Evidence map›Paper›PMID 35605973›Full record

ArticleJournal of proteome research2022

Biomarker Candidates for Tumors Identified from Deep-Profiled Plasma Stem Predominantly from the Low Abundant Area.

Marco Tognetti, Kamil Sklodowski, Sebastian Müller, Dominique Kamber, Jan Muntel, Roland Bruderer, Lukas Reiter

Open access · hybridAbstract read
In one paragraph

Article in Journal of proteome research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
7.4field-weighted citation impact, top 2% 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

34 citing papers in PubMed, 70 citations in OpenAlex.

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  6. Shared PRAME epitopes are T-cell targets in NUT carcinoma.Journal for immunotherapy of cancer · 2026
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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

7 authors at 1 institution in 1 country.

Marco TognettiBiognosys, Schlieren, Zurich 8952, Switzerland.ORCID 0000-0002-2379-1525
Kamil SklodowskiBiognosys, Schlieren, Zurich 8952, Switzerland.
Sebastian MüllerBiognosys, Schlieren, Zurich 8952, Switzerland.
Dominique KamberBiognosys, Schlieren, Zurich 8952, Switzerland.
Jan MuntelBiognosys, Schlieren, Zurich 8952, Switzerland.
Roland BrudererBiognosys, Schlieren, Zurich 8952, Switzerland.ORCID 0000-0002-1049-5524
Lukas ReiterBiognosys, Schlieren, Zurich 8952, Switzerland.ORCID 0000-0001-5751-3139
Biognosys (Switzerland) · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The plasma proteome has the potential to enable a holistic analysis of the health state of an individual. However, plasma biomarker discovery is difficult due to its high dynamic range and variability. Here, we present a novel automated analytical approach for deep plasma profiling and applied it to a 180-sample cohort of human plasma from lung, breast, colorectal, pancreatic, and prostate cancers. Using a controlled quantitative experiment, we demonstrate a 257% increase in protein identification and a 263% increase in significantly differentially abundant proteins over neat plasma. In the cohort, we identified 2732 proteins. Using machine learning, we discovered biomarker candidates such as STAT3 in colorectal cancer and developed models that classify the diseased state. For pancreatic cancer, a separation by stage was achieved. Importantly, biomarker candidates came predominantly from the low abundance region, demonstrating the necessity to deeply profile because they would have been missed by shallow profiling.

Indexed as

Pancreatic NeoplasmsProteomicsBiomarkersBlood ProteinsHumansMaleProteomeBiomarkersBlood ProteinsProteomecancerclinical proteomicsdata-independent acquisitiondepletionhigh throughputlabel-free quantificationlibraryplasma proteomicssingle shotstable isotope-based quantificationSWATH

Identifiers

PMID35605973
PMCPMC9251764
OpenAlexW4281396769

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.