Pathway and Network Approaches for Identification of Cancer Signature Markers from Omics Data

Wang, Jinlan; Zuo, Yuan; Man, Yan Gao; Avital, Itzhak; Stojadinovic, Alexander; Li, Meng; Yang, Xiaowei; Varghese, Rency S.; Tadesse, Mahlet G.; Ressom, Habtom W.

doi:10.7150/jca.10631

Cited by 48 publications

(34 citation statements)

References 144 publications

(160 reference statements)

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“…Higher levels of lipid metabolites, including fatty acids and carnitine metabolites, have been found in breast cancer patients compared to normal controls (35,38,42). Wang et al (9) reported that several lipids including phosphatidylglycerol (PG) were upregulated in highly invasive breast cancer cells. Breast tumors are described as developing a ‘lipogenic phenotype’, and while cell growth is dependent on lipogenesis, lipolysis is upregulated, and is associated with tumor aggressiveness (36,37).…”

Section: Discussionmentioning

confidence: 99%

“…This latter growing technology permits simultaneous monitoring of thousands of macro and micro metabolites that serve as substrates and products of metabolic pathways (8). Integration of these pathways and their interactions provides insight into the development of malignant processes and could lead to the discovery of cancer biomarkers (9). …”

Section: Introductionmentioning

confidence: 99%

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Metabolomic profiling of breast tumors using ductal fluid

Canto

Marian

Varghese

et al. 2016

International Journal of Oncology

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Identification of new biomarkers for breast cancer remains critical in order to enhance early detection of the disease and improve its prognosis. Towards this end, we performed an untargeted metabolomic analysis of breast ductal fluid using an ultra-performance liquid chromatography coupled with a quadrupole time-of-light (UPLC-QTOF) mass spectrometer. We investigated the metabolomic profiles of breast tumors using ductal fluid samples collected by ductal lavage (DL). We studied fluid from both the affected breasts and the unaffected contralateral breasts (as controls) from 43 women with confirmed unilateral breast cancer. Using this approach, we identified 1560 ions in the positive mode and 538 ions in the negative mode after preprocessing of the UPLC-QTOF data. Paired t-tests applied on these data matrices identified 209 ions (positive and negative modes combined) with significant change in intensity level between affected and unaffected control breasts (adjusted P-values <0.05). Among these, 83 ions (39.7%) showed a fold change (FC) >1.2 and 66 ions (31.6%) were identified with putative compound names. The metabolites that we identified included endogenous metabolites such as amino acid derivatives (N-Acetyl-DL-tryptophan) or products of lipid metabolism such as N-linoleoyl taurine, trans-2-dodecenoylcarnitine, lysophosphatidylcholine LysoPC(18:2(9Z,12Z)), glycerophospholipids PG(18:0/0:0), and phosphatidylserine PS(20:4(5Z,8Z,11Z,14Z). Generalized LASSO regression further selected 21 metabolites when race, menopausal status, smoking, grade and TNM stage were adjusted for. A predictive conditional logistic regression model, using the LASSO selected 21 ions, provided diagnostic accuracy with the area under the curve of 0.956 (sensitivity/specificity of 0.907/0.884). This is the first study that shows the feasibility of conducting a comprehensive metabolomic profiling of breast tumors using breast ductal fluid to detect changes in the cellular microenvironment of the tumors and shows the potential for this approach to be used to improve detection of breast cancer.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Metabolomic profiling of breast tumors using ductal fluid

Canto

Marian

Varghese

et al. 2016

International Journal of Oncology

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“…[31] More recent efforts at integrating -omics data for identification of targetable signaling modules have taken divergent approaches, including by multiple hypothesis testing as described here. [32] To our knowledge, a cross-platform and genome-wide search for unexpected alterations that may correlate with increased pSTAT3(Y705) expression in HNSCC has not been reported.…”

Section: Discussionmentioning

confidence: 99%

Genomic and Transcriptomic Alterations Associated with STAT3 Activation in Head and Neck Cancer

et al. 2016

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BackgroundHyperactivation of STAT3 via constitutive phosphorylation of tyrosine 705 (Y705) is common in most human cancers, including head and neck squamous carcinoma (HNSCC). STAT3 is rarely mutated in cancer and the (epi)genetic alterations that lead to STAT3 activation are incompletely understood. Here we used an unbiased approach to identify genomic and epigenomic changes associated with pSTAT3(Y705) expression using data generated by The Cancer Genome Atlas (TCGA).Methods and FindingsMutation, mRNA expression, promoter methylation, and copy number alteration data were extracted from TCGA and examined in the context of pSTAT3(Y705) protein expression. mRNA expression levels of 1279 genes were found to be associated with pSTAT3(705) expression. Association of pSTAT3(Y705) expression with caspase-8 mRNA expression was validated by immunoblot analysis in HNSCC cells. Mutation, promoter hypermethylation, and copy number alteration of any gene were not significantly associated with increased pSTAT3(Y705) protein expression.ConclusionsThese cumulative results suggest that unbiased approaches may be useful in identifying the molecular underpinnings of oncogenic signaling, including STAT3 activation, in HNSCC. Larger datasets will likely be necessary to elucidate signaling consequences of infrequent alterations.

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“…Systems biology focuses in understanding large-scale data derived from highthroughput techniques into comprehensive predictive models, quantifying all molecular entities (proteins or genes) of a biological system to evaluate their interactions into networks [18,19]. The behavior of these networks may be considered as a biomarker, as showed by models for cancer [20] and other diseases, such as respiratory illness [21][22][23][24]. Thus, the aim of this study is to identify a major network as a biomarker for CR in asthma, which may help to early distinguish patients with severe ashtma and to define new therapeutic targets to be explored.…”

Section: Introductionmentioning

confidence: 99%

Identifying a biomarker network for corticosteroid resistance in asthma from bronchoalveolar lavage samples

et al. 2016

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Corticosteroid resistance (CR) is a major barrier to the effective treatment of severe asthma. Hence, a better understanding of the molecular mechanisms involved in this condition is a priority. Network analysis is an emerging strategy to explore this complex heterogeneous disorder at system level to identify a small own network for CR in asthma. Gene expression profile of GSE7368 from bronchoalveolar lavage (BAL) of CR in subjects with asthma was downloaded from the gene expression omnibus (GEO) database and compared to BAL of corticosteroid-sensitive (CS) patients. DEGs were identified by the Limma package in R language. In addition, DEGs were mapped to STRING to acquire protein-protein interaction (PPI) pairs. Topological properties of PPI network were calculated by Centiscape, ClusterOne and BINGO. Subsequently, text-mining tools were applied to design one own cell signalling for CR in asthma. Thirty-five PPI networks were obtained; including a major network consisted of 370 nodes, connected by 777 edges. After topological analysis, a minor PPI network composed by 48 nodes was indentified, which is composed by most relevant nodes of major PPI network. In this subnetwork, several receptors (EGFR, EGR1, ESR2, PGR), transcription factors (MYC, JAK), cytokines (IL8, IL6, IL1B), one chemokine (CXCL1), one kinase (SRC) and one cyclooxygenase (PTGS2) were described to be associated with inflammatory environment and steroid resistance in asthma. We suggest a biomarker network composed by 48 nodes that could be potentially explored with diagnostic or therapeutic use.

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Pathway and Network Approaches for Identification of Cancer Signature Markers from Omics Data

Cited by 48 publications

References 144 publications

Metabolomic profiling of breast tumors using ductal fluid

Metabolomic profiling of breast tumors using ductal fluid

Genomic and Transcriptomic Alterations Associated with STAT3 Activation in Head and Neck Cancer

Identifying a biomarker network for corticosteroid resistance in asthma from bronchoalveolar lavage samples

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