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.

Identification of the Gene Expression Rules That Define the Subtypes in Glioma

“…SVM [ 24 ] is a widely used supervised-learning algorithm based on the statistical learning theory, which is applied to handle many biological problems [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. SVM performs linear classification and non-linear classification problems.…”

Clinical metagenomics

“…Classification of genes by expression profiling using RNA seq has been used to characterize several infections, including staphylococcal bacterae mia 67 , Lyme disease 68 , candidiasis 69 , tuberculosis (dis criminating between latent and active disease risk) [70][71][72] and influenza [73][74][75] . Machine learningbased analyses of RNA seq data have been used for cancer classifi cation 76 , and translation of these approaches may be promising for infectious diseases. Panels containing a limited number of host biomarkers are being developed as diagnostic assays for influenza 77 , tuberculosis 70 and bacterial sepsis 78 .…”

Bioinformatic Profiling Identifies Prognosis-Related Genes in the Immune Microenvironment of Endometrial Carcinoma

“…Among these, 4 genes (CD8A, CD3E, CCL4 and ITGAL) were reported to have close relationship with tumor immune microenvironment and to be involved in various pathological processes of EC [34][35][36]. TRBC2 encodes a speci c region of the T-cell receptor beta-2 chain [37] and has been identi ed as a promising biomarker for the distinction of multiple cancer types, including breast cancer, colorectal cancer, glioblastoma, hepatobiliary cancer, lung cancer and pancreatic cancer and so on [38]. It is worth mentioning that, our ndings demonstrated that TRBC2 had the most signi cant correlation with both OS and PFS of EC patients.…”