Abstract Background Esophageal squamous cell carcinoma (ESCC) is one of the most common malignancies, characterized by high heterogeneity an
Abstract Background Esophageal squamous cell carcinoma (ESCC) is one of the most common malignancies, characterized by high heterogeneity and poor outcomes. Effective classification for patient stratification and identifying reliable markers for prognosis prediction and treatment choice are crucial. Methods Integration of single-cell RNA-sequencing (RNA-seq) and bulk RNA-seq analyses were used to characterize ESCC. Non-negative matrix factorization (NMF) clustering was performed to stratify the ESCC patients into different subtypes and the clinical and pathological features of the ESCC subtypes were compared. Cox regression analysis and LASSO regression analysis were used to select key genes and construct a risk model for ESCC. The associations of the key genes with anti-cancer drug sensitivities in ESCC cell lines were investigated. RT-qRCR experiments, proteomics analysis, and multiplex immunohistochemistry (mIHC) experiments were used to validate the results. Furthermore, one identified gene was selected to investigate its correlation with EGFR expression and the gene effect scores of various potential gene targets across pan-cancer. Results The study identified the dysregulated distributions of epithelial cells and fibroblasts as characteristic of ESCC. ESCC patients could be classified into four distinct subtypes with unique cell type features and prognoses. With the gene makers of the cell type features, a four-gene prognostic signature for ESCC was constructed. The CCND1-PKP1-JUP-ANKRD12 model could effectively discriminate the survival status of ESCC patients, independent of various pathological and clinical features. The risk score for the samples was correlated with the expression levels of immunoregulatory genes. The prognostic effects of CCND1, PKP1, and JUP were confirmed at the protein level. The phosphorylation levels of PKP1, JUP, and ANKRD12 were found to be dysregulated in ESCC tumors. Their expression dysregulation and heterogeneity were demonstrated in ESCC cell lines. All four genes were significantly correlated with at least one of the anti-cancer drug sensitivities in ESCC cell lines. PKP1 expression was significantly correlated with EGFR expression and gene effect scores in multiple cancers. Conclusions We conclude that the CCND1-PKP1-JUP-ANKRD12 signature may serve as a novel indicator for ESCC prognosis and diagnosis. PKP1 expression might provide new clues for gene therapy efficacy in multiple cancers.
