Identification of pyroptosis‐related lncRNAs for constructing a prognostic model and their correlation with immune infiltration in breast cancer
| Autor: | Chongru Zhao, Qi Zhang, Yuping Ren, Min Wu, Yiping Wu, Yichen Wang, Wenchang Lv, Yufang Tan |
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| Rok vydání: | 2021 |
| Předmět: |
Oncology
medicine.medical_specialty Breast Neoplasms medicine.disease_cause Models Biological Correlation breast cancer lncRNA Immune system Breast cancer risk model Internal medicine Databases Genetic Biomarkers Tumor Tumor Microenvironment medicine Humans KEGG Framingham Risk Score business.industry pyroptosis Gene Expression Profiling Pyroptosis Original Articles Cell Biology Prognosis medicine.disease Biomarker (cell) ROC Curve Molecular Medicine Original Article Female RNA Long Noncoding Disease Susceptibility Carcinogenesis business |
| Zdroj: | Journal of Cellular and Molecular Medicine |
| ISSN: | 1582-4934 1582-1838 |
| Popis: | The inflammasome‐dependent cell death, which is denoted as pyroptosis, might be abnormally regulated during oncogenesis and tumour progression. Long non‐coding RNAs (LncRNAs) are pivotal orchestrators in breast cancer (BC), which have the potential to be a biomarker for BC diagnosis and therapy. The present study aims to explore the correlation between pyroptosis‐related lncRNAs and BC prognosis. In this study, a profile of 8 differentially expressed lncRNAs was screened in the TCGA database and used to construct a prognostic model. The BC patients were divided into high‐ and low‐risk groups dependent on the median cutoff of the risk score in the model. Interestingly, the risk model significantly distinguished the clinical characteristics of BC patients between high‐ and low‐risk groups. Then, the risk score of the model was identified to be an excellent independent prognostic factor. Notably, the GO, KEGG, GSEA and ssGSEA analyses revealed the different immune statuses between the high‐ and low‐risk groups. Particularly, the 8 lncRNAs expressed differentially in BC tissues between two risk subgroups in vitro validation. Collectively, this constructed well‐validated model is of high effectiveness to predict the prognosis of BC, which will provide novel means that is applicable for BC prognosis recognition. |
| Databáze: | OpenAIRE |
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