Diagnostic and prognostic role of circRNAs in pancreatic cancer: a meta-analysis

Leveraging epigenetic alterations in pancreatic ductal adenocarcinoma for clinical applications

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy characterized by late detection and poor prognosis. Recent research highlights the pivotal role of epigenetic alterations in driving PDAC development and progression. These changes, in conjunction with genetic mutations, contribute to the intricate molecular landscape of the disease. Specific modifications in DNA methylation, histone marks, and non-coding RNAs are emerging as robust predictors of disease progression and patient survival, offering the potential for more precise prognostic tools compared to conventional clinical staging. Moreover, the detection of epigenetic alterations in blood and other non-invasive samples holds promise for earlier diagnosis and improved management of PDAC. This review comprehensively summarises current epigenetic research in PDAC and identifies persisting challenges. These include the complex nature of epigenetic profiles, tumour heterogeneity, limited access to early-stage samples, and the need for highly sensitive liquid biopsy technologies. Addressing these challenges requires the standardisation of methodologies, integration of multi-omics data, and leveraging advanced computational tools such as machine learning and artificial intelligence. While resource-intensive, these efforts are essential for unravelling the functional consequences of epigenetic changes and translating this knowledge into clinical applications. By overcoming these hurdles, epigenetic research has the potential to revolutionise the management of PDAC and improve patient outcomes.

A Novel circ_0075829/miR-326/GOT1 ceRNA Crosstalk Regulates the Malignant Phenotypes and Drug Sensitivity of Gemcitabine-Resistant Pancreatic Cancer Cells

Although gemcitabine (GEM) is the cornerstone of the treatment of pancreatic cancer (PC), GEM resistance frequently arises. Circular RNA (circRNA) circ_0075829 is highly expressed in PC. However, whether circ_0075829 contributes to GEM resistance of PC is largely unknown. To generate GEM-resistant PC cells (BxPC-3/GR and SW1990/GR), we exposed GEM-sensitive PC cells to GEM. Circ_0075829, microRNA (miR)-326, and glutamic-oxaloacetic transaminase 1 (GOT1) were quantified by a qRT-PCR or western blot method. Cell survival and viability were gauged by MTS assay. Cell proliferation, apoptosis, invasion, and migration were assessed by EdU, flow cytometry, transwell, and wound-healing assays, respectively. Dual-luciferase reporter assays were used to verify the relationship between miR-326 and circ_0075829 or GOT1. Mouse xenografts were performed to evaluate the role of circ_0075829 in vivo. Our data showed that circ_0075829 was upregulated in GEM-resistant PC tissues and cells. Knockdown of circ_0075829 impeded the proliferation, invasion, migration, and glutamine metabolism, and promoted cell apoptosis and GEM sensitivity of GEM-resistant PC cells. Moreover, circ_0075829 silencing suppressed the tumorigenicity of SW1990/GR cells and sensitized them to the cytotoxic effect of GME in vivo. Mechanistically, circ_0075829 bound miR-326 and exerted regulatory effects by affecting miR-326 expression. GOT1 was a direct miR-326 target and a key downstream effector of miR-326. Furthermore, circ_0075829 modulated GOT1 expression via miR-326. Our findings establish a novel regulatory network, the circ_0075829/miR-326/GOT1 competing endogenous RNA (ceRNA) crosstalk, in the regulation of GEM resistance in PC.

ZC3H13-induced the m6A modification of hsa_circ_0081723 promotes cervical cancer progression via AMPK/p53 pathway

BACKGROUND

N6-methyladenosine (m6A) modification and circular RNAs (circRNAs) have been confirmed to participate in cervical cancer (CC) progression. However, the function of a novel circRNA, hsa_circ_0081723, has not yet been explored in CC. Therefore, this study aimed to investigate the potential role of hsa_circ_0081723 and its m6A modification in CC.

METHODS

The hsa_circ_0081723 and ZC3H13 expressions were examined by qRT-PCR in the CC tissues, and their prognostic significance was evaluated via Kaplan-Meier Plotter. The role of hsa_circ_0081723 in CC progression was checked by loss-of-function assays. The relative protein levels of AMPK/p53 pathway were determined by western blotting. The interactions of hsa_circ_0081723 and ZC3H13 were verified via MeRIP and RNA stability assays.

RESULTS

The hsa_circ_0081723 expression was elevated in CC samples, and its higher levels indicated high histological grade, high FIGO stage, poor differentiation, and poor prognosis. Functionally, silencing hsa_circ_0081723 impaired the malignant behavior of CC cells and enhanced the protein levels of key molecules of the AMPK signaling pathway. Moreover, ZC3H13 was also elevated in CC samples and demonstrated a positive association with hsa_circ_0081723. The relative enrichment of hsa_circ_0081723 m6A and its stability were enhanced in ZC3H13 overexpressed CC cells. Mechanically, ZC3H13 overexpression partially reversed the antitumor effects caused by hsa_circ_0081723 knockdown in CC cells.

CONCLUSIONS

This study innovatively demonstrates that ZC3H13-mediated m6A modification of hsa_circ_0081723 promotes CC progression by modulating AMPK/p53 pathway. Our findings may contribute to the understanding of the molecular mechanisms underlying CC and offer potential therapeutic targets for clinical treatment.

Exploring potential circRNA biomarkers for cancers based on double-line heterogeneous graph representation learning

BACKGROUND

Compared with the time-consuming and labor-intensive for biological validation in vitro or in vivo, the computational models can provide high-quality and purposeful candidates in an instant. Existing computational models face limitations in effectively utilizing sparse local structural information for accurate predictions in circRNA-disease associations. This study addresses this challenge with a proposed method, CDA-DGRL (Prediction of CircRNA-Disease Association based on Double-line Graph Representation Learning), which employs a deep learning framework leveraging graph networks and a dual-line representation model integrating graph node features.

METHOD

CDA-DGRL comprises several key steps: initially, the integration of diverse biological information to compute integrated similarities among circRNAs and diseases, leading to the construction of a heterogeneous network specific to circRNA-disease associations. Subsequently, circRNA and disease node features are derived using sparse autoencoders. Thirdly, a graph convolutional neural network is employed to capture the local graph network structure by inputting the circRNA-disease heterogeneous network alongside node features. Fourthly, the utilization of node2vec facilitates depth-first sampling of the circRNA-disease heterogeneous network to grasp the global graph network structure, addressing issues associated with sparse raw data. Finally, the fusion of local and global graph network structures is inputted into an extra trees classifier to identify potential circRNA-disease associations.

RESULTS

The results, obtained through a rigorous five-fold cross-validation on the circR2Disease dataset, demonstrate the superiority of CDA-DGRL with an AUC value of 0.9866 and an AUPR value of 0.9897 compared to existing state-of-the-art models. Notably, the hyper-random tree classifier employed in this model outperforms other machine learning classifiers.

CONCLUSION

Thus, CDA-DGRL stands as a promising methodology for reliably identifying circRNA-disease associations, offering potential avenues to alleviate the necessity for extensive traditional biological experiments. The source code and data for this study are available at https://github.com/zywait/CDA-DGRL .

Targeted Nanoparticle-Based Diagnostic and Treatment Options for Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC), one of the deadliest cancers, presents significant challenges in diagnosis and treatment due to its aggressive, metastatic nature and lack of early detection methods. A key obstacle in PDAC treatment is the highly complex tumor environment characterized by dense stroma surrounding the tumor, which hinders effective drug delivery. Nanotechnology can offer innovative solutions to these challenges, particularly in creating novel drug delivery systems for existing anticancer drugs for PDAC, such as gemcitabine and paclitaxel. By using customization methods such as incorporating conjugated targeting ligands, tumor-penetrating peptides, and therapeutic nucleic acids, these nanoparticle-based systems enhance drug solubility, extend circulation time, improve tumor targeting, and control drug release, thereby minimizing side effects and toxicity in healthy tissues. Moreover, nanoparticles have also shown potential in precise diagnostic methods for PDAC. This literature review will delve into targeted mechanisms, pathways, and approaches in treating pancreatic cancer. Additional emphasis is placed on the study of nanoparticle-based delivery systems, with a brief mention of those in clinical trials. Overall, the overview illustrates the significant advances in nanomedicine, underscoring its role in transcending the constraints of conventional PDAC therapies and diagnostics.