Survival analysis for the identified cancer gene subtype from the co-clustering algorithm

• Main Authors: Logenthiran, Machap, Kohbalan, Moorthy
• Format: Conference or Workshop Item
• Language: English; English
• Published: Institute of Electrical and Electronics Engineers Inc. 2022
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software; T Technology (General); TA Engineering (General). Civil engineering (General)
• Online Access: http://umpir.ump.edu.my/id/eprint/39408/1/Survival%20analysis%20for%20the%20identified%20cancer%20gene%20subtype%20from%20the%20co.pdf; http://umpir.ump.edu.my/id/eprint/39408/2/Survival%20analysis%20for%20the%20identified%20cancer%20gene%20subtype%20from%20the%20co-clustering%20algorithm_ABS.pdf; http://umpir.ump.edu.my/id/eprint/39408/; https://doi.org/10.1109/ICECET55527.2022.9872811

Cancer gene subtype information is significant for understanding tumour heterogeneity. The early detection of cancer and subsequent treatment can be lifesaving. However, it is hard clinically and computationally to detect cancer and its subtypes in their early stages. Therefore, we extend the analysis and results from Machap et al. (2019), to include the KaplanMeier survival analysis with the integration of gene expression and clinical features data. There are two cancer datasets used for the analysis : breast cancer and glioblastoma multiforme. The luminal type was the common subtype of breast cancer, showing a higher survival rate. Whereas the Proneural subtype in glioblastoma multiforme has a little longer survival rate than the other three subtypes. These molecular differences between subtypes have been shown to correlate very well with clinical features and survival parameters to help understand the disease and develop better therapeutic targets.