Repositioning of Anti-Inflammatory Drugs for the Treatment of Cervical Cancer Sub-Types

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.

Bioinformatics Prediction and Machine Learning on Gene Expression Data Identifies Novel Gene Candidates in Gastric Cancer

“…In this study, a well-established statistical analysis procedure [ 23 , 24 ] was used to identify DEGs. Briefly, the raw data (stored in CEL files) of each dataset were normalized by calculating the Robust Multi-Array Average (RMA) expression measure [ 25 ] implemented in the Affy package [ 26 ] of the R/Bioconductor platform (version 4.0.2) [ 27 ].…”

RGD peptide in cancer targeting: Benefits, challenges, solutions, and possible integrin–RGD interactions

“…Cervical cancer is the fourth most frequent cancer in women globally, with HPV infection being the leading risk factor. Squamous cell carcinoma (SCC) and adenocarcinoma (AC) are the two primary histological subtypes of cervical cancer 222,223 . Several integrins have been shown to interact with RGD peptides, adding to the complexity and heterogeneity of cervical cancer.…”

Unveiling HPV's hidden link: Cardiovascular diseases and the viral intrigue

“… 62 Reduction in HPV incidence could lower the risk of subsequent cardiac diseases related to persistent HPV. Anti-inflammatory Agents Target inflammation caused by HPV infection 63 Reduction in systemic inflammation, potentially benefiting cardiac health. Antioxidants Combat oxidative stress linked to HPV 64 Minimization of oxidative damage, potentially reducing cardiac complications associated with HPV-induced inflammation.…”