Hotspots and trends of risk factors in gastric cancer: A visualization and bibliometric analysis

Table 1 The top 10 most productive countries in research

Rank	Country	TP	Percent (%)	TC	CPP
1	China	1061	42.20	22402	21.11
2	Japan	467	18.58	13585	29.09
3	United States	383	15.23	21194	55.34
4	South Korea	311	12.37	6831	21.96
5	Italy	152	6.05	7327	48.20
6	Sweden	122	4.85	5669	46.47
7	Germany	116	4.61	4919	42.41
8	United Kingdom	108	4.30	7949	73.60
9	Spain	89	3.54	4225	47.47
10	Netherlands	83	3.30	4757	57.31

Total publications: Total number of publications during the observation period; Total citations: Total citations of publications; CPP: Number of citations per publication; TP: Total publications; TC: Total citations; CPP: TC/TP.

Table 2 The top 10 most productive institutions in gastroparesis research

Rank	Institution	Country	TP	TC	CPP
1	National Cancer Center	Korea	138	5475	39.67
2	Nanjing Medical University	China	122	2906	23.82
3	National Cancer Institute	United States	104	9330	89.71
4	Seoul National University	Korea	88	2336	26.55
5	China Medical University	China	81	1460	18.02
6	Karolinska Institute	Sweden	73	3111	42.62
7	Fudan University	China	57	1435	25.18
8	Shanghai Jiao Tong University	China	57	1706	29.93
9	Yonsei University	Korea	53	1628	30.72
10	Vanderbilt University	United States	50	2843	56.86

Total publications: Total number of publications during the observation period; Total citations: Total citations of publications; CPP: Number of citations per publication; TP: Total publications; TC: Total citations; CPP: TC/TP.

Table 3 The top 10 Journals with the largest number of publications in research

Rank	Journal	TP	TC	CPP	JCR	IF2021
1	International Journal of Cancer	115	6620	57.57	Q2	4.37
2	Gastric Cancer	71	1751	24.66	Q1	7.70
3	Plos One	71	1417	19.96	Q2	3.75
4	World Journal of Gastroenterology	71	2364	33.30	Q2	5.37
5	Asian Pacific Journal of Cancer Prevention	56	742	13.25	-	-
6	Cancer Epidemiology Biomarkers & Prevention	55	3968	72.15	-	-
7	Annals of Surgical Oncology	42	1350	32.14	Q1	4.34
8	BMC Cancer	42	843	20.07	Q2	4.64
9	Oncotarget	41	849	20.71	-	-
10	Medicine	40	605	15.13	Q3	1.82

Total publications: Total number of publications during the observation period; Total citations: Total citations of publications; CPP: Number of citations per publication; TP: Total publications; TC: Total citations; CPP: TC/TP.

Table 4 The top 10 authors and co-cited authors in risk factors for gastric cancer research

Rank	Author	TP	TC	CPP	Co-cited author	Co-citations
1	Il Ju Choi	36	706	19.61	P Correa	617
2	Shoichiro Tsugane	34	1021	30.03	Dm Parkin	422
3	Jeongseon Kim	31	619	19.97	Ca Gonzalez	393
4	Yuan yuan	31	526	16.97	J Ferlay	352
5	Christian C Abnet	30	1161	38.70	Em El-omar	271
6	Wong-ho Chow	23	1091	47.43	F Bray	270
7	Ping Li	22	336	15.27	A Jemal	241
8	Xiao-ou Shu	22	564	25.64	N Uemura	233
9	Li Yang	22	414	18.82	M Rugge	230
10	Wei Zheng	22	564	25.64	P Lauren	228

Total publications: Total number of publications during the observation period; Total citations: Total citations of publications; CPP: Number of citations per publication; TP: Total publications; TC: Total citations; CPP: TC/TP.

Table 5 The top 10 documents in citation analysis of publications in risk factors for gastric cancer research

Rank	Title	First author	Source	Publication year	TC
1	Interleukin-1 polymorphisms associated with increased risk of gastric cancer	Emad M El-Omar	Nature	2000	1800
2	Helicobacter pylori eradication to prevent gastric cancer in a high-risk region of China: a randomized controlled trial	Benjamin Chun-Yu Wong	Jama	2004	1045
3	Gastric cancer: descriptive epidemiology, risk factors, screening, and prevention	Parisa Karimi	Cancer Epidemiol Biomarkers Prev	2014	1015
4	Helicobacter pylori and gastric cancer: factors that modulate disease risk	Lydia E Wroblewski	Clin Microbiol Rev	2010	811
5	Increased risk of noncardia gastric cancer associated with proinflammatory cytokine gene polymorphisms	Emad M El-Omar	Gastroenterology	2003	711
6	Population attributable risks of esophageal and gastric cancers	Lawrence S Engel	J Natl Cancer Inst	2003	513
7	Prospective study of risk factors for esophageal and gastric cancers in the Linxian general population trial cohort in China	Gina D Tran	Int J Cancer	2005	494
8	Gastric cancer risk in patients with premalignant gastric lesions: a nationwide cohort study in the Netherlands	Annemarie C de Vries	Gastroenterology	2008	468
9	Progression of chronic atrophic gastritis associated with Helicobacter pylori infection increases risk of gastric cancer	Hiroshi Ohata	Int J Cancer	2004	376
10	Gastric cancer epidemiology and risk factors	Douglas E Guggenheim	J Surg Oncol	2013	346

Total citations: Total citations of publications; TC: Total citations.

Table 6 The top 10 documents in co-citation analysis of publications in risk factors for gastric cancer research

Rank	Title	First author	Source	Publication year	TC
1	Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries	Freddie Bray	Ca-cancer J Clin	2018	251
2	The two histological main types of gastric carcinoma: diffuse and so-called intestinal-type carcinoma. An attempt at a histo-clinical classification.	P Lauren	Acta Pathol Mic Sc	1965	224
3	Helicobacter pylori infection and the development of gastric cancer	N Uemura	New Engl J Med	2001	207
4	Global cancer statistics, 2002	D Max Parkin	Ca-cancer J Clin	2005	196
5	Classification and grading of gastritis. The updated Sydney System. International Workshop on the Histopathology of Gastritis, Houston 1994	M F Dixon	Am J Surg Pathol	1996	180
6	Bias in meta-analysis detected by a simple, graphical test	M Egger	BMJ	1997	159
7	Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012	Jacques Ferlay	Int J Cancer	2015	150
8	Gastric cancer and Helicobacter pylori: a combined analysis of 12 case control studies nested within prospective cohorts	Helicobacter and Cancer Collaborative Group	Gut	2001	144
9	Interleukin-1 polymorphisms associated with increased risk of gastric cancer	M Egger	BMJ	1997	137
10	Meta-analysis in clinical trials	R DerSimonian	Control Clin Trials	1986	126

Total citations: Total citations of publications; TC: Total citations.

Table 7 The top 20 risk factors for gastric cancer

Rank	Keyword	TP	Rank	Keyword	TP
1	Helicobacter pylori infection	717	11	Nutrient intake	62
2	Polymorphism	326	12	DNA methylation	40
3	Smoking	218	13	Life-style	36
4	Diet	137	14	Fruit	33
5	Alcohol	112	15	Pepsinogen	32
6	IM	99	16	Promoter polymorphism	31
7	Inflammation	98	17	Lymphadenectomy	29
8	Obesity	91	18	Necrosis-factor-alpha	29
9	Caga	63	19	s-1	29
10	Biomarker	62	20	p53	27

Total publications: Total number of publications during the observation period; TP: Total publications.

Table 8 Research characteristics of gastric cancer risk prediction models

Research characteristics				Model characteristics			Findings	Ref.
Country	Research design	Number of participants	Data types	Model types	AUC	C-index
China	Prospective cohort study	435673	①②③④	c	-	0.736	The GCRS can be an effective risk assessment tool for tailored endoscopic screening of GC in China. RESCUE, an online tool was developed to aid the use of GCRS	Zhu et al[82]
China	Retrospective study	6005	①④⑤⑥⑦	-	0.708	-	Li’s prediction model performs the best for risk stratification in the screening, detection, and diagnosis of GC and precancerous lesions, whereas the overall performance of the other three models is similar	Hu et al[83]
South Korea	Retrospective cohort study	1157	①④⑤⑦	a	0.894	-	The 4-point discriminative model may help identify patients with a normal serological test who are nonetheless at risk of developing GC	Cho et al[84]
China	Cohort study	89568	①②④⑨	b	0.97	-	This model could enable a potentially more cost- effective endoscopic surveillance program, as well as to exclude very low-risk patients from unnecessary surveillance	Leung et al[85]
China	Retrospective cohort study	2287	①②④⑩	a	0.684	-	The present study established a predictive model to assess the risk of GC using high-evidence genetic variants and detected the potential gene-environment interaction, which may be helpful in prevention of the cancer	Qiu et al[86]
China	Case-control study	383	①②③④⑤	a	0.883	-	This model is simple, convenient, and economical, has good patient compliance, is easy to implement clinically, is easy to concentrate medical resources, and is expected to identify high-risk groups at an early stage, then to increase the detection rate of GC	Tao et al[87]
America	Case-control study	140	①②③④⑦	a	0.9495	-	The addition of ethnic and cultural variables, particularly the immigration/generation, to conventional risk factor variables improved the ability of models to identify individuals at high risk for GC	In et al[88]
Japan	Case-control study	1431	①④⑦⑧	b	0.899	-	XGBoost outperformed logistic regression and showed the highest AUC value	Taninaga et al[89]
China	Cross-sectional study	14929	①②③④⑤⑥⑦	a	0.76	-	The prediction rule had good performance and showed significantly better discrimination ability to identify a patient with GC than three other alternative prediction methods	Cai et al[90]
Japan	Cohort study	5648	①②⑦⑧	c	0.790	-	We developed a risk assessment tool for gastric cancer that provides a useful guide for stratifying an individual’s risk of future gastric cancer	Iida et al[91]
China	Cross-sectional study	12112	①②③④⑤⑥⑦	c	0.811	-	A serological biopsy composed of the five stomach-specific circulating biomarkers could be used to identify high-risk individuals for further diagnostic gastroscopy, and to stratify individuals’ risk of developing GC and thus to guide targeted screening and precision prevention	Tu et al[92]
Japan	Prospective cohort study	19028	①②③④⑤⑦	c	-	0.777	In this study, the authors developed a model and a simple scoring system to estimate an individual's risk of developing GC, based on factors such as H. pylori antibodies, serum pepsinogen levels, and lifestyle habits	Charvat et al[93]
South Korea	Case-control study	217	①②③④	a	0.888	-	This study provides the first predictive model for assessing the risk factors for GC in Korea, where the incidence rate of GC is high. This study has also identified new risk factors for GC, such as drinking tap water	Lee et al[94]

^aLogistic regression model.

^bMachine learning algorithm

^cCox proportional hazards model.

① demographic data; ② personal history; ③ family history; ④ gastroscopy; ⑤ pepsinogen; ⑥ gastrin-17; ⑦ Helicobacter pylori infection status; ⑧ HbA1c; ⑨ medication history; ⑩ single nucleotide polymorphisms.

XGBoost: Extreme gradient boosting; GC: Gastric cancer; AUC: Area under the curve; RESCUE: Risk Evaluation for Stomach Cancer by Yourself; GCRS: GC risk score; H. pylori: Helicobacter pylori.