Predicting factors and top gene identification for survival data of breast cancer

Sarada Ghosh; Guruprasad Samanta; Manuel De la Sen; Sarada Ghosh; Guruprasad Samanta; Manuel De la Sen

doi:10.3934/biophy.2023006

AIMS Biophysics

2023, Volume 10, Issue 1: 67-89. doi: 10.3934/biophy.2023006

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Predicting factors and top gene identification for survival data of breast cancer

1.
Department of Statistics, Gurudas College, Phool Bagan, Kolkata-700054, India
2.
Department of Mathematics, Indian Institute of Engineering Science and Technology, Shibpur, Howrah-711103, India
3.
Institute of Research and Development of Processes, University of the Basque Country, 48940 Leioa, Bizkaia, Spain

Received: 26 September 2022 Revised: 02 February 2023 Accepted: 08 February 2023 Published: 17 February 2023

For high-throughput research with biological data-sets generated sequentially or by transcriptional micro-arrays, proteomics or other means, analytic techniques that address their high dimensional aspects remain desirable. The computation part basically predicts the tendency towards mortality due to breast cancer (BC) by using several classification methods, i.e., Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Decision Tree (DT), and compared the models' performances. We proceed with the RF method since it provides better results than any other underlying models based on accuracy. We have also demonstrated some traditional and competing risk models, illustrated the models with real data analysis, depicted their curves' natures and also compared their fits using prediction error curves and the concordance index. Furthermore, two different survival splitting rules are used by using separate Random Survival Forest (RSF) methods and also constructing the ranking of risk factors due to breast cancer. The results show that high-level grade and diameter are the most important predictors for mortality progression in the presence of competing events of death, and lymph nodes, age and angiography are other vital criteria for this purpose. We have also implemented RSF backward selection criteria, which enables top gene selection related to mortality progression due to breast cancer. This method identifies c-MYB, CDCA7, NUSAP1, BIRC5, ANGPTL4, JAG1, IL6ST, and remaining genes that are mainly responsible for mortality progression due to breast cancer. In this work, R software is used to obtain and evaluate the results.
- breast cancer,
- random forest,
- accuracy,
- brier score,
- minimal depth,
- variable importance
Citation: Sarada Ghosh, Guruprasad Samanta, Manuel De la Sen. Predicting factors and top gene identification for survival data of breast cancer[J]. AIMS Biophysics, 2023, 10(1): 67-89. doi: 10.3934/biophy.2023006

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Abstract

For high-throughput research with biological data-sets generated sequentially or by transcriptional micro-arrays, proteomics or other means, analytic techniques that address their high dimensional aspects remain desirable. The computation part basically predicts the tendency towards mortality due to breast cancer (BC) by using several classification methods, i.e., Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Decision Tree (DT), and compared the models' performances. We proceed with the RF method since it provides better results than any other underlying models based on accuracy. We have also demonstrated some traditional and competing risk models, illustrated the models with real data analysis, depicted their curves' natures and also compared their fits using prediction error curves and the concordance index. Furthermore, two different survival splitting rules are used by using separate Random Survival Forest (RSF) methods and also constructing the ranking of risk factors due to breast cancer. The results show that high-level grade and diameter are the most important predictors for mortality progression in the presence of competing events of death, and lymph nodes, age and angiography are other vital criteria for this purpose. We have also implemented RSF backward selection criteria, which enables top gene selection related to mortality progression due to breast cancer. This method identifies c-MYB, CDCA7, NUSAP1, BIRC5, ANGPTL4, JAG1, IL6ST, and remaining genes that are mainly responsible for mortality progression due to breast cancer. In this work, R software is used to obtain and evaluate the results.

Acknowledgments

The authors are grateful to the learned reviewers and Editors for their careful reading, valuable comments, and helpful suggestions, which have helped them to improve the presentation of this work significantly. They are also thankful to Nirapada Santra, SRF and Bijoy Kumar Das, SRF, and Protyusha Dutta, JRF of the IIEST, Shibpur for helping during the preparation of this manuscript. The third author (Manuel De la Sen) is grateful to the Spanish Government for its support through grant RTI2018-094336-B-I00 (MCIU/AEI/FEDER, UE) and to the Basque Government for its support through grant IT1555-22.

Data availability statement

The data used to support the findings of this study are included in the references within the article.

Conflict of interest

The authors declare that they have no conflict of interest regarding this work.

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