Identification and validation of a prognostic signature and combination drug therapy for immunotherapy of head and neck squamous cell carcinoma.
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ABSTRACT:
SUBMITTER: Qiang W
PROVIDER: S-EPMC7921014 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Head and neck squamous cell carcinoma (HNSCC) is among the most destructive of tumors, leading to considerable morbidity and mortality. Abnormal immune microenvironment is closely associated with tumor progression. This study aimed to construct a robust immune prognostic model for HNSCC. The RNA-seq transcriptome data and clinical information of HNSCC were downloaded from The Cancer Genome Atlas (TCGA) database. The key pathways and transcriptional factors (TFs) that are correlated with significantly altered immune related genes were identified. A robust immune prognostic model was constructed and further validated using a discovery-validation cohort design. An immune prognostic signature-based nomogram model was also developed. We have identified 400 significantly changed immune related genes in HNSCC. In addition, functional analysis of the altered immune related genes revealed many biological functions and pathways that might affect the tumor immune microenvironment. FOXP3, SNAI2, and STAT1 were identified as the hub TFs for regulating immunological changes in HNSCC. Moreover, an immune related gene-based prognostic signature significantly associated with the overall survival (OS) of HNSCC was constructed in the discovery cohort, and successfully validated in the validation cohort. Finally, a nomogram model based on immune prognostic signature was built and exhibited good performance for predicting the OS of HNSCC. In conclusion, the immune prognostic model is robust for predicting the prognosis of HNSCC and may evolve as a promising tool for risk evaluation and therapeutic selection.
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Project description:Long non-coding RNAs (lncRNAs) are promising cancer prognostic markers. However, the clinical significance of lncRNA signatures in evaluating overall survival (OS) outcomes of head and neck squamous cell carcinoma (HNSCC) has not been explored. This study aimed to assess the significance of lncRNA in HNSCC and to develop a lncRNA signature related to OS in HNSCC. LncRNA expression matrices were retrieved from the Cancer Genome Atlas (TCGA) data. Least Absolute Shrinkage and Selection of the Operator (LASSO), univariate and multivariate Cox regression were used for establishing a prognostic model. In vitro experiments were carried out to demonstrate the biological role of lncRNA. A prognosis model based on 7 DElncRNAs was finally established.The patients were then divided into high-risk and low-risk groups. Relative to the low-risk group, overall survival times for patients in the high-risk group were significantly low (P=2.466e-07). Risk score remained an independent prognostic factor in univariate (HR=1.329, 95%CI=1.239-1.425, p < 0.001) and multivariate (HR=1.279, 95%CI=1.184-1.382, p < 0.001) Cox regression analyses. The area under the curve (AUC) of the signature was as high as 0.78. Expressions of FOXD2-AS1 in tumor tissues were elevated, and significantly correlated with OS (P=0.008). FOXD2-AS1 silencing then significantly reduced HNSCC cell proliferation, invasion, and migration. In conclusion, a lncRNA signature was established for HNSCC prognostic prediction and FOXD2-AS1 was identified as an HNSCC oncogene.
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Project description:Increasing evidence has demonstrated that changes in alternative splicing (AS) events are closely associated with the initiation and progression of cancer. However, the concrete role of AS in tumorigenesis of head and neck squamous cell carcinoma (HNSCC) is poorly known. In this study, we aimed to investigate the AS profile in HNSCC, and build up a robust AS-based prognostic signature for HNSCC. Our results revealed a total of 4068 overall survival (OS) associated AS events in the TCGA HNSCC cohort. The whole TCGA HNSCC cohort was randomly divided into discovery cohort and validation cohort. A prognostic signature including five AS events was developed with the discovery cohort based on the most significant OS-associated AS events. Then it was further successfully validated in the validation cohort. The AS-based risk signature was an independent prognostic indicator in both discovery cohort and validation cohort. This prognostic signature-based nomogram model showed excellent performance for predicting the OS of HNSCC. Splicing network analysis have identified the most correlated splicing factor-AS network in HNSCC. Collectively, we have constructed a robust AS-based prognostic signature which might contribute to improve the clinical outcome of HNSCC.
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Project description:Head and neck squamous cell carcinoma (HNSCC) is one of the greatest public challenges because of delayed diagnosis and poor prognosis. In this study, we established an autophagy-associated long non-coding (Lnc)RNA prognostic signature to assess the prognosis of HNSCC patients. The LncRNA expression profiles and clinical information of 499 HNSCC samples were available in The Cancer Genome Atlas. Autophagic LncRNAs were analyzed using Pearson correlation. A co-expression network showed the interactions between autophagic genes and LncRNAs. An autophagic LncRNAs prognostic signature, consisting of MYOSLID, AL139287.1, AC068580.1, AL022328.2, AC104083.1, AL160006.1, AC116914.2, LINC00958, and AL450992.2, was developed through uni- and multivariate Cox regressions. High- and low-risk groups were classified based on the median risk scores. The high-risk group had significantly worse overall survival according to Kaplan-Meier curve analysis. Multivariate Cox regression demonstrated that risk scores were a significant independent prognostic factor (hazard ratio = 1.739, 95% confidence interval: 1.460-2.072), with an area under the curve of 0.735. Principal component analysis distinguished two categories based on the nine-LncRNA prognostic signature. In conclusion, this novel autophagic LncRNA signature is an independent prognostic factor and may suggest novel therapeutic targets for HNSCC.
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Project description:Head and neck cancer (HNC) is the fifth most common cancer worldwide. In this study, we performed an integrative analysis of the discovery set and established an eight-gene signature for the prediction of prognosis in patients with head and neck squamous cell carcinoma (HNSCC). Univariate Cox analysis was used to identify prognosis-related genes (with P < 0.05) in the GSE41613, GSE65858, and TCGA-HNSC RNA-Seq datasets after data collection. We performed LASSO Cox regression analysis and identified eight genes (CBX3, GNA12, P4HA1, PLAU, PPL, RAB25, EPHX3, and HLF) with non-zero regression coefficients in TCGA-HNSC datasets. Survival analysis revealed that the overall survival (OS) of GSE41613 and GSE65858 datasets and the progression-free survival(DFS)of GSE27020 and GSE42743 datasets in the low-risk group exhibited better survival outcomes compared with the high-risk group. To verify that the eight-mRNA prognostic model was independent of other clinical features, KM survival analysis of the specific subtypes with different clinical characteristics was performed. Univariate and multivariate Cox regression analyses were used to identify three independent prognostic factors to construct a prognostic nomogram. Finally, the GSVA algorithm identified six pathways that were activated in the intersection of the TCGA-HNSC, GSE65858, and GSE41613 datasets, including early estrogen response, cholesterol homeostasis, oxidative phosphorylation, fatty acid metabolism, bile acid metabolism, and Kras signaling. However, the epithelial-mesenchymal transition pathway was inhibited at the intersection of the three datasets. In conclusion, the eight-gene prognostic signature proved to be a useful tool in the prognostic evaluation and facilitate personalized treatment of HNSCC patients.
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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.
| S-EPMC8249947 | 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.
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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:BackgroundRadiotherapy is an effective treatment for head and neck squamous cell carcinoma (HNSCC), however how to predict the prognosis is not clear.MethodsHere we collected 262 radiosensitivity-associated genes, screened and constructed a prognostic nine-gene risk model through univariate COX, lasso regression, stepwise regression and multivariate COX analysis for transcriptome and clinical information of HNSCC patients obtained from the cancer genome atlas (TCGA) and gene expression omnibus (GEO) databases.ResultsThe reliability and robustness of the risk model were verified by receiver operating characteristic (ROC) curves, risk maps, and Kaplan-Meier (KM) curves analysis. Differences in immune cell infiltration and immune-related pathway enrichment between high-risk and low-risk subgroups were determined by multiple immune infiltration analyses. Meanwhile, the mutation map and the responses to immunotherapy were also differentiated by the prognostic nine-gene signature associated with radiosensitivity. These nine genes expression in HNSCC was verified in the Human Protein Atlas (HPA) database. After that, these nine genes expression was verified to be related to radiation resistance through in-vitro cell experiments.ConclusionsAll results showed that the nine-gene signature associated with radiosensitivity is a potential prognostic indicator for HNSCC patients after radiotherapy and provides potential gene targets for enhancing the efficacy of radiotherapy.
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Project description:PURPOSE OF REVIEW:Discussion of current strategies targeting the immune system related to solid tumors with emphasis on head and neck squamous cell carcinoma (HNSCC).This review will outline the current challenges with immunotherapy and future goals for treatment using these agents. RECENT FINDINGS:Agents targeting immune checkpoint receptors (IR) such as program death 1 (PD1) have been used in the clinical realm for melanoma and non-small cell lung cancer (NSCLC), and the use of these agents for these malignancies has provided crucial information about how and why patients respond or not to inhibitory checkpoint receptor blockade therapy (ICR). The anti PD1 agent, nivolumab, was recently approved by the FDA as a standard of care regimen for patients with platinum refractory recurrent/metastatic (R/M) HNSCC. Molecular pathways leading to resistance are starting to be identified, and work is underway to understand the most optimal treatment regimen with incorporation of immunotherapy. ICR has renewed interest in the immunology of cancer, but resistance is not uncommon, and thus understanding of these mechanisms will allow the clinician to appropriately select patients that will benefit from this therapy.
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