Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A Qualitative Transcriptional Signature for the Histological Classification of Non-Small Cell Lung Cancer

ABSTRACT: Histological classification by routine pathology assessment with hematoxylin-eosin staining and immunostaining for poorly differentiated tumors, particularly those from small biopsies, is still challenging. In this study, using gene expression profiles of pathologically-determined lung squamous cell carcinomas and adenocarcinomas, denoted as pSCC and pADC respectively, we developed a qualitative transcriptional signature, based on the within-sample relative gene expression orderings (REOs) of gene pairs, to individually distinguish ADC from SCC. The signature was validated in the frozen tissues, FFPE materials, mixed tumors, small biopsy specimens and poorly differentiated samples.In summary, the qualitative signature, independent of the subjective diagnosis of pathologists, would be an effective auxiliary tool in distinguishing ADC from SCC.

ORGANISM(S): Homo sapiens

PROVIDER: GSE121090 | GEO | 2018/10/11

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Expression profiling of 83 matched pairs of lung adenocarcinomas and non-malignant adjacent tissue

Project description:Background: Non-small cell lung cancers (NSCLCs) consist of adenocarcinoma (ADC), squamous cell carcinoma (SCC) and other types. Since most NSCLCs are now diagnosed from small biopsies or cytology materials, classification is not always accurate. This is a problem as many therapy regimens and clinical trials are histology-dependent. Specific Aim: To develop an RNA expression signature as an adjunct test for routine histo-pathological classification of NSCLCs. Methods: A microarray dataset of resected ADC and SCC cases was used as the learning set for an ADC-SCC signature. The Cancer Genome Atlas (TCGA) lung RNAseq dataset was used as a validation set. Another microarray dataset of ADCs and non-malignant lung was used as the learning set for a Tumor-Nonmalignant signature. The classifiers were selected as the most differentially expressed genes and sample classification was determined by a nearest distance approach. Results: We developed a 42-gene expression signature that contained many genes used in immunostains for NSCLC typing. Testing of the TCGA and other public datasets resulted in high accuracies (93-95%). We also observed that most non-malignant lung samples were classified as â??adenocarcinomasâ??, so we added 20 genes to differentiate tumor from non-malignant lung. Together, the 62-gene signature can discriminate ADC, SCC, and non-malignant lung. Additionally, a prediction score was derived that correlated both with histologic grading and survival. Summary and significance: Our histologic classifier provides a non-subjective method to aid in the pathological diagnosis of lung cancer and assist enrollment onto histology-based clinical trials 83 lung adenocarcinomas and 83 matched adjacent non-malignant lung were profiled on Illumina WG6-V3 expression arrays

2016-06-01 | E-GEOD-75037 | biostudies-arrayexpress

A proteomic landscape of diffuse-type gastric cancer

Project description:Here we present a dataset from 84 DGC patients with paired tumor and nearby tissue. Tumors and their nearby tissues were evaluated by pathologists. Nearby tissues were designated as non-cancerous and were greater than 5 cm away from the surgery margin. Hematoxylin and eosin (H&E) stained sections were examined by two expert gastrointestinal pathologists (Z.L. and Yumei Lai) independently to confirm: (1) diffuse type (Lauren type); (2) >50% tumor cell nuclei; (3) <20% necrosis in tumor tissue; (4) no tumor cells in nearby tissue.

2018-02-09 | PXD008840 | Pride

Expression profiling of 83 matched pairs of lung adenocarcinomas and non-malignant adjacent tissue

Project description:Background: Non-small cell lung cancers (NSCLCs) consist of adenocarcinoma (ADC), squamous cell carcinoma (SCC) and other types. Since most NSCLCs are now diagnosed from small biopsies or cytology materials, classification is not always accurate. This is a problem as many therapy regimens and clinical trials are histology-dependent. Specific Aim: To develop an RNA expression signature as an adjunct test for routine histo-pathological classification of NSCLCs. Methods: A microarray dataset of resected ADC and SCC cases was used as the learning set for an ADC-SCC signature. The Cancer Genome Atlas (TCGA) lung RNAseq dataset was used as a validation set. Another microarray dataset of ADCs and non-malignant lung was used as the learning set for a Tumor-Nonmalignant signature. The classifiers were selected as the most differentially expressed genes and sample classification was determined by a nearest distance approach. Results: We developed a 42-gene expression signature that contained many genes used in immunostains for NSCLC typing. Testing of the TCGA and other public datasets resulted in high accuracies (93-95%). We also observed that most non-malignant lung samples were classified as “adenocarcinomas”, so we added 20 genes to differentiate tumor from non-malignant lung. Together, the 62-gene signature can discriminate ADC, SCC, and non-malignant lung. Additionally, a prediction score was derived that correlated both with histologic grading and survival. Summary and significance: Our histologic classifier provides a non-subjective method to aid in the pathological diagnosis of lung cancer and assist enrollment onto histology-based clinical trials

2016-06-01 | GSE75037 | GEO

Transcriptional profile of lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) tumors from KRasG12D; Fbw7f/f mice

Project description:We performed lung ADC and SCC transcriptome profiling from KRasG12D; Fbw7f/f murine lung tumors by RNA-seq and compared them with a consensus lung SCC signature using data from two different SCC models (Lkb1f/f; Ptenf/f, Cancer Cell 2014 and LSL-Sox2; Ptenf/f; Cdkn2abf/f, Cancer Cell 2016) that has been validated in human lung SCC

2019-01-08 | GSE123716 | GEO

Combined clinical and genomic signatures for the prognosis of early stage non-small cell lung cancer based on gene copy number alterations [Affymetrix]

Project description:Background: The development of a more refined prognostic methodology for early non-small cell lung cancer (NSCLC) is an unmet clinical need. An accurate prognostic tool might help to select patients at early stages for adjuvant therapies. Results: A new integrated bioinformatics searching strategy, that combines gene copy number alterations and expression, together with clinical parameters was applied to derive two prognostic genomic signatures. The proposed methodology combines data from patients with and without clinical data with a priori information on the ability of a gene to be a prognostic marker. Two initial candidate sets of 513 and 150 genes for lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC), respectively, were generated by identifying genes which have both: a) significant correlation between copy number and gene expression, and b) significant prognostic value at the gene expression level in external databases. From these candidates, two panels of 7 (ADC) and 5 (SCC) genes were further identified via semi-supervised learning. These panels, together with clinical data (stage, age and sex), were used to construct the ADC and SCC hazard scores combining clinical and genomic data. The signatures were validated in two independent datasets (n=73 for ADC, n=97 for SCC), confirming that the prognostic value of both clinical-genomic models is robust, statistically significant (P=0.008 for ADC and P=0.019 for SCC) and outperforms both the clinical models (P=0.060 for ADC and P=0.121 for SCC) and the genomic models applied separately (P=0.350 for ADC and P=0.269 for SCC). Conclusion: The present work provides a methodology to generate a robust signature using copy number data that can be potentially used to any cancer. Using it, we found new prognostic scores based on DNA s that, jointly with clinical information, are able to predict overall survival (OS) in patients with early-stage ADC and SCC.

2015-08-20 | GSE72192 | GEO

Combined clinical and genomic signatures for the prognosis of early stage non-small cell lung cancer based on gene copy number alterations [Agilent]

2015-08-20 | GSE72194 | GEO

Intratumoral heterogeneity of colorectal cancers with distant metastases

Project description:Copy number analyses of regionally separated biopsies from primary and metastatic lesions of five colorectal cancers A total of 35 intratumoral biopsies were obtained from primary and metastatic lesions of five colorectal cancers. Board-certified pathologists reviewed the hematoxylin&eosin stained sections and identified tumor-rich regions (> 80% purity) to ensure minimal contamination of normal tissues. We selected two to six different areas for biopsy that were at least 5mm apart in primary and distant metastatic lesions from a same patient. Copy number profiling was performed using Agilent 180K platform according to the manufacturer's protocol. The genomic DNA obtained from the adjacent normal tissues were used as reference genomic DNA for the tumor DNA of the corresponding patients.

2014-06-16 | E-GEOD-58512 | biostudies-arrayexpress

Barrett's esophagus, Barrett's-associated adenocarcinomas and normal esophageal epithelium

Project description:Samples were obtained from 8 patients with Barrett's associated adenocarcinomas after transhiatal esophagectomy. Samples representative of the normal esophageal epithelium (N), Barrett’s esophagus (B) and esophageal adenocarcinomas (ADC) were obtained from every patient by experienced GI pathologists. RNA were extracted and samples were profiled for detection of genes differentially expressed in B and ADC relative to N and in ADC relative to B. Keywords: other

2004-11-18 | GSE1420 | GEO

Transcription profiling of esophagus samples from eight human patients with Barretts associated adenocarcinomas

Project description:Samples were obtained from 8 patients with Barrett's associated adenocarcinomas after transhiatal esophagectomy. Samples representative of the normal esophageal epithelium (N), BarrettÃ¢ÂÂs esophagus (B) and esophageal adenocarcinomas (ADC) were obtained from every patient by experienced GI pathologists. RNA were extracted and samples were profiled for detection of genes differentially expressed in B and ADC relative to N and in ADC relative to B.

2007-08-06 | E-GEOD-1420 | biostudies-arrayexpress

A robust qualitative transcriptional signature for the early diagnosis of gastric cancer

Project description:Pathological examination of gastroscopy biopsy specimens will make false diagnosis for gastric cancer (GC) due to inaccurate sampling locations and/or insufficient sampling amount. We extracted a robust qualitative transcriptional signature, based on the within-sample relative expression orderings (REOs) of gene pairs, to discriminate both GC tissues and adjacent-normal tissues from non-GC gastritis and normal gastric tissues.The qualitative transcriptional signature can be robustly applied at the individual level to aid the diagnosis of early GC.

2018-07-10 | GSE116782 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data