nCounter® Fibrosis Panel
Helping Your Research
The cellular and molecular basis for fibrotic disease is still poorly understood, and the lack of biomarkers for progression and therapeutic response have hampered efforts to develop treatments. The nCounter Fibrosis Panel helps uncover the mechanisms of disease pathogenesis, identify biomarkers of progression, and develop signatures for therapeutic response. This gene expression panel combines hundreds of genes involved in the initial tissue damage response, chronic inflammation, proliferation of pro-fibrotic cells, and tissue modification that leads to fibrotic disease of the lungs, heart, liver, kidney, and skin.
How it Works
Profile 770 genes across 51 annotated pathways in human or mouse.
Study pathogenesis and identify biomarkers for fibrotic diseases of the lungs, heart, liver, kidney, and skin
Elucidate the mechanism of action behind the four stages of fibrosis: initiation, inflammation, proliferation, and modification
Understand the signaling cascade from cell stress to inflammation
Quantify the relative abundance of 14 different immune cell types
Panel Selection Tool
Find the gene expression panel for your research with easy to use panel proFind Your Panel
Immunostimulatory cancer-associated fibroblast subpopulations can predict immunotherapy response in head and neck cancer.
Purpose: Cancer-associated fibroblasts (CAF) have been implicated as potential mediators of checkpoint immunotherapy response. However, the extensive heterogeneity of these cells has precluded rigorous understanding of their immunoregulatory role in the tumor microenvironment.
Spatial profiling reveals association between WNT pathway activation and T-cell exclusion in acquired resistance of synovial sarcoma to NY-ESO-1 transgenic T-cell therapy.
Background: Genetically engineered T-cell immunotherapies for adoptive cell transfer (ACT) have emerged as a promising form of cancer treatment, but many of these patients develop recurrent disease. Furthermore, delineating mechanisms of resistance may be challenging since the analysis of bulk tumor profiling can be complicated by spatial heterogeneity.
A Novel Artificial Intelligence–Powered Method for Prediction of Early Recurrence of Prostate Cancer After Prostatectomy and Cancer Drivers.
To develop a novel artificial intelligence (AI)–powered method for the prediction of prostate cancer (PCa) early recurrence and identification of driver regions in PCa of all Gleason Grade Group (GGG). MATERIALS AND METHODS: Deep convolutional neural networks were used to develop the AI model.