A comprehensive overview of cellular senescence from 1990 to 2021: A machine learning-based bibliometric analysis

BACKGROUND: As a cellular process, senescence functions to prevent the proliferation of damaged, old and tumor-like cells, as well as participate in embryonic development, tissue repair, etc. This study aimed to analyze the themes and topics of the scientific publications related to cellular senescence in the past three decades by machine learning. METHODS: The MeSH term “cellular senescence” was used for searching publications from 1990 to 2021 on the PubMed database, while the R platform was adopted to obtain associated data. A topic network was constructed by latent Dirichlet allocation (LDA) and the Louvain algorithm. RESULTS: A total of 21,910 publications were finally recruited in this article. Basic studies (15,382, 70.21%) accounted for the most proportion of publications over the past three decades. Physiology, drug effects, and genetics were the most concerned MeSH terms, while cell proliferation was the leading term since 2010. Three senolytics were indexed by MeSH terms, including quercetin, curcumin, and dasatinib, with the accumulated occurrence of 35, 26, and 22, separately. Three clusters were recognized by LDA and network analyses. Telomere length was the top studied topic in the cluster of physiological function, while cancer cell had been a hot topic in the cluster of pathological function, and protein kinase pathway was the most popular topic in the cluster of molecular mechanism. Notably, the cluster of physiological function showed a poor connection with other clusters. CONCLUSION: Cellular senescence has obtained increasing attention over the past three decades. While most of the studies focus on the pathological function and molecular mechanism, more researches should be conducted on the physiological function and the clinical translation of cellular senescence, especially the development and application of senotherapeutics.