GenHITS: A network science approach to driver gene detection in human regulatory network using gene's influence evaluation
Mostafa Akhavan-Safar1, Babak Teimourpour1, Mehrdad Kargari1
1Department of Information Technology Engineering, School of Systems and Industrial Engineering, Tarbiat Modares University (TMU), Tehran, Iran.
Abstract:
Cancer is among the diseases causing death, in which, cells uncontrollably grow and reproduce beyond the cell regulatory mechanism. In this disease, some genes are initiators of abnormalities and then transmit them to other genes through protein interactions. Accordingly, these genes are known as cancer driver genes (CDGs). In this regard, several methods have been previously developed for identifying cancer driver genes. Most of these methods are computational-based, which use the concept of mutation to predict CDGs. In this research, a method has been proposed for identifying CDGs in the transcription regulatory network using the concept of influence diffusion and by modifying the Hyperlink-Induced Topic Search algorithm based on the diffusion concept. Due to the type of these networks and the processes of abnormality progression in cells and the formation of cancerous tumors, high-influence genes can be the most likely considered as the driver genes. Therefore, we can use the influence diffusion concept as an acceptable theory to identify these genes. Recently, a method has been proposed to detect CDGs with the concept of the influence maximization. One of the challenges in these types of networks is finding the power of regulatory interaction between genes. Moreover, we have proposed a novel method to calculate the weight of regulatory interactions, based on the concept of diffusion. The performance of the proposed method was compared with other seventeen computational and network tools. Correspondingly, three cancer types were used as benchmarks as follows: breast invasive carcinoma (BRCA), Colon adenocarcinoma (COAD), and lung squamous cell carcinoma (LUSC). In addition, to determine the accuracy of the detected drivers using each method, CGC (Cancer Gene Census) and Mut-driver gene lists were utilized as gold standard. The results show that GenHITS performs better compared to the most of the other computational and network methods. Besides, it is also able to identify genes that have been identified by none of the other methods yet.
Insights
This study introduces GenHITS, a new computational method for identifying cancer driver genes (CDGs) in gene regulatory networks. GenHITS utilizes influence diffusion to accurately pinpoint critical genes driving cancer development.
Area of Science:
- Genomics
- Computational Biology
- Bioinformatics
Background:
- Cancer is characterized by uncontrolled cell growth, often initiated by specific cancer driver genes (CDGs) that propagate abnormalities through gene networks.
- Existing computational methods for CDG identification primarily rely on mutation data, with limited focus on network influence dynamics.
- Understanding gene regulatory networks is crucial for identifying key genes that initiate and sustain cancerous growth.
Purpose of the Study:
- To propose a novel computational method, GenHITS, for identifying cancer driver genes within transcription regulatory networks.
- To develop a new approach for calculating the weight of regulatory interactions based on influence diffusion.
- To evaluate the performance of GenHITS against existing computational and network-based tools for CDG identification.
Main Methods:
- The study modified the Hyperlink-Induced Topic Search (HITS) algorithm by incorporating the concept of influence diffusion to identify CDGs.
- A novel method was developed to calculate the weight of gene regulatory interactions using diffusion principles.
- The proposed method, GenHITS, was benchmarked against seventeen other computational tools using data from breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), and lung squamous cell carcinoma (LUSC).
Main Results:
- GenHITS demonstrated superior performance compared to most existing computational and network methods in identifying cancer driver genes.
- The method successfully identified known cancer genes and also discovered novel candidate driver genes not detected by other tools.
- Performance was validated using the Cancer Gene Census (CGC) and Mut-driver gene lists as gold standards.
Conclusions:
- GenHITS offers a robust and effective approach for identifying cancer driver genes by leveraging influence diffusion in transcription regulatory networks.
- The method's ability to uncover novel driver genes highlights its potential for advancing cancer research and therapeutic target discovery.
- Influence diffusion provides a valuable theoretical framework for understanding and predicting the behavior of cancer driver genes.
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