scholarly journals Using deep neural networks and interpretability methods to identify gene expression patterns that predict radiomic features and histology in non-small cell lung cancer

2021 ◽  
Vol 8 (03) ◽  
Author(s):  
Nova F. Smedley ◽  
Denise R. Aberle ◽  
William Hsu
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Neural Networks ◽  
Small Cell Lung Cancer ◽  
Deep Neural Networks ◽  
Expression Patterns ◽  
Small Cell ◽  
Gene Expression Patterns ◽  
Small Cell Lung ◽  
Identify Gene Expression

scholarly journals Metabolic Diversity in Human Non-Small Cell Lung Cancer Cells

2019 ◽  
Author(s):  
Pei-Hsuan Chen ◽  
Ling Cai ◽  
Kenneth Huffman ◽  
Chendong Yang ◽  
Jiyeon Kim ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cancer Cells ◽  
Cell Lines ◽  
Cell Lung Cancer ◽  
Expression Patterns ◽  
Small Cell ◽  
Metabolic Reprogramming ◽  
Small Cell Lung

SummaryIntermediary metabolism in cancer cells is regulated by diverse cell-autonomous processes including signal transduction and gene expression patterns arising from specific oncogenotypes and cell lineages. Although it is well established that metabolic reprogramming is a hallmark of cancer, we lack a full view of the diversity of metabolic programs in cancer cells and an unbiased assessment of the associations between metabolic pathway preferences and other cell-autonomous processes. Here we quantified over 100 metabolic features, mostly from 13C enrichment of molecules from central carbon metabolism, in over 80 non-small cell lung cancer (NSCLC) cell lines cultured under identical conditions. Because these cell lines were extensively annotated for oncogenotype, gene expression, protein expression and therapeutic sensitivity, the resulting database enables the user to uncover new relationships between metabolism and these orthogonal processes.

p53-regulated GML gene expression in non-small cell lung cancer: Relation to cisplatin chemosensitivity

Lung Cancer ◽  
2000 ◽  
Vol 29 (1) ◽  
pp. 193
Author(s):  
M Higashiyama ◽  
K Kodama ◽  
H Yokouchi ◽  
K Takami ◽  
Y Miyoshi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung

Effects of fine air particulates on gene expression in non-small-cell lung cancer

2017 ◽  
Vol 62 (2) ◽  
pp. 295-301 ◽  
Author(s):  
Biao Yang ◽  
Xinming Li ◽  
Dongmei Chen ◽  
Chunling Xiao
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung

scholarly journals Cell-autonomous immune gene expression is repressed in pulmonary neuroendocrine cells and small cell lung cancer

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Ling Cai ◽  
Hongyu Liu ◽  
Fang Huang ◽  
Junya Fujimoto ◽  
Luc Girard ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Neuroendocrine Cells ◽  
Immune Gene ◽  
Small Cell Lung ◽  
Immune Gene Expression ◽  
Pulmonary Neuroendocrine Cells

AbstractSmall cell lung cancer (SCLC) is classified as a high-grade neuroendocrine (NE) tumor, but a subset of SCLC has been termed “variant” due to the loss of NE characteristics. In this study, we computed NE scores for patient-derived SCLC cell lines and xenografts, as well as human tumors. We aligned NE properties with transcription factor-defined molecular subtypes. Then we investigated the different immune phenotypes associated with high and low NE scores. We found repression of immune response genes as a shared feature between classic SCLC and pulmonary neuroendocrine cells of the healthy lung. With loss of NE fate, variant SCLC tumors regain cell-autonomous immune gene expression and exhibit higher tumor-immune interactions. Pan-cancer analysis revealed this NE lineage-specific immune phenotype in other cancers. Additionally, we observed MHC I re-expression in SCLC upon development of chemoresistance. These findings may help guide the design of treatment regimens in SCLC.

scholarly journals EGFR and hTERT Expression as a Diagnostic Approach for Non-small Cell Lung Cancer in High Risk Groups

2010 ◽  
Vol 2 ◽  
pp. BIC.S3383 ◽  
Author(s):  
Radostina Cherneva ◽  
Ognian Georgiev ◽  
Ivanka Dimova ◽  
Blaga Rukova ◽  
Danail Petrov ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
High Risk ◽  
Small Cell Lung Cancer ◽  
Mrna Expression ◽  
Small Cell ◽  
Htert Mrna ◽  
Small Cell Lung ◽  
The Mean ◽  
Htert Mrna Expression

Objective The early detection of NSCLC is of importance because it provides chances for better outcomes. The aim of the study was to explore the clinical utility of EGFR and hTERT mRNA expression as markers for diagnosis of NSCLC. Methods EGFR and hTERT mRNA were quantified by quantative reverse transcription real time polymerase chain reaction in plasma of 45 non-small cell lung cancer (NSCLC) and 40 chronic obstructive pulmonary disease (COPD) patients, selected by certain spirometric characteristics that made them at high risk of developing lung cancer in future. Results The gene expression level of each gene was calculated and given as a relative quantity–-RQ. EGFR gene expression was found in all lung cancer patients. The mean level of expression was RQ = 29.39. hTERT mRNA could be detected in 88% of patients. The mean expression ratio in them was RQ = 17.31. Only 50% of the high risk patients turned to be positive for EGFR. The level of their expression was RQ = 2.09. The plasma levels of hTERT could be detected in 17 (42.5%) patients of the high risk COPD group. Their mean level of expression was RQ = 1.02. A statistically significant difference in EGFR and hTERT mRNA expression could be observed between the two groups of patients–-p = 0.0001. Conclusion EGFR and hTERT mRNA are potential markers for lung cancer diagnosis, whose clinical importance should be replicated in a larger cohort of patients.

scholarly journals Predicting Diagnostic Gene Biomarkers for Non-Small-Cell Lung Cancer

2016 ◽  
Vol 2016 ◽  
pp. 1-8
Author(s):  
Bin Liang ◽  
Yang Shao ◽  
Fei Long ◽  
Shu-Juan Jiang
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Association Network ◽  
Small Cell Lung ◽  
Functional Association ◽  
Functional Association Network ◽  
Gene Modules

Lung cancer is the primary reason for death due to cancer worldwide, and non-small-cell lung cancer (NSCLC) is the most common subtype of lung cancer. Most patients die from complications of NSCLC due to poor diagnosis. In this paper, we aimed to predict gene biomarkers that may be of use for diagnosis of NSCLC by integrating differential gene expression analysis with functional association network analysis. We first constructed an NSCLC-specific functional association network by combining gene expression correlation with functional association. Then, we applied a network partition algorithm to divide the network into gene modules and identify the most NSCLC-specific gene modules based on their differential expression pattern in between normal and NSCLC samples. Finally, from these modules, we identified genes that exhibited the most impact on the expression of their functionally associated genes in between normal and NSCLC samples and predicted them as NSCLC biomarkers. Literature review of the top predicted gene biomarkers suggested that most of them were already considered critical for development of NSCLC.

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