Secreted proteins encoded by super enhancer-driven genes could be promising biomarkers for early detection of esophageal squamous cell carcinoma.

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.
Competing Interests: Conflict of interest The author has no potential conflicts of interest to disclose.
(© 2023 The Authors. Published by Elsevier B.V. on behalf of Chang Gung University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)