Search

We focus on characterizing cell lines from young and aged-healthy and -AML (acute myeloid leukemia) cell lines, and our goal is to identify the key markers associated with the progression of AML. To characterize the age-related phenotypes in AML cell lines, we consider eigenCell analysis that effectively encapsulates the primary expression level patterns across the cell lines. However, earlier investigations utilizing eigenGenes and eigenCells analysis were based on linear combination of all features, leading to the disturbance from noise features. Moreover, the analysis based on a fully dense loading matrix makes it challenging to interpret the results of eigenCells analysis. In order to address these challenges, we develop a novel computational approach termed network-constrained eigenCells profile estimation, which employs a sparse learning strategy. The proposed method estimates eigenCell based on not only the lasso but also network constrained penalization. The use of the network-constrained penalization enables us to simultaneously select neighborhood genes. Furthermore, the hub genes and their regulator/target genes are easily selected as crucial markers for eigenCells estimation. That is, our method can incorporate insights from network biology into the process of sparse loading estimation. Through our methodology, we estimate sparse eigenCells profiles, where only critical markers exhibit expression levels. This allows us to identify the key markers associated with a specific phenotype. Monte Carlo simulations demonstrate the efficacy of our method in reconstructing the sparse structure of eigenCells profiles. We employed our approach to unveil the regulatory system of immunogenes in both young/aged-healthy and -AML cell lines. The markers we have identified for the age-related phenotype in both healthy and AML cell lines have garnered strong support from previous studies. Specifically, our findings, in conjunction with the existing literature, indicate that the activities within this subnetwork of CD79A could be pivotal in elucidating the mechanism driving AML progression, particularly noting the significant role played by the diminished activities in the CD79A subnetwork. We expect that the proposed method will be a useful tool for characterizing disease-related subsets of cell lines, encompassing phenotypes and clones. [ABSTRACT FROM AUTHOR]

Uncovering acquired drug resistance mechanisms has garnered considerable attention as drug resistance leads to treatment failure and death in patients with cancer. Although several bioinformatics studies developed various computational methodologies to uncover the drug resistance mechanisms in cancer chemotherapy, most studies were based on individual or differential gene expression analysis. However the single gene-based analysis is not enough, because perturbations in complex molecular networks are involved in anti-cancer drug resistance mechanisms. The main goal of this study is to reveal crucial molecular interplay that plays key roles in mechanism underlying acquired gastric cancer drug resistance. To uncover the mechanism and molecular characteristics of drug resistance, we propose a novel computational strategy that identified the differentially regulated gene networks. Our method measures dissimilarity of networks based on the eigenvalues of the Laplacian matrix. Especially, our strategy determined the networks' eigenstructure based on sparse eigen loadings, thus, the only crucial features to describe the graph structure are involved in the eigenanalysis without noise disturbance. We incorporated the network biology knowledge into eigenanalysis based on the network-constrained regularization. Therefore, we can achieve a biologically reliable interpretation of the differentially regulated gene network identification. Monte Carlo simulations show the outstanding performances of the proposed methodology for differentially regulated gene network identification. We applied our strategy to gastric cancer drug-resistant-specific molecular interplays and related markers. The identified drug resistance markers are verified through the literature. Our results suggest that the suppression and/or induction of COL4A1, PXDN and TGFBI and their molecular interplays enriched in the Extracellular-related pathways may provide crucial clues to enhance the chemosensitivity of gastric cancer. The developed strategy will be a useful tool to identify phenotype-specific molecular characteristics that can provide essential clues to uncover the complex cancer mechanism. [ABSTRACT FROM AUTHOR]

Gastric cancer is a leading cause of cancer-related deaths globally and chemotherapy is widely accepted as the standard treatment for gastric cancer. However, drug resistance in cancer cells poses a significant obstacle to the success of chemotherapy, limiting its effectiveness in treating gastric cancer. Although many studies have been conducted to unravel the mechanisms of acquired drug resistance, the existing studies were based on abnormalities of a single gene, that is, differential gene expression (DGE) analysis. Single gene-based analysis alone is insufficient to comprehensively understand the mechanisms of drug resistance in cancer cells, because the underlying processes of the mechanism involve perturbations of the molecular interactions. To uncover the mechanism of acquired gastric cancer drug resistance, we perform for identification of differentially regulated gene networks between drug-sensitive and drug-resistant cell lines. We develop a computational strategy for identifying phenotype-specific gene networks by extending the existing method, CIdrgn, that quantifies the dissimilarity of gene networks based on comprehensive information of network structure, that is, regulatory effect between genes, structure of edge, and expression levels of genes. To enhance the efficiency of identifying differentially regulated gene networks and improve the biological relevance of our findings, we integrate additional information and incorporate knowledge of network biology, such as hubness of genes and weighted adjacency matrices. The outstanding capabilities of the developed strategy are validated through Monte Carlo simulations. By using our strategy, we uncover gene regulatory networks that specifically capture the molecular interplays distinguishing drug-sensitive and drug-resistant profiles in gastric cancer. The reliability and significance of the identified drug-sensitive and resistance-specific gene networks, as well as their related markers, are verified through literature. Our analysis for differentially regulated gene network identification has the capacity to characterize the drug-sensitive and resistance-specific molecular interplays related to mechanisms of acquired drug resistance that cannot be revealed by analysis based solely on abnormalities of a single gene, for example, DGE analysis. Through our analysis and comprehensive examination of relevant literature, we suggest that targeting the suppressors of the identified drug-resistant markers, such as the Melanoma Antigen (MAGE) family, Trefoil Factor (TFF) family, and Ras-Associated Binding 25 (RAB25), while enhancing the expression of inducers of the drug sensitivity markers [e.g., Serum Amyloid A (SAA) family], could potentially reduce drug resistance and enhance the effectiveness of chemotherapy for gastric cancer. We expect that the developed strategy will serve as a useful tool for uncovering cancer-related phenotype-specific gene regulatory networks that provide essential clues for uncovering not only drug resistance mechanisms but also complex biological systems of cancer. [ABSTRACT FROM AUTHOR]

The complex mechanisms of diseases involve the disturbance of the molecular network, rather than disorder in a single gene, implying that single gene-based analysis is insufficient to understand these mechanisms. Gene regulatory networks (GRNs) have attracted a lot of interest and various approaches have been developed for their statistical inference and gene network-based analysis. Although various computational methods have been developed, relatively little attention has been paid to incorporation of biological knowledge into the computational approaches. Furthermore, existing studies on network-based analysis perform prediction/classification of status of cell lines based on preconstructed GRNs, implying that we cannot extract prediction/classification-specific gene networks, leading to difficulty in interpretation of biological mechanisms and marker identification related to the status of cancer cell lines. We developed a novel strategy to build a GRN-based classifier, called a GRN-classifier. The proposed GRN-classifier estimates GRNs and classifies cell lines simultaneously, where the gene network is estimated to minimize error in gene network estimation and the negative log-likelihood for classifying cell lines. Thus, we can identify biological status-specific gene regulatory systems, enabling us to achieve biologically reliable interpretation of the classification. We also propose an algorithm to implement the GRN-classifier based on coordinate descent update. Monte Carlo simulations were conducted to examine performance of the GRN-classifier. Results: Our strategy provides effective results in feature selection in the classification model and edge selection in gene network estimation. The GRN-classifier also shows outstanding classification accuracy. We apply the GRN-classifier to classify cancer cell lines into anticancer drug-related status, that is, 5-fluorouracil (5-FU)-sensitive/resistant and 5-FU target/nontarget cancer cell lines. We then identified 5-FU markers based on 5-FU-related status classification-specific gene networks. The mechanisms of the identified markers were verified through literature survey. Our results suggest that the molecular interplay between MYOF and AHNAK2 may play a crucial role in drug resistance and can provide information on the chemotherapy efficiency of 5-FU. It is also suggested that suppression of the identified 5-FU markers, including MYOF/AHNAK2 and AKR1C1/AKR1C3 may improve 5-FU resistance of cancer cell lines. [ABSTRACT FROM AUTHOR]

Uncovering mechanisms of acquired drug resistance has garnered increasing attention worldwide as drug resistance reduces antibiotic and chemotherapy effectiveness. Most bioinformatics studies have elucidated these mechanisms based on differentially expressed gene (DEG) analysis. However, considering the associated complex network of biological systems, the specific molecular interactions must also be studied to obtain a complete understanding of the mechanisms related to drug resistance. Accordingly, by analyzing sample-specific gene networks, we sought to elucidate mechanisms of acquired drug resistance of cells based on molecular interactions between genes. In the current study, we focus on gefitinib and erlotinib and characterized cell lines based on their sensitivity. We also consider CRISPR knockout screening of the target gene, epidermal growth factor receptor (EGFR), as a characteristic of cells. Subsequently, we constructed a drug sensitivity-CRISPR knockout screen-specific gene network. To identify the molecular mechanisms of drug resistance from the multiple large-scale networks, we proposed a novel computational method, designated network-constrained sparse common component analysis (NetSCCA), that extracts common structures of multiple networks characterizing molecular interaction in drug-sensitive and drug-resistant cell lines. We then applied NetSCCA to multilayer networks of candidate drug-response genes to identify common structures of the regulatory system in drug-sensitive and EGFR-dependent cells, and drug-resistant and EGFR-independent cells. NetSCCA identified crucial common targets and regulator genes that dominate multiple networks in drug-sensitive and drug-resistant cell lines, respectively. Our analysis for common structure identification based on NetSCCA has the capacity to characterize the molecular interplay between genes and crucial markers related to mechanisms of acquired drug resistance that cannot be revealed by analysis based solely on DEG analysis. The biological mechanisms associated with gefitinib and erlotinib sensitivity of identified genes were verified through the literature. We expect that the proposed method will serve as a useful tool for uncovering not only drug resistance mechanisms but also complex biological systems based on massive genomic data sets. [ABSTRACT FROM AUTHOR]

The subspace method based on principal component analysis (PCA) is a useful tool for classifying and representing patterns. In order to learn the dynamical nonlinear structure of data, there is currently much discussion about kernel subspace methods based on kernel PCA. By using a nonlinear transformation, we can efficiently capture the nonlinear structure of data, which leads to effective subspace construction for data having a dynamical nonlinear structure. However, the performance of a kernel subspace method becomes dramatically worse in the presence of noisy features, because the method is based on a fully dense loading matrix in the constructed subspace. This implies that the subspace for each class is constructed by using not only crucial features but also noisy features, which significantly disturb the PCA procedures. To resolve this issue, we propose a sparse kernel subspace method based on sparse PCA. In order to construct subspaces based only on crucial features, we incorporate sparsity into the eigenvector estimation procedures in the nonlinear feature space. We then construct the subspaces of nonlinear features based on the sparse eigenvectors, and classify the unknown vector by projecting it into the constructed subspace. The proposed method can effectively not only capture nonlinear structures of data but also estimate eigenvectors even in the presence of noisy features. The effectiveness of the proposed method leads to the high-precision of the classification. Monte Carlo simulations and a real data analysis show that the proposed sparse kernel subspace method provides effective results for classification. [ABSTRACT FROM AUTHOR]