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IRGS: an immune-related gene classifier for lung adenocarcinoma prognosis.
Shi, Xiaoshun; Li, Ruidong; Dong, Xiaoying; Chen, Allen Menglin; Liu, Xiguang; Lu, Di; Feng, Siyang; Wang, He; Cai, Kaican.
Afiliação
  • Shi X; Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, No. 1838 of North Guangzhou Avenue, Guangzhou, 510515, People's Republic of China. sxs917@163.com.
  • Li R; Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Nedlands, WA, 6009, Australia. sxs917@163.com.
  • Dong X; Graduate Program in Genetics, Genomics, and Bioinformatics, University of California, Riverside, CA, USA.
  • Chen AM; Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, No. 1838 of North Guangzhou Avenue, Guangzhou, 510515, People's Republic of China.
  • Liu X; Guangzhou Mendel Genomics and Medical Technology Co., Ltd., Guangzhou, 510535, China.
  • Lu D; Mendel Genes Inc, Manhattan Beach, CA, 90266, USA.
  • Feng S; Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, No. 1838 of North Guangzhou Avenue, Guangzhou, 510515, People's Republic of China.
  • Wang H; Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, No. 1838 of North Guangzhou Avenue, Guangzhou, 510515, People's Republic of China.
  • Cai K; Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, No. 1838 of North Guangzhou Avenue, Guangzhou, 510515, People's Republic of China.
J Transl Med ; 18(1): 55, 2020 02 04.
Article em En | MEDLINE | ID: mdl-32019546
ABSTRACT

BACKGROUND:

Tumour cells interfere with normal immune functions by affecting the expression of some immune-related genes, which play roles in the prognosis of cancer patients. In recent years, immunotherapy for tumours has been widely studied, but a practical prognostic model based on immune-related genes in lung adenocarcinoma comparable to existing model has not been established and reported.

METHODS:

We first obtained publicly accessible lung adenocarcinoma RNA expression data from The Cancer Genome Atlas (TCGA) for differential gene expression analysis and then filtered immune-related genes based on the ImmPort database. By using the lasso algorithm and multivariate Cox Proportional-Hazards (CoxPH) regression analysis, we identified candidate genes for model development and validation. The robustness of the model was further examined by comparing the model with three established gene models.

RESULTS:

Gene expression data from a total of 524 lung adenocarcinoma patients from TCGA were used for model development. We identified four biomarkers (MAP3K8, CCL20, VEGFC, and ANGPTL4) that could predict overall survival in lung adenocarcinoma (HR = 1.98, 95% CI 1.48 to 2.64, P = 4.19e-06) and this model could be used as a classifier for the evaluation of low-risk and high-risk groups. This model was validated with independent microarray data and was highly comparable with previously reported gene expression signatures for lung adenocarcinoma prognosis.

CONCLUSIONS:

In this study, we identified a practical and robust four-gene prognostic model based on an immune gene dataset with cross-platform compatibility. This model has potential value in improving TNM staging for survival predictions in patients with lung adenocarcinoma. IMPACT The study provides a method of immune relevant gene prognosis model and the identification of immune gene classifier for the prediction of lung adenocarcinoma prognosis with RNA sequencing and microarray compatibility.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Adenocarcinoma de Pulmão / Neoplasias Pulmonares Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: J Transl Med Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Adenocarcinoma de Pulmão / Neoplasias Pulmonares Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: J Transl Med Ano de publicação: 2020 Tipo de documento: Article