Dataset Information

Development and validation of a quantitative real-time polymerase chain reaction classifier for lung cancer prognosis.

ABSTRACT:

Introduction

This prospective study aimed to develop a robust and clinically applicable method to identify patients with high-risk early-stage lung cancer and then to validate this method for use in future translational studies.

Methods

Three published Affymetrix microarray data sets representing 680 primary tumors were used in the survival-related gene selection procedure using clustering, Cox model, and random survival forest analysis. A final set of 91 genes was selected and tested as a predictor of survival using a quantitative real-time polymerase chain reaction-based assay using an independent cohort of 101 lung adenocarcinomas.

Results

The random survival forest model built from 91 genes in the training set predicted patient survival in an independent cohort of 101 lung adenocarcinomas, with a prediction error rate of 26.6%. The mortality risk index was significantly related to survival (Cox model p < 0.00001) and separated all patients into low-, medium-, and high-risk groups (hazard ratio = 1.00, 2.82, 4.42). The mortality risk index was also related to survival in stage 1 patients (Cox model p = 0.001), separating patients into low-, medium-, and high-risk groups (hazard ratio = 1.00, 3.29, 3.77).

Conclusions

The development and validation of this robust quantitative real-time polymerase chain reaction platform allows prediction of patient survival with early-stage lung cancer. Utilization will now allow investigators to evaluate it prospectively by incorporation into new clinical trials with the goal of personalized treatment of patients with lung cancer and improving patient survival.

SUBMITTER: Chen G

PROVIDER: S-EPMC3167380 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

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Publications

Development and validation of a quantitative real-time polymerase chain reaction classifier for lung cancer prognosis.

Chen Guoan G Kim Sinae S Taylor Jeremy M G JM Wang Zhuwen Z Lee Oliver O Ramnath Nithya N Reddy Rishindra M RM Lin Jules J Chang Andrew C AC Orringer Mark B MB Beer David G DG

Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer 20110901 9

<h4>Introduction</h4>This prospective study aimed to develop a robust and clinically applicable method to identify patients with high-risk early-stage lung cancer and then to validate this method for use in future translational studies.<h4>Methods</h4>Three published Affymetrix microarray data sets representing 680 primary tumors were used in the survival-related gene selection procedure using clustering, Cox model, and random survival forest analysis. A final set of 91 genes was selected and te ...[more]

PMID: 21792073

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Project description:Background and aimDifferent polymerase chain reaction (PCR) techniques have and are still being used for the direct detection of Brucella DNA in serum samples of different animal species and humans without being validated or properly validated, resulting in discrepancies. Thus, this study aimed to evaluate the diagnostic performance of the TaqMan Real-Time-PCR (RT-PCR) targeting the bcsp31 gene versus conventional PCR for the accurate diagnosis of brucellosis at the genus level in cattle sera.Materials and methodsOne hundred and eighty-four serum samples were collected from bacteriologically positive and negative cows with ages ranging from 1 to 5 years old at some infected private farms in the Nile Delta under quarantine measures as well as brucellosis free farms. These samples were classified into four groups after serological diagnosis and investigated by TaqMan RT-PCR and conventional PCR targeting the IS711 gene for Brucella DNA detection. The diagnostic performance characteristics of both PCR techniques were estimated considering the bacteriological results as a gold standard.ResultsTaqMan RT-PCR revealed superiority over conventional PCR; it was able to detect Brucella DNA in 95% (67/70) and 89% (25/28) of the cattle sera samples belonging to Group 1 (serologically and bacteriologically positive) and Group 2 (serologically negative but bacteriologically positive), respectively. On evaluating the diagnostic performance, TaqMan RT-PCR showed superior diagnostic sensitivity (93.9%), diagnostic specificity (88.4%), performance index (182.3), almost perfect kappa agreement (0.825±0.042), strong positive correlation (r=0.826), high accuracy based on the receiver operating characteristic (ROC) curve, and area under the ROC curve (0.911) at p<0.05 and CI of 95%.ConclusionA cattle serum sample is not the metric of choice for targeting Brucella genomic DNA by conventional PCR. The time-saving and rapid TaqMan RT-PCR method revealed a better diagnostic performance in the detection of Brucella DNA in cattle sera. Such performance offered by TaqMan RT-PCR may be considered a step toward the possibility of using such technology in the direct differentiation between Brucella-infected and -vaccinated cattle immunized by smooth vaccines from cattle sera using primers specific for such vaccines.

Dataset Information

Development and validation of a quantitative real-time polymerase chain reaction classifier for lung cancer prognosis.

Introduction

Methods

Results

Conclusions

Publications

Development and validation of a quantitative real-time polymerase chain reaction classifier for lung cancer prognosis.

Similar Datasets

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