在任何疾病的药物研发过程中，适当的受体蛋白和药物分子的检测同等重要。本研究旨在利用综合统计和生物信息学方法探索作为抑制剂的受体及药物分子作为结肠癌(CRC)的分子标志物。为了鉴定在CRC的发病和进展中发挥重要作用的基因，我们从基因表达Omnibus数据库下载了四份微阵列数据集(GSE9348、GSE110224、GSE23878和GSE35279)和一份RNA_Seq数据集(GSE50760)。利用LIMMA软件包对数据集进行分析，确定共同差异表达基因(cDEGs)。采用蛋白质相互作用网络分析的五个拓扑测量方法检测cDEGs的关键基因(KGs)。在不同的网络工具和独立数据库中进行了CRC导致的KGs的计算验证。通过与排名前列的独立受体蛋白的最先进替代方案进行交叉验证，我们建议我们的KGs引导计算更有效的候选药物分子。我们从五个基因表达谱数据集中确定了50个共同的差异表达基因(cDEGs)，其中31个cDEGs下调，其余19个上调。然后我们将11个cDEGs(CXCL8、CEMIP、MMP7、CA4、ADH1C、GUCA2A、GUCA2B、ZG16、CLCA4、MS4A12和CLDN1) 作为KGs。基于独立数据库的相关生物信息学分析(box plot、生存概率曲线、DNA甲基化、与免疫浸润水平的相关性、疾病-KGs相互作用、GO和KEGG通路)直接或间接显示这些KGs与CRC进展显著相关。我们还检测到四个TFs蛋白(FOXC1、YY1、GATA2和NFKB)和八个microRNAs(hsa-mir-16-5p、hsa-mir-195-5p、hsa-mir-203a-3p、hsa-mir-34a-5p、hsa-mir-107、hsa-mir-27a-3p、hsa-mir-429和hsa-mir-335-5p)作为KGs的关键转录和后转录调节因子。最后，我们建议了15个分子标志物，其中包括11个KGs和4个关键TFs蛋白，引导9个小分子(Cyclosporin A、Manzamine A、Cardidigin、Staurosporine、Benzo[A]Pyrene、Sitosterol、Nocardiopsis Sp、Troglitazone和Riccardin D)作为针对CRC的治疗候选药物分子的前列候选药物。本研究的发现建议我们提出的靶蛋白和药物分子可被视为CRC的潜在诊断、预后和治疗标志物。©2023.作者。

Detection of appropriate receptor proteins and drug agents are equally important in the case of drug discovery and development for any disease. In this study, an attempt was made to explore colorectal cancer (CRC) causing molecular signatures as receptors and drug agents as inhibitors by using integrated statistics and bioinformatics approaches.To identify the important genes that are involved in the initiation and progression of CRC, four microarray datasets (GSE9348, GSE110224, GSE23878, and GSE35279) and an RNA_Seq profiles (GSE50760) were downloaded from the Gene Expression Omnibus database. The datasets were analyzed by a statistical r-package of LIMMA to identify common differentially expressed genes (cDEGs). The key genes (KGs) of cDEGs were detected by using the five topological measures in the protein-protein interaction network analysis. Then we performed in-silico validation for CRC-causing KGs by using different web-tools and independent databases. We also disclosed the transcriptional and post-transcriptional regulatory factors of KGs by interaction network analysis of KGs with transcription factors (TFs) and micro-RNAs. Finally, we suggested our proposed KGs-guided computationally more effective candidate drug molecules compared to other published drugs by cross-validation with the state-of-the-art alternatives of top-ranked independent receptor proteins.We identified 50 common differentially expressed genes (cDEGs) from five gene expression profile datasets, where 31 cDEGs were downregulated, and the rest 19 were up-regulated. Then we identified 11 cDEGs (CXCL8, CEMIP, MMP7, CA4, ADH1C, GUCA2A, GUCA2B, ZG16, CLCA4, MS4A12 and CLDN1) as the KGs. Different pertinent bioinformatic analyses (box plot, survival probability curves, DNA methylation, correlation with immune infiltration levels, diseases-KGs interaction, GO and KEGG pathways) based on independent databases directly or indirectly showed that these KGs are significantly associated with CRC progression. We also detected four TFs proteins (FOXC1, YY1, GATA2 and NFKB) and eight microRNAs (hsa-mir-16-5p, hsa-mir-195-5p, hsa-mir-203a-3p, hsa-mir-34a-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-429, and hsa-mir-335-5p) as the key transcriptional and post-transcriptional regulators of KGs. Finally, our proposed 15 molecular signatures including 11 KGs and 4 key TFs-proteins guided 9 small molecules (Cyclosporin A, Manzamine A, Cardidigin, Staurosporine, Benzo[A]Pyrene, Sitosterol, Nocardiopsis Sp, Troglitazone, and Riccardin D) were recommended as the top-ranked candidate therapeutic agents for the treatment against CRC.The findings of this study recommended that our proposed target proteins and agents might be considered as the potential diagnostic, prognostic and therapeutic signatures for CRC.© 2023. The Author(s).