Lung adenocarcinoma pathology stages related gene identification

Gaozhong Sun; Tongwei Zhao; Gaozhong Sun; Tongwei Zhao

doi:10.3934/mbe.2020038

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 1: 737-746. doi: 10.3934/mbe.2020038

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Lung adenocarcinoma pathology stages related gene identification

Gaozhong Sun ¹,
Tongwei Zhao ^{2
,
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1.
Department of Cardiothoracic Surgery, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou 310014, China
2.
Department of Oncology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou 310014, China

Received: 27 March 2019 Accepted: 15 September 2019 Published: 28 October 2019

ObjectiveLung cancer is a deadly disease with the highest 5-year survival rate. Lung adenocarcinoma is the main subtype of non-small cell lung cancer (NSCLC). Correct staging is critical as the basis of treatment. So, the identification of genes associated with pathologic stages of lung adenocarcinoma is helpful in understanding the pathological mechanism and designing targeted therapeutic drugs.

MethodsRandom forest was suitable for high-dimensional data to identify variables associated with the outcome. The variable importance-based selection method was used to rank the candidate genes associated with pathologic stages of lung adenocarcinoma. Univariate regression was used to analyze the relationship between gene expression and prognosis. The protein-protein interaction network was used to show the interactions among the identified genes. The identified genes functional enrichment analysis was performed by GSEA software.

ResultsTwelve genes significantly associated with pathologic stages of lung adenocarcinoma were identified by random forest analysis. Eight of these genes were found to play roles in survival of patients with lung adenocarcinoma. Among the 12 genes, 4 genes such as CENPH, SRSF5, PITX2 and NSG1 interacted with each other. And these genes were mainly enriched in p53 signaling pathway, cell cycle signaling pathway, JAK STAT signaling pathway and DNA replication signaling pathway.

ConclusionThe identified genes may drive the changes among pathologic stages of lung adenocarcinoma and will be helpful in understanding the molecular changes underlying the pathologic stages.
- lung adenocarcinoma,
- gene,
- pathologic stages
Citation: Gaozhong Sun, Tongwei Zhao. Lung adenocarcinoma pathology stages related gene identification[J]. Mathematical Biosciences and Engineering, 2020, 17(1): 737-746. doi: 10.3934/mbe.2020038

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Abstract

ObjectiveLung cancer is a deadly disease with the highest 5-year survival rate. Lung adenocarcinoma is the main subtype of non-small cell lung cancer (NSCLC). Correct staging is critical as the basis of treatment. So, the identification of genes associated with pathologic stages of lung adenocarcinoma is helpful in understanding the pathological mechanism and designing targeted therapeutic drugs.

MethodsRandom forest was suitable for high-dimensional data to identify variables associated with the outcome. The variable importance-based selection method was used to rank the candidate genes associated with pathologic stages of lung adenocarcinoma. Univariate regression was used to analyze the relationship between gene expression and prognosis. The protein-protein interaction network was used to show the interactions among the identified genes. The identified genes functional enrichment analysis was performed by GSEA software.

ResultsTwelve genes significantly associated with pathologic stages of lung adenocarcinoma were identified by random forest analysis. Eight of these genes were found to play roles in survival of patients with lung adenocarcinoma. Among the 12 genes, 4 genes such as CENPH, SRSF5, PITX2 and NSG1 interacted with each other. And these genes were mainly enriched in p53 signaling pathway, cell cycle signaling pathway, JAK STAT signaling pathway and DNA replication signaling pathway.

ConclusionThe identified genes may drive the changes among pathologic stages of lung adenocarcinoma and will be helpful in understanding the molecular changes underlying the pathologic stages.

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