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In silico assessment of EpCAM transcriptional expression and determination of the prognostic biomarker for human lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC).

ABSTRACT: Epithelial cell adhesion molecule (EpCAM) is a transmembrane glycoprotein which is involved in cell signaling, proliferation, maturation, and movement, all of which are crucial for the proper development of cells and tissues. Cleavage of the EpCAM protein leads to the up-regulation of c-myc, e-fabp, and cyclins A and E which promote tumorigenesis. EpCAM can act as potential diagnostic and prognostic biomarker for different types of cancers as it is also found to be expressed in epithelia and epithelial-derived neoplasms. Hence, we aimed to analyze the EpCAM gene expression and any associated feedback in the patients of two major types of lung cancer (LC) i.e., lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), based on the publicly available online databases. In this study, server-based gene expression analysis represents the up-regulation of EpCAM in both LUAD and LUSC subtypes as compared to the corresponding normal tissues. Besides, the histological sections revealed the over-expression of EpCAM protein in cancerous tissues by depicting strong staining signals. Furthermore, mutation analysis suggested missense as the predominant type of mutation both in LUAD and LUSC in the EpCAM gene. A significant correlation (P-value < 0.05) between the higher EpCAM expression and lower patient survival was also found in this study. Finally, the co-expressed genes were identified with their ontological features and signaling pathways associated in LC development. The overall study suggests EpCAM to be a significant biomarker for human LC prognosis.

SUBMITTER: Moin AT

PROVIDER: S-EPMC8319582 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundLung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients.MethodsRaw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature.ResultsA prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041-39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779-3.276]; P < 0.0001). Cox regression analysis (univariate and multivariate) demonstrated that the seven-gene signature is an independent prognostic biomarker for predicting the survival of LUAD patients. ROC curves revealed that the 7-gene prognostic signature achieved a good performance in training and validation groups (AUC = 0.91, AUC = 0.7 respectively) in predicting OS for LUAD patients. Furthermore, the stratified analysis of the signature showed another classification to predict the prognosis.ConclusionOur study suggested a new and reliable prognostic signature that has a significant implication in predicting overall survival for LUAD patients and may help with early diagnosis and making effective clinical decisions regarding potential individual treatment.

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In silico assessment of EpCAM transcriptional expression and determination of the prognostic biomarker for human lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC).

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