Reclassification of endometrial cancer and identification of key genes
based on neural-related genes
Abstract
Objective: To investigate the expression and prognostic value of
neural-related genes (NRGs) in endometrial cancer (EC). Design:
Bioinformatics Analysis. Setting: Bioinformatics Database. Population or
sample: Sample with endometrial cancer. Methods: We classified
endometrial cancer cases into two subgroups based on NRGs expression and
evaluated the differences between the two subtypes. A prognostic
prediction model was established by LASSO-Cox analysis to screen for
prognosis-associated genes. Main outcome measures: overall survival
(OS), enriched pathways, correlation analysis of clinical features,
immune cell infiltration, immune response, tumor mutation burden (TMB).
Results: EC was classified into two subtypes based on the expression of
NRGs, and significant variations in clinical staging, pathological
grading, and immune regulation were found between the two subtypes. The
prognostic model revealed that increased NRGs expression was linked to a
poor prognosis in endometrial cancer patients. Conclusions: In
endometrial cancer, the genes CHRM2, GRIN1, L1CAM, and SEMA4F were found
to be strongly linked to clinical stage, immune infiltration, immune
response, and key signaling pathways. The genes CHRM2, GRIN1, L1CAM, and
SEMA4F may serve as potential biomarker for endometrial cancer
prognosis. Funding: This work was in part supported by Innovation and
Entrepreneurship Talent Project of Lanzhou (2020-RC-52).