Re-stratification of patients with copy-number low endometrial cancer by clinicopathological characteristics

Author:

Liwei Li,He Li,Yibo Dai,Luyang Zhao,Zhihui Shen,Nan Kang,Danhua Shen,Junzhu Wang,Zhiqi Wang,Jianliu Wang

Abstract

Abstract Objective To stratify patients with copy-number low (CNL) endometrial cancer (EC) by clinicopathological characteristics. Methods EC patients who underwent surgery between June 2018 and June 2022 at Peking University People’s Hospital were included and further classified according to TCGA molecular subtyping: POLE ultramutated, microsatellite instability high (MSI-H), CNL, and copy-number high (CNH). Clinicopathological characteristics and prognosis of CNL patients were retrospectively reviewed. The Cox proportional hazards regression model was applied to perform univariate and multivariate analysis, and independent risk factors were identified. Differentially expressed genes (DEGs) according to overall survival (OS) were screened based on the transcriptome of CNL cases from the TCGA program. Finally, a nomogram was established, with an accuracy analysis performed. Results (1) A total of 279 EC patients were included, of whom 168 (60.2%) were in the CNL group. A total of 21 patients had recurrence and 6 patients deceased, and no significant difference in recurrence-free survival (RFS) was exhibited among the four molecular subtypes (P = 0.104), but that in overall survival (OS) was statistically significant (P = 0.036). (2) CNL patients were divided into recurrence and non-recurrence groups, and significant differences (P < 0.05) were found between the two groups in terms of pathological subtype, FIGO stage, ER, PR, glycated hemoglobin (HbA1c), and high-density lipoprotein cholesterol (HDL-C). All the above factors were included in univariate and multivariate Cox regression models, among which pathological subtype, PR, and HDL-C were statistically different (P < 0.05), resulting in three independent risk factors for the prognosis of patients in the CNL group. (3) By comparing the transcriptome of tumor tissues between living and deceased CNL patients from the TCGA database, 903 (4.4%) DEGs were screened, with four lipid metabolism pathways significantly enriched. Finally, a nomogram was established, and internal cross-validation was performed, showing good discrimination accuracy with an AUC of 0.831 and a C-index of 0.748 (95% CI 0.444–1.052). (4) According to the established nomogram and the median total score (85.89), patients were divided into the high score group (n = 85) and low score group (n = 83), and the 8 patients with recurrence were all in the high score group. Survival analysis was performed between the two groups, and the difference in RFS was statistically significant (P = 0.010). Conclusion In the CNL group of EC patients, pathological subtype, PR, and HDL-C were independent prognostic risk factors, the nomogram established based upon which had a good predictive ability for the recurrence risk of patients with CNL EC.

Funder

National Key Technology Research and Developmental Program of China

The National Natural Science Foundation of China

Special Scientific Research Project for Health Development in the Capital

Capital’s Funds for Health Improvement and Research

Publisher

Springer Science and Business Media LLC

Subject

Oncology,Surgery

Reference33 articles.

1. Siegel RL, Miller KD, Wagle NS, et al. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17–48.

2. Kandoth C, Schultz N, Cherniack AD, et al. Integrated genomic characterization of endometrial carcinoma. Nature. 2013;497(7447):67–73.

3. Zhao LY, Dai YB, Li LW, et al. Application and clinical significance of TCGA molecular classification in endometrial cancer. Zhonghua Fu Chan Ke Za Zhi. 2021;56(10):697–704.

4. Zong LJ, Xiang Y, Yu SN, et al. Expression and significance of immune checkpoint B7-homolog 4 in endometrial cancer. Zhonghua Fu Chan Ke Za Zhi. 2022;57(12):921–31.

5. Pecorelli S. Revised FIGO staging for carcinoma of the vulva, cervix, and endometrium. Int J Gynaecol Obstet. 2009;105(2):103–4.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3