Novel cancer subtyping method based on patient-specific gene regulatory network.
Ontology highlight
ABSTRACT:
SUBMITTER: Nakazawa MA
PROVIDER: S-EPMC8654869 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Detecting driver modules is a key challenge for understanding the mechanisms of carcinogenesis at the pathway level. Identifying cancer specific driver modules is helpful for interpreting the different principles of different cancer types. However, most methods are proposed to identify driver modules in one cancer, but few methods are introduced to detect cancer specific driver modules. We propose a network-based method to detect cancer specific driver modules (CSDM) in a certain cancer type to other cancer types. We construct the specific network of a cancer by combining specific coverage and mutual exclusivity in all cancer types, to catch the specificity of the cancer at the pathway level. To illustrate the performance of the method, we apply CSDM on 12 TCGA cancer types. When we compare CSDM with SpeMDP and HotNet2 with regard to specific coverage and the enrichment of GO terms and KEGG pathways, CSDM is more accurate. We find that the specific driver modules of two different cancers have little overlap, which indicates that the driver modules detected by CSDM are specific. Finally, we also analyze three specific driver modules of BRCA, BLCA, and LAML intersecting with well-known pathways. The source code of CSDM is freely accessible at https://github.com/fengli28/CSDM.git.
| S-EPMC6100049 | biostudies-literature
Project description:MotivationGene Essentiality Analysis based on Flux Balance Analysis (FBA-based GEA) is a promising tool for the identification of novel metabolic therapeutic targets in cancer. The reconstruction of cancer-specific metabolic networks, typically based on gene expression data, constitutes a sensible step in this approach. However, to our knowledge, no extensive assessment on the influence of the reconstruction process on the obtained results has been carried out to date.ResultsIn this article, we aim to study context-specific networks and their FBA-based GEA results for the identification of cancer-specific metabolic essential genes. To that end, we used gene expression datasets from the Cancer Cell Line Encyclopedia (CCLE), evaluating the results obtained in 174 cancer cell lines. In order to more clearly observe the effect of cancer-specific expression data, we did the same analysis using randomly generated expression patterns. Our computational analysis showed some essential genes that are fairly common in the reconstructions derived from both gene expression and randomly generated data. However, though of limited size, we also found a subset of essential genes that are very rare in the randomly generated networks, while recurrent in the sample derived networks, and, thus, would presumably constitute relevant drug targets for further analysis. In addition, we compare the in-silico results to high-throughput gene silencing experiments from Project Achilles with conflicting results, which leads us to raise several questions, particularly the strong influence of the selected biomass reaction on the obtained results. Notwithstanding, using previous literature in cancer research, we evaluated the most relevant of our targets in three different cancer cell lines, two derived from Gliobastoma Multiforme and one from Non-Small Cell Lung Cancer, finding that some of the predictions are in the right track.
| S-EPMC4856428 | biostudies-literature
Project description:Genome-wide mRNA expression profiles of 70 primary gastric tumors from the Australian patient cohort. Like many cancers, gastric adenocarcinomas (gastric cancers) show considerable heterogeneity between patients. Thus, there is intense interest in using gene expression profiles to discover subtypes of gastric cancers with particular biological properties or therapeutic vulnerabilities. Identification of such subtypes could generate insights into the mechanisms of cancer progression or lay the foundation for personalized treatments. Here we report a robust gene-xpression-based clustering of a large collection of gastric adenocarcinomas from Singaporean patients [GSE34942 and GSE15459]. We developed and validated a classifier for the three subtypes in Australian patient cohort. Profiling of 70 primary gastric tumors on Affymetrix GeneChip Human Genome U133 Plus 2.0 Array. All tumors were collected with approvals from Peter MacCallum Cancer Center, Australia; the Research Ethics Review Committee; and signed patient informed consent.
2012-07-04 | E-GEOD-35809 | biostudies-arrayexpress
Project description:BACKGROUND: Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context. RESULTS: This paper introduces the theory of network flooding, which aims to address the problem of network minimization and regulatory information flow in gene regulatory networks. Given a regulatory biological network, a set of source (input) nodes and optionally a set of sink (output) nodes, our task is to find (a) the minimal sub-network that encodes the regulatory program involving all input and output nodes and (b) the information flow from the source to the sink nodes of the network. Here, we describe a novel, scalable, network traversal algorithm and we assess its potential to achieve significant network size reduction in both synthetic and E. coli networks. Scalability and sensitivity analysis show that the proposed method scales well with the size of the network, and is robust to noise and missing data. CONCLUSIONS: The method of network flooding proves to be a useful, practical approach towards information flow analysis in gene regulatory networks. Further extension of the proposed theory has the potential to lead in a unifying framework for the simultaneous network minimization and information flow analysis across various "omics" levels.
| S-EPMC3672003 | biostudies-literature
Project description:Cryptosporidium meleagridis is a common cause of cryptosporidiosis in avian hosts and the third most common species involved in human cryptosporidiosis. Sequencing of the highly polymorphic 60-kDa glycoprotein (gp60) gene is a frequently used tool for investigation of the genetic diversity and transmission dynamics of Cryptosporidium. However, few studies have included gp60 sequencing of C. meleagridis. One explanation may be that the gp60 primers currently in use are based on Cryptosporidium hominis and Cryptosporidium parvum sequence data, potentially limiting successful amplification of the C. meleagridis gp60 gene. We therefore aimed to design primers for better gp60 subtyping of C. meleagridis. Initially, ∼1,440 bp of the gp60 locus of seven C. meleagridis isolates were amplified using primers flanking the open reading frame. The obtained sequence data (∼1,250 bp) were used to design primers for a nested PCR targeting C. meleagridis. Twenty isolates (16 from human and 4 from poultry) previously identified as C. meleagridis by analysis of small subunit (SSU) rRNA genes were investigated. Amplicons of the expected size (∼900 bp) were obtained from all 20 isolates. The subsequent sequence analysis identified 3 subtype families and 10 different subtypes. The most common subtype family, IIIb, was identified in 12 isolates, represented by 6 subtypes, 4 new and 2 previously reported. Subtype family IIIe was found in 3 isolates represented by 3 novel, distinct subtypes. Finally, IIIgA31G3R1 was found in 1 human isolate and 4 poultry isolates, all originating from a previously reported C. meleagridis outbreak at a Swedish organic farm.
| S-EPMC4097674 | biostudies-literature
Project description:Genome-wide mRNA expression profiles of 70 primary gastric tumors from the Australian patient cohort. Like many cancers, gastric adenocarcinomas (gastric cancers) show considerable heterogeneity between patients. Thus, there is intense interest in using gene expression profiles to discover subtypes of gastric cancers with particular biological properties or therapeutic vulnerabilities. Identification of such subtypes could generate insights into the mechanisms of cancer progression or lay the foundation for personalized treatments. Here we report a robust gene-xpression-based clustering of a large collection of gastric adenocarcinomas from Singaporean patients [GSE34942 and GSE15459]. We developed and validated a classifier for the three subtypes in Australian patient cohort.
2012-07-05 | GSE35809 | GEO
Project description:Cancer is an umbrella term that includes a range of disorders, from those that are fast-growing and lethal to indolent lesions with low or delayed potential for progression to death. One critical unmet challenge is that molecular disease subtypes characterized by relevant clinical differences, such as survival, are difficult to differentiate. With the advancement of multi-omics technologies, subtyping methods have shifted toward data integration in order to differentiate among subtypes from a holistic perspective that takes into consideration phenomena at multiple levels. However, these integrative methods are still limited by their statistical assumption and their sensitivity to noise. In addition, they are unable to predict the risk scores of patients using multi-omics data. Here, we present a novel approach named Subtyping via Consensus Factor Analysis (SCFA) that can efficiently remove noisy signals from consistent molecular patterns in order to reliably identify cancer subtypes and accurately predict risk scores of patients. In an extensive analysis of 7,973 samples related to 30 cancers that are available at The Cancer Genome Atlas (TCGA), we demonstrate that SCFA outperforms state-of-the-art approaches in discovering novel subtypes with significantly different survival profiles. We also demonstrate that SCFA is able to predict risk scores that are highly correlated with true patient survival and vital status. More importantly, the accuracy of subtype discovery and risk prediction improves when more data types are integrated into the analysis. The SCFA software and TCGA data packages will be available on Bioconductor.
| S-EPMC7344292 | biostudies-literature
Project description:Abstract Background Use of immunohistochemistry-based surrogates of molecular breast cancer subtypes is common in research and clinical practice, but information on their comparative validity and prognostic capacity is scarce. Methods Data from 2 PAM50-subtyped Swedish breast cancer cohorts were used: Stockholm tamoxifen trial–3 with 561 patients diagnosed 1976-1990 and Clinseq with 237 patients diagnosed 2005-2012. We evaluated 3 surrogate classifications; the immunohistochemistry-3 surrogate classifier based on estrogen receptor, progesterone receptor, and HER2 and the St. Gallen and Prolif surrogate classifiers also including Ki-67. Accuracy, kappa, sensitivity, and specificity were computed as compared with PAM50. Alluvial diagrams of misclassification patterns were plotted. Distant recurrence-free survival was assessed using Kaplan-Meier plots, and tamoxifen treatment benefit for luminal subtypes was modeled using flexible parametric survival models. Results The concordance with PAM50 ranged from poor to moderate (kappa = 0.36-0.57, accuracy = 0.54-0.75), with best performance for the Prolif surrogate classification in both cohorts. Good concordance was only achieved when luminal subgroups were collapsed (kappa = 0.71-0.69, accuracy = 0.90-0.91). The St. Gallen surrogate classification misclassified luminal A into luminal B; the reverse pattern was seen with the others. In distant recurrence-free survival, surrogates were more similar to each other than PAM50. The difference in tamoxifen treatment benefit between luminal A and B for PAM50 was not replicated with any surrogate classifier. Conclusions All surrogate classifiers had limited ability to distinguish between PAM50 luminal A and B, but patterns of misclassifications differed. PAM50 subtyping appeared to yield larger separation of survival between luminal subtypes than any of the surrogate classifications.
| S-EPMC7791620 | biostudies-literature
Project description:BackgroundMicroRNAs (miRNAs) are a class of endogenous small regulatory RNAs. Identifications of the dys-regulated or perturbed miRNAs and their key target genes are important for understanding the regulatory networks associated with the studied cellular processes. Several computational methods have been developed to infer the perturbed miRNA regulatory networks by integrating genome-wide gene expression data and sequence-based miRNA-target predictions. However, most of them only use the expression information of the miRNA direct targets, rarely considering the secondary effects of miRNA perturbation on the global gene regulatory networks.ResultsWe proposed a network propagation based method to infer the perturbed miRNAs and their key target genes by integrating gene expressions and global gene regulatory network information. The method used random walk with restart in gene regulatory networks to model the network effects of the miRNA perturbation. Then, it evaluated the significance of the correlation between the network effects of the miRNA perturbation and the gene differential expression levels with a forward searching strategy. Results show that our method outperformed several compared methods in rediscovering the experimentally perturbed miRNAs in cancer cell lines. Then, we applied it on a gene expression dataset of colorectal cancer clinical patient samples and inferred the perturbed miRNA regulatory networks of colorectal cancer, including several known oncogenic or tumor-suppressive miRNAs, such as miR-17, miR-26 and miR-145.ConclusionsOur network propagation based method takes advantage of the network effect of the miRNA perturbation on its target genes. It is a useful approach to infer the perturbed miRNAs and their key target genes associated with the studied biological processes using gene expression data.
| S-EPMC4124158 | biostudies-literature
Project description:Pathway or gene set over-representation analysis (ORA) has become a routine task in functional genomics studies. However, currently widely used ORA tools employ statistical methods such as Fisher's exact test that reduce a pathway into a list of genes, ignoring the constitutive functional non-equivalent roles of genes and the complex gene-gene interactions. Here, we develop a novel method named LEGO (functional Link Enrichment of Gene Ontology or gene sets) that takes into consideration these two types of information by incorporating network-based gene weights in ORA analysis. In three benchmarks, LEGO achieves better performance than Fisher and three other network-based methods. To further evaluate LEGO's usefulness, we compare LEGO with five gene expression-based and three pathway topology-based methods using a benchmark of 34 disease gene expression datasets compiled by a recent publication, and show that LEGO is among the top-ranked methods in terms of both sensitivity and prioritization for detecting target KEGG pathways. In addition, we develop a cluster-and-filter approach to reduce the redundancy among the enriched gene sets, making the results more interpretable to biologists. Finally, we apply LEGO to two lists of autism genes, and identify relevant gene sets to autism that could not be found by Fisher.
| S-EPMC4707541 | biostudies-literature