ArticlePeerJ2023
Construction and validation of a PANoptosis-related lncRNA signature for predicting prognosis and targeted drug response in thyroid cancer.
Article in PeerJ, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Targeting the non-coding RNA-PANoptosis axis: a novel frontier in disease diagnosis and therapy.Apoptosis : an international journal on programmed cell death · 2026Review
- PANoptosis in cancer: molecular mechanisms and therapeutic potential.Cancer gene therapy · 2025Review
- PANoptosis in cancer: bridging molecular mechanisms to therapeutic innovations.Cellular & molecular immunology · 2025Review
- Dual roles of long non-coding RNAs in thyroid cancer: regulation of programmed cell death pathways.Medical oncology (Northwood, London, England) · 2025Review
- Identification of a PANoptosis-related long noncoding rna risk signature for prognosis and immunology in colon adenocarcinoma.BMC cancer · 2025Article
- Characterization of PANoptosis-related genes and the immune landscape in moyamoya disease.Scientific reports · 2024Article
- PANoptosis-related long non-coding RNA signature to predict the prognosis and immune landscapes of pancreatic adenocarcinoma.Biochemistry and biophysics reports · 2024Article
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Authors and funding
5 authors.
Funding
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Abstract
Thyroid cancer (TC) is the most prevalent malignancy of the endocrine system. PANoptosis, a newly discovered cell death pathway, is of interest in tumor research. However, the relationship between PANoptosis-related lncRNAs (PRlncRNAs) and TC remains unclear. The study aimed to develop a prognostic model based on PRlncRNAs in TC. Gene expression data of PANoptosis-associated genes and clinical information on TC from The Cancer Genome Atlas (TCGA) database were analyzed by Pearson correlation analysis, univariate/multivariate Cox analysis, and Lasso Cox regression analysis. A PRlncRNA signature was constructed and used to develop a nomogram to predict overall survival (OS). We further explored the correlation between the risk score and tumor immune microenvironment, immune checkpoints, and drug sensitivity. Moreover, we verified the expression and biological function of lncRNAs in TC cell lines. Finally, seven PRlncRNAs were used to construct a prognostic model for predicting the OS of TC patients. We found that the risk score was associated with the tumor microenvironment (TME) and the expression of critical immune checkpoints. In addition, we screened for drugs that high- or low-risk TC groups might be sensitive to. Quantitative real-time polymerase chain reaction (qRT-PCR) results showed differential expression of four PRlncRNAs (GAPLINC, IDI2-AS1, LINC02154, and RBPMS-AS1) between tumor and normal tissues. Besides, a GEO database (GSE33630) was used to verify the expression differences of PRLncRNAs in THCA tissues and normal tissues. Finally, RBPMS-AS1 was found to inhibit the proliferation and migration of TC cells. In conclusion, we developed a PANoptosis-related lncRNA prognostic risk model that offers a comprehensive understanding of TME status in patients with TC and establishes a foundation for the choice of sensitive medications and immunotherapy.
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