scholarly journals Subgrouping by gene expression profiles to improve relapse risk prediction in paediatric B‐precursor acute lymphoblastic leukaemia

2021 ◽  
Author(s):  
Qingsheng Huang ◽  
Jiayong Zhong ◽  
Huan Gao ◽  
Kuanrong Li ◽  
Huiying Liang
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukaemia ◽  
Risk Prediction ◽  
Lymphoblastic Leukaemia ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Relapse Risk

Children and adults with acute lymphoblastic leukaemia have similar gene expression profiles

2005 ◽  
Vol 74 (6) ◽  
pp. 466-480 ◽  
Author(s):  
E. Kuchinskaya ◽  
M. Heyman ◽  
D. Grander ◽  
M. Linderholm ◽  
S. Soderhall ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukaemia ◽  
Lymphoblastic Leukaemia ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Similar Gene

scholarly journals Bioinformatics Analysis of Gene Expression Profiles for Risk Prediction in Patients with Septic Shock

2019 ◽  
Vol 25 ◽  
pp. 9563-9571 ◽  
Author(s):  
Yingchun Hu ◽  
Lingxia Cheng ◽  
Wu Zhong ◽  
Muhu Chen ◽  
Qian Zhang
Keyword(s):  
Gene Expression ◽  
Septic Shock ◽  
Risk Prediction ◽  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles

Simplifying Complex Microarray Data To Derive Gene Expression Profiles Which Identify Childhood Acute Lymphoblastic Leukaemia Patients at Risk of Relapse.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 4506-4506
Author(s):  
Dachuan Guo ◽  
Alex Fong ◽  
Andy Lail ◽  
Maree O’Sullivan ◽  
Glenn Stone ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukaemia ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Microarray Gene Expression ◽  
Treatment Regimens ◽  
Childhood Acute Lymphoblastic Leukaemia ◽  
Microarray Gene ◽  
Standard Risk ◽  
Established Treatment

Abstract The optimal treatment of patients with childhood acute lymphoblastic leukaemia (ALL) depends on establishing accurate diagnosis. Our investigations seek to strategically develop the application of microarray gene expression profiling to identify ALL patients with clinically homogenous presentations but which may respond differently to established treatment regimens. We have determined the gene expression profiles of ALL bone marrow (BM) samples taken from patients at diagnosis. Data analysis has focussed on the use of a novel and innovative statistical technology, Gene-RaVE. This series of patent protected algorithms builds a multinomial regression model using Bayesian variable selection. Gene-RaVE leads to the generation of a parsimonious and simple diagnostic gene expression signature, but which provides increased predictive ability over current analysis approaches. We describe our analysis of both Affymetrix (HU133A) and cDNA (10.5K) microarray gene expression profiles generated from diagnostic BM from >100 ALL patients covering the broad ALL subtypes including T and B lineage as well as T cell lymphoma leukaemia. Comparison of gene expression data failed to identify clearly distinguishing profiles between patients identified as ‘standard risk’ from ‘medium risk’ according to BFM95 clinical criteria. Gene expression profiles from a cohort of ALL patients, identified as ‘standard risk’ at diagnosis, were compared on the basis of their overall clinical outcome: relapse within 2 yrs vs non-relapse. Using a range of analyses including t-test, Gene-RaVE, discriminant analysis approaches and principle component analysis, we have discovered that small subsets of genes (<10), all of which included Nedd4BP3 and Ribosomal Protein L38 (RPL38), can be used to distinguish the two outcome groups. Subsequent validation using real time PCR supports the increase in Nedd4BP3 expression in standard risk patients which do not respond well to established treatment regimens. The Gene-RaVE algorithm also provides a generic framework for survival analysis. This approach indicates that the expression of these Nedd4BP3, RPL38 and inositol 1, 4, 5-triphosphate receptor, type 2 can be used to build a survival ‘index’ which correlates with the time to a relapse event in standard risk childhood ALL patients. Our results are suggestive of a way forward in the development of an informative, yet efficient diagnostic tool for this childhood malignancy using microarray gene expression analysis technology.

A lung cancer genomic risk prediction model derived from paraffin-embedded tissue

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 10059-10059
Author(s):  
D. H. Harpole ◽  
M. M. Joshi ◽  
R. P. Petersen ◽  
D. H. Conlon ◽  
K. Tanaka ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Prediction Model ◽  
Risk Prediction ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Short Term ◽  
Fresh Tissue ◽  
Kaplan Meier ◽  
Genomic Risk

10059 Background: We previously developed a validated fresh tissue-based genomic risk model in patients with early stage non-small cell lung cancer (NSCLC) using the Affymetrix U133 plus 2.0 Genechip. Limitations of this fresh tissue-based model include the need for immediate processing and limited availability; however, formalin-fixed, paraffin-embedded (FFPE) tissue is readily available and archived on every patient resected in North America. We investigated the ability of gene expression profiles generated on DNA microarrays using RNA isolated from FFPE NSCLC specimens to distinguish short-term and long-term survivors. Methods: Five to ten 5 um sections of FFPE tumor were collected from 61 NSCLC patients consisting of equal numbers of long- (+5-year) and short-term (<2 year cancer death) survivors. Fifty-five samples were microdissected (6 samples contained no tumor tissue) and RNA was extracted using a proprietary procedure of Response Genetics, Inc. For this feasibility study, Actin 300 < 30 cTs was chosen as a threshold for adequate RNA quantity for amplification to the GeneChip. Amplification and labeling of RNA were done using the Affymetrix two cycle amplification kit. The resulting cRNA was successfully hybridized to the U133 plus 2.0 GeneChip in 54/55 samples (98%). Data were analyzed using the SAM statistical software with Kaplan Meier survival analyses. Results: All analyses were performed using unsupervised hierarchical clustering and blinded duplicate samples had nearly identical gene expression profiles, indicating reproducibility. Adenocarcinoma segregated from squamous cell carcinoma with 98% accuracy (p=0.00004). A differentially expressed gene list between long and short survivors was determined. Distinct gene clusters were observed within each histological type segregating the tumors according to outcome. Kaplan Meier survival analysis stratifying on these clusters revealed significant differences in survival (cluster 1 and cluster 2 median survival>75 mos. vs. 30 mos., respectively; p<0.001). Conclusions: We have demonstrated the feasibility of creating a preliminary genomic risk prediction model using FFPE NSCLC tissue. Data will be presented on a larger training set (100+ patients) and a separate validation cohort of 100 patients. [Table: see text]

1327: Gene Expression Profiles in Benign Prostatic Hyperplasia

2004 ◽  
Vol 171 (4S) ◽  
pp. 349-350
Author(s):  
Gaelle Fromont ◽  
Michel Vidaud ◽  
Alain Latil ◽  
Guy Vallancien ◽  
Pierre Validire ◽  
...  
Keyword(s):  
Gene Expression ◽  
Benign Prostatic Hyperplasia ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Prostatic Hyperplasia
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