Abstract

Introduction: In our previous study, we constructed a Lung Cancer Explorer (LCE) database housing lung cancer-specific expression data and clinical data from over 6700 patients in 56 studies. Methods: Using this dataset of the largest collection of lung cancer gene expression along with our meta-analysis method, we systematically interrogated the association between gene expression and overall survival as well as the expression difference between tumor and normal (adjacent non-malignant tissue) samples in lung adenocarcinoma (ADC) and lung squamous cell carcinoma (SQCC). A case study for FAM83A and FAM83B was performed as a demonstration for hypothesis testing with our database. Results: We showed that the reproducibility of results across studies varied by histological subtype and analysis type. Genes and pathways unique or common to the two histological subtypes were identified and the results were integrated into LCE to facilitate user exploration. In our case study, we verified the findings from a previous study on FAM83A and FAM83B in non-small cell lung cancer. Conclusions: This study used gene expression data from a large cohort of patients to explore the molecular differences between lung ADC and SQCC.

Original languageEnglish (US)
Article number886
JournalCancers
Volume11
Issue number6
DOIs
StatePublished - Jun 1 2019

Fingerprint

Squamous Cell Carcinoma
Lung Neoplasms
Gene Expression
Databases
Neoplasm Genes
Reproducibility of Results
Non-Small Cell Lung Carcinoma
Meta-Analysis
Survival
Adenocarcinoma of lung
Genes
Neoplasms

Keywords

  • FAM83
  • Gene expression difference between tumor and normal
  • Lung cancer
  • Meta-analysis
  • Survival association analysis
  • Systematic analysis

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

Cite this

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title = "Systematic analysis of gene expression in lung adenocarcinoma and squamous cell carcinoma with a case study of FAM83A and FAM83B",
abstract = "Introduction: In our previous study, we constructed a Lung Cancer Explorer (LCE) database housing lung cancer-specific expression data and clinical data from over 6700 patients in 56 studies. Methods: Using this dataset of the largest collection of lung cancer gene expression along with our meta-analysis method, we systematically interrogated the association between gene expression and overall survival as well as the expression difference between tumor and normal (adjacent non-malignant tissue) samples in lung adenocarcinoma (ADC) and lung squamous cell carcinoma (SQCC). A case study for FAM83A and FAM83B was performed as a demonstration for hypothesis testing with our database. Results: We showed that the reproducibility of results across studies varied by histological subtype and analysis type. Genes and pathways unique or common to the two histological subtypes were identified and the results were integrated into LCE to facilitate user exploration. In our case study, we verified the findings from a previous study on FAM83A and FAM83B in non-small cell lung cancer. Conclusions: This study used gene expression data from a large cohort of patients to explore the molecular differences between lung ADC and SQCC.",
keywords = "FAM83, Gene expression difference between tumor and normal, Lung cancer, Meta-analysis, Survival association analysis, Systematic analysis",
author = "Ling Cai and Danni Luo and Bo Yao and Yang, {Donghan M.} and Shinyi Lin and Luc Girard and Deberardinis, {Ralph J.} and Minna, {John D.} and Yang Xie and Guanghua Xiao",
year = "2019",
month = "6",
day = "1",
doi = "10.3390/cancers11060886",
language = "English (US)",
volume = "11",
journal = "Cancers",
issn = "2072-6694",
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T1 - Systematic analysis of gene expression in lung adenocarcinoma and squamous cell carcinoma with a case study of FAM83A and FAM83B

AU - Cai, Ling

AU - Luo, Danni

AU - Yao, Bo

AU - Yang, Donghan M.

AU - Lin, Shinyi

AU - Girard, Luc

AU - Deberardinis, Ralph J.

AU - Minna, John D.

AU - Xie, Yang

AU - Xiao, Guanghua

PY - 2019/6/1

Y1 - 2019/6/1

N2 - Introduction: In our previous study, we constructed a Lung Cancer Explorer (LCE) database housing lung cancer-specific expression data and clinical data from over 6700 patients in 56 studies. Methods: Using this dataset of the largest collection of lung cancer gene expression along with our meta-analysis method, we systematically interrogated the association between gene expression and overall survival as well as the expression difference between tumor and normal (adjacent non-malignant tissue) samples in lung adenocarcinoma (ADC) and lung squamous cell carcinoma (SQCC). A case study for FAM83A and FAM83B was performed as a demonstration for hypothesis testing with our database. Results: We showed that the reproducibility of results across studies varied by histological subtype and analysis type. Genes and pathways unique or common to the two histological subtypes were identified and the results were integrated into LCE to facilitate user exploration. In our case study, we verified the findings from a previous study on FAM83A and FAM83B in non-small cell lung cancer. Conclusions: This study used gene expression data from a large cohort of patients to explore the molecular differences between lung ADC and SQCC.

AB - Introduction: In our previous study, we constructed a Lung Cancer Explorer (LCE) database housing lung cancer-specific expression data and clinical data from over 6700 patients in 56 studies. Methods: Using this dataset of the largest collection of lung cancer gene expression along with our meta-analysis method, we systematically interrogated the association between gene expression and overall survival as well as the expression difference between tumor and normal (adjacent non-malignant tissue) samples in lung adenocarcinoma (ADC) and lung squamous cell carcinoma (SQCC). A case study for FAM83A and FAM83B was performed as a demonstration for hypothesis testing with our database. Results: We showed that the reproducibility of results across studies varied by histological subtype and analysis type. Genes and pathways unique or common to the two histological subtypes were identified and the results were integrated into LCE to facilitate user exploration. In our case study, we verified the findings from a previous study on FAM83A and FAM83B in non-small cell lung cancer. Conclusions: This study used gene expression data from a large cohort of patients to explore the molecular differences between lung ADC and SQCC.

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