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/

Zendy Plus

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

Zendy Tools

Zendy Open

RYR2  mutation is clinically frequent in non‐small cell lung cancer (NSCLC) with its function being elusive. We downloaded lung squamous cell carcinoma and lung adenocarcinoma samples from the TCGA database, split the samples into  RYR2  mutant group ( n  = 337) and  RYR2  wild group ( n  = 634), and established Kaplan‐Meier curves. The results showed that  RYR2  mutant group lived longer than the wild group ( p  = 0.027). Weighted gene co‐expression network analysis (WGCNA) of differentially expressed genes (DEGs) yielded prognosis‐related genes. Five mRNAs and 10 lncRNAs were selected to build survival prognostic models with other clinical features. The AUCs of 2 models are 0.622 and 0.565 for predicting survival at 3 years. Among these genes, the AUCs of  DKK1  and  GS1‐115G20.1  expression levels were 0.607 and 0.560, respectively, which predicted the 3‐year survival rate of NSCLC sufferers. GSEA identified an association of high  DKK1  expression with  TP53 ,  MTOR , and  VEGF  expression. Several target miRNAs interacting with  GS1‐115G20.1  were observed to show the relationship with the phenotype, treatment, and survival of NSCLC. NSCLC patients with  RYR2  mutation may obtain better prognosis by down‐regulating  DKK1  and up‐regulating  GS1‐115G20.1 .

iet systems biology

RYR2  mutation in non‐small cell lung cancer prolongs survival via down‐regulation of  DKK1  and up‐regulation of  GS1‐115G20.1 : A weighted gene Co‐expression network analysis and risk prognostic models