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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 (05) : 497 -504. doi: 10.3877/cma.j.issn.2095-3224.2021.05.009

论著

基于生物信息学的结直肠癌枢纽基因与预后相关基因筛选
李飞1, 秦强强1, 谷战峰1, 张天祥1, 申思2, 周丽2, 张乐莎3,†()   
  1. 1. 230601　合肥，安徽医科大学第二临床医学院
    2. 230012　合肥，安徽中医药大学药学院
    3. 230032　合肥，安徽医科大学基础医学院生理学教研室
  • 收稿日期:2021-03-29 出版日期:2021-10-25
  • 通信作者: 张乐莎
  • 基金资助:
    国家自然科学基金青年科学基金项目(81903590)

Screening of hub genes and prognosis-related genes in colorectal cancer based on bioinformatics

Fei Li1, Qiangqiang Qin1, Zhanfeng Gu1, Tianxiang Zhang1, Si Shen2, Li Zhou2, Lesha Zhang3,†()   

  1. 1. Second Institute of Clinical Medicine, Anhui Medical University, Hefei 230601, China
    2. College of Pharmacy Anhui University of Chinese Medicine, Hefei 230012, China
    3. Department of Physiology, School of Medical Sciences, Anhui Medical University, Hefei 230032, China
  • Received:2021-03-29 Published:2021-10-25
  • Corresponding author: Lesha Zhang
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引用本文：

李飞, 秦强强, 谷战峰, 张天祥, 申思, 周丽, 张乐莎. 基于生物信息学的结直肠癌枢纽基因与预后相关基因筛选[J]. 中华结直肠疾病电子杂志, 2021, 10(05): 497-504.

Fei Li, Qiangqiang Qin, Zhanfeng Gu, Tianxiang Zhang, Si Shen, Li Zhou, Lesha Zhang. Screening of hub genes and prognosis-related genes in colorectal cancer based on bioinformatics[J]. Chinese Journal of Colorectal Diseases(Electronic Edition), 2021, 10(05): 497-504.

目的

筛选结直肠癌与正常大肠组织差异表达基因并分析其与预后的关系。

方法

通过基因表达综合数据库（GEO）下载结直肠癌基因芯片GSE110224、GSE41328、GSE22598。利用R软件4.0.3筛选结直肠癌组织与正常组织差异表达基因，对这些基因进行GO和KEGG分析；使用STRING数据库构建PPI网络并将结果导入Cytoscape v3.7.2筛选出枢纽基因，最后通过GEPIA数据库验证其差异表达情况及与患者预后关系。

结果

筛选出366个差异表达基因，其中128个上调，238个下调。GO分析结果表明差异表达基因主要涉及蛋白水解、细胞增殖的正性调节等生物学过程；富集于细胞外间隙、胞外区等细胞成分；介导锌离子结合、钙离子结合等分子功能。KEGG分析显示差异表达基因主要富集于细胞因子受体相互作用、趋化因子信号通路等相关信号通路。从PPI网络中筛选出10个枢纽基因分别为CXCL8、CXCL1、SPP1、CXCL12、COL1A1、SOX9、MMP3、COL1A2、CD44、CXCL5。使用GEPIA网站验证发现差异趋势均一致，其中8个有显著性差异。预后分析显示CXCL8、SPP1和COL1A2与结直肠癌患者生存率有关。

结论

CXCL8和SPP1为结直肠癌患者预后关键基因，可考虑作为潜在生物标志物为结直肠癌筛查和诊治提供分子学依据。

关键词: 结直肠肿瘤, 基因表达综合数据库, 生物信息学, 预后
Objective

To screen differentially expressed genes (DEGs) between colorectal cancer and normal colorectal mucosa tissues by bioinformatics and analyze their relationship with prognosis.

Methods

Colorectal cancer gene microarrays datasets GSE110224, GSE41328 and GSE22598 were downloaded from Gene Expression Omnibus (GEO) database. DEGs between colorectal cancer samples and normal tissue samples from those three datasets were screened by R software 4.0.3, and then GO and KEGG pathway analysis were carried out. The protein interaction network was constructed by STRING database, Cytoscape software v3.7.2 was used to screen the hub genes in the protein-protein interaction (PPI) network. Finally, the selected hub genes were validated and analyzed for the relationship with prognosis by the GEPIA database.

Results

A total of 366 DEGs were screened, of which 128 were up-regulated and 238 were down-regulated. GO function enrichment showed hub genes were mainly involved in proteolysis, positive regulation of proliferation and other biological processes, and cellular component mainly enriched in extracellular space, extracellular region, mediating zinc ion binding, calcium ion binding and other molecular functions. KEGG analysis showed that the enriched pathways are mainly related to cytokine-cytokine receptor interaction, chemokine signaling pathway and other signal transduction pathway. Ten hub genes were selected from the PPI network, including nine up-regulated genes, which were CXCL8, CXCL1, SPP1, COL1A1, SOX9, MMP3, COL1A2, CD44, CXCL5, and one down-regulated gene CXCL12. Those ten genes were verified by using GEPIA showing that eight of them existed significant difference and all of them were consistent with the above analysis. Survival analysis results showed that genes CXCL8, SPP1 and COL1A2 were significantly associated with the survival rate of colorectal tumor patients.

Conclusion

CXCL8 and SPP1 may be prognosis key genes of colorectal tumor patients and may serve as a potential biomarker providing basis on molecular level for screening and diagnosis of colorectal tumor.

Key words: Colorectal neoplasms, Gene expression omnibus (GEO) database, Bioinformatics, Prognosis
图1 数据集均一化处理。1A：GSE41328数据集均一化前；1B：GSE22598数据集均一化前；1C：GSE110224均一化前；1D：GSE41328数据集均一化后；1E：GSE22598数据集均一化后；1F：GSE100224数据集均一化后
图2 差异表达基因火山图
图3 差异表达基因的GO分析与KEGG分析信号。3A：通路1：生物学过程；3B：通路2：细胞组分；3C：通路3：分子功能；3D：KEGG信号通路富集图
表1 10个枢纽基因
差异表达基因 基因名称 连通度 中文名称
上调基因 C-X-C motif chemokine ligand 8，CXCL8 30 白细胞介素8
C-X-C motif chemokine ligand 1，CXCL1 28 白细胞介素1
CD44 28 CD44分子
Secreted phosphoprotein 1，SPP1 27 分泌磷蛋白1
Collagen type Ⅰ alpha 1 chain，COL1A1 26 Ⅰ型胶原α1
Collagen type Ⅰ alpha 2 chain，COL1A2 26 Ⅰ型胶原α2
SOX9 22 SRY-box 9
Matrix Metalloproteinase 3，MMP3 22 基质金属蛋白酶3
C-X-C motif chemokine ligand 5，CXCL5 20 白细胞介素5
下调基因 C-X-C motif chemokine ligand 1，CXCL12 26 白细胞介素12
图4 枢纽基因在正常组织与肿瘤组织中的表达情况。T：Tumor，以红色表示；N：Normal，以灰色表示。COAD：Colon adenocarcinoma，结肠癌；READ：Rectum adenocarcinoma，直肠癌
图5 预后相关基因与患者生存率的关系
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