Evidence map›Paper›PMID 39244604›Full record

ArticleClinical epigenetics2024

Discovery and validation of colorectal cancer tissue-specific methylation markers: a dual-center retrospective cohort study.

Qinxing Cao, Zhenjia Dan, Nengyi Hou, Li Yan, Xingmei Yuan, Hejiang Lu, Song Yu, Jiangping Zhang, Huasheng Xiao, Qiang Liu and 3 more

Abstract readMulticenter StudyValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical epigenetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. 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.

Qinxing Cao *Department of Geriatric General Surgery, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Zhenjia Dan *Department of Geriatric General Surgery, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Nengyi Hou *Department of Geriatric General Surgery, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Li YanDepartment of Geriatric General Surgery, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Xingmei YuanDepartment of Geriatric General Surgery, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Hejiang LuDepartment of Geriatric General Surgery, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Song YuDepartment of Geriatric General Surgery, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Jiangping ZhangChongqing Bohao Diagnostic Technology Co., Ltd, Chongqing, 410010, China.
Huasheng XiaoShanghai Biotechnology Corporation, Ltd, Shanghai, 200126, China.
Qiang LiuShanghai Biotechnology Corporation, Ltd, Shanghai, 200126, China.
Xiaoyong ZhangShanghai Biotechnology Corporation, Ltd, Shanghai, 200126, China.
Min ZhangDepartment of Outpatient, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China. 357794030@qq.com.
Minghui PangDepartment of Geriatric General Surgery, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China. mhpang@uestc.edu.cn.

Funding

Key R&D project of the Science and Technology Department of Sichuan Province 2023YFS0197Sichuan Provincial Department of Science and Technology, Sichuan Province Natural Science Foundation 2022NSFSC1541The study was funded by the Key R&D project of the Science and Technology Department of Sichuan Province 2023YFS0185University of Electronic Science and Technology of China, and the "Medical-Engineering Cross Joint Fund" of University of Electronic Science and Technology of China - Sichuan Provincial People's Hospital ZYGX2021YGLH022
6 · The paper itself

Abstract

background and purposeEarly detection, diagnosis, and treatment of colorectal cancer and its precancerous lesions can significantly improve patients' survival rates. The purpose of this research is to identify methylation markers specific to colorectal cancer tissues and validate their diagnostic capability in colorectal cancer and precancerous changes by measuring the level of DNA methylation in stool samples.

methodWe analyzed samples from six cancer tissues and adjacent normal tissues and fecal samples from 758 participants, including 62 patients with interfering diseases. Bioinformatics databases were used to screen for candidate biomarkers for CRC, and quantitative methylation-specific PCR methods were applied for identification. The methylation levels of the candidate biomarkers in fecal and tissue samples were measured. Logistic regression and random forest models were built and validated using fecal sample data from one of the centers, and the independent or combined diagnostic value of the candidate biomarkers in fecal samples for CRC and precancerous lesions was analyzed. Finally, the diagnostic capability and stability of the model were validated at another medical center.

resultsThis study identified two colorectal cancer CpG sites with tissue specificity. These two biomarkers have certain diagnostic power when used individually, but their diagnostic value for colorectal cancer and colorectal adenoma is more significant when they are used in combination.

conclusionThe results indicate that a DNA methylation biomarker combined diagnostic model based on two CpG sites, cg13096260 and cg12587766, has the potential for screening and diagnosing precancerous lesions and colorectal cancer. Additionally, compared to traditional diagnostic models, machine learning algorithms perform better but may yield more false-positive results, necessitating further investigation.

Indexed as

Biomarkers, TumorColorectal NeoplasmsDNA MethylationFecesAdultAgedCpG IslandsEarly Detection of CancerFemaleHumansMaleMiddle AgedRetrospective StudiesBiomarkers, TumorAdvanced adenomaColorectal cancerDiagnostic modelDNA methylation markersRandom forest algorithm

Identifiers

PMID39244604
PMCPMC11380779

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.