Predictors of colorectal cancer survival using cox regression and random survival forests models based on gene expression data.

Autor: Mohanad Mohammed, Innocent B Mboya, Henry Mwambi, Murtada K Elbashir, Bernard Omolo
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: PLoS ONE, Vol 16, Iss 12, p e0261625 (2021)
Druh dokumentu: article
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0261625
Popis: Understanding and identifying the markers and clinical information that are associated with colorectal cancer (CRC) patient survival is needed for early detection and diagnosis. In this work, we aimed to build a simple model using Cox proportional hazards (PH) and random survival forest (RSF) and find a robust signature for predicting CRC overall survival. We used stepwise regression to develop Cox PH model to analyse 54 common differentially expressed genes from three mutations. RSF is applied using log-rank and log-rank-score based on 5000 survival trees, and therefore, variables important obtained to find the genes that are most influential for CRC survival. We compared the predictive performance of the Cox PH model and RSF for early CRC detection and diagnosis. The results indicate that SLC9A8, IER5, ARSJ, ANKRD27, and PIPOX genes were significantly associated with the CRC overall survival. In addition, age, sex, and stages are also affecting the CRC overall survival. The RSF model using log-rank is better than log-rank-score, while log-rank-score needed more trees to stabilize. Overall, the imputation of missing values enhanced the model's predictive performance. In addition, Cox PH predictive performance was better than RSF.
Databáze: Directory of Open Access Journals
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