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中华结直肠疾病电子杂志

中华结直肠疾病电子杂志 ›› 2021, Vol. 10 ›› Issue (06) : 585 -590. doi: 10.3877/cma.j.issn.2095-3224.2021.06.004

论著

基于免疫基因的RNA-seq数据构建结直肠癌预后生存预测模型
李伟华1,†(), 赵鹏宇2, 黎鸿坚1, 刘林江1, 李运洁3, 张芳3, 邹海军4, 王玉里1   
  1. 1. 518037　深圳大学第一附属医院，深圳市第二人民医院医学影像科
    2. 150081　哈尔滨医科大学生物信息科学与技术学院
    3. 518037　深圳大学第一附属医院，深圳市第二人民医院核医学科
    4. 518037　深圳大学第一附属医院，深圳市第二人民医院药学部
  • 收稿日期:2021-11-20 出版日期:2021-12-25
  • 通信作者: 李伟华
  • 基金资助:
    深圳市科技创新委员会(JCYJ20200109120205924)

Construction of colorectal cancer prognostic survival prediction model based on RNA-seq data of immune genes

Weihua Li1,†(), Pengyu Zhao2, Hongjian Li1, Linjiang Liu1, Yunjie Li3, Fang Zhang3, Haijun Zou4, Yuli Wang1   

  1. 1. Medical Imaging Department, the First Affiliated Hospital of Shenzhen University, the Second People's Hospital of Shenzhen, Shenzhen 518037, China
    2. College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
    3. Nuclear Medicine Department, the First Affiliated Hospital of Shenzhen University, the Second People's Hospital of Shenzhen, Shenzhen 518037, China
    4. Pharmacy Department, the First Affiliated Hospital of Shenzhen University, the Second People's Hospital of Shenzhen, Shenzhen 518037, China
  • Received:2021-11-20 Published:2021-12-25
  • Corresponding author: Weihua Li
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引用本文：

李伟华, 赵鹏宇, 黎鸿坚, 刘林江, 李运洁, 张芳, 邹海军, 王玉里. 基于免疫基因的RNA-seq数据构建结直肠癌预后生存预测模型[J]. 中华结直肠疾病电子杂志, 2021, 10(06): 585-590.

Weihua Li, Pengyu Zhao, Hongjian Li, Linjiang Liu, Yunjie Li, Fang Zhang, Haijun Zou, Yuli Wang. Construction of colorectal cancer prognostic survival prediction model based on RNA-seq data of immune genes[J]. Chinese Journal of Colorectal Diseases(Electronic Edition), 2021, 10(06): 585-590.

目的

筛选结直肠癌预后相关免疫基因并构建预后预测模型。

方法

从ImmPort数据库中收集免疫相关基因。来自The Cancer Genome Atlas（TCGA）数据库的Colon adenocarcinoma（COAD）数据集用于识别预后基因特征并构建预后生存预测模型，该特征在来自Gene Expression Omnibus（GEO）数据集中得到验证。进行基因功能富集分析。

结果

差异表达分析共得到362个在结直肠癌患者中差异表达的免疫相关基因；单因素COX回归筛选得到32个预后相关免疫基因，功能富集结果显示其与T细胞介导的免疫，T细胞介导的细胞毒性的上调等正相关；多因素COX回归最终确定SCTR、XCL1、NGF、CD1B、EREG为结直肠癌关键预后风险免疫基因，通过5个基因构建的预后生存预测模型对结直肠癌患者的预后情况具有良好的预测率。

结论

免疫相关的五基因特征是 COAD 患者的可靠预后指标，可为个性化癌症管理提供见解。

关键词: 结直肠肿瘤, 免疫, 预测模型, 基因特征, 预后
Objective

To screen the immune genes related to the prognosis of colorectal cancer and construct a prognostic prediction model.

Methods

Collect immune-related genes from the ImmPort database. The Colon adenocarcinoma (COAD) dataset from The Cancer Genome Atlas (TCGA) database is used to identify prognostic gene features and construct a prognostic survival prediction model, which is validated in the Gene Expression Omnibus (GEO) dataset. Perform gene function enrichment analysis.

Results

A total of 362 immune-related genes that were differentially expressed in patients with colorectal cancer were obtained by differential expression analysis; Thirty-two prognostic-related immune genes were obtained from single-factor COX regression screening. The result of functional enrichment showed that they were related to T cell-mediated immunity. The up-regulation of cell-mediated cytotoxicity was positively correlated; multi-factor COX regression finally determines that SCTR, XCL1, NGF, CD1B, and EREG were the key prognostic risk immune genes for colorectal cancer. The prognostic survival prediction model constructed by 5 genes was used for colorectal cancer. The prognosis of cancer patients has a good predictive rate.

Conclusion

Five-gene characteristics related to immunity are reliable prognostic indicators for patients with COAD and can provide insights for personalized cancer management.

Key words: Colorectal neoplasms, Immunity, Prediction model, Gene signature, Prognosis
图1 TCGA中结肠癌免疫差异基因的鉴定。1A：结肠癌与正常样本之间差异表达mRNA的火山图；1B：识别COAD免疫差异基因的韦恩图
表1 TCGA总体生存率的单因素cox回归分析
基因 P值 HR 95%置信区间
下限 上限
TPM2 0.037 333 456 1.164 735 504 1.008 985 34 1.344 527 756
BMP5 0.015 470 686 0.902 244 216 0.830 153 608 0.980 595 18
MAPT 0.048 972 932 1.112 097 744 1.000 478 422 1.236 169 981
SCTR 0.022 720 018 1.153 509 722 1.020 144 019 1.304 310 621
PTH1R 0.001 069 271 1.257 110 43 1.096 076 917 1.441 802 676
NGFR 0.029 119 625 1.118 533 922 1.011 457 652 1.236 945 642
NRG1 0.009 517 37 0.862 798 72 0.771 726 622 0.964 618 311
XCL1 0.017 352 622 1.149 979 461 1.024 927 328 1.290289295
NGF 0.001 577 987 1.271 908 962 1.095 637 576 1.476 539 726
CD1A 0.008 754 914 0.852 017 921 0.755 872 108 0.960 393 339
CD1B 0.001 893 971 0.812 280 797 0.712 418 815 0.926 140 747
PLXNA3 0.011 929 677 1.310 237 479 1.061 376 704 1.617 448 588
IL13RA2 0.017 131 454 0.852 268 15 0.747 304 731 0.971 974 308
OXT 0.026 428 062 1.205 833 49 1.022 155 647 1.422 517 607
PGF 0.038 496 809 1.187 486 309 1.009 138 71 1.397 353 723
TNFRSF19 0.033 426 571 1.107 359 16 1.008 037 142 1.216 467 388
MC1R 0.000 401 155 1.354 699 001 1.145 071 141 1.602 703 375
LTB4R 0.006 024 796 1.257 246 435 1.067 750 88 1.480 372 088
HAMP 0.005 822 998 1.190 324 479 1.051 685 608 1.347 239 473
JAG2 0.038 627 245 1.205 457 908 1.009 821 429 1.438 995 772
LHB 0.003 690 326 1.287 330 466 1.085 538 156 1.526 634 25
NMB 0.037 105 084 1.294 390 61 1.015 548 265 1.649 795 591
VGF 0.019 353 758 1.134 601 004 1.020 658 378 1.261 263 775
GRP 0.028 857 835 1.117 979 089 1.011 571 675 1.235 579 519
INHBB 0.004 117 149 1.160 256 891 1.048 224 983 1.284 262 515
UCN 0.000 868 846 1.356 013 4 1.133 478 922 1.622 237 789
SLC11A1 0.032 705 805 1.146 338 409 1.011 299 872 1.299 408 596
OXTR 0.020 224 425 1.180 831 322 1.026 264 632 1.358 677 449
CXCL1 0.049 032 161 0.878 401 415 0.772 013 438 0.999 4502 78
TDGF1 0.037 335 37 0.917 432 088 0.845 946 482 0.994 958 491
EREG 0.035 366 082 0.924 650 707 0.859 580 693 0.994 646 503
STC2 0.032 651 23 1.130 638 218 1.010 188 758 1.265 449 421
图2 预后风险mRNA富集分析
图3 TCGA-COAD中5个基因特征模型的预后分析。3A：TCGA患者高低风险组OS的K-M曲线；3B：OS的时间相关ROC曲线
图4 TCGA-COAD中预后风险免疫mRNA的表达对比。P＜0.05表示为*；P＜0.01代表为**；P＜0.001表示为***；P＜0.0001表示为****
图5 GSE39582-COAD的验证。5A：GSE3952患者高低风险组OS的K-M曲线；5B：OS的时间相关ROC曲线
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