Zhibing Liu, Fei Wang, Weiwei Chen, Yujie Zhai, Jinbo Jian, Xiaole Wang, Yingjiang Xu, Jiajia An, Lei Han
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引用次数: 0
Abstract
Background: Ovarian cancer (OC) is a highly aggressive and often fatal disease that frequently goes undetected until it has already metastasized. The classic treatment for OC involves surgery followed by chemotherapy. However, despite the effectiveness of surgery, relapse is still a common occurrence. Unfortunately, there is currently no ideal predictive model for the progression and drug sensitivity of postoperative OC patients. Cell death patterns play an important role in tumor progression. So we aimed to investigate their potential to be used as indicators of postoperative OC prognosis and drug sensitivity.
Methods: A total of 12 programmed cell death (PCD) patterns were employed to construct novel classification and prognosis model. Bulk transcriptome, genomics, and clinical information were collected from The Cancer Genome Atlas (TCGA) Program-OV, GSE9891, GSE26712, GSE49997 and GSE63885. In addition, single-cell transcriptome data GSE210347 were procured from the Gene Expression Omnibus (GEO) database for subsequent analysis.
Results: In this study, a novel PCD classification has been employed to phenotype postoperative OC patients, revealing that patients in cluster 1 exhibited heightened sensitivity to immune-based therapies combined with high expression of chemokines, interleukins, interferons, and checkpoints. Meanwhile, a programmed cell death index (PCDI) was established using an 8-gene signature with the help of a machine learning algorithm. The patients with high-PCDI had a worse prognosis after surgery in OC. In addition, we also found that patients with low PCDI patients may exhibit sensitivity to immunotherapy, while those with high PCDI patients may display increased responsiveness to tyrosine kinase inhibitors.
Conclusions: This study provides a novel PCD model and nomogram that can effectively predict the clinical prognosis and drug sensitivity of OC patients post-surgery.
期刊介绍：
Translational Cancer Research (Transl Cancer Res TCR; Print ISSN: 2218-676X; Online ISSN 2219-6803; http://tcr.amegroups.com/) is an Open Access, peer-reviewed journal, indexed in Science Citation Index Expanded (SCIE). TCR publishes laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer; results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of cancer patients. The focus of TCR is original, peer-reviewed, science-based research that successfully advances clinical medicine toward the goal of improving patients'' quality of life. The editors and an international advisory group of scientists and clinician-scientists as well as other experts will hold TCR articles to the high-quality standards. We accept Original Articles as well as Review Articles, Editorials and Brief Articles.
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