Evidence map›Paper›PMID 38012666›Full record

ArticleJournal of translational medicine2023

Metabolomic profiles of intact tissues reflect clinically relevant prostate cancer subtypes.

Ilona Dudka, Kristina Lundquist, Pernilla Wikström, Anders Bergh, Gerhard Gröbner

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 22% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.

  1. Pooled it
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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

5 authors at 1 institution in 1 country.

Ilona DudkaDepartment of Chemistry, Umeå University, Umeå, Sweden.
Kristina LundquistDepartment of Chemistry, Umeå University, Umeå, Sweden.
Pernilla WikströmDepartment of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden. pernilla.wikstrom@umu.se.
Anders BerghDepartment of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden.
Gerhard GröbnerDepartment of Chemistry, Umeå University, Umeå, Sweden. gerhard.grobner@umu.se.ORCID 0000-0001-7380-8797
Umeå University · SE

Funding

Cancerfonden 21-1856Cancerfonden 22-2041Knut och Alice Wallenbergs Stiftelse "NMR for Life" ProgrammeVetenskapsrådet 2021-06146Vetenskapsrådet 2022-00946
6 · The paper itself

Abstract

backgroundProstate cancer (PC) is a heterogenous multifocal disease ranging from indolent to lethal states. For improved treatment-stratification, reliable approaches are needed to faithfully differentiate between high- and low-risk tumors and to predict therapy response at diagnosis.

methodsA metabolomic approach based on high resolution magic angle spinning nuclear magnetic resonance (HR MAS NMR) analysis was applied on intact biopsies samples (n = 111) obtained from patients (n = 31) treated by prostatectomy, and combined with advanced multi- and univariate statistical analysis methods to identify metabolomic profiles reflecting tumor differentiation (Gleason scores and the International Society of Urological Pathology (ISUP) grade) and subtypes based on tumor immunoreactivity for Ki67 (cell proliferation) and prostate specific antigen (PSA, marker for androgen receptor activity).

resultsValidated metabolic profiles were obtained that clearly distinguished cancer tissues from benign prostate tissues. Subsequently, metabolic signatures were identified that further divided cancer tissues into two clinically relevant groups, namely ISUP Grade 2 (n = 29) and ISUP Grade 3 (n = 17) tumors. Furthermore, metabolic profiles associated with different tumor subtypes were identified. Tumors with low Ki67 and high PSA (subtype A, n = 21) displayed metabolite patterns significantly different from tumors with high Ki67 and low PSA (subtype B, n = 28). In total, seven metabolites; choline, peak for combined phosphocholine/glycerophosphocholine metabolites (PC + GPC), glycine, creatine, combined signal of glutamate/glutamine (Glx), taurine and lactate, showed significant alterations between PC subtypes A and B.

conclusionsThe metabolic profiles of intact biopsies obtained by our non-invasive HR MAS NMR approach together with advanced chemometric tools reliably identified PC and specifically differentiated highly aggressive tumors from less aggressive ones. Thus, this approach has proven the potential of exploiting cancer-specific metabolites in clinical settings for obtaining personalized treatment strategies in PC.

Indexed as

Prostate-Specific AntigenProstatic NeoplasmsHumansKi-67 AntigenMagnetic Resonance ImagingMagnetic Resonance SpectroscopyMaleMetabolomicsKi-67 AntigenProstate-Specific AntigenBiomarkerHR MAS NMRMetabolomicsProstate cancerSubtype

Identifiers

PMID38012666
PMCPMC10683247
OpenAlexW4389048094

What OpenQuestion holds

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Registered trials

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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.