Evidence map›Paper›PMID 37835457›Full record

ArticleCancers2023

Absolute Quantification of Pan-Cancer Plasma Proteomes Reveals Unique Signature in Multiple Myeloma.

David Kotol, Jakob Woessmann, Andreas Hober, María Bueno Álvez, Khue Hua Tran Minh, Fredrik Pontén, Linn Fagerberg, Mathias Uhlén, Fredrik Edfors

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.7field-weighted citation impact, top 14% 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

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Genes · 2025
    Article
  3. Article
  4. Article
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

9 authors at 2 institutions in 1 country.

David KotolScience For Life Laboratory, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden.ORCID 0000-0002-5388-3826
Jakob WoessmannScience For Life Laboratory, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden.ORCID 0000-0002-2283-7237
Andreas HoberScience For Life Laboratory, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden.
María Bueno ÁlvezScience For Life Laboratory, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden.ORCID 0000-0002-2669-7796
Khue Hua Tran MinhScience For Life Laboratory, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden.
Fredrik PonténRudbeck Laboratory, Uppsala University, 752 36 Uppsala, Sweden.
Linn FagerbergScience For Life Laboratory, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden.ORCID 0000-0003-0198-7137
Mathias UhlénScience For Life Laboratory, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden.
Fredrik EdforsScience For Life Laboratory, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden.ORCID 0000-0002-0017-7987
KTH Royal Institute of Technology · SEUppsala University · SE

Funding

Knut and Alice Wallenberg Foundation WCPR
6 · The paper itself

Abstract

Mass spectrometry based on data-independent acquisition (DIA) has developed into a powerful quantitative tool with a variety of implications, including precision medicine. Combined with stable isotope recombinant protein standards, this strategy provides confident protein identification and precise quantification on an absolute scale. Here, we describe a comprehensive targeted proteomics approach to profile a pan-cancer cohort consisting of 1800 blood plasma samples representing 15 different cancer types. We successfully performed an absolute quantification of 253 proteins in multiplex. The assay had low intra-assay variability with a coefficient of variation below 20% (CV = 17.2%) for a total of 1013 peptides quantified across almost two thousand injections. This study identified a potential biomarker panel of seven protein targets for the diagnosis of multiple myeloma patients using differential expression analysis and machine learning. The combination of markers, including the complement C1 complex, JCHAIN, and CD5L, resulted in a prediction model with an AUC of 0.96 for the identification of multiple myeloma patients across various cancer patients. All these proteins are known to interact with immunoglobulins.

Indexed as

DIAmultiple myelomaprecision medicinetargeted proteomics

Identifiers

PMID37835457
PMCPMC10571728
OpenAlexW4387116578

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.