Study of Lipid Biomarkers of Patients With Polyps and Colorectal Câncer

Abstract

BACKGROUND: 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. OBJECTIVE: To determine the lipid profile of patients with colorectal cancer and polyps. METHODS: 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. STATISTICAL ANALYSIS: 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). RESULTS: 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). CONCLUSION: 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.

The Application of Metabolomics in Recent Colorectal Cancer Studies: A State-of-the-Art Review

“…Other methods of analysis included direct infusion mass spectrometry (1 study) [32], Capillary electrophoresis-time of flight mass spectrometry (CE-TOFMS) (3 studies) [37,40,55], and Matrix Assisted Laser Desorption Ionization-Time of Flight mass spectrometry (MALDI-TOF MS) (1 study) [58] (Figure 2b). Thirty studies [23][24][25][27][28][29][30][31][32][34][35][36][37][38][39][40][42][43][44][45][46][48][49][50]53,54,[56][57][58] employed untargeted metabolomic profiling, while 9 studies employed targeted metabolomics, either with a metabolite subset in mind to begin the study or as part of a validation of their untargeted data [22,25,26,28,35,47,51,52,55]. Of studies where the selections of analytical columns could be identified, 16 used a column that could identify polar and nonpolar compounds …”

“…Thirty studies [23][24][25][27][28][29][30][31][32][34][35][36][37][38][39][40][42][43][44][45][46][48][49][50]53,54,[56][57][58] employed untargeted metabolomic profiling, while 9 studies employed targeted metabolomics, either with a metabolite subset in mind to begin the study or as part of a validation of their untargeted data [22,25,26,28,35,47,51,52,55]. Of studies where the selections of analytical columns could be identified, 16 used a column that could identify polar and nonpolar compounds [22,[25][26][27][28]31,33,36,38,…”

“…Of studies where the selections of analytical columns could be identified, 16 used a column that could identify polar and nonpolar compounds [22,[25][26][27][28]31,33,36,38,39,44,48,49,51,55,58], 8 focused on polar compounds [23,24,30,34,47,52,54], and 2 used nonpolar compounds [45,46]. Additionally, six studies [23,25,52,[56][57][58] conducted lipidomics, a subset of metabolomics specifically targeted at lipid identification, as the lipidome is often perturbed in CRC patients [59]. Song et al, in addition to finding two individual fatty acids expressed differently in CRC compared to control in male participants, also found that the total levels of ω-6 polyunsaturated fatty acids as well as total monounsaturated fatty acids were raised in male CRC patients compared to control [57].…”

“…Twenty-one studies compared CRC directly to healthy control [24][25][26]31,33,36,38,39,[42][43][44][45][46][48][49][50][51][52][53][54]56]. Jing et al and Gao et al, conversely, compared CRC patients to an adenomatous polyp control [32,55] while eleven studies utilized CRC, healthy control, and adenomatous polyp groups [28][29][30]34,35,37,40,41,47,57,58]. Two studies (Geijson et al, Liu et al) did not include a control group [22,23], while Di Giovanni et al compared CRC groups to healthy control in both CRC models as well as CRC remission models, using separate controls but comparing CRC-remission and CRC patients using an Effect Size model [27].…”

“…None of these three authors offered extensive in-text justification for these values. Some studies also employed a Partial Least Squares Discriminant Analysis (PLS-DA) VIP score cutoff of > 1 for metabolite inclusion of diagnostic models [29,36,39,42,43,47,58]. There was no significant difference in total metabolites identified between simple p-value cutoff and p-value correction or VIP score cutoff when compared with a standard two-sample t-test (p = 0.74 vs. adjusted p value, p = 0.51 vs. VIP cutoff).…”