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Triple-negative breast cancer (TNBC) has been a challenging breast cancer subtype for oncological therapy. Normally, it can be classified into different molecular subtypes. Accurate and stable classification of the six subtypes is essential for personalized treatment of TNBC. In this study, we proposed a new framework to distinguish the six subtypes of TNBC, and this is one of the handful studies that completed the classification based on mRNA and long noncoding RNA expression data. Particularly, we developed a gene selection approach named DGGA, which takes correlation information between genes into account in the process of measuring gene importance and then effectively removes redundant genes. A gene scoring approach that combined GeneRank scores with gene importance generated by deep neural network (DNN), taking inter-subtype discrimination and inner-gene correlations into account, was came up to improve gene selection performance. More importantly, we embedded a gene connectivity matrix in the DNN for sparse learning, which takes additional consideration with weight changes during training when obtaining the measurement of the relative importance of each gene. Finally, Genetic Algorithm was used to simulate the natural evolutionary process to search for the optimal subset of TNBC subtype classification. We validated the proposed method through cross-validation, and the results demonstrate that it can use fewer genes to obtain more accurate classification results. The implementation for the proposed method is available at https://github.com/RanSuLab/TNBC. [ABSTRACT FROM AUTHOR]

Abstract Background The identification of prognostic genes that can distinguish the prognostic risks of cancer patients remains a significant challenge. Previous works have proven that functional gene sets were more reliable for this task than the gene signature. However, few works have considered the cross-talk among functional gene sets, which may result in neglecting important prognostic gene sets for cancer. Results Here, we proposed a new method that considers both the interactions among modules and the prognostic correlation of the modules to identify prognostic modules in cancers. First, dense sub-networks in the gene co-expression network of cancer patients were detected. Second, cross-talk between every two modules was identified by a permutation test, thus generating the module network. Third, the prognostic correlation of each module was evaluated by the resampling method. Then, the GeneRank algorithm, which takes the module network and the prognostic correlations of all the modules as input, was applied to prioritize the prognostic modules. Finally, the selected modules were validated by survival analysis in various data sets. Our method was applied in three kinds of cancers, and the results show that our method succeeded in identifying prognostic modules in all the three cancers. In addition, our method outperformed state-of-the-art methods. Furthermore, the selected modules were significantly enriched with known cancer-related genes and drug targets of cancer, which may indicate that the genes involved in the modules may be drug targets for therapy. Conclusions We proposed a useful method to identify key modules in cancer prognosis and our prognostic genes may be good candidates for drug targets.

Background: The identification of prognostic genes that can distinguish the prognostic risks of cancer patients remains a significant challenge. Previous works have proven that functional gene sets were more reliable for this task than the gene signature. However, few works have considered the cross-talk among functional gene sets, which may result in neglecting important prognostic gene sets for cancer. Results: Here, we proposed a new method that considers both the interactions among modules and the prognostic correlation of the modules to identify prognostic modules in cancers. First, dense sub-networks in the gene co-expression network of cancer patients were detected. Second, cross-talk between every two modules was identified by a permutation test, thus generating the module network. Third, the prognostic correlation of each module was evaluated by the resampling method. Then, the GeneRank algorithm, which takes the module network and the prognostic correlations of all the modules as input, was applied to prioritize the prognostic modules. Finally, the selected modules were validated by survival analysis in various data sets. Our method was applied in three kinds of cancers, and the results show that our method succeeded in identifying prognostic modules in all the three cancers. In addition, our method outperformed state-of-the-art methods. Furthermore, the selected modules were significantly enriched with known cancer-related genes and drug targets of cancer, which may indicate that the genes involved in the modules may be drug targets for therapy. Conclusions: We proposed a useful method to identify key modules in cancer prognosis and our prognostic genes may be good candidates for drug targets. [ABSTRACT FROM AUTHOR]

Non-small cell lung cancer (NSCLC) is a leading cause of cancer-associated mortality worldwide. Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are two primary histological subtypes of NSCLC, accounting for ~70% of lung cancer cases. Increasing evidence suggests that AC and SCC differ in the composition of genes and molecular characteristics. Previous research has focused on distinguishing AC from SCC or predicting the NSCLC patient survival rates using gene expression profiles, usually with the aid of a feature selection method. The present study conducted a pre-filtering to identify the genes that have significant expression values and a high connection with other genes in the gene network, and then used the radial coordinate visualization method to identify relevant genes. By applying the proposed procedure to NSCLC data, it was demonstrated that there is a clear segmentation between AC and SCC, however not between patients with a good prognosis and bad prognosis. The focus of discriminating AC and SCC differs from survival prediction and there are almost no overlaps between the two gene signatures. Overall, a supervised learning method is preferred and future studies aiming to identify prognostic gene signatures with an increased prediction efficiency are required. [ABSTRACT FROM AUTHOR]

The problem of identifying key genes is of fundamental importance in biology and medicine. The GeneRank model explores connectivity data to produce a prioritization of the genes in a microarray experiment that is less susceptible to variation caused by experimental noise than the one based on expression levels alone. The GeneRank algorithm amounts to solving an unsymmetric linear system. However, when the matrix in question is very large, the GeneRank algorithm is inefficient and even can be infeasible. On the other hand, the adjacency matrix is symmetric in the GeneRank model, while the original GeneRank algorithm fails to exploit the symmetric structure of the problem in question. In this paper, we discover that the GeneRank problem can be rewritten as a symmetric positive definite linear system, and propose a preconditioned conjugate gradient algorithm to solve it. Numerical experiments support our theoretical results, and show superiority of the novel algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Spermatogenesis-related genes are essential for mammalian male reproduction. To select spermatogenesis-related gene from microarray data, we integrate GeneRank with Gene Ontology (GO)-terms semantic similarity. Basing on this method, we rank genes of microarray dataset, and the results indicate that our method provides a useful framework for spermatogenesis-related gene selection. [Copyright &y& Elsevier]

Abstract: The Arnoldi-type algorithm proposed by Golub and Greif [G. Golub, C. Greif, An Arnoldi-type algorithm for computing PageRank, BIT 46 (2006) 759–771] is a restarted Krylov subspace method for computing PageRank. However, this algorithm may not be efficient when the damping factor is high and the dimension of the search subspace is small. In this paper, we first develop an extrapolation method based on Ritz values. We then consider how to periodically knit this extrapolation method together with the Arnoldi-type algorithm. The resulting algorithm is the Arnoldi-Extrapolation algorithm. The convergence of the new algorithm is analyzed. Numerical experiments demonstrate the numerical behavior of this algorithm. [Copyright &y& Elsevier]

Objective: To construct a diagnostic signature to distinguish lung adenocarcinoma from lung squamous cell carcinoma and a prognostic signature to predict the risk of death for patients with nonsmall-cell lung cancer, with satisfactory predictive performances, good stabilities, small sizes and meaningful biological implications., Methods: Pathway-based feature selection methods utilize pathway information as a priori to provide insightful clues on potential biomarkers from the biological perspective, and such incorporation may be realized by adding weights to test statistics or gene expression values. In this study, weighted gene expression profiles were generated using the GeneRank method and then the LASSO method was used to identify discriminative and prognostic genes., Results: The five-gene diagnostic signature including keratin 5 ( KRT5 ), mucin 1 ( MUC1 ), triggering receptor expressed on myeloid cells 1 ( TREM1 ), complement C3 ( C3 ) and transmembrane serine protease 2 ( TMPRSS2 ) achieved a predictive error of 12.8% and a Generalized Brier Score of 0.108, while the five-gene prognostic signature including alcohol dehydrogenase 1C (class I), gamma polypeptide ( ADH1C ), alpha-2-glycoprotein 1, zinc-binding ( AZGP1 ), clusterin ( CLU ), cyclin dependent kinase 1 ( CDK1 ) and paternally expressed 10 ( PEG10 ) obtained a log-rank P -value of 0.03 and a C-index of 0.622 on the test set., Conclusions: Besides good predictive capacity, model parsimony and stability, the identified diagnostic and prognostic genes were highly relevant to lung cancer. A large-sized prospective study to explore the utilization of these genes in a clinical setting is warranted.

Non-small cell lung cancer (NSCLC) is a leading cause of cancer-associated mortality worldwide. Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are two primary histological subtypes of NSCLC, accounting for ~70% of lung cancer cases. Increasing evidence suggests that AC and SCC differ in the composition of genes and molecular characteristics. Previous research has focused on distinguishing AC from SCC or predicting the NSCLC patient survival rates using gene expression profiles, usually with the aid of a feature selection method. The present study conducted a pre-filtering to identify the genes that have significant expression values and a high connection with other genes in the gene network, and then used the radial coordinate visualization method to identify relevant genes. By applying the proposed procedure to NSCLC data, it was demonstrated that there is a clear segmentation between AC and SCC, however not between patients with a good prognosis and bad prognosis. The focus of discriminating AC and SCC differs from survival prediction and there are almost no overlaps between the two gene signatures. Overall, a supervised learning method is preferred and future studies aiming to identify prognostic gene signatures with an increased prediction efficiency are required.