Critical limitations of prognostic signatures based on risk scores summarized from gene expression levels: a case study for resected stage I non-small-cell lung cancer

2015 ◽  
Vol 17 (2) ◽  
pp. 233-242 ◽  
Author(s):  
Lishuang Qi ◽  
Libin Chen ◽  
Yang Li ◽  
Yuan Qin ◽  
Rufei Pan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Risk Scores ◽  
Small Cell Lung ◽  
Resected Stage ◽  
Gene Expression Levels

A twenty eight-gene signature and survival in completely-resected non-small cell lung cancer (NSCLC)

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 10583-10583
Author(s):  
N. Van Zandwijk
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Overall Survival ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Gene Signature ◽  
Small Cell ◽  
Risk Scores ◽  
Small Cell Lung ◽  
Independent Validation

10583 Background: Current staging methods are imprecise for predicting the outcome of treatment of non-small-cell lung cancer (NSCLC). We have developed a 28-gene signature that is closely associated with recurrence-free and overall survival. Methods: We used whole-genome gene expression microarrays to analyze frozen-tumor samples from 174 patients (pT1&2, N0&1, MO), who had undergone complete surgical resection in 5 European institutions. Randomly generated numbers were used to assign 2/3 of the samples to an algorithm training group with the remaining 1/3 set aside for independent validation. Cox proportional hazards models were used to evaluate the association between the level of expression and patient survival. We used risk scores and nearest centroid analysis to develop a gene-expression model for the prediction of treatment outcome. Leave-one-out cross validation was used to prevent model over-training. Results: 28 genes that correlated with survival were identified by analyzing microarray data and risk scores. Based on the expression of these genes, patients in training and validation groups were classified as either high (48%) or low (52%) risk. Analysis of predicted risk groups revealed significantly different survival distributions for patients in both the training set (p<0.001) and independent validation set (p=0.006). Genes in our prognostic signature encode for several membrane-bound proteins with previously demonstrated involvement in cell cycle regulation and cell proliferation processes. Conclusions: Our 28-gene signature is closely associated with time to recurrence and overall survival of completely-resected NSCLC patients. [Table: see text]

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.

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