结直肠癌淋巴结转移相关基因表达分析。

IF 2.6 4区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY
International Journal of Genomics Pub Date : 2023-09-07 eCollection Date: 2023-01-01 DOI:10.1155/2023/9942663
Hongjie Yang, Jiafei Liu, Peishi Jiang, Peng Li, Yuanda Zhou, Zhichun Zhang, Qingsheng Zeng, Min Wang, Luciena Xiao Xiao, Xipeng Zhang, Yi Sun, Siwei Zhu
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引用次数: 0

摘要

目的:探讨癌症淋巴结转移调控基因及其与肿瘤免疫细胞浸润和预后的关系。方法:通过癌症基因图谱数据库收集CRC患者的数据集;筛选与CRC淋巴结转移相关的差异表达基因(DEGs);构建了蛋白质-蛋白质相互作用(PPI)网络;筛选出前20个hub基因;丰富和分析了基因本体论的功能和京都基因和基因组百科全书的途径。采用最小绝对收缩选择算子(LASSO)回归方法,进一步筛选20个hub基因中与CRC淋巴结转移相关的特征基因,探讨特征基因与免疫细胞浸润的相关性,对特征基因进行单变量COX分析,获得生存相关基因,构建风险评分公式,基于风险评分公式进行Kaplan-Meier分析,并对临床因素和风险评分进行多变量COX回归分析。结果:共获得62个与结直肠癌淋巴结转移相关的DEG。在通过PPI鉴定的20个枢纽基因中,只有钙激活的氯通道调节因子1(CLCA1)在淋巴结转移中表达下调,其余表达上调。使用LASSO回归方法共筛选了9个与CRC淋巴结转移相关的特征基因(KIF1A、TMEM59L、CLCA1、COL9A3、GDF5、TUBBB2、STMN2、FOXN1和SCN5A)。9个特征基因与不同类型的免疫细胞浸润显著相关,从中筛选出3个生存相关基因(TMEM59L、CLCA1和TUBB2B)。多因素COX回归显示,TMEM59L、CLCA1和TUBB2B的风险评分是独立的预后因素。对癌症直肠和结肠癌患者的组织样本进行免疫组织化学验证。结论:TMEM59L、CLCA1和TUBB2B是结直肠癌淋巴结转移的独立预后因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

An Analysis of the Gene Expression Associated with Lymph Node Metastasis in Colorectal Cancer.

An Analysis of the Gene Expression Associated with Lymph Node Metastasis in Colorectal Cancer.

An Analysis of the Gene Expression Associated with Lymph Node Metastasis in Colorectal Cancer.

An Analysis of the Gene Expression Associated with Lymph Node Metastasis in Colorectal Cancer.

Objective: This study aimed to explore the genes regulating lymph node metastasis in colorectal cancer (CRC) and to clarify their relationship with tumor immune cell infiltration and patient prognoses.

Methods: The data sets of CRC patients were collected through the Cancer Gene Atlas database; the differentially expressed genes (DEGs) associated with CRC lymph node metastasis were screened; a protein-protein interaction (PPI) network was constructed; the top 20 hub genes were selected; the Gene Ontology functions and the Kyoto Encyclopedia of Genes and Genomes pathways were enriched and analyzed. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method was employed to further screen the characteristic genes associated with CRC lymph node metastasis in 20 hub genes, exploring the correlation between the characteristic genes and immune cell infiltration, conducting a univariate COX analysis on the characteristic genes, obtaining survival-related genes, constructing a risk score formula, conducting a Kaplan-Meier analysis based on the risk score formula, and performing a multivariate COX regression analysis on the clinical factors and risk scores.

Results: A total of 62 DEGs associated with CRC lymph node metastasis were obtained. Among the 20 hub genes identified via PPI, only calcium-activated chloride channel regulator 1 (CLCA1) expression was down-regulated in lymph node metastasis, and the rest were up-regulated. A total of nine characteristic genes associated with CRC lymph node metastasis (KIF1A, TMEM59L, CLCA1, COL9A3, GDF5, TUBB2B, STMN2, FOXN1, and SCN5A) were screened using the LASSO regression method. The nine characteristic genes were significantly related to different kinds of immune cell infiltration, from which three survival-related genes (TMEM59L, CLCA1, and TUBB2B) were screened. A multi-factor COX regression showed that the risk scores obtained from TMEM59L, CLCA1, and TUBB2B were independent prognostic factors. Immunohistochemical validation was performed in tissue samples from patients with rectal and colon cancer.

Conclusion: TMEM59L, CLCA1, and TUBB2B were independent prognostic factors associated with lymphatic metastasis of CRC.

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来源期刊
International Journal of Genomics
International Journal of Genomics BIOCHEMISTRY & MOLECULAR BIOLOGY-BIOTECHNOLOGY & APPLIED MICROBIOLOGY
CiteScore
5.40
自引率
0.00%
发文量
33
审稿时长
17 weeks
期刊介绍: International Journal of Genomics is a peer-reviewed, Open Access journal that publishes research articles as well as review articles in all areas of genome-scale analysis. Topics covered by the journal include, but are not limited to: bioinformatics, clinical genomics, disease genomics, epigenomics, evolutionary genomics, functional genomics, genome engineering, and synthetic genomics.
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