Identification of key genes for guiding chemotherapeutic management in ovarian cancer using translational bioinformatics

Yuan, Danni; Zhou, Hongyang; Sun, Hongyu; Tian, Rui; Xia, Meihui; Sun, Lei; Liu, Yanan

doi:10.3892/ol.2020.11672

Cited by 4 publications

(3 citation statements)

References 50 publications

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“…Diseases-causing genes are widely used to explore pathogenetic processes and effective drug molecules. Several individual studies explored SARS-CoV-2 infections causing host genomic biomarkers, and their pathogenetic processes based on a single transcriptomics dataset [5,6,[12][13][14][15][16]. We reviewed their articles and did not find any common infection-causing genes.…”

Section: Introductionmentioning

confidence: 99%

Identification of host genomic biomarkers from multiple transcriptomics datasets for diagnosis and therapies of SARS-CoV-2 infections

et al. 2023

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The pandemic of COVID-19 is a severe threat to human life and the global economy. Despite the success of vaccination efforts in reducing the spread of the virus, the situation remains largely uncontrolled due to the random mutation in the RNA sequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which demands different variants of effective drugs. Disease-causing gene-mediated proteins are usually used as receptors to explore effective drug molecules. In this study, we analyzed two different RNA-Seq and one microarray gene expression profile datasets by integrating EdgeR, LIMMA, weighted gene co-expression network and robust rank aggregation approaches, which revealed SARS-CoV-2 infection causing eight hub-genes (HubGs) including HubGs; REL, AURKA, AURKB, FBXL3, OAS1, STAT4, MMP2 and IL6 as the host genomic biomarkers. Gene Ontology and pathway enrichment analyses of HubGs significantly enriched some crucial biological processes, molecular functions, cellular components and signaling pathways that are associated with the mechanisms of SARS-CoV-2 infections. Regulatory network analysis identified top-ranked 5 TFs (SRF, PBX1, MEIS1, ESR1 and MYC) and 5 miRNAs (hsa-miR-106b-5p, hsa-miR-20b-5p, hsa-miR-93-5p, hsa-miR-106a-5p and hsa-miR-20a-5p) as the key transcriptional and post-transcriptional regulators of HubGs. Then, we conducted a molecular docking analysis to determine potential drug candidates that could interact with HubGs-mediated receptors. This analysis resulted in the identification of top-ranked ten drug agents, including Nilotinib, Tegobuvir, Digoxin, Proscillaridin, Olysio, Simeprevir, Hesperidin, Oleanolic Acid, Naltrindole and Danoprevir. Finally, we investigated the binding stability of the top-ranked three drug molecules Nilotinib, Tegobuvir and Proscillaridin with the three top-ranked proposed receptors (AURKA, AURKB, OAS1) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore, the findings of this study might be useful resources for diagnosis and therapies of SARS-CoV-2 infections.

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Section: Introductionmentioning

confidence: 99%

Identification of host genomic biomarkers from multiple transcriptomics datasets for diagnosis and therapies of SARS-CoV-2 infections

et al. 2023

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“…In recent years, various lines of research have been performed that aimed to bypass drug resistance and improve the sensitivity to chemotherapeutic agents in cancer cells ( 7 , 8 ). The underlying mechanisms are often complicated, such as reducing the effective drug concentration in cells, establishing abnormalities in drug targets and altering the regulation of apoptosis ( 9 ).…”

Section: Introductionmentioning

confidence: 99%

Gemcitabine resistance in triple‑negative breast cancer cells can be reverted by <em>Drosophila melanogaster</em> deoxyribonucleoside kinase in the nucleus or cytosol

Zhao

Jiang

et al. 2020

Oncol Lett

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The development of drug resistance to chemotherapeutic agents has consistently presented a challenge in terms of the treatment of patients with triple-negative breast cancer (TNBC). In the present study, gemcitabine (dFdC)-resistant TNBC cells were established, and the effects of lentivirus-deoxyribonucleoside kinase (dNK) and a mutated form of dNK (lentivirus-dNKmut) on reversing the acquired drug resistance in dFdC-resistant TNBC cells were explored. Quantitative PCR and western blotting experiment results suggested that Drosophila melanogaster (Dm)-dNK was stably expressed in the lentivirus-infected MDA-MB-231 and MDA-MB-231R cells in the nucleus or cytosol, and autoradiography experiments revealed similar levels of enzymatic activity in the cells expressing dNK or dNKmut. In vitro cytotoxicity assay revealed that the IC 50 values of dFdC were decreased 30~50-fold in the dFdC-resistant MDA-MB-231 cells following lentiviral transfection with dNK or dNKmut, and this effect was associated with a significantly increased rate of apoptosis compared with the cells transfected with the negative control lentivirus. In conclusion, Dm-dNK in the nucleus or cytosol may be a potential candidate for reversing acquired dFdC resistance in TNBC cells, which may form the basis of novel strategies for the treatment of patients with drug-resistant TNBC.

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“…Genomic technologies, genome-wide association studies, high-throughput sequencing and methods have been used to analyze the network of genes that are linked to autism and also used to find biological subnetworks due to the genetic heterogeneity of the disease [3]. In ovarian cancer, overlapping differentially expressed genes have been identified in different groups of drug-resistant ovarian cancer cells and bioinformatics methods have been applied to identify hub genes to determine potentially effective treatment options [4].…”

Section: Introductionmentioning

confidence: 99%

Integrated Protein Network Analysis of Whole Exome Sequencing of Severe Preeclampsia

Schuster

Tollefson

Zarate

et al. 2020

Preprint

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To identify clusters of patients with shared networks of genes associated with early onset severe preeclampsia from whole exome sequencing data through novel bioinformatic analysis.We performed a case-control study using whole exome sequencing (WES) on early onset preeclamptic mothers with severe features delivering < 34 weeks and mothers who delivered ≥ 37 weeks. Genotype testing identified variants that were differentially abundant between cases and controls. A Protein-Protein interaction (PPI) analysis and visualization tool, Proteinarium, was implemented to identify clusters of patients with shared networks associated with severe preeclampsia.A total of 61 early onset preeclamptic women with severe features and 82 race and ethnicity matched control women at term were sequenced. We identified 8,867 predicted deleterious variants. 21 of these variants were nominally associated with preeclampsia by genotype testing. Using Proteinarium129 out of the 143 sequenced patients were assigned to statistically significant clusters, Cluster A and B (p< 0.0001). Case dominated Cluster A contained 47 of the 61 case subjects. There were 13 unique genes in the PPI network of Cluster A compared to control dominated Cluster B. Amongst these unique genes, LAMB2, PTK2, RAC1, QSOX1, FN1, and VCAM1 have known associations with the pathogenic mechanisms of preeclampsia.Our network analysis identified genes that were unique to a large cluster of patients with shared networks that provide insights for severe preeclampsia. We also identified genes imputed from the interactome that may otherwise have not been identified by conventional analysis. Strict phenotyping of both cases and controls improved the likelihood of identifying these otherwise difficult to find genetic associations. These uniquely identified genes and their associated variants are potential candidates for developing polygenic risk scores for severe preeclampsia. These results support our hypothesis on the genetic architecture of complex diseases and are generalizable to other phenotypes.

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Identification of key genes for guiding chemotherapeutic management in ovarian cancer using translational bioinformatics

Cited by 4 publications

References 50 publications

Identification of host genomic biomarkers from multiple transcriptomics datasets for diagnosis and therapies of SARS-CoV-2 infections

Identification of host genomic biomarkers from multiple transcriptomics datasets for diagnosis and therapies of SARS-CoV-2 infections

Gemcitabine resistance in triple‑negative breast cancer cells can be reverted by <em>Drosophila melanogaster</em> deoxyribonucleoside kinase in the nucleus or cytosol

Integrated Protein Network Analysis of Whole Exome Sequencing of Severe Preeclampsia

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