Evidence map›Paper›PMID 27314445›Full record

ArticleMedical science monitor : international medical journal of experimental and clinical research2016

Identification of CD20, ECM, and ITGA as Biomarkers for Osteosarcoma by Integrating Transcriptome Analysis.

Da-Wei Wang, Sheng-Yuan Yu, Yang Cao, Lei Yang, Wei Liu, Xiao-Qiang Er, Gui-Jun Yao, Zheng-Gang Bi

Open access · hybridAbstract read
In one paragraph

Article in Medical science monitor : international medical journal of experimental and clinical research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.9field-weighted citation impact, top 24% 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

7 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
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  5. Article
  6. Identification of a Two-Gene (Frontiers in oncology · 2019
    Article
  7. Identification of Key Genes Affecting Results of Hyperthermia in Osteosarcoma Based on Integrative ChIP-Seq/TargetScan Analysis.Medical science monitor : international medical journal of experimental and clinical research · 2017
    Article
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 at 2 institutions in 1 country.

Da-Wei WangDepartment of Orthopaedics, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China (mainland).
Sheng-Yuan YuDepartment of Orthopaedics, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China (mainland).
Yang CaoDepartment of Orthopaedics, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China (mainland).
Lei YangDepartment of Orthopaedics, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China (mainland).
Wei LiuDepartment of Orthopaedics, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China (mainland).
Xiao-Qiang ErDepartment of Orthopaedics, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China (mainland).
Gui-Jun YaoDepartment of Orthopaedics, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China (mainland).
Zheng-Gang BiDepartment of Orthopaedics, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China (mainland).
First Affiliated Hospital of Harbin Medical University · CNHarbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND Osteosarcoma is the most frequent primary bone cancer derived from primitive mesenchymal cells. The aim of this study was to explore the molecular mechanism of the development and progression of osteosarcoma. MATERIAL AND METHODS The gene expression profiles of osteosarcoma from 17 specimens (3 normal and 14 osteosarcoma) were downloaded from the GEO database. The differentially expressed genes were identified by use of the Limma package. DAVID and Enrichment Map were used to perform GO and KEGG pathways enrichment analysis and to integrate enrichment results of differentially expressed genes (DEGs). Protein-protein interaction network was constructed and analyzed to screen out the potential regulatory proteins using the STRING online tools. RESULTS A total of 417 DEGs were screened, including 215 up-regulated and 202 down-regulated ones, accounting for 51.56% and 48.4%, respectively. In GO term, a total of 12 up-regulated expression genes were enriched in Cellular Component. The up-regulated DEGs were enriched in 6 KEGG pathways while the down-regulated expression genes were enriched in 2 KEGG pathways. The constructed PPI network was aggregated with 1006 PPI relationships and 238 nodes, accounting for 57.07% of DEGs. We found that CD20, MCM, and CCNB1 (down-regulated) in cell cycle and ECM, ITGA, RTKin (up-regulated) in focal adhesion had important roles in the progression of osteosarcoma. CONCLUSIONS The identified DEGs and their enriched pathways provide references for the exploration of the molecular mechanism of the development and progression of osteosarcoma. Moreover, the key genes (CD20, ECM, and ITGA) may be useful in treatment and diagnosis of osteosarcoma.

Indexed as

Antigens, CD20Biomarkers, TumorBone NeoplasmsDatabases, GeneticDown-RegulationExtracellular Matrix ProteinsGene Expression ProfilingHumansIntegrinsOligonucleotide Array Sequence AnalysisOsteoporosisOsteosarcomaProtein Interaction MapsSignal TransductionTranscription FactorsUp-RegulationAntigens, CD20Biomarkers, TumorExtracellular Matrix ProteinsIntegrinsTranscription Factors

Identifiers

PMID27314445
PMCPMC4913741
OpenAlexW2427272455

What OpenQuestion holds

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Read underepoch 390

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.