Causal link between gut microbiota and four types of pancreatitis: a genetic association and bidirectional Mendelian randomization study.

Background: A number of recent observational studies have indicated a correlation between the constitution of gut microbiota and the incidence of pancreatitis. Notwithstanding, observational studies are unreliable for inferring causality because of their susceptibility to confounding, bias, and reverse causality, the causal relationship between specific gut microbiota and pancreatitis is still unclear. Therefore, our study aimed to investigate the causal relationship between gut microbiota and four types of pancreatitis. Methods: An investigative undertaking encompassing a genome-wide association study (GWAS) comprising 18,340 participants was undertaken with the aim of discerning genetic instrumental variables that exhibit associations with gut microbiota, The aggregated statistical data pertaining to acute pancreatitis (AP), alcohol-induced AP (AAP), chronic pancreatitis (CP), and alcohol-induced CP (ACP) were acquired from the FinnGen Consortium. The two-sample bidirectional Mendelian randomization (MR) approach was utilized. Utilizing the Inverse-Variance Weighted (IVW) technique as the cornerstone of our primary analysis. The Bonferroni analysis was used to correct for multiple testing, In addition, a number of sensitivity analysis methodologies, comprising the MR-Egger intercept test, the Cochran's Q test, MR polymorphism residual and outlier (MR-PRESSO) test, and the leave-one-out test, were performed to evaluate the robustness of our findings. Results: A total of 28 intestinal microflora were ascertained to exhibit significant associations with diverse outcomes of pancreatitis. Among them, Class Melainabacteria (OR = 1.801, 95% CI: 1.288-2.519, p = 0.008) has a strong causality with ACP after the Bonferroni-corrected test, in order to assess potential reverse causation effects, we used four types of pancreatitis as the exposure variable and scrutinized its impact on gut microbiota as the outcome variable, this analysis revealed associations between pancreatitis and 30 distinct types of gut microflora. The implementation of Cochran's Q test revealed a lack of substantial heterogeneity among the various single nucleotide polymorphisms (SNP). Conclusion: Our first systematic Mendelian randomization analysis provides evidence that multiple gut microbiota taxa may be causally associated with four types of pancreatitis disease. This discovery may contribute significant biomarkers conducive to the preliminary, non-invasive identification of Pancreatitis. Additionally, it could present viable targets for potential therapeutic interventions in the disease's treatment.

A deep learning model using hyperspectral image for EUS-FNA cytology diagnosis in pancreatic ductal adenocarcinoma.

BACKGROUND AND AIMS: Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) is considered to be a first-line procedure for the pathological diagnosis of pancreatic cancer owing to its high accuracy and low complication rate. The number of new cases of pancreatic ductal adenocarcinoma (PDAC) is increasing, and its accurate pathological diagnosis poses a challenge for cytopathologists. Our aim was to develop a hyperspectral imaging (HSI)-based convolution neural network (CNN) algorithm to aid in the diagnosis of pancreatic EUS-FNA cytology specimens. METHODS: HSI images were captured of pancreatic EUS-FNA cytological specimens from benign pancreatic tissues (n = 33) and PDAC (n = 39) prepared using a liquid-based cytology method. A CNN was established to test the diagnostic performance, and Attribution Guided Factorization Visualization (AGF-Visualization) was used to visualize the regions of important classification features identified by the model. RESULTS: A total of 1913 HSI images were obtained. Our ResNet18-SimSiam model achieved an accuracy of 0.9204, sensitivity of 0.9310 and specificity of 0.9123 (area under the curve of 0.9625) when trained on HSI images for the differentiation of PDAC cytological specimens from benign pancreatic cells. AGF-Visualization confirmed that the diagnoses were based on the features of tumor cell nuclei. CONCLUSIONS: An HSI-based model was developed to diagnose cytological PDAC specimens obtained using EUS-guided sampling. Under the supervision of experienced cytopathologists, we performed multi-staged consecutive in-depth learning of the model. Its superior diagnostic performance could be of value for cytologists when diagnosing PDAC.

MicroRNA-3613-3p functions as a tumor suppressor and represents a novel therapeutic target in breast cancer.

BACKGROUND: MicroRNAs have been reported to participate in tumorigenesis, treatment resistance, and tumor metastasis. Novel microRNAs need to be identified and investigated to guide the clinical prognosis or therapy for breast cancer. METHOD: The copy number variations (CNVs) of MIR3613 from Cancer Genome Atlas (TCGA) or Cancer Cell Line Encyclopedia (CCLE) were analyzed, and its correlation with breast cancer subtypes or prognosis was investigated. The expression level of miR-3613-3p in tumor tissues or serum of breast cancer patients was detected using in situ hybridization and qPCR. Gain-of-function studies were performed to determine the regulatory role of miR-3613-3p on proliferation, apoptosis, and tumor sphere formation of human breast cancer cells MDA-MB-231 or MCF-7. The effects of miR-3613-3p on tumor growth or metastasis in an immunocompromised mouse model of MDA-MB-231-luciferase were explored by intratumor injection of miR-3613-3p analogue. The target genes, interactive lncRNAs, and related signaling pathways of miR-3613-3p were identified by bioinformatic prediction and 3'-UTR assays. RESULTS: We found that MIR3613 was frequently deleted in breast cancer genome and its deletion was correlated with the molecular typing, and an unfavorable prognosis in estrogen receptor-positive patients. MiR-3613-3p level was also dramatically lower in tumor tissues or serum of breast cancer patients. Gain-of-function studies revealed that miR-3613-3p could suppress proliferation and sphere formation and promote apoptosis in vitro and impeded tumor growth and metastasis in vivo. Additionally, miR-3613-3p might regulate cell cycle by targeting SMS, PAFAH1B2, or PDK3 to restrain tumor progression. CONCLUSION: Our findings indicate a suppressive role of miR-3613-3p in breast cancer progression, which may provide an innovative marker or treatment for breast cancer patients.

Targeting LIN28B reprograms tumor glucose metabolism and acidic microenvironment to suppress cancer stemness and metastasis.

The altered metabolism and acidic microenvironment plays an important role in promoting tumor malignant characteristics. A small population of cancer stem cells (CSCs) were considered as a therapy target to reserve tumor relapse, resistance, and metastasis. However, the molecular mechanism that regulates CSCs metabolism remains poorly understood. In this study, we demonstrate a fundamental role of stemness gene LIN28B in maintaining CSCs glycolysis metabolism. Using LIN28B-expressing cancer cell lines, we found that the rate of extracellular acidification, glucose uptake, and lactate secretion are all suppressed by LIN28B knockdown in vitro and in vivo. Importantly, metabolic analyses reveal that CSCs have enhanced aerobic glycolysis metabolic characteristics and the glycolytic product lactate further promotes cancer associated stemness properties. LIN28B silencing suppresses MYC expression that further increases miR-34a-5p level. Furthermore, the glycolysis metabolism of human breast cancer cell line MDA-MB-231 is suppressed by either MYC siRNA or miR-34a-5p mimic. Clinically, high MYC and low miR-34a-5p level are correlated with high LIN28B expression and poor prognosis in human breast cancer patients. Notably, blocking LIN28B/MYC/miR-34a-5p signaling pathway by LIN28B-specific inhibitor causes dramatic inhibition of tumor growth and metastasis in immunodeficient orthotopic mouse models of human breast cancer cell MDA-MB-231. Taken together, our findings offer a preclinical investigation of targeting LIN28B to suppress CSCs glycolysis metabolism and tumor progression that may improve the therapeutic benefit for cancer patients.