Xiangjun Qi1, Guoming Chen1, Zhuangzhong Chen2, Jing Li1,3, Wenmin Chen1, Jietao Lin2, Lizhu Lin2,4. 1. The First Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, People's Republic of China. 2. Department of Oncology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, People's Republic of China. 3. Department of Oncology, The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, People's Republic of China. 4. Cancer Project Team of China Center for Evidence Based Traditional Chinese Medicine, Guangzhou, People's Republic of China.
Abstract
BACKGROUND: A growing number of studies have demonstrated that immune-related long noncoding ribonucleic acids (irlncRNAs) are potential prognostic factors for lung adenocarcinoma. Two-gene combination patterns could improve the sensitivity of prognostic models, providing us a novel signature construction concept that we applied to lung adenocarcinoma. METHODS: Gene expression and clinical data were downloaded from the Lung Adenocarcinoma project of The Cancer Genome Atlas (TCGA) database. We applied a co-expression analysis with immune genes obtained from the ImmPort database to recognize irlncRNA. The matrix of irlncRNA pairs was established by a cyclic comparison of each lncRNA pair expression level. Univariate and multivariate Cox regressions and Lasso penalized regression analysis were applied to construct the risk model. Patients with lung adenocarcinoma were divided into high- and low-risk groups, according to the Akaike Information Criterion (AIC) values of the receiver operating characteristic (ROC) curve. Then, we evaluated our signature under various clinical settings: clinical-pathological characteristics, tumor-infiltrating immune cells, checkpoint-related biomarkers, targeted therapy, and chemotherapy. RESULTS: Based on the 239 differently expressed irlncRNAs, we constructed an 11-irlncRNA pair signature. The area under the curve (AUC) of the ROC curve for the signature to predict the 4-year survival rate was 0.819, and the cut-off point was recognized as 1.003. Subsequent analysis showed that our signature can effectively distinguish unfavorable survival outcomes, prognostic clinic-pathological characteristics, and specify tumor infiltration status. Highly expressed immune checkpoint-related genes, as well as higher chemosensitivity, were correlated to the low-risk group. CONCLUSION: We constructed a novel lung adenocarcinoma irlncRNA signature with promising prognostic value using the TCGA database, based on paired irlncRNAs and not relying on lncRNAs special expression levels.
BACKGROUND: A growing number of studies have demonstrated that immune-related long noncoding ribonucleic acids (irlncRNAs) are potential prognostic factors for lung adenocarcinoma. Two-gene combination patterns could improve the sensitivity of prognostic models, providing us a novel signature construction concept that we applied to lung adenocarcinoma. METHODS: Gene expression and clinical data were downloaded from the Lung Adenocarcinoma project of The Cancer Genome Atlas (TCGA) database. We applied a co-expression analysis with immune genes obtained from the ImmPort database to recognize irlncRNA. The matrix of irlncRNA pairs was established by a cyclic comparison of each lncRNA pair expression level. Univariate and multivariate Cox regressions and Lasso penalized regression analysis were applied to construct the risk model. Patients with lung adenocarcinoma were divided into high- and low-risk groups, according to the Akaike Information Criterion (AIC) values of the receiver operating characteristic (ROC) curve. Then, we evaluated our signature under various clinical settings: clinical-pathological characteristics, tumor-infiltrating immune cells, checkpoint-related biomarkers, targeted therapy, and chemotherapy. RESULTS: Based on the 239 differently expressed irlncRNAs, we constructed an 11-irlncRNA pair signature. The area under the curve (AUC) of the ROC curve for the signature to predict the 4-year survival rate was 0.819, and the cut-off point was recognized as 1.003. Subsequent analysis showed that our signature can effectively distinguish unfavorable survival outcomes, prognostic clinic-pathological characteristics, and specify tumor infiltration status. Highly expressed immune checkpoint-related genes, as well as higher chemosensitivity, were correlated to the low-risk group. CONCLUSION: We constructed a novel lung adenocarcinoma irlncRNA signature with promising prognostic value using the TCGA database, based on paired irlncRNAs and not relying on lncRNAs special expression levels.
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