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| Prognostic analysis of endometrial cancer based on long non-coding RNA associated with ferroptosis |
| SONG Yuying1 CUI Lin2 SUN Lei3 HE Yufeng1 ZHU Huiying1 WANG Fei3 LI Yan3 |
1.The First School of Clinical Medicine, Henan University of Chinese Medicine, Henan Province, Zhengzhou 450008, China;
2.Central Laboratory and Cardiac Center, the First Affiliated Hospital of Henan University of Chinese Medicine, Henan Province, Zhengzhou 450000, China; 3.Physical Examination Center, the First Affiliated Hospital of Henan University of Chinese Medicine, Henan Province, Zhengzhou 450000, China
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Abstract Objective To construct a disease prognosis model of endometrial carcinoma (EC) by using long non-coding RNA (lncRNA) associated with ferroptosis, and to provide therapeutic targets for EC. Methods EC data was downloaded from TCGA and ferroptosis gene was downloaded from FerrDb database. Through R software, disease data and ferroptosis genes were intersected to obtain differentially expressed genes (DEGs), and GO and KEGG enrichment analysis of DEGs was performed. The lncRNA obtained according to Pearson correlation analysis. The model was constructed by Lasso-Cox, and the lncRNA used to construct the model was screened. The risk score was calculated, and the high and low risk groups were distinguished by the median value. Kaplan-Meier survival curve, receiver operating characteristics (ROC) and decision curve analysis (DCA) curves were used to evaluate the risk score of the model and clinical characteristics, and to predict the survival effect. TIMER, CIBERSORT, and other algorithms were used to analyze the immune cells and functions in high and low risk groups. R software was used to analyze the immune cell, function, checkpoint, and mRNA differential expression of m6A between the two groups of lncRNA. Results There were 248 ferroptosis genes, 1 616 lncRNA associated with ferroptosis and 80 DEGs. The results of GO analysis showed that DEGs was mainly involved in oxidative stress, regulating the basement membrane and organic anion transmembrane transporter activity. KEEG results showed that DEGs was mainly involved in ferroptosis and glutathione metabolic pathway, and so on. Univariate Cox regression analysis initially screened 45 lncRNA associated with ferroptosis. After Lasso-Cox optimization, 14 lncRNA were finally identified for the construction of prognosis models. Kaplan-Meier survival curve showed that the survival status of low risk group was better than that of high risk group (P＜0.01). ROC and DCA curves showed that risk score predicted survival better than traditional clinical characteristics; the AUC values of the first, second and third years were 0.696, 0.715, and 0.747, respectively. Univariate and multivariate Cox regression analysis results showed that model risk score was an independent influencing factor of predictor of survival in EC patients (HR [95%CI] = 1.105 [1.081-1.129]; HR [95%CI] = 1.108 [1.081-1.135], all P＜0.01). The heat map of immune cell distribution in high and low risk groups showed that there were differences in immune cell levels between the two groups (P＜0.05). The results of immune function showed that there were statistically significant differences in APC co-stimulation, CCR, and others between high and low risk groups (P＜0.05). The results of immune checkpoint showed that there were statistically significant differences in PDCD-1, CTLA-4, and others between high and low risk groups (P＜0.05). The results of m6A methylation showed that there statistically significant differences in YTHDF1, FTO and others between high and low risk groups (P＜0.05). Conclusion The 14 iron-death associated lncRNA may play an important role in tumor immunity in EC, providing potential targets for treatment of the disease.
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