Identification of NINJ1 as a novel prognostic predictor for retroperitoneal liposarcoma

Abstract

BackgroundRetroperitoneal liposarcoma (RPLS) is known for its propensity for local recurrence and short survival time. We aimed to identify a credible and specific prognostic biomarker for RPLS.MethodsCases from The Cancer Genome Atlas (TCGA) sarcoma dataset were included as the training group. Co-expression modules were constructed using weighted gene co-expression network analysis (WGCNA) to explore associations between modules and survival. Survival analysis of hub genes was performed using the Kaplan–Meier method. In addition, independent external validation was performed on a cohort of 135 Chinese RPLS patients from the REtroperitoneal SArcoma Registry (RESAR) study (NCT03838718).ResultsA total of 19 co-expression modules were constructed based on the expression levels of 26,497 RNAs in the TCGA cohort. Among these modules, the green module exhibited a positive correlation with overall survival (OS, p = 0.10) and disease-free survival (DFS, p = 0.06). Gene set enrichment analysis showed that the green module was associated with endocytosis and soft-tissue sarcomas. Survival analysis demonstrated that NINJ1, a hub gene within the green module, was positively associated with OS (p = 0.019) in the TCGA cohort. Moreover, in the validation cohort, patients with higher NINJ1 expression levels displayed a higher probability of survival for both OS (p = 0.023) and DFS (p = 0.012). Multivariable Cox analysis further confirmed the independent prognostic significance of NINJ1.ConclusionsWe here provide a foundation for the establishment of a consensus prognostic biomarker for RPLS, which should not only facilitate medical treatment but also guide the development of novel targeted drugs.