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Development and validation of a novel immune-related prognostic model in lung squamous cell carcinoma.

ABSTRACT: Background: The immune system plays an important role in the development of lung squamous cell carcinoma (LUSC). Therefore, immune-related genes (IRGs) expression may be an important predictor of LUSC prognosis. However, a prognostic model based on IRGs that can systematically assess the prognosis of LUSC patients is still lacking. This study aimed to construct a LUSC immune-related prognostic model by using IRGs. Methods: Gene expression data about LUSC were obtained from The Cancer Genome Atlas (TCGA). Differential expression analysis and univariate Cox regression analysis were performed to identify prognostic differentially expressed IRGs. A prognostic model was constructed using the Lasso and multivariate Cox regression analyses. Then we validated the performance of the prognostic model in training and test cohorts. Furthermore, associations with clinical variables and immune infiltration were also analyzed. Results: 593 differentially expressed IRGs were identified, and 8 of them were related to prognosis. Then a transcription factor regulatory network was established. A prognostic model consisted of 4 immune-related genes was constructed by using Lasso and multivariate Cox regression analyses. The prognostic value of this model was successfully validated in training and test cohorts. Further analysis showed that the prognostic model could be used independently to predict the prognosis of LUSC patients. The relationships between the risk score and immune cell infiltration indicated that the model could reflect the status of the tumor immune microenvironment. Conclusions: We constructed a risk model using four PDIRGs that can accurately predict the prognosis of LUSC patients. The risk score generated by this model can be used as an independent prognostic indicator. Moreover, the model can predict the infiltration of immune cells in patients, which is conducive to the prediction of patient sensitivity to immunotherapy.

SUBMITTER: Liu Z

PROVIDER: S-EPMC7330657 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Development and validation of a novel immune-related prognostic model in lung squamous cell carcinoma.

Liu Zeyu Z Wan Yuxiang Y Qiu Yuqin Y Qi Xuewei X Yang Ming M Huang Jinchang J Zhang Qiaoli Q

International journal of medical sciences 20200601 10

<b>Background:</b> The immune system plays an important role in the development of lung squamous cell carcinoma (LUSC). Therefore, immune-related genes (IRGs) expression may be an important predictor of LUSC prognosis. However, a prognostic model based on IRGs that can systematically assess the prognosis of LUSC patients is still lacking. This study aimed to construct a LUSC immune-related prognostic model by using IRGs. <b>Methods:</b> Gene expression data about LUSC were obtained from The Canc ...[more]

PMID: 32624696

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Project description:BackgroundCancer stem cells (CSCs) refer to cells with self-renewal capability in tumors. CSCs play important roles in proliferation, metastasis, recurrence, and tumor heterogeneity. This study aimed to identify immune-related gene-prognostic models based on stemness index (mRNAsi) in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), respectively.MethodsX-tile software was used to determine the best cutoff value of survival data in LUAD and LUSC based on mRNAsi. Tumor purity and the scores of infiltrating stromal and immune cells in lung cancer tissues were predicted with ESTIMATE R package. Differentially expressed immune-related genes (DEIRGs) between higher- and lower-mRNAsi subtypes were used to construct prognostic models.ResultsmRNAsi was negatively associated with StromalScore, ImmuneScore, and ESTIMATEScore, and was positively associated with tumor purity. LUAD and LUSC samples were divided into higher- and lower-mRNAsi groups with X-title software. The distribution of immune cells was significantly different between higher- and lower-mRNAsi groups in LUAD and LUSC. DEIRGs between those two groups in LUAD and LUSC were enriched in multiple cancer- or immune-related pathways. The network between transcriptional factors (TFs) and DEIRGs revealed potential mechanisms of DEIRGs in LUAD and LUSC. The eight-gene-signature prognostic model (ANGPTL5, CD1B, CD1E, CNTFR, CTSG, EDN3, IL12B, and IL2)-based high- and low-risk groups were significantly related to overall survival (OS), tumor microenvironment (TME) immune cells, and clinical characteristics in LUAD. The five-gene-signature prognostic model (CCL1, KLRC3, KLRC4, CCL23, and KLRC1)-based high- and low-risk groups were significantly related to OS, TME immune cells, and clinical characteristics in LUSC. These two prognostic models were tested as good ones with principal components analysis (PCA) and univariate and multivariate analyses. Tumor T stage, pathological stage, or metastasis status were significantly correlated with DEIRGs contained in prognostic models of LUAD and LUSC.ConclusionCancer stemness was not only an important biological process in cancer progression but also might affect TME immune cell infiltration in LUAD and LUSC. The mRNAsi-related immune genes could be potential biomarkers of LUAD and LUSC. Evaluation of integrative characterization of multiple immune-related genes and pathways could help to understand the association between cancer stemness and tumor microenvironment in lung cancer.

Project description:BackgroundTP53 mutation is the most common mutation in hepatocellular carcinoma (HCC), and it affects the progression and prognosis of HCC. We investigated how TP53 mutation regulates the HCC immunophenotype and thus affects the prognosis of HCC.MethodsWe investigated TP53 mutation status and RNA expression in different populations and platforms and developed an immune prognostic model (IPM) based on immune-related genes that were differentially expressed between TP53WT and TP53MUT HCC samples. Then, the influence of the IPM on the immune microenvironment in HCC was comprehensively analysed.FindingsTP53 mutation resulted in the downregulation of the immune response in HCC. Thirty-seven of the 312 immune response-related genes were differentially expressed based on TP53 mutation status. An IPM was established and validated based on 865 patients with HCC to differentiate patients with a low or high risk of poor survival. A nomogram was also established for clinical application. Functional enrichment analysis showed that the humoral immune response and immune system diseases pathway represented the major function and pathway, respectively, related to the IPM genes. Moreover, we found that the patients in the high-risk group had higher fractions of T cells follicular helper, T cells regulatory (Tregs) and macrophages M0 and presented higher expression of CTLA-4, PD-1 and TIM-3 than the low-risk group.InterpretationTP53 mutation is strongly related to the immune microenvironment in HCC. Our IPM, which is sensitive to TP53 mutation status, may have important implications for identifying subgroups of HCC patients with low or high risk of unfavourable survival. FUND: This work was supported by the International Science and Technology Cooperation Projects (2016YFE0107100), the Capital Special Research Project for Health Development (2014-2-4012), the Beijing Natural Science Foundation (L172055 and 7192158), the National Ten Thousand Talent Program, the Fundamental Research Funds for the Central Universities (3332018032), and the CAMS Innovation Fund for Medical Science (CIFMS) (2017-I2M-4-003 and 2018-I2M-3-001).

Dataset Information

Development and validation of a novel immune-related prognostic model in lung squamous cell carcinoma.

Publications

Development and validation of a novel immune-related prognostic model in lung squamous cell carcinoma.

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