A tumor-infiltrating immune cells-related pseudogenes signature based on machine-learning predicts outcomes and immunotherapy responses in ovarian cancer

被引：2

作者：

Zhang, Yuyuan ^{[1
,2
,3
]}

Guo, Manman ^{[4
]}

Wang, Libo ^{[5
]}

Weng, Siyuan ^{[1
,2
,3
]}

Xu, Hui ^{[1
,2
,3
]}

Ren, Yuqing ^{[6
]}

Liu, Long ^{[5
]}

Guo, Chunguang ^{[7
]}

Cheng, Quan ^{[8
]}

Luo, Peng

Zhang, Jian ^{[9
]}

Han, Xinwei ^{[1
,2
,3
,10
]}

机构：

[1] Zhengzhou Univ, Affiliated Hosp 1, Dept Intervent Radiol, Zhengzhou 450052, Henan, Peoples R China

[2] Zhengzhou Univ, Intervent Inst, Zhengzhou 450052, Henan, Peoples R China

[3] Intervent Treatment & Clin Res Ctr Henan Prov, Zhengzhou 450052, Henan, Peoples R China

[4] Zhengzhou Univ, Affiliated Hosp 1, Reprod Med Ctr, Zhengzhou 450052, Henan, Peoples R China

[5] Zhengzhou Univ, Affiliated Hosp 1, Dept Hepatobiliary & Pancreat Surg, Zhengzhou 450052, Henan, Peoples R China

[6] Zhengzhou Univ, Affiliated Hosp 1, Dept Resp & Crit Care Med, Zhengzhou 450052, Henan, Peoples R China

[7] Zhengzhou Univ, Affiliated Hosp 1, Dept Endovasc Surg, Zhengzhou 450052, Henan, Peoples R China

[8] Cent South Univ, Xiangya Hosp, Dept Neurosurg, Changsha 410000, Peoples R China

[9] Southern Med Univ, Zhujiang Hosp, Dept Oncol, Guangzhou 510000, Peoples R China

[10] 1 Jianshe East Rd, Zhengzhou, Henan, Peoples R China

来源：

CELLULAR SIGNALLING | 2023年 / 111卷

关键词：

Ovarian cancer; Machine-learning; Pseudogene; Prognosis; Immunotherapy; PROMOTES;

D O I：

10.1016/j.cellsig.2023.110879

中图分类号：

Q2 [细胞生物学];

学科分类号：

071009 ; 090102 ;

摘要：

Previous researches have provided evidence for the significant involvement of pseudogenes in immune-related functions across different types of cancer. However, the mechanisms by which pseudogenes regulate immunity in ovarian cancer (OV) and their potential impact on clinical outcomes remain unclear. To address this gap in knowledge, our study utilized a novel computational framework to analyze a total of 491 samples from three public datasets. We employed a combination of 10 machine-learning algorithms to construct a signature known as the tumor-infiltrating immune cells-related pseudogenes signature (TIICPS). The TIICPS, consisting of 12 pseudogenes, demonstrated independent prognostic value for overall survival, surpassing conventional clinical traits, 62 published signatures, and TP53 and BRCA mutation status in three cohorts. Patients with low TIICPS exhibited heightened immune-related pathways, intricate genomic alterations, substantial immune infiltration, and greater potential for immunotherapy efficacy. Consequently, TIICPS holds promise as a predictive tool for prognosis and immunotherapy response in ovarian cancer.

引用

页数：15

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