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. 2022 Aug 21;12(1):14245.
doi: 10.1038/s41598-022-18358-7.

Unraveling the multi-targeted curative potential of bioactive molecules against cervical cancer through integrated omics and systems pharmacology approach

Murali Aarthy  1 , Pandiyan Muthuramalingam  2 , Manikandan Ramesh  2 , Sanjeev Kumar Singh  3
Affiliations

Unraveling the multi-targeted curative potential of bioactive molecules against cervical cancer through integrated omics and systems pharmacology approach

Murali Aarthy et al. Sci Rep. .

Abstract

Molecular level understanding on the role of viral infections causing cervical cancer is highly essential for therapeutic development. In these instances, systems pharmacology along with multi omics approach helps in unraveling the multi-targeted mechanisms of novel biologically active compounds to combat cervical cancer. The immuno-transcriptomic dataset of healthy and infected cervical cancer patients was retrieved from the array express. Further, the phytocompounds from medicinal plants were collected from the literature. Network Analyst 3.0 has been used to identify the immune genes around 384 which are differentially expressed and responsible for cervical cancer. Among the 87 compounds reported in plants for treating cervical cancer, only 79 compounds were targeting the identified immune genes of cervical cancer. The significant genes responsible for the domination in cervical cancer are identified in this study. The virogenomic signatures observed from cervical cancer caused by E7 oncoproteins serve as the potential therapeutic targets whereas, the identified compounds can act as anti-HPV drug deliveries. In future, the exploratory rationale of the acquired results will be useful in optimizing small molecules which can be a viable drug candidate.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Volcano plot of the differential gene expression of immune genes of human. The scattered points indicate the up and down regulated and non-significant genes based on the threshold applied. Red spheres represent up regulation, blue spheres represent the down regulation and black spheres represent non-significant genes.
Figure 2
Figure 2
Tissue-specific PPI network for the differentially expressed genes. The green color represents the differentially expressed genes and the pink color spheres represent the interaction with various human immune responsive genes.
Figure 3
Figure 3
Enrichment network analysis observed through Network Analyst tool representing the pathways involved in differentially expressed genes.
Figure 4
Figure 4
Visualization of CTN. Red color represents the immune responsive genes and green color represents the compound and blue color represents the tumor suppressor gene targeted by HPV.
Figure 5
Figure 5
Representation of biological processes involved in the compound targeted thirty-five immune-responsive genes. The Orange color represents the immune targets and the green color represents the diverse biological processes.
Figure 6
Figure 6
Histogram of Gene Ontology enrichment analysis corresponding to the P-value.
Figure 7
Figure 7
Tissue specific PPI obtained from Network Analyst. The pink colour in the image represents the significant immune responsive genes whereas the green color represents the interacting partners present in the human whole blood tissue.
Figure 8
Figure 8
Immune targets and the molecular interactions of the cervical cancer immune target.
Figure 9
Figure 9
Molecular interactions between HPV E7 oncoprotein and the human proteins involved in cervical cancer.
See this image and copyright information in PMC

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