3.1. Basic quantified information
We found 458 articles published between 2000 and 2022 about snoRNA in the field of cancer through searching the WOS core collection database. The papers were written by 2,930 authors from 814 organizations in 50 countries and published in 235 journals, with a total of 23,322 references from 2,458 journals.
3.2. Analysis of annual publication volume change
We sorted out the collected literature data, made the annual number of published documents as a scatter plot, and drew a regression curve (Fig. 2). Since 2008, the annual output showed an obvious growth trend, reaching a peak in 2021 with a total of 51 publications. Before 2008, the number of published papers was generally low, with a total of 12 papers in 8 year, accounting for 11.13%. The average number of published papers over the past 10 years was 39. By fitting the data, we observed a statistically significant relationship between the year and the number of publications (r2 = 0.914). As can be seen from the curve, the number of published articles each year presented a rapid rising trend. In particular, in the past decade, the average annual number of published papers has stabilized at more than 20, indicating that more and more researchers are devoting themselves to this field. Based on the fitting curve, we estimated that the number of published papers in this field would grow to about 63 in 2023.
3.3. Bibliometric analysis of the authors
The analysis of the authors of the literature can help us better understand the core authors and their main research directions in a certain field. According to Price’s Law, the minimum number of publications by a core author is m = 0.749×\(\sqrt{{\text{n}}_{\text{m}\text{a}\text{x}}}\)≈1.98 [16]. In the formula, \({\text{n}}_{\text{m}\text{a}\text{x}}\) presents the author with the largest number of published papers (7 in this study). Therefore, authors who published more than two papers were identified as core authors in this field. A total of 256 people published 356 papers (77%), which was half (50%) of the standard proposed by Price’s Law. As a result, a stable group of collaborators have been formed in the field of research on snoRNA and cancer. From Fig. 3A, we could visually see which cooperative groups there were and know the amount of publication of each group and individual. From Fig. 3B, we could know the active period of each cooperative group in this field. The more yellow it was, the closer it was to today. The more purple it was, the longer it had been to today.
We further studied the highly productive authors. Table 1 showed the five most productive ones. From 2000 to 2022, Chu, Liang and Montanaro, Lorenzo published the most papers (7 papers for both) and were cited 233 and 152 times, respectively. In 2019, Chu, Liang published an article entitled “Small Nucleolar RNAs: Insight Into Their Function in Cancer”, discussing several possible mechanisms by which snoRNA is associated with cancer. It was mentioned that snoRNA may further affect the occurrence and development of cancer by regulating of p53 or phosphoinositide 3-kinase (PI3K) -AKT and Wnt/β-catenin pathways, or by affecting cancer stem cells [17]. Montanaro Lorenzo, on the other hand, was exploring the link between snoRNA host genes and cancer [18]. Jiang, Feng, the author of the most cited paper, proposed in several papers that snoRNA held great promise as a biomarker in the diagnosis of lung cancer, which opens the way for clinical use of snoRNA [19.20].
3.4. Bibliometric analysis of journals
When we performed a statistical analysis on journals, we found that the relevant journals in the research field of this paper were mostly concentrated in the field of cancer, and a few were concentrated with small molecular biology. Table 2 showed the top 9 journals in terms of publication volume, among which oncotarget, plos one and international journal of molecular sciences published more than 12 articles. These three journals were all open access, so open access journals had a great role in promoting this research field.
In terms of citation analysis, the journal with the most citations per article was Oncogene, which focused on genes and cancers, with an average 112 citations. This indicated that the quality of the articles published in this journal was very high, and its literature has been widely concerned by scholars in this field all over the world.
3.5. Bibliometric analysis of countries/regions
In order to understand which countries contributed the most in the research on snoRNA in the field of cancer, we analyzed the number of publications in 49 countries. VOSviewer was used to visualize countries with 5 or more papers. The results were shown in Fig. 4. The larger the circular node in the figure, the more publications the country has produced. We can intuitively see from the figure that the distribution of publishing countries in this field was very uneven, the top effect was very significant, and most of the papers were written by scholars from a few countries. The line of nodes represented the correlation strength. The thicker the node line, the more times the two countries published articles together. In Fig. 4A, node colors represented different clusters, and countries in the same cluster cooperated more closely. In Fig. 4B, node colors repesented the active research time of the country. As can be seen from the figure, China was a relatively active country in the field of research in recent years.
Table 3 showed the five most productive countries in this field. Chinese scholars published the most papers in the field, with a total of 147 papers, accounting for 32% of the total papers in the field, which were cited 3,259 times. It was followed by the United States, with 124 articles and 7,579 citations. England had the most citations per paper, with 27 papers cited 2489 times.
3.6. Bibliometric analysis of organizations
By analyzing the research institutions, we can better understand the input of the major research institutions in the field. In turn, it will facilitate better access to support and help for scholars, and may facilitate collaboration between organizations. Through a visual analysis of organizations with more than four papers (Fig. 5), we found that the cooperation between organizations was relatively extensive, but not fixed. Because most organizations worked with each other no more than twice. From the network map (Fig. 5A), we can see which organizations cooperated more closely together. The institutions with the most cooperation were Huazhong Uni Sci & Technol and Chinese Acad Sci. They collaborated three times, focusing on one particular snoRNA, SNORD126. Upon further investigation, they found that the activation of PI3K-AKT pathway can lead to the growth of liver cancer or colorectal cancer cells [21], as well as the proliferation of hepatocellar carcinoma cells [22]. It was worth noting that Chu, Liang, the prolific author mentioned above, participated in and led these three research collaborations. It indicated that he played an important role in the cooperation between the two organizations and might further lead the development of this field in the future.
From Fig. 5B, we can also see that several organizations in China have been more active in publishing papers in recent years, which was also consistent with the results of the country visualization analysis above.
We sorted out the top five institutions in terms of publication volume, as shown in Table 4. Wuhan Univ, China Med Univ and Guangxi Med Univ published a total of 12 articles, demonstrating their leading position in this field. In September 2022, China Med Univ published an article in this field, which stated that SNORA38 held great promise in the prognostic evaluation of breast cancer, the most common malignant tumor in women worldwide [23].
3.7. Bibliometric analysis of keywords
3.7.1. Co-occurance analysis of keywords
Keywords were the refining of the core of the article. Co-occurrence analysis of keywords can help us understand the most intensive topic selection in a field. A keyword co-occurrence network of 458 selected articles was constructed using VOSviewer, and 127 keywords with a frequency of 8 or more were selected for visual analysis. In the network map (Fig. 6A), the keywords were divided into four clusters: cluster 1: ‘biological molecule’ (red color), including gene, protein, gene expression, and telomerase; cluster 2: ‘related types of cancer’ (green color), including breast cancer, prostate cancer, hepatocellular cancer, and gastric cancer; cluter 3: ‘clinical application’ (blue color), including identification, targets, biomarkers, survival, and prognosis. cluster 4: ‘mechanism of oncogenesis’ (yellow color): including expression, promotes, oncogene, progression, and pathway.
In addition, we overlayed a visualization map (Fig. 6B) on the network discussed above. The result indicated that basic research on molecular biology like cluster 1 was relatively popular before 2015, laying the foundation of the field. However, in recent years, more research has focused on clinical applications, which was a good sign. The theoretical research on snoRNA's association with cancer has gradually been translated into applications.
Figure 6C was a density visualization map, which was similar in function to the network map, but more clearly showed which keywords were of more concern in this research field.
3.7.2. Burstness of keywords
By analyzing the burstness of keywords, we can understand which keywords received the most attention from authors in a given period of time. This can help us discover the evolution of the most intensively researched topics. As shown in Fig. 7, during a long period of time from 2000 to 2013, “dyskeratosis congenita” appeared in many articles as a high-frequency keyword. Dyskeratosis congenita was considered as a high risk factor for some cancers [24–26]. It has been suggested that snoRNA played an auxiliary role in the biological function of dyskerin. The mutation of dyskerin can cause dyskeratosis congenita and cancer [27]. Therefore, dyskeratosis congenita broke out during that period. The subsequent keywords were mainly various cancers, such as prostate cancer, colorectal cancer and cell lung cancer. With the development of the field, snoRNA's correlation with various cancers has gradually been discovered, and the research direction has gradually shifted from the theoretical to the clinical.
3.8. Bibliometric analysis of Co-citation
The main purpose of co-citation analysis was to identify the highly cited papers in the research field and the journals that published these papers. We analyzed the characteristics of journals with high citation frequency and used VOSviewer to screen journals with citation frequency higher than 80 times. We conducted a visual analysis and obtained Fig. 8A. The three most frequently cited journals were Nature, Cell and Nucleic Acids Research, all of which were among the top journals in the natural science. These highly cited journals were divided into three clusters: the red cluster on the left (cluster 1) included journals focused on cancer research; the green cluster on the right (cluster 2) focused on biochemical molecules; and the blue cluster in the middle (cluster 3) focused on genes.
After that, we also carried out a visual analysis of the papers with high co-citation frequency (Fig. 8B). In this figure, literatures with high citation frequency were divided into three categories. Cluster 1 (red) were mainly the papers on snoRNA's correlation with cancer, which can help us quickly understand the correlation; Cluster 2 (green) were mostly for articles discussing the function of snoRNA; Cluster 3 (blue) were all relatively early articles on tumors. These articles discussed the relationship between the occurrence and development of tumor and some biomolecules, including snoRNA. Most of them were groundbreaking articles in the field.
Then, we analyzed the highly cited papers and sorted out the top 10 papers with the most cited times, as shown in Table 5. It was clear that these articles laied the foundation for this research field. Reading these literatures can help subsequent researchers quickly understand the background and theoretical basis of snoRNA and cancer.