Identify asthma genes across three phases based on protein–protein interaction network

Asthma is a common inflammatory disease that is generally caused by genetic mutations or environmental factors. Recently, the emerging of omics data provides an alternative way to understand asthma. In this study, the authors present a new framework to detect asthma disease genes based on protein–protein interaction network (PPIN) and gene expression. Specifically, they construct PPINs for different stages of asthma, and detect those interactions occurred in the specific stages. By investigating the proteins in these stage‐specific interactions, they find they are more likely related to asthma, and the functional enrichment analysis indicate that the pathways enriched in the differential interactions are related to the progress of asthma. Moreover, some proteins in the differential interactions have been previously reported to be related to asthma in the literature, implying the usefulness of the proposed approach.Inspec keywords: genomics, proteins, molecular biophysics, lung, pneumodynamics, diseases, genetics, molecule‐molecule reactions, molecule‐molecule collisionsOther keywords: asthma gene identification, three‐phase gene identification, protein–protein interaction network, common inflammatory disease, genetic mutation‐caused disease, environmental factors, asthma‐associated omics data, asthma disease gene detection, PPIN construction, asthma gene expression, asthma stages, stage‐specific interaction proteins, asthma stage‐specific interactions, asthma‐related interactions, functional enrichment analysis, asthma progress‐related differential interactions, differential interaction proteins     

Induction of extrinsic and intrinsic apoptosis in cervical cancer cells by Momordica dioica mediated gold nanoparticles

Bio‐fabrication of gold nanoparticles (AuNPs) has several advantages like biocompatibility, less toxicity, and eco‐friendly in nature over their chemical and physical methods. Currently, the authors fabricated AuNPs using aqueous root extract of Momordica dioica (M. dioica) and explored their anticancer application with mechanistic approaches. Different biophysical techniques such as UV–visible spectroscopy, Fourier transform infrared, X‐ray diffraction, transmission electron microscopy, selected area electron diffraction, and dynamic light scattering were employed for AuNPs characterisation. The synthesised AuNPs were mono‐dispersed, crystalline in nature, anionic surface (−23.9 mV), and spherical particle of an average diameter of 9.4 nm. In addition, the AuNPs were stable in buffers solutions and also biocompatible towards normal human cells (human vascular endothelial cells and human lung cells). The AuNPs were exhibited anticancer activity against different cancer cell lines such as human breast cancer cells, human cervical cancer cells (HeLa) and human lung cancer cells. Further, the pro‐apoptotic genes such as Bcl₂ were down‐regulated and BAX, Caspase‐3, −8, and −9 were up‐regulated in HeLa cells as compared to untreated cells. Annexin‐V‐FITC assay results showed that the AuNPs were induced apoptosis by accumulation of intracellular reactive oxygen species. To their knowledge, this is the first report on the synthesis of bioactive metal nanoparticles from M. dioica and it may open up new avenues in therapeutic applications.Inspec keywords: nanomedicine, tumours, lung, visible spectra, drug delivery systems, cancer, transmission electron microscopy, biomedical materials, molecular biophysics, light scattering, toxicology, electron diffraction, X‐ray diffraction, ultraviolet spectra, biomembranes, drugs, gold, biochemistry, particle size, cellular biophysics, nanoparticles, nanofabrication, Fourier transform infrared spectraOther keywords: extrinsic apoptosis, intrinsic apoptosis, mediated gold nanoparticles, biofabrication, physical methods, biophysical techniques, UV‐visible spectroscopy, X‐ray diffraction, transmission electron microscopy, selected area electron diffraction, AuNPs characterisation, normal human cells, human vascular endothelial cells, cancer cell lines, human breast cancer cells, human cervical cancer cells, human lung cancer cells, HeLa cells, untreated cells, bioactive metal nanoparticles, Momordica dioica mediated gold nanoparticles, Fourier transform infrared spectra, proapoptotic genes, Bcl₂ , BAX, Caspase‐3, Caspase‐9, Caspase‐8, Annexin‐V‐FITC assay, intracellular reactive oxygen species, therapeutic applications, voltage ‐23.9 mV, size 9.4 nm, Au     

Apoptotic efficacy and antiproliferative potential of silver nanoparticles synthesised from aqueous extract of sumac (Rhus coriaria L.)

Currently, nanotechnology and nanoparticles (NPs) are recognised due to their extensive applications in medicine and the treatment of certain diseases, including cancer. Silver NPs (AgNPs) synthesised by environmentally friendly method exhibit a high medical potential. This study was conducted to determine the cytotoxic and apoptotic effects of AgNPs synthesised from sumac (Anacardiaceae family) fruit aqueous extract (AgSu/NPs) on human breast cancer cells (MCF‐7). The anti‐proliferative effect of AgSu/NPs was determined by MTT assay. The apoptotic properties of AgSu/NPs were assessed by morphological analysis and acridine orange/propidium iodide (AO/PI) and DAPI staining. The mechanism of apoptosis induction in treated cells was investigated using molecular analysis. Overall results of morphological examination and cytotoxic assay revealed that AgSu/NPs exert a concentration‐dependent inhibitory effect on the viability of MCF‐7 cells (IC50 of ∼10 µmol/48 h). AO/PI staining confirmed the occurrence of apoptosis in cells treated with AgSu/NPs. In addition, molecular analysis demonstrated that the apoptosis in MCF‐7 cells exposed to AgSu/NPs was induced via up‐regulation of Bax and down‐regulation of Bcl‐2. These findings suggested the potential use of AgSu/NP as cytotoxic and pro‐apoptotic efficacy and its possible application in modern medicine for treating certain disorders, such as cancer.Inspec keywords: nanoparticles, silver, nanomedicine, biomedical materials, toxicology, cancer, molecular biophysics, proteins, biochemistry, cellular biophysics, nanofabricationOther keywords: Ag, Bcl‐2 down‐regulation, Bax up‐regulation, MCF‐7 cell viability, concentration‐dependent inhibitory effect, cytotoxic assay, molecular analysis, DAPI staining, acridine orange‐propidium iodide staining, morphological analysis, MTT assay, human breast cancer cells, sumac fruit aqueous extract, Anacardiaceae family, cytotoxic effects, drug delivery function, diseases, Rhus coriaria L, silver nanoparticles, antiproliferative potential, apoptotic efficacy     

Biclustering‐based association rule mining approach for predicting cancer‐associated protein interactions

Protein–protein interactions (PPIs) have been widely used to understand different biological processes and cellular functions associated with several diseases like cancer. Although some cancer‐related protein interaction databases are available, lack of experimental data and conflicting PPI data among different available databases have slowed down the cancer research. Therefore, in this study, the authors have focused on various proteins that are directly related to different types of cancer disease. They have prepared a PPI database between cancer‐associated proteins with the rest of the human proteins. They have also incorporated the annotation type and direction of each interaction. Subsequently, a biclustering‐based association rule mining algorithm is applied to predict new interactions with type and direction. This study shows the prediction power of association rule mining algorithm over the traditional classifier model without choosing a negative data set. The time complexity of the biclustering‐based association rule mining is also analysed and compared to traditional association rule mining. The authors are able to discover 38 new PPIs which are not present in the cancer database. The biological relevance of these newly predicted interactions is analysed by published literature. Recognition of such interactions may accelerate a way of developing new drugs to prevent different cancer‐related diseases.Inspec keywords: cancer, medical computing, data mining, proteins, genetics, pattern clusteringOther keywords: biological processes, cancer‐related diseases, cancer research, cancer‐related protein interaction databases, protein–protein interactions, cancer‐associated protein interactions, biclustering‐based association rule mining approach, negative data set, annotation type, human proteins, cancer‐associated proteins, PPI database, cancer disease     

Evaluation of potential anti‐cancer activity of cationic liposomal nanoformulated Lycopodium clavatum in colon cancer cells

Research dealing with early diagnosis and efficient treatment in colon cancer to improve patient''s survival is still under investigation. Chemotherapeutic agent result in high systemic toxicity due to their non‐specific actions on DNA repair and/or cell replication. Traditional medicine such as Lycopodium clavatum (LC) has been claimed to have therapeutic potentials against cancer. The present study focuses on targeted drug delivery of cationic liposomal nanoformulated LC (CL‐LC) in colon cancer cells (HCT15) and comparing the efficacy with an anti‐colon cancer drug, 7‐ethyl‐10‐hydroxy‐camptothecin (SN38) along with its nanoformulated form (CL‐SN38). The colloidal suspension of LC was made using thin film hydration method. The drugs were characterised using ultraviolet, dynamic light scattering, scanning electron microscopy, energy, dispersive X‐ray spectroscopy. In vitro drug release showed kinetics of 49 and 89% of SN38 and LC, whereas CL‐SN38 and CL‐LC showed 73 and 74% of sustained drug release, respectively. Studies on morphological changes, cell viability, cytotoxicity, apoptosis, cancer‐associated gene expression analysis of Bcl‐2, Bax, p53 by real‐time polymerase chain reaction and western blot analysis of Bad and p53 protein were performed. Nanoformulated LC significantly inhibited growth and increased the apoptosis of colon cancer cells indicating its potential anti‐cancer activity against colon cancer cells.Inspec keywords: cancer, biological organs, cellular biophysics, drug delivery systems, drugs, nanomedicine, genetics, DNA, molecular biophysics, biochemistry, lipid bilayers, toxicology, suspensions, colloids, light scattering, X‐ray chemical analysis, solvation, enzymes, nanostructured materialsOther keywords: energy dispersive X‐ray spectroscopy, in vitro drug release, morphological changes, cell viability, cytotoxicity, apoptosis, cancer‐associated gene expression analysis, Bcl‐2, Bax, real‐time polymerase chain reaction, western blot analysis, Bad protein, p53 protein, scanning electron microscopy, dynamic light scattering, ultraviolet scattering, thin film hydration method, colloidal suspension, nanoformulated form CL‐SN38, 7‐ethyl‐10‐hydroxy‐camptothecin, anticolon cancer drug, colon cancer cells HCT15, cationic liposomal nanoformulated LC, targeted drug delivery, therapeutic potentials, Lycopodium clavatum, traditional medicines, cell replication, DNA repair, nonspecific actions, high systemic toxicity, chemotherapeutic agents, patient survival, colon cancer treatment, colon cancer diagnosis, CL‐LC, potential anticancer activity     

Prognostic alternative splicing signature in cervical squamous cell carcinoma

Basing on alternative splicing events (ASEs) databases, the authors herein aim to explore potential prognostic biomarkers for cervical squamous cell carcinoma (CESC). mRNA expression profiles and relevant clinical data of 223 patients with CESC were obtained from The Cancer Genome Atlas (TCGA). Correlated genes, ASEs and percent‐splice‐in (PSI) were downloaded from SpliceSeq, respectively. The PSI values of survival‐associated alternative splicing events (SASEs) were used to construct the basis of a prognostic index (PI). A protein–protein interaction (PPI) network of genes related to SASEs was generated by STRING and analysed with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Consequently, 41,776 ASEs were discovered in 19,724 genes, 2596 of which linked with 3669 SASEs. The PPI network of SASEs related genes revealed that TP53 and UBA52 were core genes. The low‐risk group had a longer survival period than high‐risk counterparts, both groups being defined according to PI constructed upon the top 20 splicing events or PI on the overall splicing events. The AUC value of ROC reached up to 0.88, demonstrating the prognostic potential of PI in CESC. These findings suggested that ASEs involve in the pathogenesis of CESC and may serve as promising prognostic biomarkers for this female malignancy.Inspec keywords: gynaecology, molecular biophysics, genomics, proteins, cellular biophysics, genetics, medical computing, cancer, ontologies (artificial intelligence), RNAOther keywords: protein‐protein interaction network, CESC pathogenesis, gene ontology, Kyoto‐encyclopedia‐of‐genes‐and‐genomes, SASEs related genes, PPI network, survival‐associated alternative splicing events, PSI values, percent‐splice‐in, Cancer Genome Atlas, mRNA expression profiles, prognostic biomarkers, alternative splicing events databases, cervical squamous cell carcinoma, prognostic alternative splicing signature     

Weighted gene co expression network analysis (WGCNA) with key pathways and hub‐genes related to micro RNAs in ischemic stroke

Ischemic stroke (IS) is one of the major causes of death and disability worldwide. However, the specific mechanism of gene interplay and the biological function in IS are not clear. Therefore, more research into IS is necessary. Dataset {"type":"entrez-geo","attrs":{"text":"GSE110993","term_id":"110993"}}GSE110993 including 20 ischemic stroke (IS) and 20 control specimens are used to establish both groups and the raw RNA‐seq data were analysed. Weighted gene co‐expression network analysis (WGCNA) was used to screen the key micro‐RNA modules. The centrality of key genes were determined by module membership (mm) and gene significance (GS). The key pathways were identified by enrichment analysis with Kyoto Protocol Gene and Genome Encyclopedia (KEGG), and the key genes were validated by protein‐protein interactions network. Result: Upon investigation, 1185 up‐ and down‐regulated genes were gathered and distributed into three modules in response to their degree of correlation to clinical traits of IS, among which the turquoise module show a trait‐correlation of 0.77. The top 140 genes were further identified by GS and MM. KEGG analysis showed two pathways may evolve in the progress of IS. Discussion: CXCL12 and EIF2a may be important biomarkers for the accurate diagnosis and treatment in IS.     

Fold change based approach for identification of significant network markers in breast,lung and prostate cancer

Cancer belongs to a class of highly aggressive diseases and a leading cause of death in the world. With more than 100 types of cancers, breast, lung and prostate cancer remain to be the most common types. To identify essential network markers (NMs) and therapeutic targets in these cancers, the authors present a novel approach which uses gene expression data from microarray and RNA‐seq platforms and utilises the results from this data to evaluate protein–protein interaction (PPI) network. Differentially expressed genes (DEGs) are extracted from microarray data using three different statistical methods in R, to produce a consistent set of genes. Also, DEGs are extracted from RNA‐seq data for the same three cancer types. DEG sets found to be common in both platforms are obtained at three fold change (FC) cut‐off levels to accurately identify the level of change in expression of these genes in all three cancers. A cancer network is built using PPI data characterising gene sets at log‐FC (LFC)>1, LFC>1.5 and LFC>2, and interconnection between principal hub nodes of these networks is observed. Resulting network of hubs at three FC levels highlights prime NMs with high confidence in multiple cancers as validated by Gene Ontology functional enrichment and maximal complete subgraphs from CFinder.Inspec keywords: cancer, proteins, RNA, bioinformatics, statistical analysis, genetics, molecular biophysics, ontologies (artificial intelligence), lungOther keywords: cancer network, PPI data, gene sets, multiple cancers, Gene Ontology functional enrichment, prostate cancer, gene expression data, RNA‐seq platforms, protein–protein interaction network, DEG, microarray data, RNA‐seq data, cancer types, lung cancer, diseases, breast cancer, network markers, differentially expressed genes, fold change based approach, CFinder, statistical methods     

In silico discovery of significant pathways in colorectal cancer metastasis using a two‐stage optimisation approach

Accurate and reliable modelling of protein–protein interaction networks for complex diseases such as colorectal cancer can help better understand mechanism of diseases and potentially discover new drugs. Different machine learning methods such as empirical mode decomposition combined with least square support vector machine, and discrete Fourier transform have been widely utilised as a classifier and for automatic discovery of biomarkers for the diagnosis of the disease. The existing methods are, however, less efficient as they tend to ignore interaction with the classifier. In this study, the authors propose a two‐stage optimisation approach to effectively select biomarkers and discover interactions among them. At the first stage, particle swarm optimisation (PSO) and differential evolution (DE) are used to optimise parameters of support vector machine recursive feature elimination algorithm, and dynamic Bayesian network is then used to predict temporal relationship between biomarkers across two time points. Results show that 18 and 25 biomarkers selected by PSO and DE‐based approach, respectively, yields the same accuracy of 97.3% and F1‐score of 97.7 and 97.6%, respectively. The stratified analysis reveals that Alpha‐2‐HS‐glycoprotein was a dominant hub gene with multiple interactions to other genes including Fibrinogen alpha chain, which is also a potential biomarker for colorectal cancer.Inspec keywords: cancer, proteins, particle swarm optimisation, evolutionary computation, support vector machines, recursive functions, Bayes methods, genetics, molecular biophysics, medical computingOther keywords: colorectal cancer metastasis, two‐stage optimisation approach, protein–protein interaction networks, biomarkers, particle swarm optimisation, differential evolution, support vector machine recursive feature elimination, dynamic Bayesian network, stratified analysis, Alpha‐2‐HS‐glycoprotein, hub gene, Fibrinogen alpha chain     

MPGraph: multi‐view penalised graph clustering for predicting drug–target interactions

Identifying drug–target interactions has been a key step for drug repositioning, drug discovery and drug design. Since it is expensive to determine the interactions experimentally, computational methods are needed for predicting interactions. In this work, the authors first propose a single‐view penalised graph (SPGraph) clustering approach to integrate drug structure and protein sequence data in a structural view. The SPGraph model does clustering on drugs and targets simultaneously such that the known drug–target interactions are best preserved in the clustering results. They then apply the SPGraph to a chemical view with drug response data and gene expression data in NCI‐60 cell lines. They further generalise the SPGraph to a multi‐view penalised graph (MPGraph) version, which can integrate the structural view and chemical view of the data. In the authors'' experiments, they compare their approach with some comparison partners, and the results show that the SPGraph could improve the prediction accuracy in a small scale, and the MPGraph can achieve around 10% improvements for the prediction accuracy. They finally give some new targets for 22 Food and Drug Administration approved drugs for drug repositioning, and some can be supported by other references.Inspec keywords: graphs, drug delivery systems, drugs, proteins, molecular biophysics, molecular configurations, optimisation, eigenvalues and eigenfunctions, Laplace equations, cancer, cellular biophysics, gene therapy, medical computingOther keywords: MPGraph, multiview penalised graph clustering, drug‐target interactions, drug repositioning, drug discovery, drug design, computational methods, single‐view penalized graph clustering approach, drug structure, protein sequence data, SPGraph model, optimisation problem, spectral clustering, eigenvalue decomposition, Laplacian model, gene expression data, NCI‐60 cell lines     

Construction and investigation of breast‐cancer‐specific ceRNA network based on the mRNA and miRNA expression data

It has been proved and widely acknowledged that messenger RNAs can talk to each other by competing for a limited pool of miRNAs. The competing endogenous RNAs are called as ceRNAs. Although some researchers have recently used ceRNAs to do biological function annotations, few of them have investigated the ceRNA network on specific disease systematically. In this work, using both miRNA expression data and mRNA expression data of breast cancer patient as well as the miRNA target relations, the authors proposed a computational method to construct a breast‐cancer‐specific ceRNA network by checking whether the shared miRNA sponges between the gene pairs are significant. The ceRNA network is shown to be scale‐free, thus the topological characters such as hub nodes and communities may provide important clues for the biological mechanism. Through investigation on the communities (the dense clusters) in the network, it was found that they are related to cancer hallmarks. In addition, through function annotation of the hub genes in the network, it was found that they are related to breast cancer. Moreover, classifiers based on the discriminative hubs can significantly distinguish breast cancer patients’ risks of distant metastasis in all the three independent data sets.Inspec keywords: cancer, genetics, medical computing, molecular biophysics, RNAOther keywords: breast‐cancer specific ceRNA network construction, miRNA expression data, mRNA expression data, gene pairs, computational method, dense clusters, cancer hallmarks, biological mechanism, discriminative hub genes     

Lung cancer prediction from microarray data by gene expression programming

Lung cancer is a leading cause of cancer‐related death worldwide. The early diagnosis of cancer has demonstrated to be greatly helpful for curing the disease effectively. Microarray technology provides a promising approach of exploiting gene profiles for cancer diagnosis. In this study, the authors propose a gene expression programming (GEP)‐based model to predict lung cancer from microarray data. The authors use two gene selection methods to extract the significant lung cancer related genes, and accordingly propose different GEP‐based prediction models. Prediction performance evaluations and comparisons between the authors’ GEP models and three representative machine learning methods, support vector machine, multi‐layer perceptron and radial basis function neural network, were conducted thoroughly on real microarray lung cancer datasets. Reliability was assessed by the cross‐data set validation. The experimental results show that the GEP model using fewer feature genes outperformed other models in terms of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. It is concluded that GEP model is a better solution to lung cancer prediction problems.Inspec keywords: lung, cancer, medical diagnostic computing, patient diagnosis, genetic algorithms, feature selection, learning (artificial intelligence), support vector machines, multilayer perceptrons, radial basis function networks, reliability, sensitivity analysisOther keywords: lung cancer prediction, cancer‐related death, cancer diagnosis, gene profiles, gene expression programming‐based model, gene selection, GEP‐based prediction models, prediction performance evaluations, representative machine learning methods, support vector machine, multilayer perceptron, radial basis function neural network, real microarray lung cancer datasets, cross‐data set validation, reliability, receiver operating characteristic curve     

Sialic acid‐conjugated PLGA nanoparticles enhance the protective effect of lycopene in chemotherapeutic drug‐induced kidney injury

Lycopene (LYC) is known to protect cells from oxidative damage caused by free radicals in human tissues. In the present study, the authors designed a LYC‐loaded sialic acid (SA)‐conjugated poly(D,L‐lactide‐co‐glycolide) (PLGA) nanoparticle (LYC‐NP) to enhance the therapeutic efficacy of LYC in acute kidney injury. The characteristics of the LYC‐NPs were defined according to particle size, morphology, and in vitro drug release. The LYC‐NPs exhibited a controlled release of LYC over 48 h. Confocal laser scanning microscopy clearly highlighted the targeting potential of SA. Enhanced green fluorescence was observed for the LYC‐NPs in H₂ O₂ ‐treated human umbilical vein endothelial cells, indicating enhanced internalisation of NPs. The LYC‐NPs showed significantly greater cell viability than H₂ O₂ ‐treated cells. In addition, the LYC‐NPs remarkably reduced proinflammatory cytokine levels, attributable mainly to the increased cellular internalisation of the SA‐based carrier delivery system. Furthermore, protein levels of caspase‐3 and ‐9 were significantly down‐regulated after treatment with the LYC‐NPs. Overall, they have demonstrated that SA‐conjugated PLGA‐NPs containing LYC could be used to treat kidney injury.Inspec keywords: fluorescence, biomedical materials, biological tissues, cellular biophysics, drugs, proteins, molecular biophysics, injuries, drug delivery systems, kidney, nanomedicine, biochemistry, optical microscopy, nanoparticles, nanofabrication, cancer, toxicology, blood vessels, particle sizeOther keywords: sialic acid‐conjugated PLGA nanoparticles, chemotherapeutic drug‐induced kidney injury, LYC‐NP, LYC‐loaded sialic acid‐conjugated poly(D,L‐lactide‐co‐glycolide) nanoparticle, SA‐conjugated PLGA‐NP, protective effect, lycopene, human tissues, particle size, in vitro drug release, confocal laser scanning microscopy, green fluorescence, human umbilical vein endothelial cells, cell viability, proinflammatory cytokine levels, cellular internalisation, SA‐based carrier delivery system, time 48.0 hour     

Biogenic synthesis of biopolymer‐based Ag–Au bimetallic nanoparticle constructs and their anti‐proliferative assessment

This study reports an eco‐friendly‐based method for the preparation of biopolymer Ag–Au nanoparticles (NPs) by using gum kondagogu (GK; Cochlospermum gossypium), as both reducing and protecting agent. The formation of GK‐(Ag–Au) NPs was confirmed by UV‐absorption, fourier transformed infrared (FTIR), atomic force microscopy (AFM), scanning electron microscope (SEM) and transmission electron microscope (TEM). The GK‐(Ag–Au) NPs were of 1–12 nm in size. The anti‐proliferative activity of nanoparticle constructs was assessed by MTT assay, confocal microscopy, flow cytometry and quantitative real‐time polymerase chain reaction (PCR) techniques. Expression studies revealed up‐regulation of p53, caspase‐3, caspase‐9, peroxisome proliferator‐activated receptors (PPAR) PPARa and PPARb, genes and down‐regulation of Bcl‐2 and Bcl‐x(K) genes, in B16F10 cells treated with GK‐(Ag–Au) NPs confirming the anti‐proliferative properties of the nanoparticles.Inspec keywords: nanomedicine, transmission electron microscopy, genetics, cellular biophysics, molecular biophysics, enzymes, nanofabrication, gold, silver, scanning electron microscopy, nanoparticles, Fourier transform infrared spectra, atomic force microscopy, biomedical materialsOther keywords: size 1.0 nm to 12.0 nm, Ag‐Au, anti‐proliferative assessment, eco‐friendly‐based method, anti‐proliferative activity, anti‐proliferative properties, biopolymer‐based Ag–Au bimetallic nanoparticle, Cochlospermum gossypium, gum kondagogu, biopolymer preparation, biogenic synthesis, UV‐absorption, Fourier transform infrared spectroscopy, scanning electron microscopy, transmission electron microscopy, atomic force microscopy, MTT assay, confocal microscopy, flow cytometry, caspase‐3, caspase‐9, peroxisome proliferator‐activated receptors, Bcl‐2 gene, Bcl‐x(K) gene, B16F10 cells     

siRNA nanotherapeutics: a promising strategy for anti‐HBV therapy

Chronic hepatitis B (CHB) is the most common cause of hepatocellular carcinoma (HCC) and liver cirrhosis worldwide. In spite of the numerous advances in the treatment of CHB, drugs and vaccines have failed because of many factors like complexity, resistance, toxicity, and heavy cost. New RNA interference (RNAi)‐based technologies have developed innovative strategies to target Achilles'' heel of the several hazardous diseases involving cancer, some genetic disease, autoimmune illnesses, and viral disorders particularly hepatitis B virus (HBV) infections. Naked siRNA delivery has serious challenges including failure to cross the cell membrane, susceptibility to the enzymatic digestion, and excretion by renal filtration, which ideally can be addressed by nanoparticle‐mediated delivery systems. cccDNA formation is a significant problem in obtaining HBV infections complete cure because of strength, durability, and lack of proper immune response. Nano‐siRNA drugs have a great potential to address this problem by silencing specific genes which are involved in cccDNA formation. In this article, the authors describe siRNA nanocarrier‐mediated delivery systems as a promising new strategy for HBV infections therapy. Simultaneously, the authors completely represent the clinical trials which use these strategies for treatment of the HBV infections.Inspec keywords: tumours, drugs, genetics, cellular biophysics, RNA, nanomedicine, diseases, molecular biophysics, microorganisms, cancer, liver, nanoparticles, patient treatmentOther keywords: siRNA nanotherapeutics, anti‐HBV therapy, chronic hepatitis B, CHB, HCC, hazardous diseases, cancer, genetic disease, autoimmune illnesses, viral disorders, hepatitis B virus infections, naked siRNA delivery, cell membrane, enzymatic digestion, renal filtration, nanoparticle‐mediated delivery systems, cccDNA formation, HBV infections complete cure, nanosiRNA drugs, siRNA nanocarrier‐mediated delivery systems, HBV infections therapy, liver cirrhosis, RNA interference, immune response, hepatocellular carcinoma