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ese data to develop M-Sig, the first platform-independent gene signature designed to predict intrinsic metastatic potential, and validate in five clinical cohorts with nearly 1800 patients. This signature has a myriad of potential clinical and research applications and represents a significant addition to the compendia of available prognostic signatures in breast cancer.datasets (Fig 3A, 3C, 3D and 3E). In addition, M-Sig was also able to stratify OS in the van de Vijver dataset (p 0.001, HR = 2.9 [1.8.7]) shown in Fig 3B. OS data was unavailable in Wang and Hatzis, and non-significant in the TCGA dataset which was unsurprising given the low number of death events captured in this clinically immature dataset. These consistent hazard ratios in all 5 cohorts for both metastasis and OS are summarized in Fig 3F. Clinical and pathological variable data were available in the van de Vijver, Hatzis, and TCGA datasets as well, and M-Sig was significantly prognostic for metastasis in van de Vijver (p 0.001, HR = 2.3 [1.5.6]), Hatzis (p 0.001, HR = 2.2 [1.4.5]), and TCGA (p 0.01, HR = 6.7 [1.94]) and OS in van de Vijver (p 0.05, HR = 1.9 [1.2.2]) on multivariable analysis (MVA) in Table 3. These results demonstrate that M-Sig can significantly stratify metastatic potential of breast cancers in clinical cohorts, and is the top predictor on MVA with stronger hazard ratios than all other clinical-pathologic variables on MVA. In the van de Vijver cohort, other significant clinical variables besides M-Sig include age (HR = 0.95 [0.92.98], p 0.001), grade (HR = 1.5 [1.1.1], p0.001), and node status (HR = 1.1 [1.0.2], p0.05) on MVA for metastasis. In the Hatzis cohort, M-Sig was once again able 10205015 to significantly predict metastasis with the largest HR of 2.2 on MVA with the other significant variables being node status (HR = 1.5 [1.2.9], p0.00001), tumor stage (HR = 1.3 [1.0.7], p0.05), and ER status (HR = 0.5 [0.3.9], p0.05), Finally, in TCGA which was an RNAseq cohort, M-Sig was the only significant predictor of metastasis on MVA.
Patients with coronary artery disease (CAD) are not only at risk of developing cardiovascular events, but may also develop malignancies. Cancer shares some risk factors with CAD, as age, smoking, and even some dietary patterns could lead to the development of both disorders [13]. Therefore, finding biomarkers that predict risk of cancer in addition to that of cardiovascular events could be useful in CAD patients. Natriuretic peptides are secreted by cancer cells [4,5] and N-terminal fragment of pro-brain natriuretic peptide (NT-proBNP) levels are increased in patients with cancer [6]. However, it has not been demonstrated whether NT-proBNP may predict the appearance of malignancies. In order to study if increased NT-proBNP plasma levels predict cancer, we studied 704 patients with CAD who were free of malignancies at baseline. We also tested these biomarkers: monocyte chemoattractant protein-1 (MCP-1) and soluble tumor necrosis factor-like weak inducer of apoptosis (sTWEAK), both involved in inflammation and atherothrombosis, among other processes [7]; galectin-3, related to malignancies, heart failure, thrombosis, and renal dysfunction [10,11]; and high-sensitivity 92831-11-3 cardiac troponin I, which has been described to have prognostic value in stable CAD [12]. High-sensitivity C-reactive protein was studied as a reference given the large amount of information published on this biomarker.
The BACS & BAMI (Biomarkers in Acute Coron

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Author: haoyuan2014