Kernel Fusion Method for Detecting Cancer Subtypes via Selecting Relevant Expression Data - Li, Shuhao; Jiang, Limin; Tang, Jijun; Gao, Nan; Guo, Fei (Frontiers in Genetics, 2020)
Recently, cancer has been characterized as a heterogeneous disease composed of many different subtypes. Early diagnosis of cancer subtypes is an important study of cancer research, which can be of tremendous help to patients after treatment. In this paper, we first extract a novel dataset, which contains gene expression, miRNA expression and isoform expression of five cancers from The Cancer Genome Atlas (TCGA). Next, to avoid the effect of noise existing in 60483 genes, we select a small number of genes by using LASSO that employs gene expression and survival time of patients. Then, we construct one similarity kernel for each expression data by using Chebyshev distance. And also, we combine three similarity kernels into one synthetical kernel by using Similarity Kernel Fusion (SKF). Finally, Spectral Clustering is applied to obtain the clustering results for predicting cancer subtypes. In the experimental results, our method has better P value in the Cox model than other methods on ten cancer data from Jiang Dataset and Novel Dataset. The difference between survival curves based on the experimental results is obvious on each cancer, and some essential genes are found to have a significant impact on cancer subtypes. For breast cancer, we find out that HSPA2A, RNASE1, CLIC6 and IFITM1 are highly expressed in some specific groups. For lung cancer, we ensure that C4BPA, SESN3 and IRS1 are highly expressed in some specific groups. The code and all supporting data files are available from https://github.com/guofei-tju/Uncovering-Cancer-Subtypes-via-LASSO.