Evidence map›Paper›PMID 30011843›Full record

ArticleMetabolites2018

Unbiased Lipidomic Profiling of Triple-Negative Breast Cancer Tissues Reveals the Association of Sphingomyelin Levels with Patient Disease-Free Survival.

Preeti Purwaha, Franklin Gu, Danthasinghe Waduge Badrajee Piyarathna, Theckelnaycke Rajendiran, Anindita Ravindran, Angela R Omilian, Sao Jiralerspong, Gokul Das, Carl Morrison, Christine Ambrosone and 3 more

Open access · goldAbstract read
In one paragraph

Article in Metabolites, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 1 pooled it
1.5field-weighted citation impact, top 17% of its field
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

29 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 3 institutions in 1 country.

Preeti PurwahaAlkek Center for Molecular Discovery and Department of Molecular and Cell Biology, Baylor College of Medicine, Houston, TX 77030, USA. purwaha@gmail.com.
Franklin GuAlkek Center for Molecular Discovery and Department of Molecular and Cell Biology, Baylor College of Medicine, Houston, TX 77030, USA. fgu@bcm.edu.
Danthasinghe Waduge Badrajee PiyarathnaAlkek Center for Molecular Discovery and Department of Molecular and Cell Biology, Baylor College of Medicine, Houston, TX 77030, USA. DanthasingheWaduge.Piyarathna@bcm.edu.
Theckelnaycke RajendiranMichigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA. tmraj@med.umich.edu.
Anindita RavindranAlkek Center for Molecular Discovery and Department of Molecular and Cell Biology, Baylor College of Medicine, Houston, TX 77030, USA. Anindita.Ravindran@bcm.edu.
Angela R OmilianRoswell Park Comprehensive Cancer Center, Buffalo, NY 14263, USA. Angela.Omilian@RoswellPark.org.
Sao JiralerspongAlkek Center for Molecular Discovery and Department of Molecular and Cell Biology, Baylor College of Medicine, Houston, TX 77030, USA. jiralers@bcm.edu.
Gokul DasRoswell Park Comprehensive Cancer Center, Buffalo, NY 14263, USA. Gokul.Das@RoswellPark.org.
Carl MorrisonRoswell Park Comprehensive Cancer Center, Buffalo, NY 14263, USA. carl.morrison@omniseq.com.
Christine AmbrosoneRoswell Park Comprehensive Cancer Center, Buffalo, NY 14263, USA. Christine.Ambrosone@RoswellPark.org.
Cristian CoarfaAlkek Center for Molecular Discovery and Department of Molecular and Cell Biology, Baylor College of Medicine, Houston, TX 77030, USA. coarfa@bcm.edu.
Nagireddy PutluriAlkek Center for Molecular Discovery and Department of Molecular and Cell Biology, Baylor College of Medicine, Houston, TX 77030, USA. putluri@bcm.edu.
Arun SreekumarAlkek Center for Molecular Discovery and Department of Molecular and Cell Biology, Baylor College of Medicine, Houston, TX 77030, USA. arun.sreekumar@bcm.edu.
Baylor College of Medicine · USRoswell Park Comprehensive Cancer Center · USUniversity of Michigan · US

Funding

Delineating racially distinct metabolic pathways in triple negative breast cancerU01CA179674 · NCI · BAYLOR COLLEGE OF MEDICINE · PI SREEKUMAR, ARUN · 2014 to 2018
$1.7M
NCI NIH HHS U01 CA179674
6 · The paper itself

Abstract

The reprogramming of lipid metabolism is a hallmark of many cancers that has been shown to promote breast cancer progression. While several lipid signatures associated with breast cancer aggressiveness have been identified, a comprehensive lipidomic analysis specifically targeting the triple-negative subtype of breast cancer (TNBC) may be required to identify novel biomarkers and therapeutic targets for this most aggressive subtype of breast cancer that still lacks effective therapies. In this current study, our global LC-MS-based lipidomics platform was able to measure 684 named lipids across 15 lipid classes in 70 TNBC tumors. Multivariate survival analysis found that higher levels of sphingomyelins were significantly associated with better disease-free survival in TNBC patients. Furthermore, analysis of publicly available gene expression datasets identified that decreased production of ceramides and increased accumulation of sphingoid base intermediates by metabolic enzymes were associated with better survival outcomes in TNBC patients. Our LC-MS lipidomics profiling of TNBC tumors has, for the first time, identified sphingomyelins as a potential prognostic marker and implicated enzymes involved in sphingolipid metabolism as candidate therapeutic targets that warrant further investigation.

Indexed as

lipidomicssphingolipidsphingomyelintriple-negative breast cancer

Identifiers

PMID30011843
PMCPMC6161031
OpenAlexW2882984667

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.