فهرست مطالب sepideh parvizpour

Glioblastoma multiforme (GBM) is incurable with routine treatments. Ascorbic acid (Asc) has antioxidant and anti-cancer properties. However, its specific anti-cancer mechanisms are only partially understood. In this study, the effect of Asc on the c-Myc, HIF-1α, and lnc-SNHG16 genes in GBM cells and their exosomes was investigated. Cells isolated from the tissue were characterized by the immunocytochemistry method (GFAP+). The cell-doubling time was determined, and FBS-free medium supplemented with Asc (5 mM) was added to the cells. The extracted exosomes in the cell culture medium were scanned by electron microscopy, Zetasizer, and BCA assay. The expression of lnc-SNHG16 in the exosomes and c-Myc and HIF-1α in the treated and control cells was evaluated by real-time PCR. The interactions between Asc and the c-Myc and HIF-1α proteins were studied using the molecular docking method. The cells showed 90–100% GFAP+ in passage 4, with a cell-doubling time of 4.8 days. Exosomal vesicles measuring 98.25–105.9 were observed. Zetasizer results showed a sharp pick at 90 nm. Protein quantitation showed 3.812 µg/ml protein in the exosomes. Lnc-SNHG16 expression was reduced (P = 0.041), and c-Myc was upregulated (P = 0.002). The expression of HIF-1α was not significant in the treated cells. Also, Asc was able to interact and affect c-Myc and HIF-1α. Asc exerts its effect by reducing lnc-SNHG16 expression in exosomes, upregulating c-Myc in GBM cells, and interacting with HIF-1α and c-Myc. Further research is necessary to achieve a full understanding of these findings.

Cancer is one of the leading causes of death worldwide and one of the greatest challenges in extending life expectancy. The paradigm of onesize- fits-all medicine has already given way to the stratification of patients by disease subtypes, clinical characteristics, and biomarkers (stratified medicine). The introduction of next-generation sequencing (NGS) in clinical oncology has made it possible to tailor cancer patient therapy to their molecular profiles. NGS is expected to lead the transition to precision medicine (PM), where the right therapeutic approach is chosen for each patient based on their characteristics and mutations. Here, we highlight how the NGS technology facilitates cancer treatment. In this regard, first, precision medicine and NGS technology are reviewed, and then, the NGS revolution in precision medicine is described. In the sequel, the role of NGS in oncology and the existing limitations are discussed. The available databases and bioinformatics tools and online servers used in NGS data analysis are also reviewed. The review ends with concluding remarks.

This study aimed to find lncRNAs and mRNAs that were expressed differently by combining microarray datasets from different studies. This was done to find important target genes in gastric cancer for anti-cancer therapy.

Gastric cancer (GC) is the fourth most frequent and second-most deadly malignancy worldwide. Thus, genetic diagnosis and treatment should focus on genetic and epigenetic variables. Based on several studies, disordered expression of non-coding RNAs (ncRNAs), such as lncRNAs, regulate gastric cancer invasion and metastasis. Besides, lncRNAs cooperatively regulate gene expression and GC progression.

We obtained differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) from three GC tissue microarray datasets by meta-analysis and screened genes using the "Limma" package. Then, using the RNAInter database, we allocated DEmRNAs to each DElncRNA. ClusterProfiler and GOplot programs were used to analyze function enrichment pathways and gene ontologies for final DEmRNAs.

A total of 9 differentially expressed lncRNAs (DElncRNAs) (5 up-regulated and 4 down-regulated), and 856 DEmRNAs (451 up-regulated and 405 down-regulated) between tumor and adjacent normal samples were found. Finally, 117 differentially expressed mRNAs were predicted as interactors of six DElncRNAs (H19, WT1-AS, EMX2OS, HOTAIR, ZEB1-AS1, and LINC00261).

In order to promote cancer therapeutics and give knowledge on the process of carcinogenesis, our study projected a network of drug-gene interactions for discovered genes and presented relevant prospective biomarkers for the prognosis of patients with stomach cancer.

Wnt/β-catenin signaling is a pathway involved in epigenetics and cancer. It regulates cell processes and malignancy in prostate cancer cells. Wnt/β-catenin signaling is also influenced by epigenetic mechanisms, such as DNA methylation, histone modifications, and non-coding RNAs. Therefore, targeting Wnt/β-catenin signaling and epigenetics may be a promising strategy for prostate cancer therapy. Blepharis persica (B. persica) is a plant with phenolic and flavonoid compounds that has anti-cancer properties. However, its mechanisms of action are unclear.We studied the effects of B. persica extract on Wnt/β-catenin signaling in prostate cancer cells (PC-3). We used MTT assay, GC-MS, RT-qPCR, and molecular docking to evaluate the cytotoxicity, composition, gene expression, and protein-ligand interactions of B. persica extract on PC-3 cells. We found that B. persica extract reduced PC-3 cell viability, down-regulated Wnt target genes (CTNNB1 and SNAIL), and up-regulated Wnt antagonists (APC and AXIN). Moreover, molecular docking analysis suggested that four compounds from B. persica extract, namely blepharone A, blepharone B, blepharone C, and blepharone D, had favorable binding energies and interactions with FZD7 protein. These results suggest that these compounds may act as potential inhibitors or modulators of FZD7 protein and thus affect the epigenetic regulation of Wnt signaling in PC-3 cells. These findings provide new insights into the molecular mechanism of B. persica extract on prostate cancer cells and suggest its potential as a natural modulator of Wnt signaling and epigenetics in prostate cancer therapy. epigenetics in PC-3 cells and may have therapeutic potential.

Cell aggregation of threedimensional (3D) culture systems (the socalled spheroids) are designed as in vitro platform to represent more accurately the in vivo environment for drug discovery by using semi-solid media. The uniform multicellular tumor spheroids can be generated based on the interaction of cells with extracellular matrix (ECM) macromolecules such as collagen and integrin. This study aimed to investigate the possible interactions between the cellulose family and collagen using both in vitro and in silico approaches.

The 3D microtissue of JIMT-1 cells was generated using hanging drop method to study the effects of charge and viscosity of the medium containing cellulose family. To determine the mode of interaction between cellulose derivatives (CDs) and collagen-integrin, docking analysis and molecular simulation were further performed using open source web servers and chemical simulations (GROMACS), respectively.

The results confirmed that the addition of CDs into the 3D medium can promote the formation of solid spheroids, where methylcellulose (MC) yielded uniform spheroids compared to carboxymethyl cellulose (CMC). Moreover, the computational analysis showed that MC interacted with both integrin and collagen, while sodium carboxymethyl cellulose (NaCMC) only interacted with collagen residues. The stated different behaviors in the 3D culture formation and collagen interaction were found in the physicochemical properties of CDs.

Based on in vitro and in silico findings, MC is suggested as an important ECMmimicking entity that can support the semi-solid medium and promote the formation of the uniform spheroid in the 3D culture.

Coronavirus disease 2019 (COVID-19) is undoubtedly the most challenging pandemic in the current century with more than 293,241 deaths worldwide since its emergence in late 2019 (updated May 13, 2020). COVID-19 is caused by a novel emerged coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Today, the world needs crucially to develop a prophylactic vaccine scheme for such emerged and emerging infectious pathogens.

In this study, we have targeted spike (S) glycoprotein, as an important surface antigen to identify its B- and T-cell immunodominant regions. We have conducted a multi-method B-cell epitope (BCE) prediction approach using different predictor algorithms to discover the most potential BCEs. Besides, we sought among a pool of MHC class I and II-associated peptide binders provided by the IEDB server through the strict cut-off values. To design a broad-coverage vaccine, we carried out a population coverage analysis for a set of candidate T-cell epitopes and based on the HLA allele frequency in the top most-affected countries by COVID-19 (update 02 April 2020).

The final determined B- and T-cell epitopes were mapped on the S glycoprotein sequence, and three potential hub regions covering the largest number of overlapping epitopes were identified for the vaccine designing (I531–N711; T717–C877; and V883–E973). Here, we have designed two domain-based constructs to be produced and delivered through the recombinant protein- and gene-based approaches, including (i) an adjuvanted domain-based protein vaccine construct (DPVC), and (ii) a self-amplifying mRNA vaccine (SAMV) construct. The safety, stability, and immunogenicity of the DPVC were validated using the integrated sequential (i.e. allergenicity, autoimmunity, and physicochemical features) and structural (i.e. molecular docking between the vaccine and human Toll-like receptors (TLRs) 4 and 5) analysis. The stability of the docked complexes was evaluated using the molecular dynamics (MD) simulations.

These rigorous in silico validations supported the potential of the DPVC and SAMV to promote both innate and specific immune responses in preclinical studies.

Triple-negative breast cancer (TNBC) is an important subtype of breast cancer, which occurs in the absence of estrogen, progesterone and HER-2 receptors. According to the recent studies, TNBC may be a cancer testis antigen (CTA)-positive tumor, indicating that the CTA-based cancer vaccine can be a treatment option for the patients bearing such tumors. Of these antigens (Ags), the MAGE-A family and NY-ESO-1 as the most immunogenic CTAs are the potentially relevant targets for the development of an immunotherapeutic way of the breast cancer treatment. In the present study, immunoinformatics approach was used to design a multi-epitope peptide vaccine to combat the TNBC. The vaccine peptide was constructed by the fusion of three crucial components, including the CD8+ cytotoxic T lymphocytes (CTLs) epitopes, helper epitopes and adjuvant. The epitopes were predicted from the MAGE-A and NY-ESO-1 Ags. In addition, the granulocyte-macrophage-colony-stimulating factor (GM-CSF) was used as an adjuvant to promote the CD4+ T cells towards the T-helper for more strong induction of CTL responses. The components were conjugated by proper linkers. The vaccine peptide was examined for different physiochemical characteristics to confirm the safety and immunogenic behavior. Furthermore, the 3D-structure of the vaccine peptide was predicted based on the homology modeling approach using the MODELLER v9.17 program. The vaccine structure was also subjected to the molecular dynamics simulation study for structure refinement. The results verified the immunogenicity and safety profile of the constructed vaccine as well as its capability for stimulating both the cellular and humoral immune responses. Based on our in-silico analyses, the proposed vaccine may be considered for the immunotherapy of TNBC.

Breast cancer, as one of the major causes of cancer death among women, is the central focus of this study. The recent advances in the development and application of computational tools and bioinformatics in the field of immunotherapy of malignancies such as breast cancer have emerged the new dominion of immunoinformatics, and therefore, next generation of immunomedicines.
Having reviewed the most recent works on the applications of computational tools, we provide comprehensive insights into the breast cancer incidence and its leading causes as well as immunotherapy approaches and the future trends. Furthermore, we discuss the impacts of bioinformatics on different stages of vaccine design for the breast cancer, which can be used to produce much more efficient vaccines through a rationalized time- and cost-effective in silico approaches prior to conducting costly experiments.
The tools can be significantly used for designing the immune system-modulating drugs and vaccines based on in silico approaches prior to in vitro and in vivo experimental evaluations. Application of immunoinformatics in the cancer immunotherapy has shown its success in the pre-clinical models. This success returns back to the impacts of several powerful computational approaches developed during the last decade.
Despite the invention of a number of vaccines for the cancer immunotherapy, more computational and clinical trials are required to design much more efficient vaccines against various malignancies, including breast cancer.