A potential diagnostic and prognostic biomarker in gastric cancer
an in silico analysis of the GPNMB gene
DOI:
https://doi.org/10.18226/25253824.v8.n13.13Keywords:
Stomach Cancer, Differential Expression, Artificial Intelligence, Gene Expression Omnibus, The Cancer Genome AtlasAbstract
Gastric cancer is the fourth most common and third deadliest cancer worldwide. Patients are usually asymptomatic or do not show specific symptoms during initial stages, which may hamper the diagnosis. A previous study identified 39 genes with biomarker potential in gastric cancer, among them the GPNMB gene. In this context, the objective of this study was to explore GPNMB as a prognostic and diagnostic biomarker for gastric cancer. Expression data was extracted from Gene Expression Omnibus (GSE33335 and GSE54129) and The Cancer Genome Atlas (TCGA-STAD). Data acquisition, preprocessing and statistical analyses were performed with an inhouse developed tool. The K-means and decision tree algorithms were applied for determining the potential of the gene as a diagnostic biomarker, whereas the survival analysis verified the influence of expression on prognosis. GPNMB expression was higher in tumoral tissue samples when compared to non-tumoral adjacent tissue (NT). K-means allowed formation of independent groups with normal and NT samples. Similarly, samples were correctly classified into normal and NT tissue groups with the decision tree according to expression values. Additionally, the survival analyses showed that the high expression of the GPNMB gene is associated with a worse prognosis. This research provided evidence on the potential of GPNMB as a biomarker for gastric cancer, given the gene demonstrated an important role in disease development.
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Copyright (c) 2024 Bianca de Andrade Lopes, Fernanda Pessi de Abreu, Pedro Lenz Casa, Marcos Vinícius Rossetto, Scheila de Avila e Silva
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