Pathway-gene identification for pancreatic cancer survival via doubly regularized Cox regression

Gong, Haijun; Wu, Tong Tong; Clarke, Edmund M.

doi:10.1186/1752-0509-8-s1-s3

Cited by 23 publications

(12 citation statements)

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“…These findings were different from another study that identified higher expression of ITGA5, SEMA3A, KIF4A, IL20RB, SLC20A1, CDC45, PXN, SSX3, and TMEM26 genes associated with shorter survival, and B3GNT1, NOSTRIN, and CADPS lower expression associated with poor outcome (Haider et al, 2014). Gong et al (2014) analyzed the high-dimensional microarray data of pancreatic cancer patients with localized and resected PDAC using doubly regularized Cox regression model, and identified and verified the following pathways and genes to be directly correlated to pancreatic cancer survival: DENN-D4A, KLF13, zinc finger protein family genes (ZNF229, ZNF233, ZNF395, and ZNF432), immune phagocytosis pathway (CYBA), SLC family (SLC22A8, SLC8A3, SLC24A6), TRP (Ca 2+ ) ion transport pathway (TRPV5, TRPM6), KCNK (K + ) ion transport pathway (KCNK3, KCNK18), and TGFb core pathway (spermatogenesis signaling set PCYT1B gene).…”

Section: Discussioncontrasting

confidence: 94%

Study of Clinical Survival and Gene Expression in a Sample of Pancreatic Ductal Adenocarcinoma by Parsimony Phylogenetic Analysis

Nalbantoglu

Abu‐Asab

Tan

et al. 2016

OMICS: A Journal of Integrative Biology

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Pancreatic ductal adenocarcinoma (PDAC) is one of the rapidly growing forms of pancreatic cancer with a poor prognosis and less than 5% 5-year survival rate. In this study, we characterized the genetic signatures and signaling pathways related to survival from PDAC, using a parsimony phylogenetic algorithm. We applied the parsimony phylogenetic algorithm to analyze the publicly available whole-genome in silico array analysis of a gene expression data set in 25 early-stage human PDAC specimens. We explain here that the parsimony phylogenetics is an evolutionary analytical method that offers important promise to uncover clonal (driver) and nonclonal (passenger) aberrations in complex diseases. In our analysis, parsimony and statistical analyses did not identify significant correlations between survival times and gene expression values. Thus, the survival rankings did not appear to be significantly different between patients for any specific gene ( p > 0.05). Also, we did not find correlation between gene expression data and tumor stage in the present data set. While the present analysis was unable to identify in this relatively small sample of patients a molecular signature associated with pancreatic cancer prognosis, we suggest that future research and analyses with the parsimony phylogenetic algorithm in larger patient samples are worthwhile, given the devastating nature of pancreatic cancer and its early diagnosis, and the need for novel data analytic approaches. The future research practices might want to place greater emphasis on phylogenetics as one of the analytical paradigms, as our findings presented here are on the cusp of this shift, especially in the current era of Big Data and innovation policies advocating for greater data sharing and reanalysis.

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Section: Discussioncontrasting

confidence: 94%

Study of Clinical Survival and Gene Expression in a Sample of Pancreatic Ductal Adenocarcinoma by Parsimony Phylogenetic Analysis

Nalbantoglu

Abu‐Asab

Tan

et al. 2016

OMICS: A Journal of Integrative Biology

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“…Hence, in order to provide more reliable and biologically meaningful results, the inclusion of a-priori biological knowledge into the models is mandatory. To address this issue, new penalized Cox methods based on the integration of genomic information have been recently proposed (Zhang et al, 2013 ; Gong et al, 2014 ; Sun et al, 2014 ). In such models, the genomic information is encoded by a network whose graph structure identifies a given relation (edges) between genes (nodes).…”

Section: Introductionmentioning

confidence: 99%

Cancer Markers Selection Using Network-Based Cox Regression: A Methodological and Computational Practice

et al. 2016

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International initiatives such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) are collecting multiple datasets at different genome-scales with the aim of identifying novel cancer biomarkers and predicting survival of patients. To analyze such data, several statistical methods have been applied, among them Cox regression models. Although these models provide a good statistical framework to analyze omic data, there is still a lack of studies that illustrate advantages and drawbacks in integrating biological information and selecting groups of biomarkers. In fact, classical Cox regression algorithms focus on the selection of a single biomarker, without taking into account the strong correlation between genes. Even though network-based Cox regression algorithms overcome such drawbacks, such network-based approaches are less widely used within the life science community. In this article, we aim to provide a clear methodological framework on the use of such approaches in order to turn cancer research results into clinical applications. Therefore, we first discuss the rationale and the practical usage of three recently proposed network-based Cox regression algorithms (i.e., Net-Cox, AdaLnet, and fastcox). Then, we show how to combine existing biological knowledge and available data with such algorithms to identify networks of cancer biomarkers and to estimate survival of patients. Finally, we describe in detail a new permutation-based approach to better validate the significance of the selection in terms of cancer gene signatures and pathway/networks identification. We illustrate the proposed methodology by means of both simulations and real case studies. Overall, the aim of our work is two-fold. Firstly, to show how network-based Cox regression models can be used to integrate biological knowledge (e.g., multi-omics data) for the analysis of survival data. Secondly, to provide a clear methodological and computational approach for investigating cancers regulatory networks.

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“…These explosively growing amount of highdimensional gene expression data can be divided into two types: static and time series. The static expression data are assumed to be independently and identically distributed (IID), and many statistical inference algorithms [ 1 - 8 ] have been developed to identify key genetic signatures and signaling pathways that are frequently altered in some diseases. Gene regulatory network plays a critical role in the cell's proliferation and differentiation, so, a comprehensive understanding of gene regulatory network (GRN) and regulatory components will help discover some drug targeted genes in cancer and other diseases.…”

Section: Introductionmentioning

confidence: 99%

A novel procedure for statistical inference and verification of gene regulatory subnetwork

et al. 2015

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BackgroundThe reconstruction of gene regulatory network from time course microarray data can help us comprehensively understand the biological system and discover the pathogenesis of cancer and other diseases. But how to correctly and efficiently decifer the gene regulatory network from high-throughput gene expression data is a big challenge due to the relatively small amount of observations and curse of dimensionality. Computational biologists have developed many statistical inference and machine learning algorithms to analyze the microarray data. In the previous studies, the correctness of an inferred regulatory network is manually checked through comparing with public database or an existing model.ResultsIn this work, we present a novel procedure to automatically infer and verify gene regulatory networks from time series expression data. The dynamic Bayesian network, a statistical inference algorithm, is at first implemented to infer an optimal network from time series microarray data of S. cerevisiae, then, a weighted symbolic model checker is applied to automatically verify or falsify the inferred network through checking some desired temporal logic formulas abstracted from experiments or public database.ConclusionsOur studies show that the marriage of statistical inference algorithm with model checking technique provides a more efficient way to automatically infer and verify the gene regulatory network from time series expression data than previous studies.

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Pathway-gene identification for pancreatic cancer survival via doubly regularized Cox regression

Cited by 23 publications

References 50 publications

Study of Clinical Survival and Gene Expression in a Sample of Pancreatic Ductal Adenocarcinoma by Parsimony Phylogenetic Analysis

Study of Clinical Survival and Gene Expression in a Sample of Pancreatic Ductal Adenocarcinoma by Parsimony Phylogenetic Analysis

Cancer Markers Selection Using Network-Based Cox Regression: A Methodological and Computational Practice

A novel procedure for statistical inference and verification of gene regulatory subnetwork

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