English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

RNA methylation modification is a key process in epigenetics that regulates posttranscriptional gene expression. With advances in next-generation sequencing technology, 5-methylcytosine (m5C) modification has also been found in multiple RNAs. Long non-coding RNAs (lncRNAs) were proved to have a key role in cancer progression and closely related to the tumor immune microenvironment. Thus, based on the PDAC patients’ clinical information and genetic transcriptome data from the TCGA database, we performed a detailed bioinformatic analysis to establish a m5C-related lncRNA prognostic risk model for PDAC patients and discovered the relationship between the risk model and PDAC immune microenvironment. Pearson correlation coefficient analysis was applied to conduct a m5C regulatory gene and m5C-related lncRNA co-expression network. Expression of m5C-related lncRNAs screened by univariate regression analysis with prognostic value showed a significant difference between pancreatic cancer and normal tissues. The least absolute shrinkage and selection operator (LASSO) Cox regression method was applied to determine an 8-m5C-related lncRNA prognostic risk model. We used principal component analysis to indicate that the risk model could distinguish all the samples clearly. The clinical nomogram also accurately predicted 1-, 1.5-, 2-, and 3-year survival time among PDAC patients. Additionally, this risk model was validated in the entire group and sub-test groups using KM analysis and ROC analysis. Combined with the clinical characteristics, the risk score was found to be an independent factor for predicting the survival of PDAC patients. Furthermore, the association between the risk model and tumor immune microenvironment was evaluated  via  the ESTIMATE R package and CIBERSORT method. Consequently, the results indicated that immune cells were associated with m5C-related lncRNA risk model scores and had different distribution in the high- and low-risk groups. Based on all these analyses, the m5C-related lncRNA risk model could be a reliable prognostic tool and therapeutic target for PDAC patients.

Prognostic Risk Model and Tumor Immune Environment Modulation of m5C-Related LncRNAs in Pancreatic Ductal Adenocarcinoma