Evidence map›Paper›PMID 35795065›Full record

ArticleFrontiers in oncology2022

Discovering Innate Driver Variants for Risk Assessment of Early Colorectal Cancer Metastasis.

Ruo-Fan Ding, Yun Zhang, Lv-Ying Wu, Pan You, Zan-Xi Fang, Zhi-Yuan Li, Zhong-Ying Zhang, Zhi-Liang Ji

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2022. 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
0.1field-weighted citation impact, top 61% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  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

8 authors at 3 institutions in 1 country.

Ruo-Fan DingState Key Laboratory of Cellular Stress Biology, National Institute for Data Science in Health and Medicine, School of Life Sciences, Xiamen University, Xiamen, China.
Yun ZhangState Key Laboratory of Cellular Stress Biology, National Institute for Data Science in Health and Medicine, School of Life Sciences, Xiamen University, Xiamen, China.
Lv-Ying WuState Key Laboratory of Cellular Stress Biology, National Institute for Data Science in Health and Medicine, School of Life Sciences, Xiamen University, Xiamen, China.
Pan YouDepartment of Clinical Laboratory, Xiamen Xianyue Hospital, Xiamen, China.
Zan-Xi FangDepartment of Clinical Laboratory, Zhongshan Hospital , affiliated to Xiamen University, Xiamen, China.
Zhi-Yuan LiDepartment of Clinical Laboratory, Zhongshan Hospital , affiliated to Xiamen University, Xiamen, China.
Zhong-Ying ZhangDepartment of Clinical Laboratory, Zhongshan Hospital , affiliated to Xiamen University, Xiamen, China.
Zhi-Liang JiState Key Laboratory of Cellular Stress Biology, National Institute for Data Science in Health and Medicine, School of Life Sciences, Xiamen University, Xiamen, China.
Xiamen University · CNZhongshan Hospital of Xiamen University · CNTianjin haihe hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metastasis is the main fatal cause of colorectal cancer (CRC). Although enormous efforts have been made to date to identify biomarkers associated with metastasis, there is still a huge gap to translate these efforts into effective clinical applications due to the poor consistency of biomarkers in dealing with the genetic heterogeneity of CRCs. In this study, a small cohort of eight CRC patients was recruited, from whom we collected cancer, paracancer, and normal tissues simultaneously and performed whole-exome sequencing. Given the exomes, a novel statistical parameter LIP was introduced to quantitatively measure the local invasion power for every somatic and germline mutation, whereby we affirmed that the innate germline mutations instead of somatic mutations might serve as the major driving force in promoting local invasion. Furthermore,

Indexed as

colorectal cancerdriver variantslocal invasionmachine learningmetastasis

Identifiers

PMID35795065
PMCPMC9252167
OpenAlexW4283158722

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.