CiteSpace-based Knowledge Graph Visualization of Mendelian Randomization Studies in Cancer

doi:10.21203/rs.3.rs-4618203/v1

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CiteSpace-based Knowledge Graph Visualization of Mendelian Randomization Studies in Cancer

https://doi.org/10.21203/rs.3.rs-4618203/v1

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Objective

This study aims to perform a visual analysis of the literature on Mendelian randomization in the field of cancer, summarizing its developmental trajectory, current research status, and hot topics to provide references for future research directions.

Methods

Literature on Mendelian randomization studies in the field of cancer was retrieved from the Web of Science Core Collection up until April 18, 2024. The analysis and visualization of publication trends, author and institutional collaboration networks, keyword co-occurrence, clustering, timeline graphs, and burst terms were conducted using CiteSpace6.3.R1.

Results

A total of 964 English-language articles were included, showing an increasing trend in the number of articles using MR for cancer-related research, with a sharp rise in recent years. Despite the large volume of publications, there has not yet been a large-scale formation of collaborative groups among authors and institutions. The research hotspots include cancers such as breast cancer, colorectal cancer, prostate cancer, and lung cancer. Discussions on key risk factors include macro indicators such as BMI, hematological micro indicators such as sex hormones, symptoms such as back pain, and other diseases such as obesity. Research methods are often used in conjunction with meta-analysis.

Conclusion

The field of cancer Mendelian randomization research is heating up, with research continuously deepening and improving. Future efforts should strengthen collaboration and communication between researchers and institutions, delve deeper into current research hotspots, and provide research directions and theoretical bases for disease prevention and the development of new therapies.

Cancer

Mendelian randomization studies༛CiteSpace༛Knowledge graph༛Bibliometric analysis༛Visualization

Cancer is one of the leading causes of death worldwide and a major obstacle to increasing life expectancy ^[1]. A research team led by Freddie Bray from the International Agency for Research on Cancer (IARC) of the World Health Organization (WHO) published the latest data report in the top journal CA: A Cancer Journal for Clinicians, revealing that nearly 20 million new cancer cases were diagnosed globally in 2022, with 9.7 million cancer-related deaths. It is estimated that about one in five men and women will develop cancer in their lifetime, and approximately one in nine men and one in twelve women will die from cancer ^[2]. The prevention and treatment of cancer remain significant challenges.

Cancer arises from the complex interactions between genetic and non-genetic determinants in somatic cell evolution ^[3]. It is a prolonged process involving multiple factors, genes, mutations, and stages. Various physical, chemical, and biological factors, as well as lifestyle choices, can induce cancer. The IARC has summarized external factors closely associated with cancer, including tobacco, alcohol, radiation, occupational exposures, microbial infections, obesity, diet, and physical activity. However, the exact causes of cancer are still not fully understood, and more importantly, the causes of cancer may continue to evolve. Therefore, further research is needed to understand the etiology of cancer better.

Exploring pathogenic factors is crucial for developing appropriate prevention strategies and treatment methods. However, even the gold standard Randomized Controlled Trials (RCTs), which are considered definitive for establishing causal effects, have numerous limitations ^[4]. Mendelian Randomization (MR) uses instrumental variables (IVs) based on genetic variations to infer causal effects from observational study data, thereby mitigating biases caused by confounding factors ^[5]. This method can address some of the limitations of traditional epidemiological research and can also investigate phenotypic risk factors that are not suitable for RCTs, such as height ^[6]. Therefore, the application of MR research in the field of cancer is of significant importance.

Bibliometrics is a quantitative and visual method used to analyze scientific journals and publications. This approach helps in understanding foundational knowledge and research hotspots. It also provides researchers with a framework for analyzing key areas within a discipline and predicting its development trends. CiteSpace is one of the most popular tools for bibliometric and visual analysis of knowledge networks. It offers strong support for researchers to extract new knowledge from existing data ^[7]. This study utilizes CiteSpace software to explore the current status and hotspots in MR research within the field of oncology, providing a theoretical basis for future research directions, exploration of disease mechanisms, disease prevention, and the development of new therapies.

Data Sources

Relevant English literature on the application of MR in cancer research was retrieved from the Web of Science Core Collection database. The search formula was TI = ("cancer" OR "tumor" OR "neoplasia" OR "neoplasm" OR "carcinoma") NOT TS= ("tumor necrosis factor") AND TS = ("Mendelian randomization" OR "Mendelian randomisation"); document type (DT) = (article); index = SCI-EXPANDED. All records were retained and exported in "plain text file" format, selecting "full record and cited references." with the search period from database inception to April 18, 2024. All data for this study are sourced from the WOS database, and ethical informed consent is not required as no human subjects are involved.

Inclusion and Exclusion Criteria

Inclusion criteria: (1) Studies focusing on Mendelian randomization research in the field of cancer; (2) Journal articles and dissertations published in English.

Exclusion criteria: (1) Duplicate publications; (2) Irrelevant literature such as conference abstracts, reviews, notices, calls for papers, and errata; (3) Literature without complete information on authors, institutions, or publication years.

Data Processing

Excel 2021 software was used to input the publication year data for descriptive analysis. CiteSpace 6.3.R1 software was used to perform co-occurrence analysis and visualization of keywords, keyword clusters, burst terms, authors, and institutions in the included studies. The time span for literature involved was processed with a time slice of "1". The g-index parameter was adjusted based on the visualization, where a larger g-index results in more nodes appearing in the map, and vice versa. All other parameters were set to default values. "Author," "Institution," and "Keyword" were selected for co-occurrence and visualization analysis of the corresponding content. For author statistics, Price's law was applied using the formula P = 0.749\(\sqrt{Nmax}\), where Nmax represents the number of papers by the most prolific author and P represents the minimum number of papers for core authors ^[8]. In the keyword analysis, three keywords related to the search terms "Mendelian randomization," "Mendelian randomisation," and "cancer" were removed. The LLR algorithm in CiteSpace was used for keyword clustering, with a modularity value Q > 0.3 and an average silhouette value S > 0.5 indicating a significant and reasonable clustering structure ^[9]. The "Burstness" option was selected in the control panel to display keyword bursts.

Trends in global publication volume

According to the search strategy, a total of 1033 English papers were retrieved from the WOS database. After browsing and screening based on titles and abstracts, 964 English papers were finally included. The screening process is shown in Fig. 1, and the annual publication trend is shown in Fig. 2. Since 2003, English MR studies have been published, and the number of articles using MR for tumor-related research has generally been on the rise, roughly divided into three stages: (1) 2003–2014, with a small and scattered number of related studies and no significant growth, indicating the initial stage of related research; (2) 2015–2018, with a significant increase in the number of publications compared to the previous stage, but the annual growth trend remained relatively flat, with an average of 27 papers per year; (3) 2019–2023, with an average of 135.6 papers per year, showing a significant annual increase, indicating that related research is in a rapid development stage. In 2024, it is expected that MR research in the field of tumors will continue to maintain a rapid growth trend.

Author Co-occurrence Network

In the co-authorship network of MR research in the field of tumors, there are 291 nodes and 1239 links (Fig. 3). According to Price's Law, half of the papers on the same topic are written by a group of highly productive authors. In this study, Nmax is 57, and after calculation, P is 5.655. Rounding up, the minimum number of publications to be considered a core author is set at 6, resulting in 89 core authors being identified. The top 10 authors by the number of publications are listed in Table 1, each having more than 25 papers.

Table.1 Top 10 core authors of MR researches in the field of oncology

Rank	Name	Country	Institution	Number of Publications
1	Martin, Richard M	United Kingdom	University of Bristol	57
2	Brenner, Hermann	Germany	German Cancer Research Center (DKFZ)	45
3	Smith, George Davey	United Kingdom	University of Bristol	42
4	Gunter, Marc J	France	Nutrition and Metabolism Section, International Agency for Research on Cancer (IARC), World Health Organization (WHO)	42
5	Giles, Graham G	Australia	University of Melbourne	38
6	Zheng, Wei	United States	Vanderbilt University, Nashville, USA	37
7	Chang-claude, Jenny	Germany	University Medical Center Hamburg-Eppendorf, Germany	33
8	Le marchand, Loic	United States	University of Hawaii Cancer Center	33
9	Peters, Ulrike	United States	Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington	28
10	Brennan, Paul	France	Genetic Epidemiology Group, International Agency for Research on Cancer (IARC), Lyon	27

Publishing Institutions

Globally, English MR research in the field of cancer involves 296 publishing institutions. Among them, 3 institutions have published more than 100 papers, accounting for 1.01%. These institutions are the University of Bristol (136 papers), Harvard University (128 papers), University of Cambridge (102 papers), International Agency for Research on Cancer (IARC) (100 papers), and the World Health Organization (100 papers). There are 24 institutions with 50–100 papers, accounting for 8.10%. The top 10 institutions by the number of publications are listed in Table 2. Although 3 institutions have published more than 100 papers and play a leading role, the centrality of the nodes of these institutions is generally low, indicating limited collaboration between institutions, with most research being conducted independently by the institutions. The visualization of publishing institutions using Citespace is shown in Fig. 4.

Table.2 Top 10 institutions of MR English research publications in the field of oncology

Rank	Institution	Studies counts	Centrality
1	University of Bristol	136	0.02
2	Harvard University	128	0
3	University of Cambridge	102	0.02
4	International Agency for Research on Cancer (IARC)	100	0.03
5	World Health Organization	100	0.02
6	Helmholtz Association	97	0.04
7	Harvard T.H. Chan School of Public Health	96	0
8	German Cancer Research Center (DKFZ)	91	0.08
9	Fred Hutchinson Cancer Center	81	0.02
10	Karolinska Institutet	80	0.02

Keyword Analysis

1 Keyword Co-occurrence Analysis

Visual analysis was conducted using keywords as nodes to create a co-occurrence knowledge map, as shown in Fig. 5. The top 15 keywords, along with their frequency and betweenness centrality, are listed in Table 3.

Table.3 High-Frequency Keywords in MR Research in the Field of cancer (Top 15)

Rank	keyword	Count	Centrality
1	risk	225	0.06
2	association	162	0.04
3	instruments	152	0.03
4	Meta-analysis	146	0.12
5	breast cancer	135	0.14
6	genome wide association	119	0.13
7	colorectal cancer	109	0.1
8	bias	97	0.03
9	epidemiology	92	0.08
10	variants	86	0.04
11	body mass index	84	0.05
12	lung cancer	82	0.03
13	prostate cancer	82	0.02
14	obesity	70	0.04
15	disease	65	0.05

2 Keyword Clustering Analysis

In the keyword clustering analysis, a total of 10 clustering modules were obtained. The keyword clustering map has a modularity value of Q = 0.3592 (Q > 0.3) and an average silhouette value of S = 0.7227 (S > 0.5), as shown in Fig. 6. This indicates that the clustering map has a significant structure and reasonable clustering ^[9]. Among them, #1 breast cancer, #3 lung cancer, #5 colorectal cancer, #6 gastric cancer, and #7 renal cell carcinoma are cancer type clusters, while #8 sex hormone and #9 fasting glucose are clusters of associated factors.

3 Keyword Burst Analysis

The keyword emergence and timeline analysis showed in Fig. 7 and Fig. 8 illustrate in how the utility and research focus of keywords change over time, reflecting the trends at the forefront of research. The red line indicates the duration of keyword emergence. The left end of the red line represents the start year of the keyword emergence, while the right end represents the end year of the emergence. The blue line represents the citation timeline. Research hotspots that have persisted for 5 years or more include genetic polymorphisms, colorectal cancer, and genome-wide association. The research hotspots of the past two years mainly include insulin, renal cell carcinoma, and endometrial cancer. The changes in keyword emergence also reflect the varying research interest in different types of cancer and influencing factors at different periods.

In the field of oncology, the number of publications utilizing Mendelian Randomization (MR) methods has been steadily increasing over the years. The earliest English literature was published in 2003, and a new rapid development phase began in 2019. According to literature searches, an article titled "Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians" was published in the British Medical Journal (BMJ) in 2018. This article focuses on helping clinicians and practitioners understand and interpret the core concepts and latest advances in MR methods, as well as guiding reporting standards and result interpretation ^[10]. The emergence of this authoritative and normative guidance literature may have contributed to the rapid growth of English publications and the initiation of Chinese research. In recent years, the large number of MR studies published indicates that more and more scholars are mastering this research method and applying it in causal inference in the field of oncology. This could potentially lead to groundbreaking research, providing theoretical foundations for tracing the causes of cancer and its prevention and treatment.

Statistics on the authors and institutions of the publications reveal that the collaboration network formed by Gunter, Marc J, Le Marchand, Loic, Chang-Claude, Jenny, and Hopper, John L is the most prominent. The high-frequency authors and their affiliated institutions are mostly from the UK, Germany, the USA, and France. The top 10 authors in terms of publication volume have each published more than 25 articles, and these authors use MR research methods to explore the co-occurrence in cancer etiology studies. In the summary of the publishing institutions, the betweenness centrality of each node is relatively low, indicating that there is more collaboration within institutions but less collaboration between institutions. Future research could further enhance inter-institutional collaboration to promote more cross-institutional research outcomes.

As for the discussion on keyword analysis, we can draw the following conclusions:

(1) Summary of the Nature and Methods of MR Research

High-frequency keywords such as "risk," "association," and "instruments" reflect the essence of MR research in exploring factors with potential causal relationships to diseases. This research method is based on data sources aggregated from "genome wide association" (GWAS) studies ^[11].

(2) Analysis of Key Cancer Types

Keyword statistics and cluster analysis from the literature identified key cancer types, including breast cancer, colorectal cancer, lung cancer, prostate cancer, gastric cancer, and renal cell carcinoma. According to epidemiological statistics published by GLOBOCAN 2022, lung cancer was the most common cancer in 2022, with approximately 2.5 million new cases, accounting for 12.4% of all new cancer cases. This was followed by female breast cancer (11.6%), colorectal cancer (9.6%), prostate cancer (7.3%), and gastric cancer (4.9%). Additionally, lung cancer also ranked first in the number of male and female malignant tumor deaths, with colorectal cancer, breast cancer, and gastric cancer also ranking high in mortality rates ^[2]. Therefore, for cancers with high incidence and mortality rates, inferring their possible causes is even more urgent.

According to the keyword emergence map and cluster timeline map from English literature, key cancer types such as breast cancer and colorectal cancer appeared in the early years. As time progressed, recent MR research has gradually expanded to cancer types with relatively smaller affected populations in epidemiological statistics, such as ovarian cancer, endometrial cancer, and renal cell carcinoma.

(3) Discussion on Risk Factors

Researchers systematically summarized and analyzed the relationships between previously reported risk factors and tumors through Mendelian randomization studies, especially large-scale GWAS studies, providing evidence for modifying the risk factor profiles of various cancer types. For example, a study on modifiable cancer risk factors for colorectal cancer summarized 39 potential modifiable cancer risk factors related to diet and lifestyle, fatty acid characteristics and metabolism, obesity, and inflammatory factors. By using genetic variants as proxies for presumed risk factors, the study provided indicative evidence for the associations between increased body fat percentage, BMI, waist circumference, basal metabolic rate, adult height, serum vitamin B12 concentration, serum iron concentration, low-density lipoprotein cholesterol, and total cholesterol with increased colorectal cancer risk^[12].An MR study on breast cancer risk evaluated the potential causal relationships of 23 known and suspected risk factors and biomarkers with overall breast cancer risk and molecular subtypes. It identified 15 significantly associated factors, including age at menarche, age at menopause, body mass index, waist-to-hip ratio, height, physical activity, smoking, sleep duration, morning preference, and six blood biomarkers ^[13].

Horizontally, the exploration of risk factors includes macro indicators such as diet and lifestyle, BMI, micro indicators such as sex hormones in hematology, symptoms like back pain, other diseases, and even socioeconomic status ^{[12, 14–19]}. According to the keyword and cluster analysis results involving cancer types, sex hormones and hormone-sensitive cancers such as breast cancer, prostate cancer, ovarian cancer, and endometrial cancer are key research areas. Additionally, epidemiological data highlight significant sex differences in the incidence rates of various cancers in non-reproductive organs, but studies on the role of sex hormones are relatively scarce. In the era of personalized medicine, understanding the molecular, cellular, and biological differences between males and females is necessary for developing more appropriate treatment interventions based on gender ^[20].

Vertically, more specific subgroup MR studies have also been conducted. For example, in terms of cancer molecular subtypes, MR research on breast cancer has found inconsistent results regarding the age of menopause and circulating sex hormone-binding globulin (SHBG) in triple-negative breast cancer compared to luminal A and B breast cancer, suggesting that the etiology of triple-negative breast cancer may differ from other breast cancer subtypes ^[13]. Regarding risk factors, for example, BMI has been explored not only in terms of its impact on specific cancers ^[21], but also in combination with different pathological subtypes ^[22], fat distribution ^[23], gender and age ^{[24, 25]}, and race ^[26–28].

(4) Combined Analysis with Other Research Methods

MR research also has limitations. The instrumental variable (IV) assumption implies no need for confounding adjustment, but in reality, although there is a strong association between genetic variants and diseases, this does not necessarily imply a causal relationship. The combined application of MR and Meta-analysis can be quite effective in improving the aggregated information from GWAS, enhancing causal inference capabilities, and providing more reliable evidence ^{[21, 29, 30]}.

This study used CiteSpace visualization software to analyze the development process, hotspots, progress, and trends of MR research in the field of cancer both domestically and internationally. MR research in the field of cancer has been conducted in various types of cancers and is increasingly becoming a research hotspot, providing a theoretical basis for etiology analysis, prevention strategy formulation, and new therapy development. However, collaboration between authors and institutions needs to be strengthened. This study has limitations, as the included literature mainly focuses on solid tumors, with relatively insufficient research on Hematologic malignancies. Moreover, bibliometric studies lack an assessment of the quality of the included literature, so the conclusions should be further interpreted in conjunction with other related studies.

1. Conflict of Interest Declaration: All authors of this article declare no conflicts of interest.

2. Funding Declaration: This study was funded by 4 projects: National Natural Science Foundation of China (82074508); Fujian Provincial Natural Science Foundation (Youth Innovation Project) (2023J05282); Fujian Provincial Natural Science Foundation (General Project) (2023J011627); Special Key Project for Supporting Traditional Chinese Medicine Development in Xiamen, Fujian Province (XWZY-2023-0603).

3. Author Contribution Statement: Zhang Kaiyue was responsible for research design, material preparation, data organization and analysis, and visualization. Wei Lai, Xiu Jiayu, and Wang Tingju assisted with data organization and visualization. Huang Yuanpeng was responsible for overall control of the manuscript and financial support. And all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

4. Ethical Approval Declaration: The data for this study comes from the Web of Science database and does not require additional ethical review.

Data Availability

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

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No competing interests reported.

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CiteSpace-based Knowledge Graph Visualization of Mendelian Randomization Studies in Cancer

Status:

Version 1

Abstract

Objective

Methods

Results

Conclusion

Figures

Introduction

Materials and Methods

Data Sources

Inclusion and Exclusion Criteria

Data Processing

Results

Trends in global publication volume

Author Co-occurrence Network

Publishing Institutions

Discussion

Conclusion

Declarations

Data Availability

References

Additional Declarations

Status:

Version 1