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Transcription profiling by array of human non-small cell lung cancer and normal adjacent lung tissue matched pairs

ABSTRACT: Affymetrix exon array data set (HuEx-1.0_st) derived from matched pairs of non-small cell lung cancer (NSCLC) and normal adjacent lung tissue (NAT). This data set includes both the adenocarcinoma (AdCa) as well as the squamous cell carcinoma (SCC) subtype of NSCLC.

ORGANISM(S): Homo sapiens

SUBMITTER: Langer Wolfram

PROVIDER: S-ECPF-MEXP-2644 | biostudies-other |

REPOSITORIES: biostudies-other

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Transcription profiling by array of human non-small cell lung cancer and normal adjacent lung tissue matched pairs

Project description:Affymetrix exon array data set (HuEx-1.0_st) derived from matched pairs of non-small cell lung cancer (NSCLC) and normal adjacent lung tissue (NAT). This data set includes both the adenocarcinoma (AdCa) as well as the squamous cell carcinoma (SCC) subtype of NSCLC.

2011-03-01 | E-MEXP-2644 | biostudies-arrayexpress

Transcription profiling by array of human non-small cell lung cancer tissue

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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

Gene expression profiling of normal lung tissue adjacent to lung adenocarcinoma (ADCA)

Project description: Not available

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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

Transcription profiling by array of histologically-normal human breast tissue

Project description: Not available

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Transcription profiling by array of human non-small cell lung cancer tissue from female non-smokers

Project description: Not available

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RNA-seq data of 20 pairs of colorectal tumor with matched adjacent normal colorectal tissue

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Project description:Total RNA was extracted from human gastric cancer tissues (n=4) and matched adjacent normal tissues (n=4) . RNA samples were analyzed by RNA sequencing based on the manufacturer’s protocols. Briefly, Illumina HiSeq 4000 platform was used to sequence the RNA samples for the subsequent generation of raw data. R package was utilized to select lncRNAs with significantly differential expression based on fold change >2 or <1/2, p value <0.05 between human gastric cancer tissues and matched adjacent normal tissues, and the top 10 upregulated lncRNAs were selected for further study.

2022-10-12 | GSE172032 | GEO

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