scholarly journals Integrative network analysis identifies potential targets and drugs for ovarian cancer

2020 ◽  
Vol 13 (S9) ◽  
Author(s):  
Tianyu Zhang ◽  
Liwei Zhang ◽  
Fuhai Li
Keyword(s):  
Ovarian Cancer ◽  
Transcription Factors ◽  
Network Analysis ◽  
Cancer Treatment ◽  
Signaling Pathways ◽  
Cancer Patients ◽  
Cancer Death ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Integrative Network

Abstract Background Though accounts for 2.5% of all cancers in female, the death rate of ovarian cancer is high, which is the fifth leading cause of cancer death (5% of all cancer death) in female. The 5-year survival rate of ovarian cancer is less than 50%. The oncogenic molecular signaling of ovarian cancer are complicated and remain unclear, and there is a lack of effective targeted therapies for ovarian cancer treatment. Methods In this study, we propose to investigate activated signaling pathways of individual ovarian cancer patients and sub-groups; and identify potential targets and drugs that are able to disrupt the activated signaling pathways. Specifically, we first identify the up-regulated genes of individual cancer patients using Markov chain Monte Carlo (MCMC), and then identify the potential activated transcription factors. After dividing ovarian cancer patients into several sub-groups sharing common transcription factors using K-modes method, we uncover the up-stream signaling pathways of activated transcription factors in each sub-group. Finally, we mapped all FDA approved drugs targeting on the upstream signaling. Results The 427 ovarian cancer samples were divided into 3 sub-groups (with 100, 172, 155 samples respectively) based on the activated TFs (with 14, 25, 26 activated TFs respectively). Multiple up-stream signaling pathways, e.g., MYC, WNT, PDGFRA (RTK), PI3K, AKT TP53, and MTOR, are uncovered to activate the discovered TFs. In addition, 66 FDA approved drugs were identified targeting on the uncovered core signaling pathways. Forty-four drugs had been reported in ovarian cancer related reports. The signaling diversity and heterogeneity can be potential therapeutic targets for drug combination discovery. Conclusions The proposed integrative network analysis could uncover potential core signaling pathways, targets and drugs for ovarian cancer treatment.

scholarly journals Potential Prognostic Immune Biomarkers of Overall Survival in Ovarian Cancer Through Comprehensive Bioinformatics Analysis: A Novel Artificial Intelligence Survival Prediction System

2021 ◽  
Vol 8 ◽  
Author(s):  
Tingshan He ◽  
Liwen Huang ◽  
Jing Li ◽  
Peng Wang ◽  
Zhiqiao Zhang
Keyword(s):  
Artificial Intelligence ◽  
Ovarian Cancer ◽  
Overall Survival ◽  
Transcription Factors ◽  
Cancer Patients ◽  
Prognostic Model ◽  
Cox Regression ◽  
Differentially Expressed ◽  
Immune Genes ◽  
Immune Biomarkers

Background: The tumour immune microenvironment plays an important role in the biological mechanisms of tumorigenesis and progression. Artificial intelligence medicine studies based on big data and advanced algorithms are helpful for improving the accuracy of prediction models of tumour prognosis. The current research aims to explore potential prognostic immune biomarkers and develop a predictive model for the overall survival of ovarian cancer (OC) based on artificial intelligence algorithms.Methods: Differential expression analyses were performed between normal tissues and tumour tissues. Potential prognostic biomarkers were identified using univariate Cox regression. An immune regulatory network was constructed of prognostic immune genes and their highly related transcription factors. Multivariate Cox regression was used to identify potential independent prognostic immune factors and develop a prognostic model for ovarian cancer patients. Three artificial intelligence algorithms, random survival forest, multitask logistic regression, and Cox survival regression, were used to develop a novel artificial intelligence survival prediction system.Results: The current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes between tumour samples and normal samples. Further univariate Cox regression identified 84 prognostic immune gene biomarkers for ovarian cancer patients in the model dataset (GSE32062 dataset and GSE53963 dataset). An immune regulatory network was constructed involving 63 immune genes and 5 transcription factors. Fourteen immune genes (PSMB9, FOXJ1, IFT57, MAL, ANXA4, CTSH, SCRN1, MIF, LTBR, CTSD, KIFAP3, PSMB8, HSPA5, and LTN1) were recognised as independent risk factors by multivariate Cox analyses. Kaplan-Meier survival curves showed that these 14 prognostic immune genes were closely related to the prognosis of ovarian cancer patients. A prognostic nomogram was developed by using these 14 prognostic immune genes. The concordance indexes were 0.760, 0.733, and 0.765 for 1-, 3-, and 5-year overall survival, respectively. This prognostic model could differentiate high-risk patients with poor overall survival from low-risk patients. According to three artificial intelligence algorithms, the current study developed an artificial intelligence survival predictive system that could provide three individual mortality risk curves for ovarian cancer.Conclusion: In conclusion, the current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes in ovarian cancer patients. Multivariate Cox analyses identified fourteen prognostic immune biomarkers for ovarian cancer. The current study constructed an immune regulatory network involving 63 immune genes and 5 transcription factors, revealing potential regulatory associations among immune genes and transcription factors. The current study developed a prognostic model to predict the prognosis of ovarian cancer patients. The current study further developed two artificial intelligence predictive tools for ovarian cancer, which are available at https://zhangzhiqiao8.shinyapps.io/Smart_Cancer_Survival_Predictive_System_17_OC_F1001/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_17_OC_F1001/. An artificial intelligence survival predictive system could help improve individualised treatment decision-making.

scholarly journals Integrative network analysis identifies an immune-based prognostic signature as the determinant for the mesenchymal subtype in epithelial ovarian cancer

Medicine ◽  
2020 ◽  
Vol 99 (41) ◽  
pp. e22549
Author(s):  
Mingyan Sheng ◽  
Haofei Tong ◽  
Xiaoyan Lu ◽  
Ni Shanshan ◽  
Xingguo Zhang ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Network Analysis ◽  
Epithelial Ovarian Cancer ◽  
Prognostic Signature ◽  
Integrative Network

scholarly journals Metabolic network analysis predicts efficacy of FDA-approved drugs targeting the causative agent of a neglected tropical disease

2012 ◽  
Vol 6 (1) ◽  
pp. 27 ◽  
Author(s):  
Arvind K Chavali ◽  
Anna S Blazier ◽  
Jose L Tlaxca ◽  
Paul A Jensen ◽  
Richard D Pearson ◽  
...  
Keyword(s):  
Network Analysis ◽  
Metabolic Network ◽  
Causative Agent ◽  
Tropical Disease ◽  
Neglected Tropical Disease ◽  
Metabolic Network Analysis ◽  
Approved Drugs ◽  
Fda Approved Drugs

scholarly journals INTEGRATIVE NETWORK ANALYSIS TO IDENTIFY ABERRANT PATHWAY NETWORKS IN OVARIAN CANCER

2011 ◽  
Author(s):  
LI CHEN ◽  
JIANHUA XUAN ◽  
JINGHUA GU ◽  
YUE WANG ◽  
ZHEN ZHANG ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Network Analysis ◽  
Integrative Network ◽  
Pathway Networks

scholarly journals Therapeutic Targeting of Transcription Factors to Control the Cytokine Release Syndrome in COVID-19

2021 ◽  
Vol 12 ◽  
Author(s):  
Clarissa S. Santoso ◽  
Zhaorong Li ◽  
Jaice T. Rottenberg ◽  
Xing Liu ◽  
Vivian X. Shen ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Cytokine Production ◽  
Lavage Fluid ◽  
Cytokine Release ◽  
Cytokine Release Syndrome ◽  
Rna Seq ◽  
Therapeutic Targeting ◽  
Inflammatory Cytokine Production ◽  
Approved Drugs ◽  
Fda Approved Drugs

Treatment of the cytokine release syndrome (CRS) has become an important part of rescuing hospitalized COVID-19 patients. Here, we systematically explored the transcriptional regulators of inflammatory cytokines involved in the COVID-19 CRS to identify candidate transcription factors (TFs) for therapeutic targeting using approved drugs. We integrated a resource of TF-cytokine gene interactions with single-cell RNA-seq expression data from bronchoalveolar lavage fluid cells of COVID-19 patients. We found 581 significantly correlated interactions, between 95 TFs and 16 cytokines upregulated in the COVID-19 patients, that may contribute to pathogenesis of the disease. Among these, we identified 19 TFs that are targets of FDA approved drugs. We investigated the potential therapeutic effect of 10 drugs and 25 drugs combinations on inflammatory cytokine production, which revealed two drugs that inhibited cytokine production and numerous combinations that show synergistic efficacy in downregulating cytokine production. Further studies of these candidate repurposable drugs could lead to a therapeutic regimen to treat the CRS in COVID-19 patients.

scholarly journals Selective killing of homologous recombination-deficient cancer cell lines by inhibitors of the RPA:RAD52 protein-protein interaction

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248941
Author(s):  
Mona Al-Mugotir ◽  
Jeffrey J. Lovelace ◽  
Joseph George ◽  
Mika Bessho ◽  
Dhananjaya Pal ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Homologous Recombination ◽  
Cancer Cells ◽  
Cell Lines ◽  
Cancer Cell ◽  
Protein Interaction ◽  
Ovarian Cancer Cells ◽  
Protein Protein Interaction ◽  
Approved Drugs ◽  
Fda Approved Drugs

Synthetic lethality is a successful strategy employed to develop selective chemotherapeutics against cancer cells. Inactivation of RAD52 is synthetically lethal to homologous recombination (HR) deficient cancer cell lines. Replication protein A (RPA) recruits RAD52 to repair sites, and the formation of this protein-protein complex is critical for RAD52 activity. To discover small molecules that inhibit the RPA:RAD52 protein-protein interaction (PPI), we screened chemical libraries with our newly developed Fluorescence-based protein-protein Interaction Assay (FluorIA). Eleven compounds were identified, including FDA-approved drugs (quinacrine, mitoxantrone, and doxorubicin). The FluorIA was used to rank the compounds by their ability to inhibit the RPA:RAD52 PPI and showed mitoxantrone and doxorubicin to be the most effective. Initial studies using the three FDA-approved drugs showed selective killing of BRCA1-mutated breast cancer cells (HCC1937), BRCA2-mutated ovarian cancer cells (PE01), and BRCA1-mutated ovarian cancer cells (UWB1.289). It was noteworthy that selective killing was seen in cells known to be resistant to PARP inhibitors (HCC1937 and UWB1 SYr13). A cell-based double-strand break (DSB) repair assay indicated that mitoxantrone significantly suppressed RAD52-dependent single-strand annealing (SSA) and mitoxantrone treatment disrupted the RPA:RAD52 PPI in cells. Furthermore, mitoxantrone reduced radiation-induced foci-formation of RAD52 with no significant activity against RAD51 foci formation. The results indicate that the RPA:RAD52 PPI could be a therapeutic target for HR-deficient cancers. These data also suggest that RAD52 is one of the targets of mitoxantrone and related compounds.

scholarly journals In vitro Targeting of Transcription Factors to Control the Cytokine Release Syndrome in COVID-19

2020 ◽  
Author(s):  
Clarissa S. Santoso ◽  
Zhaorong Li ◽  
Jaice T. Rottenberg ◽  
Xing Liu ◽  
Vivian X. Shen ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Mononuclear Cells ◽  
Cytokine Production ◽  
Lavage Fluid ◽  
Cytokine Release ◽  
Cytokine Release Syndrome ◽  
Peripheral Blood Mononuclear ◽  
Approved Drugs ◽  
Fda Approved Drugs

AbstractTreatment of the cytokine release syndrome (CRS) has become an important part of rescuing hospitalized COVID-19 patients. Here, we systematically explored the transcriptional regulators of inflammatory cytokines involved in the COVID-19 CRS to identify candidate transcription factors (TFs) for therapeutic targeting using approved drugs. We integrated a resource of TF-cytokine gene interactions with single-cell RNA-seq expression data from bronchoalveolar lavage fluid cells of COVID-19 patients. We found 581 significantly correlated interactions, between 95 TFs and 16 cytokines upregulated in the COVID-19 patients, that may contribute to pathogenesis of the disease. Among these, we identified 19 TFs that are targets of FDA approved drugs. We investigated the potential therapeutic effect of 10 drugs and 25 drug combinations on inflammatory cytokine production in peripheral blood mononuclear cells, which revealed two drugs that inhibited cytokine production and numerous combinations that show synergistic efficacy in downregulating cytokine production. Further studies of these candidate repurposable drugs could lead to a therapeutic regimen to treat the CRS in COVID-19 patients.

scholarly journals Targeting CA-125 Transcription by Development of a Conditionally Replicative Adenovirus for Ovarian Cancer Treatment

Cancers ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 4265
Author(s):  
Er Yue ◽  
Guangchao Yang ◽  
Yuanfei Yao ◽  
Guangyu Wang ◽  
Atish Mohanty ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Treatment ◽  
Cancer Cells ◽  
Cancer Patients ◽  
Essential Gene ◽  
In Vivo Studies ◽  
Ca 125 ◽  
Ovarian Cancer Cells ◽  
Specific Expression ◽  
Dna Fragment

CA-125, encoded by the MUC16 gene, is highly expressed in most ovarian cancer cells and thus serves as a tumor marker for monitoring disease progression or treatment response in ovarian cancer patients. However, targeting MUC16/CA-125 for ovarian cancer treatment has not been successful to date. In the current study, we performed multiple steps of high-fidelity PCR and obtained a 5 kb DNA fragment upstream of the human MUC16 gene. Reporter assays indicate that this DNA fragment possesses transactivation activity in CA-125-high cancer cells, but not in CA-125-low cancer cells, indicating that the DNA fragment contains the transactivation region that controls specific expression of the MUC16 gene in ovarian cancer cells. We further refined the promoter and found a 1040 bp fragment with similar transcriptional activity and specificity. We used this refined MUC16 promoter to replace the E1A promoter in the adenovirus type 5 genome DNA, where E1A is an essential gene for adenovirus replication. We then generated a conditionally replicative oncolytic adenovirus (CRAd) that replicates in and lyses CA-125-high cancer cells, but not CA-125-low or -negative cancer cells. In vivo studies showed that intraperitoneal virus injection prolonged the survival of NSG mice inoculated intraperitoneally (ip) with selected ovarian cancer cell lines. Furthermore, the CRAd replicates in and lyses primary ovarian cancer cells, but not normal cells, collected from ovarian cancer patients. Collectively, these data indicate that targeting MUC16 transactivation utilizing CRAd is a feasible approach for ovarian cancer treatment that warrants further investigation.

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