miRNAs: possible regulators of toll like receptors and inflammatory tumor microenvironment in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is ranked as the third most commonly diagnosed cancer and the third cause of cancer related deaths. CRC is greatly attributed to genetic and epigenetic mutations and immune dysregulation. Tumor aberrant expression of Toll-like Receptors (TLRs) can contribute to tumorigenesis. Recent studies suggested that microRNAs act as direct ligands of TLRs altering their expression and signaling pathways. AIM: To prove our concept that specific miRNA mimics may act as antagonists of their specific toll like receptors inhibiting their expression that could limit the release of pro-inflammatory and pro-tumorigenic cytokines leading to apoptosis of tumor cells. METHODS: From public microarray databases, we retrieved TLRs and miRNAs related to CRC followed by in silico docking of the selected miRNA ligands into the TLRs. Clinical validation after co-immunoprecipitation of TLRs and their interacting miRNA ligands was done. Expression of TLRs 1, 7,8 was determined by ELISA while miRNAs was measured by RT-qPCR. In addition, microRNA mimics of the down regulated miRNAs were transfected into human CRC cell lines. RESULTS: Our data demonstrate that TLRs 1, 7, 8 are up regulated in CRC compared to controls. Further, three miRNAs (-122, -29b and -15b) are relatively downregulated, while 4 miRNAs (-202, miRNA-98, -21 and -let7i) are upregulated in CRC patients compared to those with benign tumor and healthy controls. Transfection of down regulated miRNA mimics into CRC cell lines resulted in a significant reduction of the number and viability of cells as well as down regulating the expression of TLRs 1, 7 and 8 with ultimate reduction of downstream effector IL6 protein, suggesting that these miRNAs are negative regulators of carcinogenesis. CONCLUSION: MicroRNAs could act as antagonistic ligands of TLRs limiting the inflammatory tumor microenvironment.

A new label free spiral sensor using impedance spectroscopy to characterize hepatocellular carcinoma in tissue and serum samples.

Hepatocellular carcinoma (HCC) stands as the most prevalent form of primary liver cancer, predominantly affecting patients with chronic liver diseases such as hepatitis B or C-induced cirrhosis. Diagnosis typically involves blood tests (assessing liver functions and HCC biomarkers), imaging procedures such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), and liver biopsies requiring the removal of liver tissue for laboratory analysis. However, these diagnostic methods either entail lengthy lab processes, require expensive imaging equipment, or involve invasive techniques like liver biopsies. Hence, there exists a crucial need for rapid, cost-effective, and noninvasive techniques to characterize HCC, whether in serum or tissue samples. In this study, we developed a spiral sensor implemented on a printed circuit board (PCB) technology that utilizes impedance spectroscopy and applied it to 24 tissues and sera samples as proof of concept. This newly devised circuit has successfully characterized HCC and normal tissue and serum samples. Utilizing the distinct dielectric properties between HCC cells and serum samples versus the normal samples across a specific frequency range, the differentiation between normal and HCC samples is achieved. Moreover, the sensor effectively characterizes two HCC grades and distinguishes cirrhotic/non-cirrhotic samples from tissue specimens. In addition, the sensor distinguishes cirrhotic/non-cirrhotic samples from serum specimens. This pioneering study introduces Electrical Impedance Spectroscopy (EIS) spiral sensor for diagnosing HCC and liver cirrhosis in clinical serum-an innovative, low-cost, rapid (< 2 min), and precise PCB-based technology without elaborate sample preparation, offering a novel non-labeled screening approach for disease staging and liver conditions.

Machine-Learning-Based Identification of Key Feature RNA-Signature Linked to Diagnosis of Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is the third prime cause of malignancy-related mortality worldwide. Early and accurate identification of HCC is crucial for good prognosis, efficacy of therapy, and survival rates of the patients. We aimed to develop a machine-learning model incorporating differentially expressed RNA signatures with laboratory parameters to construct an RNA signature-based diagnostic model for HCC. Methods: We have used five classifiers (KNN, RF, SVM, LGBM, and DNNs) to predict the liver disease (HCC). The classifiers were trained on 187 samples and then tested on 80 samples. The model included 22 features (age, sex, smoking, cirrhosis, non-cirrhosis, albumin, ALT, AST bilirubin (total and direct), INR, AFP, HBV Ag, HCV Abs, RQmiR-1298, RQmiR-1262, RQmiR-106b-3p, RQmRNARAB11A, and RQSTAT1, RQmRNAATG12, RQLnc-WRAP53, RQLncRNA- RP11-513I15.6). Results: LGBM achieved the highest accuracy of 98.75% in predicting HCC among all models surpassing Random Forest (96.25%), DNN (91.25%), SVC (88.75%), and KNN (87.50%). Conclusion: Our machine-learning model incorporating the expression data of RAB11A/STAT1/ATG12/miR-1262/miR-1298/miR-106b-3p/lncRNA-RP11-513I15.6/lncRNA-WRAP53 signature and clinical data represents a potential novel diagnostic model for HCC.

Discriminatory power of a circulating multi-noncoding RNA panel in acute coronary syndrome subtypes: Towards precision detection.

BACKGROUND: Acute Coronary Syndrome (ACS) stands as a significant contributor to cardiovascular mortality, necessitating improved diagnostic tools for early detection and tailored therapeutic interventions. Current diagnostic modalities, exhibit limitations in sensitivity and specificity, urging the quest for novel biomarkers to enhance discrimination of the different stages of ACS including unstable angina, Non-ST-segment Elevation Myocardial Infarction (NSTEMI), and ST-segment Elevation Myocardial Infarction (STEMI). METHODS: This study investigated the potential of a plasma-circulating multi-noncoding RNA (ncRNA) panel, comprising four miRNAs (miR-182-5p, miR-23a-3p, miR-146a-5p, and miR-183-5p) and three lncRNAs (SNHG15, SNHG5, and RMRP), selected based on their intricate involvement in ACS pathogenesis and signaling pathways regulating post-myocardial infarction (MI) processes. The differential expression of these ncRNAs was validated in sera of ACS patients and healthy controls via real-time polymerase chain reaction (RT-PCR). RESULTS: Analysis revealed a marked upregulation of the multi-ncRNAs panel in ACS patients. Notably, miRNA-182-5p and lncRNA-RMRP exhibited exceptional discriminatory power, indicated by the high area under the curve (AUC) values (0.990 and 0.980, respectively). Importantly, this panel displayed superior efficacy in discriminating between STEMI and NSTEMI, outperforming conventional biomarkers like creatine kinase-MB and cardiac troponins. Additionally, the four miRNAs and lncRNA RMRP showcased remarkable proficiency in distinguishing between STEMI and unstable angina. CONCLUSION: The findings underscore the promising potential of the multi-ncRNA panel as a robust tool for early ACS detection, and precise differentiation among ACS subtypes, and as a potential therapeutic target.

Experimental investigation for nonalcoholic fatty pancreas management using probiotics.

BACKGROUND: Nonalcoholic fatty pancreatitis (NAFP) presents a pressing challenge within the domain of metabolic disorders, necessitating further exploration to unveil its molecular intricacies and discover effective treatments. Our focus was to delve into the potential therapeutic impact of ZBiotic, a specially engineered strain of probiotic B. subtilis, in managing NAFP by targeting specific genes linked with necroptosis and the TNF signaling pathway, including TNF, ZBP1, HSPA1B, and MAPK3, along with their upstream epigenetic regulator, miR-5192, identified through bioinformatics. METHODS: Rats were subjected to either a standard or high-fat, high-sucrose diet (HFHS) for eight weeks. Subsequently, they were divided into groups: NAFP model, and two additional groups receiving daily doses of ZBiotic (0.5 ml and 1 ml/kg), and the original B. subtilis strain group (1 ml/kg) for four weeks, alongside the HFHS diet. RESULTS: ZBiotic exhibited remarkable efficacy in modulating gene expression, leading to the downregulation of miR-5192 and its target mRNAs (p < 0.001). Treatment resulted in the reversal of fibrosis, inflammation, and insulin resistance, evidenced by reductions in body weight, serum amylase, and lipase levels (p < 0.001), and decreased percentages of Caspase and Nuclear Factor Kappa-positive cells in pancreatic sections (p < 0.01). Notably, high-dose ZBiotic displayed superior efficacy compared to the original B. subtilis strain, highlighting its potential in mitigating NAFP progression by regulating pivotal pancreatic genes. CONCLUSION: ZBiotic holds promise in curbing NAFP advancement, curbing fibrosis and inflammation while alleviating metabolic and pathological irregularities observed in the NAFP animal model. This impact was intricately linked to the modulation of necroptosis/TNF-mediated pathway-related signatures.

Comprehensive machine learning models for predicting therapeutic targets in type 2 diabetes utilizing molecular and biochemical features in rats.

Introduction: With the increasing prevalence of type 2 diabetes mellitus (T2DM), there is an urgent need to discover effective therapeutic targets for this complex condition. Coding and non-coding RNAs, with traditional biochemical parameters, have shown promise as viable targets for therapy. Machine learning (ML) techniques have emerged as powerful tools for predicting drug responses. Method: In this study, we developed an ML-based model to identify the most influential features for drug response in the treatment of type 2 diabetes using three medicinal plant-based drugs (Rosavin, Caffeic acid, and Isorhamnetin), and a probiotics drug (Z-biotic), at different doses. A hundred rats were randomly assigned to ten groups, including a normal group, a streptozotocin-induced diabetic group, and eight treated groups. Serum samples were collected for biochemical analysis, while liver tissues (L) and adipose tissues (A) underwent histopathological examination and molecular biomarker extraction using quantitative PCR. Utilizing five machine learning algorithms, we integrated 32 molecular features and 12 biochemical features to select the most predictive targets for each model and the combined model. Results and discussion: Our results indicated that high doses of the selected drugs effectively mitigated liver inflammation, reduced insulin resistance, and improved lipid profiles and renal function biomarkers. The machine learning model identified 13 molecular features, 10 biochemical features, and 20 combined features with an accuracy of 80% and AUC (0.894, 0.93, and 0.896), respectively. This study presents an ML model that accurately identifies effective therapeutic targets implicated in the molecular pathways associated with T2DM pathogenesis.

Correction: Rosavin improves insulin resistance and alleviates hepatic and kidney damage via modulating the cGAS-STING pathway and autophagy signaling in HFD/STZ-induced T2DM animals.

[This corrects the article DOI: 10.1039/D4MD00023D.].

Rosavin improves insulin resistance and alleviates hepatic and kidney damage via modulating the cGAS-STING pathway and autophagy signaling in HFD/STZ-induced T2DM animals.

Background: Inflammation-mediated insulin resistance in type 2 diabetes mellitus (T2DM) increases complications, necessitating investigation of its mechanism to find new safe therapies. This study investigated the effect of rosavin on the autophagy and the cGAS-STING pathway-related signatures (ZBP1, STING1, DDX58, LC3B, TNF-α) and on their epigenetic modifiers (miR-1976 and lncRNA AC074117.2) that were identified from in silico analysis in T2DM animals. Methods: A T2DM rat model was established by combining a high-fat diet (HFD) and streptozotocin (STZ). After four weeks from T2DM induction, HFD/STZ-induced T2DM rats were subdivided into an untreated group (T2DM group) and three treated groups which received 10, 20, or 30 mg per kg of R. rosea daily for 4 weeks. Results: The study found that rosavin can affect the cGAS-STING pathway-related RNA signatures by decreasing the expressions of ZBP1, STING1, DDX58, and miR-1976 while increasing the lncRNA AC074117.2 level in the liver, kidney, and adipose tissues. Rosavin prevented further weight loss, reduced serum insulin and glucose, improved insulin resistance and the lipid panel, and mitigated liver and kidney damage compared to the untreated T2DM group. The treatment also resulted in reduced inflammation levels and improved autophagy manifested by decreased immunostaining of TNF-α and increased immunostaining of LC3B in the liver and kidneys of the treated T2DM rats. Conclusion: Rosavin has shown potential in attenuating T2DM, inhibiting inflammation in the liver and kidneys, and improving metabolic disturbances in a T2DM animal model. The observed effect was linked to the activation of autophagy and suppression of the cGAS-STING pathway.

Prospective insight into the role of benzyl propylene glycoside as a modulator of the cGAS-STING signaling pathway in the management of nonalcoholic fatty pancreas animal model

BACKGROUND: Nonalcoholic fatty pancreatitis (NAFP) is one of the metabolic syndrome manifestations that need further studies to determine its molecular determinants and find effective medications. We aimed to investigate the potential effect of benzyl propylene glycoside on NAFP management via targeting the pancreatic cGAS-STING pathway-related genes (DDX58, NFκB1 & CHUK) and their upstream regulator miRNA (miR-1976) that were retrieved from bioinformatics analysis. METHODS: The rats were fed either normal chow or a high-fat high-sucrose diet (HFHS), as a nutritional model for NAFP. After 8 weeks, the HFHS-fed rats were subdivided randomly into 4 groups; untreated HFHS group (NAFP model group) and three treated groups which received 3 doses of benzyl propylene glycoside (10, 20, and 30 mg/kg) daily for 4 weeks, parallel with HFHS feeding. RESULTS: The molecular analysis revealed that benzyl propylene glycoside could modulate the expression of the pancreatic cGAS-STING pathway-related through the downregulation of the expression of DDX58, NFκB1, and CHUK mRNAs and upregulation of miR-1976 expression. Moreover, the applied treatment reversed insulin resistance, inflammation, and fibrosis observed in the untreated NAFP group, as evidenced by improved lipid panel, decreased body weight and the serum level of lipase and amylase, reduced protein levels of NFκB1 and caspase-3 with a significant reduction in area % of collagen fibers in the pancreatic sections of treated animals. CONCLUSION: benzyl propylene glycoside showed a potential ability to attenuate NAFP development, inhibit pancreatic inflammation and fibrosis and reduce the pathological and metabolic disturbances monitored in the applied NAFP animal model. The detected effect was correlated with modulation of the expression of pancreatic (DDX58, NFκB1, and CHUK mRNAs and miR-1976) panel.