Bibliometric Analysis of Global Scientific Research on lncRNA: A Swiftly Expanding Trend

Zhai, Xiao; Zhao, Jian; Wang, Yiran; Wei, Xianzhao; Li, Gengwu; Yang, Yilin; Chen, Ziqiang; Bai, Yushu; Wang, Qijin; Chen, Xiao; Li, Ming

doi:10.1155/2018/7625078

Cited by 25 publications

(18 citation statements)

References 34 publications

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“…Some studies analyzed research fields such as RFID' publications (de Oliveira et al, 2019); tuberculosis (Nafade et al, 2018); lncRNA (Zhai et al, 2018); urban education (Liang & Wang, 2018); environmental damage (Li et al, 2018); linguistics (Mohsen et al, 2017); information architecture (Taga et al, 2017); fuzzy research (Merigó et al, 2015); welding (Layus and Kah, 2015); computer science (Uddin et al, 2015); and entrepreneurship (Landström et al, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

A Bibliometric Analysis and Visualization of the Scientific Publications of Universities: A Study of Hamadan University of Medical Sciences during 1992-2018

Mokhtari¹,

Mirezati²,

Saberi³

et al. 2020

Preprint

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The evaluation of universities from different perspectives is important for their scientific development. Analyzing the scientific papers of a university under the bibliometric approach is one main evaluative approach. The aim of this study was to conduct a bibliometric analysis and visualization of papers published by Hamadan University of Medical Science (HUMS), Iran, during 1992-2018. This study used bibliometric and visualization techniques. Scopus database was used for data collection. 3753 papers were retrieved by applying Affiliation Search in Scopus advanced search section. Excel and VOSviewer software packages were used for data analysis and bibliometric indicator extraction. An increasing trend was seen in the numbers of HUMS's published papers and received citations. The highest rate of collaboration in national level was with Tehran University of Medical Sciences. Internationally, HUMS's researchers had the highest collaboration with the authors from the United States, the United Kingdom and Switzerland, respectively. All highly-cited papers were published in high level Q1 journals. Term clustering demonstrated four main clusters: epidemiological studies, laboratory studies, pharmacological studies, and microbiological studies. The results of this study can be beneficial to the policy-makers of this university. In addition, researchers and bibliometricians can use this study as a pattern for studying and visualizing the bibliometric indicators of other universities and research institutions.

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Section: Literature Reviewmentioning

confidence: 99%

A Bibliometric Analysis and Visualization of the Scientific Publications of Universities: A Study of Hamadan University of Medical Sciences during 1992-2018

Mokhtari¹,

Mirezati²,

Saberi³

et al. 2020

Preprint

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“…Long non-coding RNAs (lncRNAs) are defined as non-coding RNAs of more than 200 nt in length ( Zhai et al, 2018 ). The discovery of lncRNAs has revealed a new dimension to the pathological processes of many diseases, including the occurrence and development of cancer ( Martens-Uzunova et al, 2014 ; Zhou et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

A Six-lncRNA Signature for Immunophenotype Prediction of Glioblastoma Multiforme

et al. 2021

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Glioblastoma multiforme (GBM) is the most aggressive primary tumor of the central nervous system. As biomedicine advances, the researcher has found the development of GBM is closely related to immunity. In this study, we evaluated the GBM tumor immunoreactivity and defined the Immune-High (IH) and Immune-Low (IL) immunophenotypes using transcriptome data from 144 tumors profiled by The Cancer Genome Atlas (TCGA) project based on the single-sample gene set enrichment analysis (ssGSEA) of five immune expression signatures (IFN-γ response, macrophages, lymphocyte infiltration, TGF-β response, and wound healing). Next, we identified six immunophenotype-related long non-coding RNA biomarkers (im-lncRNAs, USP30-AS1, HCP5, PSMB8-AS1, AL133264.2, LINC01684, and LINC01506) by employing a machine learning computational framework combining minimum redundancy maximum relevance algorithm (mRMR) and random forest model. Moreover, the expression level of identified im-lncRNAs was converted into an im-lncScore using the normalized principal component analysis. The im-lncScore showed a promising performance for distinguishing the GBM immunophenotypes with an area under the curve (AUC) of 0.928. Furthermore, the im-lncRNAs were also closely associated with the levels of tumor immune cell infiltration in GBM. In summary, the im-lncRNA signature had important clinical implications for tumor immunophenotyping and guiding immunotherapy in glioblastoma patients in future.

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“…Epithelial to Mesenchymal Transition (emt) has long been identi ed as a mechanism by which MSCs support tumour progression and cancer cell survival within the TME (44). Furthermore, EMT has previously been identi ed as a research hotspot in cancer research, by bibliometric analysis (45). While its frequency within the literature supports its importance in MSC research and the expertise that exists within the scienti c community, the search for novelty is likely to be found elsewhere within the cluster analysis.…”

Section: Discussionmentioning

confidence: 99%

The Future of Mesenchymal Stromal Cells in cancer – A Bibliometric Analysis

O'Connor¹,

Barkley²,

Kerin³

2020

Preprint

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BackgroundMesenchymal stromal/stem cells have garnered much interest in the field of cancer biology. Bibliometric analysis of a scientific field has the potential to reveal insights pertaining to the maturity of the field, the hotspot sources of publication and temporal shifts in research subcategories within the field. Despite the benefits of such an analysis, one has not been conducted to date.MethodsThe Web of Science™database and VOSviewer software were used to analyse publication and citation data for all publications relating to Mesenchymal Stromal/Stem Cells in oncology. Contributions to the literature were also identified by country, journal and cancer type. Keyword analysis identified trends in developing research categories.Results9927 published articles were analysed. The current publication rate fits an exponential trend(r2 = 0.97). The U.S. and the People’s Republic of China have the highest overall publications but when corrected for population and citations per article, the most successful countries are Singapore, Luxembourg and Switzerland. In the past 15 years 74% of articles have been in cancer specific journals. Breast cancer publications account for 42% of common cancer articles. Most articles were published in journals that had a basic science focus (44%). Keyword analysis resulted in 3 distinct clusters aligned with 1. Characterisation and nomenclature, 2.Clinical and 3.Molecular function. The most recent publications favour topics on Molecular function. ConclusionsResearch on Mesenchymal Stromal/Stem Cells in oncology is experiencing exponential growth. There is widespread acceptance of basic science research on MSCs in cancer specific journals and a niche for more translational and clinically focused studies.

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Bibliometric Analysis of Global Scientific Research on lncRNA: A Swiftly Expanding Trend

Cited by 25 publications

References 34 publications

A Bibliometric Analysis and Visualization of the Scientific Publications of Universities: A Study of Hamadan University of Medical Sciences during 1992-2018

A Bibliometric Analysis and Visualization of the Scientific Publications of Universities: A Study of Hamadan University of Medical Sciences during 1992-2018

A Six-lncRNA Signature for Immunophenotype Prediction of Glioblastoma Multiforme

The Future of Mesenchymal Stromal Cells in cancer – A Bibliometric Analysis

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