Transcription profiling of cancerous and non cancerous lung adenocarcinoma tissue. Tumour and normal samples from human lung carcinoma from 18 patients plus tumour only from 5 patients
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ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Blum Shula
PROVIDER: S-ECPF-TABM-15 | biostudies-other |
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Comparison of gene expression of cancerous and non cancerous lung adenocarcinoma tissue. Tumour and normal samples from 18 patients plus tumour only from 5 patients.
2007-04-01 | E-TABM-15 | biostudies-arrayexpress
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:Purpose: To gain molecular insights of HBV integration that may contribute to HCC tumorigenesis, we performed whole transcriptome sequencing and whole genome copy number profiling of hepatocellular carcinoma (HCC) samples from 50 Chinese patients. Conclusions: This is the first report on the molecular basis of the MLL4 integration driving MLL4 over-expression. HBV-MLL4 integration occurred frequently in Chinese HCC patients, representing a unique molecular segment for HCC with HBV infection.
2015-03-30 | GSE65484 | GEO
Project description: Not available
| S-ECPF-MTAB-923 | biostudies-other
Project description:The heterogeneous structure in tumor tissues from colorectal cancer (CRC) patients excludes an informative comparison between tumors and adjacent normal tissues. Here, we develop and apply a strategy to compare paired cancerous (CEC) versus normal (NEC) epithelial cells enriched from patients and discover potential biomarkers and therapeutic targets for CRC. CEC and NEC cells are respectively isolated from five different tumor and normal locations in the resected colon tissue from each patient (N = 12 patients) using an optimized epithelial cell adhesion molecule (EpCAM)-based enrichment approach. An ion current-based quantitative method is employed to perform comparative proteomic analysis for each patient. A total of 458 altered proteins that are common among >75% of patients are observed and selected for further investigation. Besides known findings such as deregulation of mitochondrial function, tricarboxylic acid cycle, and RNA post-transcriptional modification, functional analysis further revealed RAN signaling pathway, small nucleolar ribonucleoproteins (snoRNPs), and infection by RNA viruses are altered in CEC cells. A selection of the altered proteins of interest is validated by immunohistochemistry analyses. The informative comparison between matched CEC and NEC enhances our understanding of molecular mechanisms of CRC development and provides biomarker candidates and new pathways for therapeutic intervention.
| S-EPMC5418098 | biostudies-literature
Project description:BackgroundThe functional role of lung microbiota has attracted an accumulating attention recently, but the profile and functional role of the lung microbiota in patients with lung cancer remained largely unknown.MethodsTo evaluate the association of the microbiota with lung cancer, we performed comparative analysis of the lung microbiota using 16S rRNA amplicon sequencing approach in the paired bronchoalveolar lavage fluid (BALF) samples (paired samples from cancerous lung and the contralateral non-cancerous lung) from 50 cancer patients with unilateral lobar masses.ResultsWe found that the relative abundance of phylum Tenericutes, its class Mollicutes, its order Entomoplasmatales, its family Spiroplasmataceae, and its genus Spiroplasma was significantly increased in cancerous lung, but the relative abundance of phylum Bacteroidetes, its class Bacteroidia, and its order Bacteroidales was significantly decreased in cancerous lung. In addition, the relative abundance of family Leuconostocaceae and its genus Weissella was significantly increased in cancerous lung.ConclusionOur findings provide insights into a change of lung microbiota community associated with the development of lung cancer.
| S-EPMC7476634 | biostudies-literature
Project description:BackgroundAlthough pemetrexed plus cis/carboplatin has become the most effective chemotherapy regimen for patients with advanced lung adenocarcinoma, predictive biomarkers are not yet available, and new tools to identify chemosensitive patients who would likely benefit from this treatment are desperately needed. In this study, we constructed and validated predictive peptide models using the serum peptidome profiles of two datasets.MethodsOne hundred eighty-three patients treated with first-line platinum-based pemetrexed treatment for advanced lung adenocarcinoma were retrospectively enrolled and randomized into the training (n = 92) or validation (n = 91) set, and pre-treatment serum samples were analyzed using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and ClinProTools software. Serum peptidome profiles from the training set were used to identify potential predictive peptide biomarkers and construct a predictive peptide model for accurate group discrimination; which was then used to classify validation samples into "good" and "poor" outcome groups. The clinical outcomes of objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), and overall survival (OS) were analyzed based on the classification result.ResultsEight potential peptide biomarkers were identified. A predictive peptide model based on four distinct m/z features (2,142.12, 3,316.19, 4,281.94, and 6,624.02 Da) was developed based on the clinical outcomes of training set patients after first-line pemetrexed plus platinum treatment. In the validation set, the good group had significantly higher ORR (49.1% vs. 8.3%, P <0.001) and DCR (96.4% vs. 47.2%, P <0.001), and longer PFS (7.3 months vs. 2.7 months, P <0.001) vs. the poor group. However, the model did not predict OS (13.6 months vs. 12.7 months, P = 0.0675).ConclusionOur predictive peptide model could predict pemetrexed plus platinum treatment outcomes in patients with advanced lung adenocarcinoma and might thus facilitate appropriate patient selection. Further studies are needed to confirm these findings.
| S-EPMC5464620 | biostudies-literature
Project description:MicroRNA (miRNA) expression profiles for lung cancers were examined to investigate the miRNA involvement in lung carcinogenesis. miRNA microarray analysis identified statistical unique profiles, which could discriminate lung cancers from noncancerous lung tissues.
| S-ECPF-TABM-47 | biostudies-other
Project description:BackgroundLung cancer is the leading cause of cancer death in both men and women. The most common lung cancer subtype is non-small cell lung carcinoma (NSCLC) comprising about 85% of all cases. NSCLC can be further divided into three subtypes: adenocarcinoma (LUAD), squamous cell carcinoma (LUSC), and large cell lung carcinoma. Specific genetic mutations and epigenetic aberrations play an important role in the developmental transition to a specific tumor subtype. The elucidation of normal lung versus lung tumor gene expression patterns and regulatory targets yields biomarker systems that discriminate lung phenotypes (i.e., biomarkers) and provide a foundation for the discovery of normal and aberrant gene regulatory mechanisms.ResultsWe built condition-specific gene co-expression networks (csGCNs) for normal lung, LUAD, and LUSC conditions. Then, we integrated normal lung tissue-specific gene regulatory networks (tsGRNs) to elucidate control-target biomarker systems for normal and cancerous lung tissue. We characterized co-expressed gene edges, possibly under common regulatory control, for relevance in lung cancer.ConclusionsOur approach demonstrates the ability to elucidate csGCN:tsGRN merged biomarker systems based on gene expression correlation and regulation. The biomarker systems we describe can be used to classify and further describe lung specimens. Our approach is generalizable and can be used to discover and interpret complex gene expression patterns for any condition or species.
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Project description:Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers worldwide; it develops in a relatively symptom-free manner, leading to rapid disease progression and metastasis, leading to a 5-year survival rate of less than 5%. A lack of dependable diagnostic markers and rapid development of resistance to conventional therapies are among the problems associated with management of the disease. A better understanding of pancreatic tumour biology and discovery of new potential therapeutic targets are important goals in pancreatic cancer research. This study describes the comparative quantitative LC-MS/MS proteomic analysis of the membrane-enriched proteome of 10 human pancreatic ductal adenocarcinomas, 9 matched adjacent-normal pancreas and patient-derived xenografts (PDXs) in mice (10 at F1 generation and 10 F2). Quantitative label-free LC-MS/MS data analysis identified 129 proteins upregulated, and 109 downregulated, in PDAC, compared to adjacent-normal tissue. In this study, analysing peptide MS/MS data from the xenografts, great care was taken to distinguish species-specific peptides definitively derived from human sequences, or from mice, which could not be distinguished. The human-only peptides from the PDXs are of particular value, since only human tumour cells survive, and stromal cells are replaced during engraftment in the mouse; this list is, therefore, enriched in tumour-associated proteins, some of which might be potential therapeutic or diagnostic targets. Using human-specific sequences, 32 proteins were found to be upregulated, and 113 downregulated in PDX F1 tumours, compared to primary PDAC. Differential expression of CD55 between PDAC and normal pancreas, and expression across PDX generations, was confirmed by Western blotting. These data indicate the value of using PDX models in PDAC research. This study is the first comparative proteomic analysis of PDAC which employs PDX models to identify patient tumour cell-associated proteins, in an effort to find robust targets for therapeutic treatment of PDAC.
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