Published online Aug 15, 2024. doi: 10.4251/wjgo.v16.i8.3732
Revised: May 19, 2024
Accepted: July 1, 2024
Published online: August 15, 2024
Processing time: 118 Days and 4.5 Hours
The primary aim of this study was to analyze the evolving trends and key focal points in research on cellular metabolism of colorectal cancer (CRC). Relevant publications on cellular metabolism in CRC were sourced from the Science Citation Index Expanded within the Web of Science Core Collection database. Bibliometric analysis and visualization were conducted using VOSviewer (version 1.6.18) software and CiteSpace 6.1.R6 (64-bit) Basic. A comprehensive compilation of 4722 English-language publications, covering the period from January 1, 1991 to December 31, 2022, was carefully identified and included in the analysis. Among the authors, “Ogino, Shuji” contributed the most publications in this field, while “Giovannucci, E” garnered the highest number of citations. The journal
Core Tip: Cellular metabolism encompasses intricate mechanisms that significantly contribute to the development of colorectal cancer. This study employs an advanced bibliometric approach to explore the evolving paradigms and prominent research areas within cellular metabolism research, particularly in the context of colorectal cancer. The findings aim to offer insights and directions for future research in this field.
- Citation: Jiang BW, Zhang XH, Ma R, Luan WY, Miao YD. Current and future research directions in cellular metabolism of colorectal cancer: A bibliometric analysis. World J Gastrointest Oncol 2024; 16(8): 3732-3737
- URL: https://www.wjgnet.com/1948-5204/full/v16/i8/3732.htm
- DOI: https://dx.doi.org/10.4251/wjgo.v16.i8.3732
We recently examined the scholarly work by Liu et al[1] titled “Global research trends and prospects of cellular metabolism in colorectal cancer”. The primary aim of this study was to analyze evolving trends and focal points in cellular metabolism research specific to colorectal cancer (CRC). Metabolic reprogramming is a hallmark of cancer, and is particularly in CRC initiation and progression. The unique metabolic characteristics of intestinal stem cells contribute to the limited efficacy of traditional therapies against CRC[2]. Key metabolic features of CRC cells include heightened glycolytic activity, exemplified by the pervasive Warburg effect[3]. Additionally, alterations in lipid metabolism promote the utilization of glycolytic intermediates for de novo lipid synthesis[4]. This increased lipid accumulation supports tumor cell membrane formation and various signaling processes that are critical for CRC initiation and progression[5]. While we acknowledge the significant contributions of Liu et al’s investigation, we would like to offer several thoughtful recommendations regarding the information retrieval methodologies employed in their study[1].
In the bibliometric research, the careful design of search strategies is crucial. The authors use the Web of Science Core Collection (WoSCC) as the primary data source, which we believe was a prudent decision. However, WoSCC comprises at least 10 subdatabases, including the Science Citation Index Expanded, Social Sciences Citation Index, among others. We believe that incorporating all of these subdatabases in the article retrieval may not be necessary or appropriate. For example, the retrieval formula used by the authors did not yield any relevant studies in Adolescent Health Concern Inventory, CCR-Expanded, or IC[6,7]. Supporting this view, some scholars advise against using a wide array of databases with different types and levels in a single bibliometric analysis[8,9]. Of these subdatabases, Science Citation Index Expanded is regarded as the most appropriate and widely accepted choice for bibliometric research[10,11].
Another critical consideration in bibliometric research is the appropriateness of the topic search approach. The topic search method defines a relevant publication if the search word is found in “Title (TI)”, “Abstract (AB)”, “Author Keywords (AK)”, or “Keywords Plus (KP)”. It is worth noting that “KP” is produced by WoSCC’s auto-algorithm without any author involvement. Therefore, including “KP” in the search process may inadvertently retrieve e numerous extraneous publications[12]. Based on our experience, a more prudent approach relies solely on “TI”, “AB”, and “AK” as qualifiers. This strategy ensures a more precise and semantically meaningful dataset for bibliometric analysis[7,9-11].
It is crucial to acknowledge that the success of a search strategy depends on its thoroughness, as an overly simplistic approach might inadvertently miss relevant publications. In the study by Liu et al[1], the authors’ exclusive use of the terms “colorectal cancer” OR “colorectal carcinoma” may be insufficient for capturing all relevant research. Using double quotes around phrases like “amino acid metabolism” ensures that the search retrieves papers specifically related to that phrase, rather than splitting the search into individual words (e.g., “amino”, “acid”, and “metabolism”), which could yield less accurate results. Finally, it is important to clarify the precise timeframe for the inclusion of literature.
To refine the retrieval strategy, we propose incorporating synonymous terms and nomenclature associated with “colorectal cancer” and “metabolism” into the search equation. For “colorectal cancer”, additional terms such as “colorectal neoplasm”, “colon cancer”, and others should be considered. Similarly, for “metabolism”, terms like “cellular metabolism”, “Metabolic reprogramming”, and “cell metabolism”, among others, could be included. Furthermore, given that terms may have both plural and singular variations, the use of wildcards (e.g., “*”) can be beneficial. For instance, “colorectal cancer*” would capture both “colorectal cancer” and “colorectal cancers”. Our proposed detailed retrieval formula is summarized in Table 1.
Search criteria | Records | |
Records identified through the WoSCC database searching (SCI-Expended) | #1: (TI= (metabolism OR “cellular metabolism” OR “Metabolic reprogramming” OR “cell metabolism” OR “Glucose metabolism” OR “Lipid metabolism” OR “Amino acid metabolism” OR “Nucleotide metabolism” OR “Acetyl-CoA and ketone body metabolism”) OR AB=(metabolism OR “cellular metabolism” OR “Metabolic reprogramming” OR “cell metabolism” OR “Glucose metabolism” OR “Lipid metabolism” OR “Amino acid metabolism” OR “Nucleotide metabolism” OR “Acetyl-CoA and ketone body metabolism”) OR AK= (metabolism OR “cellular metabolism” OR “Metabolic reprogramming” OR “cell metabolism” OR “Glucose metabolism” OR “Lipid metabolism” OR “Amino acid metabolism” OR “Nucleotide metabolism” OR “Acetyl-CoA and ketone body metabolism”)) | 609822 |
#2: (TI=(“colorectal cancer*” OR “colorectal tumor*” OR “colorectal tumour*” OR “colorectal carcinoma*” OR “colorectal neoplasm*” OR “colon cancer*” OR “colon tumor*” OR “colon tumour*” OR “colon carcinoma*” OR “colonic neoplasm*” OR “rectal cancer*” OR “rectal tumor*” OR “rectal tumour*” OR “rectal carcinoma*” OR “rectal neoplasm*”) OR AB=(“colorectal cancer*” OR “colorectal tumor*” OR “colorectal tumour*” OR “colorectal carcinoma*” OR “colorectal neoplasm*” OR “colon cancer*” OR “colon tumor*” OR “colon tumour*” OR “colon carcinoma*” OR “colonic neoplasm*” OR “rectal cancer*” OR “rectal tumor*” OR “rectal tumour*” OR “rectal carcinoma*” OR “rectal neoplasm*”) OR AK=(“colorectal cancer*” OR “colorectal tumor*” OR “colorectal tumour*” OR “colorectal carcinoma*” OR “colorectal neoplasm*” OR “colon cancer*” OR “colon tumor*” OR “colon tumour*” OR “colon carcinoma*” OR “colonic neoplasm*” OR “rectal cancer*” OR “rectal tumor*” OR “rectal tumour*” OR “rectal carcinoma*” OR “rectal neoplasm*”)) | 239132 | |
Time: January 1, 1991 to December 31, 2022 | #3 = #1 AND #2 | 4978 |
Languages: English | 4920 | |
Excluded literature | Meeting abstract (n = 137), editorial material (n = 34), others (n = 27) | 356 |
Remaining publications | 4073 articles, 685 reviews | 4722 |
By incorporating a wider array of relevant terminologies, our refined search query enabled a more exhaustive examination of the pertinent literature. This search, covering articles from January 1, 1991, to December 31, 2022, and was conducted on April 7, 2024, yielded a total of 4978 records. After meticulously excluding diverse types of literature and non-English studies, we retained 4722 publications for analysis, comprising 4073 articles and 685 reviews. The annual publication trends are illustrated in Figure 1 (Supplementary material). The publications of authors, journals, institutions, and countries, along with their respective citations, were visualized and analyzed using VOSviewer (version 1.6.18) software. The findings are presented in Figure 2. In addition, the co-occurrence analysis of the top 500 author keywords and the top 20 references with the strongest citation bursts are demonstrated in Figure 3.
Unlike the findings of Liu et al[1], our study utilized a more refined retrieval scope focusing on “TI/AK/AB”, which resulted in identifying a more accurate set of studies on cellular metabolism in CRC (7354 vs 4722 publications). This significant difference in the number of identified publications can greatly influence various quantitative metrics, including those related to the most prolific countries/regions, institutions, authors, cited-authors, source, cited academic journals, clusters, co-occurrence keyword, and bursts. Hence, to reduce potential bias, it is crucial to meticulously develop an appropriate retrieval formula when performing bibliometric analyses.
In conclusion, we congratulate Liu et al[1] for their meticulous work, particularly their innovative bibliometric analysis of subspecialty directions in the field of CRC metabolism. However, we believe that our approach enhances the precision and accuracy of data analysis regarding research trends in “cellular metabolism of colorectal cancer” over the last three decades.
1. | Liu YC, Gong ZC, Li CQ, Teng P, Chen YY, Huang ZH. Global research trends and prospects of cellular metabolism in colorectal cancer. World J Gastrointest Oncol. 2024;16:527-542. [PubMed] [DOI] [Cited in This Article: ] [Reference Citation Analysis (0)] |
2. | Yi M, Li J, Chen S, Cai J, Ban Y, Peng Q, Zhou Y, Zeng Z, Peng S, Li X, Xiong W, Li G, Xiang B. Emerging role of lipid metabolism alterations in Cancer stem cells. J Exp Clin Cancer Res. 2018;37:118. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 127] [Cited by in F6Publishing: 161] [Article Influence: 23.0] [Reference Citation Analysis (0)] |
3. | Warburg O. On the origin of cancer cells. Science. 1956;123:309-314. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 9117] [Cited by in F6Publishing: 9661] [Article Influence: 140.0] [Reference Citation Analysis (0)] |
4. | Liu Q, Luo Q, Halim A, Song G. Targeting lipid metabolism of cancer cells: A promising therapeutic strategy for cancer. Cancer Lett. 2017;401:39-45. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 184] [Cited by in F6Publishing: 245] [Article Influence: 30.6] [Reference Citation Analysis (0)] |
5. | Dai W, Xiang W, Han L, Yuan Z, Wang R, Ma Y, Yang Y, Cai S, Xu Y, Mo S, Li Q, Cai G. PTPRO represses colorectal cancer tumorigenesis and progression by reprogramming fatty acid metabolism. Cancer Commun (Lond). 2022;42:848-867. [PubMed] [DOI] [Cited in This Article: ] [Cited by in F6Publishing: 42] [Reference Citation Analysis (0)] |
6. | Ho YS. Commentary: Trends and Development in Enteral Nutrition Application for Ventilator Associated Pneumonia: A Scientometric Research Study (1996-2018). Front Pharmacol. 2019;10:1056. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 1.0] [Reference Citation Analysis (0)] |
7. | Geng JW, Li Q, Quan WX, Lin XY, Miao YD. Bibliometric analysis of worldwide research trends on tumor burden and immunotherapy: a correspondence. Int J Surg. 2024;110:3088-3090. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 4] [Reference Citation Analysis (0)] |
8. | Cheng K, He Y, Gu S, Wu H, Li C. A commentary on 'Evolutionary patterns and research frontiers in neoadjuvant immunotherapy: a bibliometric analysis'. Int J Surg. 2023;109:2829-2830. [PubMed] [DOI] [Cited in This Article: ] [Cited by in F6Publishing: 14] [Reference Citation Analysis (0)] |
9. | Pan Y, Deng X, Chen X, Lin M. Bibliometric analysis and visualization of research trends in total mesorectal excision in the past twenty years. Int J Surg. 2023;109:4199-4210. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis (0)] |
10. | He T, Zou J, Sun K, Yang J. Global research status and frontiers on autophagy in hepatocellular carcinoma: a comprehensive bibliometric and visualized analysis. Int J Surg. 2024;110:2788-2802. [PubMed] [DOI] [Cited in This Article: ] [Reference Citation Analysis (0)] |
11. | Zhao Y, Zhu Q, Bi C, Yuan J, Chen Y, Hu X. Bibliometric analysis of tumor necrosis factor in post-stroke neuroinflammation from 2003 to 2021. Front Immunol. 2022;13:1040686. [PubMed] [DOI] [Cited in This Article: ] [Cited by in F6Publishing: 12] [Reference Citation Analysis (0)] |
12. | Ho YS. Rebuttal to: Su et al. "The neurotoxicity of nanoparticles: A bibliometric analysis," Vol. 34, pp. 922-929. Toxicol Ind Health. 2019;35:399-402. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 9] [Cited by in F6Publishing: 12] [Article Influence: 2.0] [Reference Citation Analysis (0)] |