Dysregulation of MAD2L1/CAMK2A/PTTG1 Gene Cluster Maintains the Stemness Characteristics of Uterine Corpus Endometrial Carcinoma

Abstract

Objective To study the stemness characteristics of uterine corpus endometrial carcinoma(UCEC)and its potential regulatory mechanism.Methods Transcriptome sequencing data of UCEC was obtained from The Cancer Genome Atlas.Gene expression profile was normalized by edgeR package in R3.5.1.A one-class logistic regression machine learning algorithm was employed to calculated the mRNA stemness index(mRNAsi)of each UCEC sample.Then,the prognostic significance of mRNAsi and candidate genes was evaluated by survminer and survival packages.The high-frequency sub-pathways mining approach(HiFreSP)was used to identify the prognosis-related sub-pathways enriched with differentially expressed genes(DEGs).Subsequently,a gene co-expression network was constructed using WGCNA package,and the key gene modules were analyzed.The clusterProfiler package was adopted to the function annotation of the modules highly correlated with mRNAsi.Finally,the Human Protein Atlas(HPA)was retrieved for immunohistochemical validation.Results The mRNAsi of UCEC samples was significantly higher than that of normal tissues(t=25.095,P<0.001),and the lower degree of differentiation corresponded to higher mRNAsi in tumor tissues.The mRNAsi of UCEC increased gradually with tumor staging.The prognostic analysis showed that high mRNAsi was correlated with short overall survival in patients with UCEC(χ2=6.864,P=0.0088).There were 570 DEGs between the high-and low-mRNAsi groups.By using the HiFreSP algorithm,we identified that the oocyte meiosis sub-pathway(Oocyte meiosis_1)and cell cycle sub-pathway(Cell cycle_3)had significant prognostic significance.These pathways contained 11 DEGs(MAD2L1,CAMK2A,PTTG1,PLK1,CCNE1,CCNE2,ESPL1,CDC20,CCNB1,CCNB2,and SMC1B),which were significantly associated with the prognosis of UCEC patients.Gene co-expression network showed that mRNAsi,as well as MAD2L1,CAMK2A,and PTTG1,was associated with three gene modules.The immunohistochemical analysis demonstrated that MAD2L1 and PTTG1 showed up-regulated expression while CAMK2A showed down-regulated expression in UCEC,which was consistent with the results of transcriptome sequencing.Conclusions On the basis of machine learning,this study characterizes the stemness characteristics of UCEC.We identify the key sub-pathways related to prognosis and demonstrate that MAD2L1,CAMK2A,PTTG1 are closely related to the stemness of UCEC,which provides insight into the regulatory mechanism of cancer stemness and reveals the potential therapeutic targets of UCEC.