Evidence map›Paper›PMID 37637052›Full record

ArticleFrontiers in oncology2023

A scientometric analysis of research trends on targeting mTOR in breast cancer from 2012 to 2022.

Xizhou Zhang, Jinyao Wu, Qiuping Yang, Huiting Tian, Lingzhi Chen, Daitian Zheng, Zeqi Ji, Jiehui Cai, Yexi Chen, Zhiyang Li

Abstract read
In one paragraph

Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Xizhou ZhangDepartment of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Jinyao WuDepartment of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Qiuping YangDepartment of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Huiting TianDepartment of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Lingzhi ChenDepartment of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Daitian ZhengDepartment of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Zeqi JiDepartment of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Jiehui CaiDepartment of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Yexi ChenDepartment of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Zhiyang LiDepartment of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over the past decade, thousands of articles have been published on the mechanistic target of rapamycin (mTOR) and its role in breast cancer. However, the variability and heterogeneity of academic data may impact the acquisition of published research information. Due to the large number, heterogeneity, and varying quality of publications related to mTOR and breast cancer, sorting out the present state of the research in this area is critical for both researchers and clinicians. Therefore, scientometric techniques and visualization tools were employed to analyze the large number of bibliographic metadata related to the research area of mTOR and breast cancer. The features of relevant publications were searched from 2012 to 2022 to evaluate the present status of research and the evolution of research hotspots in this particular field. Web of Science was utilized to extract all relevant publications from 2012 to 2022. Subsequently, Biblioshiny and VOSviewer were utilized to obtain data on the most productive countries, authors, and institutions, annual publications and citations, the most influential journals and articles, and the most frequently occurring keywords. In total, 1,471 publications were retrieved, comprising 1,167 original articles and 304 reviews. There was a significant rise in publications between 2015 and 2018, followed by a sharp decline in 2019 and a rebound since then. The publication with the highest number of citations was a 2012 review authored by Baselga et al. The United States had the highest number of publications, citations and connections among all countries. Oncotarget had the highest number of published articles among all the journals, and José Baselga had the strongest links with other authors. Excluding the search topics, the most frequently used words were "expression" (n = 297), "growth" (n = 228), "activation" (n = 223), "pathway" (n = 205), and "apoptosis" (n = 195). mTOR is crucially involved in breast cancer pathogenesis, but its exact mechanism of action remains controversial and warrants further investigation. The scientometric analysis provides a distinct overview of the existing state of research and highlights the topical issues that deserve further exploration.

Indexed as

bibliometrixbreast cancermTORscientometricsVOSviewerWoS

Identifiers

PMID37637052
PMCPMC10448818

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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.