基于加权基因共表达网络、机器学习和ceRNA假设筛选溃疡性结肠炎生物标志物和潜在途径。

IF 2.7 3区 生物学
Ying Li, Mengyao Tang, Feng Jun Zhang, Yihan Huang, Jing Zhang, Junqi Li, Yunpeng Wang, Jinguang Yang, Shu Zhu
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引用次数: 1

摘要

背景:溃疡性结肠炎(UC)是一种难治性肠道炎症性疾病。其日益增加的发病率给患者和社会带来了巨大的负担。UC的病因尚未确定,因此筛选潜在的生物标志物对于预防疾病进展和更有效地选择最佳治疗策略至关重要。方法:从GEO数据库中选取UC患者肠黏膜活检的微阵列数据集,结合R语言筛选差异表达基因,绘制蛋白相互作用网络图。进行GO、KEGG、DO和GSEA富集分析,探讨其生物学功能。通过机器学习和WGCNA分析,筛选出可作为UC潜在生物标志物的靶点。绘制ROC曲线验证结果的可靠性,并从免疫细胞浸润、共表达分析、竞争内源性网络(ceRNA)等方面预测标记基因的作用机制。结果:整合GSE75214和GSE87466两个数据集进行筛选,共获得107个差异表达基因。它们主要与体液免疫反应和炎症反应等生物学功能有关。进一步筛选出5个标记基因,发现它们在免疫细胞浸润方面与M0巨噬细胞、静止肥大细胞、M2巨噬细胞、活化NK细胞相关。共表达网络发现54个mirna与5个标记基因之间存在显著的共表达关系。根据ceRNA假说,NEAT1-miR-342-3p/miR-650-SLC6A14、NEAT1-miR-650-IRAK3和XIST-miR-342-3p-IRAK3轴被发现是UC的潜在调控途径。结论:本研究筛选出5个可用于UC诊断和治疗的生物标志物,分别为SLC6A14、TIMP1、IRAK3、HMGCS2、APOBEC3B。证实它们在免疫浸润水平上参与UC的发生发展,并提出了一种潜在的控制UC进展的RNA调控途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Screening of ulcerative colitis biomarkers and potential pathways based on weighted gene co-expression network, machine learning and ceRNA hypothesis.

Screening of ulcerative colitis biomarkers and potential pathways based on weighted gene co-expression network, machine learning and ceRNA hypothesis.

Screening of ulcerative colitis biomarkers and potential pathways based on weighted gene co-expression network, machine learning and ceRNA hypothesis.

Screening of ulcerative colitis biomarkers and potential pathways based on weighted gene co-expression network, machine learning and ceRNA hypothesis.

Background: Ulcerative colitis (UC) refers to an intractable intestinal inflammatory disease. Its increasing incidence rate imposes a huge burden on patients and society. The UC etiology has not been determined, so screening potential biomarkers is critical to preventing disease progression and selecting optimal therapeutic strategies more effectively.

Methods: The microarray datasets of intestinal mucosal biopsy of UC patients were selected from the GEO database, and integrated with R language to screen differentially expressed genes and draw proteins interaction network diagrams. GO, KEGG, DO and GSEA enrichment analyses were performed to explore their biological functions. Through machine learning and WGCNA analysis, targets that can be used as UC potential biomarkers are screened out. ROC curves were drawn to verify the reliability of the results and predicted the mechanism of marker genes from the aspects of immune cell infiltration, co-expression analysis, and competitive endogenous network (ceRNA).

Results: Two datasets GSE75214 and GSE87466 were integrated for screening, and a total of 107 differentially expressed genes were obtained. They were mainly related to biological functions such as humoral immune response and inflammatory response. Further screened out five marker genes, and found that they were associated with M0 macrophages, quiescent mast cells, M2 macrophages, and activated NK cells in terms of immune cell infiltration. The co-expression network found significant co-expression relationships between 54 miRNAs and 5 marker genes. According to the ceRNA hypothesis, NEAT1-miR-342-3p/miR-650-SLC6A14, NEAT1-miR-650-IRAK3, and XIST-miR-342-3p-IRAK3 axes were found as potential regulatory pathways in UC.

Conclusion: This study screened out five biomarkers that can be used for the diagnosis and treatment of UC, namely SLC6A14, TIMP1, IRAK3, HMGCS2, and APOBEC3B. Confirmed that they play a role in the occurrence and development of UC at the level of immune infiltration, and proposed a potential RNA regulatory pathway that controls the progression of UC.

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来源期刊
Hereditas
Hereditas Biochemistry, Genetics and Molecular Biology-Genetics
CiteScore
3.80
自引率
3.70%
发文量
0
期刊介绍: For almost a century, Hereditas has published original cutting-edge research and reviews. As the Official journal of the Mendelian Society of Lund, the journal welcomes research from across all areas of genetics and genomics. Topics of interest include human and medical genetics, animal and plant genetics, microbial genetics, agriculture and bioinformatics.
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