Bioinformatics Based Drug Repurposing Approach for Breast and Gynecological Cancers: RECQL4/FAM13C Genes Address Common Hub Genes and Drugs.

Gizem Ayna Duran
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Abstract

Objective: The prevalence of breast cancer and gynaecological cancers is high, and these cancer types can occur consecutively as secondary cancers. The aim of our study is to determine the genes commonly expressed in these cancers and to identify the common hub genes and drug components.

Materials and methods: Gene intensity values of breast cancer, gynaecological cancers such as cervical, ovarian and endometrial cancers were used from the Gene Expression Omnibus database Affymetrix Human Genome U133 Plus 2.0 Array project. Using the linear modelling method included in the R LIMMA package, genes that differ between healthy individuals and cancer patients were identified. Hub genes were determined using cytoHubba in Cytoscape program. "ShinyGo 0.80" tool was used to determine the disease-specific biological KEGG pathways. Drug.MATADOR from the ShinyGo 0.80 tool was used to predict drug-target relationships.

Results: The RecQ Like Helicase 4 and Family with Sequence Similarity 13 Member C genes were found to be similarly expressed in breast cancer and gynaecological cancers. Upon KEGG pathway analyses with hub genes, Drug.MATADOR analysis with hub genes related to cancer related pathways was performed. We have determined these gene/drug interactions: NBN (targeted by Hydroxyurea), EP300 (targeted by Acetylcarnitine) and MAPK14 (targeted by Salicylate and Dibutyryl cyclic AMP).

Conclusion: The drugs associated with hub genes determined in our study are not routinely used in cancer treatment. Our study offers the opportunity to identify the target genes of drugs used in breast and gynaecological cancers with the drug repurposing approach.

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基于生物信息学的乳腺癌和妇科癌症药物再利用方法:RECQL4/FAM13C基因定位共同中心基因和药物
目的:乳腺癌和妇科癌症发病率高,可作为继发性癌症连续发生。我们研究的目的是确定在这些癌症中普遍表达的基因,并确定共同的中心基因和药物成分。材料与方法:乳腺癌、宫颈癌、卵巢癌、子宫内膜癌等妇科肿瘤的基因强度值来源于基因表达综合数据库Affymetrix Human Genome U133 Plus 2.0 Array项目。使用R LIMMA包中包含的线性建模方法,确定了健康个体和癌症患者之间的差异基因。利用cytoHubba在Cytoscape程序中确定枢纽基因。使用“ShinyGo 0.80”工具确定疾病特异性生物KEGG通路。药物。使用ShinyGo 0.80工具中的MATADOR来预测药物-靶标关系。结果:发现RecQ样解旋酶4和序列相似家族13成员C基因在乳腺癌和妇科癌中表达相似。通过与枢纽基因的KEGG通路分析,对与癌症相关通路相关的枢纽基因进行MATADOR分析。我们已经确定了这些基因/药物相互作用:NBN(被羟基脲靶向),EP300(被乙酰肉碱靶向)和MAPK14(被水杨酸和二丁基环AMP靶向)。结论:本研究确定的与中枢基因相关的药物在癌症治疗中并不常用。我们的研究提供了机会,以确定用于乳腺癌和妇科癌症药物的靶基因与药物再利用的方法。
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