Kinless hubs are potential target genes in prostate cancer network

Mangangcha, Irengbam Rocky; Malik, Md. Zubbair; Küçük, Ömer; Ali, Shakir

doi:10.1016/j.ygeno.2020.09.033

Cited by 19 publications

(10 citation statements)

References 48 publications

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“…PTEN plays a reverse role in the signaling effect of PI3K/AKT and dephosphorylates PIP3 [64,67]. Also, PTEN has the capability to inhibit AR in a clinical manner through AR nuclear translocation blocking as well as depletion of AR protein, and this matches AR over expression as per our findings [68][69][70]. Regulation of PTEN was targeted by miRNA-let-7b and miR-548 and down expression of miR-let-7b (tumor suppressor) was detected in our findings, but PTEN was not correlated with miR-548 in terms of expression [62].…”

Section: Discussionsupporting

confidence: 65%

Identification of Potential Key Genes in Prostate Cancer with Gene Expression, Pivotal Pathways and Regulatory Networks Analysis Using Integrated Bioinformatics Methods

Khan

Mohsen

Malik

et al. 2022

Genes

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Prostate cancer (PCa) is the most prevalent cancer (20%) in males and is accountable for a fifth (6.8%) cancer-related deaths in males globally. Smoking, obesity, race/ethnicity, diet, age, chemicals and radiation exposure, sexually transmitted diseases, etc. are among the most common risk factors for PCa. However, the basic change at the molecular level is the manifested confirmation of PCa. Thus, this study aims to evaluate the molecular signature for PCa in comparison to benign prostatic hyperplasia (BPH). Additionally, representation of differentially expressed genes (DEGs) are conducted with the help of some bioinformatics tools like DAVID, STRING, GEPIA, Cytoscape. The gene expression profile for the four data sets GSE55945, GSE104749, GSE46602, and GSE32571 was downloaded from NCBI, Gene Expression Omnibus (GEO). For the extracted DEGs, different types of analysis including functional and pathway enrichment analysis, protein–protein interaction (PPI) network construction, survival analysis and transcription factor (TF) prediction were conducted. We obtained 633 most significant upregulated genes and 1219 downregulated genes, and a sum total of 1852 DEGs were found from all four datasets after assessment. The key genes, including EGFR, MYC, VEGFA, and PTEN, are targeted by TF such as AR, Sp1, TP53, NF-KB1, STAT3, RELA. Moreover, miR-21-5p also found significantly associated with all the four key genes. Further, The Cancer Genome Atlas data (TCGA) independent database was used for validation of key genes EGFR, MYC, VEGFA, PTEN expression in prostate adenocarcinoma. All four key genes were found to be significantly correlated with overall survival in PCa. Therefore, the therapeutic target may be determined by the information of these key gene’s findings for the diagnosis, prognosis and treatment of PCa.

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Section: Discussionsupporting

confidence: 65%

Identification of Potential Key Genes in Prostate Cancer with Gene Expression, Pivotal Pathways and Regulatory Networks Analysis Using Integrated Bioinformatics Methods

Khan

Mohsen

Malik

et al. 2022

Genes

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“…Analysis of PCa‐related dynamic bio‐molecular interaction networks will improve our understanding of the complexity of the underlying molecular mechanisms during disease progression. [ 7 ] Accordingly, systems biology approaches such as network‐based methods have been utilized to dissect the crosstalk mechanisms of signaling pathways in PCa. [ 8 ] By integrating multiple expression datasets and protein interaction networks, one can identify functionally related gene modules within a gene community.…”

Section: Introductionmentioning

confidence: 99%

Co‐Targeting Plk1 and DNMT3a in Advanced Prostate Cancer

et al. 2021

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Because there is no effective treatment for late-stage prostate cancer (PCa) at this moment, identifying novel targets for therapy of advanced PCa is urgently needed. A new network-based systems biology approach, XDeath, is developed to detect crosstalk of signaling pathways associated with PCa progression. This unique integrated network merges gene causal regulation networks and protein-protein interactions to identify novel co-targets for PCa treatment. The results show that polo-like kinase 1 (Plk1) and DNA methyltransferase 3A (DNMT3a)-related signaling pathways are robustly enhanced during PCa progression and together they regulate autophagy as a common death mode. Mechanistically, it is shown that Plk1 phosphorylation of DNMT3a leads to its degradation in mitosis and that DNMT3a represses Plk1 transcription to inhibit autophagy in interphase, suggesting a negative feedback loop between these two proteins. Finally, a combination of the DNMT inhibitor 5-Aza-2'-deoxycytidine (5-Aza) with inhibition of Plk1 suppresses PCa synergistically.

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“…Non-Small Cell Lung Cancer (NSCLC) is highly malignant with only 5-year survival rate [ 40 , 41 ] and significant percentage of cancer-related deaths [ 42 ]. Network analysis and functional enrichment are well known techniques to find novel significant regulatory genes for any biological network [ 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. In our study we tried to identify potent biomarkers, by analyzing the DEGs between lung cancer and normal lung tissue samples of 550 Lung Cancer patients in a total of eight datasets from Gene Expression Omnibus (GEO).…”

Section: Discussionmentioning

confidence: 99%

CDK1 and HSP90AA1 Appear as the Novel Regulatory Genes in Non-Small Cell Lung Cancer: A Bioinformatics Approach

Bhattacharyya

Gupta

Sharma

et al. 2022

JPM

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Lung cancer is one of the most invasive cancers affecting over a million of the population. Non-small cell lung cancer (NSCLC) constitutes up to 85% of all lung cancer cases, and therefore, it is essential to identify predictive biomarkers of NSCLC for therapeutic purposes. Here we use a network theoretical approach to investigate the complex behavior of the NSCLC gene-regulatory interactions. We have used eight NSCLC microarray datasets GSE19188, GSE118370, GSE10072, GSE101929, GSE7670, GSE33532, GSE31547, and GSE31210 and meta-analyzed them to find differentially expressed genes (DEGs) and further constructed a protein–protein interaction (PPI) network. We analyzed its topological properties and identified significant modules of the PPI network using cytoscape network analyzer and MCODE plug-in. From the PPI network, top ten genes of each of the six topological properties like closeness centrality, maximal clique centrality (MCC), Maximum Neighborhood Component (MNC), radiality, EPC (Edge Percolated Component) and bottleneck were considered for key regulator identification. We further compared them with top ten hub genes (those with the highest degrees) to find key regulator (KR) genes. We found that two genes, CDK1 and HSP90AA1, were common in the analysis suggesting a significant regulatory role of CDK1 and HSP90AA1 in non-small cell lung cancer. Our study using a network theoretical approach, as a summary, suggests CDK1 and HSP90AA1 as key regulator genes in complex NSCLC network.

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Kinless hubs are potential target genes in prostate cancer network

Cited by 19 publications

References 48 publications

Identification of Potential Key Genes in Prostate Cancer with Gene Expression, Pivotal Pathways and Regulatory Networks Analysis Using Integrated Bioinformatics Methods

Identification of Potential Key Genes in Prostate Cancer with Gene Expression, Pivotal Pathways and Regulatory Networks Analysis Using Integrated Bioinformatics Methods

Co‐Targeting Plk1 and DNMT3a in Advanced Prostate Cancer

CDK1 and HSP90AA1 Appear as the Novel Regulatory Genes in Non-Small Cell Lung Cancer: A Bioinformatics Approach

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