Network analysis based on low-rank method for mining information on integrated data of multi-cancers

Joint Lp-Norm and L2,1-Norm Constrained Graph Laplacian PCA for Robust Tumor Sample Clustering and Gene Network Module Discovery

The dimensionality reduction method accompanied by different norm constraints plays an important role in mining useful information from large-scale gene expression data. In this article, a novel method named Lp-norm and L2,1-norm constrained graph Laplacian principal component analysis (PL21GPCA) based on traditional principal component analysis (PCA) is proposed for robust tumor sample clustering and gene network module discovery. Three aspects are highlighted in the PL21GPCA method. First, to degrade the high sensitivity to outliers and noise, the non-convex proximal Lp-norm (0 < p < 1)constraint is applied on the loss function. Second, to enhance the sparsity of gene expression in cancer samples, the L2,1-norm constraint is used on one of the regularization terms. Third, to retain the geometric structure of the data, we introduce the graph Laplacian regularization item to the PL21GPCA optimization model. Extensive experiments on five gene expression datasets, including one benchmark dataset, two single-cancer datasets from The Cancer Genome Atlas (TCGA), and two integrated datasets of multiple cancers from TCGA, are performed to validate the effectiveness of our method. The experimental results demonstrate that the PL21GPCA method performs better than many other methods in terms of tumor sample clustering. Additionally, this method is used to discover the gene network modules for the purpose of finding key genes that may be associated with some cancers.

An Integrated Regulatory Network Based on Comprehensive Analysis of mRNA Expression, Gene Methylation and Expression of Long Non-coding RNAs (lncRNAs) in Myelodysplastic Syndromes

Myelodysplastic syndromes (MDS) are a heterogeneous group of disorders characterized by ineffective hematopoiesis, defective differentiation of hematopoietic precursors, and expansion of the abnormal clones. The prevalence of MDS has raised great concerns worldwide, but its pathogenetic mechanisms remain elusive. To provide insights on novel biomarkers for the diagnosis and therapy of MDS, we performed high-throughput genome-wide mRNA expression profiling, DNA methylation analysis, and long non-coding RNAs (lncRNA) analysis on bone marrows from four MDS patients and four age-matched healthy controls. We identified 1,937 differentially expressed genes (DEGs), 515 methylated genes, and 214 lncRNA that showed statistically significant differences. As the most significant module-related DEGs, TCL1A, PTGS2, and MME were revealed to be enriched in regulation of cell differentiation and cell death pathways. In addition, the GeneGo pathway maps identified by top DEGs were shown to converge on cancer, immunoregulation, apoptosis and regulation of actin cytoskeleton, most of which are known contributors in MDS etiology and pathogenesis. Notably, as potential biomarkers for diagnosis of MDS, four specific genes (ABAT, FADD, DAPP1, and SMPD3) were further subjected to detailed pathway analysis. Our integrative analysis on mRNA expression, gene methylation and lncRNAs profiling facilitates further understanding of the pathogenesis of MDS, and may promote the diagnosis and novel therapeutics for this disease.