Integrated analysis of ovarian cancer patients from prospective transcription factor activity reveals subtypes of prognostic significance
Dongqing Su,
Yuqiang Xiong,
Haodong Wei,
Shiyuan Wang,
Jiawei Ke,
Pengfei Liang,
Haoxin Zhang,
Yao Yu,
Yongchun Zuo,
Lei Yang
Affiliations
Dongqing Su
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
Yuqiang Xiong
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
Haodong Wei
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
Shiyuan Wang
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
Jiawei Ke
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
Pengfei Liang
The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
Haoxin Zhang
Department of Gastrointestinal Oncology, Harbin Medical University Cancer Hospital, Harbin 150081, China
Yao Yu
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
Yongchun Zuo
The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China; Digital College, Inner Mongolia Intelligent Union Big Data Academy, Inner Mongolia Wesure Date Technology Co., Ltd., Hohhot, 010010, China; Corresponding author. The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China.
Lei Yang
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China; Corresponding author. College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Transcription factors are protein molecules that act as regulators of gene expression. Aberrant protein activity of transcription factors can have a significant impact on tumor progression and metastasis in tumor patients. In this study, 868 immune-related transcription factors were identified from the transcription factor activity profile of 1823 ovarian cancer patients. The prognosis-related transcription factors were identified through univariate Cox analysis and random survival tree analysis, and two distinct clustering subtypes were subsequently derived based on these transcription factors. We assessed the clinical significance and genomics landscape of the two clustering subtypes and found statistically significant differences in prognosis, response to immunotherapy, and chemotherapy among ovarian cancer patients with different subtypes. Multi-scale Embedded Gene Co-expression Network Analysis was used to identify differential gene modules between the two clustering subtypes, which allowed us to conduct further analysis of biological pathways that exhibited significant differences between them. Finally, a ceRNA network was constructed to analyze lncRNA-miRNA-mRNA regulatory pairs with differential expression levels between two clustering subtypes. We expected that our study may provide some useful references for stratifying and treating patients with ovarian cancer.
