Dataset Information

A Novel Immune-Related Seventeen-Gene Signature for Predicting Early Stage Lung Squamous Cell Carcinoma Prognosis.

ABSTRACT: With the increasingly early stage lung squamous cell carcinoma (LUSC) being discovered, there is an urgent need for a comprehensive analysis of the prognostic characteristics of early stage LUSC. Here, we developed an immune-related gene signature for outcome prediction of early stage LUSC based on three independent cohorts. Differentially expressed genes (DEGs) were identified using CIBERSORT and ESTMATE algorithm. Then, a 17-immune-related gene (RPRM, APOH, SSX1, MSGN1, HPR, ISM2, FGA, LBP, HAS1, CSF2, RETN, CCL2, CCL21, MMP19, PTGIS, F13A1, C1QTNF1) signature was identified using univariate Cox regression, LASSO regression and stepwise multivariable Cox analysis based on the verified DEGs from 401 cases in The Cancer Genome Atlas (TCGA) database. Subsequently, a cohort of GSE74777 containing 107 cases downloaded from Gene Expression Omnibus (GEO) database and an independent data set consisting of 36 frozen tissues collected from National Cancer Center were used to validate the predictive value of the signature. Seventeen immune-related genes were identified from TCGA cohort, which were further used to establish a classification system to construct cases into high- and low-risk groups in terms of overall survival. This classifier was still an independent prognostic factor in multivariate analysis. In addition, another two independent cohorts and different clinical subgroups validated the significant predictive value of the signature. Further mechanism research found early stage LUSC patients with high risk had special immune cell infiltration characteristics and gene mutation profiles. In conclusion, we characterized the tumor microenvironment and established a highly predictive model for evaluating the prognosis of early stage LUSC, which may provide a lead for effective immunotherapeutic options tailored for each subtype.

SUBMITTER: Fan T

PROVIDER: S-EPMC8226174 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundEarly-stage lung squamous cell carcinoma (LUSC) progression is accompanied by changes in immune microenvironments and the expression of immune-related genes (IRGs). Identifying innate IRGs associated with prognosis may improve treatment and reveal new immunotherapeutic targets.MethodsGene expression profiles and clinical data of early-stage LUSC patients were obtained from the Gene Expression Omnibus and The Cancer Genome Atlas databases and IRGs from the InnateDB database. Univariate and multivariate Cox regression and LASSO regression analyses were performed to identify an innate IRG signature model prognostic in patients with early-stage LUSC. The predictive ability of this model was assessed by time-dependent receiver operator characteristic curve analysis, with the independence of the model-determined risk score assessed by univariate and multivariate Cox regression analyses. Overall survival (OS) in early-stage LUSC patients was assessed using a nomogram and decision curve analysis (DCA). Functional and biological pathways were determined by gene set enrichment analysis, and differences in biological functions and immune microenvironments between the high- and low-risk groups were assessed by ESTIMATE and the CIBERSORT algorithm.ResultsA signature involving six IRGs (SREBF2, GP2, BMX, NR1H4, DDX41, and GOPC) was prognostic of OS. Samples were divided into high- and low-risk groups based on median risk scores. OS was significantly shorter in the high-risk than in the low-risk group in the training (P < 0.001), GEO validation (P = 0.00021) and TCGA validation (P = 0.034) cohorts. Multivariate Cox regression analysis showed that risk score was an independent risk factor for OS, with the combination of risk score and T stage being optimally predictive of clinical benefit. GSEA, ESTIMATE, and the CIBERSORT algorithm showed that immune cell infiltration was higher and immune-related pathways were more strongly expressed in the low-risk group.ConclusionA signature that includes these six innate IRGs may predict prognosis in patients with early-stage LUSC.

Project description:Background:Immune-related genes (IRGs) were linked to the prognosis of head and neck squamous cell carcinoma (HNSCC). This study aimed to identify the effects of an immune-related gene signature (IRGS) that can predict the of HNSCC prognosis. Methods:The expression data of 770 HNSCC patients from the TCGA database and the GEO database were used. To explore a predictive model, the Cox proportional hazards model was applied. The Kaplan-Meier survival analysis, as well as univariate and multivariate analyses were performed to evaluate the independent predictive value of IRGS. To explore biological functions of IRGS, enrichment analyses and pathway annotation for differentially expressed genes (DEGs) in different immune groups were applied, as well as the immune infiltration. Results:A prognostic signature comprising 27 IRGs was generated. IRGS significantly stratified HNSCC patients into high and low immune risk groups in regard to overall survival in the training cohort (HR?=?3.69, 95% CI 2.73-4.98, P?<?0.001). Likewise, IRGS could be linked to the prognosis of HNSCC in patients of the validation cohort (HR?=?1.84, 95% CI 1.21-2.81, P?<?0.01). Even after adjusting for TNM stage, IRGS was maintained as an independent predictor in the multivariate analysis (HR?=?3.62, 95% CI 2.58-5.09, P?<?0.001), and in the validation cohort (HR?=?1.73, 95% CI 1.12-2.67, P?=?0.014). The IFN-? response, the IFN-? response, IL-2/STAT5 signaling, and IL-6/JAK/STAT3 signaling were all negatively correlated with the immune risk (P?<?0.01). Immune infiltration of the high-risk group was significantly lower than that of the low-risk group (P?<?0.01). Most notably, the infiltration of CD8 T cells, memory-activated CD4 T cells, and regulatory T cells was strongly upregulated in the low immune risk groups, while memory resting CD4 T cell infiltration was downregulated (P?<?0.01). Conclusion:Our analysis provides a comprehensive prognosis of the immune microenvironments and outcomes for different individuals. Further studies are needed to evaluate the clinical application of this signature.

Project description:The present study aimed to perform screening of a gene signature for the discrimination and prognostic prediction of stage I and II lung squamous carcinoma. A microarray meta?analysis was performed to identify differentially expressed genes (DEGs) between stage I and II lung squamous carcinoma samples in seven microarray datasets collected from the Gene Expression Omnibus database via the MetaQC and MetaDE package in R. The important DEGs were selected according to the betweenness centrality value of the protein?protein interaction (PPI) network. Support vector machine (SVM) analysis was performed to screen the feature genes for discrimination and prognosis. One independent dataset downloaded from The Cancer Genome Atlas was used to validate the reliability. Pathway enrichment analysis was also performed for the feature genes. A total of 924 DEGs were identified to construct a PPI network consisting of 392 nodes and 686 edges. The top 100 of the 392 nodes were selected as crucial genes to construct an SVM classifier, and a 16?gene signature (caveolin 1, eukaryotic translation elongation factor 1?, casein kinase 2?1, tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation ?, tyrosine 3?monooxygenase/tryptophan 5?monooxygenase activation ?, pleiotrophin, insulin receptor, insulin receptor substrate 1, 3?phosphoinositide?dependent protein kinase?1, specificity protein 1, COP9 signalosome subunit 6, N?myc downstream regulated gene 1, retinoid X receptor ?, heat shock protein 90? A1, karyopherin subunit ?1 and erythrocyte membrane protein band 4.1) with high discrimination accuracy was identified. This 16?gene signature had significant prognostic value, and patients with stage II lung squamous carcinoma exhibited shorter survival rates, compared with those with stage I disease. Seven DEGs of the 16-gene signature were significantly involved in the phosphoinositide 3?kinase?Akt signaling pathway. The 16?gene signature identified in the present study may be useful for stratifying the patients with stage I or II lung squamous carcinoma and predicting prognosis.

Dataset Information

A Novel Immune-Related Seventeen-Gene Signature for Predicting Early Stage Lung Squamous Cell Carcinoma Prognosis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets