Dataset Information

Identification of prognostic immune genes in laryngeal cancer.

ABSTRACT:

Objective

In this study, we aimed to identify prognostic immune-related genes and establish a prognostic model for laryngeal cancer based on these genes.

Methods

Transcriptome profiles and clinical data of patients with laryngeal cancer were downloaded from The Cancer Genome Atlas database. Integrated bioinformatics analyses were performed to identify genes associated with prognosis.

Results

Thirty prognostic immune-related genes for laryngeal cancer were identified. We constructed a regulatory network of prognosis comprising transcription factors and immune-related genes. Multivariate Cox regression analyses identified 15 immune-related genes in the network that were used to establish the prognostic model. The model exhibited excellent prognostic prediction ability with a high area under the curve value (0.916). The calculated risk score based on expression of the 15 immune-related genes was shown to be an independent prognostic factor for laryngeal cancer.

Conclusion

We identified prognostic immune-related genes and established a prognostic model for laryngeal cancer, which might help identify novel predictive biomarkers and therapeutic targets of laryngeal cancer.

SUBMITTER: Xiao H

PROVIDER: S-EPMC7645519 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Publications

Identification of prognostic immune genes in laryngeal cancer.

Xiao Huan H Su Qi-Sheng QS Li Chao-Qian CQ

The Journal of international medical research 20201101 11

<h4>Objective</h4>In this study, we aimed to identify prognostic immune-related genes and establish a prognostic model for laryngeal cancer based on these genes.<h4>Methods</h4>Transcriptome profiles and clinical data of patients with laryngeal cancer were downloaded from The Cancer Genome Atlas database. Integrated bioinformatics analyses were performed to identify genes associated with prognosis.<h4>Results</h4>Thirty prognostic immune-related genes for laryngeal cancer were identified. We con ...[more]

PMID: 33147419

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Project description:Bladder cancer is one of the most common cancers worldwide. The immune response and immune cell infiltration play crucial roles in tumour progression. Immunotherapy has delivered breakthrough achievements in the past decade in bladder cancer. Differentially expressed genes and immune-related genes (DEIRGs) were identified by using the edgeR package. Gene ontology annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed for functional enrichment analysis of DEIRGs. Survival-associated IRGs were identified by univariate Cox regression analysis. A prognostic model was established by univariate COX regression analysis, and verified by a validation prognostic model based on the GEO database. Patients were divided into high-risk and low-risk groups based on the median risk score value for immune cell infiltration and clinicopathological analyses. A regulatory network of survival-associated IRGs and potential transcription factors was constructed to investigate the potential regulatory mechanisms of survival-associated IRGs. Nomogram and ROC curve to verify the accuracy of the model. Quantitative real-time PCR was performed to validate the expression of relevant key genes in the prognostic model. A total of 259 differentially expressed IRGs were identified in the present study. KEGG pathway analysis of IRGs showed that the "cytokine-cytokine receptor interaction" pathway was the most significantly enriched pathway. Thirteen survival-associated IRGs were selected to establish a prognostic index for bladder cancer. In both TCGA prognostic model and GEO validation model, patients with high riskscore had worse prognosis compared to low riskscore group. A high infiltration level of macrophages was observed in high-risk patients. OGN, ELN, ANXA6, ILK and TGFB3 were identified as hub survival-associated IRGs in the network. EBF1, WWTR1, GATA6, MYH11, and MEF2C were involved in the transcriptional regulation of these survival-associated hub IRGs. The present study identified several survival-associated IRGs of clinical significance and established a prognostic index for bladder cancer outcome evaluation for the first time.

Project description:BackgroundBreast cancer is one of the main malignant tumors that threaten the lives of women, which has received more and more clinical attention worldwide. There are increasing evidences showing that the immune micro-environment of breast cancer (BC) seriously affects the clinical outcome. This study aims to explore the role of tumor immune genes in the prognosis of BC patients and construct an immune-related genes prognostic index.MethodsThe list of 2498 immune genes was obtained from ImmPort database. In addition, gene expression data and clinical characteristics data of BC patients were also obtained from the TCGA database. The prognostic correlation of the differential genes was analyzed through Survival package. Cox regression analysis was performed to analyze the prognostic effect of immune genes. According to the regression coefficients of prognostic immune genes in regression analysis, an immune risk scores model was established. Gene set enrichment analysis (GSEA) was performed to probe the biological correlation of immune gene scores. P < 0.05 was considered to be statistically significant.ResultsIn total, 556 immune genes were differentially expressed between normal tissues and BC tissues (p < 0. 05). According to the univariate cox regression analysis, a total of 66 immune genes were statistically significant for survival risk, of which 30 were associated with overall survival (P < 0.05). Finally, a 15 immune genes risk scores model was established. All patients were divided into high- and low-groups. KM survival analysis revealed that high immune risk scores represented worse survival (p < 0.001). ROC curve indicated that the immune genes risk scores model had a good reliability in predicting prognosis (5-year OS, AUC = 0.752). The established risk model showed splendid AUC value in the validation dataset (3-year over survival (OS) AUC = 0.685, 5-year OS AUC = 0.717, P = 0.00048). Moreover, the immune risk signature was proved to be an independent prognostic factor for BC patients. Finally, it was found that 15 immune genes and risk scores had significant clinical correlations, and were involved in a variety of carcinogenic pathways.ConclusionIn conclusion, our study provides a new perspective for the expression of immune genes in BC. The constructed model has potential value for the prognostic prediction of BC patients and may provide some references for the clinical precision immunotherapy of patients.

Dataset Information

Identification of prognostic immune genes in laryngeal cancer.

Objective

Methods

Results

Conclusion

Publications

Identification of prognostic immune genes in laryngeal cancer.

Similar Datasets

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