Development and validation of a prediction rule for estimating gastric cancer risk in the Chinese high-risk population: a nationwide multicentre study

Pathogenicity and virulence of Helicobacter pylori: A paradigm of chronic infection

Infection with Helicobacter pylori is one of the most common infections of mankind. Infection typically occurs in childhood and persists for the lifetime of the host unless eradicated with antimicrobials. The organism colonizes the stomach and causes gastritis. Most infected individuals are asymptomatic, but infection also causes gastric and duodenal ulceration, and gastric cancer. H. pylori possesses an arsenal of virulence factors, including a potent urease enzyme for protection from acid, flagella that mediate motility, an abundance of outer membrane proteins that can mediate attachment, several immunomodulatory proteins, and an ability to adapt to specific conditions in individual human stomachs. The presence of a type 4 secretion system that injects effector molecules into gastric cells and subverts host cell signalling is associated with virulence. In this review we discuss the interplay of H. pylori colonization and virulence factors with host and environmental factors to determine disease outcome in infected individuals.

A different perspective on studying stroke predictors: joint models for longitudinal and time-to-event data in a type 2 diabetes mellitus cohort

BACKGROUND

Most predictive models rely on risk factors and clinical outcomes assessed simultaneously. This approach does not adequately reflect the progression of health conditions. By employing joint models of longitudinal and survival data, we can dynamically adjust prognosis predictions for individual patients. Our objective was to optimize the prediction of stroke or transient ischemic attack (TIA) via joint models that incorporate all available changes in the predictive variables.

METHODS

A total of 3442 patients with type 2 diabetes mellitus (T2DM) and no history of stroke, TIA or myocardial infarction were followed for 12 years. Models were constructed independently for men and women. We used proportional hazards regression models to assess the effects of baseline characteristics (excluding longitudinal data) on the risk of stroke/TIA and linear mixed effects models to assess the effects of baseline characteristics on longitudinal data development over time. Both submodels were then combined into a joint model. To optimize the analysis, a univariate analysis was first performed for each longitudinal predictor to select the functional form that gave the best fit via the deviance information criterion. The variables were then entered into a multivariate model using pragmatic criteria, and if they improved the discriminatory ability of the model, the area under the curve (AUC) was used.

RESULTS

During the follow-up period, 303 patients (8.8%) experienced their first stroke/TIA. Age was identified as an independent predictor among males. Among females, age was positively associated with atrial fibrillation (AF). The final model for males included AF, systolic blood pressure (SBP), and diastolic blood pressure (DBP), with albuminuria and the glomerular filtration rate (GFR) as adjustment variables. For females, the model included AF, blood pressure (BP), and renal function (albuminuria and GFR), with HbA1c and LDL cholesterol as adjustment variables. Both models demonstrated an AUC greater than 0.70.

CONCLUSIONS

Age, AF, and SBP have been confirmed as significant predictive factors in both sexes, whereas renal function was significant only in women. Interestingly, an increase in DBP may serve as a protective factor in our cohort. These factors were particularly relevant in the last 3-7 years of follow-up.

Associated effects of blood metal(loid) exposure and impaired glucose metabolism in patients with gastric precancerous lesions or gastric cancer

Exposure to metal(loid)s and glucose metabolism may influence the progression of gastric precancerous lesions (GPLs) or gastric cancer (GC), but their combined effects remain unclear. Our study aimed to elucidate the combined impact of metal (including metalloid and trace element) exposure and disturbances in glucose metabolism on the progression of GPLs and GC. From a prospective observational cohort of 1829 individuals, their metal(loid) levels and blood metabolism were analysed via inductively coupled plasma‒mass spectrometry and targeted metabolomics gas chromatography‒mass spectrometry, respectively. From healthy normal controls (NC) or GPLs to GC, we observed that the aluminum and arsenic levels decreased, whereas the vanadium, titanium and rubidium levels increased, but the iron, copper, zinc and barium levels initially decreased but then increased; these changes were not obvious from the NC to GPL group. With respect to glucose homeostasis, most metabolites decreased, except for phosphoenolpyruvate (PEP), which increased. Multiple logistic regression analysis revealed that titanium and phosphoenolpyruvate might be risk factors for GPLs, that barium is a protective factor for GC, and that D-glucaric acid might be a protective factor for GPLs and GC. Selenium, vanadium, titanium, succinate, maleate, isocitrate, PEP, and the tricarboxylic acid cycle (TCA) had good predictive potential for GPL and GC. Additionally, metal(loid)s such as arsenic, titanium, barium, aluminum, and vanadium were significantly correlated with multiple glucose metabolites involved in the TCA cycle in the GPL and GC groups. Our findings imply that metal(loid) exposure disrupts glucose metabolism, jointly influencing GPL and GC progression.

DKK3 and SERPINB5 as novel serum biomarkers for gastric cancer: facilitating the development of risk prediction models for gastric cancer

The existing gastric cancer (GC) risk prediction models based on biomarkers are limited. This study aims to identify new promising biomarkers for GC to develop a risk prediction model for effective assessment, screening, and early diagnosis. This study was conducted utilizing a large combined cohort for upper gastrointestinal cancer that was established in Hebei Province, China. General macro risk factors, Helicobacter pylori (H.pylori) infection status, and protein biomarkers were collected through questionnaire surveys and laboratory tests. Novel GC biomarkers were explored using data-independent acquisition (DIA) proteomics and enzyme-linked immunosorbent assay (ELISA). Multiple machine learning algorithms were used to identify key predictors for the GC risk prediction model, which was validated with an independent external cohort from multiple hospitals. A total of 530 participants aged 40 to 74 were analyzed, with 104 ultimately diagnosed with GC. Significant biomarkers in GC patients were identified by DIA combined ELISA, including elevated Keratin 7 (KRT7) and Mammary fibrostatin (SERPINB5) (P<0.001) and decreased Dickkopf-associated protein 3 (DKK3) (P<0.001). Factors such as sex, age, smoking status, alcohol consumption, family history of GC, H. pylori infection, DKK3 and SERPINB5 were used to create a multidimensional risk prediction model for GC. This model achieved an area under the curve (AUC) of 0.938 (95% confidence interval: 0.913-0.962). The risk prediction model developed in this study shows high accuracy and practical utility, serving as an effective preliminary screening tool for identifying high-risk individuals for GC.

Diagnostic efficacy of an extracellular vesicle-derived lncRNA-based liquid biopsy signature for the early detection of early-onset gastric cancer

BACKGROUND

Early-onset gastric cancer (EOGC) is a lethal malignancy. It differs from late-onset gastric cancer (LOGC) in clinical and molecular characteristics. The current strategies for EOGC detection have certain limitations in diagnostic performance due to the rising trend in EOGC.

OBJECTIVE

We developed a liquid biopsy signature for EOGC detection.

DESIGN

We use a systematic discovery approach by analysing genome-wide transcriptomic profiling data from EOGC (n=43), LOGC (n=31) and age-matched non-disease controls (n=37) tissue samples. An extracellular vesicle-derived long non-coding RNA (EV-lncRNA) signature was identified in blood samples from a training cohort (n=299), and subsequently confirmed by qPCR in two external validation cohorts (n=462 and n=438), a preoperative/postoperative cohort (n=66) and a gastrointestinal tumour cohort (n=225).

RESULTS

A three EV-lncRNA (NALT1, PTENP1 and HOTTIP) liquid biopsy signature was developed for EOGC detection with an area under the receiver operating characteristic curve (AUROC) of 0.924 (95% CI 0.889 to 0.953). This EV-lncRNA signature provided robust diagnostic performance in two external validation cohorts (Xi'an cohort: AUROC, 0.911; Beijing cohort: AUROC, 0.9323). Furthermore, the EV-lncRNA signature reliably identified resectable stage EOGC patients (stage I/II) and demonstrated better diagnostic performance than traditional GC-related biomarkers in distinguishing early-stage EOGC (stage I) from precancerous lesions. The low levels of this biomarker in postsurgery and other gastrointestinal tumour plasma samples indicated its GC specificity.

CONCLUSIONS

The newly developed EV-lncRNA signature effectively identified EOGC patients at a resectable stage with enhanced precision, thereby improving the prognosis of patients who would have otherwise missed the curative treatment window.

The artificial intelligence revolution in gastric cancer management: clinical applications

Nowadays, gastric cancer has become a significant issue in the global cancer burden, and its impact cannot be ignored. The rapid development of artificial intelligence technology is attempting to address this situation, aiming to change the clinical management landscape of gastric cancer fundamentally. In this transformative change, machine learning and deep learning, as two core technologies, play a pivotal role, bringing unprecedented innovations and breakthroughs in the diagnosis, treatment, and prognosis evaluation of gastric cancer. This article comprehensively reviews the latest research status and application of artificial intelligence algorithms in gastric cancer, covering multiple dimensions such as image recognition, pathological analysis, personalized treatment, and prognosis risk assessment. These applications not only significantly improve the sensitivity of gastric cancer risk monitoring, the accuracy of diagnosis, and the precision of survival prognosis but also provide robust data support and a scientific basis for clinical decision-making. The integration of artificial intelligence, from optimizing the diagnosis process and enhancing diagnostic efficiency to promoting the practice of precision medicine, demonstrates its promising prospects for reshaping the treatment model of gastric cancer. Although most of the current AI-based models have not been widely used in clinical practice, with the continuous deepening and expansion of precision medicine, we have reason to believe that a new era of AI-driven gastric cancer care is approaching.

A risk prediction model for gastric cancer based on endoscopic atrophy classification

BACKGROUNDS

Gastric cancer (GC) is a prevalent malignancy affecting the digestive system. We aimed to develop a risk prediction model based on endoscopic atrophy classification for GC.

METHODS

We retrospectively collected the data from January 2020 to October 2021 in our hospital and randomly divided the patients into training and validation sets in an 8:2 ratio. We used multiple machine learning algorithms such as logistic regression (LR), Decision tree, Support Vector Machine, Random forest, and so on to establish the models. We employed the Least absolute shrinkage and selection operator (LASSO) to screen variables for the LR model. However, we chose all the variables to construct the models for other machine learning algorithms. All models were evaluated using the receiver operating characteristic curve (ROC), predictive histograms, and decision curve analysis (DCA).

RESULTS

A total of 1156 patients were selected for the analysis. Five variables, including age, sex, family history of GC, HP infection status, and Kimura-Takemoto Classification (KTC), were screened using LASSO analysis. The area under the curve (AUC) of all the machine learning models ranged from 0.762 to 0.974 in the training set and from 0.608 to 0.812 in the validation set. Among them, the LR model exhibited the highest AUC value (0.812, 95%CI: 0.737-0.887) in the validation set with good calibration and clinical applicability. Finally, we constructed a nomogram to demonstrate the LR model.

CONCLUSIONS

We established a nomogram based on endoscopic atrophy classification for GC, which might be valuable in predicting GC risk and assisting clinical decision-making.

Hydrogel-Transformable Probiotic Powder for Targeted Eradication of Helicobacter pylori with Enhanced Gastric Mucosal Repair and Microbiota Preservation

Lactobacillus reuteri (L. reuteri) therapies represent a potentially effective approach to eradicating Helicobacter pylori (H. pylori). However, the difficulty in bacterial viability preservation and harsh gastric environment compromises the survival and on-target delivery of L. reuteri. This study presents a novel bacterium-mediated bacterial elimination strategy using an edible L. reuteri@HTP probiotic powder for targeted bacterial elimination. The probiotic powder is obtained by grinding a lyophilized hydrogel composed of L. reuteri, hyaluronic acid (HA), tannic acid (TA), and polyvinyl alcohol (PVA). Upon contact with water, the powder quickly transforms into a hydrogel, enhancing L. reuteri's survival in the harsh gastric environment and ensuring selective release at H. pylori-infected inflammatory sites. L. reuteri targets and reduces H. pylori colonization while secreting reuterin to eliminate the bacteria. Additionally, TA's antioxidant properties help alleviate inflammation, and HA supports gastric mucosal repair. L. reuteri@HTP powder preserves the integrity of the gut microbiota, facilitating the restoration of a healthy microbiome. In particular, the probiotic powder remains stable at room temperature for at least six months, providing a promising alternative to traditional antibiotics for H. pylori treatment. This strategy combines targeted eradication, mucosal healing, and microbiome restoration, offering a new approach to treating gastric infections.

Antibiotic-free responsive biomaterials for specific and targeted Helicobacter pylori eradication

Gastric cancer is highly correlated with Helicobacter pylori (H. pylori) infection. Approximately 50 % of the population worldwide is infected with H. pylori. However, current treatment regimens face severe challenges including drug resistance and gut microbiota disruption. An integrative treatment with slight negative influences on intestinal flora, conforming with concepts of integrative prevention of gastric cancer, is urgently needed. Non-antibiotic responsive biomaterials can respond to different stimuli, including pH, enzymes, light, ultrasound and magnetism, under which biomaterials are specifically activated to perform antibacterial capabilities, while neutral intestinal microenvironments differ from gastric microenvironments or inflammatory sites and have no or minimal irradiation via precisely controlled exogenous stimuli, which may not only overcome antibiotic resistance but also avoid gut microbiota disorders. First, the latest progress in responsive biomaterials against H. pylori without gut microbiome disturbance and their anti-H. pylori performances are profoundly summarized. Second, the mechanisms against planktonic bacteria, biofilms and intracellular bacteria are discussed respectively. Finally, the strategies of specific and targeted H. pylori elimination by responsive biomaterials are introduced. Additionally, the challenges and the focus of future research on translation into clinical application are fully proposed. Antibiotic-free responsive biomaterials for specific and targeted H. pylori eradication represent an innovative approach.

Racial/ethnic differences in risk factors for non-cardia gastric cancer: an analysis of the Multiethnic Cohort (MEC) Study

PURPOSE

Gastric cancer (GC) incidence rates show notable differences by racial/ethnic groups in the US. We sought to determine whether stratification by race/ethnicity would reveal unique risk factors for development of non-cardia gastric cancer (NCGC) for US population.

METHODS

Analysis included 1,112 incident cases of NCGC and 190,883 controls from the Multiethnic Cohort Study, a prospective US cohort study that recruited individuals living in Hawaii and California, aged 45-75 years from 5 races/ethnicities. Descriptive analysis and Cox regression models examined the association of risk factors for GC and calculate hazard ratios for each race/ethnicity, adjusting for sociodemographic and dietary variables.

RESULTS

Increasing age and male sex were risk factors for NCGC for most race/ethnicities. Higher risk was associated with: GC family history for Latino and Japanese American individuals [HRs range from 1.75 to 1.98]; foreign-born for Japanese American individuals [HR: 1.52, 95% CI 1.11-2.09]; lower education for African American, Japanese American, and Native Hawaiian individuals [HRs range from 1.30 to 1.74]; daily alcohol consumption for African American individuals[HR: 1.56, 95% CI 1.04-2.35]; current smoking for Latino and Japanese American individuals [HRs range from 1.89 to 1.94]; sodium consumption in the highest quartile for White individuals [HR: 2.55, 95% CI 1.23-5.26] compared to the lowest quartile; fruit consumption in the 2nd, 3rd, and 4th highest quartile for Native Hawaiian individuals [HRs range from 2.19 to 2.60] compared to the lowest quartile; diabetes for African American individuals [HR: 1.79, 95% CI 1.21-2.64]; and gastric/duodenal ulcers for Native Hawaiian individuals [HR: 1.82, 95% CI 1.04-3.18].

CONCLUSION

Analyses by racial/ethnic group revealed differing risk factors for NCGC. Increased knowledge of the varying pathways to GC can support personalized GC prevention strategies and risk stratification tools for early detection.

Mechanism of Yinxu Weitong Capsule in the treatment of precancerous lesions of gastric cancer based on network pharmacology and experimental validation

ETHNOPHARMACOLOGICAL RELEVANCE

Yinxu Weitong Capsule (YXWTC) is a Chinese patent medicine used to treat chronic gastritis. However, its efficacy and mechanisms of action in treating precancerous lesions of gastric cancer (PLGC) remain unclear.

AIM OF THE STUDY

To evaluate the effects of YXWTC on PLGC and explore the underlying mechanisms.

MATERIALS AND METHODS

YXWTC components were identified using ultra-high-performance liquid chromatography coupled with electrospray ionization quadrupole-exactive orbitrap mass spectrometry. A PLGC animal model was established and the protective effects of YXWTC on the gastric mucosa in PLGC rats were evaluated using hematoxylin and eosin (H&E), Alcian blue-periodic acid-Schiff and Alcian blue-high iron diamine staining, and transmission electron microscopy (TEM). The vital organs of the rats were examined using H&E staining to evaluate biosafety. Network pharmacology identified potential targets and pathways of YXWTC in PLGC treatment, followed by molecular docking validation. Various techniques, including enzyme-linked immunosorbent assay, real-time quantitative reverse transcription PCR, Western blotting, immunohistochemistry, apoptosis detection, and reactive oxygen species fluorescence staining were employed to elucidate the underlying mechanisms.

RESULTS

In total, 340 YXWTC components were identified. YXWTC effectively improves gastric mucosal pathology in rats with PLGC. Network pharmacology identified 403 targets common to PLGC and YXWTC. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses identified 2,323 biological processes and 206 signaling pathways, respectively. Molecular docking revealed that the primary target proteins and major drug molecules exhibited strong binding affinities. Animal studies demonstrated that YXWTC inhibited the IL-6/STAT3 pathway, promoted mitochondrial apoptosis, and induced ROS release.

CONCLUSIONS

We verified the pharmacodynamic effects of YXWTC in PLGC. In summary, the effects are mediated by inhibition of the IL-6/STAT3 pathway, promotion of mitochondrial apoptosis, and induction of ROS release.

Predictors of the Development of Gastric Cancer in Post-Helicobacter pylori-Eradication Patients Followed Up for More than 10 Years: A Histological, Serological, and Endoscopic Study

BACKGROUND/OBJECTIVES

Although Helicobacter pylori (H. pylori) eradication therapy is important for preventing gastric cancer (GC), the occurrence of GC after H. pylori eradication remains a problem. In this study, the aim was to identify risk factors for GC after H. pylori eradication by comparing long-term histological, endoscopic, and serological evaluations of patients with and without GC.

METHODS

Patients who underwent H. pylori eradication therapy at Oita University Hospital between June 1997 and August 2013 and were followed for at least 3 years with long-term endoscopy, histology, and serum biochemical tests were included, and the GC (215 cases) and non-GC (11 cases) groups were compared.

RESULTS

The GC group was older than the non-GC group at the time of eradication, had lower serum pepsinogen I/II levels, had severe endoscopic atrophic changes, had higher activity at the antrum, and inflammation and intestinal metaplasia (IM) at the corpus on updated Sydney system scoring. On long-term follow-up after eradication, the GC group had a wider range of endoscopic mucosal atrophy and a lower serum pepsinogen I/II ratio at any time point.

CONCLUSIONS

Endoscopic mucosal atrophy and the serum pepsinogen I/II ratio are useful predictors of GC in patients post H. pylori eradication at any time point.

Natural products based on Correa's cascade for the treatment of gastric cancer trilogy: Current status and future perspective

Gastric carcinoma (GC) is a malignancy with multifactorial involvement, multicellular regulation, and multistage evolution. The classic Correa's cascade of intestinal GC specifies a trilogy of malignant transformation of the gastric mucosa, in which normal gastric mucosa gradually progresses from inactive or chronic active gastritis (Phase I) to gastric precancerous lesions (Phase II) and finally to GC (Phase III). Correa's cascade highlights the evolutionary pattern of GC and the importance of early intervention to prevent malignant transformation of the gastric mucosa. Intervening in early gastric mucosal lesions, i.e., Phase I and II, will be the key strategy to prevent and treat GC. Natural products (NPs) have been an important source for drug development due to abundant sources, tremendous safety, and multiple pharmacodynamic mechanisms. This review is the first to investigate and summarize the multi-step effects and regulatory mechanisms of NPs on the Correa's cascade in gastric carcinogenesis. In phase I, NPs modulate Helicobacter pylori urease activity, motility, adhesion, virulence factors, and drug resistance, thereby inhibiting H. pylori-induced gastric mucosal inflammation and oxidative stress, and facilitating ulcer healing. In Phase II, NPs modulate multiple pathways and mediators regulating gastric mucosal cell cycle, apoptosis, autophagy, and angiogenesis to reverse gastric precancerous lesions. In Phase III, NPs suppress cell proliferation, migration, invasion, angiogenesis, and cancer stem cells, induce apoptosis and autophagy, and enhance chemotherapeutic drug sensitivity for the treatment of GC. In contrast to existing work, we hope to uncover NPs with sequential therapeutic effects on multiple phases of GC development, providing new ideas for gastric cancer prevention, treatment, and drug development.

Hp eradication decreased the expression level of PG II in patients of Hp negative with gastric intestinal metaplasia: a retrospective cross-sectional study

AIMS

This study aims to assess the serum levels of pepsinogen (PG)I, PG II, and gastrin (G17) in patients with gastric intestinal metaplasia (GIM) and evaluate their correlation with demographic characteristics.

METHODS

A total of 247 normal controls (NC) and 240 patients diagnosed with GIM were enrolled in this study. All participants underwent a gastroscopy procedure followed by pathological examination for diagnosis confirmation. The expression level of PGI, PG II, and G 17 was detected by fluorescence immunochromatography and Hp infection was detected by 13-carbon breath test. The demographic characteristics of the subjects were obtained through questionnaires.

RESULTS

Compared to the NC group, the GIM group showed a reduction in PG II expression level [10.71(6.40,16.89) VS 9.21(6.14,14.55), p = 0.010]. GIM patients had a higher prevalence of previous Hp eradication history (14.98% VS 23.75%, p = 0.014). The low PG II group exhibited a higher incidence rate of GIM compared to the high PG II group (54.10% VS 44.44%, p = 0 0.020). In the Hp-negative(Hp-) group, there was a decrease in both PGI and PG II expression levels when compared to the Hp-positive(Hp+) group [146.73 ± 78.53 VS 125.61 ± 68.75 and 10.19(7.27, 16.58) VS 7.36(5.62,12.53), p = 0.036 and p < 0.001]. Among patients without Hp eradication history, those with low PG II levels had a higher proportion of individuals with a history of Hp eradication than those with high PG II levels (29.31% VS 3.13%, p = 0.003). Additionally, within the subgroup that underwent Hp eradication, there was a decrease in PG II expression level compared to the subgroup without Hp eradication (6.16(5.13, 7.52) VS 8.73(5.67, 13.35), p = 0.041).

CONCLUSION

The prevalence of GIM was significantly associated with low levels of PG II. There was a significant association between HP eradication history and the prevalence of GIM. Hp eradication history resulted in reduced expression levels of PG II in Hp- GIM patients.

Identification and validation of serum MUC17 as a non-invasive early warning biomarker for screening of gastric intraepithelial neoplasia

BACKGROUND

The early diagnosis and treatment of Gastric Intraepithelial Neoplasia (GIN) are pivotal for improving the survival rates of patients with gastric cancer (GC). Regrettably, reliable noninvasive biomarkers for GIN screening are currently lacking.

METHODS

mRNA data from the GEO database, pan-cancer data from the TCGA database, and a gene list of exocrine proteins were subjected to integrated analysis to identify a noninvasive biomarker for GIN. The scRNA-seq data analysis, IHC and Elisa were employed to validate the expression of the biomarker in the serum and tissues of clinical patients across different pathological stages.

RESULTS

MUC17 has been identified as a non-invasive diagnostic marker for GIN. It is upregulated in GIN prior to the onset of gastric carcinogenesis and downregulated in other tumors, with high GC specificity. The area under the curve values of serum MUC17 for differentiating chronic gastritis (CG) from low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN), and early gastric cancer (EGC) were 0.8788, 0.8544, and 0.9513, respectively. Additionally, low plasma MUC17 levels were found to be significantly lower in gastric ulcer (GU), gastric neuroendocrine tumor (GNET), and gastrointestinal stromal tumor (GIST) compared to GIN. The AUC for differentiating between GIN and GU, GNET, or GIST was 0.7803, 0.9244 and 0.9796, respectively.

CONCLUSIONS

These findings suggest that plasma MUC17 levels hold substantial promise as a screening biomarker for individuals with GIN and EGC, effectively identifying high-risk groups that necessitate further gastroscopy.

Development of a urine-based metabolomics approach for multi-cancer screening and tumor origin prediction

Background

Cancer remains a leading cause of mortality worldwide. A non-invasive screening solution was required for early diagnosis of cancer. Multi-cancer early detection (MCED) tests have been considered to address the challenge by simultaneously identifying multiple types of cancer within a single test using minimally invasive blood samples. However, a multi-cancer screening strategy utilizing urine-based metabolomics has not yet been developed.

Methods

We enrolled 911 cancer patients with 548 lung cancer (LC), 177 with gastric cancer (GC), and 186 with colorectal cancer (CRC), alongside 563 individuals with non-cancerous benign diseases and 229 healthy controls (HC) and investigated the metabolic profiles of urine samples. Participants were randomly allocated to discovery and validation cohorts. The discovery cohort was used for identifying multi-cancer and tissue-specific signatures to build the cancer screening and tumor origin prediction models, while the validation cohort was employed for assessing the performance of these models.

Results

We identified and annotated a total of 360 metabolites from the urine samples. Using the LASSO regression algorithm, 18 metabolites were characterized as urinary metabolic biomarkers and exhibited excellent discriminative performance between cancer patients and HC with AUC of 0.96 in the validation cohort. In comparison with the performance of traditional tumor markers CEA, the screening model performed higher sensitivity across the cancer stages, with a particularly increase in sensitivity among early-stage cancer patients. Moreover, the screening model also exhibited in high classification of cancers from non-cancerous group, comprising with HC and benign disease participants. Furthermore, two non-overlapping metabolic panels were selected to differentiate LC from Non-LC and GC from CRC with the AUC values of 0.87 and 0.83 in validation cohorts, respectively. Additionally, the model accurately predicted the origin of three lethal cancers: lung, gastric, and colorectal, with an overall accuracy of 0.75. The AUC values for LC, GC, and CRC were 0.88, 0.88, and 0.80, respectively.

Discussion

Our study demonstrates the potential of urine-based metabolomics for multi-cancer early detection. The approach offers non-invasive cancer screening, promising widespread implementation in population-based programs for early detection and improved outcomes. Further validation and expansion are needed for broader clinical applicability.

Elucidating epigenetic landscape of gastric premalignant lesions through genome-wide mapping of 5-hydroxymethylcytosines: A 12-year median follow-up study

BACKGROUND

Epigenetic modifications are crucial in tumourigenesis, yet the changes in novel epigenetic regulators like 5-hydroxymethylcytosines (5hmC) during the evolution of gastric premalignant lesions remain poorly understood. This study aims to investigate the implications of 5hmC in the progression from gastric premalignant lesions to gastric adenocarcinoma (GAC).

METHODS

To our knowledge, we conducted the largest and longest longitudinal study of a Chinese population with gastric precursor lesions, involving 29,176 patients with gastritis who underwent gastroscopy and biopsy between 2001 and 2015, with follow-up until 1 August, 2022. The median follow-up time was 12.2 years, and the overall GAC incidence rate was 0.82%. Genome-wide mapping of 5hmC in gastric premalignant lesions from a subset of individuals was performed using the 5hmC-Seal assay, including 21 samples that progressed to GAC during follow-up and 48 non-progressed age- and sex-matched controls.

RESULTS

We identified 213 differentially modified gene bodies, primarily concentrated in pathways related to cell division, cell cycle, energy metabolism, inflammation and tumourigenesis. An exploratory study was conducted to summarize a 5hmC-based epigenetic model for predicting cancer progression using multivariable logistic regression and machine learning. The nine-gene model showed an area under the curve of 87.5% (95% confidence interval: 72%-100%) in the validation samples (one of three), which were set aside before model training.

CONCLUSIONS

This study is the first to explore the 5hmC molecular landscape in gastric premalignant lesions, suggesting relevant pathways implicated in their evolution to GAC as well as the feasibility of exploiting genome-wide 5hmC mapping in assessing the risk of future cancer progression.

KEY POINTS

A largest longitudinal follow-up study of gastric precursor lesions in Chinese patients. Revealing novel 5hmC molecular landscape linked to gastric premalignant lesions. The feasibility of an innovative 5hmC-based predictive model for assessing gastric cancer progression risk.

Establishment and validation of a nomogram model for early diagnosis of gastric cancer: a large-scale cohort study

Purpose

Identifying high-risk populations and diagnosing gastric cancer (GC) early remains challenging. This study aimed to establish and verify a nomogram model for the early diagnosis of GC based on conventional laboratory indicators.

Methods

We performed a retrospective analysis of the clinical data of 2,770 individuals with first diagnosis of GC and 1,513 patients with benign gastric disease from January 2018 to December 2022. The cases were divided into the training set and validation set randomly, with a ratio of 7:3. Variable screening was performed by least absolute shrinkage and selection operator (LASSO) and logistic regression analysis. A nomogram was constructed in the training set to assist in the early diagnosis of GC.

Results

There were 4283 patients included in the study, with 2998 patients assigned in the training set and 1285 patients in the validation set. Through LASSO regression and logistic regression analysis, independent variables associated with GC were identified, including CEA, CA199, LYM, HGB, MCH, MCHC, PLT, ALB, TG, HDL, and AFR. The nomogram model was constructed using the above 11 independent indicators. The AUC was 0.803 for the training set and 0.797 for the validation set, indicating that the model showed high clinical diagnostic efficacy. The calibration curves and decision curve analysis (DCA) of the nomogram presented good calibration and clinical application ability.

Conclusion

Based on the analysis of large sample size, we constructed a nomogram model with 11 routine laboratory indicators, which showed good discrimination ability and calibration.

Nomogram prediction model for gastric cancer risk in chronic atrophic gastritis: Role of blood cell ratios

BACKGROUND

Chronic atrophic gastritis (CAG) is a precancerous condition for gastric cancer. Although endoscopy is the standard method for monitoring CAG, its invasive nature and high cost limit its use.

AIM

To identify risk factors for gastric cancer in the CAG population, focusing on blood cell ratios, and develop a personalized prediction model using a nomogram.

METHODS

A retrospective analysis was conducted on 314 GAG patients admitted to Hangzhou Normal University Affiliated Hospital from January 2018 to January 2024. Data collected included demographic, serological, and blood cell parameters. Independent risk factors were identified using multivariate logistic regression and a nomogram model was constructed with R. Model performance was assessed using the area under the ROC curve (AUC), the Hosmer-Lemeshow test, and decision curve analysis (DCA).

RESULTS

Significant predictive factors for gastric cancer in the CAG population included male gender (odds ratio [OR] = 2.214, P < 0.05), Helicobacter pylori (H. pylori) infection (OR = 2.686, P < 0.05), gastrin 17 (G-17) (OR = 1.037, P < 0.05), hemoglobin-to-red blood cell distribution width ratio (HRR) (OR = 0.648, P < 0.05), and lymphocyte-to-monocyte ratio (LMR) (OR = 0.645, P < 0.05). The prediction model, with an AUC of 0.854, demonstrated good fit (Hosmer-Lemeshow test: χ² = 6.062, P = 0.640). DCA indicated the potential generalizability of the model.

CONCLUSION

The nomogram provides a noninvasive, convenient, and cost-effective tool for screening gastric cancer in CAG patients, showing excellent discrimination and calibration. Further large-scale, multicenter studies are necessary to validate its efficacy across diverse populations.

Catechin promotes endoplasmic reticulum stress-mediated gastric cancer cell apoptosis via NOX4-induced reactive oxygen species

Catechin, a polyphenolic compound in various foods and beverages, shows strong anti-cancer effects against gastric cancer (GC) cells. This study explored the effect of catechin on GC cell apoptosis and endoplasmic reticulum (ER) stress. GC cells were treated with different catechin concentrations to assess effects on cell viability, LDH release, invasion, migration, apoptosis, intracellular calcium (Ca2⁺), ER stress markers, and reactive oxygen species (ROS). siRNA knockdown targeted GRP78, PERK, CHOP, and NOX4 to examine their roles in catechin-induced ER stress and apoptosis. Catechin treatment significantly reduced GC cell viability, increased LDH release, and induced apoptosis dose-dependently. Catechins elevated intracellular Ca2⁺ and ER stress markers. Co-treatment with thapsigargin (TG) intensified these effects, implicating ER stress in apoptosis. Knocking down GRP78, PERK, and CHOP mitigated catechin-induced apoptosis and restored viability. Additionally, catechins raised ROS levels, while co-treatment with Diphenyleneiodonium (DPI) or N-acetylcysteine (NAC) lowered ROS, cell damage, and ER stress markers. NOX4 knockdown countered catechin-induced viability loss and upregulated CHOP and cleaved caspase-3. Catechin induces apoptosis in GC cells through ER stress and ROS generation. Key mediators include GRP78, PERK, CHOP, and NOX4, suggesting potential therapeutic targets for enhancing catechin efficacy in GC treatment.

Development and validation of a nomogram for predicting the risk of developing gastric cancer based on a questionnaire: a cross-sectional study

Background

Detection of gastric cancer (GC) at early stages is an effective strategy for decreasing mortality. This study aimed to construct a prediction nomogram based on a questionnaire to assess the risk of developing GC.

Methods

Our study comprised a total of 4379 participants (2326 participants from outpatient at Fengqing People's Hospital were considered for model development and internal validation, and 2053 participants from outpatients at the endoscopy center at the Seventh Affiliated Hospital of Sun Yat-Sen University were considered for independent external validation) and gastric mucosa status was determined by endoscopy and biopsies. The eligible participants in development cohort from Fengqing people's Hospital were randomly separated into a training cohort (n=1629, 70.0%) and an internal validation cohort (n=697, 30.0%). The relevant features were selected by a least absolute shrinkage and selection operator (LASSO), and the ensuing features were evaluated through multivariable logistic regression analysis. Subsequently, the variables were selected to construct a prediction nomogram. The discriminative ability and predictive accuracy of the nomogram were evaluated by the C-index and calibration plot, respectively. Decision curve analysis (DCA) curves were used for the assessment of clinical benefit of the model. This model was developed to estimate the risk of developing neoplastic lesions according to the "transparent reporting of a multivariable prediction model for individual prognosis or diagnosis" (TRIPOD) statement.

Results

Six variables, including age, sex, alcohol consumption, cigarette smoking, education level, and Hp infection status, were independent risk factors for the development of neoplastic lesions. Thus, these variables were incorporated into the final nomogram. The AUC of the nomogram were 0.701, 0.657 and 0.699 in the training, internal validation, and external validation cohorts, respectively. The calibration curve showed that the nomogram was in good agreement with the observed outcomes. Compared to treatment of all patients or none, our nomogram showed a notably higher clinical benefit.

Conclusion

This nomogram proved to be a convenient, cost-effective tool to effectively predict an individual's risk of developing neoplastic lesions, and it can act as a prescreening tool before gastroscopy.

Development and validation of a relatively accurate gastric cancer high-risk group screening scoring system in urban residents

PURPOSE

Our study aimed to develop a relatively accurate gastric cancer (GC) screening score system for urban residents and to validate the screening efficacy.

METHODS

The present study included a derivation cohort (n = 3406) and a validation cohort (n = 868) of urban residents. Applying the full-stack engineering intelligent system platform of Hualian Health Big Data of Shandong University, the clinical physical examination data of subjects were collected. Univariate and multivariate analyses were used to identify risk factors for GC, and subsequently, an optimal prediction rule was established to create three distinct scoring systems.

RESULTS

In the GC-risk scoring system I, age, plateletocrit (PCT), carcinoembryonic antigen (CEA), glucose, albumin, creatinine were independent risk factors of GC, with scores ranging from 0 to 28 and optimal cut-off was 15.5. The second scoring system consisted of age, PCT, RDW-CV, CEA, glucose, albumin, and creatinine, with scores ranging from 0 to 31. The optimal cut-off point was determined to be 15.5. The scoring system III comprise of age, sex, PCT, RDW CV, CEA, glucose, with scores ranging from 0 to 21 and optimal cut-off was 10.5. All three scoring systems demonstrated excellent discrimination for GC, achieving an AUC of 0.884, 0.89, and 0.876, respectively. In external validation, the AUC values were 0.654, 0.658, and 0.714. Notably, the GC-risk scoring system III exhibited the highest screening efficiency.

CONCLUSIONS

Urban residents benefited from the effective and verified GC-risk scoring systems, which demonstrated excellent performance in identifying individuals with an elevated risk of GC.

Comparing Participation and Interim Effectiveness of Endoscopy and Biomarker-Based Screening for Gastric Cancer: A Cluster Randomized Controlled Trial

Background: To improve compliance with endoscopic screening for gastric cancer (GC), we assessed five biomarkers-pepsinogen I (PG I), pepsinogen II (PG II), PG I/II ratio, helicobacter pylori antibody (HP-Ab), and gastrin 17 (G17) - for secondary GC screening by comparing participation and effectiveness of traditional endoscopy and biomarker-based screening in a randomized trial with baseline results. Methods: Seventy-four communities were randomly assigned to traditional endoscopy arm (TEA) or biomarker-based endoscopy arm (BEA). TEA uses a questionnaire for risk assessment, and BEA combines a questionnaire with biomarker detection. High-risk individuals in both arms underwent endoscopic screening. Participation and interim screening effectiveness in two arms were reported with baseline analysis. Results: In total, 5,798 participants in TEA and 5,158 in BEA were recruited, with a participation rate of 26.9%. BEA showed a significantly lower high-risk rate than TEA (15.2% vs. 38.9%) and a higher endoscopic participation rate for high-risk individuals (64.9% vs. 53.0%). The endoscopic screening results showed that there was no significant difference in detection rate of GC abnormalities between the two arms. Education level, frequent drinking, hot, rough and hard food consumption, family history of GC, and history of reflux esophagitis or gastropathy influenced participation rates in biomarker-based screening. Age group, sex and regular consumption of meat, eggs and milk products were associated with stomach abnormalities.Cumulative incidence and specific death rates did not significantly differ in intention-to-screen and per-protocol analyses. Conclusions: Biomarker-based screening effectively identifies high-risk individuals and increases endoscopic participation, providing value insights for improving screening efficiency as a secondary procedure.

Development and external validation of a quantitative diagnostic model for malignant gastric lesions in clinical opportunistic screening: A multicenter real-world study

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.

Application of Machine Learning in the Diagnosis of Early Gastric Cancer Using the Kyoto Classification Score and Clinical Features Collected from Medical Consultations

The early detection accuracy of early gastric cancer (EGC) determines the choice of the optimal treatment strategy and the related medical expenses. We aimed to develop a simple, affordable, and time-saving diagnostic model using six machine learning (ML) algorithms for the diagnosis of EGC. It is based on the endoscopy-based Kyoto classification score obtained after the completion of endoscopy and other clinical features obtained after medical consultation. We retrospectively evaluated 1999 patients who underwent gastrointestinal endoscopy at the China Beijing Hospital. Of these, 203 subjects were diagnosed with EGC. The data were randomly divided into training and test sets (ratio 4:1). We constructed six ML models, and the developed models were evaluated on the testing set. This procedure was repeated five times. The Kolmogorov-Arnold Networks (KANs) model achieved the best performance (mean AUC value: 0.76; mean balanced accuracy: 70.96%; mean precision: 58.91%; mean recall: 70.96%; mean false positive rate: 26.11%; mean false negative rate: 31.96%; and mean F1 score value: 58.46). The endoscopy-based Kyoto classification score was the most important feature with the highest feature importance score. The results suggest that the KAN model, the optimal ML model in this study, has the potential to identify EGC patients, which may result in a reduction in both the time cost and medical expenses in clinical practice.

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.

Diagnostic value of combined detection of three gastric functions and Helicobacter pylori typing in chronic gastritis and gastric cancer

This research attempted to clarify the clinical diagnostic value of combined detection of gastric function and Helicobacter pylori (Hp) serotyping in chronic gastritis and gastric cancer (GC). The 80 chronic non atrophic gastritis (CNAG) patients treated in our hospital from October 2021 to October 2022 received selection as the CNAG group. The 96 chronic atrophic gastritis (CAG) patients diagnosed by gastroscopy and pathology in the same period received selection as CAG group. During the same period, 50 patients diagnosed with GC received inclusion in GC group. Pepsin I (PG I), PG II (PG II), gastrin-17 (G-17) and Hp serotyping received detection and comparison in three groups. The diagnostic efficacy of PG Ⅰ, PG Ⅱ, G-17, the ratio of serum PG I to PG II (PGR), and Hp serotyping in chronic gastritis and GC received evaluation by receiver operating characteristic (ROC). Relative to in the CNAG group, PG I and PGR levels in the other two groups exhibited depletion (P < 0.05); no statistical significance was observed in the PG II level among the three groups (P > 0.05); relative to the CNAG group, the G-17 level in the other two groups exhibited elevation (P < 0.05). Total Hp positive rate was 61.06 %, among which GC group exhibited the highest positive rate (72.00 %), and type I Hp positive rate also exhibited the highest in GC group (60.00 %). The type II Hp positive rate exhibited the highest in CNAG group (15.00 %). The PG I and PGR levels in type I Hp positive patients exhibited depletion relative to those in type II Hp positive patients, whereas PG II and G-17 levels exhibited elevation. When testing each indicator alone, the area under the curve (AUC) of PG I exhibited the highest in CNAG group, which was 0.874. When testing each indicator alone, AUC of Hp typing exhibited the highest in CAG group, which was 0.515. When testing each indicator alone, AUC of G-17 exhibited the highest in GC group, which was 0.787. The performance of combined detection was better than that of individual detection, with AUCs greater than 0.9 in three groups. In conclusion, changes in PG I, PG II, PGR and G-17 levels and Hp serotyping can receive application as screening indicators for chronic gastritis and GC, which can reflect relevant status of gastric mucosa to varying degrees. Combined detection of indicators has higher diagnostic performance and can receive application as an auxiliary diagnostic indicator in addition to gastroscopy biopsy, providing a reference basis for the formulation of clinical diagnosis and treatment plans.

Combined DNA Methylation and Gastric Microbiome Marker Predicts Helicobacter pylori-Negative Gastric Cancer

Background/Aims

While DNA methylation and gastric microbiome are each associated with gastric cancer (GC), their combined role in predicting GC remains unclear. This study investigated the potential of a combined DNA methylation and gastric microbiome signature to predict Helicobacter pylori-negative GC.

Methods

In this case-control study, we conducted quantitative methylation-specific polymerase chain reaction to measure the methylation levels of DKK3, SFRP1, EMX1, NKX6-1, MIR124-3, and TWIST1 in the gastric mucosa from 75 H. pylori-negative patients, including chronic gastritis (CG), intestinal metaplasia (IM), and GC. A combined analysis of DNA methylation and gastric microbiome, using 16S rRNA gene sequencing, was performed in 30 of 75 patients.

Results

The methylation levels of DKK3, SFRP1, EMX1, MIR124-3, and TWIST1 were significantly higher in patients with GC than in controls (all q<0.05). MIR124-3 and TWIST1 methylation levels were higher in patients with IM than those with CG and also in those with GC than in those with IM (all q<0.05). A higher methylation level of TWIST1 was an independent predictor for H. pylori-negative GC after adjusting for age, sex, and atrophy (odds ratio [OR], 15.15; 95% confidence interval [CI], 1.58 to 145.46; p=0.018). The combination of TWIST1 methylation and GC microbiome index (a microbiome marker) was significantly associated with H. pylori-negative GC after adjusting for age, sex, and atrophy (OR, 50.00; 95% CI, 1.69 to 1,476; p=0.024).

Conclusions

The combination of TWIST1 methylation and GC microbiome index may offer potential as a biomarker for predicting H. pylori-negative GC.

The role of gastrin 17 and pepsinogen I:pepsinogen II ratio in pathological diagnosis and endoscopic selection in gastritis patients

BACKGROUND

The noninvasive serum markers pepsinogen I (PGI), pepsinogen II (PGII), gastrin-17 (G17), and PGI:PGII ratio (PGR) have recently been proposed as a new tool for predicting various gastric pathologies.

METHODS

A total of 83 gastritis patients confirmed by gastroscopy were enrolled, with 78 undergoing concurrent colonoscopies. The control group included 99 healthy subjects. Enzyme-linked immunosorbent assay was used to detect PGI, PGII, G17, and PGR. The performance of serological analysis for detecting gastritis pathology was evaluated using receiver operating characteristic (ROC) curves.

RESULTS

The G17 and PGII levels increased significantly (P < .001), whereas PGR levels decreased (P = .001) in the gastritis group. The ROC analysis revealed that PGR had a sensitivity and specificity of 70.83% and 86.67%, respectively, in predicting Helicobacter pylori-infected gastritis and a sensitivity and specificity of 88% and 65.52%, respectively, in predicting active gastritis. The G17 levels were significantly elevated in gastritis patients undergoing concurrent colonoscopies (P < .05).

CONCLUSION

Pepsinogen I:pepsinogen II ratio was found to be a useful predictor of active gastritis and H pylori-infected gastritis. Furthermore, G17 was found to be closely related to pathological conditions found by colonoscopy and may provide recommendations for whether gastritis patients should undergo a concurrent colonoscopy.

An antibiotic-free platform for eliminating persistent Helicobacter pylori infection without disrupting gut microbiota

Helicobacter pylori (H. pylori) infection remains the leading cause of gastric adenocarcinoma, and its eradication primarily relies on the prolonged and intensive use of two antibiotics. However, antibiotic resistance has become a compelling health issue, leading to H. pylori eradication treatment failure worldwide. Additionally, the powerlessness of antibiotics against biofilms, as well as intracellular H. pylori and the long-term damage of antibiotics to the intestinal microbiota, have also created an urgent demand for antibiotic-free approaches. Herein, we describe an antibiotic-free, multifunctional copper-organic framework (HKUST-1) platform encased in a lipid layer comprising phosphatidic acid (PA), rhamnolipid (RHL), and cholesterol (CHOL), enveloped in chitosan (CS), and loaded in an ascorbyl palmitate (AP) hydrogel: AP@CS@Lip@HKUST-1. This platform targets inflammatory sites where H. pylori aggregates through electrostatic attraction. Then, hydrolysis by matrix metalloproteinases (MMPs) releases CS-encased nanoparticles, disrupting bacterial urease activity and membrane integrity. Additionally, RHL disperses biofilms, while PA promotes lysosomal acidification and activates host autophagy, enabling clearance of intracellular H. pylori. Furthermore, AP@CS@Lip@HKUST-1 alleviates inflammation and enhances mucosal repair through delayed Cu2+ release while preserving the intestinal microbiota. Collectively, this platform presents an advanced therapeutic strategy for eradicating persistent H. pylori infection without inducing drug resistance.

A Systematic Review of Cost-Effectiveness Studies on Gastric Cancer Screening

Gastric cancer (GC) poses notable economic and health burdens in settings where the incidence of disease is prevalent. Some countries have established early screening and treatment programs to address these challenges. The objectives of this systematic review were to summarize the cost-effectiveness of gastric cancer screening presented in the literature and to identify the critical factors that influence the cost-effectiveness of screening. This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Economic evaluation studies of gastric cancer screening were reviewed from SCOPUS and PubMed. The Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) was used to assess the quality of reporting presented in the selected articles. Only primary economic evaluation studies addressing the cost-effectiveness, cost-utility, and cost-benefit of gastric cancer screening were selected. Two reviewers scrutinized the selected articles (title, abstract, and full text) to determine suitability for the systematic review based on inclusion and exclusion criteria. Authors' consensus was relied on where disagreements arose. The main outcome measures of concern in the systematic review were cost, effectiveness (as measured by either quality-adjusted life years (QALY) or life-years saved (LYS)), and incremental cost-effectiveness ratio (ICER) of screening versus either no screening or an alternative screening method. Thirty-one studies were selected for the final review. These studies investigated the cost-effectiveness of GC screening based on either primary, secondary, or a combination of primary and secondary interventions. The main primary intervention was Helicobacter pylori (Hp) screening with eradication, while the main secondary intervention was endoscopic screening. Cost-effectiveness was evaluated against no screening or screening using an alternative method in both observational and model-based studies. Screening was mainly cost-effective in Asian countries or their diasporas where the prevalence of GC was high. GC screening was generally not cost-effective among Western countries. GC screening can be cost-effective, but cost-effectiveness is dependent on context-specific factors, including geographical location, the prevalence of GC in the local population, and the screening tool adopted. However, there is benefit in targeting high-risk population groups in Asian countries and their diaspora for GC screening.

Enhancing gastric cancer early detection: A multi-verse optimized feature selection model with crossover-information feedback

Gastric cancer (GC), an acknowledged malignant neoplasm, threatens life and digestive system functionality if not detected and addressed promptly in its nascent stages. The indispensability of early detection for GC to augment treatment efficacy and survival prospects forms the crux of this investigation. Our study introduces an innovative wrapper-based feature selection methodology, referred to as bCIFMVO-FKNN-FS, which integrates a crossover-information feedback multi-verse optimizer (CIFMVO) with the fuzzy k-nearest neighbors (FKNN) classifier. The primary goal of this initiative is to develop an advanced screening model designed to accelerate the identification of patients with early-stage GC. Initially, the capability of CIFMVO is validated through its application to the IEEE CEC benchmark functions, during which its optimization efficiency is measured against eleven cutting-edge algorithms across various dimensionalities-10, 30, 50, and 100. Subsequent application of the bCIFMVO-FKNN-FS model to the clinical data of 1632 individuals from Wenzhou Central Hospital-diagnosed with either early-stage GC or chronic gastritis-demonstrates the model's formidable predictive accuracy (83.395%) and sensitivity (87.538%). Concurrently, this investigation delineates age, gender, serum gastrin-17, serum pepsinogen I, and the serum pepsinogen I to serum pepsinogen II ratio as parameters significantly associated with early-stage GC. These insights not only validate the efficacy of our proposed model in the early screening of GC but also contribute substantively to the corpus of knowledge facilitating early diagnosis.

Multi-cancer risk stratification based on national health data: a retrospective modelling and validation study

BACKGROUND

Health care is experiencing a drive towards digitisation, and many countries are implementing national health data resources. Although a range of cancer risk models exists, the utility on a population level for risk stratification across cancer types has not been fully explored. We aimed to close this gap by evaluating pan-cancer risk models built on electronic health records across the Danish population with validation in the UK Biobank.

METHODS

In this retrospective modelling and validation study, data for model development and internal validation were derived from the following Danish health registries: the Central Person Registry, the Danish National Patient Registry, the death registry, the cancer registry, and full-text medical records from secondary care records in the capital region. The development data included adults aged 16-86 years without previous malignant cancers in the time period from Jan 1, 1995, to Dec 31, 2014. The internal validation period was from Jan 1, 2015, to April 10, 2018, and the data included all adults without a previous indication of cancer aged 16-75 years on Dec 31, 2014. The external validation cohort from the UK Biobank included all adults without a previous indication of cancer aged 50-75 years. We used time-dependent Bayesian Cox hazard models built on the combined medical history of Danish individuals. A set of 1392 covariates from available clinical disease trajectories, text-mined basic health factors, and family histories were used to train predictive models of 20 major cancer types. The models were validated on cancer incidence between 2015 and 2018 across Denmark and on individuals in the UK Biobank. The primary outcomes were discrimination and calibration performance.

FINDINGS

From the Danish registries, we included 6 732 553 individuals covering 60 million hospital visits, 90 million diagnoses, and a total of 193 million life-years between Jan 1, 1978, and April 10, 2018. Danish registry data covering the period from Jan 1, 2015, to April 10, 2018, were used to internally validate risk models, containing a total of 4 248 491 individuals who remained at risk of a primary malignant cancer diagnosis and 67 401 cancer cases recorded. For the external validation, we evaluated the same time period in the UK Biobank covering 377 004 individuals with 11 486 cancer cases. The predictive performance of the models on Danish data showed good discrimination (concordance index 0·81 [SD 0·08], ranging from 0·66 [95% CI 0·65-0·67] for cervix uteri cancer to 0·91 [0·90-0·92] for liver cancer). Performance was similar on the UK Biobank in a direct transfer when controlling for shifts in the age distribution (concordance index 0·66 [SD 0·08], ranging from 0·55 [95% CI 0·44-0·66] for cervix uteri cancer to 0·78 [0·77-0·79] for lung cancer). Cancer risks were associated, in addition to heritable components, with a broad range of preceding diagnoses and health factors. The best overall performance was seen for cancers of the digestive system (oesophageal, stomach, colorectal, liver, and pancreatic) but also thyroid, kidney, and uterine cancers.

INTERPRETATION

Data available in national electronic health databases can be used to approximate cancer risk factors and enable risk predictions in most cancer types. Model predictions generalise between the Danish and UK health-care systems. With the emergence of multi-cancer early detection tests, electronic health record-based risk models could supplement screening efforts.

FUNDING

Novo Nordisk Foundation and the Danish Innovation Foundation.

Interpretable machine learning for predicting the response duration to Sintilimab plus chemotherapy in patients with advanced gastric or gastroesophageal junction cancer

Background

Sintilimab plus chemotherapy has proven effective as a combination immunotherapy for patients with advanced gastric and gastroesophageal junction adenocarcinoma (GC/GEJC). A multi-center study conducted in China revealed a median progression-free survival (PFS) of 7.1 months. However, the prediction of response duration to this immunotherapy has not been thoroughly investigated. Additionally, the potential of baseline laboratory features in predicting PFS remains largely unexplored. Therefore, we developed an interpretable machine learning (ML) framework, iPFS-SC, aimed at predicting PFS using baseline (pre-treatment) laboratory features and providing interpretations of the predictions.

Materials and methods

A cohort of 146 patients with advanced GC/GEJC, along with their baseline laboratory features, was included in the iPFS-SC framework. Through a forward feature selection process, predictive baseline features were identified, and four ML algorithms were developed to categorize PFS duration based on a threshold of 7.1 months. Furthermore, we employed explainable artificial intelligence (XAI) methodologies to elucidate the relationship between features and model predictions.

Results

The findings demonstrated that LightGBM achieved an accuracy of 0.70 in predicting PFS for advanced GC/GEJC patients. Furthermore, an F1-score of 0.77 was attained for identifying patients with PFS durations shorter than 7.1 months. Through the feature selection process, we identified 11 predictive features. Additionally, our framework facilitated the discovery of relationships between laboratory features and PFS.

Conclusion

A ML-based framework was developed to predict Sintilimab plus chemotherapy response duration with high accuracy. The suggested predictive features are easily accessible through routine laboratory tests. Furthermore, XAI techniques offer comprehensive explanations, both at the global and individual level, regarding PFS predictions. This framework enables patients to better understand their treatment plans, while clinicians can customize therapeutic approaches based on the explanations provided by the model.

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.

Pepsinogen C Interacts with IQGAP1 to Inhibit the Metastasis of Gastric Cancer Cells by Suppressing Rho-GTPase Pathway

AIM

This study systematically explored the biological effects and mechanisms of PGC on gastric cancer (GC) cells in vitro and in vivo.

METHOD

The critical biological roles of PGC in GC were assessed via EdU staining, Hoechst staining, flow cytometry, mouse models, CCK-8, wound healing, transwell, and sphere-forming assays. The interaction study with IQ-domain GTPase-activating protein 1 (IQGAP1) was used by Liquid chromatography-mass spectrometry co-immunoprecipitation, immunofluorescence staining, CHX-chase assay, MG132 assay, and qRT-PCR.

RESULTS

PGC inhibited the proliferation, viability, epithelial-mesenchymal transition, migration, invasion, and stemness of GC cells and promoted GC cell differentiation. PGC suppressed subcutaneous tumor growth and peritoneal dissemination in vivo. The interaction study found PGC inhibits GC cell migration and invasion by downregulating IQGAP1 protein and IQGAP1-mediated Rho-GTPase signaling suppression. In addition, PGC disrupts the stability of the IQGAP1 protein, promoting its degradation and significantly shortening its half-life. Moreover, the expression levels of PGC and IQGAP1 in GC tissues were significantly negatively correlated.

CONCLUSION

PGC may act as a tumor suppressor in the development and metastasis of GC. PGC can downregulate its interacting protein IQGAP1 and inhibit the Rho-GTPase pathway, thereby participating in the inhibition of GC cell migration and invasion.

Yiwei Xiaoyu granules for treatment of chronic atrophic gastritis with deficiency syndrome of the spleen and stomach

BACKGROUND

The Correa sequence, initiated by Helicobacter pylori (H. pylori), commonly progresses to gastric cancer through the stage of chronic atrophic gastritis (CAG). Although eradication of H. pylori only reduces the risk of gastric cancer, it does not eliminate the risk for neoplastic progression. Yiwei Xiaoyu granules (YWXY) are a commonly used composite preparation in Chinese clinics. However, the pursuit of excellence in clinical trials and the establishment of standardized animal experiments are still needed to contribute to full understanding and application of traditional Chinese medicine in the treatment of CAG.

AIM

To demonstrate the effectiveness of YWXY in patients with CAG and spleen-stomach deficiency syndrome (DSSS), by alleviating histological scores, improving response rates for pathological lesions, and achieving clinical efficacy in relieving DSSS symptoms.

METHODS

We designed a double-blind, randomized, controlled trial. The study enrolled seventy-two H. pylori-negative patients (mean age, 52.3 years; 38 men) who were randomly allocated to either the treatment group or control group in a 1:1 ratio, and treated with 15 g YWXY or 0.36 g Weifuchun (WFC) tablet combined with the respective dummy for 24 wk. The pre-randomization phase resulted in the exclusion of 72 patients: 50 participants did not meet the inclusion criteria, 12 participants declined to participate, and 10 participants were excluded for various other reasons. Seven visits were conducted during the study, and histopathological examination with target endoscopic biopsy of narrow-band imaging was requested before the first and seventh visits. We also evaluated endoscopic performance scores, total symptom scores, serum pepsinogen and gastrin-17.

RESULTS

Six patients did not complete the trial procedures. Treatment with YWXY improved the Operative Link on Gastric Intestinal Metaplasia Assessment (OLGIM) stage, compared with WFC (P < 0.05). YWXY provided better relief from symptoms of DSSS and better improvement in serum gastric function, compared with WFC (P < 0.05).

CONCLUSION

YWXY compared with WFC significantly reduced the risk of mild or moderate atrophic disease, according to OLGIM stage, significantly relieved symptoms of DSSS, and improved serum gastric function.

A Neoteric Feature Extraction Technique to Predict the Survival of Gastric Cancer Patients

BACKGROUND

At the time of cancer diagnosis, it is crucial to accurately classify malignant gastric tumors and the possibility that patients will survive.

OBJECTIVE

This study aims to investigate the feasibility of identifying and applying a new feature extraction technique to predict the survival of gastric cancer patients.

METHODS

A retrospective dataset including the computed tomography (CT) images of 135 patients was assembled. Among them, 68 patients survived longer than three years. Several sets of radiomics features were extracted and were incorporated into a machine learning model, and their classification performance was characterized. To improve the classification performance, we further extracted another 27 texture and roughness parameters with 2484 superficial and spatial features to propose a new feature pool. This new feature set was added into the machine learning model and its performance was analyzed. To determine the best model for our experiment, Random Forest (RF) classifier, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) (four of the most popular machine learning models) were utilized. The models were trained and tested using the five-fold cross-validation method.

RESULTS

Using the area under ROC curve (AUC) as an evaluation index, the model that was generated using the new feature pool yields AUC = 0.98 ± 0.01, which was significantly higher than the models created using the traditional radiomics feature set (p < 0.04). RF classifier performed better than the other machine learning models.

CONCLUSIONS

This study demonstrated that although radiomics features produced good classification performance, creating new feature sets significantly improved the model performance.

Chinese national clinical practice guidelines on the prevention, diagnosis, and treatment of early gastric cancer

BACKGROUND

Gastric cancer is one of the most common malignant tumors in the digestive system in China. Few comprehensive practice guidelines for early gastric cancer in China are currently available. Therefore, we created the Chinese national clinical practice guideline for the prevention, diagnosis, and treatment of early gastric cancer.

METHODS

This clinical practice guideline (CPG) was developed in accordance with the World Health Organization's recommended process and with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) in assessing evidence quality. We used the Evidence to Decision framework to formulate clinical recommendations to minimize bias and increase transparency in the CPG development process. We used the Reporting Items for practice Guidelines in HealThcare (RIGHT) statement and the Appraisal of Guidelines for Research and Evaluation II (AGREE II) as reporting and conduct guidelines to ensure completeness and transparency of the CPG.

RESULTS

This CPG contains 40 recommendations regarding the prevention, screening, diagnosis, treatment, and follow-up of early gastric cancer based on available clinical studies and guidelines. We provide recommendations for the timing of Helicobacter pylori eradication, screening populations for early gastric cancer, indications for endoscopic resection and surgical gastrectomy, follow-up interval after treatment, and other recommendations.

CONCLUSIONS

This CPG can lead to optimum care for patients and populations by providing up-to-date medical information. We intend this CPG for widespread adoption to increase the standard of prevention, screening, diagnosis, treatment, and follow-up of early gastric cancer; thereby, contributing to improving national health care and patient quality of life.

Integrating trans-omics, cellular experiments and clinical validation to identify ILF2 as a diagnostic serum biomarker and therapeutic target in gastric cancer

BACKGROUND

Gastric cancer (GC) lacks serum biomarkers with clinical diagnostic value. Multi-omics analysis is an important approach to discovering cancer biomarkers. This study aimed to identify and validate serum biomarkers for GC diagnosis by cross-analysis of proteomics and transcriptomics datasets.

METHODS

A cross-omics analysis was performed to identify overlapping differentially expressed genes (DEGs) between our previous aptamer-based GC serum proteomics dataset and the GC tissue RNA-Seq dataset in The Cancer Genome Atlas (TCGA) database, followed by lasso regression and random forest analysis to select key overlapping DEGs as candidate biomarkers for GC. The mRNA levels and diagnostic performance of these candidate biomarkers were analyzed in the original and independent GC datasets to select valuable candidate biomarkers. The valuable candidate biomarkers were subjected to bioinformatics analysis to select those closely associated with the biological behaviors of GC as potential biomarkers. The clinical diagnostic value of the potential biomarkers was validated using serum samples, and their expression levels and functions in GC cells were validated using in vitro cell experiments.

RESULTS

Four candidate biomarkers (ILF2, PGM2L1, CHD7, and JCHAIN) were selected. Their mRNA levels differed significantly between tumor and normal tissues and showed different diagnostic performances for GC, with areas under the receiver operating characteristic curve (AUROCs) of 0.629-0.950 in the TCGA dataset and 0.736-0.840 in the Gene Expression Omnibus (GEO) dataset. In the bioinformatics analysis, only ILF2 (interleukin enhancer-binding factor 2) gene levels were associated with immune cell infiltration, some checkpoint gene expression, chemotherapy sensitivity, and immunotherapy response. Serum levels of ILF2 were higher in GC patients than in controls, with an AUROC of 0.944 for the diagnosis of GC, and it was also detected in the supernatants of GC cells. Knockdown of ILF2 by siRNA significantly reduced the proliferation and colony formation of GC cells. Overexpression of ILF2 significantly promotes the proliferation and colony formation of gastric cancer cells.

CONCLUSIONS

Trans-omics analysis of proteomics and transcriptomics is an efficient approach for discovering serum biomarkers, and ILF2 is a potential diagnostic biomarker and therapeutic target of gastric cancer.

Generation and characterization of nanobodies targeting human pepsinogens

Human pepsinogens (mainly pepsinogen I and pepsinogen II) are the major inactive precursor forms of the digestive enzyme pepsin which play a crucial role in protein digestion. The levels and ratios of human pepsinogens have demonstrated potential as diagnostic biomarkers for gastrointestinal diseases, particularly gastric cancer. Nanobodies are promising tools for the treatment and diagnosis of diseases, owing to their unique recognition properties. In this study, recombinant human pepsinogens proteins were expressed and purified as immunized antigens. We constructed a VHH phage library and identified several nanobodies via phage display bio-panning. We determined the binding potency and cross-reactivity of these nanobodies. Our study provides technical support for developing immunodiagnostic reagents targeting human pepsinogens.

Pepsinogen II and a no-pickled food diet are risk factors for female patients with anxiety: a cross-sectional study

OBJECTIVES

No studies have evaluated the relationship between lifestyle and Pepsinogen (PG)I, PGII and Gastrin (G)17 in patients with anxiety. Using data from the Affiliated Hospital of Xuzhou Medical University study, we aimed to identify factors associated with anxiety.

METHODS

We conducted a retrospective cross-sectional observational study involving 779 Chinese healthy checkup participants (301 males; mean age, 47.60±16.17 years) who underwent stomach-related health examinations.

RESULTS

Anxiety was defined as a Hamilton Anxiety Scale (HAM-A) Scale score ≥14. The odds ratios, with 95% confidence intervals, were calculated using binary logistic analysis to assess the risk of anxiety and healthy checkup participants while adjusting for several covariates. In the HAM-A≥14 group (anxiety group), sex, PGII and pickled dishes were independent influencing factors. Binary logistic regression analysis revealed a significant difference in anxiety risk between the high PGII group and the low PGII group for females (P=0.005). There was also a significant difference in anxiety risk between the groups consuming pickled and non-pickled food for females (P=0.010). Logistic regression analysis indicated a higher risk of anxiety in females aged ≤50 years who belonged to the high PGII + no pickled foods group.

CONCLUSIONS

Our study revealed that in females aged ≤50 years, high levels of PGII and no pickled foods were associated with a higher risk of anxiety.

Cell-free DNA methylation profiles enable early detection of colorectal and gastric cancer

Colorectal cancer (CRC) and gastric cancer (GC) rank the top five common and lethal cancers worldwide. Early detection can significantly reduce the mortality of CRC and GC. However, current clinical screening methods including invasive endoscopic techniques and noninvasive fecal occult blood test screening tests/fecal immunochemical test have shown low sensitivity or unsatisfactory patient's compliance. Aberrant DNA methylation occurs frequently in tumorigenesis and cell-free DNA (cfDNA) methylation has shown the potential in multi-cancer detection. Herein, we aimed to explore the value of cfDNA methylation in the gastrointestinal cancer detection and develop a noninvasive method for CRC and GC detection. We applied targeted methylation sequencing on a total of 407 plasma samples from patients diagnosed with CRC, GC, and noncancerous gastrointestinal benign diseases (Non-Ca). By analyzing the methylation profiles of 34 CRC, 62 GC and 107 Non-Ca plasma samples in the training set (n=203), we identified 40,110 gastrointestinal cancer-specific markers and 63 tissue of origin (TOO) prediction markers. A new integrated model composed of gastrointestinal cancer detection and TOO prediction for three types of classification of CRC, GC and Non-Ca patients was further developed through logistic regression algorithm and validated in an independent validation set (n=103). The model achieved overall sensitivities of 83% and 81.3% at specificities of 81.5% and 80% for identifying gastrointestinal cancers in the test set and validation set, respectively. The detection sensitivities for GC and CRC were respectively 81.4% and 83.3% in the cohort of the test and validation sets. Among these true positive cancer samples, further TOO prediction showed accuracies of 95.8% and 95.8% for GC patients and accuracies of 86.7% and 93.3% for CRC patients, in test set and validation set, respectively. Collectively, we have identified novel cfDNA methylation biomarkers for CRC and GC detection and shown the promising potential of cfDNA as a noninvasive gastrointestinal cancer detection tool.

Correlation of chronic atrophic gastritis with gastric-specific circulating biomarkers

BACKGROUND AND STUDY AIMS

It has been suggested that the combined detection of multiple serum biomarkers can effectively screen out the high-risk population of chronic atrophic gastritis in the general population. Therefore, it is necessary to establish an effective predictive model of chronic atrophic gastritis.

PATIENTS AND METHODS

Serum biopsies were assessed using five stomach-specific circulating biomarkers pepsinogen I (PGI), PGII, PGI/II ratio, anti- H. pylori antibody, and gastrin-17 (G-17) to identify high-risk individuals and evaluate the risk of developing chronic atrophic gastritis.

RESULTS

In the cross-sectional analysis, PGII, the PG ratio, G17, anti- H. pylori IgG were positively associated with the presence of chronic atrophic gastritis, and combined prediction of the five biomarkers was more accurate than single-factor prediction ((0.692 vs 0.54(PG1), 0.604 (PGⅡ), 0.616(PGI/II ratio), 0.629(G-17)).

CONCLUSION

The combination of PGI, PGII, the PGI/II ratio, G17, and anti-H. pylori antibodies for serological analysis are helpful to screen chronic atrophic gastritis high-risk subjects from the general population and recommend that these people carry out further endoscopy and biopsy.

The Most Recent Insights into the Roots of Gastric Cancer

Helicobacter pylori (H. pylori) is the most common bacterial infection worldwide, usually being acquired during childhood, and its persistence into adulthood represents one of the main contributors of gastric carcinogenesis. Based on these statements, it would be of great importance to know if the most early premalignant transformation occurs in children or later since, this would enable the development of effective anti-tumorigenesis strategies. The interplay between H. pylori virulence factors, the host's responses modified by this infection, and the gastric microecology are complex and eventually lead to the development of gastric cancer in susceptible individuals. Several biomarkers were identified as major contributors of this long-lasting process, such as pepsinogens, gastrin 17, lipid-, glucose- and iron-metabolism parameters, immunity players, aberrant bacterial DNA methylation, H. pylori virulence factors, and hallmarks of gastric dysbiosis. Several of these biomarkers were also identified in children with H. pylori infection, independently of the presence of premalignant lesions, which were also proven to be present in a subgroup of H. pylori-infected children, especially those carrying extremely virulent strains. Therefore, the most incipient premalignant gastric changes might indeed occur early during childhood, opening a promising research gate for further studies to delineate the border between infection and cancer.

Rubropunctatin-silver composite nanoliposomes for eradicating Helicobacter pylori in vitro and in vivo

Helicobacter pylori (H. pylori) is a major factor in peptic ulcer disease and gastric cancer, and its infection rate is rising globally. The efficacy of traditional antibiotic treatment is less effective, mainly due to bacterial biofilms and the formation of antibiotic resistance. In addition, H. pylori colonizes the gastrointestinal epithelium covered by mucus layers, the drug must penetrate the double barrier of mucus layer and biofilm to reach the infection site and kill H. pylori. The ethanol injection method was used to synthesize nanoliposomes (EPI/R-AgNPs@RHL/PC) with a mixed lipid layer containing rhamnolipids (RHL) and phosphatidylcholine (PC) as a carrier, loaded with the urease inhibitor epiberberine (EPI) and the antimicrobial agent rubropunctatin silver nanoparticles (R-AgNPs). EPI/R-AgNPs@RHL/PC had the appropriate size, negative charge, and acid sensitivity to penetrate mucin-rich mucus layers and achieve acid-responsive drug release. In vitro experiments demonstrated that EPI/R-AgNPs@RHL/PC exhibited good antibacterial activity, effectively inhibited urease activity, removed the mature H. pylori biofilm, and inhibited biofilm regeneration. In vivo antibacterial tests showed that EPI/R-AgNPs@RHL/PC exhibited excellent activity in eradicating H. pylori and protecting the mucosa compared to the traditional clinical triple therapy, providing a new idea for the treatment of H. pylori infection.

A feasibility study on utilizing machine learning technology to reduce the costs of gastric cancer screening in Taizhou, China

Aim

To optimize gastric cancer screening score and reduce screening costs using machine learning models.

Methods

This study included 228,634 patients from the Taizhou Gastric Cancer Screening Program. We used three machine learning models to optimize Li's gastric cancer screening score: Gradient Boosting Machine (GBM), Distributed Random Forest (DRF), and Deep Learning (DL). The performance of the binary classification models was evaluated using the area under the curve (AUC) and area under the precision-recall curve (AUCPR).

Results

In the binary classification model used to distinguish low-risk and moderate- to high-risk patients, the AUC in the GBM, DRF, and DL full models were 0.9994, 0.9982, and 0.9974, respectively, and the AUCPR was 0.9982, 0.9949, and 0.9918, respectively. Excluding Helicobacter pylori IgG antibody, pepsinogen I, and pepsinogen II, the AUC in the GBM, DRF, and DL models were 0.9932, 0.9879, and 0.9900, respectively, and the AUCPR was 0.9835, 0.9716, and 0.9752, respectively. Remodel after removing variables IgG, PGI, PGII, and G-17, the AUC in GBM, DRF, and DL was 0.8524, 0.8482, 0.8477, and AUCPR was 0.6068, 0.6008, and 0.5890, respectively. When constructing a tri-classification model, we discovered that none of the three machine learning models could effectively distinguish between patients at intermediate and high risk for gastric cancer (F1 scores in the GBM model for the low, medium and high risk: 0.9750, 0.9193, 0.5334, respectively; F1 scores in the DRF model for low, medium, and high risks: 0.9888, 0.9479, 0.6694, respectively; F1 scores in the DL model for low, medium, and high risks: 0.9812, 0.9216, 0.6394, respectively).

Conclusion

We concluded that gastric cancer screening indicators could be optimized when distinguishing low-risk and moderate to high-risk populations, and detecting gastrin-17 alone can achieve a good discriminative effect, thus saving huge expenditures.