Project description:Breast cancer is a heterogeneous disease for which prognosis and treatment strategies are largely governed by the receptor status (estrogen, progesterone and Her2-neu) of the tumor cells. Gene expression profiling of whole breast tumors further stratifies breast cancer into several molecular subtypes which also co-segregate with the receptor status of the tumor cells. We postulated that cancer associated fibroblasts (CAFs) within the tumor stroma may exhibit subtype specific gene expression profiles and thus contribute to the biology of the disease in a subtype specific manner. Several studies have reported gene expression profile differences between CAFs and normal breast fibroblasts but in none of these studies were the results stratified based on tumor subtypes. To address whether gene expression in breast cancer associated fibroblasts varies between breast cancer subtypes, we compared the gene expression profiles of early passage primary CAFs isolated from twenty human breast cancer samples representing three main subtypes; seven ER+, seven triple negative (TNBC) and six Her2+. We observed significant expression differences between CAFs derived from Her2+ breast cancer and CAFs from TNBC and ER+ cancers, particularly in pathways associated with cytoskeleton and integrin signaling. In the case of Her2+ breast cancer, the signaling pathways found to be selectively up regulated in CAFs may contribute to the more invasive properties and unfavorable prognosis of Her2+ breast cancer. These data demonstrate that in addition to the distinct molecular profiles that characterize the neoplastic cells, CAF gene expression is also differentially regulated in distinct subtypes of breast cancer. We isolated CAFs from twenty primary breast cancer samples representing three main subtypes (ER+ (n=7), TNBC (n=7), Her2+ (n=6)) and performed gene expression profile analyses on RNA isolated from these early passage CAFs. Those samples were done in two batches with 4 samples repeated in both batches. One TNBC sample was found to be an outlier and not used in the analysis.

Project description:Background: Using proteomics, we strove to reveal novel molecular subtypes of human atherosclerotic lesions, study their associations with histology and imaging and relate them to long-term cardiovascular outcomes. Methods: 219 samples were obtained from 120 patients undergoing carotid endarterectomy. Sequential protein extraction was combined with multiplexed, discovery proteomics. Parallel reaction monitoring for 135 proteins was deployed for targeted validation. A combination of statistical, bioinformatics and machine learning methods was used to perform differential expression, network, pathway enrichment analysis and train and evaluate prognostic models. Results: Our extensive proteomics analysis from the core and periphery of plaques doubled the coverage of the plaque proteome compared to the largest proteomics study on atherosclerosis thus far. Plaque inflammation and calcification signatures were inversely correlated and validated with targeted proteomics. The inflammation signature was enriched with neutrophil-derived proteins, including calprotectin (S100A8/9) and myeloperoxidase. The calcification signature contained fetuin-A, osteopontin, and gamma-carboxylated proteins. Sex differences in the proteome of atherosclerosis were explained by a higher proportion of calcified plaques in women. Single-cell RNA sequencing data attributed the inflammation signature predominantly to neutrophils and macrophages and the calcification signature to smooth muscle cells, except for certain plasma proteins that were not expressed but retained in the plaque, i.e., fetuin-A. Echogenic lesions reflect the collagen content and calcification of plaque but carotid Duplex ultrasound fails to capture the extent of inflammatory protein changes in symptomatic plaques. Applying dimensionality reduction and machine learning on the proteomics data defined 4 distinct plaque phenotypes and revealed key protein signatures linked to smooth muscle cell content, plaque calcification and structural extracellular matrix, which improved the 9-year prognostic AUC by 25% compared to ultrasound and histology. A biosignature of four proteins (CNN1, PROC, SERPH, and CSPG2) independently predicted the progression of atherosclerosis and cardiovascular mortality with an AUC of 75% Conclusion: We combined discovery and targeted proteomics with network reconstruction and clustering techniques to provide molecular insights into protein changes in atherosclerotic plaques. The application of proteomics and machine learning techniques revealed distinct clusters of plaques that inform on disease progression and future adverse cardiovascular events.

Project description:INTRODUCTION Ischemia and reperfusion injury (IRI)-elicited tissue injury contributes to morbidity and mortality in a wide range of pathologies, including myocardial infarction, ischemic stroke, acute kidney injury, trauma, circulatory arrest 1. Ischemia-reperfusion injury is also a major challenge during organ transplantation and cardiothoracic, vascular and general surgery 1. IRI, one of the biggest challenges for organ transplantation, continues to be a vital source of morbidity among recipients, especially in liver transplantation 2. With the enlarging shortage of available donor livers, the increased use of extended criteria donor grafts further increases IRI, adversely affecting both short-term and long-term outcomes of graft and patient survival 3. Numerous studies have investigated the benefits of pharmacological, heat shock, and ischemic preconditioning interventions aimed at decreasing liver IRI 4. However, the benefit was limited. Our center has been making great effort in conquering IRI and have developed a novel surgical technique called ischemia-free liver transplantation 5. It is an ultimate method to overcome IRI in liver transplantation, but there is still a long way to popularize it. As a result, it is still of great significance to study IRI and identify the core genes in the process and the underlying mechanism. Comprehensive bioinformatics analysis has been increasingly important as a method to study various pathological and physiological condition 6. By enrolling multiple omics or combining different types of omics, comprehensive bioinformatics analysis was able to recognize key factors that could have potentially pathogenic impact such as gene expression, protein function, and downstream pathways. With the rapid development of high-throughput sequencing technologies, several transcriptomic datasets on IRI of liver transplantation have become available in the Gene Expression Omnibus (GEO) database. Herein, we recruited 3 GEO datasets to conduct comprehensive analysis with the GEO dataset from our center. Moreover, we performed the first proteome of liver tissues to study liver IRI. Then the transcriptome and proteome were used for combined analysis to reveal key factors in liver IRI.

Dataset Information

Gene Expression Profiles in Peripheral Blood Mononuclear Cells Can Distinguish Patients with NonSmall Cell Lung Cancer.

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Gene expression profiles in peripheral blood mononuclear cells can distinguish patients with non-small cell lung cancer from patients with nonmalignant lung disease.

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