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Original Paper

Knowledge Graph for Breast Cancer Prevention andTreatment: Literature-Based Data Analysis Study
Shuyan Jin1, MPH; Haobin Liang2, MSc; Wenxia Zhang1, PhD; Huan Li1, MM
1Health Department, Shenzhen Maternity and Child Healthcare Hospital, Shenzhen, China
School of Economics and Statistics, Guangzhou University, Guangzhou, China

2

Corresponding Author:
Shuyan Jin, MPH
Health Department
Shenzhen Maternity and Child Healthcare Hospital
2004 Hongli Road, Futian District
Shenzhen, 518000
China
Phone: 86 13823373712
Email: 13823373712@163.com

Abstract
Background: The incidence of breast cancer has remained high and continues to rise since the 21st century. Consequently,
there has been a significant increase in research efforts focused on breast cancer prevention and treatment. Despite theextensive body of literature available on this subject, systematic integration is lacking. To address this issue, knowledge graphs
have emerged as a valuable tool. By harnessing their powerful knowledge integration capabilities, knowledge graphs offer acomprehensive and structured approach to understanding breast cancer prevention and treatment.
Objective: We aim to integrate literature data on breast cancer treatment and prevention, build a knowledge graph, andprovide support for clinical decision-making.
Methods: We used Medical Subject Headings terms to search for clinical trial literature on breast cancer prevention andtreatment published on PubMed between 2018 and 2022. We downloaded triplet data from the Semantic MEDLINE Database(SemMedDB) and matched them with the retrieved literature to obtain triplet data for the target articles. We visualized thetriplet information using NetworkX for knowledge discovery.
Results: Within the scope of literature research in the past 5 years, malignant neoplasms appeared most frequently (587/1387,
42.3%). Pharmacotherapy (267/1387, 19.3%) was the primary treatment method, with trastuzumab (209/1805, 11.6%) being
the most commonly used therapeutic drug. Through the analysis of the knowledge graph, we have discovered a complex
network of relationships between treatment methods, therapeutic drugs, and preventive measures for different types of breast
cancer.
Conclusions: This study constructed a knowledge graph for breast cancer prevention and treatment, which enabled theintegration and knowledge discovery of relevant literature in the past 5 years. Researchers can gain insights into treatment
methods, drugs, preventive knowledge regarding adverse reactions to treatment, and the associations between different
knowledge domains from the graph.
JMIR Med Inform 2024;12:e52210; doi: 10.2196/52210
Keywords: knowledge graph; breast cancer; treatment; prevention; adverse reaction

Introduction
Breast cancer is the most common malignant tumor in women
worldwide, with a reported death toll exceeding 600,000
in 2018 alone [1]. Breast cancer has emerged as the mostprevalent cancer and a primary cause of mortality among
women. The global incidence of new cases of female breast
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cancer witnessed a sharp increase from 1.05 million in2000 to 2.09 million in 2018 [2]. In 2020, global cancer
burden data revealed that new breast cancer cases reached2.26 million, constituting 11.7% of all newly diagnosed
cancer cases worldwide. The newly reported mortality cases
numbered 0.68 million, representing 6.9% of global newly
reported deaths [3]. Factors such as old age, young age
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at menarche, family history of breast cancer, smoking, anddrinking alcohol increase the risk of breast cancer [4-6].
On the contrary, regular physical exercise; breastfeeding;regular work and rest; and intake of fruits, vegetables, wholegrains, and dietary fiber can appropriately reduce the riskof breast cancer [7]. Various treatment methods are used
for patients with breast cancer, including surgery, radiation therapy, endocrine therapy, chemotherapy, and targeted
therapy. So far, most countries have primarily focused on
population education for breast cancer prevention, including
encouraging increased physical activity, controlling BMI, andlimiting alcohol intake [8]. Despite the increasing numberof research literature, a large amount of literature on breast
cancer prevention and treatment has not been systematically integrated. Knowledge graph technology allows for the
independent connection and integration of disparate literature,
resulting in a more comprehensive and cohesive knowledge
framework.
Knowledge Graph is a knowledge repository proposedby Google in 2012 to enhance the functionality of searchengines. It describes concepts and their relationships in thereal world using triplets in the form of entity-relation-entity[9]. Knowledge graphs can integrate information from diverse
sources and domains, including text, databases, and web
pages, and intricately interlink them. These integrations serveto mitigate information silos, fostering the establishmentof a more comprehensive knowledge framework. Knowledge graphs have been widely used in various fields, suchas medicine, network security, journalism, finance, andeducation [10]. Knowledge graphs in the biomedical domain
have applications in studies related to disease associations[11], genomics [12], drug interactions [13], and support forphysicians in formulating individualized treatment regimens[14]. At present, there are well-established knowledge graphs,
including DisGeNET [15], which integrate information on
the associations between genes and diseases; DrugBank[16], a comprehensive bioinformatics and cheminformatics
knowledge base; and ClinVar [17], a compilation of genetic
variation information from diverse laboratories worldwide.
One study extracted breast cancer–related features from
Chinese breast cancer mammography reports and built aknowledge graph for diagnosing breast cancer by combining diagnosis and treatment guidelines and insights fromclinical experts [18]. Another study integrated triples fromclinical guidelines, medical encyclopedias, and electronic
medical records to build a breast cancer knowledge graph[19]. Despite a small number of scholars having constructed
knowledge graphs for breast cancer, the varied emphases
and diverse data sources employed render their applicability limited. A knowledge graph specifically focused on theprevention and treatment of breast cancer has not been
constructed at present. Therefore, this study primarily collects
information related to the prevention and treatment of breast
cancer to construct a knowledge graph.
In the biomedical field, there are already mature tools(eg, SemRep) for extracting knowledge from medical texts.
SemRep is a natural language processing program basedon the Unified Medical Language System (UMLS), whichhttps://medinform.jmir.org/2024/1/e52210

Jin et al
performs operations such as text tokenization, syntactic
analysis, part-of-speech disambiguation, phrase mapping,semantic predicate normalization, and syntactic constraints[20]. It extracts entities and relationships from biomedical texts and outputs triplets stored in the Semantic MEDLINE Database (SemMedDB) [21]. SemMedDB currently
encompasses details on approximately 96.3 million predications derived from all PubMed citations (around 29.1 million
citations) and serves as the foundation for the Semantic
MEDLINE application [22]. We downloaded the entity
and relationship data provided by SemMedDB. NetworkX
is an open-source library for Python, primarily designed
for creating, analyzing, and visualizing complex network
structures. NetworkX plays a significant role in knowledge
visualization, facilitating users in intuitively presenting andcomprehending intricate knowledge graphs or network data.

Methods
Ethics Approval
This study was approved by the Board of Medical Ethics
Committee of Shenzhen Maternal and Child Health Hospital(SFYLS[2022]003).

Data Source
We conducted a search on PubMed using Medical Subject Headings terms “breast cancer,” “prevention,” and“treatment,” covering the period from January 1, 2018, toDecember 31, 2022, and the study type was clinical trials. A
total of 3589 articles were retrieved. We obtained the entity
and relationship data from SemMedDB.

Data Processing and Construction of
Knowledge Graph
We matched the PMIDs of the retrieved articles with the
database and extracted the corresponding triplet information.
We initially obtained 33,060 Subject-Predicate-Object (SPO)
triplets of data.
Next, we made improvements according to the SPO
cleaning principles proposed by Fiszman et al [9] (ie,relevance, connectivity, novelty, and significance). We
combined them with expert manual screening to ensure thatthe selected SPO triplets have a higher relevance. In the
improved process, we did not predefine semantic patterns.
Instead, we used a series of cleaning operations to select core
SPO triplets and connected SPO triplets, eliminating SPO
triplets lacking specific information and those that appeared
only once in the frequency. The specific process is as follows:
1. In the same article, there may be repeated occurrencesof identical SPO triplets. To maintain equal contribution from each article, we counted the repeated SPO
triplets once within the same article.
2. To ensure statistical reliability, we calculated theoccurrence frequency of each SPO triplet acrossdifferent articles. SPO triplets with low occurrence
frequencies may lack statistical significance. Therefore,
we filtered SPO triplets with frequencies greater than orequal to 2.
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3.

Based on expert domain knowledge, we manually
screened the selected SPO triplets with frequencies
greater than or equal to 2 to identify those of researchvalue.

Finally, we obtained 25,449 SPO triplets data. We imported
the filtered SPO triplets information into the NetworkX for
visual analysis to explore knowledge and information related
to breast cancer prevention and treatment.
All analyses were conducted in a Python program (version
3.11.3; Python Software Foundation), primarily using Pandas,
Matplotlib, WordCloud, and NetworkX packages [23-26].

Results
Summary of Included Literatures
A total of 3589 articles were published in 618 different
journals. Among them, 191 articles were published in thesame journal, while 293 journals had only 1 article published.
The journals were ranked based on the number of publications, and the top 100 journals accounted for 2631 articles,
which is 73.30% of the total.

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and related diseases (Table S1 in Multimedia Appendix
1). Regarding treatment and prevention, the relationships
include TREATS, ADMINISTERED_TO, USES, andPREVENTS, representing treatment drugs, surgeries, andpreventive measures for breast cancer. Regarding influencing or associated factors, the relationships include ASSOCIATED_WITH, AFFECTS, and CAUSES, which represent
diseases’ impact and etiological factors. Regarding relateddiseases, the relationship COEXISTS_WITH represents thecoexistence between different diseases. In the semantic
patterns involving treatment (TREATS), the topp-TREATSneop and topp-TREATS-podg have appeared over 1000
times.

Summary of SPO Triples
In terms of breast tumors, malignant neoplasms had thehighest frequency, accounting for 42.3% (587/1387) of thetotal, followed by triple-negative breast neoplasms (56/1387,
4%) and human epidermal growth factor receptor 2 (HER2)–
positive carcinoma of breast (54/1387, 4%; Table 1 andMultimedia Appendix 2).

Semantic Relationships and Semantic
Patterns
We mainly summarize semantic associations into 3 types:
treatment and prevention, influencing or associated factors,
Table 1. Summary of breast cancer subtypes and stages, treatment methods, and treatment drugs. The top 30 subtypes, treatment methods, andtreatment drugs with higher frequencies in all data are presented for each group.
Group
Breast cancer subtypes and stages (n=1387)
Malignant neoplasm of breast
Triple-negative breast neoplasms

HER2a-positive carcinoma of breast

Values, n (%)
587 (42.3)
56 (4)

54 (3.9)

Carcinoma breast stage IV

48 (3.5)

Early-stage breast carcinoma

42 (3)

Breast cancer metastatic
Malignant neoplasms
Neoplasm

Metastatic triple-negative breast carcinoma
High-risk cancer

Neoplasm metastasis
Advanced cancer

Advanced breast carcinoma

HER2-negative breast cancer

Locally advanced malignant neoplasm
Advanced malignant neoplasm
Nonsmall cell lung carcinoma

Noninfiltrating intraductal carcinoma
Locally advanced breast cancer
Breast cancer stage III

Treatment of breast cancer (n=1387)
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47 (3.4)
31 (2.2)
30 (2.2)

26 (1.9)24 (1.7)

21 (1.5)
19 (1.4)
19 (1.4)
18 (1.3)

17 (1.2)

15 (1.1)

15 (1.1)
14 (1)

13 (0.9)
11 (0.8)

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Values, n (%)

Pharmacotherapy

267 (19.3)

Hormone therapy

68 (4.9)

Neoadjuvant therapy
Chemotherapy (adjuvant)
Therapeutic procedure
Radiation therapy

Treatment protocols
Adjuvant therapy

Breast-conserving surgery
First-line treatment

Single-agent therapy
Mastectomy

Operative surgical proceduresInterventional procedureRadiotherapy (adjuvant)Excision of axillary lymph nodes group
Combined modality therapy
ExcisionTargeted therapy
Placebos

Drugs for breast cancer (n=1805)

88 (6.3)
54 (3.9)53 (3.8)

48 (3.5)
43 (3.1)

36 (2.6)
35 (2.5)
31 (2.2)
27 (1.9)27 (1.9)20 (1.4)

16 (1.2)
14 (1)

13 (0.9)
12 (0.9)

11 (0.8)
11 (0.8)
10 (0.7)

Trastuzumab

209 (11.6)

Paclitaxel

81 (4.5)

Capecitabine
Aromatase inhibitors

Immunologic adjuvants
Letrozole

Bevacizumab
Tamoxifen

Gemcitabine
Pertuzumab
Fulvestrant

Cyclophosphamide
Pembrolizumab
Docetaxel
TaxaneAdo-trastuzumab emtansine

130-nm albumin-bound paclitaxel
Carboplatin
Eribulin

Palbociclib

Exemestane
Everolimus
Olaparib

Talazoparib

Pharmaceutical preparations

Protein-tyrosine kinase inhibitor
Cisplatin

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88 (4.9)64 (3.5)
62 (3.4)
58 (3.2)
48 (2.7)
40 (2.2)
36 (2)
36 (2)
36 (2)

32 (1.8)30 (1.7)

27 (1.5)
27 (1.5)
22 (1.2)

22 (1.2)

22 (1.2)
21 (1.2)
19 (1.1)
19 (1.1)
19 (1.1)
18 (1)

17 (0.9)

16 (0.9)

15 (0.8)
14 (0.8)

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Group
Lapatinib

Fluorouracil
Preservative free ingredientaHER2: human epidermal growth factor receptor 2.

Pharmacotherapy is the most common treatment method,
accounting for 19.2% (267/1387) of the overall frequency.
Additionally, other high-frequency treatment modalities
include neoadjuvant therapy (88/1387, 6%), hormone therapy(68/1387, 5%), adjuvant chemotherapy (54/1387, 4%), andradiation therapy (48/1387, 3%; Table 1 and Multimedia
Appendix 3). In breast cancer treatment drugs, trastuzumab(209/1805, 11.6%), capecitabine (88/1805, 5%), paclitaxel(81/1805, 4%), aromatase inhibitors (64/1805, 4%), andimmunologic adjuvants (62/1805, 3%) have a relatively highfrequency of occurrence (Table 1 and Multimedia Appendix
4).

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Values, n (%)
14 (0.8)

13 (0.7)
13 (0.7)

relevant preventive measures. Figure 1 shows the relationshipbetween different subtypes and stages of breast cancer andtreatment methods. In different subtypes of breast cancer,
the highest frequency is observed in malignant neoplasmof the breast, with pharmacotherapy having the highest
frequency among various treatment modalities. Different
subtypes simultaneously correspond to multiple treatment
modalities; likewise, a single treatment modality corresponds
to multiple breast cancer subtypes.

Breast Cancer Knowledge Graph
We visualized the SPO triples and displayed 3 subgroups:
breast cancer treatment methods, therapeutic drugs, andFigure 1. Relationship between different subtypes and stages of breast cancer and treatment methods. HER2: human epidermal growth factor receptor
2.

Figure 2 shows the relationship between different subtypes
and stages of breast cancer and drugs. Among the therapeutic drugs for breast cancer, trastuzumab has the highest frequency and corresponds to the most types of breast

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cancer. Capecitabine, paclitaxel, aromatase inhibitors, andimmunologic adjuvants also have relatively high frequencies. In comparison, immunologic adjuvants have the fewest
connections with different types of breast cancer.

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Figure 2. Relationship between different subtypes and stages of breast cancer and drugs. HER2: human epidermal growth factor receptor 2.

Figure 3 shows the relationship between breast cancer treatment and adverse reactions. Pharmacotherapy is
associated with neuropathy, onycholysis, heart neutropenia failure, alopecia, febrile neutropenia, anemia, stomatitis, leukopenia, thrombocytopenia, premature menopause,and gastrointestinal dysfunction. Additionally, multiple
nodes are connected, forming multiple pathways, such as
pharmacotherapy-febrile neutropenia-adjuvant chemotherapy
and pharmacotherapy-leukopenia-breast cancer therapeutic
procedure-osteoporosis.

Figure 3. Relationship between breast cancer treatment and adverse reactions.

Figure 4 shows the relationship between adverse events after
breast cancer treatment and preventive measures. Peripheral neuropathy is associated with cryotherapy, low-level
laser therapy, compression procedure, acupuncture procedure, pharmacotherapy, and massage. Lymphedema is
associated with resistance education, axillary lymph node

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dissection, physical therapy, excision of axillary lymph nodesgroup, and drainage of lymphatics. Early radiation dermatitis is associated with topical administration and bleomycin, cisplatin, or methotrexate protocol. In addition, there
are some adverse reactions with relatively few treatment
measures, such as stomatitis-diet, alopecia-scalp cooling.

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Figure 4. Relationship between adverse reactions after breast cancer treatment and preventive measures.

We performed a relationship visualization to gain a better
understanding of the association between types of breast
cancer, treatments, drugs, and genes. Figure 5 intuitively
reflects the high frequency of malignant neoplasm of thebreast, pharmacotherapy, and trastuzumab. In addition, breast
malignant tumors are associated with multiple genes, such as

the phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic
subunit alpha (PIK3CA) gene, platelet-derived growth factor
receptor beta (PDGFRB) gene, phosphatase and tensin
homolog (PTEN) gene, and erb-B2 receptor tyrosine kinase
2 (ERBB2) gene.

Figure 5. Relationship between types of breast cancer, treatments, drugs, and genes.

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Discussion
Principal Findings
The knowledge graphs constructed in this study help
researchers understand the research hot spots in breast
cancer over the past 5 years. The complex network involving treatment methods, drugs, adverse reactions, preventive
measures, and genes in breast cancer can assist clinicians
in making decisions that comprehensively consider multiple
aspects, ultimately aiding in decisions that are the mostbeneficial to patients. Additionally, the knowledge graphallows for personalized considerations based on specificgenes for individualized patients.
This study found that from 2018 to 2022, breast malignancies appeared most frequently in the literature and were
the primary concern for researchers. Research interest in
triple-negative breast neoplasms is higher than in othersubtypes. This phenomenon may be due to the higher
risk of recurrence and poor prognosis in patients withearly-stage triple-negative breast neoplasms [10], making
it a subject of greater concern to clinicians and researchers. Among treatment modalities, pharmacotherapy receives
the highest attention. Pharmacotherapy for breast cancer
primarily involves chemotherapy, endocrine therapy, andtargeted therapy [27]. Compared to traditional surgery andradiotherapy, pharmacotherapy can more precisely intervenein the growth and division of cancer cells by targeting specificmolecules or cellular structures, which reduces damage tonormal cells and allows for the formulation of personalized
treatment plans based on the patient’s genotype and molecular characteristics [28]. Medications circulating through thebloodstream can also act on cancer cells throughout thebody, preventing cancer cell metastasis. These advantages ofpharmacotherapy may be related to the heightened emphasis on pharmacotherapy over the past 5 years. Trastuzumab
receives the highest attention in breast cancer pharmacotherapy; it is a specific cancer-targeting medication used in thetreatment of cancers characterized by elevated levels of HER2
protein [29].
Pharmacotherapy is associated with various adverse
reactions, including neutropenia, neuropathy, onycholysis,
heart failure, alopecia, and febrile neutropenia. Amongthese adverse reactions, peripheral neuropathy and lymphedema have the most corresponding preventive and treatment
measures, with lymphedema being a common complication
after surgery [30]. However, there is limited research on
how to prevent and treat the potential adverse reactions ofpharmacotherapy, and further studies are needed. Various
adverse effects of breast cancer treatment may reduce
patients’ adherence to treatment. Therefore, when clinicians
choose different treatments and drugs, they should pay close
attention to their potential adverse reactions and how toprevent or mitigate them.
In existing knowledge graphs related to breast cancer,
one study from China constructed a knowledge graph using
electronic medical records, clinical guidelines, and expert
opinions, primarily focusing on breast cancer diagnosis
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Jin et al[18]. Another study by Chinese scholars also used datafrom various sources, including clinical guidelines, medical
encyclopedias, and electronic medical records, to constructa knowledge graph primarily applied to medical knowledge
question-answering and medical record retrieval [19]. Thesestudies used data from multiple sources, including structured, unstructured, and semistructured data. Data extraction
and accuracy face challenges. Therefore, they used neuralnetwork models for training and calculated a series of metrics
to ensure data accuracy. For instance, they utilized BERT
+ Bi-LSTM+ CRF for textual data to achieve named entity
recognition. In this study, SemMedDB was used as thedata source, and the database was constructed by extracting
semantic information from PubMed using SemRep, which
demonstrated good performance in a biomedical text [21].
In summary, the knowledge graph constructed in thisstudy for breast cancer treatment and prevention encompasses information on different stages, subtypes of breast
cancer, treatment modalities, medications, adverse reactions,and preventive measures. This knowledge forms a complex
network, providing clinical practitioners with a comprehensive and referenced knowledge base. We recommend thatclinical practitioners apply our research findings in several
aspects. First, clinicians can gain insights into the current
state of breast cancer treatment and prevention researchthrough our study. Additionally, there is a relative lack ofpreventive measures and strategies for mitigating postoperative and postmedication adverse reactions compared tobreast cancer treatment, and more efforts are needed inthese areas. Furthermore, our research can assist clinicians
in making comprehensive decisions. For instance, whenselecting a treatment approach for patients, the knowledge
graph facilitates linking to available medications, associated adverse reactions, and measures to mitigate or prevent
adverse effects.
Our research still has several limitations. First, SemRep, asa natural language processing program based on the UMLS,
still exhibits shortcomings. Despite the extensive coverageand scale of the UMLS Metathesaurus, it has a relatively
limited ability to recognize entities. There are still areas
for improvement in processing natural language texts [20].
Second, clinical researchers often prefer causal relationships
rather than pure correlations; however, our study can only
reveal the connections between pieces of information andcannot determine the magnitude and direction of their effects.
Third, with the release of new literature, the knowledge graphalso needs to be updated promptly, increasing the burden on
researchers. Future improvements should focus on automating
the mining of literature data to ensure timely updates to theknowledge graph for breast cancer prevention and treatment,
thereby alleviating the burden on researchers.

Conclusions
This study successfully constructed a knowledge graph forbreast cancer prevention and treatment by integrating relevant
literature from the past 5 years and conducting knowledge
discovery. Through this knowledge graph, researchers canlearn about breast cancer treatment methods, medications, andJMIR Med Inform 2024 | vol. 12 | e52210 | p. 8
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adverse reactions to preventive treatments and gain insights
into the relationships between different pieces of knowledge.
Acknowledgments
The authors would like to thank Feng Xixi, associate chief physician and member of the Chronic Disease Special Committee of
the Chengdu City Preventive Medicine Association, for her suggestions at the initial stage of the study.
Conflicts of Interest
None declared.
Multimedia Appendix 1
Table depicting the semantic relationship and semantic schema of breast cancer.
[DOCX File (Microsoft Word File), 19 KB-Multimedia Appendix 1]
Multimedia Appendix 2
Different subtypes and stages of breast cancer.
[PNG File (Portable Network Graphics File), 158 KB-Multimedia Appendix 2]
Multimedia Appendix 3
Treatments of breast cancer.
[PNG File (Portable Network Graphics File), 214 KB-Multimedia Appendix 3]
Multimedia Appendix 4
Drugs for breast cancer.
[PNG File (Portable Network Graphics File), 161 KB-Multimedia Appendix 4]
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Abbreviations
ERBB2: erb-B2 receptor tyrosine kinase 2
HER2: human epidermal growth factor receptor 2
PDGFRB: platelet-derived growth factor receptor beta
PIK3CA: phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha
PTEN: phosphatase and tensin homolog
SemMedDB: Semantic MEDLINE Database
SPO: Subject-Predicate-Object
UMLS: Unified Medical Language System

Edited by Arriel Benis; peer-reviewed by Christophe Gaudet-Blavignac, Shu Yang, Yuanchia Chu; submitted 26.08.2023;
final revised version received 02.01.2024; accepted 06.01.2024; published 22.02.2024
Please cite as:
Jin S, Liang H, Zhang W, Li H
Knowledge Graph for Breast Cancer Prevention and Treatment: Literature-Based Data Analysis StudyJMIR Med Inform 2024;12:e52210
URL: https://medinform.jmir.org/2024/1/e52210
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