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Diagnostic marker for lung cancer through microarray analysis

ABSTRACT: Dr. Kate Olson's Lab is interested to expand these analysis to comparisons between human normal lung tissue and human lung tumor tissue. In hopes that this will help in finding a diagnostic marker for lung cancer. Since there will be more variability between these samples, we would like to get preliminary data on ten normal lung tissue and ten lung tumor tissues from the same patient. We have already analyzed 4 cell lines and we saw gene expression variability related to how metastatic the cell lines were. This has been written and submitted for publication. We would like to expand these analysis to comparisons between human normal lung tissue and human lung tumor tissue. We hope that this will help in finding a diagnostic marker for lung cancer. Since there will be more variability between these samples, we would like to get preliminary data on ten normal lung tissue and ten lung tumor tissues from the same patient. The samples were dissected by a pathologist prior to extraction to maximize the amount of normal tissue included in the tumor samples. RNA samples from non tumor and tumor lung cancer patients were received by Core E. The RNA was amplified, labeled, and hybridized to the GLYCOv3 microarrays.

ORGANISM(S): Homo sapiens

SUBMITTER: Steven Head

PROVIDER: E-GEOD-29927 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Diagnostic marker for lung cancer through microarray analysis

Project description:Dr. Kate Olson's Lab is interested to expand these analysis to comparisons between human normal lung tissue and human lung tumor tissue. In hopes that this will help in finding a diagnostic marker for lung cancer. Since there will be more variability between these samples, we would like to get preliminary data on ten normal lung tissue and ten lung tumor tissues from the same patient.

2011-06-14 | GSE29927 | GEO

Quantification of Normal Cell Fraction and Copy Number Neutral LOH in Clinical Lung Cancer Samples Using SNP Array Data

Project description:Technologies based on DNA microarrays have the potential to provide detailed information on genomic aberrations in tumor cells. In practice a major obstacle for quantitative detection of aberrations is the heterogeneity of clinical tumor tissue. Tumor tissue consists of a mixture of tumor cells and normal cells, including inflammatory and stromal cells, which may lead to a failure to detect aberrations in the tumor cells. Principal finding: Using SNP array data from 43 non-small cell lung cancer samples we have developed a bioinformatic algorithm that accurately models the fractions of normal and tumor cells in clinical tumor samples. The proportion of normal cells and SNP array data can be used to detect and quantify copy number neutral loss-of-heterozygosity (CNNLOH) in the tumor cells both in crude tumor tissue and in samples enriched for tumor cells by laser capture microdissection. Conclusion: Genome-wide quantitative analysis of CNNLOH using the CNNLOH Quantifier method can help to identify recurrent aberrations contributing to tumor development in clinical tumor samples. In addition, SNP-array based analysis of CNNLOH may become important for detection of aberrations that can be used for diagnostic and prognostic purposes, as well as to predict response to therapy.

2010-04-09 | E-GEOD-16092 | biostudies-arrayexpress

Mus musculus

Project description:EML4-ALK transgenic mouse lung normal tissue vs. lung tumor tissue

| PRJNA369211 | ENA

Homo sapiens

Project description:Non-small-cell lung tumor and normal lung tissue methylation

| PRJNA138213 | ENA

Quantification of Normal Cell Fraction and Copy Number Neutral LOH in Clinical Lung Cancer Samples Using SNP Array Data

2009-08-11 | GSE16092 | GEO

Liquid chromatography-mass spectrometry based tissue metabolic profiling reveals major metabolic pathway alterations and potential biomarkers of lung cancer

Project description:MS spectrum of lung tissue metabolic analysis in 131 patients with lung cancer or benign lung tumor. These include lung cancer tissues/benign tumor tissues, adjacent normal tissues and distal normal tissues.

2020-06-19 | PXD019969 |

Proteomic Analysis Enables Distinction of Early- Versus Advanced-Stage Lung Adenocarcinomas

Project description:Background: A gel-free proteomic approach was utilized to perform in-depth tissue protein profiling of lung adenocarcinoma (ADC) and normal lung tissues from early and advanced stages of the disease. The long-term goal of this study is to generate a large-scale, label-free proteomic data set from histologically well-classified lung ADC that can be used to increase further our understanding of disease progression and aid in identifying novel biomarkers. Methods and Results: Cases of early-stage (I-II) and advanced-stage (III-IV) lung ADCs were selected and paired with normal lung tissues from 22 patients. The histologically and clinically stratified human primary lung adenocarcinomas were analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS). From the analysis of ADC and normal specimens, 5,933 protein groups were identified. To examine the protein expression profile of ADC, a peak area-based quantitation method was used. In early- and advanced-stage ADC, 33 and 39 proteins were differentially-expressed respectively between normal and tumor tissue (adjusted p-value < 0.01, fold change ≥ 4). For early- and advanced stage ADC tumors compared to normal patient-matched tissue, 11 and 22 proteins and 23 and 16 proteins were identified as down- and up-regulated, respectively. In silico functional analysis of the up-regulated proteins in both tumor groups revealed that most of the enriched pathways are involved in mRNA metabolism. Furthermore, the most over-represented pathways in the proteins that were unique to ADC are related to mRNA metabolic processes. Conclusions: Further analysis of these data may provide an insight into the molecular pathways involved in disease etiology and may lead to the identification of biomarker candidates and potential targets for therapy. Our study provides potential diagnostic biomarkers for lung ADC and novel stage-specific drug targets for rational intervention.

2021-09-09 | PXD019259 | Pride

A microRNA-based primary tumor site classification of liver core biopsies

Project description:To develop a classifier based on microRNAs for primary tumor site identification of liver core biopsies and to explore the influence of surrounding normal liver tissue on classification. Tissue samples from 333 patients, corresponding to one of the following ten assay classes, were obtained from archives of the pathology department, Copenhagen University Hospital, Rigshospitalet, Denmark: Lung cancer, breast cancer, gastric/cardia cancer, colorectal cancer, bladder cancer, pancreatic cancer, hepatocellular carcinoma, cholangiocarcinoma, squamous cell cancers of different origin, and normal liver tissue

2013-10-19 | E-GEOD-51429 | biostudies-arrayexpress

Combined expression of plasma CA19-9 and thrombospondin-2 for accurate diagnosis of pancreatic cancer and distal cholangiocarcinoma– A proteome approach

Project description:Minimally- invasive diagnostic biomarkers for patients with pancreatic ductal adenocarcinoma (PDAC) and distal cholangiocarcinoma (CCA) are warranted to enhance early and accurate diagnosis. This study identified tumor tissue secreted proteins, the “secretome”, which was analysed for differential candidate diagnostic plasma proteins. Secretome of tumor and normal tissue was collected directly after resection of the primary tumor from 4 patients with PDAC and distal CCA. The tissue secretome was investigated by label-free LC-MSMS, and database searching identified a total of 5179 proteins. 696 proteins were differentially enriched in tumor secretome compared to normal tissue. Thrombospondin-2 (THBS2) emerged as most promising candidate diagnostic biomarker. Subsequent analysis of THBS2 in plasma samples of patients with PDAC and distal CCA by ELISA showed significantly higher abundance compared to healthy individuals (p<0.001) and resulted in an AUC of 0.844.

2020-05-26 | PXD012480 | Pride

Genome-wide DNA methylation analysis of driver gene-negative and EGFR-mutation lung adenocarcinoma and ajacent normal tissue

Project description:Genome-wide DNA methylation profiling of normal and lung adenocarcinoma fresh tissue samples. The Illumina Infinium MethylationEPIC BeadChip (850K) was used to obtain DNA methylation profiles across 860,000 CpGs in fresh tissue of lung adenocarcinoma and adjacent histological normal lung tissue samples. Samples included 30 paired tumor-normal driver gene-negative lung adenocarcinoma tissues and 35 paired tumor-normal EGFR-mutation positive lung adenocarcinoma tissues.

2024-05-28 | GSE235414 | GEO

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