Evidence map›Paper›PMID 39263143›Full record

ArticleHeliyon2024

Mutation patterns in colorectal cancer and their relationship with prognosis.

Zhaoran Su, Maria El Hage, Michael Linnebacher

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

14 citing papers in PubMed, 1 synthesis or guideline 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

3 authors.

Zhaoran SuDepartment of Gastrointestinal Surgery, People's Hospital of Tongling City, China.
Maria El HageMolecular Oncology and Immunotherapy, Clinic of General Surgery, University Medical Center Rostock, Rostock 18057, Germany.
Michael LinnebacherMolecular Oncology and Immunotherapy, Clinic of General Surgery, University Medical Center Rostock, Rostock 18057, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Colorectal cancer (CRC) is a prevalent malignancy and a leading cause of cancer-related mortality. Extensive research into the aetiology of CRC has revealed that somatic mutations in certain genes play a crucial role in CRC development.

aimIn this study, we utilized data from public databases to investigate prevalent mutation patterns in CRC and developed a prognostic predictive model for CRC patients based on mutant genetic characteristics and other relevant clinical features. Methods: We initially gathered mutation information from CRC patients by analysing data from 15 datasets to identify genes with a mutation frequency of ≥10 %. Next, log-rank analyses were used to determine the relationship between prognosis and the mutational status of the most commonly mutated genes; the SIGnaling database was utilized to generate a protein‒protein interaction network. We consolidated and classified the gene mutation patterns of CRC patients in the database based on frequently mutated genes related to prognosis. A predictive nomogram was constructed, including age, sex, TNM stage, and mutation partner, based on available clinical, mutational, and prognostic information for CRC patients at our institution. Finally, the reliability of the model was verified using time-dependent ROC curve analysis. Results: The top 7 genes somatically mutated ≥10 % in 4477 samples from 4255 patients were Conclusion: Common mutational patterns based on frequently mutated genes are associated with prognosis in CRC patients. Our study provides a valuable and concise prognostic predictor for determining outcomes in patients with CRC.

Indexed as

BiomarkerColorectal cancerMutationPrognosis

Identifiers

PMID39263143
PMCPMC11387246

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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.