BACKGROUND: Esophageal squamous cell cancer (ESCC) is the most common type of esophageal cancer. This study aimed to elucidate the role of Saccharomyces cerevisiae-like 4 (SEC14L4) in ESCC. METHODS: To elucidate the role of SEC14L4 in ESCC, this study analyzed the clinical data, gene sequencing data, and other relevant data retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information. The methodology involved several analytical approaches, including nomogram model analysis, co-expression analysis, gene set enrichment and variation analysis, weighted correlation network analysis, drug susceptibility analysis, and single-cell analysis. These methods were employed to evaluate the significance of SEC14L4 in ESCC. The expression of SEC14L4 was evaluated via quantitative real-time polymerase chain reaction (qRT-PCR). RESULTS: SEC14L4 expression (P<0.001) was significantly elevated in those with ESCC, especially in patients with locally advanced disease (P=0.005), and indicated a poor prognosis (P=0.045). Findings from the nomogram model analysis identified the contribution of clinical indicators to survival prediction with good efficacy. Subsequently, the single-nucleotide polymorphisms and co-expressed genes of SEC14L4 were identified. Furthermore, pathways associated with SEC14L4, including DNA metabolic process, transcription factor binding, apoptosis, and others, were examined. Notably, SEC14L4 expression was predominantly observed in monocytes. Drug sensitivity analysis indicated the association of SEC14L4 expression with sensitivity of ESCC to the common chemotherapy drugs AICAR, BMS.708163, GNF.2, Nutlin.3a, PD.0325901, and RDEA119. Verification of the high expression of SEC14L4 in KYSE520 and KYSE150 was conducted, thereby confirming the study's findings. CONCLUSIONS: High expression of SEC14L4 is associated with poorer clinical outcomes, highlighting its potential as a therapeutic target and suggesting its involvement in the molecular mechanisms underlying ESCC.

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Wang A, Wang Y, Chen Y, Wan P, Saeed A, Ma Q, Chen X

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