Evidence map›Paper›PMID 40670358›Full record

ArticleScientific data2025

A comprehensive database for identifying and interpreting ctDNA driver genes and variants in cancer.

Yuncong Wang, Jing Gan, Haoyu Hu, Manyi Xu, Yongle Xu, Yusen Zhao, Wenbo Dong, Xinrong Li, Hongbo Zhu, Shuangshuang Wang and 4 more

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2025. 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. 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

14 authors.

Yuncong Wang *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China.
Jing Gan *College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China.
Haoyu HuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China.
Manyi XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China.
Yongle XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China.
Yusen ZhaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China.
Wenbo DongCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China.
Xinrong LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China.
Hongbo ZhuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China.
Shuangshuang WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China.
Jiaheng HeCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China.
Shangwei NingCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China. ningsw@ems.hrbmu.edu.cn.
Peng WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China. wpgqy@hrbmu.edu.cn.
Hui ZhiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China. zhihui@ems.hrbmu.edu.cn.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 32170674
6 · The paper itself

Abstract

Circulating tumor DNA (ctDNA) variants hold significant promise as cancer biomarkers in liquid biopsies, owing to their minimally invasive testing approach and capacity to capture the comprehensive tumor landscape. However, a comprehensive resource for studying ctDNA variants is still lacking. Here, we developed CTDdgv to systematically identify ctDNA variants and interpret their clinical relevance. We manually curated 1674 experimentally validated clinical interpretations for ctDNA variants, concerning prognosis, drug resistance, tumor characteristics, metastasis monitoring, therapy, prospect for detection, and others. Furthermore, we developed and integrated a pipeline that identifies tumor driver genes (TDGs) and variants (TDVs) and evaluates the prognostic significance of potential TDGs. Using publicly available ctDNA mutation spectra, we identified potential TDGs and TDVs from 38 datasets across 17 cancer types. Based on these datasets, we provide a multi-dimensional analysis of TDG gene sets in specific cancer types, providing insights into their driving effects. Collectively, CTDdgv has significant potential to serve as a valuable resource for molecular diagnostics and therapeutic decision-making in cancer via liquid biopsy.

Indexed as

Circulating Tumor DNADatabases, GeneticNeoplasmsBiomarkers, TumorHumansLiquid BiopsyMutationBiomarkers, TumorCirculating Tumor DNA

Identifiers

PMID40670358
PMCPMC12267690

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.