Evidence map›Paper›PMID 33850871›Full record

ArticleAnnals of translational medicine2021

Development and validation of a prediction model for lung adenocarcinoma based on RNA-binding protein.

Longjun Yang, Rusi Zhang, Guangran Guo, Gongming Wang, Yingsheng Wen, Yongbin Lin, Xuewen Zhang, Xiangyang Yu, Zirui Huang, Dechang Zhao and 2 more

Open access · diamondAbstract read
In one paragraph

Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
3.0field-weighted citation impact, top 7% 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

19 citing papers in PubMed, 26 citations in OpenAlex.

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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

12 authors at 1 institution in 1 country.

Longjun YangState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Rusi ZhangState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Guangran GuoState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Gongming WangState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Yingsheng WenState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Yongbin LinState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Xuewen ZhangState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Xiangyang YuDepartment of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Zirui HuangState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Dechang ZhaoState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Lanjun ZhangState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
written on behalf of the AME Thoracic Surgery Collaborative Group
Sun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRNA-binding proteins (RBPs) have been found to participate in the development and progression of cancer. This present study aimed to construct a RBP-based prognostic prediction model for lung adenocarcinoma (LUAD).

methodsRNA sequencing data and corresponding clinical information were acquired from The Cancer Genome Atlas (TCGA) and served as a training set. The prediction model was validated using the dataset in Gene Expression Omnibus (GEO) databases. Univariate and multivariate Cox regression analyses were conducted to identify the RBPs associated with survival. R software (http://www.r-project.org) was used for analysis in this study.

resultsNine hub prognostic RBPs (

conclusionsOur nine-gene signature model could be used as a marker to predict the prognosis of LUAD and has potential for use in treatment individualization.

Indexed as

Lung adenocarcinoma (LUAD)overall survival (OS)prognostic modelRNA-binding protein (RBPs)

Identifiers

PMID33850871
PMCPMC8039651
OpenAlexW3141345935

What OpenQuestion 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 OpenQuestion graph.