Imaging Biomarker Discovery for Lung Cancer Survival Prediction

2016 ◽  
pp. 649-657 ◽  
Author(s):  
Jiawen Yao ◽  
Sheng Wang ◽  
Xinliang Zhu ◽  
Junzhou Huang
Keyword(s):  
Lung Cancer ◽  
Cancer Survival ◽  
Biomarker Discovery ◽  
Imaging Biomarker ◽  
Survival Prediction ◽  
Lung Cancer Survival

scholarly journals PD-0496: Multi-centric learning with a federated IT infrastructure: application to 2-year lung-cancer survival prediction

2013 ◽  
Vol 106 ◽  
pp. S193-S194 ◽  
Author(s):  
A. Dekker ◽  
G. Nalbantov ◽  
C. Oberije ◽  
W. Wiessler ◽  
M. Eble ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Survival ◽  
Survival Prediction ◽  
It Infrastructure ◽  
Lung Cancer Survival

PO-1544: Comparing Clinical Variables and Quantitative Imaging Features for Lung Cancer Survival Prediction

2020 ◽  
Vol 152 ◽  
pp. S835-S836
Author(s):  
C. Zhang ◽  
Z. Shi ◽  
C. Zhu ◽  
P. Kalendralis ◽  
I. Bermejo ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Survival ◽  
Quantitative Imaging ◽  
Survival Prediction ◽  
Imaging Features ◽  
Clinical Variables ◽  
Lung Cancer Survival

scholarly journals Gene Expression Profiles of Non-Small Cell Lung Cancer: Survival Prediction and New Biomarkers

Oncology ◽  
2010 ◽  
Vol 79 (3-4) ◽  
pp. 283-292 ◽  
Author(s):  
Kristjan Välk ◽  
Tõnu Vooder ◽  
Raivo Kolde ◽  
Mari-Ann Reintam ◽  
Cathleen Petzold ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Cancer Survival ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Small Cell ◽  
Survival Prediction ◽  
Small Cell Lung ◽  
New Biomarkers ◽  
Lung Cancer Survival

scholarly journals Analysis of Survival-Related lncRNA Landscape Identifies A Role for LINC01537 in Energy Metabolism and Lung Cancer Progression

2019 ◽  
Vol 20 (15) ◽  
pp. 3713 ◽  
Author(s):  
Wei Gong ◽  
Lei Yang ◽  
Yuanyuan Wang ◽  
Jianfeng Xian ◽  
Fuman Qiu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Energy Metabolism ◽  
Cancer Progression ◽  
Cancer Survival ◽  
Metabolic Reprogramming ◽  
Survival Prediction ◽  
Rna Seq ◽  
Lung Cancer Patients ◽  
Rna Interaction ◽  
Lung Cancer Survival

Many long non-coding RNAs (lncRNAs) have emerged as good biomarkers and potential therapeutic targets for various cancers. We aimed to get a detailed understanding of the lncRNA landscape that is associated with lung cancer survival. A comparative analysis between our RNA sequencing (RNA-seq) data and TCGA datasets was conducted to reveal lncRNAs with significant correlations with lung cancer survival and then the association of the most promising lncRNA was validated in a cohort of 243 lung cancer patients. Comparing RNA-seq data with TCGA ones, 84 dysregulated lncRNAs were identified in lung cancer tissues, among which 10 lncRNAs were significantly associated with lung cancer survival. LINC01537 was the most significant one (p = 2.95 × 10−6). Validation analysis confirmed the downregulation of LINC01537 in lung cancer. LINC01537 was observed to inhibit tumor growth and metastasis. It also increased cellular sensitivity to nilotinib. PDE2A (phosphodiesterase 2A) was further identified to be a target of LINC01537 and it was seen that LINC01537 promoted PDE2A expression via RNA–RNA interaction to stabilize PDE2A mRNA and thus echoed effects of PDE2A on energy metabolism including both Warburg effect and mitochondrial respiration. Other regulators of tumor energy metabolism were also affected by LINC01537. These results elucidate a suppressed role of LINC01537 in lung cancer development involving tumor metabolic reprogramming, and we believe that it might be a biomarker for cancer survival prediction and therapy.

scholarly journals The effect of imputing missing clinical attribute values on training lung cancer survival prediction model performance

2017 ◽  
Vol 5 (1) ◽  
Author(s):  
Mohamed S. Barakat ◽  
Matthew Field ◽  
Aditya Ghose ◽  
David Stirling ◽  
Lois Holloway ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Prediction Model ◽  
Cancer Survival ◽  
Model Performance ◽  
Survival Prediction ◽  
Lung Cancer Survival
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