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Novel Prognostic Model Based on Immune Signature for Head and Neck Squamous Cell Carcinoma.

ABSTRACT: Background:Deciphering the immune characteristics within tumors and identifying the immune signals related to the prognostic factor are helpful for the treatment and management of tumor patients. However, systematic analysis of immune signatures in head and neck squamous cell carcinoma (HNSCC) remains largely unstudied. Methods:A total of 718 immune-related genes were extracted from RNA sequencing data from 519 HNSCC patients in the TCGA database, and survival analysis with integrated bioinformatics analyses was performed to build the final predictive prognosis model. Results:The 178 survival-associated genes (P < 0.05) participated in important immune functions, including immune cell activation and migration. Multivariate regression analysis using 93 genes (P < 0.01), together with survival-associated clinicopathological parameters, identified 35 independent prognostic factors. The most significant 8 independent factors were CD3E, CD40LG, TNFRSF4, CD3G, CD5, ITGA2B, ABCB1, and TNFRSF13b. The final prognostic model achieved outstanding predictive efficiency with the highest AUC of 0.963. Conclusion:Our prognostic model based on the immune signature could effectively predict the prognosis of HNSCC patients, providing novel predictive biomarkers and potential therapeutic targets for HNSCC patients.

SUBMITTER: Wang Y

PROVIDER: S-EPMC7593737 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Novel Prognostic Model Based on Immune Signature for Head and Neck Squamous Cell Carcinoma.

Wang Yingying Y Xu Yu Y Hua Qingquan Q Jiang Yang Y Liu Peiqiang P Zhang Wei W Xiang Rong R

BioMed research international 20201019

<h4>Background</h4>Deciphering the immune characteristics within tumors and identifying the immune signals related to the prognostic factor are helpful for the treatment and management of tumor patients. However, systematic analysis of immune signatures in head and neck squamous cell carcinoma (HNSCC) remains largely unstudied.<h4>Methods</h4>A total of 718 immune-related genes were extracted from RNA sequencing data from 519 HNSCC patients in the TCGA database, and survival analysis with integr ...[more]

PMID: 33134377

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Project description:BackgroundHead and neck squamous cell carcinoma (HNSCC) is an extremely heterogeneous malignant cancer with poor prognosis. Pyroptosis is defined as a novel inflammation-dependent programmed cell death. However, the pyroptosis-associated gene expression in HNSCC and their relationship with prognosis are still indistinct.Material and methodsWe acquired the mRNA expression information of HNSCC patients from publicly available TCGA and GEO databases. We compared the tumor issues and adjacent normal tissues in terms of the gene expression for the purpose of identifying differentially expressed genes (DEGs). Based on these genes, we established a risk signature by the LASSO Cox regression in the TCGA cohort and validated the results in a GEO cohort. We also verified the levels of relevant mRNA expression in the model by RT-qPCR analysis. Eventually, functional enrichment approach was carried out to explore the potential mechanisms.ResultsOur team found a total of 18 differentially expressed genes (DEGs) between the HNSCC and healthy samples, and 4 DEGs displayed a remarkable association with the overall survival (OS) (P < 0.05). A 4-gene signature was constructed, presenting beneficial forecast power in both TCGA and GEO cohorts. Our team categorized patients into a group with high risk and another group with low risk as per the average risk value of the 4-gene feature. The individuals in the low risk group displayed a notably greater OS compared with the high risk one (P < 0.01). The Cox regression study demonstrated the independent forecast capability of the risk score. The receiver operating characteristic approach facilitated the verification of the forecast function of the gene signature. Posterior to verification, 4 genes were aberrantly expressed in the HNSCC and healthy samples. Functional study displayed that these groups presented diverse immunity conditions.ConclusionPyroptosis-associated genes are pivotal for the prognosis of HNSCC and can serve as potential therapeutic targets.

Project description:Simple Summary:Clinically, aberrant lipid metabolism is responsible for overweight and/or obesity. Overweight is considered as an independent factor of cancer risk in 2019. Therefore, lipid metabolic reprogramming is an emerging hallmark of malignancy. It is an urgent need to comprehensively understand the relationship among lipid metabolism and HNSCC and identify a valuable biomarker for predicting prognosis of HNSCC patients. Three new findings were found in this study. Firstly, we identified the lipid-related differentially expressed genes (DEGs) by using the GEO microarrays and TCGA dataset. A novel lipid-related mRNA prognostic signature (LRPS, consisting of ADCY2, LIPE and OLR1) was developed, which could predict the survival and prognosis of HNSCC patients as an independent effective prognostic factor. Secondly, we found that the LRPS could indicate the type of infiltrated immune cells in HNSCC tumor microenvironment. Thirdly, we verified that the LPPS score could interpret the TP53 status of HNSCC. Our new findings indicated that LRPS has a potential to be a promising indicator of overall survival, TP53 status, and immune characteristics in HNSCC, and perhaps can monitor and guide the treatment efficacy and prognosis of HNSCC in the future. Background:Head and neck squamous cell carcinoma (HNSCC) is characterized by a high frequency of lymph node metastasis and a high mortality. Lipid metabolic reprogramming is an emerging carcinogen as its role in fulfilling cancer growth and spread. However, little is known about the correlation between lipid metabolism and HNSCC. Materials and Methods:Expressions of lipid-related genes were obtained from the Cancer Genome Atlas (TCGA) and Gene expression Omnibus (GEO) databases for differential and functional analyses. A total number of 498 patients from TCGA with complete information were included to identify a lipid-related prognostic signature (LRPS), based on ADCY2, LIPE, and OLR1, by using univariate and multivariate Cox regression analyses. LRPS-high and LRPS-low groups were accordingly divided to pathway and cell enrichment analyses. Results:LRS-low patients had a better overall survival and relapse - free survival than LRS-high ones in HNSCC. The LRPS-high group was significantly related to perineural invasion of cancer, cancer-related pathways, high TP53 mutation rate, high proportion of natural killer T cells (NKT), dendritic cells, monocytes, Treg, and M1 and M2 macrophage infiltration in HNSCC tumor tissues. Conversely, the LRPS-low group correlated with DNA damage-related and T-cell-regulated pathways, low frequency of mutated TP53, and high infiltration of B cells and CD4+ effector cells including Th1 and Th2. Conclusion:LRPS has a potential to be a promising indicator of overall survival, prognosis, TP53 status, and immune characteristics in HNSCC.

Project description:The immune response within the tumor microenvironment plays a key role in tumorigenesis and determines the clinical outcomes of head and neck squamous cell carcinoma (HNSCC). However, to date, a paucity of robust, reliable immune-related biomarkers has been identified that are capable of estimating prognosis in HNSCC patients. High-throughput RNA sequencing was performed in tumors and matched adjacent tissues from five HNSCC patients, and the immune signatures expression of 730 immune-related transcripts selected from the nCounter PanCancer Immune Profiling Panel were assessed. Survival analyzes were performed in a training cohort, consisting of 416 HNSCC cases, retrieved from The Cancer Genome Atlas (TCGA) database. A prognostic signature was built, using elastic net-penalized Cox regression and backward, stepwise Cox regression analyzes. The outcomes were validated by an independent cohort of 115 HNSCC patients, using tissue microarrays and immunohistochemistry staining. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was also used to estimate the relative fractions of 22 immune-cell types and their correlations coefficients with prognostic biomarkers. Collectively, 248 immune-related genes were differentially expressed in paired tumors and normal tissues using RNA sequencing. After process screening in the training TCGA cohort, four immune-related genes (PVR, TNFRSF12A, IL21R, and SOCS1) were significantly associated with overall survival (OS). Integrating these genes with Path_N stage, a multiplex model was built and suggested better performance in determining 5 years OS (receiver operating characteristic (ROC) analysis, area under the curve (AUC)=0.709) than others. Further protein-based validation was conducted in 115 HNSCC patients. Similarly, high expression of PVR and TNFRSF12A were associated with poor OS (Kaplan-Meier p=0.017 and 0.0032), while high expression of IL21R and SOCS1 indicated favorable OS (Kaplan-Meier p<0.0001 and =0.0018). The integrated model with Path_N stage still demonstrated efficacy in OS evaluation (Kaplan-Meier p<0.0001, ROC AUC=0.893). Besides, the four prognostic genes were significantly correlated with activated CD8+ T cells, CD4+ T cells, follicular helper T cells and regulatory T cells, implying the possible involvement of these genes in the immunoregulation and development of HNSCC. The well-established model encompassing both immune-related biomarkers and clinicopathological factor might serve as a promising tool for the prognostic prediction of HNSCC.

Dataset Information

Novel Prognostic Model Based on Immune Signature for Head and Neck Squamous Cell Carcinoma.

Publications

Novel Prognostic Model Based on Immune Signature for Head and Neck Squamous Cell Carcinoma.

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