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Identification of Subtype-Specific Prognostic Genes for Early-Stage Lung Adenocarcinoma and Squamous Cell Carcinoma Patients Using an Embedded Feature Selection Algorithm.

Tian S - PLoS ONE (2015)

Bottom Line: In this article, we continue our effort on identification of subtype-specific prognostic genes for AC and SCC, and propose a novel embedded feature selection method by extending Threshold Gradient Descent Regularization (TGDR) algorithm and minimizing on a corresponding negative partial likelihood function.Using real-world datasets and simulated ones, we show these two proposed methods have comparable performance whereas the new proposal is superior in terms of model parsimony.Our analysis provides some evidence on the existence of such subtype-specific prognostic genes, more investigation is warranted.

View Article: PubMed Central - PubMed

Affiliation: Division of Clinical Epidemiology, The First Hospital of Jilin University, Changchun, Jilin, People's Republic of China.

ABSTRACT
The existence of fundamental differences between lung adenocarcinoma (AC) and squamous cell carcinoma (SCC) in their underlying mechanisms motivated us to postulate that specific genes might exist relevant to prognosis of each histology subtype. To test on this research hypothesis, we previously proposed a simple Cox-regression model based feature selection algorithm and identified successfully some subtype-specific prognostic genes when applying this method to real-world data. In this article, we continue our effort on identification of subtype-specific prognostic genes for AC and SCC, and propose a novel embedded feature selection method by extending Threshold Gradient Descent Regularization (TGDR) algorithm and minimizing on a corresponding negative partial likelihood function. Using real-world datasets and simulated ones, we show these two proposed methods have comparable performance whereas the new proposal is superior in terms of model parsimony. Our analysis provides some evidence on the existence of such subtype-specific prognostic genes, more investigation is warranted.

No MeSH data available.


Related in: MedlinePlus

Study schema.A graphical illustration showed how Cox-TGDR-specific and Cox-filter were applied to select relevant subtype-specific prognostic genes for AC and SCC lung cancer.
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pone.0134630.g001: Study schema.A graphical illustration showed how Cox-TGDR-specific and Cox-filter were applied to select relevant subtype-specific prognostic genes for AC and SCC lung cancer.

Mentions: We applied Cox-TGDR-specific method to both NSCLC datasets. First, we used the microarray dataset as the training set and the RNA-seq data as the test set. Second, we reversed the order and repeated the analysis using the RNA-seq data as the training set, and then tested the resulting markers on the microarray data (Fig 1).


Identification of Subtype-Specific Prognostic Genes for Early-Stage Lung Adenocarcinoma and Squamous Cell Carcinoma Patients Using an Embedded Feature Selection Algorithm.

Tian S - PLoS ONE (2015)

Study schema.A graphical illustration showed how Cox-TGDR-specific and Cox-filter were applied to select relevant subtype-specific prognostic genes for AC and SCC lung cancer.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4520527&req=5

pone.0134630.g001: Study schema.A graphical illustration showed how Cox-TGDR-specific and Cox-filter were applied to select relevant subtype-specific prognostic genes for AC and SCC lung cancer.
Mentions: We applied Cox-TGDR-specific method to both NSCLC datasets. First, we used the microarray dataset as the training set and the RNA-seq data as the test set. Second, we reversed the order and repeated the analysis using the RNA-seq data as the training set, and then tested the resulting markers on the microarray data (Fig 1).

Bottom Line: In this article, we continue our effort on identification of subtype-specific prognostic genes for AC and SCC, and propose a novel embedded feature selection method by extending Threshold Gradient Descent Regularization (TGDR) algorithm and minimizing on a corresponding negative partial likelihood function.Using real-world datasets and simulated ones, we show these two proposed methods have comparable performance whereas the new proposal is superior in terms of model parsimony.Our analysis provides some evidence on the existence of such subtype-specific prognostic genes, more investigation is warranted.

View Article: PubMed Central - PubMed

Affiliation: Division of Clinical Epidemiology, The First Hospital of Jilin University, Changchun, Jilin, People's Republic of China.

ABSTRACT
The existence of fundamental differences between lung adenocarcinoma (AC) and squamous cell carcinoma (SCC) in their underlying mechanisms motivated us to postulate that specific genes might exist relevant to prognosis of each histology subtype. To test on this research hypothesis, we previously proposed a simple Cox-regression model based feature selection algorithm and identified successfully some subtype-specific prognostic genes when applying this method to real-world data. In this article, we continue our effort on identification of subtype-specific prognostic genes for AC and SCC, and propose a novel embedded feature selection method by extending Threshold Gradient Descent Regularization (TGDR) algorithm and minimizing on a corresponding negative partial likelihood function. Using real-world datasets and simulated ones, we show these two proposed methods have comparable performance whereas the new proposal is superior in terms of model parsimony. Our analysis provides some evidence on the existence of such subtype-specific prognostic genes, more investigation is warranted.

No MeSH data available.


Related in: MedlinePlus