Novel Prognostic Model Based on Immune Signature for Head and Neck Squamous Cell Carcinoma.
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ABSTRACT:
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]
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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, very limited robust and reliable immunological biomarkers have been developed that are capable of estimating prognosis in HNSCC patients. In this study, we aimed to identify the effects of novel immune-related gene signatures (IRGs) that can predict HNSCC prognosis. Based on gene expression profiles and clinical data of HNSCC patient cohorts from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, a total of 439 highly variable expressed immune-related genes (including 239 upregulated and 200 downregulated genes) were identified by using differential gene expression analysis. Pathway enrichment analysis indicated that these immune-related differentially expressed genes were enriched in inflammatory functions. After process screening in the training TCGA cohort, six immune-related genes (PLAU, STC2, TNFRSF4, PDGFA, DKK1, and CHGB) were significantly associated with overall survival (OS) based on the LASSO Cox regression model. Integrating these genes with clinicopathological features, a multivariable model was built and suggested better performance in determining patients' OS in the testing cohort, and the independent validation cohort. In conclusion, a well-established model encompassing both immune-related gene signatures and clinicopathological factors would serve as a promising tool for the prognostic prediction of HNSCC.
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Project description:Background: Head and neck squamous cell carcinoma (HNSCC) is a common malignancy of the mucosal epithelium of the oral cavity, pharynx, and larynx. Laryngeal squamous cell carcinoma (LSCC) and oral squamous cell carcinoma are common HNSCC subtypes. Patients with metastatic HNSCC have a poor prognosis. Therefore, identifying molecular markers for the development and progression of HNSCC is essential for improving early diagnosis and predicting patient outcomes. Methods: Gene expression RNA-Seq data and patient clinical traits were obtained from The Cancer Genome Atlas-Head and Neck Squamous Cell Carcinoma (TCGA-HNSC) and Gene Expression Omnibus databases. Differentially expressed gene (DEG) screening was performed using the TCGA-HNSC dataset. Intersection analysis between the DEGs and a list of core matrisome genes obtained from the Matrisome Project was used to identify differentially expressed matrisome genes. A prognostic model was established using univariate and multivariate Cox regression analyses, least absolute shrinkage, and selection operator (LASSO) regression analysis. Immune landscape analysis was performed based on the single-sample gene set enrichment analysis algorithm, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, prognostic value, receiver operating characteristic curve analysis, and gene mutation analyses. Immunohistochemical results regarding prognostic protein levels were obtained from the Human Protein Atlas. Single-gene RNA-sequencing data were obtained from GSE150321 and GSE172577 datasets. CCK-8 and Transwell assays were used to confirm cell proliferation and migration. Results: A total of 1,779 DEGs, including 939 upregulated and 840 downregulated genes, between tumor and normal samples were identified using the TCGA-HNSC microarray data. Intersection analysis revealed 52 differentially expressed matrisome-related genes. After performing univariate and multivariate Cox regression and LASSO analyses, a novel prognostic model based on six matrisome genes (FN1, LAMB4, LAMB3, DMP1, CHAD, and MMRN1) for HNSCC was established. This risk model can successfully predict HNSCC survival. The high-risk group had worse prognoses and higher enrichment of pathways related to cancer development than the low-risk group. Silencing LAMB4 in HNSCC cell lines promoted cell proliferation and migration. Conclusion: This study provides a novel prognostic model for HNSCC. Thus, FN1, LAMB4, LAMB3, DMP1, CHAD, and MMRN1 may be the promising biomarkers for clinical practice.
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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.
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Project description:PurposeThis study aimed to construct a prognostic signature consisting of immune-related RNA-binding proteins (RBPs) to predict the prognosis of patients with head and neck squamous cell carcinoma (HNSCC) effectively.MethodsThe transcriptome and clinical data of HNSCC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. First, we ascertained the immunological differences in HNSCC, through single-sample gene set enrichment analysis, stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE), and cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) deconvolution algorithm. Then we used univariate proportional hazards (Cox) regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis to screen immune-related RBPs and acquire the risk score of each sample. Subsequently, we further investigated the difference in prognosis, immune status, and tumor mutation burden in high- and low-risk groups. Finally, the efficacy of immunotherapy was measured by the tumor immune dysfunction and exclusion (TIDE) score.ResultsWe derived 15 immune-related RBPs, including FRMD4A, ASNS, RAB11FIP1, FAM120C, CFLAR, CTTN, PLEKHO1, SELENBP1, CHCHD2, NPM3, ATP2A3, CFDP1, IGF2BP2, NQO1, and DENND2D. There were significant differences in the prognoses of patients in the high- and low-risk groups in the training set (p < 0.001) and the validation set (p < 0.01). Furthermore, there were statistical differences between the high-risk group and low-risk group in immune cell infiltration and pathway and tumor mutation load (p < 0.001). In the end, we found that patients in the low-risk group were more sensitive to immunotherapy (p < 0.001), and then we screened 14 small-molecule chemotherapeutics with higher sensitivity to the high-risk group (p < 0.001).ConclusionThe study constructed a prognostic signature of HNSCC, which might guide clinical immunotherapy in the future.
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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.
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