Yari Amir Hossein, Aghbash Parisa Shiri, Bayat Mobina, Lahouti Shiva, Jalilzadeh Nazila, Zadeh Leila Nariman, Yari Amir Mohammad, Tabrizi-Nezhadi Parinaz, Nahand Javid Sadri, MotieGhader Habib, Baghi Hossein Bannazadeh
Department of Biology, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
Heliyon. 2024 Nov 14;10(22):e40179. doi: 10.1016/j.heliyon.2024.e40179. eCollection 2024 Nov 30.
The goal of this bioinformatics research is to get a comprehensive understanding of the driver genes and their function in the development, progression, and treatment of cervical cancer. This study constitutes a pioneering attempt, adding to our knowledge of genetic diversity and its ramifications.
In this project, we use bioinformatics and systems biology methods to identify candidate transcription factors and the genes they regulate in order to identify microRNAs and LncRNAs that regulate these transcription factors and lead to the discovery of new medicines for the treatment of cervical cancer. From the differentially expressed genes available via GEO's GSE63514 accession, we use driver genes to choose these candidates. We then used the WGCNA tool in R to rebuild the co-expression network and its modules. The hub genes of each module were determined using CytoHubba, a Cystoscope plugin. The biomarker potential of hub genes was analyzed using the UCSC Xena browser and the GraphPad prism program. The TRRUST database is used to locate the TFs that regulate the expression of these genes. In order to learn how drugs, MicroRNAs, and LncRNAs interact with transcription factors, we consulted the Drug Target Information Database (DGIDB), the miRWalk database, and the LncHub database. Finally, the online database Enrichr is utilized to analyze the enrichment of Gene Ontology and KEGG pathways.
By combining the mRNA expression levels of 2041 driver genes from 14 early-stage Cervical cancer and 24 control samples, a co-expression network was built. The cluster analysis shows that the collection of shared genes may be broken down into seven distinct groups, or "modules." According to the average linkage hierarchical clustering and S smaller than 2, we found five modules (represented by the colors blue, brown, red, green, and grey) in our research. Then, we identify 5 high-degree genes from these modules that may serve as diagnostic biomarkers (ZBBX, PLCH1, TTC7B, DNAH7, and ZMYND10). In addition, we identify four transcription factors (SRF, RELA, NFKB1, and SP1) that regulate the expression of genes in the co-expression module. Drugs, microRNAs, and long noncoding RNAs are then shown to cooperate with transcription factors. At last, the KEGG database's pathways were mined for information on how the co-expression module fits within them. More clinical trials are required for more trustworthy outcomes, and we collected this data using bioinformatics methods.
The major goal of this research was to identify diagnostic and therapeutic targets for cervical cancer by learning more about the involvement of driver genes in cancer's earliest stages.
这项生物信息学研究的目标是全面了解驱动基因及其在宫颈癌发生、发展和治疗中的作用。本研究是一次开创性的尝试，增加了我们对基因多样性及其影响的认识。
在本项目中，我们运用生物信息学和系统生物学方法来识别候选转录因子及其调控的基因，以确定调控这些转录因子的微小RNA（miRNA）和长链非编码RNA（LncRNA），从而发现治疗宫颈癌的新药。我们从通过GEO的GSE63514登录号获取的差异表达基因中，利用驱动基因来选择这些候选基因。然后我们使用R语言中的WGCNA工具重建共表达网络及其模块。使用Cystoscope插件CytoHubba确定每个模块的中心基因。使用UCSC Xena浏览器和GraphPad prism程序分析中心基因的生物标志物潜力。利用TRRUST数据库定位调控这些基因表达的转录因子（TF）。为了了解药物、miRNA和LncRNA如何与转录因子相互作用，我们查阅了药物靶点信息数据库（DGIDB）、miRWalk数据库和LncHub数据库。最后，利用在线数据库Enrichr分析基因本体（Gene Ontology）和京都基因与基因组百科全书（KEGG）通路的富集情况。
通过整合来自14个早期宫颈癌样本和24个对照样本的2041个驱动基因的mRNA表达水平，构建了一个共表达网络。聚类分析表明，共享基因的集合可分为七个不同的组，即“模块”。根据平均连锁层次聚类和S小于2的标准，我们在研究中发现了五个模块（分别以蓝色、棕色、红色、绿色和灰色表示）。然后，我们从这些模块中识别出5个可能作为诊断生物标志物的高表达基因（ZBBX、PLCH1、TTC7B、DNAH7和ZMYND10）。此外，我们还识别出四个调控共表达模块中基因表达的转录因子（SRF、RELA、NFKB1和SP1）。结果表明，药物、miRNA和长链非编码RNA与转录因子相互协作。最后，在KEGG数据库的通路中挖掘共表达模块如何与之匹配的数据。需要更多的临床试验才能得出更可靠的结果，我们使用生物信息学方法收集了这些数据。
本研究的主要目标是通过更多地了解驱动基因在癌症早期阶段的作用，来识别宫颈癌的诊断和治疗靶点。
