Abstract
Background. The aim of this study was to identify novel biomarkers associated with esophageal squamous cell carcinoma (ESCC) prognosis. Methods. 81 ESCC samples collected from The Cancer Genome Atlas (TCGA) were used as the training set, and 179 ESCC samples collected from the Gene Expression Omnibus database (GEO) were used as the validation set. The protein-coding genes of 25 samples from patients who completed the follow-up in TCGA were analyzed to construct a coexpression network by weighted gene coexpression network analysis (WGCNA). Gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analyses were performed for the selected genes. The least absolute shrinkage and selection operator (LASSO) Cox regression model was constructed to analyze survival-related genes, and an optimal prognostic model was developed as well as evaluated by Kaplan–Meier and ROC curves. Results. In this study, a module containing 43 protein-coding genes and strongly related to overall survival (OS) was identified through WGCNA. These genes were significantly enriched in retina homeostasis, antimicrobial humoral response, and epithelial cell differentiation. Besides, through the LASSO regression model, 3 genes (PDLIM2, DNASE1L3, and KRT81) significantly related to ESCC survival were screened and an optimal prognostic 3-gene risk prediction model was constructed. ESCC patients with low and high OS in both sets could be successfully discriminated by calculating a risk score with the linear combination of the expression level of each gene multiplied by the LASSO coefficient. Conclusions. Our study identified three novel biomarkers that have potential in the prognosis prediction of ESCC.
Highlights
Esophageal cancer (EC) is a highly aggressive malignancy and one of the leading causes of cancer-related death worldwide [1]. ere are two histologic subtypes of EC: esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC)
81 ESCC samples collected from e Cancer Genome Atlas (TCGA) were used as the training set, and 179 ESCC samples collected from the Gene Expression Omnibus database (GEO) were used as the validation set. e protein-coding genes of 25 samples from patients who completed the follow-up in TCGA were analyzed to construct a coexpression network by weighted gene coexpression network analysis (WGCNA)
Our study identified three novel biomarkers that have potential in the prognosis prediction of ESCC
Summary
Esophageal cancer (EC) is a highly aggressive malignancy and one of the leading causes of cancer-related death worldwide [1]. ere are two histologic subtypes of EC: esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). ESCC is the main type of EC, accounting for about 90% [2]. The main treatments for ESCC include chemotherapy, radiation, and surgery [3]. Despite significant advances in the treatment of ESCC in recent years, the overall 5-year survival rate for ESCC patients is still less than 25%, and the prognosis is still poor, with metastasis and recurrence frequently occurring [4]. Due to late diagnosis and the lack of effective targeted therapy, most patients are diagnosed at an advanced stage [5]. Many genes and mechanisms have been shown to be closely related to the occurrence and development of ESCC, the overall genes and regulation of ESCC Due to late diagnosis and the lack of effective targeted therapy, most patients are diagnosed at an advanced stage [5]. erefore, it is urgent to explore new therapeutic methods and new therapeutic targets.
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