Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh.
Center for Systems Biology, Soochow University, Suzhou, China.
PLoS One. 2022 May 26;17(5):e0268967. doi: 10.1371/journal.pone.0268967. eCollection 2022.
Integrated bioinformatics and statistical approaches are now playing the vital role in identifying potential molecular biomarkers more accurately in presence of huge number of alternatives for disease diagnosis, prognosis and therapies by reducing time and cost compared to the wet-lab based experimental procedures. Breast cancer (BC) is one of the leading causes of cancer related deaths for women worldwide. Several dry-lab and wet-lab based studies have identified different sets of molecular biomarkers for BC. But they did not compare their results to each other so much either computationally or experimentally. In this study, an attempt was made to propose a set of molecular biomarkers that might be more effective for BC diagnosis, prognosis and therapies, by using the integrated bioinformatics and statistical approaches. At first, we identified 190 differentially expressed genes (DEGs) between BC and control samples by using the statistical LIMMA approach. Then we identified 13 DEGs (AKR1C1, IRF9, OAS1, OAS3, SLCO2A1, NT5E, NQO1, ANGPT1, FN1, ATF6B, HPGD, BCL11A, and TP53INP1) as the key genes (KGs) by protein-protein interaction (PPI) network analysis. Then we investigated the pathogenetic processes of DEGs highlighting KGs by GO terms and KEGG pathway enrichment analysis. Moreover, we disclosed the transcriptional and post-transcriptional regulatory factors of KGs by their interaction network analysis with the transcription factors (TFs) and micro-RNAs. Both supervised and unsupervised learning's including multivariate survival analysis results confirmed the strong prognostic power of the proposed KGs. Finally, we suggested KGs-guided computationally more effective seven candidate drugs (NVP-BHG712, Nilotinib, GSK2126458, YM201636, TG-02, CX-5461, AP-24534) compared to other published drugs by cross-validation with the state-of-the-art alternatives top-ranked independent receptor proteins. Thus, our findings might be played a vital role in breast cancer diagnosis, prognosis and therapies.
综合生物信息学和统计学方法在疾病诊断、预后和治疗方面具有巨大的潜力，通过与基于湿实验的实验程序相比，减少时间和成本，可以更准确地识别潜在的分子生物标志物。乳腺癌（BC）是全球女性癌症相关死亡的主要原因之一。已有多项干实验和湿实验研究为 BC 鉴定了不同的分子生物标志物集合。但它们要么在计算上，要么在实验上，都没有相互比较它们的结果。在这项研究中，我们尝试通过综合生物信息学和统计学方法，提出一组可能对 BC 诊断、预后和治疗更有效的分子生物标志物。首先，我们使用统计 LIMMA 方法在 BC 和对照样本之间鉴定出 190 个差异表达基因（DEGs）。然后，我们通过蛋白质-蛋白质相互作用（PPI）网络分析鉴定出 13 个 DEGs（AKR1C1、IRF9、OAS1、OAS3、SLCO2A1、NT5E、NQO1、ANGPT1、FN1、ATF6B、HPGD、BCL11A 和 TP53INP1）作为关键基因（KGs）。然后，我们通过 GO 术语和 KEGG 通路富集分析研究 DEGs 的致病过程，突出 KGs。此外，我们通过与转录因子（TFs）和 micro-RNAs 的相互作用网络分析，揭示了 KGs 的转录和转录后调节因子。监督和无监督学习，包括多变量生存分析结果，都证实了所提出的 KGs 具有很强的预后能力。最后，我们通过与最先进的替代方法的顶级独立受体蛋白进行交叉验证，建议了 KGs 指导的计算上更有效的七种候选药物（NVP-BHG712、尼罗替尼、GSK2126458、YM201636、TG-02、CX-5461、AP-24534），与其他已发表的药物相比。因此，我们的研究结果可能在乳腺癌的诊断、预后和治疗中发挥重要作用。
