Evidence map›Paper›PMID 30569177›Full record

ArticleMolecular medicine reports2019

Identification of gene biomarkers in patients with postmenopausal osteoporosis.

Chenggang Yang, Jing Ren, Bangling Li, Chuandi Jin, Cui Ma, Cheng Cheng, Yaolan Sun, Xiaofeng Shi

Open access · hybridAbstract read
In one paragraph

Article in Molecular medicine reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed, 1 pooled it
1.3field-weighted citation impact, top 19% 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

47 citing papers in PubMed, 1 synthesis or guideline pooled it, 68 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

8 authors.

Chenggang YangDepartment of Research and Development, Gu'an Bojian Bio‑Technology Co., Ltd., Langfang, Hebei 065000, P.R. China.
Jing RenDepartment of Big Data, Beijing Medintell Bioinformatic Technology Co., Ltd., Beijing 100081, P.R. China.
Bangling LiDepartment of Big Data, Beijing Medintell Bioinformatic Technology Co., Ltd., Beijing 100081, P.R. China.
Chuandi JinDepartment of Big Data, Beijing Medintell Bioinformatic Technology Co., Ltd., Beijing 100081, P.R. China.
Cui MaDepartment of Research and Development, Gu'an Bojian Bio‑Technology Co., Ltd., Langfang, Hebei 065000, P.R. China.
Cheng ChengDepartment of Big Data, Beijing Medintell Bioinformatic Technology Co., Ltd., Beijing 100081, P.R. China.
Yaolan SunDepartment of Big Data, Beijing Medintell Bioinformatic Technology Co., Ltd., Beijing 100081, P.R. China.
Xiaofeng ShiDepartment of Research and Development, Gu'an Bojian Bio‑Technology Co., Ltd., Langfang, Hebei 065000, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Postmenopausal osteoporosis (PMOP) is a major public health concern worldwide. The present study aimed to provide evidence to assist in the development of specific novel biomarkers for PMOP. Differentially expressed genes (DEGs) were identified between PMOP and normal controls by integrated microarray analyses of the Gene Expression Omnibus (GEO) database, and the optimal diagnostic gene biomarkers for PMOP were identified with LASSO and Boruta algorithms. Classification models, including support vector machine (SVM), decision tree and random forests models, were established to test the diagnostic value of identified gene biomarkers for PMOP. Functional annotations and protein‑protein interaction (PPI) network constructions were also conducted. Integrated microarray analyses (GSE56815, GSE13850 and GSE7429) of the GEO database were employed, and 1,320 DEGs were identified between PMOP and normal controls. An 11‑gene combination was also identified as an optimal biomarker for PMOP by feature selection and classification methods using SVM, decision tree and random forest models. This combination was comprised of the following genes: Dehydrogenase E1 and transketolase domain containing 1 (DHTKD1), osteoclast stimulating factor 1 (OSTF1), G protein‑coupled receptor 116 (GPR116), BCL2 interacting killer, adrenoceptor β1 (ADRB1), neogenin 1 (NEO1), RB binding protein 4 (RBBP4), GPR87, cylicin 2, EF‑hand calcium binding domain 1 and DEAH‑box helicase 35. RBBP4 (degree=12) was revealed to be the hub gene of this PMOP‑specific PPI network. Among these 11 genes, three genes (OSTF1, ADRB1 and NEO1) were speculated to serve roles in PMOP by regulating the balance between bone formation and bone resorption, while two genes (GPR87 and GPR116) may be involved in PMOP by regulating the nuclear factor‑κB signaling pathway. Furthermore, DHTKD1 and RBBP4 may be involved in PMOP by regulating mitochondrial dysfunction and interacting with ESR1, respectively. In conclusion, the findings of the current study provided an insight for exploring the mechanism and developing novel biomarkers for PMOP. Further studies are required to test the diagnostic value for PMOP prior to use in a clinical setting.

Indexed as

Gene Regulatory NetworksAgedBiomarkersCase-Control StudiesComputational BiologyDatabases, GeneticDecision TreesFemaleGene Expression ProfilingGene Expression RegulationGene OntologyHumansIntracellular Signaling Peptides and ProteinsKetoglutarate Dehydrogenase ComplexKetone OxidoreductasesMicroarray AnalysisADGRF5 protein, humanADRB1 protein, humanBiomarkersDHTKD1 protein, humanIntracellular Signaling Peptides and ProteinsKetoglutarate Dehydrogenase ComplexKetone OxidoreductasesNEO1 protein, humanNerve Tissue ProteinsOSTF1 protein, humanProteinsRBBP4 protein, humanReceptors, Adrenergic, beta-1Receptors, Cell SurfaceReceptors, G-Protein-CoupledRetinoblastoma-Binding Protein 4biomarkerdifferentially expressed genesGene Expression Omnibuspostmenopausal osteoporosis

Identifiers

PMID30569177
PMCPMC6323213
OpenAlexW2905537553

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.