Interleukin 36 receptor-inducible matrix metalloproteinase 13 mediates intestinal fibrosis.

Background: Fibrostenotic disease is a common complication in Crohn's disease (CD) patients hallmarked by transmural extracellular matrix (ECM) accumulation in the intestinal wall. The prevention and medical therapy of fibrostenotic CD is an unmet high clinical need. Although targeting IL36R signaling is a promising therapy option, downstream mediators of IL36 during inflammation and fibrosis have been incompletely understood.

Pustular psoriasis: Molecular pathways and effects of spesolimab in generalized pustular psoriasis.

Background: The IL-36 pathway plays a key role in the pathogenesis of generalized pustular psoriasis (GPP). In a proof-of-concept clinical trial, treatment with spesolimab, an anti-IL-36 receptor antibody, resulted in rapid skin and pustular clearance in patients presenting with GPP flares.

Objective: We sought to compare the molecular profiles of lesional and nonlesional skin from patients with GPP or palmoplantar pustulosis (PPP) with skin from healthy volunteers, and to investigate the molecular changes after spesolimab treatment in the skin and blood of patients with GPP flares.

Differential Effects of Trp53 Alterations in Murine Colorectal Cancer.

Background: Colorectal cancer (CRC) development is a multi-step process resulting in the accumulation of genetic alterations. Despite its high incidence, there are currently no mouse models that accurately recapitulate this process and mimic sporadic CRC. We aimed to develop and characterize a genetically engineered mouse model (GEMM) of Apc/Kras/Trp53 mutant CRC, the most frequent genetic subtype of CRC.

Silenced ZNF154 Is Associated with Longer Survival in Resectable Pancreatic Cancer.

Pancreatic cancer has become the third leading cause of cancer-related death in the Western world despite advances in therapy of other cancerous lesions. Late diagnosis due to a lack of symptoms during early disease allows metastatic spread of the tumor. Most patients are considered incurable because of metastasized disease.

Peripheral blood proteomic profiling of idiopathic pulmonary fibrosis biomarkers in the multicentre IPF-PRO Registry.

Background: Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease for which diagnosis and management remain challenging. Defining the circulating proteome in IPF may identify targets for biomarker development. We sought to quantify the circulating proteome in IPF, determine differential protein expression between subjects with IPF and controls, and examine relationships between protein expression and markers of disease severity.

Meta-analysis of Cancer Gene Profiling Data.

The simultaneous measurement of thousands of genes gives the opportunity to personalize and improve cancer therapy. In addition, the integration of meta-data such as protein-protein interaction (PPI) information into the analyses helps in the identification and prioritization of genes from these screens. Here, we describe a computational approach that identifies genes prognostic for outcome by combining gene profiling data from any source with a network of known relationships between genes.

Gene Expression Analysis in the Age of Mass Sequencing: An Introduction.

During the last years the technology used for gene expression analysis has changed dramatically. The old mainstay, DNA microarray, has served its due course and will soon be replaced by next-generation sequencing (NGS), the Swiss army knife of modern high-throughput nucleic acid-based analysis. Therefore preparation technologies have to adapt to suit the emerging NGS technology platform.

Network information improves cancer outcome prediction.

Disease progression in cancer can vary substantially between patients. Yet, patients often receive the same treatment. Recently, there has been much work on predicting disease progression and patient outcome variables from gene expression in order to personalize treatment options.

Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes.

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance.