Development and validation of a risk assessment tool for gastric cancer in a general Japanese population

Risk Prediction Models for Gastric Cancer: A Scoping Review

Background

Gastric cancer is a significant contributor to the global cancer burden. Risk prediction models aim to estimate future risk based on current and past information, and can be utilized for risk stratification in population screening programs for gastric cancer. This review aims to explore the research design of existing models, as well as the methods, variables, and performance of model construction.

Methods

Six databases were searched through to November 4, 2023 to identify appropriate studies. PRISMA extension for scoping reviews and the Arksey and O'Malley framework were followed. Data sources included PubMed, Embase, Web of Science, CNKI, Wanfang, and VIP, focusing on gastric cancer risk prediction model studies.

Results

A total of 29 articles met the inclusion criteria, from which 28 original risk prediction models were identified that met the analysis criteria. The risk prediction model is screened, and the data extracted includes research characteristics, prediction variables selection, model construction methods and evaluation indicators. The area under the curve (AUC) of the models ranged from 0.560 to 0.989, while the C-statistics varied between 0.684 and 0.940. The number of predictor variables is mainly concentrated between 5 to 11. The top 5 most frequently included variables were age, helicobacter pylori (Hp), precancerous lesion, pepsinogen (PG), sex, and smoking. Age and Hp were the most consistently included variables.

Conclusion

This review enhances understanding of current gastric cancer risk prediction research and its future directions. The findings provide a strong scientific basis and technical support for developing more accurate gastric cancer risk models. We expect that these conclusions will point the way for future research and clinical practice in this area to assist in the early prevention and treatment of gastric cancer.

Using the Electronic Health Record to Develop a Gastric Cancer Risk Prediction Model

Background and Aims

Gastric cancer (GC) is a leading cause of cancer incidence and mortality globally. Population screening is limited by the low incidence and prevalence of GC in the United States. A risk prediction algorithm to identify high-risk patients allows for targeted GC screening. We aimed to determine the feasibility and performance of a logistic regression model based on electronic health records to identify individuals at high risk for noncardia gastric cancer (NCGC).

Methods

We included 614 patients who had a diagnosis of NCGC between ages 40 and 80 years and who were seen at our large tertiary medical center in multiple states between 2010 and 2021. Controls without a diagnosis of NCGC were randomly selected in a 1:10 ratio of cases to controls. Multiple imputation by chained equations for missing data followed by logistic regression on imputed datasets was used to estimate the probability of NCGC. Area under the curve and the 0.632 estimator was used as the estimate for discrimination.

Results

The 0.632 estimator value was 0.731, indicating robust model performance. Probability of NCGC was higher with increasing age (odds ratio [OR] = 1.16, 95% confidence interval [CI]: 1.04-1.3), male sex (OR = 1.97; 95% CI: 1.64-2.36), Black (OR = 3.07; 95% CI: 2.46-3.83) or Asian race (OR = 4.39; 95% CI: 2.60-7.42), tobacco use (OR = 1.61; 95% CI: 1.34-1.94), anemia (OR = 1.35; 95% CI: 1.09-1.68), and pernicious anemia (OR = 6.12, 95% CI: 3.42-10.95).

Conclusion

We demonstrate the feasibility and good performance of an electronic health record-based logistic regression model for estimating the probability of NCGC. Future studies will refine and validate this model, ultimately identifying a high-risk cohort who could be eligible for NCGC screening.

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

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.

Prediction algorithm for gastric cancer in a general population: A validation study

BACKGROUND

Worldwide, gastric cancer is a leading cause of cancer incidence and mortality. This study aims to devise and validate a scoring system based on readily available clinical data to predict the risk of gastric cancer in a large Chinese population.

METHODS

We included a total of 6,209,697 subjects aged between 18 and 70 years who have received upper digestive endoscopy in Hong Kong from 1997 to 2018. A binary logistic regression model was constructed to examine the predictors of gastric cancer in a derivation cohort (n = 4,347,224), followed by model evaluation in a validation cohort (n = 1,862,473). The algorithm's discriminatory ability was evaluated as the area under the curve (AUC) of the mathematically constructed receiver operating characteristic (ROC) curve.

RESULTS

Age, male gender, history of Helicobacter pylori infection, use of proton pump inhibitors, non-use of aspirin, non-steroidal anti-inflammatory drugs (NSAIDs), and statins were significantly associated with gastric cancer. A scoring of ≤8 was designated as "average risk (AR)". Scores at 9 or above were assigned as "high risk (HR)". The prevalence of gastric cancer was 1.81% and 0.096%, respectively, for the HR and LR groups. The AUC for the risk score in the validation cohort was 0.834, implying an excellent fit of the model.

CONCLUSIONS

This study has validated a simple, accurate, and easy-to-use scoring algorithm which has a high discriminatory capability to predict gastric cancer. The score could be adopted to risk stratify subjects suspected as having gastric cancer, thus allowing prioritized upper digestive tract investigation.

Development, validation, and evaluation of a risk assessment tool for personalized screening of gastric cancer in Chinese populations

BACKGROUND

Effective risk prediction models are lacking for personalized endoscopic screening of gastric cancer (GC). We aimed to develop, validate, and evaluate a questionnaire-based GC risk assessment tool for risk prediction and stratification in the Chinese population.

METHODS

In this three-stage multicenter study, we first selected eligible variables by Cox regression models and constructed a GC risk score (GCRS) based on regression coefficients in 416,343 subjects (aged 40-75 years) from the China Kadoorie Biobank (CKB, development cohort). In the same age range, we validated the GCRS effectiveness in 13,982 subjects from another independent Changzhou cohort (validation cohort) as well as in 5348 subjects from an endoscopy screening program in Yangzhou. Finally, we categorized participants into low (bottom 20%), intermediate (20-80%), and high risk (top 20%) groups by the GCRS distribution in the development cohort.

RESULTS

The GCRS using 11 questionnaire-based variables demonstrated a Harrell's C-index of 0.754 (95% CI, 0.745-0.762) and 0.736 (95% CI, 0.710-0.761) in the two cohorts, respectively. In the validation cohort, the 10-year risk was 0.34%, 1.05%, and 4.32% for individuals with a low (≤ 13.6), intermediate (13.7~30.6), and high (≥ 30.7) GCRS, respectively. In the endoscopic screening program, the detection rate of GC varied from 0.00% in low-GCRS individuals, 0.27% with intermediate GCRS, to 2.59% with high GCRS. A proportion of 81.6% of all GC cases was identified from the high-GCRS group, which represented 28.9% of all the screened participants.

CONCLUSIONS

The GCRS can be an effective risk assessment tool for tailored endoscopic screening of GC in China. Risk Evaluation for Stomach Cancer by Yourself (RESCUE), an online tool was developed to aid the use of GCRS.

Evaluation of polygenic risk score for risk prediction of gastric cancer

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.

Prediction models for gastric cancer risk in the general population: a systematic review

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.

Development and validation of a questionnaire-based risk scoring system to identify individuals at high risk for gastric cancer in Chinese populations

OBJECTIVE

This study aimed to develop and validate a risk scoring system to identify high-risk individuals carrying malignant lesions in stomach for tailored gastric cancer screening.

METHODS

A gastric cancer risk scoring system (GC-RSS) was developed based on questionnaire-based predictors for gastric cancer derived from systematic literature review. To assess the capability of this system for discrimination, risk scores for 8,214 and 7,235 outpatient subjects accepting endoscopic examination in two endoscopy centers, and 32,630 participants in a community-based cohort in China were calculated to plot receiver operating characteristic curves and generate area under the curve (AUC). To evaluate the performance of GC-RSS, the screening proportion, sensitivity and detection rate ratio compared to universal screening were used under different risk score cutoff values.

RESULTS

GC-RSS comprised nine predictors including advanced age, male gender, low body mass index (<18.5 kg/m2), family history of gastric cancer, cigarette smoking, consumption of alcohol, preference for salty food, irregularity of meals and consumption of preserved food. This tool performed well in determining the risk of malignant gastric lesions with AUCs of 0.763, 0.706 and 0.696 in three validation sets. When subjects with risk scores ≥5 were evaluated with endoscopy, nearly 50% of these endoscopies could be saved with a detection rate of over 1.5 times achieved. When the cutoff was set at 8, only about 10% of subjects with the highest risk would be offered endoscopy, and detection rates for gastric cancer could be increased 2-4 fold compared to universal screening.

CONCLUSIONS

An effective questionnaire-based GC-RSS was developed and validated. This tool may play an important role in establishing a tailored screening strategy for gastric cancer in China.

Temporal trend of gastric cancer burden along with its risk factors in China from 1990 to 2019, and projections until 2030: comparison with Japan, South Korea, and Mongolia

Background

Identifying and projecting the epidemiological burden of gastric cancer (GC) can optimize the control strategies, especially in high-burden areas.

Methods

We collected incidence, deaths, disability-adjusted life-years (DALYs), age-standardized incidence rate (ASIR), age-standardized mortality rate (ASMR), age-standardized DALY rate (ASDR) of GC from 1990 to 2019 in China, Japan, South Korea, and Mongolia from the Global Burden of Disease Study 2019. The average annual percentage change (AAPC) was calculated to quantify the temporal trends, and the projection was estimated by applying the Bayesian age-period-cohort model.

Results

In China, the ASIR of GC declined slightly from 37.56/100000 in 1990 to 30.64/100000 in 2019 (AAPC of − 0.41), while the declines of ASMR and ASDR were pronounced (AAPC of − 1.68 and − 1.98, respectively), which were weaker than Japan and South Korea. Although the age-standardized rates of gastric cancer in most countries have declined overall in the past 30 years, the downward trend in the last 4 years has become flattened. Smoking remained one main contributor to DALYs of GC in China, Japan, South Korea, and Mongolia, with more than 24%. The contribution from high-sodium diet was similar between men and women, and kept relatively stable over the three decades. The predicted ASMRs among the four East Asian countries continued to decline until 2030, but the absolute deaths would still increase significantly, especially in South Korea and Mongolia.

Conclusions

Although the age-standardized rates of GC in most countries have declined, the absolute burden of GC in the world, especially in China and Mongolia, is on the rise gradually. Low socio-demographic index and aging along with Helicobacter pylori infection, smoking, and high-salt diet were the main risk factors of GC occurrence and should be paid more attention.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40364-021-00340-6.

Risk Prediction for Gastric Cancer Using GWAS-Identifie Polymorphisms, Helicobacter pylori Infection and Lifestyle-Related Risk Factors in a Japanese Population

Simple Summary

Gastric cancer remains the major cancer in Japan and worldwide. It is expected that practical intervention strategies for prevention, such as personalized approaches based on genetic risk models, will be developed. Here, we developed and validated a risk prediction model for gastric cancer using genetic, biological, and lifestyle-related risk factors. Results showed that the combination of selected GWAS-identified SNP polymorphisms and other predictors provided high discriminatory accuracy and good calibration in both the derivation and validation studies; however, the contribution of genetic factors to risk prediction was limited. The greatest contributor to risk prediction was ABCD classification (Helicobacter pylori infection-related factor).

Abstract

Background: As part of our efforts to develop practical intervention applications for cancer prevention, we investigated a risk prediction model for gastric cancer based on genetic, biological, and lifestyle-related risk factors. Methods: We conducted two independent age- and sex-matched case–control studies, the first for model derivation (696 cases and 1392 controls) and the second (795 and 795) for external validation. Using the derivation study data, we developed a prediction model by fitting a conditional logistic regression model using the predictors age, ABCD classification defined by H. pylori infection and gastric atrophy, smoking, alcohol consumption, fruit and vegetable intake, and 3 GWAS-identified polymorphisms. Performance was assessed with regard to discrimination (area under the curve (AUC)) and calibration (calibration plots and Hosmer–Lemeshow test). Results: A combination of selected GWAS-identified polymorphisms and the other predictors provided high discriminatory accuracy and good calibration in both the derivation and validation studies, with AUCs of 0.77 (95% confidence intervals: 0.75–0.79) and 0.78 (0.77–0.81), respectively. The calibration plots of both studies stayed close to the ideal calibration line. In the validation study, the environmental model (nongenetic model) was significantly more discriminative than the inclusive model, with an AUC value of 0.80 (0.77–0.82). Conclusion: The contribution of genetic factors to risk prediction was limited, and the ABCD classification (H. pylori infection-related factor) contributes most to risk prediction of gastric cancer.

A model for predicting prognosis in patients with esophageal squamous cell carcinoma based on joint representation learning

Esophageal squamous cell carcinoma (ESCC) is one of the deadliest cancer types with a poor prognosis due to the lack of symptoms in the early stages and a delayed diagnosis. The present study aimed to identify the risk factors significantly associated with prognosis and to search for novel effective diagnostic modalities for patients with early-stage ESCC. mRNA and methylation data of patients with ESCC and the corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA) database, and the representation features were screened using deep learning autoencoder. The univariate Cox regression model was used to select the prognosis-related features from the representation features. K-means clustering was used to cluster the TCGA samples. Support vector machine classifier was constructed based on the top 75 features mostly associated with the risk subgroups obtained from K-means clustering. Two ArrayExpress datasets were used to verify the reliability of the obtained risk subgroups. The differentially expressed genes and methylation genes (DEGs and DMGs) between the risk subgroups were analyzed, and pathway enrichment analysis was performed. A total of 500 representation features were produced. Using K-means clustering, the TCGA samples were clustered into two risk subgroups with significantly different overall survival rates. Joint multimodal representation strategy, which showed a good model fitness (C-index=0.760), outperformed early-fusion autoencoder strategy. The joint representation learning-based classification model had good robustness. A total of 1,107 DEGs and 199 DMGs were screened out between the two risk subgroups. The DEGs were involved in 70 pathways, the majority of which were correlated with metastasis and proliferation of various cancer types, including cytokine-cytokine receptor interaction, cell adhesion molecules PPAR signaling pathway, pathways in cancer, transcriptional misregulation in cancer and ECM-receptor interaction pathways. The two survival subgroups obtained via the joint representation learning-based model had good robustness, and had prognostic significance for patients with ESCC.

Evaluation of a Low-Dose Computed Tomography Lung Cancer Screening Program in Henan, China

IMPORTANCE

Lung cancer screening has been widely implemented in Europe and the US. However, there is little evidence on participation and diagnostic yields in population-based lung cancer screening in China.

OBJECTIVE

To assess the participation rate and detection rate of lung cancer in a population-based screening program and the factors associated with participation.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study used data from the Cancer Screening Program in Urban China from October 2013 to October 2019, with follow-up until March 10, 2020. The program is conducted at centers in 8 cities in Henan Province, China. Eligible participants were aged 40 to 74 and were evaluated for a high risk for lung cancer using an established risk score system.

MAIN OUTCOMES AND MEASURES

Overall and group-specific participation rates by common factors, such as age, sex, and educational level, were calculated. Differences in participation rates between those groups were compared. The diagnostic yield of both screening and nonscreening groups was calculated.

RESULTS

The study recruited 282 377 eligible participants and included 55 428 with high risk for lung cancer; the mean (SD) age was 55.3 (8.1) years, and 34 966 participants (63.1%) were men. A total of 22 260 participants underwent LDCT (participation rate, 40.16%; 95% CI, 39.82%-40.50%). The multivariable logistic regression model showed that female sex (odds ratio [OR], 1.64; 95% CI, 1.52-1.78), former smoking (OR, 1.26; 95% CI, 1.13-1.41), lack of physical activity (OR, 1.19; 95% CI, 1.14-1.24), family history of lung cancer (OR, 1.73; 95% CI, 1.66-1.79), and 7 other factors were associated with increased participation of LDCT screening. Overall, at 6-year follow-up, 78 participants in the screening group (0.35%; 95% CI, 0.29%-0.42%) and 125 in the nonscreening group (0.38%; 95% CI, 0.33%-0.44%) had lung cancer detected, which resulted in an odds ratio of 0.93 (95% CI, 0.70-1.23; P = .61).

CONCLUSIONS AND RELEVANCE

The low participations rate in the program studied suggests that an improved strategy is needed. These findings may provide useful information for designing effective population-based lung cancer screening strategies in the future.

A Medical Decision Support System to Assess Risk Factors for Gastric Cancer Based on Fuzzy Cognitive Map

Gastric cancer (GC), one of the most common cancers around the world, is a multifactorial disease and there are many risk factors for this disease. Assessing the risk of GC is essential for choosing an appropriate healthcare strategy. There have been very few studies conducted on the development of risk assessment systems for GC. This study is aimed at providing a medical decision support system based on soft computing using fuzzy cognitive maps (FCMs) which will help healthcare professionals to decide on an appropriate individual healthcare strategy based on the risk level of the disease. FCMs are considered as one of the strongest artificial intelligence techniques for complex system modeling. In this system, an FCM based on Nonlinear Hebbian Learning (NHL) algorithm is used. The data used in this study are collected from the medical records of 560 patients referring to Imam Reza Hospital in Tabriz City. 27 effective features in gastric cancer were selected using the opinions of three experts. The prediction accuracy of the proposed method is 95.83%. The results show that the proposed method is more accurate than other decision-making algorithms, such as decision trees, Naïve Bayes, and ANN. From the perspective of healthcare professionals, the proposed medical decision support system is simple, comprehensive, and more effective than previous models for assessing the risk of GC and can help them to predict the risk factors for GC in the clinical setting.

An optimal prognostic model based on gene expression for clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of RCC; however, prognostic prediction tools for ccRCC are scant. Developing mRNA or long non-coding RNA (lncRNA)-based risk assessment tools may improve the prognosis in patients with ccRCC. RNA-sequencing and prognostic data from patients with ccRCC were downloaded from The Cancer Genome Atlas and the European Bioinformatics Institute Array database at the National Center for Biotechnology Information. Differentially expressed (DE) RNAs (DERs) and prognostic DERs were screened between less favorable and favorable prognoses using the limma package in R 3.4.1, and analyzed using univariate and multivariate Cox regression analyses, respectively. Risk score models were constructed using optimal combinations of DEmRNAs and DElncRNAs identified using the Least Absolute Shrinkage And Selection Operator Cox regression model of the penalized package. Associations between risk score models and overall survival time were evaluated. Independent prognostic clinical factors were screened using univariate and multivariate Cox regression analyses, and nomogram models were constructed. Gene Ontology biological processes and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted using the clusterProfiler package in R3.4.1. A total of 451 DERs were identified, including 404 mRNAs and 47 lncRNAs, between less favorable and favorable prognoses, and 269 DERs, including 233 mRNAs and 36 lncRNAs, were identified as independent prognostic factors. Optimal combinations including 10 DEmRNAs or 10 DElncRNAs were screened using four risk score models based on the status or expression levels of the 10 DEmRNAs or 10 DElncRNAs. The model based on the expression levels of the 10 DEmRNAs had the highest prognostic power. These prognostic DEmRNAs may be involved in biological processes associated with the inflammatory response, complement and coagulation cascades and neuroactive ligand-receptor interaction pathways. The present validated risk assessment tool based on the expression levels of these 10 DEmRNAs may help to identify patients with ccRCC at a high risk of mortality. These 10 DEmRNAs in optimal combinations may serve as prognostic biomarkers and help to elucidate the pathogenesis of ccRCC.

Utilizing Cultural and Ethnic Variables in Screening Models to Identify Individuals at High Risk for Gastric Cancer: A Pilot Study

Identifying persons at high risk for gastric cancer is needed for targeted interventions for prevention and control in low-incidence regions. Combining ethnic/cultural factors with conventional gastric cancer risk factors may enhance identification of high-risk persons. Data from a prior case-control study (40 gastric cancer cases and 100 controls) were used. A "conventional model" using risk factors included in the Harvard Cancer Risk Index's gastric cancer module was compared with a "parsimonious model" created from the most predictive variables of the conventional model as well as ethnic/cultural and socioeconomic variables. Model probability cutoffs aimed to identify a cohort with at least 10 times the baseline risk using Bayes' Theorem applied to baseline U.S. gastric cancer incidence. The parsimonious model included age, U.S. generation, race, cultural food at ages 15-18 years, excessive salt, education, alcohol, and family history. This 11-item model enriched the baseline risk by 10-fold, at the 0.5 probability level cutoff, with an estimated sensitivity of 72% [95% confidence interval (CI), 64-80], specificity of 94% (95% CI, 90-97), and ability to identify a subcohort with gastric cancer prevalence of 128.5 per 100,000. The conventional model was only able to reach a risk level of 9.8 times baseline with a corresponding sensitivity of 31% (95% CI, 23-39) and specificity of 97% (95% CI, 94-99). Cultural and ethnic data may add important information to models for identifying U.S. individuals at high risk for gastric cancer, who then could be targeted for interventions to prevent and control gastric cancer. The findings of this pilot study remain to be validated in an external dataset.

A novel risk score model for stomach adenocarcinoma based on the expression levels of 10 genes

Stomach adenocarcinoma (STAD) accounts for 95% of cases of malignant gastric cancer, which is the third leading cause of cancer-associated mortality worldwide. The pathogenesis and effective diagnosis of STAD have become popular topics for research in the previous decade. In the present study, high-throughput RNA sequencing expression profiles and clinical data from patients with STAD were obtained from The Cancer Genome Atlas database and were used as a training dataset to screen differentially expressed genes (DEGs). Prognostic DEGs were identified using univariate Cox regression analysis and were further screened by the least absolute shrinkage and selection operator regularization regression algorithm. The resulting genes were used to construct a risk score model, the validation and effectiveness evaluation of which were performed on an independent dataset downloaded from the Gene Expression Omnibus database. Stratified and functional pathway (gene set enrichment) analyses were performed on groups with different estimated prognosis. A total of 92 genes significantly associated with STAD prognosis were obtained by univariate Cox regression analysis, and 10 prognosis-associated DEGs; hemoglobin b, chromosome 4 open reading frame 48, Dickkopf WNT signaling pathway inhibitor 1, coagulation factor V, serpin family E member 1, transmembrane protein 200A, NADPH oxidase organizer 1, C-X-C motif chemokine ligand 3, mannosidase endo-α-like and tripartite motif-containing 31; were selected for the development of the risk score model. The reliability of this prognostic method was verified using a validation set, and the results of multivariate Cox analysis indicated that the risk score may serve as an independent prognostic factor. In functional DEG analysis, eight Kyoto Encyclopedia of Genes and Genomes pathways were identified to be significantly associated with STAD risk factors. Thus, the 10-gene risk score model established in the present study was regarded as credible. This risk assessment tool may help identify patients with a high risk of STAD, and the proposed prognostic mRNAs may be useful in elucidating STAD pathogenesis.

Classifying risk level of gastric cancer: Evaluation of questionnaire-based prediction model

Objective

This study aimed at evaluating the efficacy of the questionnaire-based prediction model in an independent prospective cohort.

Methods

A cluster-randomized controlled trial was conducted in Changsha, Harbin, Luoshan, and Sheyang in eastern China in 2015−2017. A total of 182 villages/communities were regarded as clusters, and allocated to screening arm or control arm randomly. Face-to-face interview through a questionnaire interview, including of relevant risk factors of gastric cancer, was administered for each subject. Participants were further classified into high-risk or low-risk groups based on their exposure to risk factors. All participants were followed up until December 31, 2019. Cumulative incidence rates from gastric cancer between high-risk and low-risk groups were calculated and compared using the log-rank test. Cox proportional hazard regression models were applied to estimate hazard ratio (HR) and 95% confidence interval (95% CI).

Results

Totally, 89,914 residents were recruited with a mean follow-up of 3.47 years. And 42,015 (46.73%) individuals were classified into high-risk group and 47,899 (53.27%) subjects were categorized into low-risk group. Gastric cancer was diagnosed in 131 participants, of which 91 were in high-risk group. Compared with the low-risk participants, high-risk individuals were more likely to develop gastric cancer (adjusted HR=2.15, 95% CI, 1.23−3.76). The sensitivity of the questionnaire-based model was estimated at 61.82% (95% CI, 47.71−74.28) in a general population.

Conclusions

Our questionnaire-based model is effective at identifying high-risk individuals for gastric cancer.

Determinants of participation and detection rate of upper gastrointestinal cancer from population-based screening program in China

Upper gastrointestinal cancer (UGC) screening has been widely implemented in many Asian countries. However, there is little evidence of participation and diagnostic yields in population-based UGC screening in China. The participation rate and detection of upper gastrointestinal lesions in this program were reported and related factors were explored. The analysis was conducted in the context of the Cancer Screening Program in Urban China, which recruited 179 002 eligible participants aged 40-74 years from three cities in Henan province from 2013 to 2017. A total of 43 423 participants were evaluated to be high risk for esophageal cancer or gastric cancer by an established risk score system and were subsequently recommended for endoscopy. Of 43 423 with high risk for UGC, 7996 subjects undertook endoscopy (participation rate of 18.4%). We found that male sex, high level of education, marriage, smoking, current alcohol drinking, lack of physical activity, history of upper gastrointestinal system disease, and family history of UGC were associated with increased participation of endoscopy screening. Overall, 15 UGC (0.19%), 275 squamous epithelial dysplasia (3.44%), and 33 intraepithelial neoplasm (0.41%) cases were detected. Several factors including age, sex, smoking, current alcohol drinking, lack of physical activity, and dietary intake of processed meat were identified to be associated with the presence of upper gastrointestinal lesions. Health promotion campaigns targeting the specific group of individuals identified in our study will be helpful for improvement of the adherence of UGC screening in population-based cancer screening programs. Participant rate and yield of UGC screening will provide important references for evaluating the effectiveness and cost-effectiveness of cancer screening in China.

A novel risk score system for assessment of ovarian cancer based on co-expression network analysis and expression level of five lncRNAs

Background

Ovarian cancer (OC) is the most deadly gynaecological cancer, contributing significantly to female cancer-related deaths worldwide. Improving the outlook for OC patients depends on the identification of more reliable prognostic biomarkers for early diagnosis and survival prediction. The various roles of long non-coding RNAs (lncRNAs) in OC have attracted increasing attention. This study aimed to identify a lncRNA-based signature for survival prediction in OC patients.

Methods

RNA expression data and clinical information from a large number of OC patients were downloaded from a public database. These data were regarded as a training set to construct a weighed gene co-expression network analysis (WGCNA) network, mine stable modules, and screen differentially expressed lncRNAs. The prognostic lncRNAs were screened using univariate Cox regression analysis and the optimal prognosis lncRNA combination was screened using a Cox-PH model. The finalised lncRNA combination was used to construct the risk score system, which was validated and assessed for effectiveness using other independent datasets. Further functional pathway enrichment was performed using gene set enrichment analysis (GSEA).

Results

A co-expression network was constructed and four stable modules with OC-related biological functions were obtained. A total of 19 lncRNAs significantly related to prognosis of ovarian cancer were obtained using univariate Cox regression analysis, and the 5 prognostic signature lncRNAs GAS5, HCP5, PART1, SNHG11, and SNHG5 were used to establish a risk assessment system. The reliability of the prognostic scoring system was further confirmed using validation sets, which indicated that the risk assessment system could be used as an independent prognostic factor. Pathway enrichment analysis revealed that the network modules related to the above five prognostic genes were significantly associated with cell local adhesion, cancer signaling pathways, JAK-STAT signalling, and endogenous cell receptor interaction.

Conclusions

The risk score system established in this study could provide a novel reliable method to identify individuals at high risk of OC. In addition, the five prognostic lncRNAs identified here are promising potential prognostic biomarkers that could help to elucidate the pathogenesis of OC.

Validation of a Blood Biomarker for Identification of Individuals at High Risk for Gastric Cancer

BACKGROUND

Helicobacter pylori is the leading cause of gastric cancer, yet the majority of infected individuals will not develop neoplasia. Previously, we developed and replicated serologic H. pylori biomarkers for gastric cancer risk among prospective cohorts in East Asia and now seek to validate the performance of these biomarkers in identifying individuals with premalignant lesions.

METHODS

This cross-sectional study included 1,402 individuals from Linqu County screened by upper endoscopy. H. pylori protein-specific antibody levels were assessed using multiplex serology. Multivariable-adjusted logistic regression models were used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for prevalent intestinal metaplasia, indefinite dysplasia, or dysplasia, compared with superficial or mild atrophic gastritis.

RESULTS

Compared with individuals seronegative to Omp and HP0305, individuals seropositive to both were seven times more likely to have precancerous lesions (OR, 7.43; 95% CI, 5.59-9.88). A classification model for precancerous lesions that includes age, smoking, and seropositivity to H. pylori, Omp, and HP0305 resulted in an area under the curve (AUC) of 0.751 (95% CI, 0.725-0.777), which is significantly better than the same model, including the established gastric cancer risk factor CagA (AUC, 0.718; 95% CI, 0.691-0.746, P _difference = 0.0002).

CONCLUSIONS

The present study of prevalent precancerous gastric lesions provides support for two new serum biomarkers of gastric cancer risk, Omp and HP 0305.

IMPACT

Our results support further research into the serological biomarkers Omp and HP0305 as possible improvements over the established virulence marker CagA for identifying individuals with precancerous lesions in East Asia.