Immune-related gene signature for predicting the prognosis of head and neck squamous cell carcinoma

She, Yangyang; Kong, Xiangbo; Ge, Yaping; Yin, Ping; Li, Yong; Chen, Jieyu; Gao, Feng; Fang, Si-Lian

doi:10.1186/s12935-020-1104-7

Cited by 59 publications

(61 citation statements)

References 37 publications

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“…In the present study, we developed a prognostic signature based on 10 immune-related genes from TCGA datasets and validated them using GEO datasets. The patients in the high risk group were considered to have short survival times in both datasets, in accordance with previous studies [15]. ROC analysis indicated that our immune signature exhibited better sensitivity and specificity for survival prediction at 2-, 3-and 5-years, even exceeding the predictive ability of TNM stage.…”

Section: Genetic Alterations and Gsea Analysis In The High-risk Groupssupporting

confidence: 84%

“…Checkpoint blockade immunotherapeutics, such as nivolumab and pembrolizumab, are primarily used in the patients with recurrent or metastatic disease, but the observed objective response rates are in the range of only 16-25% [12,13]. Increasing evidence has also revealed that a prognostic signature containing several to dozens of genes have laid a foundation for predicting the survival of HNSCC [14][15][16]. To our knowledge, there is no immune-related prognostic signature which can not only predict survival, but may be associated with the immune checkpoint.…”

Section: Introductionmentioning

confidence: 99%

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A novel comprehensive immune-related gene signature as a promising survival predictor for head and neck squamous cell carcinoma

Fang

Chen

Liao

et al. 2020

Preprint

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Background: Head and neck squamous cell carcinoma (HNSCC), the most frequent subtype of head and neck cancer, continues to have a poor prognosis with no improvement. Growing evidence has demonstrated that the immune system plays a crucial role in the development and progression of HNSCC. The goal of our study was to develop an immune-related signature for accurately predicting the survival of HNSCC patients. Methods: Gene expression profiles were established from a total of 546 HNSCC and normal tissues to establish a training set and 83 HNSCC tissues for a validation set. Differentially expressed prognostic immune genes were identified by univariate Cox regression analysis and a corresponding network of differentially expressed transcription factors (TFs) were identified using Cytoscape. The immune-related gene signature was established and validated by univariate Cox regression analysis, least absolute shrinkage and selector operation (LASSO), and multivariate Cox regression analyses. In addition, the prognostic value of the immune-related signature was analyzed by survival and Cox regression analysis. Finally, the correlation between the immune-related signature and the immune microenvironment was established.Results: In this study, the TF-mediated network revealed that Foxp3 plays a central role in the regulatory mechanism of most immune genes. A prognostic signature based on 10 immune-related genes, which divided patients into high and low risk groups, was developed and successfully validated using two independent databases. Our prognostic signature was significantly related to worse survival and predicted prognosis in patients with different clinicopathological factors. A nomogram including clinical characteristics was also constructed for accurate prediction. Furthermore, it was determined that our prognostic signature may act as an independent factor for predicting the survival of HNSCC patients. ROC analysis also revealed that our signature had superior predictive value compared with TNM stage. As for the immune microenvironment, our signature showed a positive correlation with activated mast cells and M0 macrophages, a negative correlation with Tregs, and immune checkpoint molecules PD-1 and CLTA-4. Conclusions: Our study established an immune-related gene signature, which not only provides a promising biomarker for survival prediction, but may be evaluated as an indicator for personalized immunotherapy in patients with HNSCC.

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Section: Genetic Alterations and Gsea Analysis In The High-risk Groupssupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

A novel comprehensive immune-related gene signature as a promising survival predictor for head and neck squamous cell carcinoma

Fang

Chen

Liao

et al. 2020

Preprint

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“…Tumor-in ltrating immune cells played a key function in the genesis and progress of tumors [21]. Therefore, the potential association between risk score and level of immune in ltration in the whole TCGA set was analyzed.…”

Section: Clinical Utility Of the Prognostic Risk Model In The Whole Tmentioning

confidence: 99%

A novel RNA binding protein-associated prognostic model to predict overall survival in hepatocellular carcinoma patients

Liu

Qin

Hai

et al. 2020

Preprint

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Abstract Background Hepatocellular carcinoma (HCC) represents one of the deadliest malignancies worldwide. Despite significant advances in diagnosis and treatment, the mortality rate from HCC persists at a substantial level. This research strives to establish a prognostic model based on the RNA binding proteins (RBPs) that can predict HCC patients’ OS. Methods There was an RNA-seq data set derived from the Cancer Genome Atlas (TCGA) databank which was included in our research as well as a Microarray data set (GSE14520). The differentially expressed RBPs between HCC and normal tissues were investigated in TCGA dataset. Subsequently, the TCGA data set was randomly split into a training and a testing cohort. The prognostic model of the training cohort was developed by applying univariate Cox regression and lasso Cox regression analyses and multivariate Cox regression analysis. In order to evaluate the prognostic value of the model, a comprehensive survival assessment was conducted. Results A total of 133 differentially expressed RBPs were identified. Five RBPs (RPL10L, EZH2, PPARGC1A, ZNF239, IFIT1) were used to construct the model. The model accurately predicted the prognosis of liver cancer patients in both the TCGA cohort and the GSE14520 validation cohort. HCC patients could be assigned into a high-risk group and a low-risk group by this model, and the overall survival of these two groups was significantly different. Furthermore, the risk scores obtained by our model were highly correlated with immune cell infiltration. . Conclusions Five RBPs-related prognostic models were constructed and validated to predict OS reliably in HCC individuals. It helps to identify patients at high risk of mortality with the risk prediction score, which optimizes personalized therapeutic decision-making.

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“…By acting on the immune checkpoints, it has been used to treat a variety of cancers, including sarcoma [15]. More importantly, previous studies indicated that immune-related genes(IRGs) can serve as an effective prognostic biomarkers in many tumors, such as lung cancer [16][17][18][19], ovarian cancer [20,21], hepatocellular carcinoma [22,23], head and neck squamous cell carcinoma [24], papillary thyroid cancer [25], bladder urothelial carcinoma [26], and renal cancer [27]. Nonetheless, the prognostic signi cance of IRGs in sarcoma remains unclear but urgent.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of an immune gene-set based prognostic signature for soft tissue sarcoma

Shen

Meng

et al. 2020

Preprint

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Background Sarcomas is a group of heterogeneous malignant tumors originated from mesenchymal tissue and different types of sarcomas have disparate outcomes. The present study aims to identify the prognostic value of immune-related genes (IRGs) in sarcoma and establish a prognostic signature based on IRGs. Methods We collected the expression profile and clinical information of 255 soft tissue sarcoma samples from The Cancer Genome Atlas (TCGA) database and 2498 IRGs from the ImmPort database. The LASSO algorithm and Cox regression analysis were used to identify the best candidate genes and construct a signature. The prognostic ability of the signature was evaluated by ROC curves and Kaplan-Meier survival curves and validated in an independent cohort. Besides, a nomogram based on the IRGs and independent prognostic clinical variables was developed. Results A total of 19 IRGs were incorporated into the signature. In the training cohort, the AUC values of signature at 1-, 2-, and 3-years were 0.938, 0.937 and 0.935, respectively. The Kaplan-Meier survival curve indicated that high-risk patients were significantly worse prognosis(P < 0.001). In the validation cohort, the AUC values of signature at 1-, 2-, and 3-years were 0.730, 0.717 and 0.647, respectively. The Kaplan-Meier survival curve also showed significant distinct survival outcome between two risk groups. Furthermore, a nomogram based on the signature and four prognostic variables showed great accuracy in whole sarcoma patients and subgroup analyses. More importantly, the results of the TF regulatory network and immune infiltration analysis revealed the potential molecular mechanism of IRGs. Conclusions In general, we identified and validated an IRG-based signature, which can be used as an independent prognostic signature in evaluating the prognosis of sarcoma patients and provide potential novel immunotherapy targets.

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Immune-related gene signature for predicting the prognosis of head and neck squamous cell carcinoma

Cited by 59 publications

References 37 publications

A novel comprehensive immune-related gene signature as a promising survival predictor for head and neck squamous cell carcinoma

A novel comprehensive immune-related gene signature as a promising survival predictor for head and neck squamous cell carcinoma

A novel RNA binding protein-associated prognostic model to predict overall survival in hepatocellular carcinoma patients

Development and validation of an immune gene-set based prognostic signature for soft tissue sarcoma

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