STUDY OF LIPID BIOMARKERS OF PATIENTS WITH POLYPS AND COLORECTAL CÂNCER

• Published in: Arquivos de gastroenterologia 2019-10, Vol.56 (4), p.399-404
• Main Authors: Serafim, Patricia Valeria Pereira, Figueiredo, Jr, Adiel Goes de, Felipe, Aledson Vitor, Turco, Edson Guimaraes Lo, Silva, Ismael Dale Cotrim Guerreiro da, Forones, Nora Manoukian
• Format: Article
• Language: English
• Subjects: Adult; Aged; Aged, 80 and over; Biomarkers; Biomarkers, Tumor - blood; Case-Control Studies; Colonic Polyps - blood; Colonic Polyps - diagnosis; Colonoscopy; Colorectal neoplasms; Colorectal Neoplasms - blood; Colorectal Neoplasms - diagnosis; Early Detection of Cancer; Female; GASTROENTEROLOGY & HEPATOLOGY; Humans; Lipid metabolism; Lipids - blood; Male; Mass spectrometry; Middle Aged; Neoplasm Staging
• ISSN: 0004-2803; 1678-4219; 1678-4219
• DOI: 10.1590/s0004-2803.201900000-80

Colorectal cancer (CRC) is one of the leading causes of cancer worldwide. Early diagnostic methods using serum biomarkers are required. The study of omics, most recently lipidomics, has the purpose of analyzing lipids for a better understanding of human lipidoma. The evolution of mass spectrometry methods, such as MALDI-MS technology, has enabled the detection and identification of a wide variety of lipids with great potential to open new avenues for predictive and preventive medicine. To determine the lipid profile of patients with colorectal cancer and polyps. Patients with stage I-III CRC, adenomatous polyps and individuals with normal colonoscopy were selected. All patients underwent peripheral blood collection for lipid extraction. The samples were analyzed by MALDI-MS technique for lipid identification. Univariate and multivariate (principal component analysis [PCA] and discriminant analysis by partial least squares [PLS-DA]) analyses workflows were applied to the dataset, using MetaboAnalyst 3.0 software. The ions were identified according to the class of lipids using the online database Lipid Maps (http://www.lipidmaps.org). We included 88 individuals, 40 with CRC, 12 with polyps and 32 controls. Boxplot analysis showed eight VIP ions in the three groups. Differences were observed between the cancer and control groups, as well as between cancer and polyp, but not between polyps and control. The polyketide (810.1) was the lipid represented in cancer and overrepresented in polyp and control. Among the patients with CRC we observed differences between lipids with lymph node invasion (N1-2) compared to those without lymph node invasion (N). Possible lipid biomarkers were identified among cancer patients compared to control and polyp groups. The polyketide lipid (810.1) was the best biomarker to differentiate the cancer group from control and polyp. We found no difference between the biomarkers in the polyp group in relation to the control.