Prioritizing cervical cancer candidate genes using chaos game and fractal-based time series approach.

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.

Multifractal and cross-correlation analysis on mitochondrial genome sequences using chaos game representation.

We characterized the multifractality and power-law cross-correlation of mitochondrial genomes of various species through the recently developed method which combines the chaos game representation theory and 2D-multifractal detrended cross-correlation analysis. In the present paper, we analyzed 32 mitochondrial genomes of different species and the obtained results show that all the analyzed data exhibit multifractal nature and power-law cross-correlation behaviour. Further, we performed a cluster analysis from the calculated scaling exponents to identify the class affiliation and its outcome is represented as a dendrogram. We suggest that this integrative approach may help the researchers to understand the phylogeny of any kingdom with their varying genome lengths and also this approach may find applications in characterizing the protein sequences, mRNA sequences, next-generation sequencing, and drug development, etc.

A graph centrality-based approach for candidate gene prediction for type 1 diabetes.

Type 1 diabetes mellitus (T1DM) or insulin-dependent diabetes is an autoimmune disease that may pose life-threatening situations to individuals. In most cases, cytotoxic T lymphocytes (CTLs) promotes killing of islets of Langerhans in the pancreas, which harbour insulin-producing beta cells. The trigger for autoimmune attack is still unclear; therefore, identifying and targeting candidate genes are imperative to hinder its deleterious effects. In the present study, we focused on identification of new candidate genes for T1DM. For our study, we exclusively selected immune-related genes as they play a crucial role in T1DM. We constructed and analysed a human immunome signalling network (directed network) to identify the new candidate genes through various graph centrality measures combining with Gene Ontology (GO). As a result, we identified 4 new candidate genes which may act as potential drug targets for T1DM. We further validated for their disease relevance through literature survey and pathway analysis and found that 3 out of 4 predicted genes mirrored their well-established roles as potential targets for T1DM.