scholarly journals Differences in the early stage gene expression profiles of lung adenocarcinoma and lung squamous cell carcinoma

2019 ◽  
Author(s):  
Nitin Venugopal ◽  
Justin Yeh ◽  
Sai Kodeboyina ◽  
Tae Lee ◽  
Shruti Sharma ◽  
...  
Keyword(s):  
Gene Expression ◽  
Squamous Cell Carcinoma ◽  
Lung Adenocarcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Early Stage ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Lung Squamous Cell Carcinoma

scholarly journals Weighted gene expression profiles identify diagnostic and prognostic genes for lung adenocarcinoma and squamous cell carcinoma

2019 ◽  
Vol 48 (3) ◽  
pp. 030006051989383 ◽  
Author(s):  
Xing Wu ◽  
Linlin Wang ◽  
Fan Feng ◽  
Suyan Tian
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Squamous Cell Carcinoma ◽  
Lung Adenocarcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Prognostic Signature ◽  
Diagnostic Signature

Objective To construct a diagnostic signature to distinguish lung adenocarcinoma from lung squamous cell carcinoma and a prognostic signature to predict the risk of death for patients with nonsmall-cell lung cancer, with satisfactory predictive performances, good stabilities, small sizes and meaningful biological implications. Methods Pathway-based feature selection methods utilize pathway information as a priori to provide insightful clues on potential biomarkers from the biological perspective, and such incorporation may be realized by adding weights to test statistics or gene expression values. In this study, weighted gene expression profiles were generated using the GeneRank method and then the LASSO method was used to identify discriminative and prognostic genes. Results The five-gene diagnostic signature including keratin 5 ( KRT5), mucin 1 ( MUC1), triggering receptor expressed on myeloid cells 1 ( TREM1), complement C3 ( C3) and transmembrane serine protease 2 ( TMPRSS2) achieved a predictive error of 12.8% and a Generalized Brier Score of 0.108, while the five-gene prognostic signature including alcohol dehydrogenase 1C (class I), gamma polypeptide ( ADH1C), alpha-2-glycoprotein 1, zinc-binding ( AZGP1), clusterin ( CLU), cyclin dependent kinase 1 ( CDK1) and paternally expressed 10 ( PEG10) obtained a log-rank P-value of 0.03 and a C-index of 0.622 on the test set. Conclusions Besides good predictive capacity, model parsimony and stability, the identified diagnostic and prognostic genes were highly relevant to lung cancer. A large-sized prospective study to explore the utilization of these genes in a clinical setting is warranted.

scholarly journals Females and Males Show Differences in Early-Stage Transcriptomic Biomarkers of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 347
Author(s):  
Quewang Liu ◽  
Yueying Wang ◽  
Meiyu Duan ◽  
Yusi Fan ◽  
Xingyuan Pan ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Lung Adenocarcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Early Stage ◽  
Lung Squamous Cell Carcinoma ◽  
Disease Diagnosis ◽  
Diagnostic Model ◽  
Lung Cancers ◽  
Incidence And Mortality

The incidence and mortality rates of lung cancers are different between females and males. Therefore, sex information should be an important part of how to train and optimize a diagnostic model. However, most of the existing studies do not fully utilize this information. This study carried out a comparative investigation between sex-specific models and sex-independent models. Three feature selection algorithms and five classifiers were utilized to evaluate the contribution of the sex information to the detection of early-stage lung cancers. Both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) showed that the sex-specific models outperformed the sex-independent detection of early-stage lung cancers. The Venn plots suggested that females and males shared only a few transcriptomic biomarkers of early-stage lung cancers. Our experimental data suggested that sex information should be included in optimizing disease diagnosis models.

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