A novel comprehensive immune-related gene signature as a promising survival predictor for the patients with head and neck squamous cell carcinoma.
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ABSTRACT:
SUBMITTER: Fang R
PROVIDER: S-EPMC8109104 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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:Infection with high-risk types of human papilloma virus (HPV) is currently the best-established prognostic marker for head and neck squamous cell carcinoma (HNSCC), one of the most common and lethal human malignancies worldwide. Clinical trials have been launched to address the concept of treatment de-escalation for HPV-positive HNSCC with the final aim to reduce treatment related toxicity and debilitating long-term impacts on the quality of life. However, HPV-related tumors are mainly restricted to oropharyngeal SCC (OPSCC) and there is an urgent need to establish reliable biomarkers for all patients at high risk for treatment failure. A patient cohort (n = 295) with mainly non-OPSCC (72.9%) and a low prevalence of HPV16-related tumors (8.8%) was analyzed by MassARRAY to determine a previously established prognostic methylation score (MS). Kaplan-Meier revealed a highly significant correlation between a high MS and a favorable survival for OPSCC (P = 0.0004) and for non-OPSCC (P<0.0001), which was confirmed for all HNSCC by multivariate Cox regression models (HR: 9.67, 95% CI [4.61-20.30], P<0.0001). Next, we established a minimal methylation signature score (MMSS), which consists of ten most informative of the originally 62 CpG units used for the MS. The prognostic value of the MMSS was confirmed by Kaplan-Meier analysis for all HNSCC (P<0.0001) and non-OPSCC (P = 0.0002), and was supported by multivariate Cox regression models for all HNSCC (HR: 2.15, 95% CI [1.36-3.41], P = 0.001). In summary, the MS and the MMSS exhibit an excellent performance as prognosticators for survival, which is not limited by the anatomical site, and both could be implemented in future clinical trials.
| S-EPMC4846111 | biostudies-literature
Project description:Myeloid cells are a major heterogeneous cell population in the tumor immune microenvironment (TIME). Imbalance of myeloid response remains a major obstacle to a favorable prognosis and successful immune therapy. Therefore, we aimed to construct a risk model to evaluate the myeloid contexture, which may facilitate the prediction of prognosis and immune infiltration in patients with head and neck squamous cell carcinoma (HNSCC). In our study, six myeloid signature genes (including CCL13, CCR7, CD276, IL1B, LYVE1 and VEGFC) analyzed from 52 differentially expressed myeloid signature genes were finally pooled to establish a prognostic risk model, termed as myeloid gene score (MGS) in a training cohort and validated in a test cohort and an independent external cohort. Furthermore, based on the MGS subgroups, we were able to effectively identify patients with a poor prognosis, aggressive clinical parameters, immune cell infiltration status and immunotherapy response. Thus, MGS may serve as an effective prognostic signature and predictive indicator for immunotherapy response in patients with HNSCC.
| S-EPMC8116959 | biostudies-literature
Project description:Patients with head and neck squamous cell carcinoma (HNSCC) demonstrate poor survival and significant treatment morbidity with standard therapy. The immune profile in HNSCC, whether caused by carcinogen exposure or human papillomavirus (HPV), is notably immunosuppressive. Early clinical trials of immunotherapy in HNSCC were troubled by systemic toxicity or difficulties in local administration. Now, interest in immunotherapy has been revitalized by mechanistic insights into immune evasion by HNSCC, coupled to ongoing development of novel immunotherapies. This review will summarize immune escape mechanisms in HNSCC, namely downregulation of tumor antigen (TA) presentation, aberrant regulation of the signal transducer and activator of transcription (STAT) family, the immunosuppressive cytokine milieu, and dysregulation of immune effector cells. Therapeutic strategies hypothesized to specifically counter HNSCC immunosuppression will then be discussed. We will survey TA- targeted monoclonal antibodies (mAb), including the prototype cetuximab, as well as adjunctive strategies to enhance antibody-dependent cell-mediated cytotoxicity. We will review immunomodulation to restore STAT1/STAT3 activation balance. Examples of mAb therapy to block immunosuppressive cytokines, such as interleukin-6 or VEGF, will be provided. mAbs which release co-inhibitory T cell receptors such as CTLA-4 and PD-1, overexpressed in HNSCC, also hold therapeutic promise. Finally, we will describe principles for therapeutic vaccination in HPV-associated HNSCC, where non-host TAs such as viral oncoproteins represent ideal targets, and HPV-negative HNSCC, where p53 is a promising target. Insights into immunosuppression in HNSCC have elucidated mechanistic targets for immunotherapy. Rational clinical investigation may lead to effective stand alone or combinatorial treatment approaches.
| S-EPMC4861147 | 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:Background: Long non-coding RNA (lncRNA) plays a significant role in the development, establishment, and progression of head and neck squamous cell carcinoma (HNSCC). This article aims to develop an immune-related lncRNA (irlncRNA) model, regardless of expression levels, for risk assessment and prognosis prediction in HNSCC patients. Methods: We obtained clinical data and corresponding full transcriptome expression of HNSCC patients from TCGA, downloaded GTF files to distinguish lncRNAs from Ensembl, discerned irlncRNAs based on co-expression analysis, distinguished differentially expressed irlncRNAs (DEirlncRNAs), and paired these DEirlncRNAs. Univariate Cox regression analysis, LASSO regression analysis, and stepwise multivariate Cox regression analysis were then performed to screen lncRNA pairs, calculate the risk coefficient, and establish a prognosis model. Finally, the predictive power of this model was validated through the AUC and the ROC curves, and the AIC values of each point on the five-year ROC curve were calculated to select the maximum inflection point, which was applied as a cut-off point to divide patients into low- or high-risk groups. Based on this methodology, we were able to more effectively differentiate between these groups in terms of survival, clinico-pathological characteristics, tumor immune infiltrating status, chemotherapeutics sensitivity, and immunosuppressive molecules. Results: A 13-irlncRNA-pair signature was built, and the ROC analysis demonstrated high sensitivity and specificity of this signature for survival prediction. The Kaplan-Meier analysis indicated that the high-risk group had a significantly shorter survival rate than the low-risk group, and the chi-squared test certified that the signature was highly related to survival status, clinical stage, T stage, and N stage. Additionally, the signature was further proven to be an independent prognostic risk factor via the Cox regression analyses, and immune infiltrating analyses showed that the high-risk group had significant negative relationships with various immune infiltrations. Finally, the chemotherapeutics sensitivity and the expression level of molecular markers were also significantly different between high- and low-risk groups. Conclusion: The signature established by paring irlncRNAs, with regard to specific expression levels, can be utilized for survival prediction and to guide clinical therapy in HNSCC.
| S-EPMC8313825 | biostudies-literature
Project description:Background: Head and neck squamous cell carcinoma (HNSC) is a prevalent and heterogeneous malignancy with poor prognosis and high mortality rates. There is significant evidence of alternative splicing (AS) contributing to tumor development, suggesting its potential in predicting prognosis and therapeutic efficacy. This study aims to establish an AS-based prognostic signature in HNSC patients. Methods: The expression profiles and clinical information of 486 HNSC patients were downloaded from the TCGA database, and the AS data were downloaded from the TCGA SpliceSeq database. The survival-associated AS events were identified by conducting a Cox regression analysis and utilized to develop a prognostic signature by fitting into a LASSO-regularized Cox regression model. Survival analysis, univariate and multivariate Cox regression analysis, and receiver operating characteristic (ROC) curve analysis were performed to evaluate the signature and an independent cohort was used for validation. The immune cell function and infiltration were analyzed by CIBERSORT and the ssGSEA algorithm. Results: Univariate Cox regression analysis identified 2726 survival-associated AS events from 1714 genes. The correlation network reported DDX39B, PRPF39, and ARGLU1 as key splicing factors (SF) regulating these AS events. Eight survival-associated AS events were selected and validated by LASSO regression to develop a prognostic signature. It was confirmed that this signature could predict HNSC outcomes independent of other variables via multivariate Cox regression analysis. The risk score AUC was more than 0.75 for 3 years, highlighting the signature's prediction capability. Immune infiltration analysis reported different immune cell distributions between the two risk groups. The immune cell content was higher in the high-risk group than in the low-risk group. The correlation analysis revealed a significant correlation between risk score, immune cell subsets, and immune checkpoint expression. Conclusion: The prognostic signature developed from survival-associated AS events could predict the prognosis of HNSC patients and their clinical response to immunotherapy. However, this signature requires further research and validation in larger cohort studies.
| S-EPMC9633855 | biostudies-literature
Project description:Increasing evidence has shown that long non-coding RNAs (lncRNAs) have important biological functions and can be used as a prognostic biomarker in human cancers. However, investigation of the prognostic value of lncRNAs in head and neck squamous cell carcinoma (HNSCC) is in infancy. In the present study, we analyzed the lncRNA expression data in a large number of HNSCC patients (n=425) derived from The Cancer Genome Atlas (TCGA) to identify an lncRNA expression signature for improving the prognosis of HNSCC. Three lncRNAs are identified to be significantly associated with survival in the training dataset using Cox regression analysis. Three lncRNAs were integrated to construct an lncRNA expression signature that could stratify patients of training dataset into the high-risk group and low-risk group with significantly different survival time (median survival 1.85 years vs. 5.48 years; P=0.0018, log-rank test). The prognostic value of this three-lncRNA signature was confirmed in the testing and entire datasets, respectively. Further analysis revealed that the prognostic power of three-lncRNA signature was independent of clinical features by multivariate Cox regression and stratified analysis. These three lncRNAs were significantly associated with known genetic and epigenetic events by means of functional enrichment analysis. Therefore, our results indicated that the three-lncRNA expression signature can predict HNSCC patients' survival.
| S-EPMC6246764 | biostudies-literature
Project description:The prognosis of head and neck squamous cell carcinoma (HNSCC) patients remains poor. High-throughput sequencing data have laid a solid foundation for identifying genes related to cancer prognosis, but a gene marker is needed to predict clinical outcomes in HNSCC. In our study, we downloaded RNA Seq, single nucleotide polymorphism, copy number variation, and clinical follow-up data from TCGA. The samples were randomly divided into training and test. In the training set, we screened genes and used random forests for feature selection. Gene-related prognostic models were established and validated in a test set and GEO verification set. Six genes (PEX11A, NLRP2, SERPINE1, UPK, CTTN, D2HGDH) were ultimately obtained through random forest feature selection. Cox regression analysis confirmed the 6-gene signature is an independent prognostic factor in HNSCC patients. This signature effectively stratified samples in the training, test, and external verification sets (P < 0.01). The 5-year survival AUC in the training and verification sets was greater than 0.74. Thus, we have constructed a 6-gene signature as a new prognostic marker for predicting survival of HNSCC patients.
| S-EPMC6977678 | biostudies-literature
Project description:Background: Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous tumor with a high incidence and poor prognosis. Therefore, effective predictive models are needed to evaluate patient outcomes and optimize treatment. Methods: Robust Rank Aggregation (RRA) method was used to identify highly robust differentially-expressed genes (DEGs) between HNSCC and normal tissue in 9 GEO and TCGA datasets. Univariate Cox regression analysis and Lasso Cox regression analysis were performed to identify DEGs related to the Overall survival (OS) and to construct a prognostic gene signature (HNSCCSig). External validation was performed using GSE65858 dataset. Moreover, comprehensive bioinformatics analyses were used to identify the association between HNSCCSig and tumor immune environment. Results: A total of 257 reliable DEGs were identified by differentially analysis result of TCGA and GSE65858 datasets. The HNSCCSig including 7 mRNAs (SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3) were developed and validated to identify high-risk group who had a worse OS than low-risk group in TCGA and GSE65858 datasets. Cox regression analysis showed that the HNSCCSig could independently predict OS in both the TCGA and the GSE65858 datasets. Further research demonstrated that the infiltration bundance of CD8 + T cells, B cells, neutrophils, and NK cells were significantly lower in the high-risk group. A nomogram was also constructed by combining the HNSCCSig and clinical characters. Conclusion: We established and validated the HNSCCSig consisting of SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3. A nomogram combining HNSCCSig and some clinical parameters was constructed to identify high-risk HNSCC-patients with poor prognosis.
| S-EPMC8498039 | biostudies-literature