Cuprotosis Programmed-Cell-Death-Related lncRNA Signature Predicts Prognosis and Immune Landscape in PAAD Patients.
Hao ChiGaoge PengRui WangFengyi YangXixi XieJinhao ZhangKe XuTao GuXiaoli YangGang TianPublished in: Cells (2022)
In terms of mortality and survival, pancreatic cancer is one of the worst malignancies. Known as a unique type of programmed cell death, cuprotosis contributes to tumor cell growth, angiogenesis, and metastasis. Cuprotosis programmed-cell-death-related lncRNAs (CRLs) have been linked to PAAD, although their functions in the tumor microenvironment and prognosis are not well understood. This study included data from the TCGA-PAAD cohort. Random sampling of PAAD data was conducted, splitting the data into two groups for use as a training set and test set (7:3). We searched for differentially expressed genes that were substantially linked to prognosis using univariate Cox and Lasso regression analysis. Through the use of multivariate Cox proportional risk regression, a risk-rating system for prognosis was developed. Correlations between the CRL signature and clinicopathological characteristics, tumor microenvironment, immunotherapy response, and chemotherapy sensitivity were further evaluated. Lastly, qRT-PCR was used to compare CRL expression in healthy tissues to that in tumors. Some CRLs are thought to have strong correlations with PAAD outcomes. These CRLs include AC005332.6, LINC02041, LINC00857, and AL117382.1. The CRL-based signature construction exhibited outstanding predictive performance and offers a fresh approach to evaluating pre-immune effectiveness, paving the way for future studies in precision immuno-oncology.
Keyphrases
- long non coding rna
- electronic health record
- poor prognosis
- long noncoding rna
- big data
- data analysis
- cell proliferation
- end stage renal disease
- ejection fraction
- systematic review
- prognostic factors
- newly diagnosed
- gene expression
- cardiovascular events
- type diabetes
- machine learning
- genome wide
- squamous cell carcinoma
- locally advanced
- dna methylation
- metabolic syndrome
- endothelial cells
- current status
- radiation therapy
- artificial intelligence
- insulin resistance
- weight loss
- genome wide identification
- deep learning
- case control
- free survival
- network analysis
- real time pcr