Head and neck squamous cell carcinoma-specific prognostic signature and drug sensitive subtypes based on programmed cell death-related genes
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ABSTRACT:
SUBMITTER: Xu C
PROVIDER: S-EPMC10668860 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:Human papillomavirus (HPV) positive HNSCC patients generally have a favorable survival and promising responsiveness to radiotherapy, chemoradiotherapy and checkpoint blockades. However, HPV negative patients, the majority of HNSCC patients, have been largely overlooked. Cell death has been involved in the therapeutic resistance of cancers. We constructed a cell death index (CDI), based on autophagy, apoptosis and pyroptosis related genes, to predict the prognosis for HNSCC using TCGA dataset, and validated in a cohort from Qilu Hospital of Shandong University. We performed RNA sequencing of 28 paraffin-embedded tissue from HNSCC patients and determined HPV status by immunohistochemistry of p16. We found that CDI was an independent prognostic indicator for overall survival. Notably, the prognostic value of CDI was more profound in HPV negative HNSCC patients compared with those with HPV positivity.
2023-02-07 | GSE208576 | GEO
Project description:BackgroundHead and neck squamous cell carcinomas (HNSCC) are the most common type of head and neck cancer with an unimproved prognosis over the past decades. Although the role of cancer-associated-fibroblast (CAF) has been demonstrated in HNSCC, the correlation between CAF-derived gene expression and patient prognosis remains unknown.MethodsA total of 528 patients from TCGA database and 270 patients from GSE65858 database were contained in this study. After extracting 66 CAF-related gene expression data from TCGA database, consensus clustering was performed to identify different HNSCC subtypes. Limma package was used to distinguish the differentially expression genes (DEGs) between these subtypes, followed by Lasso regression analysis to construct a prognostic model. The model was validated by performing Kaplan-Meier survival, ROC and risk curve, univariate and multivariate COX regression analysis. GO, KEGG, GSEA, ESTIMATE and ssGSEA analyses was performed to explort the potential mechanism leading to different prognosis.ResultsBased on the 66 CAF-related gene expression pattern we stratitied HNSCC patients into two previously unreported subtypes with different clinical outcomes. A prognostic model composed of 15 DEGs was constructed and validated. In addition, bioinformatics analysis showed that the prognostic risk of HNSCC patients was also negatively correlated to immune infiltration, implying the role of tumor immune escape in HNSCC prognosis and treatment option.ConclusionsThe study develops a reliable prognostic prediction tool and provides a theoretical treatment guidance for HNSCC patients.
| S-EPMC10028128 | biostudies-literature
Project description:Head and neck squamous cell carcinoma (HNSCC) is a common malignant cancer that accounts for 5-10% of all cancers. This study aimed to identify essential genes associated with the prognosis of HNSCC and construct a powerful prognostic model for the risk assessment of HNSCC. RNAseq expression profile data for the patients with HNSCC were obtained from the TCGA database (GEO). A total of 500 samples with full clinical following-up were randomly divided into a training set and a validation set. The training set was used to screen for differentially expressed lncRNAs. Single-factor survival analysis was performed to obtain lncRNAs that associated with prognosis. A robust likelihood-based survival model was constructed to identify the lncRNAs that are essential for the prognosis of HNSCC. A co-expression network between genes and lncRNAs was also constructed to identify lncRNAs co-expressed with genes to serve as the final signature lncRNAs for prognosis. Finally, the prognostic effect of the signature lncRNAs was tested by multi-factor survival analysis and a scoring model for the prognosis of HNSCC was constructed. Moreover, the results of the validation set and the relative expression levels of the signature lncRNAs in the tumour and the adjacent tissue were consistent with the results of the training set. The 5 lncRNAs were distributed among 3 expression modules. Further KEGG pathway enrichment analysis showed that these 3 co-expressed modules participate in different pathways, and many of these pathways are associated with the development and progression of disease. Therefore, we proposed that the 5 validated lncRNAs can be used to predict the prognosis of HNSCC patients and can be applied in postoperative treatment and follow-up.
| S-EPMC6189101 | biostudies-literature
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.
| S-EPMC8249947 | biostudies-literature
Project description:BackgroundHead and neck squamous cell carcinoma (HNSCC) is one of the most common cancers, worldwide. Considering the role of human papilloma virus (HPV) in tumor development and sensitivity to treatment of HNSCC, we aimed to explore the prognostic classification ability of HPV-related signatures in head and neck cancer.MethodsHPV-related signatures were screened out based on Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases. HPV-related signatures with prognostic value were identified through univariate Cox regression analysis and a risk signature was established by least absolute shrinkage and selection operator (LASSO). Further, we developed a nomogram by integrating independent prognostic factors.ResultsA total of 55 HPV-associated signatures were differentially expressed and ten of them were associated with prognosis of HNSCC patients. The prognostic signature based on CDKN2A, CELSR3, DMRTA2, SERPINE1, TJP3, FADD, and IGF2BP2 expression was constructed. Univariate and multivariate regression analyses demonstrated that the novel prognostic signature was an independent prognostic factor of HNSCC. The nomogram integrating the prognostic signature and other independent prognostic factors was developed.ConclusionIn summary, the prognostic signature of the HPV-related signatures might serve as an important prognostic biomarker for patients with HNSCC.
| S-EPMC9681561 | biostudies-literature
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.
| S-EPMC9445128 | biostudies-literature
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.
| S-EPMC9761089 | biostudies-literature
Project description:BackgroundDeciphering 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.MethodsA 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.ResultsThe 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.ConclusionOur 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.
| S-EPMC7593737 | biostudies-literature
Project description:BackgroundHead and neck squamous cell carcinoma (HNSCC) is still a menace to public wellbeing globally. However, the underlying molecular events influencing the carcinogenesis and prognosis of HNSCC are poorly known.MethodsGene expression profiles of The Cancer Genome Atlas (TCGA) HNSCC dataset and GSE37991 were downloaded from the TCGA database and gene expression omnibus, respectively. The common differentially expressed metabolic enzymes (DEMEs) between HNSCC tissues and normal controls were screened out. Then a DEME-based molecular signature and a clinically practical nomogram model were constructed and validated.ResultsA total of 23 commonly upregulated and 9 commonly downregulated DEMEs were identified in TCGA HNSCC and GSE37991. Gene ontology analyses of the common DEMEs revealed that alpha-amino acid metabolic process, glycosyl compound metabolic process, and cellular amino acid metabolic process were enriched. Based on the TCGA HNSCC cohort, we have built up a robust DEME-based prognostic signature including HPRT1, PLOD2, ASNS, TXNRD1, CYP27B1, and FUT6 for predicting the clinical outcome of HNSCC. Furthermore, this prognosis signature was successfully validated in another independent cohort GSE65858. Moreover, a potent prognostic signature-based nomogram model was constructed to provide personalized therapeutic guidance for treating HNSCC. In vitro experiment revealed that the knockdown of TXNRD1 suppressed malignant activities of HNSCC cells.ConclusionOur study has successfully developed a robust DEME-based signature for predicting the prognosis of HNSCC. Moreover, the nomogram model might provide useful guidance for the precision treatment of HNSCC.
| S-EPMC8810637 | biostudies-literature
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.
| S-EPMC9576890 | biostudies-literature