Dataset Information

Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach.

ABSTRACT: Understanding the biological factors that are characteristic of metastasis in melanoma remains a key approach to improving treatment. In this study, we seek to identify a gene signature of metastatic melanoma. We configured a new network-based computational pipeline, combined with a machine learning method, to mine publicly available transcriptomic data from melanoma patient samples. Our method is unbiased and scans a genome-wide protein-protein interaction network using a novel formulation for network scoring. Using this, we identify the most influential, differentially expressed nodes in metastatic as compared to primary melanoma. We evaluated the shortlisted genes by a machine learning method to rank them by their discriminatory capacities. From this, we identified a panel of 6 genes, ALDH1A1, HSP90AB1, KIT, KRT16, SPRR3 and TMEM45B whose expression values discriminated metastatic from primary melanoma (87% classification accuracy). In an independent transcriptomic data set derived from 703 primary melanomas, we showed that all six genes were significant in predicting melanoma specific survival (MSS) in a univariate analysis, which was also consistent with AJCC staging. Further, 3 of these genes, HSP90AB1, SPRR3 and KRT16 remained significant predictors of MSS in a joint analysis (HR?=?2.3, P?=?0.03) although, HSP90AB1 (HR?=?1.9, P?=?2?×?10^-4) alone remained predictive after adjusting for clinical predictors.

SUBMITTER: Metri R

PROVIDER: S-EPMC5725601 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach.

Metri Rahul R Mohan Abhilash A Nsengimana Jérémie J Pozniak Joanna J Molina-Paris Carmen C Newton-Bishop Julia J Bishop David D Chandra Nagasuma N

Scientific reports 20171211 1

Understanding the biological factors that are characteristic of metastasis in melanoma remains a key approach to improving treatment. In this study, we seek to identify a gene signature of metastatic melanoma. We configured a new network-based computational pipeline, combined with a machine learning method, to mine publicly available transcriptomic data from melanoma patient samples. Our method is unbiased and scans a genome-wide protein-protein interaction network using a novel formulation for ...[more]

PMID: 29229936

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Project description:Malignant melanoma (MM) is a malignant tumor originating from melanocytes, with high aggressiveness, high metastasis and extremely poor prognosis. MM accounts for 4% of skin cancers and 80% of mortality, and the median survival of patients with metastatic melanoma is only about 6 months, with a five-year survival rate of less than 10%. In recent years, the incidence of melanoma has gradually increased and has become one of the serious diseases that endanger human health. Competitive endogenous RNA (ceRNA) is the main model of the mechanism by which long chain non-coding RNAs (lncRNAs) play a regulatory role in the disease. LncRNAs can act as a "sponge", competitively attracting small RNAs (micoRNAs; miRNAs), thus interfering with miRNA function, and affect the expression of target gene messenger RNAs (mRNAs), ultimately promoting tumorigenesis and progression. Bioinformatics analysis can identify potentially prognostic and therapeutically relevant differentially expressed genes in MM, finding lncRNAs, miRNAs and mRNAs that are interconnected through the ceRNA network, providing further insight into gene regulation and prognosis of metastatic melanoma. Weighted co-expression networks were used to identify lncRNA and mRNA modules associated with the metastatic phenotype, as well as the co-expression genes contained in the modules. A total of 17 lncRNAs, six miRNAs, and 11 mRNAs were used to construct a ceRNA interaction network that plays a regulatory role in metastatic melanoma patients. The prognostic risk model was used as a sorter to classify the survival prognosis of melanoma patients. Four groups of ceRNA interaction triplets were finally obtained, which miR-3662 might has potential implication for the treatment of metaststic melanoma patients, and futher experiments confirmed the regulating relationship and phenotype of this assumption. This study provides new targets to regulate metastatic process, predict metastatic potential and indicates that the miR-3662 can be used in the treatment of melanoma.

Project description:BACKGROUND:The identification of prognostic biomarkers for cancer patients is essential for cancer research. These days, DNA methylation has been proved to be associated with cancer prognosis. However, there are few methods which identify the prognostic markers based on DNA methylation data systematically, especially considering the interaction among DNA methylation sites. METHODS:In this paper, we first evaluated the stabilities of microRNA, mRNA, and DNA methylation data in prognosis of cancer. After that, a rank-based method was applied to construct a DNA methylation interaction network. In this network, nodes with the largest degrees (10% of all the nodes) were selected as hubs. Cox regression was applied to select the hubs as prognostic signature. In this prognostic signature, DNA methylation levels of each DNA methylation site are correlated with the outcomes of cancer patients. After obtaining these prognostic genes, we performed the survival analysis in the training group and the test group to verify the reliability of these genes. RESULTS:We applied our method in three cancers (ovarian cancer, breast cancer and Glioblastoma Multiforme). In all the three cancers, there are more common ones of prognostic genes selected from different samples in DNA methylation data, compared with gene expression data and miRNA expression data, which indicates the DNA methylation data may be more stable in cancer prognosis. Power-law distribution fitting suggests that the DNA methylation interaction networks are scale-free. And the hubs selected from the three networks are all enriched by cancer related pathways. The gene signatures were obtained for the three cancers respectively, and survival analysis shows they can distinguish the outcomes of tumor patients in both the training data sets and test data sets, which outperformed the control signatures. CONCLUSIONS:A computational method was proposed to construct DNA methylation interaction network and this network could be used to select prognostic signatures in cancer.

Project description:Comparison of gene expression profiles between a primary melanoma and an early metastatic specimen from the same patient will provide essential biological insight into early metastatic processes. The DASL (cDNA mediated annealing, selection, extension and ligation) assay has been used to generate gene expression data for 502 cancer-related genes from very small formalin-fixed sentinel node biopsy (SNB) melanoma samples, this data has been further compared with gene expression of the matched formalin-fixed primary melanoma. Tissue was sampled from twenty-five SNB deposits using laser capture microdissection. The mean number of genes detected using DASL with SNB samples was lower than when using a core of primary melanoma tumor (242 versus 434 genes). A large proportion of SNB samples failed (<240 genes detected) the assay (57.7%). Area of tissue microdissected, RNA concentration and qRT-PCR quality control did not predict performance of samples on the array but age of sampled tissue negatively correlated with number of genes detected (p=0.01). For samples that performed successfully, matched primary samples were available for 10 samples. Gene expression profiles correlated between all matched tumor pairs (Spearman’s rho 0.15-0.80, p<0.01), although a number of genes were differentially expressed between nodal and primary tumors in all tumor pairs. This study demonstrates that the DASL assay can be used to generate gene expression data from small formalin-fixed samples, but not consistently. Differentially expressed genes were identified across 10 matched primary and nodal tumor pairs suggesting that the DASL assay could be used to derive essential biological information about early metastasis. Formalin-fixed paraffin-embedded SNB samples and primary tumors were identified from two study sets. In Study 1, population ascertained melanoma patients were recruited to a cohort in the period from 2000 till the present day. Eight positive SNB samples were identified, from patients whose primary tumors had already been sampled for DASL studies. In Study 2, seventeen positive SNB samples were identified in a study designed to identify predictors of sentinel node positivity and relapse (SNB study). One nodal RNA sample, microdissected from a diagnostic H&E slide, was not sent for DASL analysis because of low RNA concentration (0.37 ng/μl), therefore a total of 26 nodal samples (including 2 replicate samples) were supplied to the Illumina DASL service provider. Of the 11 samples successfully yielding gene expression data, 10 had matched primary samples (e.g., Node_1 and Primary_1) for which gene expression data were available and is presented in this data set.

Project description:Malignancy of cancer has been linked to distinct subsets of stem-like cells, the so-called cancer stem cells (CSCs), which persist during treatment and seem to lead to drug-resistant recurrence. Metastatic spread of cancer cells is one of the hallmarks of malignancy and contributes to most human melanoma-related deaths. Recently, overlapping groups of proteins and pathways were shown to regulate stem cell migration and cancer metastasis, raising the question of whether genes/proteins involved in stem cell pluripotency may have important implications when applied to the biology of cancer metastasis. Furthermore, it is well known that ion channels and receptors, particularly those responsible for calcium (Ca2+) signal generation, are critical in determining the cellular fate of stem cells (SCs). In the present study, we searched for evidence of altered stem cell pluripotency and Ca2+ signaling-related genes in the context of melanoma metastasis. We did this by using network analysis of gene expression in tissue biopsies from three different independent datasets of patients. First, we created an in silico network model ("STEMCa" interactome) showing the landscape of interactions between stem cell pluripotency and Ca2+ signaling-related genes/proteins, and demonstrated that around 51% (151 out of 294) of the genes within this model displayed significant changes of expression (False Discovery Rate (FDR), corrected p-value < 0.05) in at least one of the datasets of melanoma metastasis when compared with primary tumor biopsies (controls). Analysis of the properties (degree and betweenness) of the topological network revealed 27 members as the most central hub (HB) and nonhub-bottlenecks (NH-B) among the 294 genes/proteins of the whole interactome. From those representative genes, CTNNB1, GNAQ, GSK3B, GSTP1, MAPK3, PPP1CC, PRKACA, and SMAD4 showed equal up- or downregulation (corrected p-value < 0.05) in at least 2 independent datasets of melanoma metastases samples and PTPN11 showed upregulation (corrected p-value < 0.05) in three of them when compared with control samples. We postulate that altered expression of stem cell pluripotency and Ca2+ signaling pathway-related genes may contribute to the metastatic transformation, with these central members being an optimal candidate group of biomarkers and in silico therapeutic targets for melanoma metastasis, which deserve further investigation.

Dataset Information

Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach.

Publications

Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach.

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