Evidence map›Paper›PMID 38559975›Full record

ArticleACS omega2024

Integrative Analysis of Multi-Omic Data for the Characteristics of Endometrial Cancer.

Tong Li, Zhijun Ruan, Chunli Song, Feng Yin, Tuanjie Zhang, Liyun Shi, Min Zuo, Linlin Lu, Yuhao An, Rui Wang and 1 more

Abstract read
In one paragraph

Article in ACS omega, 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

11 authors.

Tong LiDepartment of Gynecology, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China.
Zhijun RuanShenzhen Bay Laboratory, Pingshan Translational Medicine Center, Shenzhen 518118, China.
Chunli SongSchool of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Nanshan, Shenzhen 518055, China.
Feng YinSchool of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Nanshan, Shenzhen 518055, China.
Tuanjie ZhangShenzhen Bay Laboratory, Pingshan Translational Medicine Center, Shenzhen 518118, China.
Liyun ShiDepartment of Gynecology, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China.
Min ZuoDepartment of Pathology, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China.
Linlin LuInternational Institute for Translational Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, China.
Yuhao AnShenzhen Bay Laboratory, Pingshan Translational Medicine Center, Shenzhen 518118, China.ORCID https://orcid.org/0009-0006-0224-9709
Rui WangShenzhen Bay Laboratory, Pingshan Translational Medicine Center, Shenzhen 518118, China.
Xiyang YeDepartment of Gynecology, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Endometrial cancer (EC) is a frequently diagnosed gynecologic cancer. Identifying reliable prognostic genes for predicting EC onset is crucial for reducing patient morbidity and mortality. Here, a comprehensive strategy with transcriptomic and proteomic data was performed to measure EC's characteristics. Based on the publicly available RNA-seq data, death-associated protein kinase 3, recombination signal-binding protein for the immunoglobulin kappa J region, and myosin light chain 9 were screened out as potential biomarkers that affect the EC patients' prognosis. A linear model was further constructed by multivariate Cox regression for the prediction of the risk of being malignant. From further integrative analysis, exosomes were found to have a highly enriched role that might participate in EC occurrence. The findings were validated by qRT-polymerase chain reaction (PCR) and western blotting. Collectively, we constructed a prognostic-gene-based model for EC prediction and found that exosomes participate in EC incidents, revealing significantly promising support for the diagnosis of EC.

Identifiers

PMID38559975
PMCPMC10975631

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Registered trials

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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.