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.