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Abstract Background Exosomes assume a pivotal role as essential mediators of intercellular communication within tumor microenvironments. Within this context, long noncoding RNAs (LncRNAs) have been observed to be preferentially sorted into exosomes, thus exerting regulatory control over the initiation and progression of cancer through diverse mechanisms. Results Exosomes were successfully isolated from cholangiocarcinoma (CCA) CTCs organoid and healthy human serum. Notably, the LncRNA titin-antisense RNA1 (TTN-AS1) exhibited a conspicuous up-regulation within CCA CTCs organoid derived exosomes. Furthermore, a significant elevation of TTN-AS1 expression was observed in tumor tissues, as well as in blood and serum exosomes from patients afflicted with CCA. Importantly, this hightened TTN-AS1 expression in serum exosomes of CCA patients manifested a strong correlation with both lymph node metastasis and TNM staging. Remarkably, both CCA CTCs organoid-derived exosomes and CCA cells-derived exosomes featuring pronounced TTN-AS1 expression demonstrated the capability to the proliferation and migratory potential of CCA cells. Validation of these outcomes was conducted in vivo experiments. Conclusions In conclusion, our study elucidating that CCA CTCs-derived exosomes possess the capacity to bolster the metastasis tendencies of CCA cells by transporting TTN-AS1. These observations underscore the potential of TTN-AS1 within CTCs-derived exosomes to serve as a promising biomarker for the diagnosis and therapeutic management of CCA.

Abstract Background In this study, we aimed to investigate the short-term clinical outcomes of laparoscopic duodenum-preserving pancreatic-head resection (LDPPHR) for the management of pancreatic-head cystic neoplasms. Methods This retrospective study included 60 patients who were treated with pancreatic-head cystic neoplasms at the Shandong Provincial Hospital Affiliated to Shandong First Medical University from December 2019 to July 2022. Results No significant difference was found between the two groups in terms of the baseline and pathological characteristics of patients (P > 0.05). The postoperative exhaust time was shorter in the LDPPHR group compared with the laparoscopic pancreaticoduodenectomy (LPD) group (2 (2 and 4) vs. 4 (3 and 5) days; P = 0.003). No significant difference was found between the two groups in terms of operative time, estimated blood loss, intraoperative transfusion, hemoglobin levels on the first postoperative day, total bilirubin before discharge, direct bilirubin before discharge, postoperative hospital stay, postoperative pancreatic fistula, bile leakage, hemorrhage, peritoneal effusion, abdominal infection, delayed gastric emptying, interventional embolization hemostasis, reoperation, and 30-day readmission (P > 0.05). No conversion and 90-day mortality were found in the two groups. The LDPPHR group showed a higher 3-month postoperative PNI, 6-month postoperative TG and 6-month postoperative BMI than the LPD group (P

Introduction Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a poor response to chemotherapy and an extremely poor prognosis. Recent studies have revealed that phospholysine phosphohistidine inorganic pyrophosphate phosphatase (LHPP) can inhibit the growth of various cancers. Therefore, the current study was conducted to investigate the antitumor effects of LHPP in PDAC and to explore its mechanism using proteomics analysis. Methods and Results Immunohistochemical analysis of clinical samples demonstrated that LHPP expression levels were lower in tumor tissues compared to adjacent nontumor tissues. Moreover, multivariate COX regression analysis showed that LHPP expression level was an independent prognostic factor for the patients with PDAC. Patients with high LHPP expression had a better prognosis. The lentiviral vectors for normal control (NC), LHPP knockdown (KD), and LHPP overexpression (OE) were infected with BxPC-3 and PANC-1 cell lines. Cell counting kit-8 assay, Transwell assay, and flow cytometry analyses showed that LHPP overexpression significantly inhibited the cell viability, migration, and proliferation of BxPC-3 and PANC-1 cells. Moreover, xenograft tumor model demonstrated that LHPP overexpression inhibited xenograft tumor growth in vivo. Subsequently, proteins with significantly altered expression in BxPC-3 cells after lentivirus infection were detected using proteomics analyses. Interestingly, compared to the NC group, the expression of Syndecan 1 (SDC1) was significantly upregulated in the KD group, while that of S100P was significantly downregulated in the OE group. Conclusion LHPP might emerge as an important target for delaying the advancement of PDAC, thereby providing a novel therapeutic approach for the treatment of PDAC.

MicroRNAs (miRNAs) play a crucial role in various biological processes and human diseases, and are considered as therapeutic targets for small molecules (SMs). Due to the time-consuming and expensive biological experiments required to validate SM-miRNA associations, there is an urgent need to develop new computational models to predict novel SM-miRNA associations. The rapid development of end-to-end deep learning models and the introduction of ensemble learning ideas provide us with new solutions. Based on the idea of ensemble learning, we integrate graph neural networks (GNNs) and convolutional neural networks (CNNs) to propose a miRNA and small molecule association prediction model (GCNNMMA). Firstly, we use GNNs to effectively learn the molecular structure graph data of small molecule drugs, while using CNNs to learn the sequence data of miRNAs. Secondly, since the black-box effect of deep learning models makes them difficult to analyze and interpret, we introduce attention mechanisms to address this issue. Finally, the neural attention mechanism allows the CNNs model to learn the sequence data of miRNAs to determine the weight of sub-sequences in miRNAs, and then predict the association between miRNAs and small molecule drugs. To evaluate the effectiveness of GCNNMMA, we implement two different cross-validation (CV) methods based on two different datasets. Experimental results show that the cross-validation results of GCNNMMA on both datasets are better than those of other comparison models. In a case study, Fluorouracil was found to be associated with five different miRNAs in the top 10 predicted associations, and published experimental literature confirmed that Fluorouracil is a metabolic inhibitor used to treat liver cancer, breast cancer, and other tumors. Therefore, GCNNMMA is an effective tool for mining the relationship between small molecule drugs and miRNAs relevant to diseases.