Cell cycle and aging, morphogenesis, and response to stimuli genes are individualized biomarkers of glioblastoma progression and survival

<p>Abstract</p> <p>Background</p> <p>Glioblastoma is a complex multifactorial disorder that has swift and devastating consequences. Few genes have been consistently identified as prognostic biomarkers of glioblastoma survival. The goal of this study was to identify general and clinical-dependent biomarker genes and biological processes of three complementary events: lifetime, overall and progression-free glioblastoma survival.</p> <p>Methods</p> <p>A novel analytical strategy was developed to identify general associations between the biomarkers and glioblastoma, and associations that depend on cohort groups, such as race, gender, and therapy. Gene network inference, cross-validation and functional analyses further supported the identified biomarkers.</p> <p>Results</p> <p>A total of 61, 47 and 60 gene expression profiles were significantly associated with lifetime, overall, and progression-free survival, respectively. The vast majority of these genes have been previously reported to be associated with glioblastoma (35, 24, and 35 genes, respectively) or with other cancers (10, 19, and 15 genes, respectively) and the rest (16, 4, and 10 genes, respectively) are novel associations. <it>Pik3r1</it>, <it>E2f3, Akr1c3</it>, <it>Csf1</it>, <it>Jag2</it>, <it>Plcg1</it>, <it>Rpl37a</it>, <it>Sod2</it>, <it>Topors</it>, <it>Hras</it>, <it>Mdm2, Camk2g</it>, <it>Fstl1</it>, <it>Il13ra1</it>, <it>Mtap </it>and <it>Tp53 </it>were associated with multiple survival events.</p> <p>Most genes (from 90 to 96%) were associated with survival in a general or cohort-independent manner and thus the same trend is observed across all clinical levels studied. The most extreme associations between profiles and survival were observed for <it>Syne1</it>, <it>Pdcd4</it>, <it>Ighg1</it>, <it>Tgfa</it>, <it>Pla2g7</it>, and <it>Paics</it>. Several genes were found to have a cohort-dependent association with survival and these associations are the basis for individualized prognostic and gene-based therapies. <it>C2</it>, <it>Egfr</it>, <it>Prkcb</it>, <it>Igf2bp3</it>, and <it>Gdf10 </it>had gender-dependent associations; <it>Sox10</it>, <it>Rps20</it>, <it>Rab31</it>, and <it>Vav3 </it>had race-dependent associations; <it>Chi3l1</it>, <it>Prkcb</it>, <it>Polr2d</it>, and <it>Apool </it>had therapy-dependent associations. Biological processes associated glioblastoma survival included morphogenesis, cell cycle, aging, response to stimuli, and programmed cell death.</p> <p>Conclusions</p> <p>Known biomarkers of glioblastoma survival were confirmed, and new general and clinical-dependent gene profiles were uncovered. The comparison of biomarkers across glioblastoma phases and functional analyses offered insights into the role of genes. These findings support the development of more accurate and personalized prognostic tools and gene-based therapies that improve the survival and quality of life of individuals afflicted by glioblastoma multiforme.</p

Particle-induced pulmonary acute phase response correlates with neutrophil influx linking inhaled particles and cardiovascular risk

BACKGROUND: Particulate air pollution is associated with cardiovascular disease. Acute phase response is causally linked to cardiovascular disease. Here, we propose that particle-induced pulmonary acute phase response provides an underlying mechanism for particle-induced cardiovascular risk. METHODS: We analysed the mRNA expression of Serum Amyloid A (Saa3) in lung tissue from female C57BL/6J mice exposed to different particles including nanomaterials (carbon black and titanium dioxide nanoparticles, multi- and single walled carbon nanotubes), diesel exhaust particles and airborne dust collected at a biofuel plant. Mice were exposed to single or multiple doses of particles by inhalation or intratracheal instillation and pulmonary mRNA expression of Saa3 was determined at different time points of up to 4 weeks after exposure. Also hepatic mRNA expression of Saa3, SAA3 protein levels in broncheoalveolar lavage fluid and in plasma and high density lipoprotein levels in plasma were determined in mice exposed to multiwalled carbon nanotubes. RESULTS: Pulmonary exposure to particles strongly increased Saa3 mRNA levels in lung tissue and elevated SAA3 protein levels in broncheoalveolar lavage fluid and plasma, whereas hepatic Saa3 levels were much less affected. Pulmonary Saa3 expression correlated with the number of neutrophils in BAL across different dosing regimens, doses and time points. CONCLUSIONS: Pulmonary acute phase response may constitute a direct link between particle inhalation and risk of cardiovascular disease. We propose that the particle-induced pulmonary acute phase response may predict risk for cardiovascular disease

A Computational Pipeline to Identify New Potential Regulatory Motifs in Melanoma Progression

Molecular biology experiments allow to obtain reliable data about the expression of different classes of molecules involved in several cellular processes. This information is mostly static and does not give much clue about the causal relationships (i.e., regulation) among the different molecules. A typical scenario is the presence of a set of modulated mRNAs (up or down regulated) along with an over expression of one or more small non-coding RNAs molecules like miRNAs. To computationally identify the presence of transcriptional or post-transcriptional regulatory modules between one or more miRNAs and a set of target modulated genes, we propose a computational pipeline designed to integrate data from multiple online data repositories. The pipeline produces a set of three types of putative regulatory motifs involving coding genes, intronic miRNAs, and transcription factors. We used this pipeline to analyze the results of a set of expression experiments on a melanoma cell line that showed an over expression of miR-214 along with the modulation of a set of 73 other genes. The results suggest the presence of 27 putative regulatory modules involving miR-214, NFKB1, SREBPF2, miR-33a and 9 out of the 73 miR-214 modulated genes (ALCAM, POSTN, TFAP2A, ADAM9, NCAM1, SEMA3A, PVRL2, JAG1, EGFR1). As a preliminary experimental validation we focused on 9 out of the 27 identified regulatory modules that involve miR-33a and SREBF2. The results confirm the importance of the predictions obtained with the presented computational approac