Large-scale phosphoproteomics reveals activation of the MAPK/GADD45ß/P38 axis and cell cycle inhibition in response to BMP9 and BMP10 stimulation in endothelial cells.

BACKGROUND: BMP9 and BMP10 are two major regulators of vascular homeostasis. These two ligands bind with high affinity to the endothelial type I kinase receptor ALK1, together with a type II receptor, leading to the direct phosphorylation of the SMAD transcription factors. Apart from this canonical pathway, little is known. Interestingly, mutations in this signaling pathway have been identified in two rare cardiovascular diseases, hereditary hemorrhagic telangiectasia and pulmonary arterial hypertension. METHODS: To get an overview of the signaling pathways modulated by BMP9 and BMP10 stimulation in endothelial cells, we employed an unbiased phosphoproteomic-based strategy. Identified phosphosites were validated by western blot analysis and regulated targets by RT-qPCR. Cell cycle analysis was analyzed by flow cytometry. RESULTS: Large-scale phosphoproteomics revealed that BMP9 and BMP10 treatment induced a very similar phosphoproteomic profile. These BMPs activated a non-canonical transcriptional SMAD-dependent MAPK pathway (MEKK4/P38). We were able to validate this signaling pathway and demonstrated that this activation required the expression of the protein GADD45ß. In turn, activated P38 phosphorylated the heat shock protein HSP27 and the endocytosis protein Eps15 (EGF receptor pathway substrate), and regulated the expression of specific genes (E-selectin, hyaluronan synthase 2 and cyclooxygenase 2). This study also highlighted the modulation in phosphorylation of proteins involved in transcriptional regulation (phosphorylation of the endothelial transcription factor ERG) and cell cycle inhibition (CDK4/6 pathway). Accordingly, we found that BMP10 induced a G1 cell cycle arrest and inhibited the mRNA expression of E2F2, cyclinD1 and cyclinA1. CONCLUSIONS: Overall, our phosphoproteomic screen identified numerous proteins whose phosphorylation state is impacted by BMP9 and BMP10 treatment, paving the way for a better understanding of the molecular mechanisms regulated by BMP signaling in vascular diseases.

COL7A1 Expression Improves Prognosis Prediction for Patients with Clear Cell Renal Cell Carcinoma Atop of Stage.

Clear-cell renal cell carcinoma (ccRCC) accounts for 75% of kidney cancers. Due to the high recurrence rate and treatment options that come with high costs and potential side effects, a correct prognosis of patient survival is essential for the successful and effective treatment of patients. Novel biomarkers could play an important role in the assessment of the overall survival of patients. COL7A1 encodes for collagen type VII, a constituent of the basal membrane. COL7A1 is associated with survival in many cancers; however, the prognostic value of COL7A1 expression as a standalone biomarker in ccRCC has not been investigated. With five publicly available independent cohorts, we used Kaplan-Meier curves and the Cox proportional hazards model to investigate the prognostic value of COL7A1, as well as gene set enrichment analysis to investigate genes co-expressed with COL7A1. COL7A1 expression stratifies patients in terms of aggressiveness, where the 5-year survival probability of each of the four groups was 72.4%, 59.1%, 34.15%, and 8.6% in order of increasing expression. Additionally, COL7A1 expression was successfully used to further divide patients of each stage and histological grade into groups of high and low risk. Similar results were obtained in independent cohorts. In vitro knockdown of COL7A1 expression significantly affected ccRCC cells' ability to migrate, leading to the hypothesis that COL7A1 may have a role in cancer aggressiveness. To conclude, we identified COL7A1 as a new prognosis marker that can stratify ccRCC patients.

Optimal microRNA Sequencing Depth to Predict Cancer Patient Survival with Random Forest and Cox Models.

(1) Background: tumor profiling enables patient survival prediction. The two essential parameters to be calibrated when designing a study based on tumor profiles from a cohort are the sequencing depth of RNA-seq technology and the number of patients. This calibration is carried out under cost constraints, and a compromise has to be found. In the context of survival data, the goal of this work is to benchmark the impact of the number of patients and of the sequencing depth of miRNA-seq and mRNA-seq on the predictive capabilities for both the Cox model with elastic net penalty and random survival forest. (2) Results: we first show that the Cox model and random survival forest provide comparable prediction capabilities, with significant differences for some cancers. Second, we demonstrate that miRNA and/or mRNA data improve prediction over clinical data alone. mRNA-seq data leads to slightly better prediction than miRNA-seq, with the notable exception of lung adenocarcinoma for which the tumor miRNA profile shows higher predictive power. Third, we demonstrate that the sequencing depth of RNA-seq data can be reduced for most of the investigated cancers without degrading the prediction abilities, allowing the creation of independent validation sets at a lower cost. Finally, we show that the number of patients in the training dataset can be reduced for the Cox model and random survival forest, allowing the use of different models on different patient subgroups.

Prognosis of lasso-like penalized Cox models with tumor profiling improves prediction over clinical data alone and benefits from bi-dimensional pre-screening.

BACKGROUND: Prediction of patient survival from tumor molecular '-omics' data is a key step toward personalized medicine. Cox models performed on RNA profiling datasets are popular for clinical outcome predictions. But these models are applied in the context of "high dimension", as the number p of covariates (gene expressions) greatly exceeds the number n of patients and e of events. Thus, pre-screening together with penalization methods are widely used for dimensional reduction. METHODS: In the present paper, (i) we benchmark the performance of the lasso penalization and three variants (i.e., ridge, elastic net, adaptive elastic net) on 16 cancers from TCGA after pre-screening, (ii) we propose a bi-dimensional pre-screening procedure based on both gene variability and p-values from single variable Cox models to predict survival, and (iii) we compare our results with iterative sure independence screening (ISIS). RESULTS: First, we show that integration of mRNA-seq data with clinical data improves predictions over clinical data alone. Second, our bi-dimensional pre-screening procedure can only improve, in moderation, the C-index and/or the integrated Brier score, while excluding irrelevant genes for prediction. We demonstrate that the different penalization methods reached comparable prediction performances, with slight differences among datasets. Finally, we provide advice in the case of multi-omics data integration. CONCLUSIONS: Tumor profiles convey more prognostic information than clinical variables such as stage for many cancer subtypes. Lasso and Ridge penalizations perform similarly than Elastic Net penalizations for Cox models in high-dimension. Pre-screening of the top 200 genes in term of single variable Cox model p-values is a practical way to reduce dimension, which may be particularly useful when integrating multi-omics.