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Author Spotlight: Exploring the Role of FAM83A in Cervical Cancer
Published on: February 9, 2024
In-silico analysis reveals Quinic acid as a multitargeted inhibitor against Cervical Cancer
Shaban Ahmad1, Salwa Sayeed1, Nagmi Bano1
1Department of Computer Science, Jamia Millia Islamia, New Delhi, India.
Abstract:
The cervix is the lowermost part of the uterus that connects to the vagina, and cervical cancer is a malignant cervix tumour. One of this cancer's most important risk factors is HPV infection. In the approach to finding an effective treatment for this disease, various works have been done around genomics and drug discovery. Finding the major altered genes was one of the most significant studies completed in the field of cervical cancer by TCGA (The Cancer Genome Atlas), and these genes are TGFBR2, MED1, ERBB3, CASP8, and HLA-A. The greatest genomic alterations were found in the PI3K/MAPK and TGF-Beta signalling pathways, suggesting that numerous therapeutic targets may come from these pathways in the future. We, therefore, conducted a combined enrichment analysis of genes gathered from various works of literature for this study. The final six key genes from the list were obtained after enrichment analysis using GO, KEGG, and Reactome methods. The six proteins against the identified genes were then subjected to a docking-based screening against a library of 6,87,843 prepared natural compounds from the ZINC15 database. The most stable compound was subsequently discovered through virtual screening to be the natural substance Quinic acid, which also had the highest binding affinity for all six proteins and a better docking score. To examine their stability, the study was extended to MM/GBSA and MD simulations on the six docked proteins, and comparative docking-based calculations led us to identify the Quinic Acid as a multitargeted compound. The overall deviation of the compound was less than 2 Å for all the complexes considered best for the biological molecules, and the simulation interaction analysis reveals a huge web of interaction during the simulation.Communicated by Ramaswamy H. Sarma.
Insights
Researchers identified Quinic acid as a potential multi-targeted drug for cervical cancer by analyzing key genes and signaling pathways. This natural compound showed high binding affinity and stability against six identified cervical cancer proteins.
Area of Science:
- Oncology
- Genomics
- Drug Discovery
Background:
- Cervical cancer, a major health concern, is strongly linked to HPV infection.
- Genomic studies, including The Cancer Genome Atlas (TCGA), have identified key altered genes like TGFBR2, MED1, ERBB3, CASP8, and HLA-A in cervical cancer.
- The PI3K/MAPK and TGF-Beta signaling pathways are significantly altered, presenting potential therapeutic targets.
Purpose of the Study:
- To identify novel therapeutic targets for cervical cancer through combined gene enrichment analysis.
- To virtually screen natural compounds for potential efficacy against identified cervical cancer targets.
- To validate the binding stability and multi-targeted potential of promising drug candidates.
Main Methods:
- Enrichment analysis using Gene Ontology (GO), KEGG, and Reactome databases to identify key genes.
- Docking-based virtual screening of over 680,000 natural compounds from the ZINC15 database against six identified cervical cancer proteins.
- Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) and Molecular Dynamics (MD) simulations to assess binding stability and interactions.
Main Results:
- Six key genes were identified through enrichment analysis.
- Quinic acid demonstrated the highest binding affinity and best docking score against all six target proteins.
- MM/GBSA and MD simulations confirmed the stability of Quinic acid-protein complexes, with deviations under 2 Å, indicating a strong web of interactions.
Conclusions:
- Quinic acid is identified as a promising multi-targeted therapeutic compound for cervical cancer.
- The study highlights the potential of natural compounds in developing novel cervical cancer treatments.
- Virtual screening and molecular dynamics simulations are effective tools for identifying potent drug candidates.

