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

IF 1.5 4区医学 Q4 ONCOLOGY Translational cancer research Pub Date : 2024-11-30 Epub Date: 2024-11-21 DOI:10.21037/tcr-24-656

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

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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.

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多组学破译免疫微环境，揭示卵巢癌术后患者的治疗策略。

背景：卵巢癌（OC）是一种高度侵袭性且往往致命的疾病，通常直到已经转移才被发现。卵巢癌的经典治疗包括手术加化疗。然而，尽管手术有效，复发仍然是一个常见的现象。不幸的是，目前还没有理想的预测模型来预测卵巢癌术后患者的进展和药物敏感性。细胞死亡模式在肿瘤进展中起重要作用。因此，我们的目的是探讨它们作为卵巢癌术后预后和药物敏感性指标的潜力。方法：采用12种程序性细胞死亡（PCD）模式构建新的分类和预后模型。大量转录组、基因组学和临床信息收集自The Cancer Genome Atlas (TCGA) Program-OV、GSE9891、GSE26712、GSE49997和GSE63885。此外，从Gene Expression Omnibus （GEO）数据库中获取单细胞转录组数据GSE210347进行后续分析。结果：在这项研究中，一种新的PCD分类被用于对术后OC患者进行表型分析，结果显示，第1类患者对基于免疫的治疗以及趋化因子、白细胞介素、干扰素和检查点的高表达表现出更高的敏感性。同时，借助机器学习算法，利用8基因签名建立了程序性细胞死亡指数（PCDI）。高pcdi患者术后预后较差。此外，我们还发现低PCDI患者可能对免疫治疗敏感，而高PCDI患者可能对酪氨酸激酶抑制剂表现出更高的反应性。结论：本研究提供了一种新的PCD模型和nomogram，可有效预测OC患者的临床预后和术后药物敏感性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Translational cancer research ONCOLOGY-

CiteScore

2.10

自引率

0.00%

发文量

252

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.