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All Journal Eksakta : Berkala Ilmiah Bidang MIPA Indonesian Journal of Medical Chemistry and Bioinformatics
Fajarido, Ariski
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Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Mechanical Engineering

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Metabolite Biomarker Discovery for Lung Cancer Using Machine Learning Fajarido, Ariski; Erlina, Linda; Tedjo, Aryo; Fadilah, Fadilah; Arozal, Wawaimuli
Indonesian Journal of Medical Chemistry and Bioinformatics
Publisher : UI Scholars Hub

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Abstract

Lung cancer is the leading cause of cancer death worldwide. About 2.1 million lung cancer patients were diagnosed in 2018, accounting for about 11.6% of all newly diagnosed cancer cases. For lung cancer, blood is the first choice as a source of screening biomarker candidates. Blood biomarkers provide a snapshot of the patient's entire body, including the primary tumor, metastatic disease, immune response, and peritumoral stroma. However, sputum sampling, bronchial lavage or aspiration, exhaled breath (EB), and airway epithelial sampling represent unique samples for lung cancer and other airway cancers as potential sources for alternative biomarkers. Metabolites are products of cell metabolism that are unique biomarkers in a disease. In this article, we aim to find metabolite biomarkers using machine learning. Metabolite data were obtained from Metabolomic workbench, while detection and identification were performed in silico. From 82 samples, controls and cancers, we found 158 metabolites and analyzed them. From the analysis, we found 3 metabolites that play an important role in lung cancer and found 1 metabolite that is the most influential. From there we found that glutamic acid is one of the best biomarker candidates we provide for detecting lung cancer. However, this simulation still needs to be improved in order to find other biomarkers that can provide a better detection of lung cancer
Identification of Potential Biomarkers for Hypertensive Nephropathy by Bioinformatics Analysis Fajarido, Ariski; Fadilah, Fadilah; Wawaimuli Arozal
EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 25 No. 04 (2024): Eksakta : Berkala Ilmiah Bidang MIPA (E-ISSN : 2549-7464)
Publisher : Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Negeri Padang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/eksakta/vol25-iss04/537

Abstract

Hypertensive nephropathy (HN) is a common complication of chronic hypertension that leads to kidney damage. This study aimed to identify potential biomarkers and key pathways associated with HN using bioinformatics tools. Gene data related to HN were retrieved from GeneCards and the Comparative Toxicogenomics Database (CTD), resulting in 89 genes from GeneCards and 10,898 genes from CTD. A Venn diagram revealed 58 overlapping genes, which were then analyzed using Protein-Protein Interaction (PPI) networks and the CytoHubba plugin in Cytoscape. The Maximal Clique Centrality (MCC) algorithm identified 10 hub genes, including ACE, AGT, ACE2, AGTR1, and AGTR2, integral to the renin- angiotensin-aldosterone system (RAAS). Functional enrichment analysis using Gene Ontology (GO) and KEGG pathways revealed that the most significant biological process was regulating systemic arterial blood pressure by the Renin-Angiotensin system, with the renin-angiotensin system pathway being the most highly enriched. Further visualization using ShinyGo highlighted the involvement of key genes in the RAAS pathway. These findings provide valuable insights into the molecular mechanisms underlying HN and suggest that bioinformatics approaches can aid in the identification of specific biomarkers for early diagnosis, non-invasive monitoring, and targeted treatments for HN in the future.
Co-Authors Aryo Tedjo Erlina, Linda Fadilah Fadilah, Fadilah Wawaimuli Arozal Wawaimuli Arozal