Identification of novel biomarkers with potential for diagnosis and prognosis of gastric cancer: a Bioinformatics Approach

Abstract

Introduction: Gastric cancer (GC) is the fifth most diagnosed neoplasia and the third leading cause of cancer-related deaths. A substantial number of patients exhibit an advanced GC stage once diagnosed. Therefore, the search for biomarkers contributes to the improvement and development of therapies. Objective: This study aimed to identify potential GC biomarkers making use of in silico tools. Methods: Gastric tissue microarray data available in Gene Expression Omnibus and The Cancer Genome Atlas Program was extracted. We applied statistical tests in the search for differentially expressed genes between tumoral and non-tumoral adjacent tissue samples. The selected genes were submitted to an in-house tool for analyses of functional enrichment, survival rate, histological and molecular classifications, and clinical follow-up data. A decision tree analysis was performed to evaluate the predictive power of the potential biomarkers. Results: In total, 39 differentially expressed genes were found, mostly involved in extracellular structure organization, extracellular matrix organization, and angiogenesis. The genes SLC7A8, LY6E, and SIDT2 showed potential as diagnostic biomarkers considering the differential expression results coupled with the high predictive power of the decision tree models. Moreover, GC samples showed lower SLC7A8 and SIDT2 expression, whereas LY6E was higher. SIDT2 demonstrated a potential prognostic role for the diffuse type of GC, given the higher patient survival rate for lower gene expression. Conclusion: Our study outlines novel biomarkers for GC that may have a key role in tumor progression. Nevertheless, complementary in vitro analyses are still needed to further support their potential.

Keywords

• stomach neoplasms
• computational biology
• gene expression
• biomarkers, tumor
• tissue array analysis
• decision trees