SBV - Gene Expression Profiles of Lung Cancer Tumors - Adenocarcinomas and Squamous Cell Carcinomas
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE43580 | GEO | 2013/10/18
SECONDARY ACCESSION(S): PRJNA186757
REPOSITORIES: GEO
Similar Datasets
Project description:This dataset encompassing the profiles of 150 lung cancer tumors was developed to serve as test dataset in the SBV IMPROVER Diagnostic Signature Challenge (sbvimprover.com). The aim of this subchallenge was to verify that it is possible to extract a robust diagnostic signature from gene expression data that can identify stages of different types of lung cancer. Participants were asked to develop and submit a classifier that can stratify lung cancer patients in one of four groups M-bM-^@M-^S Stage 1 of Adenocarcinoma (AC Stage 1), Stage 2 of Adenocarcinoma (AC Stage 2), Stage 1 of Squamous cell carcinoma (SCC Stage 1) or Stage 2 of Squamous cell carcinoma (SCC Stage 2). The classifier could be built by using any publicly available gene expression data with related histopathological information and was tested on the independent dataset described here. 150 non-small cell lung cancer tumors (adenocarcinoma, AC and squamous cell carcinoma, SCC) of stages I and II were collected by surgical resection from patients who have provided consent. Adenosquamous and large cell tumor samples were excluded. The number of smokers and non-smokers was balanced: there were 41 AC1 (adenocarcinoma stage I), 36 AC2, 34 SCC1, and 39 SCC2 samples. Study pathologists at each of the seven sites (Lebanon, Republic of Moldova, Romania, Russian Federation, Ukraine, Vietnam and United States of America) reviewed both the tumor permanent sections and the frozen sections of the samples. Clinical information was also collected about tumor staging, history of prior cancers, lymph node involvement by lymph node sampling/dissection, smoking history, age, gender.
2013-10-18 | E-GEOD-43580 | biostudies-arrayexpress
Project description:Non-small cell lung cancer (NSCLC, n=22) and normal adjacent control biopsies (n=18) from patients with lung cancer were obtained for Affymetrix GeneChip analysis. NSCLC samples were grouped into squamous cell carcinoma (SCC, n=11) and adenocarcinoma (AC, n=11) samples.
2017-07-01 | E-MTAB-5231 | biostudies-arrayexpress
Project description:Non-small cell lung cancer (NSCLC) can be classified into the major subtypes adenocarcinoma (AC) and squamous cell carcinoma (SCC) subtypes. Although explicit molecular, histological and clinical characteristics have been reported for both subtypes, no specific therapy exists so far. However, the characterization of suitable molecular targets holds great promises to develop novel therapies in NSCLC. In the present study, global gene expression profiling of 58 human high grade NSCLC specimens revealed large transcriptomic differences between AC and SCC subtypes: More than 1.700 genes were found to be differentially expressed. Experiment Overall Design: The NSCLC patient collective was composed of the histological subtype adenocarcinoma (n=40) and squamous cell carcinoma (n=18). We subjected gene expression profiles of 40 AC and 18 SCC samples into further analysis. Unsupervised hierarchical clustering of all 58 NSCLC tumors using the 500 most variably expressed transcripts revealed two different clusters, which were strongly associated with the histological subtypes AC and SCC of NSCLC. Our result indicated that the major impact on global transcriptional changes was due to the NSCLC histology.
2009-10-11 | E-GEOD-10245 | biostudies-arrayexpress
Project description:Non-small cell lung cancer (NSCLC) can be classified into the major subtypes adenocarcinoma (AC) and squamous cell carcinoma (SCC) subtypes. Although explicit molecular, histological and clinical characteristics have been reported for both subtypes, no specific therapy exists so far. However, the characterization of suitable molecular targets holds great promises to develop novel therapies in NSCLC. In the present study, global gene expression profiling of 58 human high grade NSCLC specimens revealed large transcriptomic differences between AC and SCC subtypes: More than 1.700 genes were found to be differentially expressed. Keywords: disease subtype analysis
2009-10-01 | GSE10245 | GEO
Project description:Degradation and chemical modification of RNA in formalin-fixed paraffin-embedded (FFPE) samples hamper their use in expression profiling studies. In this study, we investigated the feasibility of gene expression signature generation from archival FFPE materials. Nineteen cervical squamous cell carcinoma (SCC) and nine adenocarcinoma (AC) 10~16-year-old FFPE samples were profiled using Affymetrix Exon 1.0 ST arrays. A comparison of the global gene expression changes between SCC and AC revealed 1217 differentially expressed genes. Of these, 1062 showed significantly higher expression levels in SCC relative to AC, and 155 genes were found to be specifically upregulated in AC. When the 1217-gene signature was tested on a fresh-frozen human non-small cell lung cancer (NSCLC) series, it correctly separated the 58 NSCLC samples into SCC and AC. In conclusion, our results showed that clinically and biologically relevant gene expression profiles can be derived from FFPE samples with Exon array profiling.
2011-02-17 | GSE27388 | GEO
Project description:Degradation and chemical modification of RNA in formalin-fixed paraffin-embedded (FFPE) samples hamper their use in expression profiling studies. In this study, we investigated the feasibility of gene expression signature generation from archival FFPE materials. Nineteen cervical squamous cell carcinoma (SCC) and nine adenocarcinoma (AC) 10~16-year-old FFPE samples were profiled using Affymetrix Exon 1.0 ST arrays. A comparison of the global gene expression changes between SCC and AC revealed 1217 differentially expressed genes. Of these, 1062 showed significantly higher expression levels in SCC relative to AC, and 155 genes were found to be specifically upregulated in AC. When the 1217-gene signature was tested on a fresh-frozen human non-small cell lung cancer (NSCLC) series, it correctly separated the 58 NSCLC samples into SCC and AC. In conclusion, our results showed that clinically and biologically relevant gene expression profiles can be derived from FFPE samples with Exon array profiling. The study population contains 28 FFPE human cervix samples (19 SCC and 9 AC). Histopathology assessment and p63 IHC were performed on all samples to confirm their histological subtypes. Total RNA was extracted and hybridised to Affymetrix Human Exon 1.0 ST arrays. Expression values were normalised using RMA. Only exonic probesets that were flagged as present (DABG< 0.01) in at least three samples were retained for further analysis. LIMMA was used to identify probesets that were differentially expressed between SCC and AC subtypes, using FDR ≤ 0.01 and absolute fold-change ≥ 2 as cutoff.
2011-02-17 | E-GEOD-27388 | biostudies-arrayexpress
Project description:To identify genes associated with lung cancer progression, we examined gene expression profiles of tumor cells from 20 patients with primary, untreated non-small cell lung cancer (10 adenocarcinomas (AC) and 10 squamous cell carcinomas (SCC)) in comparison to lung tissue of 23 patients with stage IIIB or stage IV non-small cell lung cancer (15 AC and 8 SCC). Bronchoscopical biopsies from patient with recurrent lung tumor were taken after initial treatment. Cancer cells were isolated using laser capture microdissection in order to obtain pure samples of tumor cells. For expression analysis, microarrays covering 8793 defined genes (Human HG Focus Array, Affymetrix) were used. Array data were normalized and analysed for significant differences using variance stabilizing transformation (VSN) and significance analysis of microarrays (SAM), respectively. Genes were considered to be up- or down-regulated when the ratio between primary and recurrent tumor samples were at least 1.5-fold differentially expressed with an estimated false discovery rate: < 5%. Based on differentially expressed genes, primary cancer samples could be separated from recurrent tumor samples. We identified 115 and 124 significantly regulates genes in AC and SCC, respectively. For example, in recurrent AC we found increased expression of genes related to the wingless (FZD6, RYK, MYC) and calcium (CALM1, ATB2B1, S100A2) signalling pathways which might play a role in metastasis of tumor cells. Other differentially expressed genes were related to cell cycle (CCND1, CDK2), transcription factors (TTF1, TAF2, YY1), nuclear mRNA splicing and mRNA processing (SFRS1, HNRPL), protein-nucleus import (NUTF2, KPNB1, NUP50) and chromatin modification (HIST1H4C, SMARCC1). In SCC, we found an increased expression of CTNNB1, an important mediator in wingless signalling pathway. Among the down-regulated genes in SCC, the utmost fraction belonged to genes coding for ubiquitin mediated proteolysis (UCHL1, PSMA3, COPS6) and ribosomal proteins (RPS26, RPL7A, RPS15). Other down regulated genes were related to transcription factors (TCEA2, TAF10), nuclear mRNA splicing and mRNA processing (SNRPD2, HNRPM). In conclusion, a distinct pattern of gene expression is found during the progression from primary carcinoma to recurrent NSCLC. Our microarray-based expression profiling revealed interesting novel candidate genes and pathways that may contribute to lung cancer progression. Experiment Overall Design: - 20 patients with primary, untreated non-small cell lung cancer (10adenocarcinomas (AC) and 10 squamous cell carcinomas (SCC)) in comparison to lung tissue of 23 patients with stage IIIB or stage IV non-small cell lung cancer (15 AC and 8 SCC) Experiment Overall Design: - Human HG Focus Array, Affymetrix) were used Experiment Overall Design: - Array data were normalized and analysed for significant differences using variance stabilizing transformation (VSN) and significance analysis of microarrays (SAM) Experiment Overall Design: - Genes were considered to be up- or down-regulated when the ratio between primary and recurrent tumor samples were at least 1.5-fold differentially expressed with an estimated false discovery rate: < 5%
2008-06-15 | E-GEOD-7880 | biostudies-arrayexpress
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: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:Cervical cancers is the second most malignancy in women. It has been clinically important histological variants such as squamous cell carcinoma (SCC) and adenocarcinoma (AC) and adenosquamous carcinomas (ASC). It has been postulated that AC and ASC has a worse prognosis than pure SCC. However, many of the mixed or other types confuses its diagnosis and aggressive/resistant behavior of some tumors has resulted in debate for prognostic role of empirical pathological classification. In addition, the prognosis of adenosquamous carcinoma is still under debate. To establish a novel molecular classification of cervical cancer, we investigated intrinsic characteristics using expression profile. A total of 80 cevical cancer samples of following histology were included in this study: 54 SCC, 18 ASC, 6 AC, and 2 others. Replicate number: 2. No control/no reference sample was included. One-color.
2010-09-28 | E-GEOD-20167 | biostudies-arrayexpress