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一种基于生物信息学的方法用于发现肝细胞癌中的新型生物标志物。

A Bioinformatics-Based Approach to Discover Novel Biomarkers in Hepatocellular Carcinoma.

作者信息

Shourideh Amir, Maddah Reza, Amiri Bahareh Shateri, Basharat Zarrin, Shadpirouz Marzieh

机构信息

Faculty of Pharmacy, Eastern Mediterranean University, Famagusta, Cyprus.

Department of Bioprocess Engineering, Institute of Industrial and Environmental Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran.

出版信息

Iran J Public Health. 2024 Jun;53(6):1332-1342.

PMID:39430152
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11488550/
Abstract

BACKGROUND

Liver hepatocellular carcinoma (LIHC) is a common cancer with a poor prognosis and high recurrence rate. We aimed to identify potential biomarkers for LIHC by investigating the involvement of hub genes, microRNAs (miRNAs), transcription factors (TFs), and protein kinases (PKs) in its occurrence.

METHODS

we conducted a bioinformatics analysis using microarray datasets, the TCGA-LIHC dataset, and text mining to identify differentially expressed genes (DEGs) associated with LIHC. They then performed functional enrichment analysis and gene-disease association analysis. The protein-protein interaction network of the genes was established, and hub genes were identified. The expression levels and survival analysis of these hub genes were evaluated, and their association with miRNAs, TFs, and PKs was assessed.

RESULTS

The analysis identified 122 common genes involved in LIHC pathogenesis. Ten hub genes were filtered out, including and . The expression level of all hub genes was confirmed, and high expression levels of all hub genes were correlated with poor overall survival of LIHC patients.

CONCLUSION

Identifying potential biomarkers for LIHC can aid in the design of targeted treatments and improve the survival of LIHC patients. The findings of this study provide a basis for further research in the field of LIHC and contribute to the understanding of its molecular pathogenesis.

摘要

背景

肝细胞肝癌（LIHC）是一种常见癌症，预后较差且复发率高。我们旨在通过研究核心基因、微小RNA（miRNA）、转录因子（TF）和蛋白激酶（PK）在其发生过程中的作用，来确定LIHC的潜在生物标志物。

方法

我们使用微阵列数据集、TCGA-LIHC数据集和文本挖掘进行生物信息学分析，以识别与LIHC相关的差异表达基因（DEG）。然后进行功能富集分析和基因-疾病关联分析。建立基因的蛋白质-蛋白质相互作用网络，并识别核心基因。评估这些核心基因的表达水平和生存分析，并评估它们与miRNA、TF和PK的关联。

结果

分析确定了122个参与LIHC发病机制的常见基因。筛选出10个核心基因，包括和。所有核心基因的表达水平均得到证实，所有核心基因的高表达水平与LIHC患者较差的总生存率相关。

结论

确定LIHC的潜在生物标志物有助于设计靶向治疗并提高LIHC患者的生存率。本研究结果为LIHC领域的进一步研究提供了基础，并有助于理解其分子发病机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a646/11488550/b6270dfa44c2/IJPH-53-1332-g007.jpg
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本文引用的文献

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NCAPG is a prognostic biomarker associated with vascular invasion in hepatocellular carcinoma.NCAPG 是与肝细胞癌血管侵犯相关的预后生物标志物。
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An Integrative Pan-Cancer Analysis of the Prognostic and Immunological Role of Casein Kinase 2 Alpha Protein 1 (CSNK2A1) in Human Cancers: A Study Based on Bioinformatics and Immunohistochemical Analysis.酪蛋白激酶2α蛋白1（CSNK2A1）在人类癌症中的预后和免疫作用的综合泛癌分析：一项基于生物信息学和免疫组织化学分析的研究
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ASPM promotes hepatocellular carcinoma progression by activating Wnt/β-catenin signaling through antagonizing autophagy-mediated Dvl2 degradation.
ASPM 通过拮抗自噬介导的 Dvl2 降解来激活 Wnt/β-catenin 信号通路，从而促进肝癌的进展。
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Centromere Protein F () Serves as a Potential Prognostic Biomarker and Target for Human Hepatocellular Carcinoma.着丝粒蛋白F（）作为人类肝细胞癌潜在的预后生物标志物和靶点。
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Comprehensive Analysis of Gene Expression Changes and Validation in Hepatocellular Carcinoma.肝细胞癌中基因表达变化的综合分析及验证
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Transcription factor E2F4 is an indicator of poor prognosis and is related to immune infiltration in hepatocellular carcinoma.转录因子E2F4是预后不良的一个指标，且与肝细胞癌中的免疫浸润有关。
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The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets.2021 年的 STRING 数据库：可定制的蛋白质-蛋白质网络，以及用户上传的基因/测量集的功能特征分析。
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