Preparation and Optimization of MiR-375 Nano-Vector Using Two Novel Chitosan-Coated Nano-Structured Lipid Carriers as Gene Therapy for Hepatocellular Carcinoma.

Currently, there is still a lack of effective carriers with minimal side effects to deliver therapeutic miRNA. Thus, it is crucial to optimize novel drug delivery systems. MiR-375 has proven superior therapeutic potency in Hepatocellular carcinoma (HCC). The purpose of this study was to fabricate 2 novel and smart nano-carriers for the transportation efficiency of miR-375 in HCC cells and enhance its anti-tumor effects. We established the miR-375 construct through the pEGP- miR expression vector. Two nano-carriers of solid/liquid lipids and chitosan (CS) were strategically selected, prepared by high-speed homogenization, and optimized by varying nano-formulation factors. Thus, the two best nano-formulations were designated as F1 (0.5% CS) and F2 (1.5% CS) and were evaluated for miR-375 conjugation efficiency by gel electrophoresis and nanodrop assessment. Then, physio-chemical characteristics and stability tests for the miR-375 nano-plexes were all studied. Next, its efficiencies as replacement therapy in HepG2 cells have been assessed by fluorescence microscopy, flow cytometry, and cytotoxicity assay. The obtained data showed that two cationic nanostructured solid/liquid lipid carriers (NSLCs); F1 and F2 typically had the best physio-chemical parameters and long-term stability. Moreover, both F1 and F2 could form nano-plexes with the anionic miR-375 construct at weight ratios 250/1 and 50/1 via electrostatic interactions. In addition, these nano-plexes exhibited physical stability after three months and protected miR-375 from degradation in the presence of 50% fetal bovine serum (FBS). Furthermore, both nano-plexes could simultaneously deliver miR-375 into HepG2 cells and they ensure miR re-expression even in the presence of 50% FBS compared to free miR-375 (p-value < 0.001). Moreover, both F1 and F2 alone significantly exhibited minimal cytotoxicity in treated cells. In contrast, the nano-plexes significantly inhibited cell growth compared to free miR-375 or doxorubicin (DOX), respectively. More importantly, F2/miR-375 nano-plex exhibited more anti-proliferative activity in treated cells although its IC50 value was 55 times lower than DOX (p-value < 0.001). Collectively, our findings clearly emphasized the multifunctionality of the two CS-coated NSLCs in terms of their enhanced biocompatibility, biostability, conjugation, and transfection efficiency of therapeutic miR-375. Therefore, the NSLCs/miR-375 nano-plexes could serve as a novel and promising therapeutic strategy for HCC.

SIRT1 as a potential key regulator for mediating apoptosis in oropharyngeal cancer using cyclophosphamide and all-trans retinoic acid.

Although cyclophosphamide (CTX) has been used for recurrent or metastatic head and neck cancers, resistance is usually expected. Thus, we conducted this study to examine the effect of adding all-trans retinoic acid (ATRA) to CTX, to increase efficacy of CTX and reduce the risk of resistance developed. In this study, we investigated the combined effect of ATRA and CTX on the expression of apoptotic and angiogenesis markers in oropharyngeal carcinoma cell line (NO3), and the possible involved mechanisms. ATRA and CTX in combination significantly inhibited the proliferation of NO3 cells. Lower dose of CTX in combination with ATRA exhibited significant cytotoxicity than that of CTX when used alone, implying lower expected toxicity. Results showed that ATRA and CTX modulated oxidative stress; increased NOx and MDA, reduced GSH, and mRNA expression of Cox-2, SIRT1 and AMPK. Apoptosis was induced through elevating mRNA expressions of Bax and PAR-4 and suppressing that of Bcl-xl and Bcl-2, parallel with increased caspases 3 and 9 and decreased VEGF, endothelin-1 and CTGF levels. The primal action of the combined regimen on inflammatory signaling highlights its impact on cell death in NO3 cell line which was mediated by oxidative stress associated with apoptosis and suppression of angiogenesis.

A structural-based virtual screening and in vitro validation reveals novel effective inhibitors for SARS-CoV-2 helicase and endoribonuclease.

Researchers worldwide are looking for molecules that might disrupt the COVID-19 life cycle. Endoribonuclease, which is responsible for processing viral RNA to avoid detection by the host defense system, and helicase, which is responsible for unwinding the RNA helices for replication, are two key non-structural proteins. This study performs a hierarchical structure-based virtual screening approach for NSP15 and helicase to reach compounds with high binding probabilities. In this investigation, we incorporated a variety of filtering strategies for predicting compound interactions. First, we evaluated 756,275 chemicals from four databases using a deep learning method (NCI, Drug Bank, Maybridge, and COCONUT). Following that, two docking techniques (extra precision and induced fit) were utilized to evaluate the compounds' binding affinity, followed by molecular dynamic simulation supported by the MM-GBSA free binding energy calculation. Remarkably, two compounds (90616 and CNP0111740) exhibited high binding affinity values of -66.03 and -12.34 kcal/mol for helicase and NSP15, respectively. The VERO-E6 cell line was employed to test their in vitro therapeutic impact. The CC50 for CNP0111740 and 90616 were determined to be 102.767 µg/ml and 379.526 µg/ml, while the IC50 values were 140.176 µg/ml and 5.147 µg/ml, respectively. As a result, the selectivity index for CNP0111740 and 90616 is 0.73 and 73.73, respectively. Finally, these compounds were found to be novel, effective inhibitors for the virus; however, further in vivo validation is needed.Communicated by Ramaswamy H. Sarma.

In Silico Analysis of MicroRNA Expression Data in Liver Cancer.

Abnormal miRNA expression has been evidenced to be directly linked to HCC initiation and progression. This study was designed to detect possible prognostic, diagnostic, and/or therapeutic miRNAs for HCC using computational analysis of miRNAs expression. Methods: miRNA expression datasets meta-analysis was performed using the YM500v2 server to compare miRNA expression in normal and cancerous liver tissues. The most significant differentially regulated miRNAs in our study undergone target gene analysis using the mirWalk tool to obtain their validated and predicted targets. The combinatorial target prediction tool; miRror Suite was used to obtain the commonly regulated target genes. Functional enrichment analysis was performed on the resulting targets using the DAVID tool. A network was constructed based on interactions among microRNAs, their targets, and transcription factors. Hub nodes and gatekeepers were identified using network topological analysis. Further, we performed patient data survival analysis based on low and high expression of identified hubs and gatekeeper nodes, patients were stratified into low and high survival probability groups. Results: Using the meta-analysis option in the YM500v2 server, 34 miRNAs were found to be significantly differentially regulated (P-value ⩽ .05); 5 miRNAs were down-regulated while 29 were up-regulated. The validated and predicted target genes for each miRNA, as well as the combinatorially predicted targets, were obtained. DAVID enrichment analysis resulted in several important cellular functions that are directly related to the main cancer hallmarks. Among these functions are focal adhesion, cell cycle, PI3K-Akt signaling, insulin signaling, Ras and MAPK signaling pathways. Several hub genes and gatekeepers were found that could serve as potential drug targets for hepatocellular carcinoma. POU2F1 and PPARA showed a significant difference between low and high survival probabilities (P-value ⩽ .05) in HCC patients. Our study sheds light on important biomarker miRNAs for hepatocellular carcinoma along with their target genes and their regulated functions.

Targeting SARS-CoV-2 endoribonuclease: a structure-based virtual screening supported by in vitro analysis.

Researchers are focused on discovering compounds that can interfere with the COVID-19 life cycle. One of the important non-structural proteins is endoribonuclease since it is responsible for processing viral RNA to evade detection of the host defense system. This work investigates a hierarchical structure-based virtual screening approach targeting NSP15. Different filtering approaches to predict the interactions of the compounds have been included in this study. Using a deep learning technique, we screened 823,821 compounds from five different databases (ZINC15, NCI, Drug Bank, Maybridge, and NCI Diversity set III). Subsequently, two docking protocols (extra precision and induced fit) were used to assess the binding affinity of the compounds, followed by molecular dynamic simulation supported by the MM-GBSA free binding energy. Interestingly, one compound (ZINC000104379474) from the ZINC15 database has been found to have a good binding affinity of - 7.68 kcal/Mol. The VERO-E6 cell line was used to investigate its therapeutic effect in vitro. Half-maximal cytotoxic concentration and Inhibitory concentration 50 were determined to be 0.9 mg/ml and 0.01 mg/ml, respectively; therefore, the selectivity index is 90. In conclusion, ZINC000104379474 was shown to be a good hit for targeting the virus that needs further investigations in vivo to be a drug candidate.

Derivation of "Egyptian varices prediction (EVP) index": A novel noninvasive index for diagnosing esophageal varices in HCV Patients.

Introduction: Esophageal Varices (EVs) is one of the major dangerous complications of liver fibrosis. Upper Gastrointestinal (UGI) Endoscopy is necessary for its diagnosis. Repeated examinations for EVs screening severely burden endoscopic units in terms of cost and other side implications; moreover, the lack of public health resources in rural areas and primary hospitals should be considered, particularly in developing countries. So, an accurate noninvasive marker for EV is highly needed for liver disease patients. Objectives: This study sought to evaluate the values of several indices to determine how adequate are they in predicting EV and build a novel accurate prediction index. Methods: Five thousand and thirteen patients were enrolled. The laboratory tests, abdominal ultrasonography, liver stiffness measurement using Fibro-scan, and UGI endoscopy were performed. Ten common indices: Fib-4 score, AST-to-platelet ratio index, Fibrosis index, AST/ALT ratio Varices Prediction Rule, Baveno VI, APRI-Fib4 Combo, King score, "Model for End-Stage Liver Disease", and Lok Score were calculated. The significant predictors for EVs were identified by using "P-value Correlation-based Filter Selection" method, where a novel Egyptian Varices Prediction (EVP) index was developed using binary logistic regression. The diagnostic performance was evaluated by some parameters and the Area Under Curve (AUC). Results: EVP Index was correlated to EVs at 0.5; it achieved higher performance (AUC 0.788, accuracy 73.3%, and sensitivity 78%) than the other indices at a cutoff point of 0.423. Conclusion: EVP Index was a good noninvasive predictor. It had an acceptable performance for diagnosing EVs and it was only required regular laboratory tests and imaging data. It can provide a tool for classifying or arranging the patients according to the degree pre-emptive for selective endoscopy and the degree of severity. Also, it will enable clinicians to concentrate on one marker instead of a wide set of parameters.

Targeted delivery of miR-218 via decorated hyperbranched polyamidoamine for liver cancer regression.

Hepatocellular carcinoma (HCC) is one of most common causes of cancer death worldwide. MicroRNA (miRNA) replacement gene therapy is a novel approach for HCC management. MiR-218 is a promising tumor suppressor miRNA that is down-regulated in HCC. Here, our aim was the targeted delivery of miR-218 expressing DNA plasmid (pmiR-218) to suppress HCC in vitro and in vivo. Hyperbranched polyamidoamine was synthesized via simple and economically one-pot reaction followed by decoration with lactobionic acid (LA-PAMAM) to selectively deliver and restore miR-218 expression in HCC. In vitro cytotoxicity investigations revealed the high biocompatibility of LA-PAMAM. Furthermore, decoration of hyperbranched polymer with LA moieties enabled LA-PAMAM to deliver pmiR-218 more efficiently to HepG2 cells compared to both PMAMA and naked pmiR-218. Such efficient delivery of miR-218 resulted in suppression of HepG2 proliferation and down-regulation of its oncogenic HOXA1 target. In vivo, LA-PAMAM/pmiR-218 treatment of HCC induced by DEN and CCl4 in mice leads to an obvious decrease in the number and size of HCC nodules. In addition, LA-PAMAM/pmiR-218 significantly improved the liver histological features, as well as down-regulated the HOXA1 in liver tissue. In conclusion, this study showed the potential of LA-PAMAM carrier for the targeted delivery of tumor suppressor miR-218 as a therapeutic candidate for HCC.

Integrative assessment of CIP2A overexpression and mutational effects in human malignancies identifies possible deleterious variants.

KIAA1524 is the gene encoding the human cancerous inhibitor of PP2A (CIP2A) protein which is regarded as a novel target for cancer therapy. It is overexpressed in 65%-90% of tissues in almost all studied human cancers. CIP2A expression correlates with cancer progression, disease aggressivity in lung cancer besides poor survival and resistance to chemotherapy in breast cancer. Herein, a pan-cancer analysis of public gene expression datasets was conducted showing significant upregulation of CIP2A in cancerous and metastatic tissues. CIP2A overexpression also correlated with poor survival of cancer patients. To determine the non-coding variants associated with CIP2A overexpression, 5'UTR and 3'UTR variants were annotated and scored using RegulomeDB and Enformer deep learning model. The 5'UTR variants rs1239349555, rs1576326380, and rs1231839144 were predicted to be potential regulators of CIP2A overexpression scoring best on RegulomeDB annotations with a high "2a" rank of supporting experimental data. These variants also scored the highest on Enformer predictions. Analysis of the 3'UTR variants of CIP2A predicted rs56255137 and rs58758610 to alter binding sites of hsa-miR-500a-5 and (hsa-miR-3671, hsa-miR-5692a) respectively. Both variants were also found in linkage disequilibrium with rs11709183 and rs147863209 respectively at r2 ≥ 0.8. The aforementioned variants were found to be eQTL hits significantly associated with CIP2A overexpression. Further, analysis of rs11709183 and rs147863209 revealed a high "2b" rank on RegulomeDB annotations indicating a probable effect on DNAse transcription factors binding. The MuTarget analysis indicated that somatic mutations in TP53 are significantly associated with upregulated CIP2A in human cancers. Analysis of missense SNPs on CIP2A solved structure predicted seven deleterious effects. Four of these variants were also predicted as structurally and functionally destabilizing to CIP2A including; rs375108755, rs147942716, rs368722879, and rs367941403. Variant rs1193091427 was predicted as a potential intronic splicing mutation that might be responsible for the novel CIP2A variant (NOCIVA) in multiple myeloma. Finally, Enrichment of the Wnt/ß-catenin pathway within the CIP2A regulatory gene network suggested potential of therapeutic combinations between FTY720 with Wnt/ß-catenin, Plk1 and/or HDAC inhibitors to downregulate CIP2A which has been shown to be essential for the survival of different cancer cell lines.

In Silico and In Vivo Evaluation of SARS-CoV-2 Predicted Epitopes-Based Candidate Vaccine.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2, the causative agent of coronavirus disease (COVID-19)) has caused relatively high mortality rates in humans throughout the world since its first detection in late December 2019, leading to the most devastating pandemic of the current century. Consequently, SARS-CoV-2 therapeutic interventions have received high priority from public health authorities. Despite increased COVID-19 infections, a vaccine or therapy to cover all the population is not yet available. Herein, immunoinformatics and custommune tools were used to identify B and T-cells epitopes from the available SARS-CoV-2 sequences spike (S) protein. In the in silico predictions, six B cell epitopes QTGKIADYNYK, TEIYQASTPCNGVEG, LQSYGFQPT, IRGDEVRQIAPGQTGKIADYNYKLPD, FSQILPDPSKPSKRS and PFAMQMAYRFNG were cross-reacted with MHC-I and MHC-II T-cells binding epitopes and selected for vaccination in experimental animals for evaluation as candidate vaccine(s) due to their high antigenic matching and conserved score. The selected six peptides were used individually or in combinations to immunize female Balb/c mice. The immunized mice raised reactive antibodies against SARS-CoV-2 in two different short peptides located in receptor binding domain and S2 region. In combination groups, an additive effect was demonstrated in-comparison with single peptide immunized mice. This study provides novel epitope-based peptide vaccine candidates against SARS-CoV-2.

Impact of type 2 diabetes mellitus on the immunoregulatory characteristics of adipose tissue-derived mesenchymal stem cells.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder associated with several complications. Adipose tissue-derived mesenchymal stem cells (AT-MSCs) represent an emerging type of MSCs with high plasticity and immunoregulatory capabilities and are useful for treating inflammation-related disorders such as T2DM. However, the pathogenic microenvironment of T2DM may affect their therapeutic potential. We aimed to examine the impact of the diabetic milieu on the immunomodulatory/anti-inflammatory potential of AT-MSCs. METHODS: We assessed the proliferation potential, cell surface expression of MSC-characteristic markers and immunomodulatory markers, along with the gene expression and protein secretion of pro-inflammatory and anti-inflammatory cytokines and adipokines in AT-MSCs derived from T2DM patients (dAT-MSCs) vs. those derived from non-diabetic volunteers (ndAT-MSCs). Furthermore, we evaluated the IFN-γ priming effect on both groups. RESULTS: Our data revealed comparable proliferative activities in both groups. Flow cytometric analysis results showed a lower expression of CD200 and CD276 on dAT-MSCs vs. ndAT-MSCs. qPCR demonstrated upregulation of IL-1ß associated with a downregulation of IL-1RN in dAT-MSCs vs. ndAT-MSCs. IFN-γ priming induced an elevation in CD274 expression associated with IDO1 and ILRN overexpression and IL-1ß downregulation in both groups. ELISA analysis uncovered elevated levels of secreted IL-1ß, TNF, and visfatin/NAMPT in dAT-MSCs, whereas IL-1RA and IDO levels were reduced. ELISA results were also evident in the secretome of dAT-MSCs upon IFN-γ priming. CONCLUSIONS: This study suggests that the T2DM milieu alters the immunomodulatory characteristics of AT-MSCs with a shift towards a proinflammatory phenotype which may restrain their autologous therapeutic use. Furthermore, our findings indicate that IFN-γ priming could be a useful strategy for enhancing dAT-MSC anti-inflammatory potential.

Machine Learning Prediction Models for Diagnosing Hepatocellular Carcinoma with HCV-related Chronic Liver Disease.

BACKGROUND AND OBJECTIVE: Considered as one of the most recurrent types of liver malignancy, Hepatocellular Carcinoma (HCC) needs to be assessed in a non-invasive way. The objective of the current study is to develop prediction models for Chronic Hepatitis C (CHC)-related HCC using machine learning techniques. METHODS: A dataset, for 4423 CHC patients, was investigated to identify the significant parameters for predicting HCC presence. In this study, several machine learning techniques (Classification and regression tree, alternating decision tree, reduce pruning error tree and linear regression algorithm) were used to build HCC classification models for prediction of HCC presence. RESULTS: Age, alpha-fetoprotein (AFP), alkaline phosphate (ALP), albumin, and total bilirubin attributes were statistically found to be associated with HCC presence. Several HCC classification models were constructed using several machine learning algorithms. The proposed HCC classification models provide adequate area under the receiver operating characteristic curve (AUROC) and high accuracy of HCC diagnosis. AUROC ranges between 95.5% and 99%, plus overall accuracy between 93.2% and 95.6%. CONCLUSION: Models with simplistic factors have the power to predict the existence of HCC with outstanding performance.

Effect of Tumor Suppressor MiR-34a Loaded on ZSM-5 Nanozeolite in Hepatocellular Carcinoma: In Vitro and In Vivo Approach.

BACKGROUND: MicroRNA modulation therapy has shown great promise to treat hepatocellular carcinoma (HCC), however Efficient tissue-specific and safe delivery remains a major challenge. OBJECTIVE: We sought to develop an inorganic-organic hybrid vehicle for the systemic delivery of the tumor suppressor miR-34a, and to investigate the efficiency of the delivered miR-34a in the treatment of HCC in vitro and in vivo. METHODS: In the present study, pEGP-miR cloning and expression vector, expressing miR-34a, was electrostatically bound to polyethyleneimine (PEI), and then loaded onto ZSM-5 zeolite nanoparticles (ZNP). Qualitative and quantitative assessment of the transfection efficiency of miR-34a construct in HepG2 cells was applied by GFP screening and qRT-PCR, respectively. The expression of miR-34a target genes was investigated by qRT-PCR in vitro and in vivo. RESULTS: ZNP/PEI/miR-34a nano-formulation could efficiently deliver into HepG2 cells with low cytotoxicity, indicating good biocompatibility of generated nanozeolite. Furthermore, five injected doses of ZNP/PEI/miR-34a nano-formulation in HCC induced male Balb-c mice, significantly inhibited tumor growth, and demonstrated improved cell structure, in addition to a significant decrease in alphafetoprotein level and liver enzymes activities, as compared to the positive control group. Moreover, injected ZNP/PEI/miR-34a nano-formulation led to a noticeable decrease in the CD44 and c-Myc levels. Results also showed that ZNP/PEI/miR-34a nano-formulation inhibited several target oncogenes including AEG-1, and SOX-9, in vitro and in vivo. CONCLUSION: Our results suggested that miR-34a is a powerful candidate in HCC treatment and that AEG-1 and SOX-9 are novel oncotargets of miR-34a in HCC. Results also demonstrated that our nano-formulation may serve as a candidate approach for miR-34a restoration for HCC therapy, and generally for safe gene delivery.

The Promise of miRNA Replacement Therapy for Hepatocellular Carcinoma.

Hepatocellular carcinoma is a devastating tumor which accounts for death mortality rate 94% globally, and about 780,000 new cases each year. Tumor suppressor miRNAs represent a class of noncoding RNAs, which exhibit decreased or inhibited expression in the case of carcinogenesis. Therefore, the replacement of these molecules leads to post-transcriptional regulation of tens to hundreds of oncogenic targets and limiting the tumor. Interestingly, there is a group of tumor silencer miRNAs that have been highlighted in HCC and herein, our review will discuss the prominent examples of these miRs in terms of their efficient delivery using vectors, nano-delivery systems, their successful models either in vitro or in vivo and pre-clinical trials. Collectively, tumor suppressor miRNAs can act as novel therapeutics for HCC and more studies should be directed towards these promising therapeutics.

Co-regulatory Network of Oncosuppressor miRNAs and Transcription Factors for Pathology of Human Hepatic Cancer Stem Cells (HCSC).

Hepatic cancer stem cells (HCSCs) are considered as main players for the hepatocellular carcinoma (HCC) initiation, metastasis, drug resistance and recurrence. There is a growing evidence supporting the down-regulated miRNAs in HCSCs as key suppressors for the stemness traits, but still more details are vague about how these miRNAs modulate the HCC development. To uncover some of these miRNA regulatory aspects in HCSC, we compiled 15 down-regulated miRNA and their validated and predicted up-regulated targets in HCSC. The targets were enriched for several cancer cell stemness hallmarks and CSC pre-metastatic niche, which support these miRNAs role in suppression of HCSCs neoplastic transformation. Further, we constructed miRNA-Transcription factor (TF) regulatory networks, which provided new insights on the role of the proposed miRNA-TF co-regulation in the cancer stemness axis and its cross talk with the surrounding microenvironment. Our analysis revealed HCSC important hubs as candidate regulators for targeting hepatic cancer stemness such as, miR-148a, miR-214, E2F family, MYC and SLC7A5. Finally, we proposed a possible model for miRNA and TF co-regulation of HCSC signaling pathways. Our study identified an HCSC signature and set bridges between the reported results to give guide for future validation of HCC therapeutic strategies avoiding drug resistance.

A review study: Computational techniques for expecting the impact of non-synonymous single nucleotide variants in human diseases.

Non-Synonymous Single-Nucleotide Variants (nsSNVs) and mutations can create a diversity effect on proteins as changing genotype and phenotype, which interrupts its stability. The alterations in the protein stability may cause diseases like cancer. Discovering of nsSNVs and mutations can be a useful tool for diagnosing the disease at a beginning stage. Many studies introduced the various predicting singular and consensus tools that based on different Machine Learning Techniques (MLTs) using diverse datasets. Therefore, we introduce the current comprehensive review of the most popular and recent unique tools that predict pathogenic variations and Meta-tool that merge some of them for enhancing their predictive power. Also, we scanned the several types computational techniques in the state-of-the-art and methods for predicting the effect both of coding and noncoding variants. We then displayed, the protein stability predictors. We offer the details of the most common benchmark database for variations including the main predictive features used by the different methods. Finally, we address the most common fundamental criteria for performance assessment of predictive tools. This review is targeted at bioinformaticians attentive in the characterization of regulatory variants, geneticists, molecular biologists attentive in understanding more about the nature and effective role of such variants from a functional point of views, and clinicians who may hope to learn about variants in human associated with a specific disease and find out what to do next to uncover how they impact on the underlying mechanisms.

MicroRNA-150 down Regulation in Acute Myeloid Leukaemia Patients and Its Prognostic Implication.

BACKGROUND: MicroRNAs (miRNAs) are small, non-coding RNAs that are important for post-transcriptional gene regulation in both healthy and morbid conditions. Numerous miRNAs promote tumorigenesis, while others have a tumour suppressive effects. Acute myeloid leukaemia (AML) is a heterogeneous group of genetically diverse hematopoietic malignancies with variable response to treatment. AIM: Our study aimed to investigate the possible role of miR-150 in de novo adult AML and the impact of its level on survival, and we used in the silicon analysis to predict the main target genes involved in miR-150 mediated cancer pathway. MATERIAL AND METHODS: We evaluated miR-150 expression profiling assay using TaqMan primer probes RT-PCR in the plasma of 50 adult AML patients, before the start of treatment and at day 28 of treatment, along with 20 normal adult control samples. miR-16 was used as an endogenous reference for standardisation. Follow-up of patients during treatment at day 28 of induction chemotherapy and after one year was done. RESULTS: In this study, we found a significantly lower level of miR-150 in AML patients when compared to controls (p = 0.005) with 0.62 fold change than in healthy controls. Patients were divided into two groups: the low miR-150 group (miR-150 < 1) and the high miR-150 group (miR-150 > 1). A statistically significant difference was found between the two groups regarding initial total leukocytic count and initial PB blast count while for the TLC, HB and PLT count at follow up. No difference in the overall survival between the low and the high miR-150 groups could be demonstrated. CONCLUSION: Our results suggest that miR-150 functions as a tumour suppressor and gatekeeper in inhibiting cell transformation and that its downregulation is required for leukemogenesis.

Comparison of Machine Learning Approaches for Prediction of Advanced Liver Fibrosis in Chronic Hepatitis C Patients.

BACKGROUND/AIM: Using machine learning approaches as non-invasive methods have been used recently as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy. This study aims to evaluate different machine learning techniques in prediction of advanced fibrosis by combining the serum bio-markers and clinical information to develop the classification models. METHODS: A prospective cohort of 39,567 patients with chronic hepatitis C was divided into two sets-one categorized as mild to moderate fibrosis (F0-F2), and the other categorized as advanced fibrosis (F3-F4) according to METAVIR score. Decision tree, genetic algorithm, particle swarm optimization, and multi-linear regression models for advanced fibrosis risk prediction were developed. Receiver operating characteristic curve analysis was performed to evaluate the performance of the proposed models. RESULTS: Age, platelet count, AST, and albumin were found to be statistically significant to advanced fibrosis. The machine learning algorithms under study were able to predict advanced fibrosis in patients with HCC with AUROC ranging between 0.73 and 0.76 and accuracy between 66.3 and 84.4 percent. CONCLUSIONS: Machine-learning approaches could be used as alternative methods in prediction of the risk of advanced liver fibrosis due to chronic hepatitis C.

Down-regulation of circulating microRNA let-7a in Egyptian smokers.

Altered miRNAs were associated with cigarette smoking. The study aimed to examine the gene expression level of plasma let-7a among healthy smokers and compared it with the non-smokers. Forty subjects were recruited for the present study and classified into 21 smokers and 19 non-smokers, age, and sex were matched. The software that used to design functional primers was MIRprimer. Quantitative real-time PCR was employed to compare the relative expression of plasma let-7a. Results showed that the level of let-7a was down-regulated in smokers to 0.34fold (p = 0.006) that of the non-smokers. Plasma let-7a showed an area under curve (AUC) of 0.749 with sensitivity 43% and specificity 100%. In conclusion, plasma let-7a was significantly down-regulated in the smokers, and it might be considered a candidate biomarker to discriminate between smokers and non-smokers.

In silico identification of potential key regulatory factors in smoking-induced lung cancer.

BACKGROUND: Lung cancer is a leading cause of cancer-related death worldwide and is the most commonly diagnosed cancer. Like other cancers, it is a complex and highly heterogeneous disease involving multiple signaling pathways. Identifying potential therapeutic targets is critical for the development of effective treatment strategies. METHODS: We used a systems biology approach to identify potential key regulatory factors in smoking-induced lung cancer. We first identified genes that were differentially expressed between smokers with normal lungs and those with cancerous lungs, then integrated these differentially expressed genes (DEGs) with data from a protein-protein interaction database to build a network model with functional modules for pathway analysis. We also carried out a gene set enrichment analysis of DEG lists using the Kinase Enrichment Analysis (KEA), Protein-Protein Interaction (PPI) hubs, and KEGG (Kyoto Encyclopedia of Genes and Genomes) databases. RESULTS: Twelve transcription factors were identified as having potential significance in lung cancer (CREB1, NUCKS1, HOXB4, MYCN, MYC, PHF8, TRIM28, WT1, CUX1, CRX, GABP, and TCF3); three of these (CRX, GABP, and TCF) have not been previously implicated in lung carcinogenesis. In addition, 11 kinases were found to be potentially related to lung cancer (MAPK1, IGF1R, RPS6KA1, ATR, MAPK14, MAPK3, MAPK4, MAPK8, PRKCZ, and INSR, and PRKAA1). However, PRKAA1 is reported here for the first time. MEPCE, CDK1, PRKCA, COPS5, GSK3B, BRCA1, EP300, and PIN1 were identified as potential hubs in lung cancer-associated signaling. In addition, we found 18 pathways that were potentially related to lung carcinogenesis, of which 12 (mitogen-activated protein kinase, gonadotropin-releasing hormone, Toll-like receptor, ErbB, and insulin signaling; purine and ether lipid metabolism; adherens junctions; regulation of autophagy; snare interactions in vesicular transport; and cell cycle) have been previously identified. CONCLUSION: Our systems-based approach identified potential key molecules in lung carcinogenesis and provides a basis for investigations of tumor development as well as novel drug targets for lung cancer treatment.

MicroTarget: MicroRNA target gene prediction approach with application to breast cancer.

MicroRNAs are known to play an essential role in gene regulation in plants and animals. The standard method for understanding microRNA-gene interactions is randomized controlled perturbation experiments. These experiments are costly and time consuming. Therefore, use of computational methods is essential. Currently, several computational methods have been developed to discover microRNA target genes. However, these methods have limitations based on the features that are used for prediction. The commonly used features are complementarity to the seed region of the microRNA, site accessibility, and evolutionary conservation. Unfortunately, not all microRNA target sites are conserved or adhere to exact seed complementary, and relying on site accessibility does not guarantee that the interaction exists. Moreover, the study of regulatory interactions composed of the same tissue expression data for microRNAs and mRNAs is necessary to understand the specificity of regulation and function. We developed MicroTarget to predict a microRNA-gene regulatory network using heterogeneous data sources, especially gene and microRNA expression data. First, MicroTarget employs expression data to learn a candidate target set for each microRNA. Then, it uses sequence data to provide evidence of direct interactions. MicroTarget scores and ranks the predicted targets based on a set of features. The predicted targets overlap with many of the experimentally validated ones. Our results indicate that using expression data in target prediction is more accurate in terms of specificity and sensitivity. Available at: https://bioinformatics.cs.vt.edu/~htorkey/microTarget .