Abstract
Long non-coding RNAs (lncRNAs) play key roles in tumors and function not only as important molecular markers for cancer prognosis, but also as molecular characteristics at the pan-cancer level. Because of the poor prognosis of pancreatic cancer, accurate assessment of prognosis is a key issue in the development of treatment plans for pancreatic cancer. Here we analyzed pancreatic cancer data from The Cancer Genome Atlas and The Genotype Tissue Expression database using Cox regression and lasso regression in analyses using a combination of the two databases as well as only The Cancer Genome Atlas database (Cancer Genome Atlas Research Network et al., 2013). A prognostic risk score model with significant correlation with pancreatic cancer survival was constructed, and two lncRNAs were investigated. Additional analysis of 33 cancers using the two lncRNAs showed that lncRNA TsPOAP1-AS1 was a prognostic marker of seven cancers, among which pancreatic cancer was the most significant, and lncRNA mi600hg was a prognostic marker of ovarian cancer and pancreatic cancer. LncRNA TsPOAP1-AS1 is associated with clinical stage and tumor mutation burden of some cancers as well as a strong degree of immune infiltration in many cancers, while a strong correlation between lncRNA mi600hg and microsatellite instability was observed in several cancers. The results of this study help further our understanding of the different functions of lncRNAs in cancer and may aid in the clinical application of lncRNAs as prognostic factors for cancer.
Highlights
Research on pancreatic cancer has made some progress, and several pancreatic cancerrelated genes have been discovered (He et al, 2014; Wolpin et al, 2014)
Zhou et al established a prognostic model for pancreatic cancer containing five long non-coding RNAs (lncRNAs) (RP11-159F24.5, RP11-744N12.2, RP11-388M20.1, RP11-356C4.5, and CTC-459F4.9) through analysis of The Cancer Genome Atlas (TCGA) data; this model provides the possibility for survival prediction of pancreatic cancer patients and selection of biological treatment targets (Zhou et al, 2019)
To examine potential lncRNAs related to the prognosis of pancreatic cancer patients, we used datasets from TCGA and GTEx databases as a combined dataset
Summary
Research on pancreatic cancer has made some progress, and several pancreatic cancerrelated genes have been discovered (He et al, 2014; Wolpin et al, 2014). Zhou et al established a prognostic model for pancreatic cancer containing five lncRNAs (RP11-159F24.5, RP11-744N12.2, RP11-388M20.1, RP11-356C4.5, and CTC-459F4.9) through analysis of The Cancer Genome Atlas (TCGA) data; this model provides the possibility for survival prediction of pancreatic cancer patients and selection of biological treatment targets (Zhou et al, 2019). The authors constructed a pancreatic cancer prognostic risk scoring model that can be used as a potential target for pancreatic cancer immunotherapy (Wei et al, 2019) These studies have several shortcomings, including limitations regarding data samples, algorithms and efficacy evaluations. The studies did not apply their models to pan-cancer research or explore the function of the lncRNAs
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