Author:
Wang Yangwei,Yu Tong,Chen Jiaping,Zhao Rong,Diao Mingxin,Mei Peiyuan,He Shiwen,Qiu Wenlin,Ye Guanchao,Jiang Lijuan,Xiao Han,Liao Yongde
Abstract
AbstractLung adenocarcinoma (LUAD) is a common type of malignant tumor with poor prognosis and high mortality. In our previous studies, we found that estrogen is an important risk factor for LUAD, and different estrogen statuses can predict different prognoses. Therefore, in this study, we constructed a prognostic signature related to estrogen reactivity to determine the relationship between different estrogen reactivities and prognosis. We downloaded the LUAD dataset from The Cancer Genome Atlas (TCGA) database, calculated the estrogen reactivity of each sample, and divided them into a high-estrogen reactivity group and a low-estrogen reactivity group. The difference in overall survival between the groups was significant. We also analyzed the status of immune cell infiltration and immune checkpoint expression between the groups. We analyzed the differential gene expression between the groups and screened four key prognostic factors by the least absolute shrinkage and selection operator (LASSO) regression and univariable and multivariable Cox regression. Based on the four genes, a risk signature was established. To a certain extent, the receiver operating characteristic (ROC) curve showed the predictive ability of the risk signature, which was further verified using the GSE31210 dataset. We also determined the role of estrogen in LUAD using an orthotopic mouse model. Additionally, we developed a predictive nomogram combining the risk signature with other clinical characteristics. In conclusion, our four-gene prognostic signature based on estrogen reactivity had prognostic value and can provide new insights into the development of treatment strategies for LUAD.
Funder
National Natural Science Foundation of China
Department of Science and Technology, Hubei Provincial People’s Government
Publisher
Springer Science and Business Media LLC
Subject
Cancer Research,Genetics,Oncology