Deletion of pancreas-specific miR-216a reduces beta-cell mass and inhibits pancreatic cancer progression in mice

The roles of lncRNAs and miRNAs in pancreatic cancer: a focus on cancer development and progression and their roles as potential biomarkers

Pancreatic ductal adenocarcinoma (PDAC) is among the most penetrative malignancies affecting humans, with mounting incidence prevalence worldwide. This cancer is usually not diagnosed in the early stages. There is also no effective therapy against PDAC, and most patients have chemo-resistance. The combination of these factors causes PDAC to have a poor prognosis, and often patients do not live longer than six months. Because of the failure of conventional therapies, the identification of key biomarkers is crucial in the early diagnosis, treatment, and prognosis of pancreatic cancer. 65% of the human genome encodes ncRNAs. There are different types of ncRNAs that are classified based on their sequence lengths and functions. They play a vital role in replication, transcription, translation, and epigenetic regulation. They also participate in some cellular processes, such as proliferation, differentiation, metabolism, and apoptosis. The roles of ncRNAs as tumor suppressors or oncogenes in the growth of tumors in a variety of tissues, including the pancreas, have been demonstrated in several studies. This study discusses the key roles of some lncRNAs and miRNAs in the growth and advancement of pancreatic carcinoma. Because they are involved not only in the premature identification, chemo-resistance and prognostication, also their roles as potential biomarkers for better management of PDAC patients.

In vivo techniques for assessment of insulin sensitivity and glucose metabolism

Metabolic tests are vital to determine in vivo insulin sensitivity and glucose metabolism in preclinical models, usually rodents. Such tests include glucose tolerance tests, insulin tolerance tests, and glucose clamps. Although these tests are not standardized, there are general guidelines for their completion and analysis that are constantly being refined. In this review, we describe metabolic tests in rodents as well as factors to consider when designing and performing these tests.

A novel saliva-based miRNA profile to diagnose and predict oral cancer

Oral cancer (OC) is the most common form of head and neck cancer. Despite the high incidence and unfavourable patient outcomes, currently, there are no biomarkers for the early detection of OC. This study aims to discover, develop, and validate a novel saliva-based microRNA signature for early diagnosis and prediction of OC risk in oral potentially malignant disorders (OPMD). The Cancer Genome Atlas (TCGA) miRNA sequencing data and small RNA sequencing data of saliva samples were used to discover differentially expressed miRNAs. Identified miRNAs were validated in saliva samples of OC (n = 50), OPMD (n = 52), and controls (n = 60) using quantitative real-time PCR. Eight differentially expressed miRNAs (miR-7-5p, miR-10b-5p, miR-182-5p, miR-215-5p, miR-431-5p, miR-486-3p, miR-3614-5p, and miR-4707-3p) were identified in the discovery phase and were validated. The efficiency of our eight-miRNA signature to discriminate OC and controls was: area under curve (AUC): 0.954, sensitivity: 86%, specificity: 90%, positive predictive value (PPV): 87.8% and negative predictive value (NPV): 88.5% whereas between OC and OPMD was: AUC: 0.911, sensitivity: 90%, specificity: 82.7%, PPV: 74.2% and NPV: 89.6%. We have developed a risk probability score to predict the presence or risk of OC in OPMD patients. We established a salivary miRNA signature that can aid in diagnosing and predicting OC, revolutionising the management of patients with OPMD. Together, our results shed new light on the management of OC by salivary miRNAs to the clinical utility of using miRNAs derived from saliva samples.

Small RNA Changes in Plasma Have Potential for Early Diagnosis of Alzheimer's Disease before Symptom Onset

Alzheimer's disease (AD), due to its multifactorial nature and complex etiology, poses challenges for research, diagnosis, and treatment, and impacts millions worldwide. To address the need for minimally invasive, repeatable measures that aid in AD diagnosis and progression monitoring, studies leveraging RNAs associated with extracellular vesicles (EVs) in human biofluids have revealed AD-associated changes. However, the validation of AD biomarkers has suffered from the collection of samples from differing points in the disease time course or a lack of confirmed AD diagnoses. Here, we integrate clinical diagnosis and postmortem pathology data to form more accurate experimental groups and use small RNA sequencing to show that EVs from plasma can serve as a potential source of RNAs that reflect disease-related changes. Importantly, we demonstrated that these changes are identifiable in the EVs of preclinical patients, years before symptom manifestation, and that machine learning models based on differentially expressed RNAs can help predict disease conversion or progression. This research offers critical insight into early disease biomarkers and underscores the significance of accounting for disease progression and pathology in human AD studies.

Human pancreatic capillaries and nerve fibers persist in type 1 diabetes despite beta cell loss

The autonomic nervous system regulates pancreatic function. Islet capillaries are essential for the extension of axonal projections into islets, and both of these structures are important for appropriate islet hormone secretion. Because beta cells provide important paracrine cues for islet glucagon secretion and neurovascular development, we postulated that beta cell loss in type 1 diabetes (T1D) would lead to a decline in intraislet capillaries and reduction of islet innervation, possibly contributing to abnormal glucagon secretion. To define morphological characteristics of capillaries and nerve fibers in islets and acinar tissue compartments, we analyzed neurovascular assembly across the largest cohort of T1D and normal individuals studied thus far. Because innervation has been studied extensively in rodent models of T1D, we also compared the neurovascular architecture between mouse and human pancreas and assembled transcriptomic profiles of molecules guiding islet angiogenesis and neuronal development. We found striking interspecies differences in islet neurovascular assembly but relatively modest differences at transcriptome level, suggesting that posttranscriptional regulation may be involved in this process. To determine whether islet neurovascular arrangement is altered after beta cell loss in T1D, we compared pancreatic tissues from non-diabetic, recent-onset T1D (<10-yr duration), and longstanding T1D (>10-yr duration) donors. Recent-onset T1D showed greater islet and acinar capillary density compared to non-diabetic and longstanding T1D donors. Both recent-onset and longstanding T1D had greater islet nerve fiber density compared to non-diabetic donors. We did not detect changes in sympathetic axons in either T1D cohort. Additionally, nerve fibers overlapped with extracellular matrix (ECM), supporting its role in the formation and function of axonal processes. These results indicate that pancreatic capillaries and nerve fibers persist in T1D despite beta cell loss, suggesting that alpha cell secretory changes may be decoupled from neurovascular components.NEW & NOTEWORTHY Defining the neurovascular architecture in the pancreas of individuals with type 1 diabetes (T1D) is crucial to understanding the mechanisms of dysregulated glucagon secretion. In the largest T1D cohort of biobanked tissues analyzed to date, we found that pancreatic capillaries and nerve fibers persist in human T1D despite beta cell loss, suggesting that alpha cell secretory changes may be decoupled from neurovascular components. Because innervation has been studied extensively in rodent T1D models, our studies also provide the first rigorous direct comparisons of neurovascular assembly in mouse and human, indicating dramatic interspecies differences.

Human pancreatic islet microRNAs implicated in diabetes and related traits by large-scale genetic analysis

Genetic studies have identified ≥240 loci associated with the risk of type 2 diabetes (T2D), yet most of these loci lie in non-coding regions, masking the underlying molecular mechanisms. Recent studies investigating mRNA expression in human pancreatic islets have yielded important insights into the molecular drivers of normal islet function and T2D pathophysiology. However, similar studies investigating microRNA (miRNA) expression remain limited. Here, we present data from 63 individuals, the largest sequencing-based analysis of miRNA expression in human islets to date. We characterized the genetic regulation of miRNA expression by decomposing the expression of highly heritable miRNAs into cis- and trans-acting genetic components and mapping cis-acting loci associated with miRNA expression [miRNA-expression quantitative trait loci (eQTLs)]. We found i) 84 heritable miRNAs, primarily regulated by trans-acting genetic effects, and ii) 5 miRNA-eQTLs. We also used several different strategies to identify T2D-associated miRNAs. First, we colocalized miRNA-eQTLs with genetic loci associated with T2D and multiple glycemic traits, identifying one miRNA, miR-1908, that shares genetic signals for blood glucose and glycated hemoglobin (HbA1c). Next, we intersected miRNA seed regions and predicted target sites with credible set SNPs associated with T2D and glycemic traits and found 32 miRNAs that may have altered binding and function due to disrupted seed regions. Finally, we performed differential expression analysis and identified 14 miRNAs associated with T2D status-including miR-187-3p, miR-21-5p, miR-668, and miR-199b-5p-and 4 miRNAs associated with a polygenic score for HbA1c levels-miR-216a, miR-25, miR-30a-3p, and miR-30a-5p.

Diagnostic and prognostic potential clustered miRNAs in bladder cancer

At specific genomic loci, miRNAs are in clusters and their association with copy number variations (CNVs) may exhibit abnormal expression in several cancers. Hence, the current study aims to understand the expression of miRNA clusters residing within CNVs and the regulation of their target genes in bladder cancer. To achieve this, we used extensive bioinformatics resources and performed an integrated analysis of recurrent CNVs, clustered miRNA expression, gene expression, and drug–gene interaction datasets. The study identified nine upregulated miRNA clusters that are residing on CNV gain regions and three miRNA clusters (hsa-mir-200c/mir-141, hsa-mir-216a/mir-217, and hsa-mir-15b/mir-16-2) are correlated with patient survival. These clustered miRNAs targeted 89 genes that were downregulated in bladder cancer. Moreover, network and gene enrichment analysis displayed 10 hub genes (CCND2, ETS1, FGF2, FN1, JAK2, JUN, KDR, NOTCH1, PTEN, and ZEB1) which have significant potential for diagnosis and prognosis of bladder cancer patients. Interestingly, hsa-mir-200c/mir-141 and hsa-mir-15b/mir-16-2 cluster candidates showed significant differences in their expression in stage-specific manner during cancer progression. Downregulation of NOTCH1 by hsa-mir-200c/mir-141 may also sensitize tumors to methotrexate thus suggesting potential chemotherapeutic options for bladder cancer subjects. To overcome some computational challenges and reduce the complexity in multistep big data analysis, we developed an automated pipeline called CmiRClustFinder v1.0 (https://github.com/msls-bioinfo/CmiRClustFinder_v1.0), which can perform integrated data analysis of 35 TCGA cancer types.