Identification of Potential Biomarkers in Stomach Adenocarcinoma using Machine Learning Approaches

Author:

Nazari Elham12,Pourali Ghazaleh1,Khazaei Majid3,Asadnia Alireza14,Dashtiahangar Mohammad5,Mohit Reza6,Maftooh Mina1,Nassiri Mohammadreza7,Hassanian Seyed Mahdi12,Ghayour-Mobarhan Majid1,Ferns Gordon A.8,Shahidsales Soodabeh9,Avan Amir124

Affiliation:

1. Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

2. Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran

3. Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.

4. Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

5. School of Medicine, Gonabad University of Medical Sciences, Gonabad, Iran

6. Department of Anesthesia, Bushehr University of Medical Sciences, Bushehr, Iran

7. Recombinant Proteins Research Group, The Research Institute of Biotechnology, Ferdowsi University of Mashhad, Mashhad, Iran

8. Brighton & Sussex Medical School, Division of Medical Education, Falmer, Brighton, Sussex, BN1 9PH, UK

9. Cancer Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

Abstract

Background: Stomach adenocarcinoma (STAD) is a common cancer with poor clinical outcomes globally. Due to a lack of early diagnostic markers of disease, the majority of patients are diagnosed at an advanced stage. Objective: The aim of the present study is to provide some new insights into the available biomarkers for patients with STAD using bioinformatics. Methods: RNA-Sequencing and other relevant data of patients with STAD from The Cancer Genome Atlas (TCGA) database were evaluated to identify differentially expressed genes (DEGs). Then, Machine Learning algorithms were undertaken to predict biomarkers. Additionally, Kaplan–Meier analysis was used to detect prognostic biomarkers. Furthermore, the Gene Ontology and Reactome pathways, protein-protein interactions (PPI), multiple sequence alignment, phylogenetic mapping, and correlation between clinical parameters were evaluated. Results: The results showed 61 DEGs, and the key dysregulated genes associated with STAD are MTHFD1L (Methylenetetrahydrofolate dehydrogenase 1-like), ZWILCH (Zwilch Kinetochore Protein), RCC2 (Regulator of chromosome condensation 2), DPT (Dermatopontin), GCOM1 (GRINL1A complex locus 1), and CLEC3B (C-Type Lectin Domain Family 3 Member B). Moreover, the survival analysis reported ASPA (Aspartoacylase) as a prognostic marker. Conclusion: Our study provides a proof of concept of the potential value of ASPA as a prognostic factor in STAD, requiring further functional investigations to explore the value of emerging markers.

Publisher

Bentham Science Publishers Ltd.

Subject

Computational Mathematics,Genetics,Molecular Biology,Biochemistry

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