BACKGROUND: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are commonly used for glucose lowering and weight-loss. However, their association with gastrointestinal cancer remains uncertain. This meta-analysis assesses the risk of gastrointestinal cancer in patients treated with GLP-1 RAs.
METHODS: We searched Medline/PubMed, Embase, and Scopus databases from inception to November 15, 2023, for randomized controlled trials (RCTs) with at least 24 weeks of safety follow-up. Pooled risk ratios (RRs) were calculated using fixed- and random-effect models. Risk of bias was assessed using the revised Cochrane risk-of-bias tool, and certainty of evidence was determined using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework.
RESULTS: We included 90 RCTs with 124,791 participants, with an average follow-up of 3.1 years per participant. No significant association was found between GLP-1 RAs and the risk of any gastrointestinal cancer (RRrandom=0.99, 95% CI: 0.86-1.13), or site-specific gastrointestinal cancers including biliary tract (RR=0.98, 0.54-1.78), colorectal (RR=1.13, 0.92-1.39), gallbladder (RR=1.32, 0.43-4.00), gastric (RR=0.88, 0.58-1.33), hepatic (RR=0.79, 0.51-1.21), oesophageal (RR=0.70, 0.38-1.28), pancreatic (RR=1.05, 0.77-1.43), and small intestine cancer (RR=0.78, 0.20-3.04). The corresponding absolute risk differences excluded important impacts on risk. Additional analyses, limited to placebo-controlled trials, high-dose studies, or those with a follow-up duration of ≥5 years, confirmed these findings. Risk of bias was generally low and the certainty of evidence was high for all outcomes.
CONCLUSIONS: This meta-analysis found no significant impact of GLP-1 RAs on gastrointestinal cancer risk. Long-term safety monitoring of these agents remains important.
BACKGROUND: CRD42023476762.

Numerous genomic-based early detection screening tests are being developed. These tests have the potential to revolutionize current single-organ screening paradigms, especially in gastrointestinal cancers. In this review, we underscore the performance of these genomic-based early detection tests based on prospective clinical trials. Moreover, we discuss a professional advancement for gastroenterologists in the diagnostic assessment of individuals who are cancer signal positive.

BACKGROUND: Older adults are at risk of adverse effects during chemotherapy including nausea and fatigue, but many also suffer from dizziness and peripheral neuropathy. This may lead to balance and walking impairments and increased risk of falls and affect health-related quality of life. Moreover, these symptoms are often underreported with inadequate awareness among health professionals leading to deficient focus on the need for targeted assessment and rehabilitation. We aimed to examine the prevalence of dizziness, impaired walking balance, and neuropathy and falls in older adults ≥65 years with gastrointestinal cancer receiving chemotherapy and the associations between these symptoms. Further, we aimed to examine the quantity of patients reporting these symptoms to the oncologist.
METHODS: This is a cross-sectional study among patients ≥65 years with gastrointestinal cancers who have completed three or more series of chemotherapy. The prevalence of dizziness, impaired walking balance, neuropathy, and reporting of these adverse effects was examined through structured questionnaires.
RESULTS: Of two hundred patients (57 % male, mean age 74.4 years) the prevalence of dizziness was 54 % and the prevalence of patients experiencing impaired walking balance was 48 %. Symptoms of neuropathy was present in 32 % of patients and 11 % experienced falls during chemotherapy. Symptoms of neuropathy was associated with experiencing dizziness: odds ratio (OR) 1.98 (95 % confidence interval [CI]: 1.06; 3.71) and impaired balance: OR 3.61 (95 % CI: 1.87; 6.96). Less than half the patients (48 %) told the oncologist about these symptoms.
CONCLUSIONS: Dizziness and impaired walking balance during chemotherapy are underreported yet profound symptoms among older patients with cancer. Dizziness and impaired balance should be systematically assessed during chemotherapy among older patients.

BACKGROUND: Aggressive malignancies, such as pancreatic cancer, are increasingly impacting young, female populations. Our investigation centered on whether the observed trends in cancer incidence were unique to pancreatic cancer or indicative of a broader trend across various cancer types. To delve deeper into this phenomenon, we analyzed cancer incidence trends across different age and sex groups. Furthermore, we explored differences in cancer incidence within specific young subgroups aged 18 to 26 and 27 to 34, to better understand the emerging incidence trend among young individuals.
METHODS: This study collected cancer incidence data from one of the Surveillance, Epidemiology, and End Results cancer registry databases (SEER22), with 10,183,928 total cases from 2000 to 2020. Data were analyzed through Joinpoint trend analysis approach to evaluate sex- and age-specific trends in cancer incidence. Exposure rates were reported as Average Annual Percentage Changes (AAPCs).
RESULTS: The analysis revealed significant age and sex-specific disparities, particularly among individuals aged 18-26 and 27-34. Pancreatic cancer incidence rates increased more in females aged 18-26 (AAPC, 9.37% [95% CI, 7.36-11.41%]; p < .0001) than in males (4.43% [95% CI, 2.36-6.53%]; p < .0001). Notably, among gender, age, and other malignancies, young females had the highest AAPCs for pancreatic cancer. Additionally, the incidence of gastric cancer, myeloma, and colorectal malignancies also showed higher AAPCs in young females compared to males.
CONCLUSIONS: Recognizing emerging risk populations for highly lethal malignancies is crucial for early detection and effective disease management.

BACKGROUND: Surgical resection is the primary treatment for gastrointestinal (GI) cancers, but postoperative skeletal muscle loss (SML) is common and linked to poor prognosis. This study aims to identify patterns of muscle change, examine its association with quality of life (QoL), and explore predictors of SML in the first 3 months.
METHODS: A prospective cohort study was conducted on patients newly diagnosed with GI cancer and undergoing surgery in China between September 2021 and May 2022. Skeletal muscle mass (SMM) and QoL were assessed at admission, 7 days, 1 month, and 3 months post-surgery. Demographic, clinical data, and biomarkers were collected. Missing data were imputed using multiple imputation. Data were analyzed using growth mixture modelling, bivariate analyses, and logistic regression.
RESULTS: A total of 483 patients completed baseline assessment. Of the 242 patients with complete muscle assessments, 92% experienced SML. Three distinct patterns of muscle change were identified: 57% had normal preoperative SMM with mild postoperative SML, 16% had low preoperative SMM with moderate SML, and 27% had normal preoperative mass but severe postoperative SML. Moderate/severe SML was associated with more postoperative complications, poorer health, and higher symptom burden. Independent predictors included advanced age, preoperative sarcopenia, advanced cancer stage, and low prognostic nutrition index (PNI ≤ 45). The results did not change when using imputed values.
CONCLUSIONS: Although SML is prevalent, patterns of muscle change are heterogeneous among patients. Advanced age, preoperative sarcopenia, advanced cancer stage, and cancer-related inflammation are predictors for moderate/severe SML, highlighting the need for early detection and management.

Duodenal adenocarcinoma is a rare and aggressive gastrointestinal malignancy that frequently presents with symptoms like gastric outlet obstruction and biliary obstruction, leading to delayed diagnosis and challenging prognosis. This case report explores the clinical presentation, diagnostic hurdles, and therapeutic management of late-stage duodenal adenocarcinoma in a 53-year-old woman with no significant prior medical history. The patient presented with severe epigastric pain radiating to the right upper quadrant, nausea, and decreased appetite. Elevated liver enzymes and imaging revealed multiple liver masses and a primary duodenal mass. Biopsies confirmed moderately differentiated adenocarcinoma. Tumor markers were evaluated during the staging phase, showing markedly elevated levels. The patient underwent systemic chemotherapy with FOLFOX but faced complications, including pulmonary emboli and neurological symptoms. Management required a multidisciplinary approach, integrating palliative and supportive care to address symptoms and improve quality of life. The case highlights the necessity of considering duodenal adenocarcinoma when diagnosing persistent gastrointestinal symptoms. It highlights the need for a holistic treatment approach, including tailored chemotherapy regimens and vigilant monitoring of complications. Molecular profiling was crucial in guiding treatment decisions, although MSI, HER2, and PD-1 were negative, and the tumor showed no mismatch repair protein deficiency. This article emphasizes the importance of early integration of palliative care and the value of comprehensive pathological analysis in managing advanced duodenal adenocarcinoma, providing insights into diagnostic and therapeutic strategies for this complex case.

Gastric cancer has become a serious worldwide health concern, emphasizing the crucial importance of early diagnosis measures to improve patient outcomes. While traditional histological image analysis is regarded as the clinical gold standard, it is labour intensive and manual. In recognition of this problem, there has been a rise in interest in the use of computer-aided diagnostic tools to help pathologists with their diagnostic efforts. In particular, deep learning (DL) has emerged as a promising solution in this sector. However, current DL models are still restricted in their ability to extract extensive visual characteristics for correct categorization. To address this limitation, this study proposes the use of ensemble models, which incorporate the capabilities of several deep-learning architectures and use aggregate knowledge of many models to improve classification performance, allowing for more accurate and efficient gastric cancer detection. To determine how well these proposed models performed, this study compared them with other works, all of which were based on the Gastric Histopathology Sub-Size Images Database, a publicly available dataset for gastric cancer. This research demonstrates that the ensemble models achieved a high detection accuracy across all sub-databases, with an average accuracy exceeding 99%. Specifically, ResNet50, VGGNet, and ResNet34 performed better than EfficientNet and VitNet. For the 80 × 80-pixel sub-database, ResNet34 exhibited an accuracy of approximately 93%, VGGNet achieved 94%, and the ensemble model excelled with 99%. In the 120 × 120-pixel sub-database, the ensemble model showed 99% accuracy, VGGNet 97%, and ResNet50 approximately 97%. For the 160 × 160-pixel sub-database, the ensemble model again achieved 99% accuracy, VGGNet 98%, ResNet50 98%, and EfficientNet 92%, highlighting the ensemble model\'s superior performance across all resolutions. Overall, the ensemble model consistently provided an accuracy of 99% across the three sub-pixel categories. These findings show that ensemble models may successfully detect critical characteristics from smaller patches and achieve high performance. The findings will help pathologists diagnose gastric cancer using histopathological images, leading to earlier identification and higher patient survival rates.

Gastrointestinal (GI) cancers impose a substantial global health burden, highlighting the necessity for deeper understanding of their intricate pathogenesis and treatment strategies. This review explores the interplay between intratumoral microbiota, tumor metabolism, and major types of GI cancers (including esophageal, gastric, liver, pancreatic, and colorectal cancers), summarizing recent studies and elucidating their clinical implications and future directions. Recent research revealed altered microbial signatures within GI tumors, impacting tumor progression, immune responses, and treatment outcomes. Dysbiosis-induced alterations in tumor metabolism, including glycolysis, fatty acid metabolism, and amino acid metabolism, play critical roles in cancer progression and therapeutic resistance. The integration of molecular mechanisms and potential biomarkers into this understanding further enhances the prognostic significance of intratumoral microbiota composition and therapeutic opportunities targeting microbiota-mediated tumor metabolism. Despite advancements, challenges remain in understanding the dynamic interactions within the tumor microenvironment (TME). Future research directions, including advanced omics technologies and prospective clinical studies, offer promising avenues for precision oncology and personalized treatment interventions in GI cancer. Overall, integrating microbiota-based approaches and molecular biomarkers into GI cancer management holds promise for improving patient outcomes and survival.

Gastrointestinal cancer is a worldwide health challenge due to its dramatically increasing prevalence and as a leading cause of cancer-related mortality. Increasing evidence has illustrated the vital role of gut microbes-derived metabolites in gastrointestinal cancer progression and treatment. Microbial metabolites are produced by the gut microbiota that utilizes both extrinsic dietary components and intrinsic host-generated compounds. Meanwhile, certain categories of metabolites such as short-chain fatty acids, bile acids, tryptophan, and indole derivatives, are linked to gastrointestinal malignancy. In this review, the major classes of microbial metabolites and their impacts on various gastrointestinal cancers including colorectal cancer, gastric cancer, and hepatocellular carcinoma, have been introduced. The application of microbial metabolites as predictive biomarkers for early diagnosis and prognosis of gastrointestinal cancer has also been explored. In addition, therapeutic potential of strategies that target microbial metabolites against gastrointestinal cancer is further evaluated.

Cancer is a leading cause of death worldwide. Around one-third of the total global cancer incidence and mortality are related to gastrointestinal (GI) cancers. Over the past few years, rapid developments have been made in patient-derived organoid (PDO) models for gastrointestinal cancers. By closely mimicking the molecular properties of their parent tumors in vitro, PDOs have emerged as powerful tools in personalized medicine and drug discovery. Here, we review the current literature on the application of PDOs of common gastrointestinal cancers in the optimization of drug treatment strategies in the clinic and their rising importance in pre-clinical drug development. We discuss the advantages and limitations of gastrointestinal cancer PDOs and outline the microfluidics-based strategies that improve the throughput of PDO models in order to extract the maximal benefits in the personalized medicine and drug discovery process.