Pairwise analysis of gene expression for oral squamous cell carcinoma via a large-scale transcriptome integration

IF 5.3
Nan Li, Zunkai Hu, Ning Zhang, Yining Liang, Yating Feng, Wanfu Ding, Lixin Cheng, Yuyan Zheng
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引用次数: 0

Abstract

Among all cancers occurring in the head and neck region, oral squamous cell carcinoma (OSCC) is the most common oral malignant tumours characterized by its aggressiveness and metastasis. The development of transcriptomics technology has greatly facilitated the diagnosis of various cancers. However, identifying genetic biomarkers is limited by data from a single batch of OSCC samples, and integrating analysis across different platforms remains a great challenge. In this study, we integrated five OSCC transcriptome datasets using an innovative strategy capable of mitigating batch effect, and extracting information from different datasets based on changes in the relative expression of gene pairs. By leveraging a machine learning method, we developed a prediction model including 27 differential gene pairs (DGPs) to discriminate OSCC from control samples, achieving an area under the receiver operating characteristic curve (AUC) of 0.8987 for the training set. Moreover, the model demonstrated commendable performance in four external validation sets, with AUCs of 0.9926, 0.9688, 0.8052 and 0.8565, respectively. Subsequently, a prognostic model was constructed based on six key gene pairs through univariate and multivariate Cox regression analysis. The AUCs of the model at 1-year and 3-year overall survival time prediction were 0.717 and 0.779 in an independent dataset. Our result demonstrates the effectiveness of this new method of integrating data and identifying DGPs. Using DGPs can significantly improve the performance of both diagnostic and prognostic models.

Abstract Image

通过大规模转录组整合对口腔鳞状细胞癌的基因表达进行配对分析。
在所有发生在头颈部的癌症中,口腔鳞状细胞癌(OSCC)是最常见的口腔恶性肿瘤,其特点是侵袭性和转移性。转录组学技术的发展极大地促进了各种癌症的诊断。然而,基因生物标志物的鉴定受到来自单批 OSCC 样本数据的限制,整合不同平台的分析仍是一项巨大的挑战。在本研究中,我们采用一种创新策略整合了五个OSCC转录组数据集,该策略能够减轻批次效应,并根据基因对相对表达量的变化从不同数据集中提取信息。利用机器学习方法,我们建立了一个包含 27 个差异基因对(DGPs)的预测模型,用于区分 OSCC 和对照样本,训练集的接收者操作特征曲线下面积(AUC)达到了 0.8987。此外,该模型在四个外部验证集上的表现也值得称赞,AUC 分别为 0.9926、0.9688、0.8052 和 0.8565。随后,通过单变量和多变量 Cox 回归分析,构建了基于六个关键基因对的预后模型。在一个独立的数据集中,该模型在预测1年和3年总生存时间时的AUC分别为0.717和0.779。我们的结果证明了这种整合数据和识别 DGPs 的新方法的有效性。使用 DGPs 可以大大提高诊断和预后模型的性能。
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来源期刊
CiteScore
11.50
自引率
0.00%
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0
期刊介绍: The Journal of Cellular and Molecular Medicine serves as a bridge between physiology and cellular medicine, as well as molecular biology and molecular therapeutics. With a 20-year history, the journal adopts an interdisciplinary approach to showcase innovative discoveries. It publishes research aimed at advancing the collective understanding of the cellular and molecular mechanisms underlying diseases. The journal emphasizes translational studies that translate this knowledge into therapeutic strategies. Being fully open access, the journal is accessible to all readers.
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