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突变状态相关特征在子宫内膜癌进展和预后中的意义。

Significance of Mutational Status-Associated Signature in the Progression and Prognosis of Endometrial Carcinoma.

机构信息

Department of Ultrasound, Xiaoshan Traditional Chinese Medical Hospital, Zhouhang, China.

Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.

出版信息

Oxid Med Cell Longev. 2022 Jul 6;2022:1817339. doi: 10.1155/2022/1817339. eCollection 2022.

DOI:10.1155/2022/1817339
PMID:35847579
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9280614/
Abstract

BACKGROUND

TP53 mutations are associated with poor outcome for patients with endometrial carcinoma (EC). However, to date, there have been no studies focused on the construction of TP53 mutational status-associated signature in EC. In this study, we aim to conduct a TP53 mutation-associated prognostic gene signature for EC.

METHODS

Hence, we explored the mutational landscape of TP53 in patients with EC based on the simple nucleotide variation data downloaded from The Cancer Genome Atlas (TCGA) database. Differential expression analysis and least absolute shrinkage and selection operator (LASSO)-Cox analysis was used to establish TP53 mutation-associated prognostic gene signature. The overall survival rate between the high-risk and low-risk groups was compared by the Kaplan-Meier (K-M) method.

RESULTS

We found that the TP53 mutation was associated with poor outcome, older age, lower BMI, and higher grade and stage of EC in patients. A TP53 mutational status-associated signature was established based on transcriptome profiling data. Moreover, the patients in TCGA database were categorized into high- and low-risk groups. Kaplan-Meier (K-M) analysis indicated that the patients in the high-risk group have poor survival outcome. Furthermore, receiver operating characteristic (ROC) curves confirmed the robust prognostic prediction efficiency of the TP53 mutational status-associated signature. Finally, the prognostic ability was successfully verified in the other two datasets from cBioPortal database as well as in 60 clinical specimens. Univariate (hazard ratio (HR) = 1.041, 95%CI = 1.031-1.051, < 0.001) and multivariate (hazard ratio (HR) = 1.029, 95%CI = 1.018-1.040, < 0.001) Cox regression analyses indicated that the TP53 mutational status-associated signature could be used as an independent prognostic factor for EC patients.

CONCLUSION

In summary, our research constructed a powerful TP53 mutational status-associated signature that could be a potential novel prognostic biomarker and therapeutic target for EC.

摘要

背景

TP53 突变与子宫内膜癌(EC)患者的不良预后相关。然而,迄今为止,尚无研究专注于构建 EC 中 TP53 突变状态相关的特征。在这项研究中,我们旨在构建一个用于 EC 的 TP53 突变相关的预后基因特征。

方法

因此,我们基于从癌症基因组图谱(TCGA)数据库下载的简单核苷酸变异数据,探索了 EC 患者中 TP53 的突变情况。差异表达分析和最小绝对值收缩和选择算子(LASSO)-Cox 分析用于建立 TP53 突变相关的预后基因特征。通过 Kaplan-Meier(K-M)方法比较高低风险组之间的总生存率。

结果

我们发现,TP53 突变与患者的不良预后、年龄较大、BMI 较低、EC 分级和分期较高有关。基于转录组谱数据建立了一个 TP53 突变状态相关的特征。此外,TCGA 数据库中的患者被分为高风险组和低风险组。Kaplan-Meier(K-M)分析表明,高风险组的患者生存结局较差。此外,受试者工作特征(ROC)曲线证实了 TP53 突变状态相关特征的稳健预后预测效率。最后,该预后能力在 cBioPortal 数据库中的另外两个数据集以及 60 个临床标本中得到了成功验证。单因素(风险比(HR)=1.041,95%CI=1.031-1.051, < 0.001)和多因素(HR=1.029,95%CI=1.018-1.040, < 0.001)Cox 回归分析表明,TP53 突变状态相关特征可作为 EC 患者的独立预后因素。

结论

总之,我们的研究构建了一个强大的 TP53 突变状态相关的特征,它可能成为 EC 的一个有潜力的新的预后生物标志物和治疗靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e60a/9280614/d83026a15e73/OMCL2022-1817339.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e60a/9280614/9445810fc093/OMCL2022-1817339.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e60a/9280614/7edc99fd077f/OMCL2022-1817339.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e60a/9280614/daf2fe5ecc98/OMCL2022-1817339.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e60a/9280614/b619714703ed/OMCL2022-1817339.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e60a/9280614/f75d8401376d/OMCL2022-1817339.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e60a/9280614/b49080bc996f/OMCL2022-1817339.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e60a/9280614/d83026a15e73/OMCL2022-1817339.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e60a/9280614/9445810fc093/OMCL2022-1817339.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e60a/9280614/7edc99fd077f/OMCL2022-1817339.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e60a/9280614/daf2fe5ecc98/OMCL2022-1817339.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e60a/9280614/b619714703ed/OMCL2022-1817339.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e60a/9280614/f75d8401376d/OMCL2022-1817339.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e60a/9280614/b49080bc996f/OMCL2022-1817339.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e60a/9280614/d83026a15e73/OMCL2022-1817339.007.jpg

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