Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Gachibowli, Hyderabad, 500046, India.
Department of Animal Biology, School of Life Sciences, University of Hyderabad, Gachibowli, Hyderabad, 500046, India.
Theory Biosci. 2024 Sep;143(3):183-193. doi: 10.1007/s12064-024-00418-3. Epub 2024 May 28.
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.
宫颈癌是全球范围内对女性健康最严重的威胁之一，其致死率位居第四。据估计，2020 年全球报告宫颈癌病例 604127 例。随着高通量技术和生物信息学的发展，已经提出了几种宫颈癌候选基因，以制定更好的治疗策略。在本文中，我们旨在通过分形时间序列交叉相关方法优先考虑参与宫颈癌进展的候选基因。我们应用混沌游戏表示理论，结合二维多重分形去趋势交叉相关方法，对已知和候选基因进行分析，以确定候选基因的优先级。我们获得了 16 个与已知癌症基因有交叉相关的候选基因。候选基因的功能富集分析表明，它们涉及 GO 术语：生物过程、细胞-细胞连接组装、细胞-细胞连接组织、细胞形状调节、皮质肌动蛋白细胞骨架组织和肌动球蛋白结构组织。KEGG 通路分析显示，这些基因在 Rap1 信号通路、ErbB 信号通路、MAPK 信号通路、PI3K-Akt 信号通路、mTOR 信号通路、急性髓细胞白血病、慢性髓细胞白血病、乳腺癌、甲状腺癌、膀胱癌和胃癌中发挥作用。此外，我们进行了生存分析，并将 CDH2、PAIP1、BRAF、EPB41L3、OSMR 和 RUNX1 这六个基因优先作为宫颈癌的潜在候选基因，这些基因在肿瘤进展中起着关键作用。我们发现，我们通过这种综合方法进行的研究是一种有效的工具，并为优先考虑候选基因开辟了新的途径，这些基因可以通过实验进行评估，以验证其潜在价值。我们建议，这可能有助于分析核苷酸序列和蛋白质序列，用于聚类、分类、类别归属等。
