Dataset Information

Constructing a T-Cell Receptor-Related Gene Signature for Prognostic Stratification and Therapeutic Guidance in Head and Neck Squamous Cell Carcinoma.

ABSTRACT: The stratification of head and neck squamous cell carcinoma (HNSCC) patients based on prognostic differences is critical for therapeutic guidance. This study was designed to construct a predictive signature derived from T-cell receptor-related genes (TCRRGs) to forecast the clinical outcomes in HNSCC. We sourced gene expression profiles from The Cancer Genome Atlas (TCGA) HNSCC dataset, GSE41613, and GSE65858 datasets. Utilizing consensus clustering analysis, we identified two distinct HNSCC clusters according to TCRRG expression. A TCRRG-based signature was subsequently developed and validated across diverse independent HNSCC cohorts. Moreover, we established a nomogram model based on TCRRGs. We further explored differences in immune landscapes between high- and low-risk groups. The TCGA HNSCC dataset was stratified into two clusters, displaying marked variations in both overall survival (OS) and immune cell infiltration. Furthermore, we developed a robust prognostic signature based on TCRRG utilizing the TCGA HNSCC train cohort, and its prognostic efficacy was validated in the TCGA HNSCC test cohort, GSE41613, and GSE65858. Importantly, the high-risk group was characterized by a suppressive immune microenvironment, in contrast to the low-risk group. Our study successfully developed a robust TCRRG-based signature that accurately predicts clinical outcomes in HNSCC, offering valuable strategies for improved treatments.

SUBMITTER: Lu Y

PROVIDER: S-EPMC10705130 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Constructing a T-Cell Receptor-Related Gene Signature for Prognostic Stratification and Therapeutic Guidance in Head and Neck Squamous Cell Carcinoma.

Lu Ye Y Mai Zizhao Z Zheng Jiarong J Lin Pei P Lin Yunfan Y Cui Li L Zhao Xinyuan X

Cancers 20231121 23

<h4>Backgroud</h4>The stratification of head and neck squamous cell carcinoma (HNSCC) patients based on prognostic differences is critical for therapeutic guidance. This study was designed to construct a predictive signature derived from T-cell receptor-related genes (TCRRGs) to forecast the clinical outcomes in HNSCC.<h4>Methods</h4>We sourced gene expression profiles from The Cancer Genome Atlas (TCGA) HNSCC dataset, GSE41613, and GSE65858 datasets. Utilizing consensus clustering analysis, we ...[more]

PMID: 38067199

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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.

Project description:BackgroundGlucose metabolism (GM) plays a crucial role in cancer cell proliferation, tumor growth, and survival. However, the identification of glucose metabolism-related genes (GMRGs) for effective prediction of prognosis in head and neck squamous cell carcinoma (HNSC) is still lacking.MethodsWe conducted differential analysis between HNSC and Normal groups to identify differentially expressed genes (DEGs). Key module genes were obtained using weighted gene co-expression network analysis (WGCNA). Intersection analysis of DEGs, GMRGs, and key module genes identified GMRG-DEGs. Univariate and multivariate Cox regression analyses were performed to screen prognostic-associated genes. Independent prognostic analysis of clinical traits and risk scores was implemented using Cox regression. Gene set enrichment analysis (GSEA) was used to explore functional pathways and genes between high- and low-risk groups. Immune infiltration analysis compared immune cells between the two groups in HNSC samples. Drug prediction was performed using the Genomics of Drug Sensitivity in Cancer (GDSC) database. Quantitative real-time fluorescence PCR (qRT-PCR) validated the expression levels of prognosis-related genes in HNSC patients.ResultsWe identified 4973 DEGs between HNSC and Normal samples. Key gene modules, represented by black and brown module genes, were identified. Intersection analysis revealed 76 GMRG-DEGs. Five prognosis-related genes (MTHFD2, CDKN2A, TPM2, MPZ, and DNMT1) were identified. A nomogram incorporating age, lymph node status (N), and risk score was constructed for survival prediction in HNSC patients. Immune infiltration analysis showed significant differences in five immune cell types (Macrophages M0, memory B cells, Monocytes, Macrophages M2, and Dendritic resting cells) between the high- and low-risk groups. GDSC database analysis identified 53 drugs with remarkable differences between the groups, including A.443654 and AG.014699. DNMT1 and MTHFD2 were up-regulated, while MPZ was down-regulated in HNSC.ConclusionOur study highlights the significant association of five prognosis-related genes (MTHFD2, CDKN2A, TPM2, MPZ, and DNMT1) with HNSC. These findings provide further evidence of the crucial role of GMRGs in HNSC.

Project description:BackgroundHead and neck squamous cell carcinoma (HNSCC) is a life-threatening disease with poor prognosis. Pyroptosis has been recently disclosed as a programmed cell death triggered by invasive infection, involved in cancer development. However, the prognosis role of pyroptosis-related genes in HNSCC has not been discussed.MethodsThe RNA sequence data of pyroptosis-related genes were obtained from The Cancer Genome Atlas (TCGA) database. Cox regression and the least absolute shrinkage and selection operator (LASSO) analysis were performed to screen the HNSCC survival-related signature genes. We established a HNSCC risk model with the identified prognostic genes, then divided the HNSCC patients into low- and high-risk subgroups according to median risk score. Moreover, we utilized Gene Expression Omnibus (GEO) dataset to validate the risk model. Go and KEGG analyses were conducted to reveal the potential function of differential expression of genes that identified between low- and high-risk subgroups. ESTIMATE algorithm was performed to investigate the immune infiltration of tumors. Correlation between signature gene expression and drug-sensitivity was disclosed by Spearman's analysis.ResultsWe constructed a HNSCC risk model with identified seven pyroptosis-related genes (CASP1, GSDME, IL6, NLRP1, NLRP2, NLRP6, and NOD2) as prognostic signature genes. High-risk subgroup of HNSCC patients in TCGA cohort correlated with lower survival probability than patients from low-risk subgroup (p < .001), and the result is verified with GEO dataset. In addition, 161 genes were identified differentially expressed between the low- and high-risk subgroups in the TCGA cohort, mainly related to immune response. Higher PD-L1 expression level was found in the high-risk subgroup that indicated the possible employment of immune checkpoint inhibitors. IL6 was positively correlated with WZ3105 and MPS-1-IN-1 in the cancer therapeutics response portal database.ConclusionWe built and verified a risk model for HNSCC prognosis using seven pyroptosis-related signature genes, which could predict the overall survival of HNSCC patients and facilitate treatment.

Project description:Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous group of tumors that retain their poor prognosis despite recent advances in their standard of care. As the involvement of the immune system against HNSCC development is well-recognized, characterization of the immune signature and the complex interplay between HNSCC and the immune system could lead to the identification of novel therapeutic targets that are required now more than ever. In this study, we investigated RNA sequencing data of 530 HNSCC patients from The Cancer Genome Atlas (TCGA) for which the immune composition (CIBERSORT) was defined by the relative fractions of 10 immune-cell types and expression data of 45 immune checkpoint ligands were quantified. This initial investigation was followed by immunohistochemical (IHC) staining for a curated selection of immune cell types and checkpoint ligands markers in tissue samples of 50 advanced stage HNSCC patients. The outcome of both analyses was correlated with clinicopathological parameters and patient overall survival. Our results indicated that HNSCC tumors are in close contact with both cytotoxic and immunosuppressive immune cells. TCGA data showed prognostic relevance of dendritic cells, M2 macrophages and neutrophils, while IHC analysis associated T cells and natural killer cells with better/worse prognostic outcome. HNSCC tumors in our TCGA cohort showed differential RNA over- and underexpression of 28 immune inhibitory and activating checkpoint ligands compared to healthy tissue. Of these, CD73, CD276 and CD155 gene expression were negative prognostic factors, while CD40L, CEACAM1 and Gal-9 expression were associated with significantly better outcomes. Our IHC analyses confirmed the relevance of CD155 and CD276 protein expression, and in addition PD-L1 expression, as independent negative prognostic factors, while HLA-E overexpression was associated with better outcomes. Lastly, the co-presence of both (i) CD155 positive cells with intratumoral NK cells; and (ii) PD-L1 expression with regulatory T cell infiltration may hold prognostic value for these cohorts. Based on our data, we propose that CD155 and CD276 are promising novel targets for HNSCC, possibly in combination with the current standard of care or novel immunotherapies to come.

Project description:BackgroundHead and neck squamous cell carcinoma (HNSCC) are the most common cancers in the head and neck. Therapeutic response-related genes (TRRGs) are closely associated with carcinogenesis and prognosis in HNSCC. However, the clinical value and prognostic significance of TRRGs are still unclear. We aimed to construct a prognostic risk model to predict therapy response and prognosis in TRRGs-defined subgroups of HNSCC.MethodsThe multiomics data and clinical information of HNSCC patients were downloaded from The Cancer Genome Atlas (TCGA). The profile data GSE65858 and GSE67614 chip was downloaded from public functional genomics data Gene Expression Omnibus (GEO). Based on TCGA-HNSC database, patients were divided into a remission group and a non-remission group according to therapy response, and differentially expressed TRRGs between those two groups were screened. Using Cox regression analysis and Least absolute shrinkage and selection operator (LASSO) analysis, candidate TRRGs that can predict the prognosis of HNSCC were identified and used to construct a TRRGs-based signature and a prognostic nomogram.ResultA total of 1896 differentially expressed TRRGs were screened, including 1530 upregulated genes and 366 downregulated genes. Then, 206 differently expressed TRRGs that was significantly associated with the survival were chosen using univariate Cox regression analysis. Finally, a total of 20 candidate TRRGs genes were identified by LASSO analysis to establish a signature for risk prediction, and the risk score of each patient was calculated. Patients were divided into a high-risk group (Risk-H) and a low-risk group (Risk-L) based on the risk score. Results showed that the Risk-L patients had better overall survival (OS) than Risk-H patients. Receiver operating characteristic (ROC) curve analysis revealed great predictive performance for 1-, 3-, and 5-year OS in TCGA-HNSC and GEO databases. Moreover, for patients treated with post-operative radiotherapy, Risk-L patients had longer OS and lower recurrence than Risk-H patients. The nomogram involves risk score and other clinical factors had good performance in predicting survival probability.ConclusionsThe proposed risk prognostic signature and Nomogram based on TRRGs are novel promising tools for predicting therapy response and overall survival in HNSCC patients.

Dataset Information

Constructing a T-Cell Receptor-Related Gene Signature for Prognostic Stratification and Therapeutic Guidance in Head and Neck Squamous Cell Carcinoma.

Publications

Constructing a T-Cell Receptor-Related Gene Signature for Prognostic Stratification and Therapeutic Guidance in Head and Neck Squamous Cell Carcinoma.

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