Indonesian Journal of Medical Chemistry and Bioinformatics


Background: Colorectal cancer is one of the deadliest diseases with a high prevalence worldwide and is characterized by the appearance of adenomatous polyps in the colon mucosa which are at high risk of developing into colorectal cancer. This study aims to use serum metabolites as promising non-invasive biomarkers for colorectal cancer detection and prognostication. Differences in serum metabolites in patients with adenomatous polyps, colorectal cancer, and healthy controls are considered to be able to support the prognosis of colorectal cancer. Methods: Metabolite dataset is taken from the Metabolomic Workbench. Analysis and validation are carried out in silico using machine learning methods. Results: From a total of 234 samples, 113 metabolites were found and 5 metabolites; histidine, lysine, glyceraldehyde, linolenic acid, and aspartic acid were identified as the most significant in differentiating the sample groups. CTD analysis showed that aspartic acid and histidine are associated with the biological pathways of colorectal cancer progression and significant metabolites are associated with cancer-related phenotypes. Conclusion: The serum metabolites differ in colorectal cancer and healthy control. The significant metabolites can be used as a consideration in selecting colorectal cancer biomarkers, but improvisation is needed to obtain more accurate biomarkers.

Bahasa Abstract

Latar belakang: Kanker kolorektal adalah salah satu penyakit mematikan dengan prevalensi tinggi di dunia dan diawali dengan munculnya polip adenomatosa di mukosa usus besar yang berisiko tinggi berkembang menjadi kanker kolorektal. Penelitian ini bertujuan untuk menggunakan metabolit serum sebagai biomarker non-invasif yang menjanjikan untuk deteksi dan prognostikasi kanker kolorektal. Perbedaan metabolit serum pada pasien polip adenomatosa, kanker kolorektal, dan kontrol sehat dianggap dapat mendukung prognosis kanker kolorektal. Metode: Data metabolit diambil dari Metabolomic Workbench dan identifikasi serta validasi dilakukan secara in silico menggunakan metode machine learning. Hasil: Dari total 234 sampel, ditemukan 113 metabolit dan diidentifikasi 5 metabolit yang paling signifikan membedakan kelompok sampel, yaitu histidine, lysine, glyceraldehyde, linolenic acid, dan aspartic acid. Analisis CTD menunjukan bahwa aspartic acid dan histidin berhubungan dengan jalur biologis progresivitas kanker kolorektal dan berhubungan dengan beberapa fenotip terkait kanker. Simpulan: Metabolit serum dilaporkan berbeda pada kanker kolorektal dan control sehat. Metabolit yang signifikan dapat dipertimbangkan untuk digunakan sebagai biomarker kanker kolorektal, namun diperlukan improvisasi untuk mendapatkan biomarker yang lebih akurat.


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