Identification of the prognostic value of immune gene signature and infiltrating immune cells for esophageal cancer patients
| Autor: | Lin Wang, Lin Zhao, Miao He, Zinan Li, Chenyi Zhou, Qian Wei, Minjie Wei, Lianze Chen, Ming Zhang |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Oncology Male medicine.medical_specialty Esophageal Neoplasms medicine.medical_treatment Immunology Kaplan-Meier Estimate Targeted therapy Correlation 03 medical and health sciences 0302 clinical medicine Immune system Internal medicine medicine Biomarkers Tumor Leukocytes Immunology and Allergy Humans Survival rate Pharmacology Receiver operating characteristic business.industry Macrophages Area under the curve Immunotherapy Dendritic Cells Esophageal cancer Middle Aged medicine.disease Prognosis Gene Expression Regulation Neoplastic 030104 developmental biology 030220 oncology & carcinogenesis Female business Transcriptome |
| Zdroj: | International immunopharmacology. 87 |
| ISSN: | 1878-1705 |
| Popis: | Background Esophageal cancer (ESCA) is one of the deadliest solid malignancies with worse survival rate worldwide. Here, we aimed to establish an immune-gene prognostic signature for predicting patients’ survival and providing accurate targets for personalized therapy or immunotherapy. Methods Gene expression profile of patients with ESCA were download from The Cancer Genome Atlas (TCGA) database (dataset 1: n = 159) and immune-related genes from the ImmPORT database. Dataset 1 was subdivided into two groups (dataset 2: n = 80; dataset 3: n = 79). Kaplan-Meier and receiver operating characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signature on the three datasets. TIMER and CIBERSORT analysis were used to evaluate the correlation between the prognostic signature and infiltrating immune cells. Results We constructed a prognostic signature composed of six immune genes (HSPA6, S100A12, FABP3, DKK1, OSM and NR2F2). Kaplan-Meier curves validated the good predictive ability of the prognostic signature in datasets 1, 2 and 3 (P = 0.0034, P = 0.0081, and P = 0.0363, respectively). The area under the curve (AUC) of the ROC curves validated the predictive accuracy of the immune signature (AUCs = 0.757, 0.800, and 0.701, respectively). We also revealed the good prognostic value of the immune cells, including activated memory CD4 T cells, T follicular helper cells and monocytes. Potential target drugs, including Olopatadine and Amlexanox, were identified for clinical therapies to improve patients’ survival outcomes. Conclusion Our study indicated that the immune-related prognostic signature could serve as a novel biomarker for predicting patients’ prognosis and providing new immunotherapy targets in ESCA. |
| Databáze: | OpenAIRE |
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