Ataei Atousa, Arab Seyed Shahriar, Zahiri Javad, Rajabpour Azam, Kletenkov Konstantin, Rizvanov Albert
Institute of Fundamental Medicine and Biology, Kazan (Volga Region) Federal University, Kazan, Russia.
Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran.
Iran J Biotechnol. 2021 Jul 1;19(3):e2643. doi: 10.30498/ijb.2021.209370.2643. eCollection 2021 Jul.
Gene expression profiling and prediction of drug responses based on the molecular signature indicate new molecular biomarkers which help to find the most effective drugs according to the tumor characteristics.
In this study two independent datasets, GSE28646 and GSE15372 were subjected to meta-analysis based on Affymetrix microarrays.
In-silico methods were used to determine differentially expressed genes (DEGs) in the previously reported sensitive and resistant A2780 cell lines to Cisplatin. Gene Fuzzy Scoring (GFS) and Principle Component Analysis (PCA) were then used to eliminate batch effects and reduce data dimension, respectively. Moreover, SVM method was performed to classify sensitive and resistant data samples. Furthermore, Wilcoxon Rank sum test was performed to determine DEGs. Following the selection of drug resistance markers, several networks including transcription factor-target regulatory network and miRNA-target network were constructed and Differential correlation analysis was performed on these networks.
The trained SVM successfully classified sensitive and resistant data samples. Moreover, Performing DiffCorr analysis on the sensitive and resistant samples resulted in detection of 27 and 25 significant (with correlation ≥|0.9|) pairs of genes that respectively correspond to newly constructed correlations and loss of correlations in the resistant samples.
Our results indicated the functional genes and networks in Cisplatin resistance of ovarian cancer cells and support the importance of differential expression studies in ovarian cancer chemotherapeutic agent responsiveness.
基于分子特征的基因表达谱分析和药物反应预测可发现新的分子生物标志物，有助于根据肿瘤特征找到最有效的药物。
本研究基于Affymetrix芯片对两个独立数据集GSE28646和GSE15372进行荟萃分析。
采用计算机模拟方法确定先前报道的对顺铂敏感和耐药的A2780细胞系中的差异表达基因（DEGs）。然后分别使用基因模糊评分（GFS）和主成分分析（PCA）消除批次效应并降低数据维度。此外，采用支持向量机（SVM）方法对敏感和耐药数据样本进行分类。进一步进行Wilcoxon秩和检验以确定差异表达基因。在选择耐药标志物后，构建了包括转录因子-靶标调控网络和miRNA-靶标网络在内的多个网络，并对这些网络进行了差异相关性分析。
训练后的支持向量机成功对敏感和耐药数据样本进行了分类。此外，对敏感和耐药样本进行差异相关性分析，分别检测到27对和25对显著（相关性≥|0.9|）的基因对，它们分别对应于耐药样本中新构建的相关性和相关性丧失。
我们的结果表明了卵巢癌细胞顺铂耐药中的功能基因和网络，支持了差异表达研究在卵巢癌化疗药物反应性中的重要性。
