Dataset Information

Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma

ABSTRACT:

A discovery-based lipid profiling study of serum samples from a cohort that included patients with clear cell Renal Cell Carcinoma (ccRCC) stage I, II, III, and IV (n=112) and controls (n=52) was performed using ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry and machine learning techniques. Multivariate models based on support vector machines and the LASSO variable selection method yielded two discriminant lipid panels for ccRCC detection and early diagnosis. A 16-lipid panel allowed discriminating ccRCC patients from controls with 95.7% accuracy in a training set under cross- validation, and 77.1% accuracy in an independent test set. A second model trained to discriminate early (I and II) from late (III and IV) stage ccRCC yielded a panel of 26 compounds that classified stage I patients from an independent test set with 82.1% accuracy. Thirteen species, including cholic acid, undecylenic acid, lauric acid, LPC(16:0/0:0), and PC(18:2/18:2) identified with level 1 exhibited significant lower levels in samples from ccRCC patients compared to controls. Moreover, 3α-hydroxy-5α-androstan-17-one 3-sulfate, cis-5-dodecenoic acid, arachidonic acid, cis-13-docosenoic acid, PI(16:0/18:1), PC(16:0/18:2), and PC(O-16:0/20:4) contributed to discriminate early from late ccRCC stage patients. The results are auspicious for early ccRCC diagnosis after validation of the panels in larger and different cohorts.

INSTRUMENT(S): Liquid Chromatography MS - negative - reverse phase

SUBMITTER: Malena Manzi

PROVIDER: MTBLS1839 | MetaboLights | 2020-11-20

REPOSITORIES: MetaboLights

ACCESS DATA

Publications

Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma.

Manzi Malena M Palazzo Martín M Knott María Elena ME Beauseroy Pierre P Yankilevich Patricio P Giménez María Isabel MI Monge María Eugenia ME

Journal of proteome research 20201118 1

A discovery-based lipid profiling study of serum samples from a cohort that included patients with clear cell renal cell carcinoma (ccRCC) stages I, II, III, and IV (n = 112) and controls (n = 52) was performed using ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry and machine learning techniques. Multivariate models based on support vector machines and the LASSO variable selection method yielded two discriminant lipid panels for ccRCC d ...[more]

PMID: 33207877

Similar Datasets

Project description:The GOLDN study was initiated to assess how genetic factors interact with environmental (diet and drug) interventions to influence blood levels of triglycerides and other atherogenic lipid species and inflammation markers (registered at <a href="http://clinicaltrials.gov/ct2/show/NCT00083369">clinicaltrails.gov</a>, number NCT00083369). The study recruited Caucasian participants primarily from three-generational pedigrees from two NHLBI Family Heart Study (FHS) field centers (Minneapolis, MN and Salt Lake City, UT). Only families with at least two siblings were recruited and only participants who did not take lipid-lowering agents (pharmaceuticals or nutraceuticals) for at least 4 weeks prior to the initial visit were included. A total of 1048 GOLDN participants were included in the diet intervention. The diet intervention followed the protocol of Patsch et al. (<a href="http://www.ncbi.nlm.nih.gov/pubmed/1420093">1992</a>). The whipping cream (83% fat) meal had 700 Calories/m2 body surface area (2.93 MJ/m2 body surface area): 3% of calories were derived from protein (instant nonfat dry milk) and 14% from carbohydrate (sugar). The ratio of polyunsaturated to saturated fat was 0.06 and the cholesterol content of the average meal was 240 mg. The mixture was blended with ice and flavorings. Blood samples were drawn immediately before (fasting) and at 3.5 and 6 hours after consuming the high-fat meal. For the GOLDN lipidomics study, sterols and fatty acids were measured from stored plasma (-80 degrees Celsius) collected at fasting and 3.5 hours after the diet intervention using TrueMass Panels from Lipomics (West Sacramento, CA). A total of 11 sterols were quantified in nmols/gram of sample including total cholesterol, 7-dehydrocholesterol, desmosterol, lanosterol, lathasterol, cholestanol, coprostanol, beta-sitosterol, campesterol, stigmasterol, and 7alpha-hydroxycholesterol. A total of 35 fatty acids were quantified in nmols/gram of sample inlcuding myristic acid (14:0); pentadecanoic acid (15:0); palmitic acid (16:0); stearic acid (18:0); arachidic acid (20:0); behenic acid (22:0); lignoceric acid (24:0); myristoleic acid (14:1n5); palmitoleic acid (16:1n7); palmitelaidic acid (t16:1n7); oleic acid (18:1n9); elaidic acid (t18:1n9); vaccenic acid (18:1n7); linoleic acid (18:2n6); gamma-linolenic acid (18:3n6); alpha-linolenic acid (18:3n3); stearidonic acid (18:4n3); eicosenoic acid (20:1n9); eicosadienoic acid (20:2n6); mead acid (20:3n9); di-homo-gamma-linolenic acid (20:3n6); arachidonic acid (20:4n6); eicsoatetraenoic acid (20:4n3); eicosapentaenoic acid (20:5n3); erucic acid (22:1n9); docosadienoic acid (22:2n6); adrenic acid (22:4n6); docosapentaenoic acid (22:5n6); docosapentaenoic acid (22:5n3); docosahexaenoic acid (22:6n3); nervonic acid (24:1n9); and plasmalogen derivatives of 16:0, 18:0, 18:1n9, and 18:1n7.

Project description:Pseudoexfoliation syndrome (PEX) is systemic disorder that manifests as white, fluffy, proteinaceous fibrillar material throughout the body. In the eye such deposits result in Pseudoexfoliation glaucoma (PEXG), due to impeding aqueous humor outflow. Serum lipid alterations and increased lipid peroxidation have been reported in PEX. We report first ever comprehensive lipid profiling of the aqueous humor (AH) of PEXG. Our untargeted lipidomic analysis of 23 non-glaucomatous control, 19 primary open angle glaucoma, 9 PEX, and 14 PEXG AH with 13 deuterated lipid internal standards for normalization among the lipid classes resulted in the combined identification of 489 lipid species within 26 lipid classes across PEX, PEXG, POAG, and control AH. The mean total lipid content in the AH across samples showed that control AH (mean peak area 13.54 ± 56.1) had, on average, greater total lipid content than PEX (4.21 ± 10.90), PEXG (9.08 ± 25.97), and POAG (5.66 ± 15.75) samples. Multiple cholesterol esters (ChE), phosphatidylcholines (PC), triglycerides (TG), and ceramides (Cer) were present in higher concentrations for the PEXG AH samples. Some of the lipids found in high concentrations in the PEXG samples are ChE(16:0), ChE(20:3), ChE(18:1), ChE(18:3), ChE(22:6), ChE(18:2), ChE(20:4), PC(16:0/16:0), PC(16:0/18:2), TG(18:1/18:1/20:4), and Cer(t18:0/24:0). The CerG2GNAc1(d34:1) was enriched in control samples and depleted both in PEX and PEXG samples. The PC (18:0/18:2), PC (36:2), and PC (34:1e) are in low concentrations for PEX but highly concentrated in PEXG, despite both having similar material deposits, suggesting they are fundamentally different in composition. Elevations in Apolipoprotein A-I (APOA1) correlated to increase abundance of PC lipid species in the AH of patients with PEXG. Machine learning prediction with three supervised logistic regression binary classification tasks showed 1) POAG vs control, with 86% accuracy 2) PEXG vs control, with 71% accuracy and 3) PEX vs control, with 86% accuracy, respectively.

Project description:INTRODUCTION: Differences in the metabolite profiles between serum and plasma are incompletely understood. OBJECTIVES: To evaluate metabolic profile differences between serum and plasma and among plasma sample subtypes. METHODS: We analyzed serum, platelet rich plasma (PRP), platelet poor plasma (PPP), and platelet free plasma (PFP), collected from 8 non-fasting apparently healthy women, using untargeted standard 1D and CPMG 1H NMR and reverse phase and hydrophilic (HILIC) UPLC-MS. Differences between metabolic profiles were evaluated using validated principal component and orthogonal partial least squares discriminant analysis. RESULTS: Explorative analysis showed the main source of variation among samples was due to inter-individual differences with no grouping by sample type. After correcting for inter-individual differences, lipoproteins, lipids in VLDL/LDL, lactate, glutamine, and glucose were found to discriminate serum from plasma in NMR analyses. In UPLC-MS analyses, lysophosphatidylethanolamine (lysoPE)(18:0) and lysophosphatidic acid(20:0) were higher in serum, and phosphatidylcholines (PC)(16:1/18:2, 20:3/18:0, O-20:0/22:4), lysoPC(16:0), PE(O-18:2/20:4), sphingomyelin(18:0/22:0), and linoleic acid were lower. In plasma subtype analyses, isoleucine, leucine, valine, phenylalanine, glutamate, and pyruvate were higher among PRP samples compared with PPP and PFP by NMR while lipids in VLDL/LDL, citrate, and glutamine were lower. By UPLC-MS, PE(18:0/18:2) and PC(P-16:0/20:4) were higher in PRP compared with PFP samples. CONCLUSIONS: Correction for inter-individual variation was required to detect metabolite differences between serum and plasma. Our results suggest the potential importance of inter-individual effects and sample type on the results from serum and plasma metabolic phenotyping studies. NMR protocols and data are reported in the current study MTBLS470. UPLC-MS protocols and data associated to this study are reported in MTBLS465. Linked Studies: <a href='https://www.ebi.ac.uk/metabolights/MTBLS465' target='_blank'>MTBLS465</a>

Project description:INTRODUCTION: Differences in the metabolite profiles between serum and plasma are incompletely understood. OBJECTIVES: To evaluate metabolic profile differences between serum and plasma and among plasma sample subtypes. METHODS: We analyzed serum, platelet rich plasma (PRP), platelet poor plasma (PPP), and platelet free plasma (PFP), collected from 8 non-fasting apparently healthy women, using untargeted standard 1D and CPMG 1H NMR and reverse phase and hydrophilic (HILIC) UPLC-MS. Differences between metabolic profiles were evaluated using validated principal component and orthogonal partial least squares discriminant analysis. RESULTS: Explorative analysis showed the main source of variation among samples was due to inter-individual differences with no grouping by sample type. After correcting for inter-individual differences, lipoproteins, lipids in VLDL/LDL, lactate, glutamine, and glucose were found to discriminate serum from plasma in NMR analyses. In UPLC-MS analyses, lysophosphatidylethanolamine (lysoPE)(18:0) and lysophosphatidic acid(20:0) were higher in serum, and phosphatidylcholines (PC)(16:1/18:2, 20:3/18:0, O-20:0/22:4), lysoPC(16:0), PE(O-18:2/20:4), sphingomyelin(18:0/22:0), and linoleic acid were lower. In plasma subtype analyses, isoleucine, leucine, valine, phenylalanine, glutamate, and pyruvate were higher among PRP samples compared with PPP and PFP by NMR while lipids in VLDL/LDL, citrate, and glutamine were lower. By UPLC-MS, PE(18:0/18:2) and PC(P-16:0/20:4) were higher in PRP compared with PFP samples. CONCLUSIONS: Correction for inter-individual variation was required to detect metabolite differences between serum and plasma. Our results suggest the potential importance of inter-individual effects and sample type on the results from serum and plasma metabolic phenotyping studies. UPLC-MS protocols and data are reported in the current study MTBLS465. NMR protocols and data associated to this study are reported in MTBLS470. Linked Studies: <a href='https://www.ebi.ac.uk/metabolights/MTBLS470' target='_blank'>MTBLS470</a>

Dataset Information

Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma

Publications

Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure