Hajipour Sara, Hosseini Sayed Mostafa, Irani Shiva, Tavallaie Mahmood
Biology Department, Science and Research Branch, Islamic Azad University, Tehran 14155-4933, Iran.
Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran 14359-16471, Iran.
Genomics Inform. 2023 Sep;21(3):e38. doi: 10.5808/gi.23039. Epub 2023 Sep 27.
Non-small cell lung cancer (NSCLC) is an important cause of cancer-associated deaths worldwide. Therefore, the exact molecular mechanisms of NSCLC are unidentified. The present investigation aims to identify the miRNAs with predictive value in NSCLC. The two datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DEmiRNA) and mRNAs (DEmRNA) were selected from the normalized data. Next, miRNA-mRNA interactions were determined. Then, co-expression network analysis was completed using the WGCNA package in R software. The co-expression network between DEmiRNAs and DEmRNAs was calculated to prioritize the miRNAs. Next, the enrichment analysis was performed for DEmiRNA and DEmRNA. Finally, the drug-gene interaction network was constructed by importing the gene list to dgidb database. A total of 3,033 differentially expressed genes and 58 DE miRNA were recognized from two datasets. The co-expression network analysis was utilized to build a gene co-expression network. Next, four modules were selected based on the Zsummary score. In the next step, a bipartite miRNA-gene network was constructed and hub miRNAs (let-7a-2-3p, let-7d-5p, let-7b-5p, let-7a-5p, and let-7b-3p) were selected. Finally, a drug-gene network was constructed while SUNITINIB, MEDROXYPROGESTERONE ACETATE, DOFETILIDE, HALOPERIDOL, and CALCITRIOL drugs were recognized as a beneficial drug in NSCLC. The hub miRNAs and repurposed drugs may act a vital role in NSCLC progression and treatment, respectively; however, these results must validate in further clinical and experimental assessments.
非小细胞肺癌（NSCLC）是全球癌症相关死亡的重要原因。因此，NSCLC的确切分子机制尚不清楚。本研究旨在鉴定在NSCLC中具有预测价值的miRNA。从基因表达综合数据库（GEO）下载了两个数据集。从标准化数据中选择差异表达的miRNA（DEmiRNA）和mRNA（DEmRNA）。接下来，确定miRNA-mRNA相互作用。然后，使用R软件中的WGCNA包完成共表达网络分析。计算DEmiRNA和DEmRNA之间的共表达网络以对miRNA进行优先级排序。接下来，对DEmiRNA和DEmRNA进行富集分析。最后，通过将基因列表导入dgidb数据库构建药物-基因相互作用网络。从两个数据集中共识别出3033个差异表达基因和58个DE miRNA。利用共表达网络分析构建基因共表达网络。接下来，根据Zsummary评分选择四个模块。下一步，构建二分miRNA-基因网络并选择枢纽miRNA（let-7a-2-3p、let-7d-5p、let-7b-5p、let-7a-5p和let-7b-3p）。最后，构建药物-基因网络，同时确定舒尼替尼、醋酸甲羟孕酮、多非利特、氟哌啶醇和骨化三醇药物为NSCLC中的有益药物。枢纽miRNA和重新利用的药物可能分别在NSCLC进展和治疗中发挥重要作用；然而，这些结果必须在进一步的临床和实验评估中得到验证。
