Multi-omics decipher the immune microenvironment and unveil therapeutic strategies for postoperative ovarian cancer patients

Zhibing Liu; Fei Wang; Weiwei Chen; Yujie Zhai; Jinbo Jian; Xiaole Wang; Yingjiang Xu; Jiajia An; Lei Han

doi:10.21037/tcr-24-656

Multi-omics decipher the immune microenvironment and unveil therapeutic strategies for postoperative ovarian cancer patients

Transl Cancer Res. 2024 Nov 30;13(11):6028-6044. doi: 10.21037/tcr-24-656. Epub 2024 Nov 21.

Authors

Zhibing Liu^#^{1

2}, Fei Wang^#³, Weiwei Chen^#³, Yujie Zhai¹, Jinbo Jian¹, Xiaole Wang¹, Yingjiang Xu⁴, Jiajia An⁵, Lei Han⁶

Affiliations

¹ Department of Oncology, Binzhou Medical University Hospital, Binzhou, China.
² Department of Oncology, Qilu Hospital of Shandong University, Jinan, China.
³ Medical Research Center, Binzhou Medical University Hospital, Binzhou, China.
⁴ Department of Interventional Vascular Surgery, Binzhou Medical University Hospital, Binzhou, China.
⁵ Department of Clinical Laboratory, Binzhou Medical University Hospital, Binzhou, China.
⁶ Department of Reproductive Medicine, Binzhou Medical University Hospital, Binzhou, China.

^# Contributed equally.

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.

Keywords: Programmed cell death (PCD); classification; drug sensitivity; microenvironment; ovarian carcinoma.