Recent Updates on Oncogenic Signaling of Aurora Kinases in Chemosensitive, Chemoresistant Cancers: Novel Medicinal Chemistry Approaches for Targeting Aurora Kinases.

The Aurora Kinase family (AKI) is composed of serine-threonine protein kinases involved in the modulation of the cell cycle and mitosis. These kinases are required for regulating the adherence of hereditary-related data. Members of this family can be categorized into aurora kinase A (Ark-A), aurora kinase B (Ark-B), and aurora kinase C (Ark-C), consisting of highly conserved threonine protein kinases. These kinases can modulate cell processes such as spindle assembly, checkpoint pathway, and cytokinesis during cell division. The main aim of this review is to explore recent updates on the oncogenic signaling of aurora kinases in chemosensitive/chemoresistant cancers and to explore the various medicinal chemistry approaches to target these kinases. We searched Pubmed, Scopus, NLM, Pubchem, and Relemed to obtain information pertinent to the updated signaling role of aurora kinases and medicinal chemistry approaches and discussed the recently updated roles of each aurora kinases and their downstream signaling cascades in the progression of several chemosensitive/chemoresistant cancers; subsequently, we discussed the natural products (scoulerine, Corynoline, Hesperidin Jadomycin-B, fisetin), and synthetic, medicinal chemistry molecules as aurora kinase inhibitors (AKIs). Several natural products' efficacy was explained as AKIs in chemosensitization and chemoresistant cancers. For instance, novel triazole molecules have been used against gastric cancer, whereas cyanopyridines are used against colorectal cancer and trifluoroacetate derivatives could be used for esophageal cancer. Furthermore, quinolone hydrazine derivatives can be used to target breast cancer and cervical cancer. In contrast, the indole derivatives can be preferred to target oral cancer whereas thiosemicarbazone-indole could be used against prostate cancer, as reported in an earlier investigation against cancerous cells. Moreover, these chemical derivatives can be examined as AKIs through preclinical studies. In addition, the synthesis of novel AKIs through these medicinal chemistry substrates in the laboratory using in silico and synthetic routes could be beneficial to develop prospective novel AKIs to target chemoresistant cancers. This study is beneficial to oncologists, chemists, and medicinal chemists to explore novel chemical moiety synthesis to target specifically the peptide sequences of aurora kinases in several chemoresistant cancer cell types.

Trimethoxy Crown Chalcones as Multifunctional Class of Monoamine Oxidase Enzyme Inhibitors.

BACKGROUND: Chalcones with methoxy substituent are considered as a promising framework for the inhibition of monoamine oxidase (MAO) enzymes. METHODS: A series of nine trimethoxy substituted chalcones (TMa-TMi) was synthesized and evaluated as a multifunctional class of MAO inhibitors. All the synthesized compounds were investigated for their in vitro MAO inhibition, kinetics, reversibility, blood-brain barrier (BBB) permeation, and cytotoxicity and antioxidant potentials. RESULTS: In the present study, compound (2E)-3-(4-nitrophenyl)-1-(3,4,5-trimethoxyphenyl)prop- 2-en-1-one (TMf) was provided with a MAO-A inhibition constant value equal to 3.47±0.09 µM with a selectivity of 0.008, thus comparable to that of moclobemide, a well known potent hMAOA inhibitor (SI=0.010). Compound (2E)-3-(4-bromophenyl)-1-(3,4,5-trimethoxyphenyl)prop-2- en-1-one (TMh) show good MAO-B inhibition with inhibition constant of 0.46±0.009 µM. The PAMPA assay demonstrated that all the synthesized derivatives can cross the BBB successfully. The cytotoxicity studies revealed that TMf and TMh have 88.22 and 80.18 % cell viability at 25 µM. Compound TMf appeared as the most promising antioxidant molecule with IC50 values, relative to DPPH and H2O2 radical activities equal to 6.02±0.17 and 7.25±0.07 µM. To shed light on the molecular interactions of TMf and TMh towards MAO-A and MAO-B, molecular docking simulations and MM/GBSA calculations have been carried out. CONCLUSION: The lead molecules TMf and TMh with multi-functional nature can be further employed for the treatment of various neurodegenerative disorders and depressive states.

Identification of Key Pathways and Genes in Obesity Using Bioinformatics Analysis and Molecular Docking Studies.

Obesity is an excess accumulation of body fat. Its progression rate has remained high in recent years. Therefore, the aim of this study was to diagnose important differentially expressed genes (DEGs) associated in its development, which may be used as novel biomarkers or potential therapeutic targets for obesity. The gene expression profile of E-MTAB-6728 was downloaded from the database. After screening DEGs in each ArrayExpress dataset, we further used the robust rank aggregation method to diagnose 876 significant DEGs including 438 up regulated and 438 down regulated genes. Functional enrichment analysis was performed. These DEGs were shown to be significantly enriched in different obesity related pathways and GO functions. Then protein-protein interaction network, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. The module analysis was performed based on the whole PPI network. We finally filtered out STAT3, CORO1C, SERPINH1, MVP, ITGB5, PCM1, SIRT1, EEF1G, PTEN and RPS2 hub genes. Hub genes were validated by ICH analysis, receiver operating curve (ROC) analysis and RT-PCR. Finally a molecular docking study was performed to find small drug molecules. The robust DEGs linked with the development of obesity were screened through the expression profile, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for obesity.

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis.

INTRODUCTION: Heart failure (HF) is a heterogeneous clinical syndrome and affects millions of people all over the world. HF occurs when the cardiac overload and injury, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental HF as well as to explore active drug molecules. METHODS: The expression profiling by high throughput sequencing of GSE141910 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which contained 366 samples, including 200 heart failure samples and 166 non heart failure samples. The raw data was integrated to find differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. Gene ontology (GO) and REACTOME enrichment analyses were performed via ToppGene; protein-protein interaction (PPI) networks of the DEGs was constructed based on data from the HiPPIE interactome database; modules analysis was performed; target gene-miRNA regulatory network and target gene-TF regulatory network were constructed and analyzed; hub genes were validated; molecular docking studies was performed. RESULTS: A total of 881 DEGs, including 442 up regulated genes and 439 down regulated genes were observed. Most of the DEGs were significantly enriched in biological adhesion, extracellular matrix, signaling receptor binding, secretion, intrinsic component of plasma membrane, signaling receptor activity, extracellular matrix organization and neutrophil degranulation. The top hub genes ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 were identified from the PPI network. Module analysis revealed that HF was associated with adaptive immune system and neutrophil degranulation. The target genes, miRNAs and TFs were identified from the target gene-miRNA regulatory network and target gene-TF regulatory network. Furthermore, receiver operating characteristic (ROC) curve analysis and RT-PCR analysis revealed that ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 might serve as prognostic, diagnostic biomarkers and therapeutic target for HF. The predicted targets of these active molecules were then confirmed. CONCLUSION: The current investigation identified a series of key genes and pathways that might be involved in the progression of HF, providing a new understanding of the underlying molecular mechanisms of HF.

Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gestational diabetes mellitus.

Gestational diabetes mellitus (GDM) is the metabolic disorder that appears during pregnancy. The current investigation aimed to identify central differentially expressed genes (DEGs) in GDM. The transcription profiling by array data (E-MTAB-6418) was obtained from the ArrayExpress database. The DEGs between GDM samples and non-GDM samples were analyzed. Functional enrichment analysis were performed using ToppGene. Then we constructed the protein-protein interaction (PPI) network of DEGs by the Search Tool for the Retrieval of Interacting Genes database (STRING) and module analysis was performed. Subsequently, we constructed the miRNA-hub gene network and TF-hub gene regulatory network. The validation of hub genes was performed through receiver operating characteristic curve (ROC). Finally, the candidate small molecules as potential drugs to treat GDM were predicted by using molecular docking. Through transcription profiling by array data, a total of 869 DEGs were detected including 439 up-regulated and 430 down-regulated genes. Functional enrichment analysis showed these DEGs were mainly enriched in reproduction, cell adhesion, cell surface interactions at the vascular wall and extracellular matrix organization. Ten genes, HSP90AA1, EGFR, RPS13, RBX1, PAK1, FYN, ABL1, SMAD3, STAT3 and PRKCA were associated with GDM, according to ROC analysis. Finally, the most significant small molecules were predicted based on molecular docking. This investigation identified hub genes, signal pathways and therapeutic agents, which might help us, enhance our understanding of the mechanisms of GDM and find some novel therapeutic agents for GDM.

Investigation of candidate genes and mechanisms underlying obesity associated type 2 diabetes mellitus using bioinformatics analysis and screening of small drug molecules.

BACKGROUND: Obesity associated type 2 diabetes mellitus is a metabolic disorder ; however, the etiology of obesity associated type 2 diabetes mellitus remains largely unknown. There is an urgent need to further broaden the understanding of the molecular mechanism associated in obesity associated type 2 diabetes mellitus. METHODS: To screen the differentially expressed genes (DEGs) that might play essential roles in obesity associated type 2 diabetes mellitus, the publicly available expression profiling by high throughput sequencing data (GSE143319) was downloaded and screened for DEGs. Then, Gene Ontology (GO) and REACTOME pathway enrichment analysis were performed. The protein - protein interaction network, miRNA - target genes regulatory network and TF-target gene regulatory network were constructed and analyzed for identification of hub and target genes. The hub genes were validated by receiver operating characteristic (ROC) curve analysis and RT- PCR analysis. Finally, a molecular docking study was performed on over expressed proteins to predict the target small drug molecules. RESULTS: A total of 820 DEGs were identified between healthy obese and metabolically unhealthy obese, among 409 up regulated and 411 down regulated genes. The GO enrichment analysis results showed that these DEGs were significantly enriched in ion transmembrane transport, intrinsic component of plasma membrane, transferase activity, transferring phosphorus-containing groups, cell adhesion, integral component of plasma membrane and signaling receptor binding, whereas, the REACTOME pathway enrichment analysis results showed that these DEGs were significantly enriched in integration of energy metabolism and extracellular matrix organization. The hub genes CEBPD, TP73, ESR2, TAB1, MAP 3K5, FN1, UBD, RUNX1, PIK3R2 and TNF, which might play an essential role in obesity associated type 2 diabetes mellitus was further screened. CONCLUSIONS: The present study could deepen the understanding of the molecular mechanism of obesity associated type 2 diabetes mellitus, which could be useful in developing therapeutic targets for obesity associated type 2 diabetes mellitus.

Identification of hub genes related to the progression of type 1 diabetes by computational analysis.

BACKGROUND: Type 1 diabetes (T1D) is a serious threat to childhood life and has fairly complicated pathogenesis. Profound attempts have been made to enlighten the pathogenesis, but the molecular mechanisms of T1D are still not well known. METHODS: To identify the candidate genes in the progression of T1D, expression profiling by high throughput sequencing dataset GSE123658 was downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and gene ontology (GO) and pathway enrichment analyses were performed. The protein-protein interaction network (PPI), modules, target gene - miRNA regulatory network and target gene - TF regulatory network analysis were constructed and analyzed using HIPPIE, miRNet, NetworkAnalyst and Cytoscape. Finally, validation of hub genes was conducted by using ROC (Receiver operating characteristic) curve and RT-PCR analysis. A molecular docking study was performed. RESULTS: A total of 284 DEGs were identified, consisting of 142 up regulated genes and 142 down regulated genes. The gene ontology (GO) and pathways of the DEGs include cell-cell signaling, vesicle fusion, plasma membrane, signaling receptor activity, lipid binding, signaling by GPCR and innate immune system. Four hub genes were identified and biological process analysis revealed that these genes were mainly enriched in cell-cell signaling, cytokine signaling in immune system, signaling by GPCR and innate immune system. ROC curve and RT-PCR analysis showed that EGFR, GRIN2B, GJA1, CAP2, MIF, POLR2A, PRKACA, GABARAP, TLN1 and PXN might be involved in the advancement of T1D. Molecular docking studies showed high docking score. CONCLUSIONS: DEGs and hub genes identified in the present investigation help us understand the molecular mechanisms underlying the advancement of T1D, and provide candidate targets for diagnosis and treatment of T1D.

Identification of key pathways and genes in polycystic ovary syndrome via integrated bioinformatics analysis and prediction of small therapeutic molecules.

To enhance understanding of polycystic ovary syndrome (PCOS) at the molecular level; this investigation intends to examine the genes and pathways associated with PCOS by using an integrated bioinformatics analysis. Based on the expression profiling by high throughput sequencing data GSE84958 derived from the Gene Expression Omnibus (GEO) database, the differentially expressed genes (DEGs) between PCOS samples and normal controls were identified. We performed a functional enrichment analysis. A protein-protein interaction (PPI) network, miRNA- target genes and TF - target gene networks, were constructed and visualized, with which the hub gene nodes were identified. Validation of hub genes was performed by using receiver operating characteristic (ROC) and RT-PCR. Small drug molecules were predicted by using molecular docking. A total of 739 DEGs were identified, of which 360 genes were up regulated and 379 genes were down regulated. GO enrichment analysis revealed that up regulated genes were mainly involved in peptide metabolic process, organelle envelope and RNA binding and the down regulated genes were significantly enriched in plasma membrane bounded cell projection organization, neuron projection and DNA-binding transcription factor activity, RNA polymerase II-specific. REACTOME pathway enrichment analysis revealed that the up regulated genes were mainly enriched in translation and respiratory electron transport and the down regulated genes were mainly enriched in generic transcription pathway and transmembrane transport of small molecules. The top 10 hub genes (SAA1, ADCY6, POLR2K, RPS15, RPS15A, CTNND1, ESR1, NEDD4L, KNTC1 and NGFR) were identified from PPI network, miRNA - target gene network and TF - target gene network. The modules analysis showed that genes in modules were mainly associated with the transport of respiratory electrons and signaling NGF, respectively. We find a series of crucial genes along with the pathways that were most closely related with PCOS initiation and advancement. Our investigations provide a more detailed molecular mechanism for the progression of PCOS, detail information on the potential biomarkers and therapeutic targets.

Development of methylthiosemicarbazones as new reversible monoamine oxidase-B inhibitors for the treatment of Parkinson's disease.

Selective monoamine oxidase-B (MAO-B) inhibition is an attractive subject for the treatment of Parkinson's disease (PD). In the current study, we synthesized some selected derivatives of methylthiosemicarbazones and investigated their MAOs and acetylcholinesterase (AChE) inhibitory activities. Among the series synthesized, compounds SM5, SM4, and SM9 most inhibited MAO-B with IC50 values of 5.48, 7.06, and 8.03 µM, respectively. All compounds tested weakly inhibited MAO-A at 10 µM with the residual activities of >50%. Compound SM5 had the highest selectivity index (SI) value for MAO-B (>7.30), followed by SM4 (>5.67). Kinetic experiments revealed that SM5 competitively inhibited MAO-B, with a mean Ki value of 2.39 ± 0.15 µM. Reversibility experiments showed that SM5 reversibly inhibited MAO-B, and 3-(4,5-dimethylthiazolyl-2)-2,5-diphenyltetrazolium bromide (MTT) assays revealed that SM5 was not toxic to Vero cells (IC50 = 198.96 µg/mL). The SM5/MAO-B interaction was ascertained by molecular docking and dynamics studies. The study shows that SM5 competitively inhibits MAO-B in a reversible, moderate selective manner, and that it is non-toxic to Vero cells.Communicated by Ramaswamy H. Sarma.

Identification of potential mRNA panels for severe acute respiratory syndrome coronavirus 2 (COVID-19) diagnosis and treatment using microarray dataset and bioinformatics methods.

The goal of the present investigation is to identify the differentially expressed genes (DEGs) between SARS-CoV-2 infected and normal control samples to investigate the molecular mechanisms of infection with SARS-CoV-2. The microarray data of the dataset E-MTAB-8871 were retrieved from the ArrayExpress database. Pathway and Gene Ontology (GO) enrichment study, protein-protein interaction (PPI) network, modules, target gene-miRNA regulatory network, and target gene-TF regulatory network have been performed. Subsequently, the key genes were validated using an analysis of the receiver operating characteristic (ROC) curve. In SARS-CoV-2 infection, a total of 324 DEGs (76 up- and 248 down-regulated genes) were identified and enriched in a number of associated SARS-CoV-2 infection pathways and GO terms. Hub and target genes such as TP53, HRAS, MAPK11, RELA, IKZF3, IFNAR2, SKI, TNFRSF13C, JAK1, TRAF6, KLRF2, CD1A were identified from PPI network, target gene-miRNA regulatory network, and target gene-TF regulatory network. Study of the ROC showed that ten genes (CCL5, IFNAR2, JAK2, MX1, STAT1, BID, CD55, CD80, HAL-B, and HLA-DMA) were substantially involved in SARS-CoV-2 patients. The present investigation identified key genes and pathways that deepen our understanding of the molecular mechanisms of SARS-CoV-2 infection, and could be used for SARS-CoV-2 infection as diagnostic and therapeutic biomarkers.

Bioinformatics analyses of significant genes, related pathways, and candidate diagnostic biomarkers and molecular targets in SARS-CoV-2/COVID-19.

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infection is a leading cause of pneumonia and death. The aim of this investigation is to identify the key genes in SARS-CoV-2 infection and uncover their potential functions. We downloaded the expression profiling by high throughput sequencing of GSE152075 from the Gene Expression Omnibus database. Normalization of the data from primary SARS-CoV-2 infected samples and negative control samples in the database was conducted using R software. Then, joint analysis of the data was performed. Pathway and Gene ontology (GO) enrichment analyses were performed, and the protein-protein interaction (PPI) network, target gene - miRNA regulatory network, target gene - TF regulatory network of the differentially expressed genes (DEGs) were constructed using Cytoscape software. Identification of diagnostic biomarkers was conducted using receiver operating characteristic (ROC) curve analysis. 994 DEGs (496 up regulated and 498 down regulated genes) were identified. Pathway and GO enrichment analysis showed up and down regulated genes mainly enriched in the NOD-like receptor signaling pathway, Ribosome, response to external biotic stimulus and viral transcription in SARS-CoV-2 infection. Down and up regulated genes were selected to establish the PPI network, modules, target gene - miRNA regulatory network, target gene - TF regulatory network revealed that these genes were involved in adaptive immune system, fluid shear stress and atherosclerosis, influenza A and protein processing in endoplasmic reticulum. In total, ten genes (CBL, ISG15, NEDD4, PML, REL, CTNNB1, ERBB2, JUN, RPS8 and STUB1) were identified as good diagnostic biomarkers. In conclusion, the identified DEGs, hub genes and target genes contribute to the understanding of the molecular mechanisms underlying the advancement of SARS-CoV-2 infection and they may be used as diagnostic and molecular targets for the treatment of patients with SARS-CoV-2 infection in the future.

Identification of differentially expressed genes regulated by molecular signature in breast cancer-associated fibroblasts by bioinformatics analysis.

OBJECTIVE: Breast cancer is a severe risk to public health and has adequately convoluted pathogenesis. Therefore, the description of key molecular markers and pathways is of much importance for clarifying the molecular mechanism of breast cancer-associated fibroblasts initiation and progression. Breast cancer-associated fibroblasts gene expression dataset was downloaded from Gene Expression Omnibus database. METHODS: A total of nine samples, including three normal fibroblasts, three granulin-stimulated fibroblasts and three cancer-associated fibroblasts samples, were used to identify differentially expressed genes (DEGs) between normal fibroblasts, granulin-stimulated fibroblasts and cancer-associated fibroblasts samples. The gene ontology (GO) and pathway enrichment analysis was performed, and protein-protein interaction (PPI) network of the DEGs was constructed by NetworkAnalyst software. RESULTS: Totally, 190 DEGs were identified, including 66 up-regulated and 124 down-regulated genes. GO analysis results showed that up-regulated DEGs were significantly enriched in biological processes (BP), including cell-cell signalling and negative regulation of cell proliferation; molecular function (MF), including insulin-like growth factor II binding and insulin-like growth factor I binding; cellular component (CC), including insulin-like growth factor binding protein complex and integral component of plasma membrane; the down-regulated DEGs were significantly enriched in BP, including cell adhesion and extracellular matrix organization; MF, including N-acetylgalactosamine 4-sulfate 6-O-sulfotransferase activity and calcium ion binding; CC, including extracellular space and extracellular matrix. WIKIPATHWAYS analysis showed the up-regulated DEGs were enriched in myometrial relaxation and contraction pathways. WIKIPATHWAYS, REACTOME, PID_NCI and KEGG pathway analysis showed the down-regulated DEGs were enriched endochondral ossification, TGF beta signalling pathway, integrin cell surface interactions, beta1 integrin cell surface interactions, malaria and glycosaminoglycan biosynthesis-chondroitin sulfate/dermatan sulphate. The top 5 up-regulated hub genes, CDKN2A, MME, PBX1, IGFBP3, and TFAP2C and top 5 down-regulated hub genes VCAM1, KRT18, TGM2, ACTA2, and STAMBP were identified from the PPI network, and subnetworks revealed these genes were involved in significant pathways, including myometrial relaxation and contraction pathways, integrin cell surface interactions, beta1 integrin cell surface interaction. Besides, the target hsa-mirs for DEGs were identified. hsa-mir-759, hsa-mir-4446-5p, hsa-mir-219a-1-3p and hsa-mir-26a-5p were important miRNAs in this study. CONCLUSIONS: We pinpoint important key genes and pathways closely related with breast cancer-associated fibroblasts initiation and progression by a series of bioinformatics analysis on DEGs. These screened genes and pathways provided for a more detailed molecular mechanism underlying breast cancer-associated fibroblasts occurrence and progression, holding promise for acting as molecular markers and probable therapeutic targets.