Figure 1 from Utilizing Serum-Derived Lipidomics with Protein Biomarkers and Machine Learning for Early Detection of Ovarian Cancer in the Symptomatic Population

<p>Lipidomics analysis. <b>A,</b> Lipid class assignments as a percentage of species reaching <i>P</i> < 0.05 by the Mann–Whitney u test comparing controls with ovarian cancer. <b>B,</b> PLSDA analysis of controls and ovarian cancer (OC) for each cohort. <b>C,</b> Lipid class assignments as a percentage of species reaching <i>P</i> < 0.05 by the Mann Whitney U test comparing controls with early-stage ovarian cancer. <b>D,</b> PLSDA analysis of controls and early-stage ovarian cancer (Early OC) for each cohort. <b>E,</b> Scatter plots depicting relative abundance of representative lipid species across cohorts 1 and 2 for control, ovarian cancer (OC), and early-stage ovarian cancer (EOC). Cer, ceramide; CL, cardiolipin; DG, diacylglycerol; FFA, free fatty acid; GlcCer, glucosylceramide; GM3, GM3 ganglioside; LacCer, lactosylceramide; LPA, lysophosphatidic acid; LPC, lysophosphatidylcholine; LPE, lysophosphatidylethanolamine; LPG, lysophosphatidylglycerol; LPI, lysophosphatidylinositol; LPS, lysophosphatidylserine; MG, monoacylglycerol; PA, phosphatidic acid; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PG, phosphatidylglycerol; PGP, phosphatidylglycerophosphate; PI, phosphatidylinositol; PS, phosphatidylserine; SM, sphingomyelin; TG, triacylglycerol. Statistical testing used Mann Whitney U with significance defined as: ns, <i>P</i> > 0.05; **, <i>P</i> ≤ 0.01; ***, <i>P</i> ≤ 0.001; ****, <i>P</i> ≤ 0.0001.</p>

Authors

Publication Details

Published
2025-09-04
DOI
https://doi.org/10.1158/2767-9764.30051290
Primary Topic
Metabolomics and Mass Spectrometry Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Figure 1 from Utilizing Serum-Derived Lipidomics with Protein Biomarkers and Machine Learning for Early Detection of Ovarian Cancer in the Symptomatic Population

Brendan M. Giles, Emma J. Crosbie, Benjamin G. Bitler, Connor Hansen et al.
Metabolomics and Mass Spectrometry Studies
article

Figure 1 from Utilizing Serum-Derived Lipidomics with Protein Biomarkers and Machine Learning for Early Detection of Ovarian Cancer in the Symptomatic Population

Brendan M. Giles, Emma J. Crosbie, Benjamin G. Bitler, Connor Hansen, Anna Jeter, Rachel Culp‐Hill, James R. White, Kian Behbakht, Emily C. Kubota, Robert Adger Law, Violeta Beleva Guthrie, Leonardo N. Hagmann, Abigail McElhinny, Vuna S. Fa, Charles M. Nichols, Enkhtuya Radnaa, Chloe E. Barr, Maria Pik Wong, Collin Hill, Moises Zapata, Melvyn Goldberg
article en

Abstract

<p>Lipidomics analysis. <b>A,</b> Lipid class assignments as a percentage of species reaching <i>P</i> < 0.05 by the Mann–Whitney u test comparing controls with ovarian cancer. <b>B,</b> PLSDA analysis of controls and ovarian cancer (OC) for each cohort. <b>C,</b> Lipid class assignments as a percentage of species reaching <i>P</i> < 0.05 by the Mann Whitney U test comparing controls with early-stage ovarian cancer. <b>D,</b> PLSDA analysis of controls and early-stage ovarian cancer (Early OC) for each cohort. <b>E,</b> Scatter plots depicting relative abundance of representative lipid species across cohorts 1 and 2 for control, ovarian cancer (OC), and early-stage ovarian cancer (EOC). Cer, ceramide; CL, cardiolipin; DG, diacylglycerol; FFA, free fatty acid; GlcCer, glucosylceramide; GM3, GM3 ganglioside; LacCer, lactosylceramide; LPA, lysophosphatidic acid; LPC, lysophosphatidylcholine; LPE, lysophosphatidylethanolamine; LPG, lysophosphatidylglycerol; LPI, lysophosphatidylinositol; LPS, lysophosphatidylserine; MG, monoacylglycerol; PA, phosphatidic acid; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PG, phosphatidylglycerol; PGP, phosphatidylglycerophosphate; PI, phosphatidylinositol; PS, phosphatidylserine; SM, sphingomyelin; TG, triacylglycerol. Statistical testing used Mann Whitney U with significance defined as: ns, <i>P</i> > 0.05; **, <i>P</i> ≤ 0.01; ***, <i>P</i> ≤ 0.001; ****, <i>P</i> ≤ 0.0001.</p>

Openalex Percentile: Top 79%
Metabolomics and Mass Spectrometry Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.