Gene expression profiling of lung adenocarcinomas and normal lung tissue
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE43458 | GEO | 2013/08/06
SECONDARY ACCESSION(S): PRJNA186452
REPOSITORIES: GEO
Similar Datasets
Project description:Lung cancer is still the leading cause of cancer-related deaths in the US and worldwide. Understanding the global molecular profiles or transcriptome of lung cancers would strengthen our understanding of the biology of this malignancy. We performed gene expression profiling using the Human Gene 1.0 ST platform of 80 lung adenocarcinomas and 30 normal lung tissues to better understand the biology of this significant fraction of non-small cell lung carcinomas (NSCLCs) Lung adenocarcinomas were comrpised of never-smoker (n=40) and smoker (n=40) adenocarcinomas. Normal lung tissue (n=30) were paired to 30 of the never-smoker cases. Gene expression profiling was performed on the samples to identify differentially expressed profiles between lung adenocarcinomas and normal lung tissues.
2013-08-06 | E-GEOD-43458 | biostudies-arrayexpress
Project description:Transcriptomes of lung adenocarcinomas
| PRJNA638137 | ENA
Project description:Transcriptomes of lung adenocarcinomas
| PRJNA593979 | ENA
Project description:We performed RNA-seq using 19 human surgically-resected lung adenocarcinomas to investigate of difference between EGFR-mutated and wild-type lung adenocarcinomas.
2020-02-25 | GSE141569 | GEO
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
Project description:Lung cancer is the leading cause of cancer-related deaths worldwide and lung adenocarcinoma is the most common form of lung cancer. Genomic studies of lung adenocarcinoma (LUAD) have advanced our understanding of the disease's biology and accelerated targeted therapy. However, the proteomic characteristics of LUAD are still insufficiently understood. Prognosis for lung cancer patients remains poor and is strongly related to the stage of disease at the time of diagnosis. Therefore, our study focuses on metastasis formation in LUAD. We performed high-performance liquid chromatography (HPLC) and electrospray ionization tandem mass spectrometry (ESI-MS/MS) on a total of 40 FFPE samples and compared proteomic profiles of primary tumors to those of matched distant metastases. Using differential expression analysis and unsupervised clustering we identified candidate proteins playing a role in metastatic spread. Selected proteins were validated by immunoblotting. Our findings give a better understanding of tumor progression and metastasis formation in LUAD and might help to develop biomarker specific therapy.
2023-11-28 | PXD042604 | Pride
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:The study was designed to identify the molecular changes that occur in EGFR mutant NSCLCs that become resistant to TKI by transforming to SCLC. Tyrosine kinase inhibitors (TKIs) are effective treatments for non-small cell lung cancers (NSCLCs) with epidermal growth factor receptor (EGFR) mutations. However, they do not lead to cures, and, on average, relapse occurs after one year of continuous treatment. In a subset of patients, a fundamental histological transformation from NSCLC to small cell lung cancer (SCLC) is observed in the resistant cancers, but the molecular changes associated with this transformation remain unknown. Analysis of a cohort of tumor samples and cell lines derived from resistant EGFR mutant patients with SCLC transformation revealed that RB is lost in 100% of these cases, but rarely in those that remain NSCLC. Global changes in gene expression, including increased neuroendocrine marker expression and absence of EGFR expression, are observed in cancers that transformed to SCLC. Consistent with their genetic and epigenetic similarities to classical SCLC, cell lines derived from resistant EGFR mutant SCLC biopsies are substantially more sensitive to ABT-263 treatment compared to those derived from resistant EGFR mutant NSCLCs. Together, these findings suggest that despite developing initially as EGFR mutant adenocarcinomas, this subset of resistant cancers ultimately take on many of the molecular and phenotypic characteristics of classical SCLC. Overall, we completed array CGH analysis on 4 tumor specimens from EGFR mutant, TKI-resistant patients. Three of these samples had transformed to SCLC and one remained NSCLC.
2015-01-30 | E-GEOD-64765 | biostudies-arrayexpress
Project description:The study was designed to identify the molecular changes that occur in EGFR mutant NSCLCs that become resistant to TKI by transforming to SCLC. Tyrosine kinase inhibitors (TKIs) are effective treatments for non-small cell lung cancers (NSCLCs) with epidermal growth factor receptor (EGFR) mutations. However, they do not lead to cures, and, on average, relapse occurs after one year of continuous treatment. In a subset of patients, a fundamental histological transformation from NSCLC to small cell lung cancer (SCLC) is observed in the resistant cancers, but the molecular changes associated with this transformation remain unknown. Analysis of a cohort of tumor samples and cell lines derived from resistant EGFR mutant patients with SCLC transformation revealed that RB is lost in 100% of these cases, but rarely in those that remain NSCLC. Global changes in gene expression, including increased neuroendocrine marker expression and absence of EGFR expression, are observed in cancers that transformed to SCLC. Consistent with their genetic and epigenetic similarities to classical SCLC, cell lines derived from resistant EGFR mutant SCLC biopsies are substantially more sensitive to ABT-263 treatment compared to those derived from resistant EGFR mutant NSCLCs. Together, these findings suggest that despite developing initially as EGFR mutant adenocarcinomas, this subset of resistant cancers ultimately take on many of the molecular and phenotypic characteristics of classical SCLC.
2015-01-30 | GSE64765 | GEO
Project description:The study was designed to identify the molecular changes that occur in EGFR mutant NSCLCs that become resistant to TKI by transforming to SCLC. Tyrosine kinase inhibitors (TKIs) are effective treatments for non-small cell lung cancers (NSCLCs) with epidermal growth factor receptor (EGFR) mutations. However, they do not lead to cures, and, on average, relapse occurs after one year of continuous treatment. In a subset of patients, a fundamental histological transformation from NSCLC to small cell lung cancer (SCLC) is observed in the resistant cancers, but the molecular changes associated with this transformation remain unknown. Analysis of a cohort of tumor samples and cell lines derived from resistant EGFR mutant patients with SCLC transformation revealed that RB is lost in 100% of these cases, but rarely in those that remain NSCLC. Global changes in gene expression, including increased neuroendocrine marker expression and absence of EGFR expression, are observed in cancers that transformed to SCLC. Consistent with their genetic and epigenetic similarities to classical SCLC, cell lines derived from resistant EGFR mutant SCLC biopsies are substantially more sensitive to ABT-263 treatment compared to those derived from resistant EGFR mutant NSCLCs. Together, these findings suggest that despite developing initially as EGFR mutant adenocarcinomas, this subset of resistant cancers ultimately take on many of the molecular and phenotypic characteristics of classical SCLC.
2015-01-30 | GSE64322 | GEO