Transcription profiling by array of human non-small cell lung cancer and normal adjacent lung tissue matched pairs
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Langer Wolfram
PROVIDER: S-ECPF-MEXP-2644 | biostudies-other |
REPOSITORIES: biostudies-other
Similar Datasets
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
Project description:PURPOSE The development of reliable gene expression profiling technology is having an increasing impact on our understanding of lung cancer biology. The present study aims to determine whether the phenotypic heterogeneity and genetic diversity of lung cancer are correlated. PATIENTS AND METHODS In this study, microarray analysis was performed in a set of 91 non-small cell lung cancer (NSCLC) samples in order to: establish gene signatures in primary adenocarcinomas and squamous-cell carcinomas; determine differentially expressed gene sequences at different stages of the disease; and identify sequences with biological significance for tumor progression. After microarray analysis, the expression level of 92 selected genes was validated by qPCR in an independent set of 70 samples. RESULTS Gene sequences were differentially expressed as a function of tumor type, stage, and differentiation grade. High upregulation was observed for KRT15 and PKP1, which may be good markers to distinguish squamous cell carcinoma samples. High downregulation was observed for DSG3 in stage IA adenocarcinomas. CONCLUSION Expression signatures in NSCLC distinguish tumor type, stage, and differentiation grade. Keywords: Tumor vs control comparative genomics study 91 samples studied, 46 tumors and 45 controls. All samples are paired except three.
| S-ECPF-GEOD-18842 | biostudies-other
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
Project description:Due to increasing evidence of the complexity of the transcriptome, we have, through a combination of cDNA sequencing, gene expression profiling and public sequence data mining, characterized the transcriptome of Non-Small Cell Lung Carcinoma. We used this information to create a unique disease focused microarray capable of measuring ~60,000 individual transcripts, many of which are undetectable on standard microarrays. Expression profiling demonstrated significant detection of the additional unique/novel transcriptome content. In this technical assessment experiment, one patient sample was profiled. Frozen pairs of lung squamous cell carcinoma and adjacent normal lung tissue originating from a single donor were obtained from Asterand (Detroit, MI). All sample pairs were processed immediately and under identical conditions. For both normal and tumour tissue, 5 technical replicates were profiled.
| S-ECPF-TABM-387 | biostudies-other
Project description:Individual variations in the transcriptional profile of normal lung tissue may reflect the lung ADCA patients’ predisposition to a specific clinical stage Associations between clinical outcome of cancer patients and the gene expression signature in primary tumors at time of diagnosis have been reported. To test whether gene expression patterns in the normal tissue surrounding the cancerous tissue might correlate with clinical stage, we compared transcriptome of normal lung samples of adenocarcinoma (ADCA) smoker patients of clinical stage I versus stage >I, and identified 55 differentially expressed genes. Eight out of 10 genes validated by quantitative real-time PCR confirmed statistical association with clinical stage, with 6 genes downregulated in high clinical stage patients, including the TMEM100 gene showing the best statistical association. Five of these 6 genes were also downregulated in lung ADCA tissue as compared to normal tissue. Functional studies in vitro indicated that four of them (SLC14A1, SMAD6, TMEM100, and TXNIP) inhibited colony formation when over-expressed by transfection in lung cancer cell lines, suggesting their potential tumor-suppressor activity. Our findings indicated that individual variations in the transcriptional profile of normal lung tissue may reflect the lung ADCA patients’ predisposition to a specific clinical stage. The 120 RNA samples from normal lung were combined in 24 small pools, with 12 pools constituted by stage I patients and 12 pools by stage >I patients (5 samples per pool)
| S-ECPF-GEOD-31267 | biostudies-other
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
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Project description:Next-generation sequencing (NGS) has revolutionized systems-based analysis of gene expression. The goals of this study is to compare gene expressions in colorectal tumor versus adjacent normal colorectal tissue.
2022-12-16 | GSE142279 | GEO
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