Exploiting systems biology to investigate the gene modules and drugs in ovarian cancer: A hypothesis based on the weighted gene co-expression network analysis

Nomiri, Samira; Karami, Hassan; Javadrashid, Darya; Derakhshani, Afshin; Nourbakhsh, Niloufar Sadat; Baradaran, Behzad; Solimando, Antonio Giovanni; Tabrizi, Neda Jalili; Brunetti, Oronzo; Nasseri, Saeed; Racanelli, Vito; Safarpour, Hossein; Silvestris, Nicola

doi:10.1016/j.biopha.2021.112537

Cited by 28 publications

(13 citation statements)

References 25 publications

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“…High expression of ADORA1 is associated with worse disease-free survival in OC patients. Activated ADORA1 affects cAMP signaling by inhibiting adenylate cyclase activity and, consequently, decreases intracellular cAMP levels [ 98 ].…”

Section: The Bioinformatics Prediction In Ovarian Cancermentioning

confidence: 99%

cAMP-Dependent Signaling and Ovarian Cancer

Kilanowska

Ziółkowska

Stasiak

et al. 2022

Cells

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cAMP-dependent pathway is one of the most significant signaling cascades in healthy and neoplastic ovarian cells. Working through its major effector proteins—PKA and EPAC—it regulates gene expression and many cellular functions. PKA promotes the phosphorylation of cAMP response element-binding protein (CREB) which mediates gene transcription, cell migration, mitochondrial homeostasis, cell proliferation, and death. EPAC, on the other hand, is involved in cell adhesion, binding, differentiation, and interaction between cell junctions. Ovarian cancer growth and metabolism largely depend on changes in the signal processing of the cAMP-PKA-CREB axis, often associated with neoplastic transformation, metastasis, proliferation, and inhibition of apoptosis. In addition, the intracellular level of cAMP also determines the course of other pathways including AKT, ERK, MAPK, and mTOR, that are hypo- or hyperactivated among patients with ovarian neoplasm. With this review, we summarize the current findings on cAMP signaling in the ovary and its association with carcinogenesis, multiplication, metastasis, and survival of cancer cells. Additionally, we indicate that targeting particular stages of cAMP-dependent processes might provide promising therapeutic opportunities for the effective management of patients with ovarian cancer.

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Section: The Bioinformatics Prediction In Ovarian Cancermentioning

confidence: 99%

cAMP-Dependent Signaling and Ovarian Cancer

Kilanowska

Ziółkowska

Stasiak

et al. 2022

Cells

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“…KIF11 also appears to have non-mitotic functions. Moreover, it regulates axonal branching and growth cone motility, and has recently been proven to be involved in cell motility ( 30 , 31 ).…”

Section: Discussionmentioning

confidence: 99%

KIF11: A potential prognostic biomarker for predicting bone metastasis‑free survival of prostate cancer

Wang

Liu

et al. 2022

Oncol Lett

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Most prostate cancer (PCa) cases remain indolent with a relatively good prognosis. However, bone metastasis of PCa can quickly worsen prognoses and lead to mortality. Metastasis-free survival (MFS), a strong surrogate for overall survival, is widely used in PCa prognosis research. The present study identified molecules that affect bone MFS in PCa, with clinical validation. Three datasets (GSE32269, GSE74367 and GSE77930) were downloaded from the Gene Expression Omnibus database. Hub genes most relevant to clinical traits (bone metastasis-associated morbidity) were identified by weighted gene co-expression network analysis (WGCNA) and subjected to logistic regression analysis. Patient samples were obtained between January 2014 and December 2016, with a clinically annotated follow-up in December 2021. Clinical data and follow-up information for 60 patients with PCa were used in MFS analysis. Tumor samples were retrieved, and immunohistochemistry was performed to detect vascular endothelial growth factor (VEGF). The prognostic potential of the two molecules was assessed using Cox proportional hazards regression analysis. A total of 16 gene modules were obtained via WGCNA, and the tan module, containing 147 genes, was most closely linked to bone metastasis. In total, 877 differentially expressed genes (DEGs) were detected. The DEG-tan module intersection yielded seven hub genes [BUB1, kinesin family member (KIF)2C, RACGAP1, CENPE, KIF11, TTK and KIF20A]. Using univariate and multivariate logistic regression analyses for independent risk factors of bone metastasis, KIF11 and VEGF were found to be significantly associated with a higher T stage, prostate-specific antigen level and Gleason score. In addition, KIF11 and VEGF expression levels were positively correlated (P<0.001). Using univariate Cox analysis, KIF11 and VEGF were found to exhibit a significant association with poor MFS (P<0.05). However, only KIF11 was significantly associated with MFS upon multivariate analysis (P=0.007; hazard ratio, 2.776; 95% confidence interval, 1.315-5.859). Markers of bone metastasis in PCa were identified. Overall, KIF11 is an independent indicator that can predict bone metastasis for patients with PCa, which could be used to guide clinical practice.

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“…This study first discovered potential interested modules responsible for the progression of OP through the WGCNA method, which is a powerful analytic algorithm based on holistic data mining ( 41 ). The application of WGCNA has been widely used in a variety of diseases, such as blood disease, disc degeneration, and neoplasms ( 42 – 44 ), which builds a bridge between sample characteristics and gene expression matrix. A total of 16 distinct co-expression modules were generated according to WGCNA in this study, among which the brown module was determined as the key module in the development of OP, with the highest correlations and lowest P value.…”

Section: Discussionmentioning

confidence: 99%

Revealing oxidative stress-related genes in osteoporosis and advanced structural biological study for novel natural material discovery regarding MAPKAPK2

Zhao

Zhang

et al. 2022

Front. Endocrinol.

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ObjectivesThis study aimed to find novel oxidative stress (OS)-related biomarkers of osteoporosis (OP), together with targeting the macromolecule Mitogen-activated protein kinase-activated protein kinase 2 (MAPKAPK2) protein to further discover potential novel materials based on an advanced structural biology approach.MethodsGene expression profiles of GSE35958 were obtained from the Gene Expression Omnibus (GEO) database, which were included for weighted gene co-expression network analysis (WGCNA) and differential analysis to identify the most correlated module, to identify OS-related hub genes in the progression of OP. Functional annotations were also analyzed on the interested module to get a comprehensive understanding of these genes. Then, a series of advanced structural biology methods, including high-throughput screening, pharmacological characteristic prediction, precise molecular docking, molecular dynamics simulation, etc., was implemented to discover novel natural inhibitor materials against the MAPKAPK2 protein.ResultsThe brown module containing 720 genes was identified as the interested module, and a group set of genes was determined as the hub OS-related genes, including PPP1R15A, CYB5R3, BCL2L1, ABCD1, MAPKAPK2, HSP90AB1, CSF1, RELA, P4HB, AKT1, HSP90B1, and CTNNB1. Functional analysis demonstrated that these genes were primarily enriched in response to chemical stress and several OS-related functions. Then, Novel Materials Discovery demonstrated that two compounds, ZINC000014951634 and ZINC000040976869, were found binding to MAPKAPK2 with a favorable interaction energy together with a high binding affinity, relatively low hepatoxicity and carcinogenicity, high aqueous solubility and intestinal absorption levels, etc., indicating that the two compounds were ideal potential inhibitor materials targeting MAPKAPK2.ConclusionThis study found a group set of OS-related biomarkers of OP, providing further insights for OS functions in the development of OP. This study then focused on one of the macromolecules, MAPKAPK2, to further discover potential novel materials, which was of great significance in guiding the screening of MAPKAPK2 potential materials.

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Exploiting systems biology to investigate the gene modules and drugs in ovarian cancer: A hypothesis based on the weighted gene co-expression network analysis

Cited by 28 publications

References 25 publications

cAMP-Dependent Signaling and Ovarian Cancer

cAMP-Dependent Signaling and Ovarian Cancer

KIF11: A potential prognostic biomarker for predicting bone metastasis‑free survival of prostate cancer

Revealing oxidative stress-related genes in osteoporosis and advanced structural biological study for novel natural material discovery regarding MAPKAPK2

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