scholarly journals Identification of immune prognostic signature of lung carcinoma based on Genome-wide immune expression profile

2020 ◽  
Author(s):  
Bo Ling ◽  
Guangbin Ye ◽  
Qiuhua Zhao ◽  
Yan Jiang ◽  
Lingling Liang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Cancer Patients ◽  
Expression Profile ◽  
Immune Checkpoint ◽  
Immune Gene ◽  
Clinical Prognosis ◽  
Checkpoint Gene ◽  
Lung Cancer Patients ◽  
Resistance Pathway

Abstract Background : Lung cancer is one of the most common types of cancer with low early diagnosis rate and poor prognosis. The integration of immune checkpoint gene expression data and patient prognosis information can help identify the immune subtypes of lung cancer and provide reference for individualized gene immunotherapy in patients with lung cancer. Methods : The data of immune gene expression for lung cancer patients were obtained from TCGA and GEO databases. The relationship between the expressions of 45 immune checkpoint genes (ICGs) and prognosis were analysed. In the other hand, the correlation between the expressions of 45 biomarkers , tumor mutation load (TMB), MMRs, neoantigens and other immunotherapy biomarkers were been identified. Ultimately, prognosis-related ICGs were combined with IDO1, CD274, and CTLA4 to divide lung cancer immune subgroups and the prognostic differences between lung cancer immune subgroups were identified. Results: Based on TCGA database and GEO database, 9 and 11 ICGs were obtained respectively, which were closely related to prognosis. There was a certain synergistic relationship between them. The expression of CD200R1 had a significant negative correlation with TMB and neoantigens. CD200R1 showed a significant positive correlation with CD8A, CD68 and GZMB genes, indicating that it may cause the expression disorder of adaptive immune resistance pathway genes. Based on CD200R1 and combination with IDO1, CD274 and CTLA4, the group with high expression of CD200R1 and low expression of IDO1, CD274 and CTLA4 had the best prognosis among the immune subtypes. Conclusion : Our research provides a method of integrating immune checkpoint gene expression profile and clinical prognosis information to identify immune subtypes of lung cancer, which can provide a unique reference for gene immunotherapy of lung cancer patients.

scholarly journals Gene expression profile of A549 cells from tissue of 4D model predicts poor prognosis in lung cancer patients

2013 ◽  
Vol 134 (4) ◽  
pp. 789-798 ◽  
Author(s):  
Dhruva K. Mishra ◽  
Chad J. Creighton ◽  
Yiqun Zhang ◽  
Don L. Gibbons ◽  
Jonathan M. Kurie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Cancer Patients ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Poor Prognosis ◽  
A549 Cells ◽  
Lung Cancer Patients

scholarly journals Identification of an Immunologic Signature of Lung Adenocarcinomas Based on Genome-Wide Immune Expression Profiles

2021 ◽  
Vol 7 ◽  
Author(s):  
Bo Ling ◽  
Guangbin Ye ◽  
Qiuhua Zhao ◽  
Yan Jiang ◽  
Lingling Liang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Expression Profiles ◽  
External Validation ◽  
Immune Gene ◽  
Clinical Prognosis ◽  
Resistance Pathway ◽  
Lung Adenocarcinomas

Background: Lung cancer is one of the most common types of cancer, and it has a poor prognosis. It is urgent to identify prognostic biomarkers to guide therapy.Methods: The immune gene expression profiles for patients with lung adenocarcinomas (LUADs) were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The relationships between the expression of 45 immune checkpoint genes (ICGs) and prognosis were analyzed. Additionally, the correlations between the expression of 45 biomarkers and immunotherapy biomarkers, including tumor mutation burden (TMB), mismatch repair defects, neoantigens, and others, were identified. Ultimately, prognostic ICGs were combined to determine immune subgroups, and the prognostic differences between these subgroups were identified in LUAD.Results: A total of 11 and nine ICGs closely related to prognosis were obtained from the GEO and TCGA databases, respectively. CD200R1 expression had a significant negative correlation with TMB and neoantigens. CD200R1 showed a significant positive correlation with CD8A, CD68, and GZMB, indicating that it may cause the disordered expression of adaptive immune resistance pathway genes. Multivariable Cox regression was used to construct a signature composed of four prognostic ICGs (IDO1, CD274, CTLA4, and CD200R1): Risk Score = −0.002*IDO1+0.031*CD274−0.069*CTLA4−0.517*CD200R1. The median Risk Score was used to classify the samples for the high- and low-risk groups. We observed significant differences between groups in the training, testing, and external validation cohorts.Conclusion: Our research provides a method of integrating ICG expression profiles and clinical prognosis information to predict lung cancer prognosis, which will provide a unique reference for gene immunotherapy for LUAD.

scholarly journals Higher CD4/CD8 ratio of pleural effusion predicts better survival for lung cancer patients receiving immune checkpoint inhibitors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Po-Hsin Lee ◽  
Tsung-Ying Yang ◽  
Kun-Chieh Chen ◽  
Yen-Hsiang Huang ◽  
Jeng-Sen Tseng ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Pleural Effusion ◽  
Cancer Patients ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Lung Cancer Patients ◽  
Predicting Factor ◽  
Type 3

AbstractPleural effusion is a rare immune-related adverse event for lung cancer patients receiving immune checkpoint inhibitors (ICIs). We enrolled 281 lung cancer patients treated with ICIs and 17 were analyzed. We categorized the formation of pleural effusion into 3 patterns: type 1, rapid and massive; type 2, slow and indolent; and type 3, with disease progression. CD4/CD8 ratio of 1.93 was selected as the cutoff threshold to predict survival. Most patients of types 1 and 2 effusions possessed pleural effusion with CD4/CD8 ratios ≥ 1.93. The median OS time in type 1, 2, and 3 patients were not reached, 24.8, and 2.6 months, respectively. The median PFS time in type 1, 2, and 3 patients were 35.5, 30.2, and 1.4 months, respectively. The median OS for the group with pleural effusion CD4/CD8 ≥ 1.93 and < 1.93 were not reached and 2.6 months. The median PFS of those with pleural effusion CD4/CD8 ≥ 1.93 and < 1.93 were 18.4 and 1.2 months. In conclusion, patients with type 1 and 2 effusion patterns had better survival than those with type 3. Type 1 might be interpreted as pseudoprogression of malignant pleural effusion. CD4/CD8 ratio ≥ 1.93 in pleural effusion is a good predicting factor for PFS.

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