Evidence map›Paper›PMID 36457747›Full record

ArticleFrontiers in genetics2022

Construction and validation of a novel and superior protein risk model for prognosis prediction in esophageal cancer.

Yang Liu, Miaomiao Wang, Yang Lu, Shuyan Zhang, Lin Kang, Guona Zheng, Yanan Ren, Xiaowan Guo, Huanfen Zhao, Han Hao

Open access · goldAbstract read
In one paragraph

Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 75% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

10 authors at 2 institutions in 1 country.

Yang LiuDepartment of Pathology, Hebei General Hospital, Shijiazhuang, China.
Miaomiao WangBasic Medical College, Hebei Medical University, Shijiazhuang, China.
Yang LuBasic Medical College, Hebei Medical University, Shijiazhuang, China.
Shuyan ZhangDepartment of Pathology, Hebei General Hospital, Shijiazhuang, China.
Lin KangDepartment of Pathology, Hebei General Hospital, Shijiazhuang, China.
Guona ZhengDepartment of Pathology, Hebei General Hospital, Shijiazhuang, China.
Yanan RenDepartment of Gynecology, Hebei General Hospital, Shijiazhuang, China.
Xiaowan GuoDepartment of Radiology, Hebei General Hospital, Shijiazhuang, China.
Huanfen ZhaoDepartment of Pathology, Hebei General Hospital, Shijiazhuang, China.
Han HaoDepartment of Pharmacology, The Key Laboratory of New Drug Pharmacology and Toxicology, Center of Innovative Drug Research and Evaluation, Hebei Medical University, Shijiazhuang, China.
Hebei General Hospital · CNHebei Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Esophageal cancer (EC) is recognized as one of the most common malignant tumors in the word. Based on the biological process of EC occurrence and development, exploring molecular biomarkers can provide a good guidance for predicting the risk, prognosis and treatment response of EC. Proteomics has been widely used as a technology that identifies, analyzes and quantitatively acquires the composition of all proteins in the target tissues. Proteomics characterization applied to construct a prognostic signature will help to explore effective biomarkers and discover new therapeutic targets for EC. This study showed that we established a 8 proteins risk model composed of ASNS, b-Catenin_pT41_S45, ARAF_pS299, SFRP1, Vinculin, MERIT40, BAK and Atg4B

Indexed as

esophageal cancerprognosisproteomicsrisk modelTCGAtreatment

Identifiers

PMID36457747
PMCPMC9705836
OpenAlexW4309215437

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.