Evidence map›Paper›PMID 36900401›Full record

ArticleCancers2023

Development and Experimental Validation of a Novel Prognostic Signature for Gastric Cancer.

Chengcheng Liu, Yuying Huo, Yansong Zhang, Fumei Yin, Taoyu Chen, Zhenyi Wang, Juntao Gao, Peng Jin, Xiangyu Li, Minglei Shi and 1 more

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In one paragraph

Article in Cancers, 2023. 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
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.

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

11 authors.

Chengcheng LiuSchool of Software Engineering, Beijing Jiaotong University, Beijing 100044, China.
Yuying HuoSchool of Software Engineering, Beijing Jiaotong University, Beijing 100044, China.ORCID 0000-0003-3907-7032
Yansong ZhangSchool of Life Sciences, Peking University, Beijing 100871, China.
Fumei YinMedical School of Chinese PLA, Beijing 100853, China.ORCID 0000-0002-5388-0481
Taoyu ChenDepartment of Bioinformatics, School of Basic Medicine, Peking University Health Center, Beijing 100191, China.
Zhenyi WangMOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, Tsinghua University, Beijing 100084, China.ORCID 0000-0002-9407-0135
Juntao GaoMOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, Tsinghua University, Beijing 100084, China.
Peng JinSenior Department of Gastroenterology, First Medical Center of Chinese PLA General Hospital, Beijing 100036, China.
Xiangyu LiSchool of Software Engineering, Beijing Jiaotong University, Beijing 100044, China.ORCID 0000-0002-6017-0701
Minglei ShiMOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, Tsinghua University, Beijing 100084, China.
Michael Q ZhangMOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, Tsinghua University, Beijing 100084, China.

Funding

National Key Research and Development Program of China 2017YFA0505503Natural Science Foundation of China 62003028Research Funds for the Central Universities 2019RC045
6 · The paper itself

Abstract

backgroundGastric cancer is a malignant tumor with high morbidity and mortality. Therefore, the accurate recognition of prognostic molecular markers is the key to improving treatment efficacy and prognosis.

methodsIn this study, we developed a stable and robust signature through a series of processes using machine-learning approaches. This PRGS was further experimentally validated in clinical samples and a gastric cancer cell line.

resultsThe PRGS is an independent risk factor for overall survival that performs reliably and has a robust utility. Notably, PRGS proteins promote cancer cell proliferation by regulating the cell cycle. Besides, the high-risk group displayed a lower tumor purity, higher immune cell infiltration, and lower oncogenic mutation than the low-PRGS group.

conclusionsThis PRGS could be a powerful and robust tool to improve clinical outcomes for individual gastric cancer patients.

Indexed as

clinical outcomesgastric cancermachine learningoncogenic mutationprognostic signaturetumor microenvironment

Identifiers

PMID36900401
PMCPMC10000504

What Socratic holds

Textfull text, public
LicenceCC BY
measurements read42
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.