In-silico study reveals immunological signaling pathways, their genes, and potential herbal drug targets in ovarian cancer

Ovarian cancer is a leading cause of death due to the fact that most of the patients are diagnosed at an advanced stage(s) of disease progression because it cannot be detected at the early stage. An immunogenic tumor and immunotherapy is strongly pursued through targeting the immune checkpoints. In previous study, the immune signaling pathways and their components have been shown as a key factor for cancer initiation, progression, and finally a scope for diagnosis. Thus, in ovarian cancer the immune-based prognostic signature remains a potential that can be explored further. We have used gene expression GEO dataset with the focus to understand how the signaling pathways and their components altered are relevant to the immune system related functions. Finally, we have explored the possible herbal drug targets (Apigenin, Quercetin, and Resveratrol) based on gene expression profiling. The herbal drug structures have been retrieved from PubChem database and the 3D protein structures for the top-ranked genes have been obtained by using SwissModel tool and in the next step SwissDock tool has been applied for performing docking studies of the herbal drugs against the top ranked differentially expressed genes. We concluded that for ovarian cancer, immune systems associated pathways are TCR, UPS, Foxo, neuroactive ligand-receptor interaction, adrenergic, NK cell, TGF, TNF, NF-kB, BCR, and APC and docking profiling reveals that the most likely targets for quercetin are GNAI3, FGFR1, and LGALS3 while apigenin and resveratrol follow the similar trends in terms of delta G. Network-based study of the docked proteins reveals that FGFR1, GNAI3, RAC1, and RHEB control a large number of DEGs of immune system and the cluster of these genes also connect each other. •The novelty of this study is that it starts from simplified approach where the gene expression public dataset (GEO) has been used to ISP in ovarian cancer.•By using multiple approach such as data analysis to docking to predict putative nutraceutical (Apigenin, Quercetin, and Resveratrol) targets in ovarian cancer.•The basis of shortlisting genes was expression profiles and TCGA dataset clinical relevance.