Evidence map›Paper›PMID 31666584›Full record

SynthesisScientific reports2019

An algorithm-based meta-analysis of genome- and proteome-wide data identifies a combination of potential plasma biomarkers for colorectal cancer.

Danuta R Gawel, Eun Jung Lee, Xinxiu Li, Sandra Lilja, Andreas Matussek, Samuel Schäfer, Renate Slind Olsen, Margaretha Stenmarker, Huan Zhang, Mikael Benson

Open access · goldAbstract readMeta-Analysis
In one paragraph

Synthesis in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 24% 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Cancers · 2025
    Article
  4. Article
  5. Lipocalin-2 and intestinal diseases.World journal of gastroenterology · 2024
    Review
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. 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

10 authors at 3 institutions in 2 countries.

Danuta R GawelCentre for Personalized Medicine, Linköping University, Linköping, Sweden. danuta.r.gawel@gmail.com.
Eun Jung LeeCentre for Personalized Medicine, Linköping University, Linköping, Sweden.
Xinxiu LiCentre for Personalized Medicine, Linköping University, Linköping, Sweden.
Sandra LiljaCentre for Personalized Medicine, Linköping University, Linköping, Sweden.
Andreas MatussekLaboratory Medicine, Division of Psychiatrics & Rehabilitation & Diagnostics, Region Jönköping County, Jönköping, Sweden.
Samuel SchäferCentre for Personalized Medicine, Linköping University, Linköping, Sweden.ORCID http://orcid.org/0000-0002-3101-9367
Renate Slind OlsenPathology Laboratory, Division of Psychiatrics & Rehabilitation & Diagnostics, Region Jönköping County, Jönköping, Sweden.
Margaretha StenmarkerDepartment of Paediatrics, Jönköping, Region Jönköping County, and Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden.
Huan ZhangCentre for Personalized Medicine, Linköping University, Linköping, Sweden. huan.zhang@liu.se.ORCID http://orcid.org/0000-0001-9339-7999
Mikael BensonCentre for Personalized Medicine, Linköping University, Linköping, Sweden.
Linköping University · SEKarolinska Institutet · SEKarolinska University Hospital · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Screening programs for colorectal cancer (CRC) often rely on detection of blood in stools, which is unspecific and leads to a large number of colonoscopies of healthy subjects. Painstaking research has led to the identification of a large number of different types of biomarkers, few of which are in general clinical use. Here, we searched for highly accurate combinations of biomarkers by meta-analyses of genome- and proteome-wide data from CRC tumors. We focused on secreted proteins identified by the Human Protein Atlas and used our recently described algorithms to find optimal combinations of proteins. We identified nine proteins, three of which had been previously identified as potential biomarkers for CRC, namely CEACAM5, LCN2 and TRIM28. The remaining proteins were PLOD1, MAD1L1, P4HA1, GNS, C12orf10 and P3H1. We analyzed these proteins in plasma from 80 patients with newly diagnosed CRC and 80 healthy controls. A combination of four of these proteins, TRIM28, PLOD1, CEACAM5 and P4HA1, separated a training set consisting of 90% patients and 90% of the controls with high accuracy, which was verified in a test set consisting of the remaining 10%. Further studies are warranted to test our algorithms and proteins for early CRC diagnosis.

Indexed as

AlgorithmsBiomarkers, TumorColorectal NeoplasmsHumansProteomicsBiomarkers, Tumor

Identifiers

PMID31666584
PMCPMC6821706
OpenAlexW2982643407

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.