuracy.</p></div><div><h3>Results</h3><p>Serum EFNA1 and MMP13 were identified, and showed significantly higher levels in ESCC patients compared to normal controls. An integrated Five-Biomarker Panel (iFBPanel) established by combining EFNA1, MMP13, carcino-embryonic antigen, Cyfra21-1 and squmaous cell carcinoma antigen had AUCs of 0.881 and 0.880 for ESCC in test and validation cohorts, respectively. Importantly, the iFBPanel also exhibited good performance in detecting early-stage ESCC patients (0.872 and 0.864). Furthermore, the iFBPanel was further empowered by AI technology which showed excellent diagnostic performance in early-stage ESCC (0.927 and 0.907).</p></div><div><h3>Conclusions</h3><p>Our study suggested that serum EFNA1 and MMP13 could potentially assist ESCC detection, and provided an easy-to-use detection model that might help the diagnosis of early-stage ESCC.</p></div>","PeriodicalId":8934,"journal":{"name":"Biomedical Journal","volume":"47 4","pages":"Article 100662"},"PeriodicalIF":4.1000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2319417023000999/pdfft?md5=4504f2fd3690b113780b1da191002a7e&pid=1-s2.0-S2319417023000999-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Journal","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2319417023000999","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background
Early detection of cancer remains an unmet need in clinical practice, and high diagnostic sensitivity and specificity biomarkers are urgently required. Here, we attempted to identify secreted proteins encoded by super-enhancer (SE)-driven genes as diagnostic biomarkers for esophageal squamous cell carcinoma (ESCC).
Methods
We conducted an integrative analysis of multiple data sets including ChIP-seq data, secretome data, CCLE data and GEO data to screen secreted proteins encoded by SE-driven genes. Using ELISA, we further identified up-regulated secreted proteins through a small size of clinical samples and verified in a multi-centre validation stage (345 in test cohort and 231 in validation cohort). Receiver operating characteristic curves were used to calculate diagnostic accuracy. Artificial intelligence (AI) method named gradient boosting machine (GBM) were applied for model construction to enhance diagnostic accuracy.
Results
Serum EFNA1 and MMP13 were identified, and showed significantly higher levels in ESCC patients compared to normal controls. An integrated Five-Biomarker Panel (iFBPanel) established by combining EFNA1, MMP13, carcino-embryonic antigen, Cyfra21-1 and squmaous cell carcinoma antigen had AUCs of 0.881 and 0.880 for ESCC in test and validation cohorts, respectively. Importantly, the iFBPanel also exhibited good performance in detecting early-stage ESCC patients (0.872 and 0.864). Furthermore, the iFBPanel was further empowered by AI technology which showed excellent diagnostic performance in early-stage ESCC (0.927 and 0.907).
Conclusions
Our study suggested that serum EFNA1 and MMP13 could potentially assist ESCC detection, and provided an easy-to-use detection model that might help the diagnosis of early-stage ESCC.
期刊介绍：
Biomedical Journal publishes 6 peer-reviewed issues per year in all fields of clinical and biomedical sciences for an internationally diverse authorship. Unlike most open access journals, which are free to readers but not authors, Biomedical Journal does not charge for subscription, submission, processing or publication of manuscripts, nor for color reproduction of photographs.
Clinical studies, accounts of clinical trials, biomarker studies, and characterization of human pathogens are within the scope of the journal, as well as basic studies in model species such as Escherichia coli, Caenorhabditis elegans, Drosophila melanogaster, and Mus musculus revealing the function of molecules, cells, and tissues relevant for human health. However, articles on other species can be published if they contribute to our understanding of basic mechanisms of biology.
A highly-cited international editorial board assures timely publication of manuscripts. Reviews on recent progress in biomedical sciences are commissioned by the editors.
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