Yang Zhai1,2, Bin Zhao3,4, Yuzhen Wang1, Lina Li1, Jingjin Li5, Xu Li1, Linhan Chang6, Qian Chen7, Zijun Liao8. 1. Department of Oncology, Tumor Hospital of Shaanxi Province, Xi'an, 710061, People's Republic of China. 2. Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, 710049, PR China. 3. Department of Epidemiology, Shaanxi Provincial Tumor Hospital, Xi'an, 710061, China. 4. The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China. 5. Department of Vasculocardiology, First Affiliated Hospital, Xi'an Jiaotong University Medical College, Xi'an, 710061, PR China. 6. Xi'an Medical University, Xi'an, 710061, PR China. 7. Department of Reproduction, First Affiliated Hospital, Xi'an Jiaotong University Medical College, Xi'an, Shaanxi, 710061, PR China. chenqian1006@126.com. 8. Department of Oncology, Tumor Hospital of Shaanxi Province, Xi'an, 710061, People's Republic of China. 472176491@qq.com.
Abstract
BACKGROUND: Lung adenocarcinoma (LUAD) is the most common pathology subtype of lung cancer. In recent years, immunotherapy, targeted therapy and chemotherapeutics conferred a certain curative effects. However, the effect and prognosis of LUAD patients are different, and the efficacy of existing LUAD risk prediction models is unsatisfactory. METHODS: The Cancer Genome Atlas (TCGA) LUAD dataset was downloaded. The differentially expressed immune genes (DEIGs) were analyzed with edgeR and DESeq2. The prognostic DEIGs were identified by COX regression. Protein-protein interaction (PPI) network was inferred by STRING using prognostic DEIGs with p value< 0.05. The prognostic model based on DEIGs was established using Lasso regression. Immunohistochemistry was used to assess the expression of FERMT2, FKBP3, SMAD9, GATA2, and ITIH4 in 30 cases of LUAD tissues. RESULTS: In total,1654 DEIGs were identified, of which 436 genes were prognostic. Gene functional enrichment analysis indicated that the DEIGs were involved in inflammatory pathways. We constructed 4 models using DEIGs. Finally, model 4, which was constructed using the 436 DEIGs performed the best in prognostic predictions, the receiver operating characteristic curve (ROC) was 0.824 for 3 years, 0.838 for 5 years, 0.834 for 10 years. High levels of FERMT2, FKBP3 and low levels of SMAD9, GATA2, ITIH4 expression are related to the poor overall survival in LUAD (p < 0.05). The prognostic model based on DEIGs reflected infiltration by immune cells. CONCLUSIONS: In our study, we built an optimal prognostic signature for LUAD using DEIGs and verified the expression of selected genes in LUAD. Our result suggests immune signature can be harnessed to obtain prognostic insights.
BACKGROUND:Lung adenocarcinoma (LUAD) is the most common pathology subtype of lung cancer. In recent years, immunotherapy, targeted therapy and chemotherapeutics conferred a certain curative effects. However, the effect and prognosis of LUAD patients are different, and the efficacy of existing LUAD risk prediction models is unsatisfactory. METHODS: The Cancer Genome Atlas (TCGA) LUAD dataset was downloaded. The differentially expressed immune genes (DEIGs) were analyzed with edgeR and DESeq2. The prognostic DEIGs were identified by COX regression. Protein-protein interaction (PPI) network was inferred by STRING using prognostic DEIGs with p value< 0.05. The prognostic model based on DEIGs was established using Lasso regression. Immunohistochemistry was used to assess the expression of FERMT2, FKBP3, SMAD9, GATA2, and ITIH4 in 30 cases of LUAD tissues. RESULTS: In total,1654 DEIGs were identified, of which 436 genes were prognostic. Gene functional enrichment analysis indicated that the DEIGs were involved in inflammatory pathways. We constructed 4 models using DEIGs. Finally, model 4, which was constructed using the 436 DEIGs performed the best in prognostic predictions, the receiver operating characteristic curve (ROC) was 0.824 for 3 years, 0.838 for 5 years, 0.834 for 10 years. High levels of FERMT2, FKBP3 and low levels of SMAD9, GATA2, ITIH4 expression are related to the poor overall survival in LUAD (p < 0.05). The prognostic model based on DEIGs reflected infiltration by immune cells. CONCLUSIONS: In our study, we built an optimal prognostic signature for LUAD using DEIGs and verified the expression of selected genes in LUAD. Our result suggests immune signature can be harnessed to obtain prognostic insights.
Authors: M Ashburner; C A Ball; J A Blake; D Botstein; H Butler; J M Cherry; A P Davis; K Dolinski; S S Dwight; J T Eppig; M A Harris; D P Hill; L Issel-Tarver; A Kasarskis; S Lewis; J C Matese; J E Richardson; M Ringwald; G M Rubin; G Sherlock Journal: Nat Genet Date: 2000-05 Impact factor: 38.330
Authors: Taiwen Li; Jingyu Fan; Binbin Wang; Nicole Traugh; Qianming Chen; Jun S Liu; Bo Li; X Shirley Liu Journal: Cancer Res Date: 2017-11-01 Impact factor: 12.701
Authors: Emily R Penick; Nicholas W Bateman; Christine Rojas; Cuauhtemoc Magana; Kelly Conrads; Ming Zhou; Brian L Hood; Guisong Wang; Niyati Parikh; Ying Huang; Kathleen M Darcy; Yovanni Casablanca; Paulette Mhawech-Fauceglia; Thomas P Conrads; G Larry Maxwell Journal: Clin Proteomics Date: 2022-10-04 Impact factor: 5.000