Updates on global epidemiology, risk and prognostic factors of gastric cancer

Yang, Wenjuan; Zhao, Heping; Yu, Yan; Wang, Ji-Han; Guo, Lei; Liu, Jun-Ye; Pu, Jie; Lv, Jing

doi:10.3748/wjg.v29.i16.2452

Cited by 72 publications

(40 citation statements)

References 155 publications

(223 reference statements)

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“…GC is de ned as the primary epithelial malignancy derived from the stomach, and it is a complicated and heterogeneous disease with multiple risk factors [20] . GC is one of the most common cancers in the world, particularly in developing countries, and the mortality is the second leading cause of cancer-related deaths [21] .…”

Section: Discussionmentioning

confidence: 99%

CACNA1E, C4BPA is causally associated with gastric cancer: A Mendelian randomization study

Zhang,

Zhao,

Liu

et al. 2023

Preprint

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Background: Gastric cancer (GC) is influenced by several risk factors such as smoking and high intake of cured foods. However, the effect of some molecules on incidence of GC have not been demonstrated. In this study, Single-Variable Mendelian Randomization (SVMR) and Multi-Variable Mendelian Randomization (MVMR) were utilized for estimating the causal association between calcium voltage-gated channel subunit alpha 1E (CACNA1E), C4b binding protein alpha (C4BPA) and GC. Methods: The Genome-Wide Association Study (GWAS) ids for GC and exposure factors were derived from Integrative Epidemiology Unit (IEU) Open GWAS database. Including 6563 GC samples and 195,745 control samples, and there were 8,885,324 Single Nucleotide Polymorphisms (SNPs) for GC. The sample size for CACNA1E was 31,684, and the number of SNPs was 18,413. The sample size for C4BPA was 14,263, and the number of SNPs was 15,879. The SVMR studies were conducted to estimate the risk of CACNA1E and C4BPA in GC using MR Egger, weighted median, inverse variance weighted (IVW), simple mode and weighted mode. Importantly, IVW was the most major method. In addition, sensitivity analysis was conducted to assess the reliability of MR results, which mainly consisted of the heterogeneity, horizontal pleiotropy and Leave-One-Out (LOO). Finally, the MVMR analysis was carried out. Results: The SVMR indicated that CACNA1E and C4BPA are causally related to GC, with C4BPA (P=0.006, OR=1.032) as a risk factor and CACNA1E (P=0.005, OR=0.827) as a protective factor. The reliability of SVMR results was demonstrated by sensitivity analysis. The MVMR analysis was consistent with the outcome of SVMR, implicating C4BPA as a cause of GC, whereas CACNA1E is a protective factor against GC. Conclusion: A causal relationship between CACNA1E, C4BPA and GC was confirmed by our study, and increased C4BPA expression elevates the prevalence of GC and vice versa for CACNA1E.

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Section: Discussionmentioning

confidence: 99%

CACNA1E, C4BPA is causally associated with gastric cancer: A Mendelian randomization study

Zhang,

Zhao,

Liu

et al. 2023

Preprint

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“…Moreover, Ge et al (18) utilized RAG technology to develop Li Versa, a specialized model for liver diseases, Illustrates the hit rates of the text-embedding-ada-002 model, the gte-base-zh model, and the fine-tuned model. (23,24). Given the substantial patient population and the complexity of managing gastrointestinal diseases, implementing RAG is paramount for enhancing the accuracy of diagnosis and treatment for these diseases.…”

Section: Previous Research Backgroundmentioning

confidence: 99%

GastroBot: a Chinese gastrointestinal disease chatbot based on the retrieval-augmented generation

Zhou,

Liu,

Duan

et al. 2024

Front. Med.

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IntroductionLarge Language Models (LLMs) play a crucial role in clinical information processing, showcasing robust generalization across diverse language tasks. However, existing LLMs, despite their significance, lack optimization for clinical applications, presenting challenges in terms of illusions and interpretability. The Retrieval-Augmented Generation (RAG) model addresses these issues by providing sources for answer generation, thereby reducing errors. This study explores the application of RAG technology in clinical gastroenterology to enhance knowledge generation on gastrointestinal diseases.MethodsWe fine-tuned the embedding model using a corpus consisting of 25 guidelines on gastrointestinal diseases. The fine-tuned model exhibited an 18% improvement in hit rate compared to its base model, gte-base-zh. Moreover, it outperformed OpenAI’s Embedding model by 20%. Employing the RAG framework with the llama-index, we developed a Chinese gastroenterology chatbot named “GastroBot,” which significantly improves answer accuracy and contextual relevance, minimizing errors and the risk of disseminating misleading information.ResultsWhen evaluating GastroBot using the RAGAS framework, we observed a context recall rate of 95%. The faithfulness to the source, stands at 93.73%. The relevance of answers exhibits a strong correlation, reaching 92.28%. These findings highlight the effectiveness of GastroBot in providing accurate and contextually relevant information about gastrointestinal diseases. During manual assessment of GastroBot, in comparison with other models, our GastroBot model delivers a substantial amount of valuable knowledge while ensuring the completeness and consistency of the results.DiscussionResearch findings suggest that incorporating the RAG method into clinical gastroenterology can enhance the accuracy and reliability of large language models. Serving as a practical implementation of this method, GastroBot has demonstrated significant enhancements in contextual comprehension and response quality. Continued exploration and refinement of the model are poised to drive forward clinical information processing and decision support in the gastroenterology field.

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“…According to the latest epidemiological data in 2023, gastric cancer is one of the most prevalent malignant tumors worldwide, ranking fifth in incidence and fourth in mortality among all malignancies; it has a profound impact on the lives of individuals. Due to the atypical symptoms of early-stage gastric cancer, patients are often diagnosed at an advanced stage .…”

Section: Introductionmentioning

confidence: 99%

Ultrasound-Assisted Extraction of Atractylodes chinensis (DC.) Koidz. Polysaccharides and the Synergistic Antigastric Cancer Effect in Combination with Oxaliplatin

Liang,

Wu,

Sun

et al. 2024

ACS Omega

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Oxaliplatin (OXA) is recognized as a first-line drug for gastric cancer. However, low accumulation of the OXA in the target site and the development of drug resistance directly led to treatment failure. In the present study, an ultrasonic extraction method for Atractylodes chinensis (DC.) Koidz. polysaccharides (AKUs) and the combination treatment with OXA in vitro were studied. Results showed that when the pH level was 11, the ultrasound power at 450 W, the solid−liquid ratio was 1:20, and the ultrasound treatment for 30 min, the yield of AKUs was significantly increased to 13.20 ± 0.35%. The molecular weights of the AKUs ranged from 7.21 to 185.94 kDa, and its monosaccharides were mainly composed of arabinose (Ara), galactose (Gal), and glucose (Glu) with a ratio of 58.36, 16.90, and 15.49%, respectively. Cell experiments showed that, compared to OXA alone (2 μg/mL, inhibition rate of 18%), the treatment of OXA with AKUs had a significant synergistic inhibitory effect on MKN45 proliferation, which increased to 33, 41, and 45% with increasing AKUs concentrations (5−50 μg/mL), respectively, representing a 2.5-fold inhibition. Inductively coupled plasma-mass spectrometry (ICP-MS) determination confirmed that AKUs significantly increased the intracellular uptake of OXA by 29%, compared to that of OXA alone. We first demonstrated that the combined synergistic inhibitory effect of AKUs and OXA on gastric cancer cells was mediated by reducing the expression of efflux proteins (MRP1 and MRP2) and increasing the expression of ingested protein (OCT2). As a result of the above, AKUs deserved to be an effective adjuvant combined with chemotherapeutics in a clinical setting.

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Updates on global epidemiology, risk and prognostic factors of gastric cancer

Cited by 72 publications

References 155 publications

CACNA1E, C4BPA is causally associated with gastric cancer: A Mendelian randomization study

CACNA1E, C4BPA is causally associated with gastric cancer: A Mendelian randomization study

GastroBot: a Chinese gastrointestinal disease chatbot based on the retrieval-augmented generation

Ultrasound-Assisted Extraction of Atractylodes chinensis (DC.) Koidz. Polysaccharides and the Synergistic Antigastric Cancer Effect in Combination with Oxaliplatin

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