A 25 Immune-Related Gene Pair Signature Predicts Overall Survival in Cervical Cancer

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Cancer Informatics Pub Date : 2022-01-01 DOI:10.1177/11769351221090921

Huaqiu Chen, Huanyu Xie, Pengyu Wang, S. Yan, Yuanyuan Zhang, Guangming Wang

引用次数: 1

Abstract

Mounting evidence suggests that the tumor microenvironment plays an important role in the occurrence and development of cancer, with immune system dysfunction being closely related to malignant cancers. We aimed to screen immune-related genes (IRGs) to generate an IRG pair (IRGP)-based prognostic signature for cervical cancer (CC). Datasets were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases and used as training and validation cohorts, respectively. Using the ImmPort database, IRGs in control and CC samples were compared, and differentially expressed genes were identified to construct an IRGP prognostic signature. Based on this analysis, 25 IRGPs were identified as important factors for the prognosis of CC. Univariate and multivariate Cox regression analyses further showed that the IRGP signature was an independent prognostic factor of overall survival. In summary, we successfully constructed an IRGP prognostic signature of CC, providing insights into immunotherapy for CC.

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25个免疫相关基因对标记可预测宫颈癌患者的总生存率

越来越多的证据表明，肿瘤微环境在癌症的发生和发展中起着重要作用，免疫系统功能障碍与恶性肿瘤密切相关。我们旨在筛选免疫相关基因（IRG），以产生基于IRG对（IRGP）的宫颈癌症（CC）预后标志。数据集来自癌症基因组图谱和基因表达综合数据库，分别用作训练和验证队列。使用ImmPort数据库，比较对照和CC样本中的IRG，并鉴定差异表达基因以构建IRGP预后标志。基于这一分析，25个IRGP被确定为CC预后的重要因素。单变量和多变量Cox回归分析进一步表明，IRGP特征是影响总生存率的独立预后因素。总之，我们成功构建了CC的IRGP预后标志，为CC的免疫治疗提供了见解。

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

Cancer Informatics Medicine-Oncology

CiteScore

3.00

自引率

5.00%

发文量

审稿时长

8 weeks

期刊介绍： The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.