A Qualitative Transcriptional Signature for the Histological Classification of Non-Small Cell Lung Cancer
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PROVIDER: PRJNA495620 | ENA |
REPOSITORIES: ENA
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Project description:Histological classification by routine pathology assessment with hematoxylin-eosin staining and immunostaining for poorly differentiated tumors, particularly those from small biopsies, is still challenging. In this study, using gene expression profiles of pathologically-determined lung squamous cell carcinomas and adenocarcinomas, denoted as pSCC and pADC respectively, we developed a qualitative transcriptional signature, based on the within-sample relative gene expression orderings (REOs) of gene pairs, to individually distinguish ADC from SCC. The signature was validated in the frozen tissues, FFPE materials, mixed tumors, small biopsy specimens and poorly differentiated samples.In summary, the qualitative signature, independent of the subjective diagnosis of pathologists, would be an effective auxiliary tool in distinguishing ADC from SCC.
2018-10-11 | GSE121090 | GEO
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Project description:Pathological examination of gastroscopy biopsy specimens will make false diagnosis for gastric cancer (GC) due to inaccurate sampling locations and/or insufficient sampling amount. We extracted a robust qualitative transcriptional signature, based on the within-sample relative expression orderings (REOs) of gene pairs, to discriminate both GC tissues and adjacent-normal tissues from non-GC gastritis and normal gastric tissues.The qualitative transcriptional signature can be robustly applied at the individual level to aid the diagnosis of early GC.
2018-07-10 | GSE116782 | GEO
Project description:BACKGROUND: Global gene expression analysis provides a comprehensive molecular characterization of non-small cell lung cancer. The aim of this study was to evaluate the feasibility of integrating expression profiling into routine clinical work-up by including minute bronchoscopic biopsies and develop a robust prognostic gene expression signature METHODS: Tissue samples from a series of 41 chemotherapy-naïve non-small cell lung cancer patients and 15 control patients with inflammatory lung diseases were obtained during routine clinical work-up and gene expression profiles were gained using a highly sensitive oligonucleotide array platform (Novachip ; 34'207 transcripts). Gene expression signatures were analyzed by correlation with histological and clinical parameters and validated on independent published datasets and immunohistochemistry. RESULTS: Tumor tissue classification based on the gene expression results was strongly dependent on the proportion of tumor cells present in the biopsies and showed an overall sensitivity of 80% and specificity of 89%. For prognostication we developed a metagene consisting of 13 genes, which was validated on 4 independent published datasets. The robustness of this metagene has been demonstrated by a virtual independence from tumor cells present in the biopsies. Furthermore, vascular endothelial growth factor-beta, one of the key prognostic genes was validated by immunohistochemistry on 508 independent tumor samples. CONCLUSIONS: The proposed strategy of integrating functional genomics into routine clinical work-up allows molecular tumor classification and prediction of survival in patients with non-small cell lung cancer of all stages and is suitable for an integration in the daily clinical practice. Keywords: Gene expression profiling for disease state analysis in lung cancer patients 56 lung biopsies, 4 different Phenotypes: NSCLC-squa., NSCLC-NOS, NSCLC-Adeno, Ctr.-Infl.
2008-04-10 | E-GEOD-11117 | biostudies-arrayexpress
Project description:BACKGROUND: Global gene expression analysis provides a comprehensive molecular characterization of non-small cell lung cancer. The aim of this study was to evaluate the feasibility of integrating expression profiling into routine clinical work-up by including minute bronchoscopic biopsies and develop a robust prognostic gene expression signature METHODS: Tissue samples from a series of 41 chemotherapy-naïve non-small cell lung cancer patients and 15 control patients with inflammatory lung diseases were obtained during routine clinical work-up and gene expression profiles were gained using a highly sensitive oligonucleotide array platform (Novachip ; 34'207 transcripts). Gene expression signatures were analyzed by correlation with histological and clinical parameters and validated on independent published datasets and immunohistochemistry. RESULTS: Tumor tissue classification based on the gene expression results was strongly dependent on the proportion of tumor cells present in the biopsies and showed an overall sensitivity of 80% and specificity of 89%. For prognostication we developed a metagene consisting of 13 genes, which was validated on 4 independent published datasets. The robustness of this metagene has been demonstrated by a virtual independence from tumor cells present in the biopsies. Furthermore, vascular endothelial growth factor-beta, one of the key prognostic genes was validated by immunohistochemistry on 508 independent tumor samples. CONCLUSIONS: The proposed strategy of integrating functional genomics into routine clinical work-up allows molecular tumor classification and prediction of survival in patients with non-small cell lung cancer of all stages and is suitable for an integration in the daily clinical practice. Keywords: Gene expression profiling for disease state analysis in lung cancer patients
2008-04-10 | GSE11117 | GEO
Project description:A robust qualitative transcriptional signature for the early diagnosis of gastric cancer
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Project description:Lupus nephritis (LN) is characterized by immune-complex deposition in kidney glomeruli and has been classified according to histological features, but has not been characterized on a molecular level. This study aimed to characterize the relationship between histological and molecular phenotypes in LN. Renal compartmental mRNA expression was measured in 54 kidney biopsy specimens from patients with LN and correlated to histological phenotypes. The top identified transcripts were compared to a separate longitudinal cohort of 36 patients with paired kidney biopsies obtained at the time of flare and at follow up. Unsupervised clustering based on mRNA abundance resulted in clear separation by renal compartment, but did not demonstrate a relationship with LN class based on the International Society of Nephrology/Renal Pathology Society (ISN/RPS) classification, or the NIH activity and chronicity indices. A strong interferon gene signature was observed in both cohorts. FN1, SPP1, and LGALS-3 correlated with disease activity in both cohorts. The relationship between mRNA expression and ISN/RPS classification is modest. However, correlation of mRNA expression with individual histologic lesions identifies transcripts that change with resolution of disease flare, and provide insights into the molecular pathways potentially responsible for pathologic kidney lesions. Combining molecular and pathologic kidney biopsy phenotype may hold promise to better classify disease and identify actionable treatment targets.
2019-10-29 | GSE127797 | GEO
Project description:<p><strong>INTRODUCTION:</strong> To decrease antibiotic resistance, their use as growth promoters in the agricultural sector has been largely abandoned. This may lead to decreased health due to infectious disease or microbiome changes leading to gut inflammation.</p><p><strong>OBJECTIVES:</strong> We aimed to generate a m/z signature classifying chicken health in blood, and obtain biological insights from the resulting m/z signature.</p><p><strong>METHODS:</strong> We used direct infusion mass-spectrometry to determine a machine-learned metabolomics signature that classifies chicken health from a blood sample. We then challenged the resulting models by investigating the classification capability of the signature on novel data obtained at poultry houses in previously unseen countries using a leave-one-country-out (LOCO) cross-validation strategy. Additionally, we optimised the number of mass/charge (m/z) values required to maximise the classification capability of Random Forest models, by developing a novel ranking system based on combined univariate t-test and fold-change analyses and building models based on this ranking through forward and reverse feature selection.</p><p><strong>RESULTS:</strong> The multi-country and LOCO models could classify chicken health. Both resulting 25-m/z and 3784-m/z signatures reliably classified chicken health worldwide. Through mummichog enrichment analysis on the large m/z signature, we found changes to amino acid metabolism, including branched chain amino acids and polyamines.</p><p><strong>CONCLUSION:</strong> We reliably classified chicken health from blood, independent of genetic-, farm-, feed- and country-specific confounding factors. The 25-m/z signature can be used to aid development of a per-metabolite panel. The extended 3784-m/z version can be used to gain a deeper understanding of the metabolic causes and consequences of low chicken health. Together, they may facilitate future treatment, prevention and intervention.</p>
2023-08-08 | MTBLS5065 | MetaboLights
Project description:This SuperSeries is composed of the following subset Series: GSE28571: Gene Copy Number Aberrations are Associated with Survival in Histological Subgroups of Non-Small Cell Lung Cancer (expression data) GSE28572: Gene Copy Number Aberrations are Associated with Survival in Histological Subgroups of Non-Small Cell Lung Cancer (copy number data) Refer to individual Series
2011-11-01 | E-GEOD-28582 | biostudies-arrayexpress