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A signature of 17 immune-related gene pairs predicts prognosis and immune status in HNSCC patients.

ABSTRACT: Head and neck squamous cell carcinoma (HNSCC) is an invasive malignancy with high worldwide mortality. Growing evidence has indicated a pivotal correlation between HNSCC prognosis and immune signature. This study investigated an immune-related gene pairs (IRGPs) signature to predict the prognostic value of HNSCC patients. We constructed IRGPs via integrating multiple IRG expression data sets. Moreover, we established the predictive model base on the IRGPs for HNSCC, and utilized multidimensional bioinformatics methods to validate the robustness of prognostic value of the IRGPs signature. In addition, we explored the relationship between the IRGPs model and immune status. Seventeen IRGPs signature was built as the predictive model which predicted prognosis independently and reliably for HNSCC. Compared to the high-risk group, the low-risk group demonstrated a distinctly favorable prognosis including overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS). The low-risk group showed higher-immune score and lower-tumor purity than the high-risk group. In addition, the low-risk group exhibited higher expression of Programmed cell death 1 ligand 1 (PD-L1) and Microsatellite instability (MSI) score, and lower expression of Tumor Immune Dysfunction and Exclusion (TIDE), which indicated the low-risk group was much more sensitive to immunotherapy. Lastly, the IRGs signature has achieved a higher accuracy than clinical properties for estimation of survival. The IRGPs model is an independent biomarker for estimating the prognosis, and could be also used to predict immunotherapeutic response in HNSCC patients. These findings may provide new ideas for novel biomarkers and may be helpful to formulate personalized immunotherapy strategy.

SUBMITTER: Jiang P

PROVIDER: S-EPMC7689340 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundLung adenocarcinoma (LUAD) is the most common form of lung cancer, accounting for 30% of all cases and 40% of all non-small-cell lung cancer cases. Immune-related genes play a significant role in predicting the overall survival and monitoring the status of the cancer immune microenvironment. The present study was aimed at finding an immune-related gene signature for predicting LUAD patient outcomes.MethodsFirst, we chose the TCGA-LUAD project in the TCGA database as the training cohort for model training. For model validating, we found the datasets of GSE72094 and GSE68465 in the GEO database and took them as the candidate cohorts. We obtained 1793 immune-related genes from the ImmPort database and put them into a univariate Cox proportional hazard model to initially look for the genes with potential prognostic ability using the data of the training cohort. These identified genes then entered into a random survival forests-variable hunting algorithm for the best combination of genes for prognosis. In addition, the LASSO Cox regression model tested whether the gene combination can be further shrinkage, thereby constructing a gene signature. The Kaplan-Meier, Cox model, and ROC curve were deployed to examine the gene signature's prognosis in both cohorts. We conducted GSEA analysis to study further the mechanisms and pathways that involved the gene signature. Finally, we performed integrating analyses about the 22 TICs, fully interpreted the relationship between our signature and each TIC, and highlighted some TICs playing vital roles in the signature's prognostic ability.ResultsA nine-gene signature was produced from the data of the training cohort. The Kaplan-Meier estimator, Cox proportional hazard model, and ROC curve confirmed the independence and predictive ability of the signature, using the data from the validation cohort. The GSEA analysis results illustrated the gene signature's mechanism and emphasized the importance of immune-related pathways for the gene signature. 22 TICs immune infiltration analysis revealed resting mast cells' key roles in contributing to gene signature's prognostic ability.ConclusionsThis study discovered a novel immune-related nine-gene signature (BTK, CCR6, S100A10, SEMA3C, GPI, SCG2, TNFRSF11A, CCL20, and DKK1) that predicts LUAD prognosis precisely and associates with resting mast cells strongly.

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A signature of 17 immune-related gene pairs predicts prognosis and immune status in HNSCC patients.

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