A Unique Four-Hub Protein Cluster Associates to Glioblastoma Progression

Simeone, Pasquale; Trerotola, Marco; Urbanella, Andrea; Lattanzio, Rossano; Ciavardelli, Domenico; Giuseppe, Fabrizio Di; Eleuterio, Enrica; Sulpizio, M.J.; Eusebi, Vincenzo; Pession, Annalisa; Piantelli, Mauro; Alberti, Saverio

doi:10.1371/journal.pone.0103030

Cited by 24 publications

(16 citation statements)

References 82 publications

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“…To verify the strength of this unsupervised analysis, and to further build on it, we performed a supervised PLS-DA [ 50 ]. Datasets of pathological/experimental parameters were grouped using a dichotomic classification (metastatic relapse versus no relapse).…”

Section: Resultsmentioning

confidence: 99%

p53, cathepsin D, Bcl-2 are joint prognostic indicators of breast cancer metastatic spreading

et al. 2016

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BackgroundTraditional prognostic indicators of breast cancer, i.e. lymph node diffusion, tumor size, grading and estrogen receptor expression, are inadequate predictors of metastatic relapse. Thus, additional prognostic parameters appear urgently needed. Individual oncogenic determinants have largely failed in this endeavour. Only a few individual tumor growth drivers, e.g. mutated p53, Her-2, E-cadherin, Trops, did reach some prognostic/predictive power in clinical settings. As multiple factors are required to drive solid tumor progression, clusters of such determinants were expected to become stronger indicators of tumor aggressiveness and malignant progression than individual parameters. To identify such prognostic clusters, we went on to coordinately analyse molecular and histopathological determinants of tumor progression of post-menopausal breast cancers in the framework of a multi-institutional case series/case-control study.MethodsA multi-institutional series of 217 breast cancer cases was analyzed. Twenty six cases (12 %) showed disease relapse during follow-up. Relapsed cases were matched with a set of control patients by tumor diameter, pathological stage, tumor histotype, age, hormone receptors and grading. Histopathological and molecular determinants of tumor development and aggressiveness were then analyzed in relapsed versus non-relapsed cases. Stepwise analyses and model structure fitness assessments were carried out to identify clusters of molecular alterations with differential impact on metastatic relapse.Resultsp53, Bcl-2 and cathepsin D were shown to be coordinately associated with unique levels of relative risk for disease relapse. As many Ras downstream targets, among them matrix metalloproteases, are synergistically upregulated by mutated p53, whole-exon sequence analyses were performed for TP53, Ki-RAS and Ha-RAS, and findings were correlated with clinical phenotypes. Notably, TP53 insertion/deletion mutations were only detected in relapsed cases. Correspondingly, Ha-RAS missense oncogenic mutations were only found in a subgroup of relapsing tumors.ConclusionsWe have identified clusters of specific molecular alterations that greatly improve prognostic assessment with respect to singularly-analysed indicators. The combined analysis of these multiple tumor-relapse risk factors promises to become a powerful approach to identify patients subgroups with unfavourable disease outcome.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-016-2713-3) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

p53, cathepsin D, Bcl-2 are joint prognostic indicators of breast cancer metastatic spreading

et al. 2016

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“…This identified DAPK1 , MDGI , BRCA1 , P15 , PGK1 , PGR , SYK , THBS1 , 14-3-3σ , APAF1 , CALCA and CCND2 as the highest differentially methylated genes between progressing and non-progressing breast cancers. This group of genes contained controllers of cell proliferation ( P15, CCND2, PGR ) and apoptosis ( APAF1, DAPK1 ), p53 interactors ( THBS1, DAPK1 ), signaling kinases ( SYK, PGK1, DAPK1 ), drivers of tumor development ( BRCA1, 14-3-3 [ 50 ]), suggesting direct relevance of differentially-methylated/regulated genes for breast cancer development or progression.…”

Section: Resultsmentioning

confidence: 99%

A seven-gene CpG-island methylation panel predicts breast cancer progression

Melnikov

Levenson

et al. 2015

BMC Cancer

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BackgroundDNA methylation regulates gene expression, through the inhibition/activation of gene transcription of methylated/unmethylated genes. Hence, DNA methylation profiling can capture pivotal features of gene expression in cancer tissues from patients at the time of diagnosis. In this work, we analyzed a breast cancer case series, to identify DNA methylation determinants of metastatic versus non-metastatic tumors.MethodsCpG-island methylation was evaluated on a 56-gene cancer-specific biomarker microarray in metastatic versus non-metastatic breast cancers in a multi-institutional case series of 123 breast cancer patients. Global statistical modeling and unsupervised hierarchical clustering were applied to identify a multi-gene binary classifier with high sensitivity and specificity. Network analysis was utilized to quantify the connectivity of the identified genes.ResultsSeven genes (BRCA1, DAPK1, MSH2, CDKN2A, PGR, PRKCDBP, RANKL) were found informative for prognosis of metastatic diffusion and were used to calculate classifier accuracy versus the entire data-set. Individual-gene performances showed sensitivities of 63–79 %, 53–84 % specificities, positive predictive values of 59–83 % and negative predictive values of 63–80 %. When modelled together, these seven genes reached a sensitivity of 93 %, 100 % specificity, a positive predictive value of 100 % and a negative predictive value of 93 %, with high statistical power. Unsupervised hierarchical clustering independently confirmed these findings, in close agreement with the accuracy measurements. Network analyses indicated tight interrelationship between the identified genes, suggesting this to be a functionally-coordinated module, linked to breast cancer progression.ConclusionsOur findings identify CpG-island methylation profiles with deep impact on clinical outcome, paving the way for use as novel prognostic assays in clinical settings.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-015-1412-9) contains supplementary material, which is available to authorized users.

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“…Tying together the interactions of multiple subnetworks are centralized conduits, or hub proteins, such as MYC, P53, and EGFR, which play information-trafficking roles in the cell. Hub proteins are defined as the proteins with the highest numbers of interactions with other proteins in the proteome [19–21]. Due to their essential functions, the mutation, deletion, or functional alteration of hub proteins causes severe, though rare, phenotypic outcomes [22–24].…”

Section: Introductionmentioning

confidence: 99%

A Systems Biology Comparison of Ovarian Cancers Implicates Putative Somatic Driver Mutations through Protein-Protein Interaction Models

Yang

Elnitski

2016

PLoS ONE

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Ovarian carcinomas can be aggressive with a high mortality rate (e.g., high-grade serous ovarian carcinomas, or HGSOCs), or indolent with much better long-term outcomes (e.g., low-malignant-potential, or LMP, serous ovarian carcinomas). By comparing LMP and HGSOC tumors, we can gain insight into the mechanisms underlying malignant progression in ovarian cancer. However, previous studies of the two subtypes have been focused on gene expression analysis. Here, we applied a systems biology approach, integrating gene expression profiles derived from two independent data sets containing both LMP and HGSOC tumors with protein-protein interaction data. Genes and related networks implicated by both data sets involved both known and novel disease mechanisms and highlighted the different roles of BRCA1 and CREBBP in the two tumor types. In addition, the incorporation of somatic mutation data revealed that amplification of PAK4 is associated with poor survival in patients with HGSOC. Thus, perturbations in protein interaction networks demonstrate differential trafficking of network information between malignant and benign ovarian cancers. The novel network-based molecular signatures identified here may be used to identify new targets for intervention and to improve the treatment of invasive ovarian cancer as well as early diagnosis.

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A Unique Four-Hub Protein Cluster Associates to Glioblastoma Progression

Cited by 24 publications

References 82 publications

p53, cathepsin D, Bcl-2 are joint prognostic indicators of breast cancer metastatic spreading

p53, cathepsin D, Bcl-2 are joint prognostic indicators of breast cancer metastatic spreading

A seven-gene CpG-island methylation panel predicts breast cancer progression

A Systems Biology Comparison of Ovarian Cancers Implicates Putative Somatic Driver Mutations through Protein-Protein Interaction Models

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