Evidence map›Paper›PMID 36711577›Full record

ArticlebioRxiv : the preprint server for biology2023

Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer.

Olatomiwa O Bifarin, Samyukta Sah, David A Gaul, Samuel G Moore, Ruihong Chen, Murugesan Palaniappan, Jaeyeon Kim, Martin M Matzuk, Facundo M Fernández

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 1 country.

Olatomiwa O BifarinSchool of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
Samyukta SahSchool of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
David A GaulSchool of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
Samuel G MoorePetit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
Ruihong ChenDepartment of Pathology & Immunology, Baylor College of Medicine, Houston, TX 77030, United States.
Murugesan PalaniappanDepartment of Pathology & Immunology, Baylor College of Medicine, Houston, TX 77030, United States.
Jaeyeon KimDepartment of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, Indiana, 46202, United States.
Martin M MatzukDepartment of Pathology & Immunology, Baylor College of Medicine, Houston, TX 77030, United States.
Facundo M FernándezSchool of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
Georgia Institute of Technology · USBaylor College of Medicine · USIndiana University Health · US

Funding

Deep Ovarian Cancer MetabolomicsR01CA218664 · NCI · GEORGIA INSTITUTE OF TECHNOLOGY · PI Facundo Martin Fernandez, Jaeyeon Kim · 2018 to 2026
$4.3M
Development of a Precision Drug to target Y537S Mutant Estrogen Receptor in Metastatic Breast CancerR03CA259664 · NCI · BAYLOR COLLEGE OF MEDICINE · PI PALANIAPPAN, MURUGESAN · 2022 to 2023
$160k
NCI NIH HHS R01 CA218664NCI NIH HHS R03 CA259664
6 · The paper itself

Abstract

Ovarian cancer (OC) is one of the deadliest cancers affecting the female reproductive system. It may present little or no symptoms at the early stages, and typically unspecific symptoms at later stages. High-grade serous ovarian cancer (HGSC) is the subtype responsible for most ovarian cancer deaths. However, very little is known about the metabolic course of this disease, particularly in its early stages. In this longitudinal study, we examined the temporal course of serum lipidome changes using a robust HGSC mouse model and machine learning data analysis. Early progression of HGSC was marked by increased levels of phosphatidylcholines and phosphatidylethanolamines. In contrast, later stages featured more diverse lipids alterations, including fatty acids and their derivatives, triglycerides, ceramides, hexosylceramides, sphingomyelins, lysophosphatidylcholines, and phosphatidylinositols. These alterations underscored unique perturbations in cell membrane stability, proliferation, and survival during cancer development and progression, offering potential targets for early detection and prognosis of human ovarian cancer. Teaser: Time-resolved lipidome remodeling in an ovarian cancer model is studied through lipidomics and machine learning.

Identifiers

PMID36711577
PMCPMC9881992
OpenAlexW4313490190

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.