Sahebnasagh Roxana, Azizi Zahra, Komeili-Movahhed Tahereh, Zendehdel Kazem, Ghahremani Mohammad Hossein
Department of Molecular Medicine, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, IRN.
Cellular and Molecular Research Center, Qom University of Medical Sciences, Qom, IRN.
Cureus. 2024 Aug 7;16(8):e66393. doi: 10.7759/cureus.66393. eCollection 2024 Aug.
Background Acquired resistance to 5-fluorouracil (5-FU) frequently results in chemotherapy failure and disease recurrence in advanced colorectal cancer (CRC) patients. Research has demonstrated that dysregulation of long non-coding RNAs (lncRNAs) mediates the development of chemotherapy resistance in cancerous cells. The present study aims to identify key lncRNAs associated with 5-FU resistance in CRC using bioinformatic and experimental validation approaches. Methods The Gene Expression Omnibus (GEO) dataset GSE119481, which contains miRNA expression profiles of the parental CRC HCT116 cell line (HCT116/P) and its in-vitro established 5-FU-resistant sub-cell line (HCT116/FUR), was downloaded. Firstly, differentially expressed microRNAs (DEmiRNAs) between the parental and 5-FU resistance cells were identified. LncRNAs and mRNAs were then predicted using online databases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to uncover relevant biological mechanisms and pathways. Networks integrating lncRNAs, miRNAs, and mRNAs interactions were constructed, and topological analyses were used to identify key lncRNAs associated with 5-FU resistance. An in-vitro model of the HCT116/FUR sub-cell line was developed by exposing the HCT116/P cell line to increasing concentrations of 5-FU. Finally, real-time quantitative PCR (RT-qPCR) was performed on total RNA extracted from the HCT116/P cell line and the HCT116/FUR sub-cell line to validate the in-silico predictions of key lncRNAs. Results A total of 32 DEmiRNAs were identified. Enrichment analysis demonstrated that these DEmiRNAs were mainly enriched in several cancer hallmark pathways that regulate cell growth, cell cycle, cell survival, inflammation, immune response, and apoptosis. The predictive analysis identified 237 unique lncRNAs and 123 mRNAs interacting with these DEmiRNAs. The pathway analysis indicated that most of these predicted genes were enriched in the cellular response to starvation, protein polyubiquitination, chromatin remodeling, and negative regulation of gene expression. Topological analyses of the lncRNA-miRNA-mRNA network highlighted the nuclear enriched abundant transcript 1 (NEAT1), metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), and Opa interacting protein 5 antisense RNA 1 (OIP5-AS1) as central lncRNAs. Experimental analysis by RT-qPCR confirmed that the expression levels of NEAT1 and MALAT1 were significantly increased in HCT116/FUR cells compared to HCT116/P cells. However, no significant difference was observed in the OIP5-AS1 expression level between the two cells. Conclusion Our findings specifically highlight MALAT1 and NEAT1 as significant contributors to 5-FU resistance in CRC. These lncRNAs are promising biomarkers for diagnosing and predicting outcomes in CRC.
获得性5-氟尿嘧啶(5-FU)耐药常导致晚期结直肠癌(CRC)患者化疗失败和疾病复发。研究表明,长链非编码RNA(lncRNA)失调介导癌细胞化疗耐药的发生。本研究旨在通过生物信息学和实验验证方法,鉴定与CRC中5-FU耐药相关的关键lncRNA。方法:下载基因表达综合数据库(GEO)数据集GSE119481,其包含亲代CRC HCT116细胞系(HCT116/P)及其体外建立的5-FU耐药亚细胞系(HCT116/FUR)的miRNA表达谱。首先,鉴定亲代细胞与5-FU耐药细胞之间差异表达的微小RNA(DEmiRNA)。然后使用在线数据库预测lncRNA和mRNA。进行基因本体(GO)和京都基因与基因组百科全书(KEGG)富集分析,以揭示相关生物学机制和途径。构建整合lncRNA、miRNA和mRNA相互作用的网络,并通过拓扑分析鉴定与5-FU耐药相关的关键lncRNA。通过将HCT116/P细胞系暴露于浓度递增的5-FU中,建立HCT116/FUR亚细胞系的体外模型。最后,对从HCT116/P细胞系和HCT116/FUR亚细胞系中提取的总RNA进行实时定量PCR(RT-qPCR),以验证关键lncRNA的计算机预测结果。结果:共鉴定出32个DEmiRNA。富集分析表明,这些DEmiRNA主要富集于调节细胞生长、细胞周期、细胞存活、炎症、免疫反应和凋亡的几种癌症特征途径。预测分析鉴定出237个与这些DEmiRNA相互作用的独特lncRNA和123个mRNA。通路分析表明,这些预测基因大多富集于对饥饿的细胞反应、蛋白质多聚泛素化、染色质重塑和基因表达的负调控。lncRNA-miRNA-mRNA网络的拓扑分析突出了核富集丰富转录本1(NEAT1)、转移相关肺腺癌转录本1(MALAT1)和Opa相互作用蛋白5反义RNA 1(OIP5-AS1)作为核心lncRNA。RT-qPCR实验分析证实,与HCT116/P细胞相比,HCT116/FUR细胞中NEAT1和MALAT1的表达水平显著升高。然而,两种细胞之间OIP5-AS1表达水平未观察到显著差异。结论:我们的研究结果特别突出了MALAT1和NEAT1是CRC中5-FU耐药的重要促成因素。这些lncRNA有望成为CRC诊断和预测预后的生物标志物。