Evidence map›Paper›PMID 36768152›Full record

ArticleInternational journal of molecular sciences2023

Detectable Lipidomes and Metabolomes by Different Plasma Exosome Isolation Methods in Healthy Controls and Patients with Advanced Prostate and Lung Cancer.

Alex C Soupir, Yijun Tian, Paul A Stewart, Yury O Nunez-Lopez, Brandon J Manley, Bruna Pellini, Amanda M Bloomer, Jingsong Zhang, Qianxing Mo, Douglas C Marchion and 4 more

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
3.8field-weighted citation impact, top 6% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
  7. Article
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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

14 authors at 2 institutions in 1 country.

Alex C SoupirDepartment of Tumor Biology, Moffitt Cancer Center, Tampa, FL 33612, USA.ORCID 0000-0003-1251-9179
Yijun TianDepartment of Tumor Biology, Moffitt Cancer Center, Tampa, FL 33612, USA.
Paul A StewartDepartment of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL 33612, USA.ORCID 0000-0003-0882-308X
Yury O Nunez-LopezAdvent Health, Translational Research Institute for Metabolism and Diabetes, Orlando, FL 32804, USA.ORCID 0000-0003-0382-9124
Brandon J ManleyDepartment of Genitourinary Oncology, Moffitt Cancer Center, Tampa, FL 33612, USA.ORCID 0000-0002-7927-1510
Bruna PelliniDepartment of Thoracic Oncology, Moffitt Cancer Center, Tampa, FL 33612, USA.ORCID 0000-0003-0898-0202
Amanda M BloomerDepartment of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL 33612, USA.ORCID 0000-0002-2737-8631
Jingsong ZhangDepartment of Genitourinary Oncology, Moffitt Cancer Center, Tampa, FL 33612, USA.
Qianxing MoDepartment of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL 33612, USA.ORCID 0000-0002-0021-7694
Douglas C MarchionTissue Core, Moffitt Cancer Center, Tampa, FL 33612, USA.
Min LiuProteomics & Metabolomics Core, Moffitt Cancer Center, Tampa, FL 33612, USA.
John M KoomenDepartment of Molecular Oncology, Moffitt Cancer Center, Tampa, FL 33612, USA.
Erin M SiegelDepartment of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL 33612, USA.ORCID 0000-0003-1779-2510
Liang WangDepartment of Tumor Biology, Moffitt Cancer Center, Tampa, FL 33612, USA.ORCID 0000-0002-9364-8572
Moffitt Cancer Center · USTranslational Research Institute for Metabolism and Diabetes · US

Funding

TRANSLATIONAL RESEARCHP30CA076292 · NCI · UNIVERSITY OF SOUTH FLORIDA · PI Christine O'Connell · 1998 to 2026
$93.5M
Cell free nucleic acid-based biomarkers in advanced prostate cancerR01CA212097 · NCI · MEDICAL COLLEGE OF WISCONSIN · PI KOHLI, MANISH, WANG, LIANG · 2017 to 2022
$3.1M
Functional characterization of prostate cancer risk loci by high throughput sequencingR01CA250018 · NCI · H. LEE MOFFITT CANCER CTR & RES INST · PI WANG, LIANG · 2020 to 2023
$1.5M
NCI NIH HHS P30 CA076292NCI NIH HHS R01 CA212097NCI NIH HHS R01 CA250018NIH HHS P30-CA076292NIH HHS R01CA212097
6 · The paper itself

Abstract

Circulating exosomes in the blood are promising tools for biomarker discovery in cancer. Due to their heterogeneity, different isolation methods may enrich distinct exosome cargos generating different omic profiles. In this study, we evaluated the effects of plasma exosome isolation methods on detectable multi-omic profiles in patients with non-small cell lung cancer (NSCLC), castration-resistant prostate cancer (CRPC), and healthy controls, and developed an algorithm to quantify exosome enrichment. Plasma exosomes were isolated from CRPC (n = 10), NSCLC (n = 14), and healthy controls (n = 10) using three different methods: size exclusion chromatography (SEC), lectin binding, and T-cell immunoglobulin domain and mucin domain-containing protein 4 (TIM4) binding. Molecular profiles were determined by mass spectrometry of extracted exosome fractions. Enrichment analysis of uniquely detected molecules was performed for each method with MetaboAnalyst. The exosome enrichment index (EEI) scores methods based on top differential molecules between patient groups. The lipidomic analysis detected 949 lipids using exosomes from SEC, followed by 246 from lectin binding and 226 from TIM4 binding. The detectable metabolites showed SEC identifying 191 while lectin binding and TIM4 binding identified 100 and 107, respectively. When comparing uniquely detected molecules, different methods showed preferential enrichment of different sets of molecules with SEC enriching the greatest diversity. Compared to controls, SEC identified 28 lipids showing significant difference in NSCLC, while only 1 metabolite in NSCLC and 5 metabolites in CRPC were considered statistically significant (FDR < 0.1). Neither lectin-binding- nor TIM4-binding-derived exosome lipids or metabolites demonstrated significant differences between patient groups. We observed the highest EEI from SEC in lipids (NSCLC: 871.33) which was also noted in metabolites. These results support that the size exclusion method of exosome extraction implemented by SBI captures more heterogeneous exosome populations. In contrast, lectin-binding and TIM4-binding methods bind surface glycans or phosphatidylserine moieties of the exosomes. Overall, these findings suggest that specific isolation methods select subpopulations which may significantly impact cancer biomarker discovery.

Indexed as

Carcinoma, Non-Small-Cell LungExosomesLung NeoplasmsProstatic Neoplasms, Castration-ResistantHumansLectinsLipidomicsLipidsMaleMetabolomeProstateLectinsLipidscancer multiomecirculating lipidscirculating metabolitesexosome extractionplasma omics

Identifiers

PMID36768152
PMCPMC9916336
OpenAlexW4316804660

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.