1
|
Li M, Gao N, Wang SL, Guo YF, Liu Z. Hotspots and trends of risk factors in gastric cancer: A visualization and bibliometric analysis. World J Gastrointest Oncol 2024; 16:2200-2218. [PMID: 38764808 PMCID: PMC11099465 DOI: 10.4251/wjgo.v16.i5.2200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 02/08/2024] [Accepted: 03/11/2024] [Indexed: 05/09/2024] Open
Abstract
BACKGROUND The lack of specific symptoms of gastric cancer (GC) causes great challenges in its early diagnosis. Thus it is essential to identify the risk factors for early diagnosis and treatment of GC and to improve the survival rates. AIM To assist physicians in identifying changes in the output of publications and research hotspots related to risk factors for GC, constructing a list of key risk factors, and providing a reference for early identification of patients at high risk for GC. METHODS Research articles on risk factors for GC were searched in the Web of Science core collection, and relevant information was extracted after screening. The literature was analyzed using Microsoft Excel 2019, CiteSpace V, and VOSviewer 1.6.18. RESULTS A total of 2514 papers from 72 countries and 2507 research institutions were retrieved. China (n = 1061), National Cancer Center (n = 138), and Shoichiro Tsugane (n = 36) were the most productive country, institution, or author, respectively. The research hotspots in the study of risk factors for GC are summarized in four areas, namely: Helicobacter pylori (H. pylori) infection, single nucleotide polymorphism, bio-diagnostic markers, and GC risk prediction models. CONCLUSION In this study, we found that H. pylori infection is the most significant risk factor for GC; single-nucleotide polymorphism (SNP) is the most dominant genetic factor for GC; bio-diagnostic markers are the most promising diagnostic modality for GC. GC risk prediction models are the latest current research hotspot. We conclude that the most important risk factors for the development of GC are H. pylori infection, SNP, smoking, diet, and alcohol.
Collapse
Affiliation(s)
- Meng Li
- Department of Gastroenterology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Ning Gao
- Department of Acupuncture and Moxibustion, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Shao-Li Wang
- Department of Gastroenterology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Yu-Feng Guo
- Department of Acupuncture and Moxibustion, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Zhen Liu
- Department of Gastroenterology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| |
Collapse
|
2
|
Li S, Che J, Gu B, Li Y, Han X, Sun T, Pan K, Lv J, Zhang S, Wang C, Zhang T, Wang J, Xue F. Metabolites, Healthy Lifestyle, and Polygenic Risk Score Associated with Upper Gastrointestinal Cancer: Findings from the UK Biobank Study. J Proteome Res 2024; 23:1679-1688. [PMID: 38546438 DOI: 10.1021/acs.jproteome.3c00827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2024]
Abstract
Previous metabolomics studies have highlighted the predictive value of metabolites on upper gastrointestinal (UGI) cancer, while most of them ignored the potential effects of lifestyle and genetic risk on plasma metabolites. This study aimed to evaluate the role of lifestyle and genetic risk in the metabolic mechanism of UGI cancer. Differential metabolites of UGI cancer were identified using partial least-squares discriminant analysis and the Wilcoxon test. Then, we calculated the healthy lifestyle index (HLI) score and polygenic risk score (PRS) and divided them into three groups, respectively. A total of 15 metabolites were identified as UGI-cancer-related differential metabolites. The metabolite model (AUC = 0.699) exhibited superior discrimination ability compared to those of the HLI model (AUC = 0.615) and the PRS model (AUC = 0.593). Moreover, subgroup analysis revealed that the metabolite model showed higher discrimination ability for individuals with unhealthy lifestyles compared to that with healthy individuals (AUC = 0.783 vs 0.684). Furthermore, in the genetic risk subgroup analysis, individuals with a genetic predisposition to UGI cancer exhibited the best discriminative performance in the metabolite model (AUC = 0.770). These findings demonstrated the clinical significance of metabolic biomarkers in UGI cancer discrimination, especially in individuals with unhealthy lifestyles and a high genetic risk.
Collapse
Affiliation(s)
- Shuting Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Jiajing Che
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Bingbing Gu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Yunfei Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Xinyue Han
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Tiantian Sun
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Keyu Pan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Jiali Lv
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Shuai Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Cheng Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Tao Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Jialin Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| |
Collapse
|
3
|
Zheng H, Liu Z, Chen Y, Ji P, Fang Z, He Y, Guo C, Xiao P, Wang C, Yin W, Li F, Chen X, Liu M, Pan Y, Liu F, Liu Y, He Z, Ke Y. Development and external validation of a quantitative diagnostic model for malignant gastric lesions in clinical opportunistic screening: A multicenter real-world study. Chin Med J (Engl) 2024:00029330-990000000-00966. [PMID: 38403900 DOI: 10.1097/cm9.0000000000002903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Indexed: 02/27/2024] Open
Abstract
BACKGROUND Clinical opportunistic screening is a cost-effective cancer screening modality. This study aimed to establish an easy-to-use diagnostic model serving as a risk stratification tool for identification of individuals with malignant gastric lesions for opportunistic screening. METHODS We developed a questionnaire-based diagnostic model using a joint dataset including two clinical cohorts from northern and southern China. The cohorts consisted of 17,360 outpatients who had undergone upper gastrointestinal endoscopic examination in endoscopic clinics. The final model was derived based on unconditional logistic regression, and predictors were selected according to the Akaike information criterion. External validation was carried out with 32,614 participants from a community-based randomized controlled trial. RESULTS This questionnaire-based diagnostic model for malignant gastric lesions had eight predictors, including advanced age, male gender, family history of gastric cancer, low body mass index, unexplained weight loss, consumption of leftover food, consumption of preserved food, and epigastric pain. This model showed high discriminative power in the development set with an area under the receiver operating characteristic curve (AUC) of 0.791 (95% confidence interval [CI]: 0.750-0.831). External validation of the model in the general population generated an AUC of 0.696 (95% CI: 0.570-0.822). This model showed an ideal ability for enriching prevalent malignant gastric lesions when applied to various scenarios. CONCLUSION This easy-to-use questionnaire-based model for diagnosis of prevalent malignant gastric lesions may serve as an effective prescreening tool in clinical opportunistic screening for gastric cancer.
Collapse
Affiliation(s)
- Hongchen Zheng
- State Key Laboratory of Molecular Oncology, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Genetics, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Zhen Liu
- State Key Laboratory of Molecular Oncology, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Genetics, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Yun Chen
- Department of Ultrasound, Peking University Shenzhen Hospital, Shenzhen, Guangdong 516473, China
- Shenzhen Key Laboratory for Drug Addiction and Medication Safety, Shenzhen Peking University-Hong Kong University of Science and Technology Medical Center, Shenzhen, Guangdong 516473, China
| | - Ping Ji
- Clinical Research Institute, Shenzhen Peking University-Hong Kong University of Science and Technology Medical Center, Shenzhen, Guangdong 516473, China
| | - Zhengyu Fang
- Clinical Research Institute, Shenzhen Peking University-Hong Kong University of Science and Technology Medical Center, Shenzhen, Guangdong 516473, China
| | - Yujie He
- Endoscopy Center, Hua County People's Hospital, Anyang, Henan 456483, China
| | - Chuanhai Guo
- State Key Laboratory of Molecular Oncology, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Genetics, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Ping Xiao
- Clinical Research Institute, Shenzhen Peking University-Hong Kong University of Science and Technology Medical Center, Shenzhen, Guangdong 516473, China
| | - Chengwen Wang
- Endoscope Group, Department of Gastroenterology, Peking University Shenzhen Hospital, Shenzhen, Guangdong 516473, China
| | - Weihua Yin
- Department of Pathology, Peking University Shenzhen Hospital, Shenzhen, Guangdong 516473, China
| | - Fenglei Li
- Hua County People's Hospital, Anyang, Henan 456483, China
| | - Xiujian Chen
- Department of Pathology, Hua County People's Hospital, Anyang, Henan 456483, China
| | - Mengfei Liu
- State Key Laboratory of Molecular Oncology, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Genetics, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Yaqi Pan
- State Key Laboratory of Molecular Oncology, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Genetics, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Fangfang Liu
- State Key Laboratory of Molecular Oncology, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Genetics, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Ying Liu
- State Key Laboratory of Molecular Oncology, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Genetics, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Zhonghu He
- State Key Laboratory of Molecular Oncology, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Genetics, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Yang Ke
- State Key Laboratory of Molecular Oncology, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Genetics, Peking University Cancer Hospital & Institute, Beijing 100142, China
| |
Collapse
|
4
|
Pyun H, Gunathilake M, Lee J, Choi IJ, Kim YI, Sung J, Kim J. Functional Annotation and Gene Set Analysis of Gastric Cancer Risk Loci in a Korean Population. Cancer Res Treat 2024; 56:191-198. [PMID: 37340842 PMCID: PMC10789951 DOI: 10.4143/crt.2022.958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Accepted: 06/17/2023] [Indexed: 06/22/2023] Open
Abstract
PURPOSE We aimed to identify the associated single nucleotide polymorphisms (SNPs) with gastric cancer (GC) risk by genome-wide association study (GWAS) and to explore the pathway enrichment of implicated genes and gene-sets with expression patterns. MATERIALS AND METHODS The study population was comprised of 1,253 GC cases and 4,827 controls from National Cancer Center and an urban community of the Korean Genome Epidemiology Study and their genotyping was performed. SNPs were annotated, and mapped to genes to prioritize by three mapping approaches by functional mapping and annotation (FUMA). The gene-based analysis and gene-set analysis were conducted with full GWAS summary data using MAGMA. Gene-set pathway enrichment test with those prioritized genes were performed. RESULTS In GWAS, rs2303771, a nonsynonymous variant of KLHDC4 gene was top SNP associated significantly with GC (odds ratio, 2.59; p=1.32×10-83). In post-GWAS, 71 genes were prioritized. In gene-based GWAS, seven genes were under significant p < 3.80×10-6 (0.05/13,114); DEFB108B had the lowest p=5.94×10-15, followed by FAM86C1 (p=1.74×10-14), PSCA (p=1.81×10-14), and KLHDC4 (p=5.00×10-10). In gene prioritizing, KLDHC4 was the only gene mapped with all three gene-mapping approaches. In pathway enrichment test with prioritized genes, FOLR2, PSCA, LY6K, LYPD2, and LY6E showed strong enrichment related to cellular component of membrane; a post-translation modification by synthesis of glycosylphosphatidylinositol (GPI)-anchored proteins pathway. CONCLUSION While 37 SNPs were significantly associated with the risk of GC, genes involved in signaling pathways related to purine metabolism and GPI-anchored protein in cell membrane are pinpointed to be playing important role in GC.
Collapse
Affiliation(s)
- Hyojin Pyun
- Division of Genome and Health Big Data, Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul,
Korea
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, Goyang,
Korea
| | - Madhawa Gunathilake
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, Goyang,
Korea
| | - Jeonghee Lee
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, Goyang,
Korea
| | - Il Ju Choi
- Center for Gastric Cancer, National Cancer Center Hospital, National Cancer Center, Goyang,
Korea
| | - Young-Il Kim
- Center for Gastric Cancer, National Cancer Center Hospital, National Cancer Center, Goyang,
Korea
| | - Joohon Sung
- Division of Genome and Health Big Data, Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul,
Korea
- Institute of Health and Environment, Seoul National University, Seoul,
Korea
| | - Jeongseon Kim
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, Goyang,
Korea
| |
Collapse
|
5
|
Wang XY, Wang LL, Xu L, Liang SZ, Yu MC, Zhang QY, Dong QJ. Evaluation of polygenic risk score for risk prediction of gastric cancer. World J Gastrointest Oncol 2023; 15:276-285. [PMID: 36908320 PMCID: PMC9994049 DOI: 10.4251/wjgo.v15.i2.276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 01/11/2023] [Accepted: 02/02/2023] [Indexed: 02/14/2023] Open
Abstract
Genetic variations are associated with individual susceptibility to gastric cancer. Recently, polygenic risk score (PRS) models have been established based on genetic variants to predict the risk of gastric cancer. To assess the accuracy of current PRS models in the risk prediction, a systematic review was conducted. A total of eight eligible studies consisted of 544842 participants were included for evaluation of the performance of PRS models. The overall accuracy was moderate with Area under the curve values ranging from 0.5600 to 0.7823. Incorporation of epidemiological factors or Helicobacter pylori (H. pylori) status increased the accuracy for risk prediction, while selection of single nucleotide polymorphism (SNP) and number of SNPs appeared to have little impact on the model performance. To further improve the accuracy of PRS models for risk prediction of gastric cancer, we summarized the association between gastric cancer risk and H. pylori genomic variations, cancer associated bacteria members in the gastric microbiome, discussed the potentials for performance improvement of PRS models with these microbial factors. Future studies on comprehensive PRS models established with human SNPs, epidemiological factors and microbial factors are indicated.
Collapse
Affiliation(s)
- Xiao-Yu Wang
- Central Laboratories and Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao University, Qingdao 266071, Shandong Province, China
| | - Li-Li Wang
- Central Laboratories and Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao University, Qingdao 266071, Shandong Province, China
| | - Lin Xu
- Central Laboratories and Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao University, Qingdao 266071, Shandong Province, China
| | - Shu-Zhen Liang
- Central Laboratories and Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao University, Qingdao 266071, Shandong Province, China
| | - Meng-Chao Yu
- Central Laboratories and Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao University, Qingdao 266071, Shandong Province, China
| | - Qiu-Yue Zhang
- Department of Clinical Laboratory, the Eighth Medical Center of the General Hospital of the People’s Liberation Army, Beijing 100000, China
| | - Quan-Jiang Dong
- Central Laboratories and Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao University, Qingdao 266071, Shandong Province, China
| |
Collapse
|
6
|
Yue Q, Bai J, Wang F, Xue F, Li L, Duan X. Novel classification and risk model based on ferroptosis-related lncRNAs to predict oncologic outcomes for gastric cancer patients. J Biochem Mol Toxicol 2022; 36:e23052. [PMID: 35315178 DOI: 10.1002/jbt.23052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 01/07/2022] [Accepted: 03/10/2022] [Indexed: 12/24/2022]
Abstract
Gastric cancer (GC) is a highly heterogeneous malignancy, characterized by high mortality and poor prognosis. Ferroptosis is a newly defined nonapoptotic programmed cell death mechanism that has been implicated in the development of various pathological conditions. We aimed to identify ferroptosis-related long noncoding RNA (lncRNAs) that might be used to predict GC prognosis. The data were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus database. Two subtypes, C1 and C2, were identified, which had significant variations in prognosis and immune cell infiltrations. Differentially expressed genes between the subtypes were found to be involved in multiple tumor-associated pathways. Subsequently, a training dataset and a testing dataset were created from the TCGA dataset. A predictive model for GC patients based on six ferroptosis-related lncRNAs (including STX18-AS1, MIR99AHG, LINC01197, LINC00968, LINC00865, and LEF1-AS1) was developed. The model could stratify patients into a high- and low-risk group, showing good predictive performance. The testing dataset, entire TCGA dataset, and GSE62254 cohort both confirmed the predictive value of the model. Compared to the clinical parameters (including gender, age, and grade), the risk model was an independent risk factor for GC patients. Moreover, a nomogram (containing our risk score model and clinical parameters) was constructed, which might provide great potential to improve prediction accuracy. Moreover, the single-sample gene set enrichment analysis revealed that the high-risk group was linked to various signaling pathways involved in the regulation of GC progression. Conclusively, a novel classification and risk model based on ferroptosis-related lncRNAs that can predict oncologic outcomes for GC patients has been developed.
Collapse
Affiliation(s)
- Qingfang Yue
- Department of Medical Oncology, Shaanxi Provincial People's Hospital, Affiliated Hospital of Northwestern Polytechnical University, Xi'an, Shaanxi, P. R. China
- Postdoctoral Station, Institute of Medical Research, Northwestern Polytechnic University, Xi'an, Shaanxi, P. R. China
| | - Jun Bai
- Department of Medical Oncology, Shaanxi Provincial People's Hospital, Affiliated Hospital of Northwestern Polytechnical University, Xi'an, Shaanxi, P. R. China
| | - Fei Wang
- Department of Gynecology, Shaanxi Provincial People's Hospital, Affiliated Hospital of Northwestern Polytechnical University, Xi'an, Shaanxi, P. R. China
| | - Fei Xue
- Second Department of General Surgery, Shaanxi Provincial People's Hospital, Affiliated Hospital of Northwestern Polytechnical University, Xi'an, Shaanxi, P. R. China
| | - Lianxiang Li
- Department of Gynecology, Shaanxi Provincial People's Hospital, Affiliated Hospital of Northwestern Polytechnical University, Xi'an, Shaanxi, P. R. China
| | - Xianglong Duan
- Second Department of General Surgery, Shaanxi Provincial People's Hospital, Affiliated Hospital of Northwestern Polytechnical University, Xi'an, Shaanxi, P. R. China
- Medical College, Xizang Mingzu University, Xianyang, Shaanxi, P. R. China
| |
Collapse
|
7
|
Gu J, Chen R, Wang SM, Li M, Fan Z, Li X, Zhou J, Sun K, Wei W. Prediction models for gastric cancer risk in the general population: a systematic review. Cancer Prev Res (Phila) 2022; 15:309-318. [PMID: 35017181 DOI: 10.1158/1940-6207.capr-21-0426] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 11/15/2021] [Accepted: 01/07/2022] [Indexed: 11/16/2022]
Abstract
Risk prediction models for gastric cancer (GC) could identify high-risk individuals in the general population. The objective of this study was to systematically review the available evidence about the construction and verification of GC predictive models. We searched PubMed, Embase, and Cochrane Library databases for articles that developed or validated GC risk prediction models up to November 2021. Data extracted included study characteristics, predictor selection, missing data, and evaluation metrics. Risk of bias (ROB) was assessed using the Prediction model study Risk Of Bias Assessment Tool (PROBAST). We identified a total of 12 original risk prediction models that fulfilled the criteria for analysis. The area under the receiver operating characteristic curve ranged from 0.73 to 0.93 in derivation sets (n=6), 0.68 to 0.90 in internal validation sets (n=5), 0.71 to 0.92 in external validation sets (n=7). The higher-performing models usually include age, salt preference, Helicobacter pylori, smoking, BMI, family history, pepsinogen and sex. According to PROBAST, at least one domain with a high ROB was present in all studies mainly due to methodologic limitations in the analysis domain. In conclusion, although some risk prediction models including similar predictors have displayed sufficient discriminative abilities, many have a high ROB due to methodological limitations and are not externally validated efficiently. Future prediction models should adherence to well-established standards and guidelines to benefit GC screening.
Collapse
Affiliation(s)
- Jianhua Gu
- National Central Cancer Registry, National Cancer Center/ National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
| | - Ru Chen
- National Central Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
| | - Shao-Ming Wang
- National Central Cancer Registry Office, National Cancer Center/National Clinical Research Center for Cancer/ Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
| | - Minjuan Li
- National Central Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
| | - Zhiyuan Fan
- National Cancer Registry Office, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
| | - Xinqing Li
- 1. Office of National Central Cancer Registry, Cancer Institute/Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Jiachen Zhou
- Department of Epidemiology and Biostatistics, School of Public Health, Xi'an Jiaotong University Health Science Center
| | - Kexin Sun
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College
| | - Wenqiang Wei
- National Central Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
| |
Collapse
|
8
|
Qiu L, Qu X, He J, Cheng L, Zhang R, Sun M, Yang Y, Wang J, Wang M, Zhu X, Guo W. Predictive model for risk of gastric cancer using genetic variants from genome-wide association studies and high-evidence meta-analysis. Cancer Med 2020; 9:7310-7316. [PMID: 32777176 PMCID: PMC7541133 DOI: 10.1002/cam4.3354] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 07/15/2020] [Accepted: 07/16/2020] [Indexed: 02/05/2023] Open
Abstract
Genome-wide association studies (GWAS) have identified some single nucleotide polymorphisms (SNPs) associated with the risk of gastric cancer (GCa). However, currently, there is no published predictive model to assess the risk of GCa. In the present study, risk-associated SNPs derived from GWAS and large meta-analyses were selected to construct a predictive model to assess the risk of GCa. A total of 1115 GCa cases and 1172 controls from the eastern Chinese population were included. Logistic regression models were used to identify SNPs that correlated with the risk of GCa. A predictive model to assess the risk of GCa was established by receiver operating characteristic curve analysis. Multifactor dimensionality reduction (MDR) and classification and regression tree (CART) were applied to calculate the effect of high-order gene-environment interactions on risk of the cancer. A total of 42 SNPs were selected for further analysis. The results revealed that ASH1L rs80142782, PKLR rs3762272, PRKAA1 rs13361707, MUC1 rs4072037, PSCA rs2294008, and PLCE1 rs2274223 polymorphisms were associated with a risk of GCa. The area under curve considering both genetic factors and BMI was 3.10% higher than that of BMI alone. MDR analysis revealed that rs13361707 and rs4072307 variants and BMI had interaction effects on susceptibility to GCa, with the highest predictive accuracy (61.23%) and cross-validation consistency (100/100). CART analysis also supported this interaction model that non-overweight status and a six SNP panel could synergistically increase the susceptibility to GCa. The six SNP panel for predicting the risk of GCa may provide new tools for prevention of the cancer based on GWAS and large meta-analyses derived genetic variants.
Collapse
Affiliation(s)
- Lixin Qiu
- Department of Medical OncologyFudan University Shanghai Cancer CenterDepartment of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
- Cancer InstituteCollaborative Innovation Center for Cancer MedicineFudan University Shanghai Cancer CenterShanghaiChina
| | - Xiaofei Qu
- Cancer InstituteCollaborative Innovation Center for Cancer MedicineFudan University Shanghai Cancer CenterShanghaiChina
| | - Jing He
- Cancer InstituteCollaborative Innovation Center for Cancer MedicineFudan University Shanghai Cancer CenterShanghaiChina
| | - Lei Cheng
- Department of Medical OncologyFudan University Shanghai Cancer CenterDepartment of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
- Cancer InstituteCollaborative Innovation Center for Cancer MedicineFudan University Shanghai Cancer CenterShanghaiChina
| | - Ruoxin Zhang
- Cancer InstituteCollaborative Innovation Center for Cancer MedicineFudan University Shanghai Cancer CenterShanghaiChina
| | - Menghong Sun
- Department of PathologyFudan University Shanghai Cancer CenterShanghaiChina
| | - Yajun Yang
- Ministry of Education Key Laboratory of Contemporary Anthropology and State Key Laboratory of Genetic EngineeringSchool of Life SciencesFudan UniversityShanghaiChina
- Fudan‐Taizhou Institute of Health SciencesTaizhouChina
| | - Jiucun Wang
- Ministry of Education Key Laboratory of Contemporary Anthropology and State Key Laboratory of Genetic EngineeringSchool of Life SciencesFudan UniversityShanghaiChina
- Fudan‐Taizhou Institute of Health SciencesTaizhouChina
| | - Mengyun Wang
- Cancer InstituteCollaborative Innovation Center for Cancer MedicineFudan University Shanghai Cancer CenterShanghaiChina
| | - Xiaodong Zhu
- Department of Medical OncologyFudan University Shanghai Cancer CenterDepartment of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Weijian Guo
- Department of Medical OncologyFudan University Shanghai Cancer CenterDepartment of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| |
Collapse
|