Evidence map›Paper›PMID 38383634›Full record

ArticleScientific reports2024

Plasma metabolomic differences in early-onset compared to average-onset colorectal cancer.

Thejus Jayakrishnan, Arshiya Mariam, Nicole Farha, Daniel M Rotroff, Federico Aucejo, Shimoli V Barot, Madison Conces, Kanika G Nair, Smitha S Krishnamurthi, Stephanie L Schmit and 3 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.5field-weighted citation impact, top 9% 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, 9 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Young Onset Colorectal Cancer.South Asian journal of cancer · 2024
    Article
  6. Article
  7. Review
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

13 authors at 4 institutions in 1 country.

Thejus JayakrishnanDepartment of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA.
Arshiya Mariam *Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, USA.
Nicole Farha *Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA.
Daniel M RotroffDepartment of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, USA.
Federico AucejoDepartment of Surgery, Digestive Disease & Surgery Institute, Cleveland Clinic, Cleveland, USA.
Shimoli V BarotDepartment of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA.
Madison ConcesCase Comprehensive Cancer Center, Cleveland, USA.
Kanika G NairDepartment of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA.
Smitha S KrishnamurthiDepartment of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA.
Stephanie L SchmitCenter for Young-Onset Colorectal Cancer, Cleveland Clinic, Cleveland, USA.
David LiskaCase Comprehensive Cancer Center, Cleveland, USA.
Alok A Khorana *Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA.
Suneel D Kamath *Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA. kamaths@ccf.org.
Cleveland Clinic · USCase Comprehensive Cancer CenterCleveland Clinic Lerner College of Medicine · USUniversity Hospitals Seidman Cancer Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deleterious effects of environmental exposures may contribute to the rising incidence of early-onset colorectal cancer (eoCRC). We assessed the metabolomic differences between patients with eoCRC, average-onset CRC (aoCRC), and non-CRC controls, to understand pathogenic mechanisms. Patients with stage I-IV CRC and non-CRC controls were categorized based on age ≤ 50 years (eoCRC or young non-CRC controls) or  ≥ 60 years (aoCRC or older non-CRC controls). Differential metabolite abundance and metabolic pathway analyses were performed on plasma samples. Multivariate Cox proportional hazards modeling was used for survival analyses. All P values were adjusted for multiple testing (false discovery rate, FDR P < 0.15 considered significant). The study population comprised 170 patients with CRC (66 eoCRC and 104 aoCRC) and 49 non-CRC controls (34 young and 15 older). Citrate was differentially abundant in aoCRC vs. eoCRC in adjusted analysis (Odds Ratio = 21.8, FDR P = 0.04). Metabolic pathways altered in patients with aoCRC versus eoCRC included arginine biosynthesis, FDR P = 0.02; glyoxylate and dicarboxylate metabolism, FDR P = 0.005; citrate cycle, FDR P = 0.04; alanine, aspartate, and glutamate metabolism, FDR P = 0.01; glycine, serine, and threonine metabolism, FDR P = 0.14; and amino-acid t-RNA biosynthesis, FDR P = 0.01. 4-hydroxyhippuric acid was significantly associated with overall survival in all patients with CRC (Hazards ratio, HR = 0.4, 95% CI 0.3-0.7, FDR P = 0.05). We identified several unique metabolic alterations, particularly the significant differential abundance of citrate in aoCRC versus eoCRC. Arginine biosynthesis was the most enriched by the differentially altered metabolites. The findings hold promise in developing strategies for early detection and novel therapies.

Indexed as

Colorectal NeoplasmsMetabolomicsArginineCitratesCitric AcidHumansMiddle AgedArginineCitratesCitric AcidArginine biosynthesis pathwayCitric acid cycleEarly onset colorectal cancerMetabolomicsPathway analysisSynthetic lethalityTranslational research

Identifiers

PMID38383634
PMCPMC10881959
OpenAlexW4392004128

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.