Bibliometric analysis of autophagy in the diagnosis and treatment of osteosarcoma: a bibliometric analysis (2007-2023)

Abstract

Osteosarcoma is the most common primary bone tumor in children and adolescents. Its pathogenesis is complex and poses difficulties in treatment. Autophagy is a cell biological process that plays a crucial role in the mechanistic study and treatment of osteosarcoma. The objective of this study is to evaluate the past research progress from 2007 to 2023 and visualize the key research directions through bibliometric methods. Relevant publications published from the start of 2007 to the end of 2023 were searched and screened in the Web of Science Core Collection. They were analyzed and visualized using CiteSpace and the Bibliometric online analysis platform in terms of country, institution, author, journal, cited references, and keywords. In total, 619 publications from 522 journals with 682 authors from 42 countries were screened. The country with the highest number of publications is China (n = 445, 71.890%), followed by the United States (n = 60, 9.693%). The research institution with the highest number of publications is Shanghai Jiao Tong University (n = 42, 6.785%). The author with the highest number of publications is Cai, Zhengdong (n = 7), while the most cited author is Mizushimma N (n = 93). Among many journals, AUTOPHAGY has the most citations (n = 342), while CANCER LETT shows the greatest centrality (Centrality = 0.05). "Autophagy" is the most cited keyword (n = 177), and the keyword with the largest burst intensity is "cancer cells" (Strength = 6.27), which lasted from 2011 to 2014. China is a major contributor to autophagy research in the field of osteosarcoma, followed by the United States. All publications are in high-quality journals. "Autophagy" is a hot research topic in this field.

Keywords: Autophagy; bibliometric; citeSpace; osteosarcoma; visualization.

Figure 1.

Publications screening flowchart.

Figure 2.

Number of publications in different countries at different times (a, b).

Figure 3.

Visualization map of the cooperative relationships between various countries (a) and affiliations (b). Pie charts and co-occurrence plots of the number of articles issued in different countries (c, d). Nodes represent countries or institutions, with node size indicating publication volume and edges representing collaboration strength.

Figure 4.

Visualized network co-occurrence maps for authors (a) and cited authors (b). Nodes represent authors or cited authors, node size indicates the number of author publications or cited authors, and lines indicate the strength of the partnership.

Figure 5.

Co-occurrence visualization (a) and citation bursts (b) analysis of cited journals. Nodes represent journals, node size indicates the number of journal publications, and lines indicate the strength of the partnership.

Figure 6.

The dual-map overlay of journals on the autophagy studies in osteosarcoma. Dual-map distinguishes the disciplines of citation links by color, allowing a clear view of where the citations come from and where they go. Lower-colored bars indicate years with relatively stable publication venues, while higher-colored bars indicate a more diverse distribution of publications.

Figure 7.

Visualisation of cited references (a) and top ten references with strong citation bursts (b).

Figure 8.

Keyword visualization for the study of osteosarcoma (a, b) and top ten keywords with strong citation bursts (c).

Figure 9.

Cluster analysis plot of keywords for the study of osteosarcoma. Similar keywords are clustered together and labels are generated for each cluster, which are usually based on the commonality of the keywords in the cluster.

Figure 10.

Map of keyword landscape (a) and time zones (b). In landscape graphs, nodes represent keywords, the size of the nodes is usually proportional to the frequency of keyword occurrence, and the color indicates the classification. In a time zones graph, the keywords for each time period are fixed to the year in which they first appear, and the same keywords appearing in subsequent years add up to the frequency of the first appearance, thus forming a kind of hotspot distribution graph over the time series.