Construction of prognostic model for endometrial carcinoma based on bioinformatics
10.3969/j.issn.1673-9701.2024.03.011
- VernacularTitle:基于生物信息学的子宫内膜癌预后模型构建
- Author:
Peng LIN
1
,
2
,
3
,
4
;
Pei SUN
5
,
6
;
Shuxia XU
5
,
6
Author Information
1. Department of Pathology, Fujian Children&rsquo
2. s Hospital (Fujian Branch of Shanghai Children&rsquo
3. s Medical Center), College of Clinical Medicine for Obstetrics &
4. Gynecology and Pediatrics, Fujian Medical University, Fujian Fuzhou, 350000, China
5. Department of Pathology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics &
6. Gynecology and Pediatrics, Fujian Medical University, Fuzhou 350000, Fujian, China
- Publication Type:Journal Article
- Keywords:
Endometrial carcinoma;
Bioinformatics;
Prognosis;
Predictive model
- From:
China Modern Doctor
2024;62(3):47-53
- CountryChina
- Language:Chinese
-
Abstract:
Objective Differential genes related to prognosis of endometrial carcinoma(EC)were screened and prognostic models were constructed.Methods Gene Expression data of EC and normal control samples were downloaded from The Cancer Genome Atlas(TCGA)database and Gene Expression Omnibus(GEO)dataset GSE63678 to screen out common differential genes.LASSO regression analysis was used to screen out the genes with prognostic significance and construct prognostic characteristics.EC patients with complete information were obtained from the TCGA database and randomly divided into the training group and the validation group in a ratio of 1:1.In the training group,survival curves were constructed based on prognostic characteristics.Functional annotation and pathway enrichment analysis were conducted using gene ontology(GO)analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)analysis.Combined with prognostic features and clinical risk factors,a calibration curve and C-index were used to evaluate the performance of the histogram.Finally,use a verification group for validation.Results A total of 4800 and 257 differentially expressed genes were screened from TCGA and GEO databases respectively,of which 73 up-regulated genes and 52 down-regulated genes were co-expressed.6 prognostic genes(ORMDL2,BNC2,TTK,MAMLD1,KCTD7 and DCLK2)were screened out by LASSO regression analysis.The survival curve showed that the overall survival of patients in the high-risk group was significantly lower than that in the low-risk group(P<0.01).GO analysis and KEGG results exhibited that prognostic signature was associated with cell cycle.The nomogram showed powerful predictive ability in the training and validation groups.Conclusion We constructed a predictive model based on prognostic genes,which can accurately predict the prognosis of patients with EC and provide new theoretical support for clinical diagnosis and treatment.
