Repositioning of Anti-Inflammatory Drugs for the Treatment of Cervical Cancer Sub-Types
TLDR
This study employed systems biology approaches to unveil the diagnostic and treatment options from a precision medicine perspective by delineating differential inflammation-associated biomarkers associated with carcinogenesis for both subtypes by performing a meta-analysis of cervical cancer-associated transcriptomic datasets.Abstract:
Cervical cancer is the fourth most commonly diagnosed cancer worldwide and, in almost all cases is caused by infection with highly oncogenic Human Papillomaviruses (HPVs). On the other hand, inflammation is one of the hallmarks of cancer research. Here, we focused on inflammatory proteins that classify cervical cancer patients by considering individual differences between cancer patients in contrast to conventional treatments. We repurposed anti-inflammatory drugs for therapy of HPV-16 and HPV-18 infected groups, separately. In this study, we employed systems biology approaches to unveil the diagnostic and treatment options from a precision medicine perspective by delineating differential inflammation-associated biomarkers associated with carcinogenesis for both subtypes. We performed a meta-analysis of cervical cancer-associated transcriptomic datasets considering subtype differences of samples and identified the differentially expressed genes (DEGs). Using gene signature reversal on HPV-16 and HPV-18, we performed both signature- and network-based drug reversal to identify anti-inflammatory drug candidates against inflammation-associated nodes. The anti-inflammatory drug candidates were evaluated using molecular docking to determine the potential of physical interactions between the anti-inflammatory drug and inflammation-associated nodes as drug targets. We proposed 4 novels anti-inflammatory drugs (AS-601245, betamethasone, narciclasin, and methylprednisolone) for the treatment of HPV-16, 3 novel drugs for the treatment of HPV-18 (daphnetin, phenylbutazone, and tiaprofenoic acid), and 5 novel drugs (aldosterone, BMS-345541, etodolac, hydrocortisone, and prednisolone) for the treatment of both subtypes. We proposed anti-inflammatory drug candidates that have the potential to be therapeutic agents for the prevention and/or treatment of cervical cancer.read more
Citations
More filters
Journal ArticleDOI
Drug repurposing for viral cancers: A paradigm of machine learning, deep learning, and virtual screening‐based approaches
Faheem Ahmed,In-Suk Kang,Kyung Hwan Kim,Arun Asif,Chethikkattuveli Salih Abdul Rahim,Anupama Samantasinghar,Fida Hussain Memon,Kyung Hyun Choi +7 more
TL;DR: A critical review of viral cancer related databases, tools, and different machine learning, deep learning and virtual screening-based drug repurposing studies focusing on viral cancers is provided in this article .
Journal ArticleDOI
A Pan-Cancer Atlas of Differentially Interacting Hallmarks of Cancer Proteins
TL;DR: In this article , a pan-cancer analysis to map differentially interacting hallmarks of cancer proteins (DIHCPs) was performed and the differential interactome algorithm was applied to determine DIHCP and their interacting partners.
Journal ArticleDOI
Daphnetin, a Coumarin with Anticancer Potential against Human Melanoma: In Vitro Study of Its Effective Combination with Selected Cytostatic Drugs
TL;DR: In this article , the authors evaluated the anticancer potential of daphnetin (7,8-dihydroxycoumarin) and its combinations with five different cytostatic drugs (mitoxantrone, docetaxel, vemurafenib, epirubicin and cisplatin).
Journal ArticleDOI
Tumor microenvironment promotes lymphatic metastasis of cervical cancer: its mechanisms and clinical implications
Yuting Li,Xiaofan Gao,Yibao Huang,Xiaoran Zhu,Yingying Chen,Liru Xue,Qingqing Zhu,Bo Wang,Ming Wu +8 more
TL;DR: In this paper , the authors summarize the complex interactions between tumor cells and immune cells within the tumor microenvironment (TME), as well as potential therapeutic strategies to target the TME.
Journal ArticleDOI
Bioinformatics Prediction and Machine Learning on Gene Expression Data Identifies Novel Gene Candidates in Gastric Cancer
TL;DR: In this article , the authors integrated three different GC gene expression data with three different biological networks for the first time and captured biologically significant (i.e., reporter) transcripts, hub proteins, transcription factors, and receptor molecules of GC.
References
More filters
Journal ArticleDOI
Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
Freddie Bray,Jacques Ferlay,Isabelle Soerjomataram,Rebecca L. Siegel,Lindsey A. Torre,Ahmedin Jemal +5 more
TL;DR: A status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions.
Journal ArticleDOI
Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.
Hyuna Sung,Jacques Ferlay,Rebecca L. Siegel,Mathieu Laversanne,Isabelle Soerjomataram,Ahmedin Jemal,Freddie Bray +6 more
TL;DR: The GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer (IARC) as mentioned in this paper show that female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung cancer, colorectal (11 4.4%), liver (8.3%), stomach (7.7%) and female breast (6.9%), and cervical cancer (5.6%) cancers.
Journal ArticleDOI
Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks
Paul Shannon,Andrew Markiel,Owen Ozier,Nitin S. Baliga,Jonathan T. Wang,Daniel Ramage,Nada Amin,Benno Schwikowski,Trey Ideker +8 more
TL;DR: Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
Journal ArticleDOI
limma powers differential expression analyses for RNA-sequencing and microarray studies
Matthew E. Ritchie,Belinda Phipson,Di Wu,Yifang Hu,Charity W. Law,Wei Shi,Gordon K. Smyth,Gordon K. Smyth +7 more
TL;DR: The philosophy and design of the limma package is reviewed, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
Journal ArticleDOI
AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
Oleg Trott,Arthur J. Olson +1 more
TL;DR: AutoDock Vina achieves an approximately two orders of magnitude speed‐up compared with the molecular docking software previously developed in the lab, while also significantly improving the accuracy of the binding mode predictions, judging by tests on the training set used in AutoDock 4 development.