Evidence map›Paper›PMID 32432324›Full record

ArticleBioscience reports2020

Comprehensive analysis of the value of RAB family genes in prognosis of breast invasive carcinoma.

Shitong Lin, Canhui Cao, Yifan Meng, Ping Wu, Peipei Gao, Wenhua Zhi, Ting Peng, Peng Wu, Lingli Gui

Open access · goldAbstract read
In one paragraph

Article in Bioscience reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.8field-weighted citation impact, top 15% 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

6 citing papers in PubMed, 24 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

9 authors at 1 institution in 1 country.

Shitong LinCancer Biology Research Center (Key Laboratory of The Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Canhui CaoCancer Biology Research Center (Key Laboratory of The Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Yifan MengCancer Biology Research Center (Key Laboratory of The Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Ping WuCancer Biology Research Center (Key Laboratory of The Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Peipei GaoCancer Biology Research Center (Key Laboratory of The Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Wenhua ZhiCancer Biology Research Center (Key Laboratory of The Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Ting PengCancer Biology Research Center (Key Laboratory of The Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Peng WuCancer Biology Research Center (Key Laboratory of The Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Lingli GuiDepartment of Anesthesiology and Pain Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Tongji Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeSeveral RAB family genes have been studied extensively and proven to play pivotal roles in the occurrence and development of certain cancers. Here, we explored commonly expressed RAB family genes in humans and their prognostic significance using bioinformatics, and then identified potential biomarkers of breast invasive carcinoma (BRCA). MATERIALS AND

methodsThe prognostic values (overall survival) of RAB family genes in BRCA were obtained using Gene Expression Profiling Interactive Analysis (GEPIA). The expression patterns of RAB family genes and their relationships with clinicopathological parameters in BRCA were measured using the ONCOMINE and UALCAN databases, respectively. Genetic mutations and survival analysis were investigated using the cBio Cancer Genomics Portal (c-BioPortal). Interacting genes of potential biomarkers were identified using STRING, and functional enrichment analyses were performed using FunRich v3.1.3.

resultsIn total, 64 RAB genes were identified and analyzed in our study. Results showed that RAB1B, RAB2A, and RAB18 were up-regulated and significantly associated with poor overall survival in BRCA. Furthermore, their higher expression was positively correlated with clinicopathological parameters (e.g. cancer stage and nodal metastasis status). DNA copy number amplifications and mRNA up-regulation were the main genetic mutations, and the altered group showed significantly poorer overall survival compared with the unaltered group. Functional enrichment analysis of RAB1B, RAB2A, and RAB18 indicated they were closely involved in GTPase activity.

conclusionsRAB1B, RAB2A, and RAB18 were up-regulated and significantly correlated with poor prognosis in BRCA. Thus, they could be applied as novel biomarkers of BRCA in future studies.

Indexed as

TranscriptomeBiomarkers, TumorBreast NeoplasmsComputational BiologyDatabases, GeneticFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticGenetic Predisposition to DiseaseHumansNeoplasm InvasivenessPhenotypePrognosisProtein Interaction Mapsrab1 GTP-Binding Proteinsrab2 GTP-Binding ProteinBiomarkers, TumorRAB18 protein, humanRab1B protein, humanrab1 GTP-Binding Proteinsrab2 GTP-Binding Proteinrab GTP-Binding ProteinsBiologic biomarkersBreast invasive carcinomaRAB family genes

Identifiers

PMID32432324
PMCPMC7260355
OpenAlexW3027854802

What OpenQuestion holds

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LicenceCC BY
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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.