Suppr超能文献

开发和验证一个 6 基因标志物,用于预测 HPV 阴性局部晚期头颈部鳞癌患者术后放化疗后的局部区域控制情况。

Development and validation of a 6-gene signature for the prognosis of loco-regional control in patients with HPV-negative locally advanced HNSCC treated by postoperative radio(chemo)therapy.

机构信息

German Cancer Research Center (DKFZ), Heidelberg, Germany, and German Cancer Consortium (DKTK), Partner Site Dresden, Germany; OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Germany; Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany.

German Cancer Research Center (DKFZ), Heidelberg, Germany, and German Cancer Consortium (DKTK), Partner Site Dresden, Germany; OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany; National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany, German Cancer Research Center (DKFZ), Heidelberg, Germany, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, and Helmholtz Association/Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Germany.

出版信息

Radiother Oncol. 2022 Jun;171:91-100. doi: 10.1016/j.radonc.2022.04.006. Epub 2022 Apr 13.

Abstract

PURPOSE

The aim of this study was to develop and validate a novel gene signature from full-transcriptome data using machine-learning approaches to predict loco-regional control (LRC) of patients with human papilloma virus (HPV)-negative locally advanced head and neck squamous cell carcinoma (HNSCC), who received postoperative radio(chemo)therapy (PORT-C).

MATERIALS AND METHODS

Gene expression analysis was performed using Affymetrix GeneChip Human Transcriptome Array 2.0 on a multicentre retrospective training cohort of 128 patients and an independent validation cohort of 114 patients from the German Cancer Consortium - Radiation Oncology Group (DKTK-ROG). Genes were filtered based on differential gene expression analyses and Cox regression. The identified gene signature was combined with clinical parameters and with previously identified genes related to stem cells and hypoxia. Technical validation was performed using nanoString technology.

RESULTS

We identified a 6-gene signature consisting of four individual genes CAV1, GPX8, IGLV3-25, TGFBI, and one metagene combining the highly correlated genes INHBA and SERPINE1. This signature was prognostic for LRC on the training data (ci = 0.84) and in validation (ci = 0.63) with a significant patient stratification into two risk groups (p = 0.005). Combining the 6-gene signature with the clinical parameters T stage and tumour localisation as well as the cancer stem cell marker CD44 and the 15-gene hypoxia-associated signature improved the validation performance (ci = 0.69, p = 0.001).

CONCLUSION

We have developed and validated a novel prognostic 6-gene signature for LRC of HNSCC patients with HPV-negative tumours treated by PORT-C. After successful prospective validation the signature can be part of clinical trials on the individualization of radiotherapy.

摘要

目的

本研究旨在通过机器学习方法从全转录组数据中开发和验证一种新的基因签名,以预测接受术后放化疗(PORT-C)的 HPV 阴性局部晚期头颈部鳞状细胞癌(HNSCC)患者的局部区域控制(LRC)。

材料和方法

对来自德国癌症联合会-放射肿瘤学组(DKTK-ROG)的 128 例患者的多中心回顾性训练队列和 114 例独立验证队列进行 Affymetrix GeneChip Human Transcriptome Array 2.0 基因表达分析。基于差异基因表达分析和 Cox 回归对基因进行筛选。确定的基因签名与临床参数以及先前与干细胞和缺氧相关的基因相结合。使用 NanoString 技术进行技术验证。

结果

我们确定了一个由 6 个基因组成的基因签名,其中包括 4 个独立基因 CAV1、GPX8、IGLV3-25 和 TGFBI,以及一个将高度相关基因 INHBA 和 SERPINE1 结合在一起的元基因。该签名在训练数据(ci=0.84)和验证数据(ci=0.63)中对 LRC 具有预后价值,并且可以将患者分为两个风险组(p=0.005)。将 6 个基因签名与临床参数 T 分期和肿瘤定位以及癌症干细胞标记物 CD44 和 15 个基因缺氧相关签名相结合,可提高验证性能(ci=0.69,p=0.001)。

结论

我们已经开发并验证了一种新的 HPV 阴性肿瘤接受 PORT-C 治疗的 HNSCC 患者 LRC 的预后 6 个基因签名。在成功的前瞻性验证后,该签名可以成为放疗个体化临床试验的一部分。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验