Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

DNA methylation-based classification of sinonasal tumors [Proteomics data]

ABSTRACT: This dataset includes raw label-free mass spectrometry proteomics data of different sinonasal tumor entities as well as normal sinonasal tissue. 72 samples were processed on a Q Exactive HF-X instrument coupled to an easy nanoLC 1200 system using one microgram of peptides and an 110 minutes gradient.

PROVIDER: EGAD00010002381 | EGA |

REPOSITORIES: EGA

ACCESS DATA

Json Xml

Similar Datasets

DNA methylation-based classification of sinonasal tumors [test set]

Project description:The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, whereas tumors that are driven by SMARCB1-deficiency and tumors that represent previously misclassified adenoid cystic carcinomas are highly aggressive. Our findings have the potential to dramatically improve the diagnostic classification of sinonasal tumors and will fundamentally change the current perception of SNUCs.

2022-09-25 | GSE196227 | GEO

DNA methylation-based classification of sinonasal tumors [Proteomics data]

Project description:The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent previously misclassified adenoid cystic carcinomas. The repository includes the raw mass spectrometry-based proteomics data

| EGAS00001006712 | EGA

DNA methylation-based classification of sinonasal tumors [reference set]

Project description:The histopathological diagnosis of sinonasal tumors is challenging as it encompasses a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we show that a machine learning algorithm based on DNA methylation is able to classify sinonasal tumors with clinical-grade reliability. We further show that tumors with SNUC morphology are not as undifferentiated as their current terminology suggests, but can be assigned to four molecular classes defined by distinct epigenic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SWI/SNF chromatin remodeling complex mutations and overall favorable clinical course, highly aggressive tumors that are driven by SMARCB1-deficiency and tumors that represent previously misclassified adenoid-cystic carcinomas. Our findings have the potential to dramatically improve the diagnostic of challenging sinonasal tumors and could fundamentally change the current perception of SNUCs.

2022-09-25 | GSE189778 | GEO

DNA methylation-based classification of sinonasal tumors

Project description:DNA methylation-based classification of sinonasal tumors

| PRJNA803926 | ENA

DNA methylation-based classification of sinonasal tumors [DNA sequencing]

Project description:The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent previously misclassified adenoid cystic carcinomas. This repository includes the results from DNA sequencing and mass spectrometry-based proteomics.

| EGAS00001006713 | EGA

Peptidome in Chouguiyu at different fermentation time

Project description:Peptidome in Chouguiyu after fermentation for 0,4,and 8d using Easy-nLC 1200 UPLC-Q Exactive HF-X tandem mass.

2022-10-15 | PXD033251 | Pride

CC10 gene deletion: Effect on gene expression in the sinonasal mucosa

Project description:Comparative analysis of gene expression in murine sinonasal mucosa in wild-type and CC10-knockout littermates with allergic eosinophilic chronic rhinosinusitis. The data provide a comprehensive overview of genes expressed in the mouse sinonasal mucosa and show that the expression of several known and unidentified genes is modified by disruption of the CC10 gene. Total RNA isolated from sinonasal mucosae of 6- to 8-week-old mice, C57BL/6 strain, was used for this comparison. Three groups: wild-type control, wild-type with allergic eosinophilic chronic rhinosinusitis, and CC10-knockout with allergic eosinophilic chronic rhinosinusitis.

2010-12-01 | E-GEOD-20969 | biostudies-arrayexpress

DNA methylation-based classification of sinonasal tumors [test set]

Project description:DNA methylation-based classification of sinonasal tumors [test set]

| PRJNA803928 | ENA

DNA methylation-based classification of sinonasal tumors [reference set]

Project description:DNA methylation-based classification of sinonasal tumors [reference set]

| PRJNA784516 | ENA

Human sinonasal cavity

Project description:Human sinonasal cavity Targeted loci environmental

| PRJNA407725 | ENA