Identifying and analyzing different cancer subtypes using RNA-seq data of blood platelets

Abstract

Detection and diagnosis of cancer are especially important for early prevention and effective treatments. Traditional methods of cancer detection are usually time-consuming and expensive. Liquid biopsy, a newly proposed noninvasive detection approach, can promote the accuracy and decrease the cost of detection according to a personalized expression profile. However, few studies have been performed to analyze this type of data, which can promote more effective methods for detection of different cancer subtypes. In this study, we applied some reliable machine learning algorithms to analyze data retrieved from patients who had one of six cancer subtypes (breast cancer, colorectal cancer, glioblastoma, hepatobiliary cancer, lung cancer and pancreatic cancer) as well as healthy persons. Quantitative gene expression profiles were used to encode each sample. Then, they were analyzed by the maximum relevance minimum redundancy method. Two feature lists were obtained in which genes were ranked rigorously. The incremental feature selection method was applied to the mRMR feature list to extract the optimal feature subset, which can be used in the support vector machine algorithm to determine the best performance for the detection of cancer subtypes and healthy controls. The ten-fold cross-validation for the constructed optimal classification model yielded an overall accuracy of 0.751. On the other hand, we extracted the top eighteen features (genes), including TTN, RHOH, RPS20, TRBC2, in another feature list, the MaxRel feature list, and performed a detailed analysis of them. The results indicated that these genes could be important biomarkers for discriminating different cancer subtypes and healthy controls.

Keywords: RNA-seq data; cancer detection; liquid biopsy; maximum relevance minimum redundancy; support vector machine.

Figure 1. IFS-curves for the results yielded in the first stage of the IFS method

The Y-axis represents the overall accuracy, and the X-axis represents the number of features used for classification. The high overall accuracies (no less than 0.740) all cluster between 2000 and 2200.

Figure 2. IFS-curves for the results yielded in the second stage of the IFS method

The Y-axis represents the overall accuracy, and the X-axis represents the number of features used for classification. The highest overall accuracy was 0.751 when 2047 features were used.

Figure 3. The performance of the optimal classification model evaluated by ten-fold cross-validation

Figure 4. The heat map of all samples using the important eighteen genes

Figure 5. The eighteen important genes found in the MaxRel feature list were clustered into three groups

Figure 6. The flow chart of constructing the mRMR feature list in the mRMR method