Essential proteins in cancer networks: a graph-based perspective using Dijkstra’s algorithm

Identifying essential proteins within cancer-related PPI networks is a significant challenge due to the heterogeneity and complexity of cancer diseases. Identifying these proteins is crucial for developing effective therapeutic strategies and understanding cancer biology. This study introduces a nov...

Full description

Saved in:
Bibliographic Details
Published in:Network modeling and analysis in health informatics and bioinformatics (Wien) 2024-08, Vol.13 (1), p.42
Main Authors: Rout, Trilochan, Mohapatra, Anjali, Kar, Madhabananda, Muduly, Dillip Kumar
Format: Article
Language:English
Subjects:
1-Phosphatidylinositol 3-kinase
AKT protein
Algorithms
Applications of Graph Theory and Complex Networks
Bioinformatics
Biological effects
Biology
Biomarkers
Breast
Breast cancer
Cancer
Cell cycle
Cell growth
Complexity
Computational Biology/Bioinformatics
Computer Science
Disease
Genes
Health Informatics
Heterogeneity
Identification
Impact analysis
Lung cancer
Lungs
Medical diagnosis
Medical research
Methods
Networks
Original Article
Ovarian cancer
Permutations
Precision medicine
Proteins
Raf protein
Shortest-path problems
Signal transduction
Therapeutic targets
TOR protein
Wnt protein
β-Catenin
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Identifying essential proteins within cancer-related PPI networks is a significant challenge due to the heterogeneity and complexity of cancer diseases. Identifying these proteins is crucial for developing effective therapeutic strategies and understanding cancer biology. This study introduces a novel graph-based approach to identify essential cancer proteins within PPI networks, focusing on breast, lung, colorectal, and ovarian cancers. The proposed methodology involves a multi-step process beginning with identifying and preprocessing common genes associated with breast, colorectal, lung, and ovarian cancers. The PPI networks are constructed using these common genes. The PPI networks are analyzed to find the shortest paths using centrality measures. Centrality measures, particularly betweenness centrality, prioritize proteins with the highest impact on cancer progression. Betweenness centrality is used as a threshold to exclude nonessential proteins. The identified proteins are validated and categorized into cancer-related pathways through permutation and enrichment tests. The proposed approach successfully identified 64 essential proteins across breast, lung, colorectal, and ovarian cancers. These proteins were categorized into 14 cancer-related pathways, including cell cycle regulation, Wnt/ β -Catenin signaling, RTK/RAS/RAF/MEK/ERK signaling, and PI3K/AKT/mTOR signaling. The identified pathways highlight complex interactions of these proteins, pivotal functions in cancer progression, and therapeutic targets. The validation process, through permutation and enrichment tests, confirmed the robustness and relevance of these findings, indicating their significant impact on understanding and potentially treating cancer. Identifying essential cancer proteins using this novel graph-based approach has significant clinical relevance, particularly for precision medicine. These findings can guide personalized treatment strategies and enhance the understanding of cancer biology. Future research will extend this methodology to other types of cancers and clinical applicability.
ISSN:2192-6662
2192-6670
DOI:10.1007/s13721-024-00477-y