The search strategy yielded 1550 studies related to tumor organoids. Year-standardized studies and the corresponding organoid search index in Google are shown in Fig. 1. Also, 2773 major topic terms were extracted from the studies on PubMed. As described in the high-frequency term table (Table 1), the frequency number before the 21st word was larger than its original number, and the 22nd and 23rd words shared the same frequency number with the former one. However, after the 24th word, the ordinary number was higher. The terms ranked before the 21st word were related to 736 studies and defined as frequent terms. Then, a co-occurrence analysis of the high-frequency terms was applied. A row was set by identifying studies with their PMID number, and a column was set as MeSH terms (Table 2). In this table, “1” represented the term present and “0” represented the term absent in the reference. These references were distinguished by their PMID number. Finally, a co-word matrix was established (Table 4). This matrix indicated terms present in the selected studies, which showed the association between two topics and their accumulative number (Table 4).
The packed bubble graph was used to visualize based on PubMed so as to describe the hot topic distribution in the field of tumor organoids. In the PubMed database, the larger the weight of the topic, the larger the area, and the more central the module (Fig. 3).
Subsequently, a peak map and a double-clustering heat map were generated, and gCLUTO was used to perform data visualization based on the high-frequency terms in the literature set, which could directly detect the relationship between studies. Moreover, peak, volume, height, and color were all used to describe the associated cluster. According to the literature set, six clusters from 0 to 5 were recognized. Figure 1 shows the heat map of double-clustering visualization, where rows comprise high-frequency major MeSH terms, with the columns of corresponding terms located on the right. The bottom of the heat map showed the PubMed unique identifier of each study. A deep red grid represented a relatively higher frequency of major MeSH terms in the study, while a white grid represented a value closer to zero; negative values were green. The double-clustering matrix visualization showed that 21 highly frequent major MeSH terms were clustered in 6 peaks. In Fig. 1, the hierarchical tree on the left side denotes the relationship between high-frequency MeSH terms, while the hierarchical tree on the top denotes the relationship between studies. The highest expression of MeSH terms in each category was also examined. In Fig. 2, each category is numbered from 0 to 5. Peak, volume, height, and color were all used to provide information about the associated cluster. The peak is the specific area in each topic. The volume is the accumulative article volume in the topic. The more the papers, the higher the height. Moreover, red indicates a low deviation, and blue indicates a high deviation (Fig. 2). The detailed cluster is summarized in Table 3.
Table 3
Descriptive and discriminating features and representative articles
Descriptive and Descriminating features
|
Cluster 0
|
Size 3
|
ISim: 0.794
|
Esim: 0.024
|
|
Descriptive
|
10048709
|
10340047
|
1054896
|
10078927
|
Descriminating
|
10048709
|
10340047
|
1054896
|
1026920
|
Cluster 1
|
Size 3
|
ISim: 0.533
|
Esim: 0.025
|
|
Descriptive
|
1057351
|
10399173
|
10571348
|
1032517
|
Descriminating
|
1057351
|
10399173
|
10571348
|
10048709
|
Cluster 2
|
Size 4
|
ISim: 0.451
|
Esim: 0.049
|
|
Descriptive
|
1026920
|
10417695
|
10463034
|
10403302
|
Descriminating
|
1026920
|
10463034
|
10417695
|
10403302
|
Cluster 3
|
Size 4
|
ISim: 0.339
|
Esim: 0.032
|
|
Descriptive
|
10323079
|
1032522
|
10095889
|
10483587
|
Descriminating
|
10323079
|
1032522
|
10095889
|
10483587
|
Cluster 4
|
Size 5
|
ISim: 0.335
|
Esim: 0.047
|
|
Descriptive
|
10470114
|
1032517
|
10342010
|
10459830
|
Descriminating
|
10470114
|
10342010
|
10459830
|
1032517
|
Cluster 5
|
Size 5
|
ISim: 0.306
|
Esim: 0.032
|
|
Descriptive
|
1057348
|
10416596
|
1057348
|
10392634
|
Descriminating
|
1057348
|
10416596
|
1057348
|
10392634
|
Table 4
A co-word matrix of high-frequency major topic (localized).
No.
|
Topic Words
|
Skin Neoplasms / pathology
|
Organoids
|
…
|
Carcinoid Tumor / pathology
|
1
|
Skin Neoplasms / pathology
|
76
|
2
|
…
|
0
|
2
|
Organoids
|
2
|
63
|
…
|
0
|
3
|
Organoids / ultrastructure
|
2
|
0
|
…
|
0
|
…
|
…
|
…
|
…
|
…
|
…
|
24
|
Carcinoid Tumor / pathology
|
0
|
0
|
…
|
21
|
In addition, the main groups and the current trend of research in the tumor organoid field was determined by investigating the studies corresponding to each category of clusters. In this way, some clusters could be subdivided or integrated into different topics:
-
Pathology for skin cancer and nevus (cluster 0)
-
Pathology for thymus cancer (cluster 1)
-
Pathology for lung carcinoma (cluster 1)
-
Pancreatic cancer organoid and biology model (cluster 2)
-
Organoid for antineoplastic agents of liver cancer (cluster 3)
-
Organoid for genetic study of colorectal carcinoma (cluster 4)
-
Organoid for metabolism study of breast cancer (cluster 4)
-
Pathology for adenocarcinoma (cluster 5)
-
Pathology for carcinoid tumor (cluster 5)
-
Pathology for teratoma (cluster 5).