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Updated: Jun 25, 2025
Author Spotlight: Exploring the Role of FAM83A in Cervical Cancer
Published on: February 9, 2024
Prioritizing cervical cancer candidate genes using chaos game and fractal-based time series approach
T Mallikarjuna1, N B Thummadi2, Vaibhav Vindal1
1Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Gachibowli, Hyderabad, 500046, India.
Abstract:
Cervical cancer is one of the most severe threats to women worldwide and holds fourth rank in lethality. It is estimated that 604, 127 cervical cancer cases have been reported in 2020 globally. With advancements in high throughput technologies and bioinformatics, several cervical candidate genes have been proposed for better therapeutic strategies. In this paper, we intend to prioritize the candidate genes that are involved in cervical cancer progression through a fractal time series-based cross-correlations approach. we apply the chaos game representation theory combining a two-dimensional multifractal detrended cross-correlations approach among the known and candidate genes involved in cervical cancer progression to prioritize the candidate genes. We obtained 16 candidate genes that showed cross-correlation with known cancer genes. Functional enrichment analysis of the candidate genes shows that they involve GO terms: biological processes, cell-cell junction assembly, cell-cell junction organization, regulation of cell shape, cortical actin cytoskeleton organization, and actomyosin structure organization. KEGG pathway analysis revealed genes' role in Rap1 signaling pathway, ErbB signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, mTOR signaling pathway, Acute myeloid leukemia, chronic myeloid leukemia, Breast cancer, Thyroid cancer, Bladder cancer, and Gastric cancer. Further, we performed survival analysis and prioritized six genes CDH2, PAIP1, BRAF, EPB41L3, OSMR, and RUNX1 as potential candidate genes for cervical cancer that has a crucial role in tumor progression. We found that our study through this integrative approach an efficient tool and paved a new way to prioritize the candidate genes and these genes could be evaluated experimentally for potential validation. We suggest this may be useful in analyzing the nucleotide sequences and protein sequences for clustering, classification, class affiliation, etc.
Insights
This study identifies key genes involved in cervical cancer progression using a novel fractal cross-correlation analysis. Six genes, including CDH2 and BRAF, show significant potential for future therapeutic strategies and experimental validation.
Area of Science:
- Oncology
- Bioinformatics
- Genetics
Background:
- Cervical cancer is a leading cause of death in women globally, necessitating improved therapeutic strategies.
- Advancements in high-throughput technologies have identified numerous candidate genes for cervical cancer.
- Prioritizing these candidate genes is crucial for developing effective treatments.
Purpose of the Study:
- To prioritize candidate genes involved in cervical cancer progression using a fractal time series-based cross-correlations approach.
- To identify novel genetic markers for cervical cancer through advanced bioinformatics analysis.
- To provide a framework for experimental validation of prioritized candidate genes.
Main Methods:
- Application of chaos game representation theory and two-dimensional multifractal detrended cross-correlations analysis.
- Cross-correlation analysis between known and candidate cervical cancer genes.
- Functional enrichment analysis (Gene Ontology and KEGG pathways) and survival analysis.
Main Results:
- Identified 16 candidate genes exhibiting cross-correlation with known cancer genes.
- Functional analysis revealed involvement in cell-cell junctions, cytoskeleton organization, and key signaling pathways (e.g., MAPK, PI3K-Akt).
- Six genes (CDH2, PAIP1, BRAF, EPB41L3, OSMR, RUNX1) were prioritized based on survival analysis for their crucial role in tumor progression.
Conclusions:
- The fractal time series-based cross-correlations approach is an efficient tool for prioritizing candidate genes in cervical cancer.
- The prioritized genes represent promising targets for further experimental validation and potential therapeutic development.
- This integrative approach offers a new pathway for analyzing nucleotide and protein sequences for various applications.
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