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中华结直肠疾病电子杂志 ›› 2020, Vol. 09 ›› Issue (03) : 245 -253. doi: 10.3877/cma.j.issn.2095-3224.2020.03.007

所属专题: 文献

论著

基于生物信息学筛选炎症相关结直肠癌基因及其特征分析
刘欣然1, 康悦2, 于守江3, 于永生1, 刘玉伟1, 王洪伟1,()   
  1. 1. 150000 哈尔滨医科大学附属第四医院松北普外科
    2. 150000 哈尔滨医科大学附属第二医院普通外科十病房
    3. 150000 哈尔滨医科大学附属第四医院普通外科四病房
  • 收稿日期:2019-11-11 出版日期:2020-06-25
  • 通信作者: 王洪伟

Screening and characterization of inflammation-related genes in colorectal cancer based on bioinformatics

Xinran Liu1, Yue Kang2, Shoujiang Yu3, Yongsheng Yu1, Yuwei Liu1, Hongwei Wang1,()   

  1. 1. Department of General Surgery, the Fourth Affiliated Hospital, Harbin Medical University, Harbin 150000, China
    2. the 10th Department of General Surgery, the Second Affiliated Hospital, Harbin 150000, China
    3. the 4th Department of General Surgery, the Fourth Affiliated Hospital, Harbin 150000, China
  • Received:2019-11-11 Published:2020-06-25
  • Corresponding author: Hongwei Wang
  • About author:
    Corresponding author: Wang Hongwei, Email:
引用本文:

刘欣然, 康悦, 于守江, 于永生, 刘玉伟, 王洪伟. 基于生物信息学筛选炎症相关结直肠癌基因及其特征分析[J]. 中华结直肠疾病电子杂志, 2020, 09(03): 245-253.

Xinran Liu, Yue Kang, Shoujiang Yu, Yongsheng Yu, Yuwei Liu, Hongwei Wang. Screening and characterization of inflammation-related genes in colorectal cancer based on bioinformatics[J]. Chinese Journal of Colorectal Diseases(Electronic Edition), 2020, 09(03): 245-253.

目的

通过对炎症性肠病差异基因的筛选以及结直肠癌队列生存特征和表达模式的探究,为炎症相关结直肠癌的发生与发展的后续研究提供候选基因。

方法

从GEO数据库中选择RNA测序表达谱数据集GSE95473和GSE107597,通过常规转录组表达谱差异分析,筛选出炎症性肠病差异表达基因(DEG),利用GO数据库获取DEG的功能注释,并利用KEGG数据库进行通路富集分析。同时基于TCGA数据库进一步在结直肠癌数据集中筛选具有预后意义的基因,并评价其在结直肠肿瘤中的表达特征。

结果

两个数据集筛选到了共有DEGs 100个,通过主成分分析证实这些基因能够对结直肠癌肿瘤和黏膜区分良好。进一步筛选,获得ALDOB,SPINK4,REG4,IL1B,C2CD4A,CXCL8,NOS2,CXCL3等候选基因。这些基因在结直肠肿瘤中高表达,并且这些基因的高表达往往提示患者预后较好。

结论

ALDOB,SPINK4,REG4,IL1B,C2CD4A,CXCL8,NOS2,CXCL3可能在炎症相关结直肠癌的发生发展中发挥重要作用,有待于后续炎癌转化相关功能验证和机制探究。

Objective

To explore the differential characteristics of inflammatory bowel disease and the survival characteristics and expression patterns of colorectal cancer cohort. Further, candidate genes are provided for subsequent studies on the development of inflammation-related colorectal cancer.

Methods

The RNA sequencing expression profile data sets GSE95473 and GSE107597 were selected from the GEO database. The inflammatory bowel disease expression differential gene (DEG) was screened by routine transcriptome expression profiling analysis. The GO database and the KEGG database were used to obtain functional annotations of the DEG and path enrichment analysis respectively. At the same time, based on the TCGA database, the prognostic genes were further screened in the colorectal cancer dataset, and their expression characteristics in colorectal tumors were evaluated.

Results

Two datasets were screened for 100 common DEGs, which were confirmed by principal component analysis to distinguish well between colorectal cancer tumors and mucosa. Further screening, candidate genes such as ALDOB, SPINK4, REG4, IL1B, C2CD4A, CXCL8, NOS2, CXCL3 were obtained. These genes are highly expressed in colorectal tumors, and high expression of these genes often suggests a better prognosis.

Conclusion

ALDOB, SPINK4, REG4, IL1B, C2CD4A, CXCL8, NOS2, CXCL3 may play an important role in the development of inflammation-related colorectal cancer, and it is necessary to verify the function and mechanism of subsequent inflammation transformation.

表1 来自GEO数据库的数据集的相关信息
表2 GSE95473差异表达基因(|logFoldChange|由大到小的顺序排名前30的基因)
序号 基因名称 logFC 平均表达量 t P 错误发生率
1 REG3A 7.12945015 5.35991907 8.98286166 1.29E-13 1.15E-10
2 SAA1 6.61037098 3.13313861 8.85880963 2.24E-13 1.86E-10
3 REG1A 6.50845391 3.46165362 8.71808649 4.19E-13 3.07E-10
4 DEFA5 6.04915717 3.45098582 8.98411099 1.29E-13 1.15E-10
5 SAA2 5.63967487 2.52282751 8.36990207 1.97E-12 9.79E-10
6 DUOXA2 5.60844504 3.78865364 10.1090722 8.95E-16 3.71E-12
7 REG1B 5.02387265 2.11303898 6.1411882 3.33E-08 2.09E-06
8 CLDN2 4.96197645 3.31048518 10.6399129 8.83E-17 5.78E-13
9 DUOX2 4.6360948 5.47400015 9.13550211 6.57E-14 9.08E-11
10 AQP8 -4.3346199 9.00765296 -6.4999099 7.24E-09 6.20E-07
11 PRSS2 4.32103832 0.46539972 7.04238494 6.91E-10 1.02E-07
12 MMP3 4.17229187 1.51818864 7.00124231 8.27E-10 1.15E-07
13 CXCL1 4.01976566 3.22821575 9.43206609 1.77E-14 4.39E-11
14 CHI3L1 3.99563705 1.5138968 8.58940446 7.43E-13 4.86E-10
15 LCN2 3.97927133 10.1165101 10.6280447 9.29E-17 5.78E-13
16 RTEL1-TNFRSF6B 3.86524064 7.82693999 9.1486058 6.20E-14 9.08E-11
17 PRSS22 3.81273137 2.27155773 9.03193455 1.04E-13 1.08E-10
18 CXCL6 3.75838981 2.05789779 8.05859903 7.84E-12 3.36E-09
19 CLDN8 -3.6395069 1.82221013 -7.2778418 2.46E-10 4.71E-08
20 CTB-50L17.14 3.54157744 2.32057953 5.64376507 2.65E-07 1.13E-05
21 S100A8 3.46245438 2.47242413 7.07698594 5.94E-10 9.12E-08
22 CXCL8 3.43166532 2.02228011 7.94884412 1.28E-11 4.67E-09
23 CXCR1 3.34649551 0.2073486 5.82048953 1.28E-07 6.43E-06
24 CXCL11 3.32026553 1.31840884 6.62443737 4.24E-09 4.05E-07
25 HMGCS2 -3.2687519 4.11977255 -7.0292313 7.32E-10 1.06E-07
26 TRIM40 3.23355971 4.23626365 7.79529848 2.52E-11 7.81E-09
27 SLC6A14 3.2068548 0.09136297 7.36713335 1.67E-10 3.57E-08
28 C2CD4A 3.20258293 2.87430461 9.92560621 2.00E-15 6.23E-12
29 S100A9 3.19861277 3.4516047 7.42556042 1.29E-10 3.07E-08
30 VNN1 3.18265305 2.88103846 8.04650274 8.27E-12 3.43E-09
表3 GSE107597差异表达基因(|logFoldChange|由大到小的顺序排名前30的基因)
序号 基因名称 logFC 平均表达量 t P 错误发生率
1 AQP8 8.13287631 5.50455958 12.2975316 1.4381E-16 2.2135E-14
2 ABCA12 -7.5234783 -0.2067304 -13.194841 1.0067E-17 3.9842E-15
3 MMP7 -7.0940435 0.56595938 -15.418348 2.1267E-20 1.1784E-16
4 DUOXA2 -6.9353318 3.96245364 -11.950725 4.1323E-16 4.6724E-14
5 SERPINB7 -6.9179921 -1.0384118 -8.2511274 8.1E-11 6.7526E-10
6 REG1A -6.6368582 3.89659826 -8.7646257 1.3621E-11 1.4818E-10
7 REG3A -6.4884436 2.2170506 -8.417081 4.5409E-11 4.1158E-10
8 DUOX2 -6.3785271 6.94202937 -13.288015 7.6825E-18 3.6241E-15
9 PITX2 6.34689026 -0.4895965 7.34931594 1.9461E-09 1.094E-08
10 REG1B -6.3435076 0.3794701 -7.2310842 2.9619E-09 1.6017E-08
11 DEFB4A -6.2538015 -2.4175337 -8.9587453 6.9869E-12 8.4101E-11
12 TNFRSF6B -6.2454125 1.90456199 -8.3199574 6.3697E-11 5.4976E-10
13 TCN1 -6.092247 -0.2042264 -13.279921 7.8647E-18 3.6241E-15
14 SLC6A14 -6.0442668 3.31669018 -11.712515 8.6089E-16 7.7049E-14
15 MMP3 -5.9639817 2.46225938 -9.7111745 5.4564E-13 1.0249E-11
16 AQP9 -5.9492106 -0.4457837 -10.691668 2.1722E-14 8.2439E-13
17 CHI3L1 -5.827216 3.55109431 -13.634077 2.8424E-18 2.0543E-15
18 SAA1 -5.7525892 0.13005259 -8.6577378 1.9702E-11 1.9979E-10
19 IGHG3 -5.7260612 6.91681515 -10.462714 4.5627E-14 1.4284E-12
20 KRT17 -5.6467977 -1.6447763 -10.228013 9.8307E-14 2.6236E-12
21 CXCL8 -5.5574099 1.3626185 -9.4416309 1.3505E-12 2.1585E-11
22 HMGCS2 5.52517836 6.84005602 13.0339606 1.6094E-17 4.7496E-15
23 SLC38A4 5.51179389 1.62296651 8.94747537 7.2622E-12 8.6911E-11
24 PI3 -5.4595694 5.79958827 -11.295666 3.1652E-15 1.9273E-13
25 FCGR3B -5.3781545 0.53296654 -10.599263 2.9285E-14 1.0314E-12
26 IGHG1 -5.3470302 10.0018955 -11.651824 1.0391E-15 8.9038E-14
27 CXCR1 -5.3332773 -1.1794448 -11.177864 4.5916E-15 2.6229E-13
28 CXCL5 -5.3196348 0.90093457 -5.6433743 8.3269E-07 2.7212E-06
29 MMP10 -5.318136 0.57934216 -9.5703044 8.7529E-13 1.5031E-11
30 PRSS2 -5.2485065 -0.2392651 -8.8359436 1.0654E-11 1.2072E-10
图1 GO富集和KEGG通路富集分析结果。该图包含交互式条形图,显示使用Enrichr生成的GO富集分析和KEGG信号通路的结果。条形图的长度表示每个条目的富集分数
图2 主成分析结果。基于两个队列重合的100个差异基因在TCGA数据库中对结肠肿瘤及正常黏膜、直肠肿瘤及正常黏膜表达谱数据进行主成分分析。2A:是降维过程中每一个主成分中的比例。2B和2C:显示了结直肠癌病灶及正常黏膜测序数据的散点图(二维和三维)。每个点代表RNA-seq样品。具有相似基因表达谱的样品在三维空间中更接近
图3 基于TCGA数据集候选基因在结直肠癌中的预后意义。根据TCGA的数据,候选基因的表达情况对结直肠癌的总生存期的影响,采用对数秩检验进行计算
图4 基于TCGA数据集候选基因在结直肠癌中的表达模式。结直肠癌原发灶和正常黏膜中的候选基因的表达情况,肿瘤灶用红色表示,正常组织用绿色表示,*表示P<0.05
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