Faculty of Engineering, Department of Computer Engineering, Dokuz Eylul University, Izmir, Turkey.
Department of Translational Oncology, Institute of Health Sciences, Dokuz Eylul University, Izmir, Turkey.
PLoS One. 2022 Apr 29;17(4):e0267973. doi: 10.1371/journal.pone.0267973. eCollection 2022.
Adenomatous polyps of the colon are the most common neoplastic polyps. Although most of adenomatous polyps do not show malign transformation, majority of colorectal carcinomas originate from neoplastic polyps. Therefore, understanding of this transformation process would help in both preventive therapies and evaluation of malignancy risks. This study uncovers alterations in gene expressions as potential biomarkers that are revealed by integration of several network-based approaches. In silico analysis performed on a unified microarray cohort, which is covering 150 normal colon and adenomatous polyp samples. Significant gene modules were obtained by a weighted gene co-expression network analysis. Gene modules with similar profiles were mapped to a colon tissue specific functional interaction network. Several clustering algorithms run on the colon-specific network and the most significant sub-modules between the clusters were identified. The biomarkers were selected by filtering differentially expressed genes which also involve in significant biological processes and pathways. Biomarkers were also validated on two independent datasets based on their differential gene expressions. To the best of our knowledge, such a cascaded network analysis pipeline was implemented for the first time on a large collection of normal colon and polyp samples. We identified significant increases in TLR4 and MSX1 expressions as well as decrease in chemokine profiles with mostly pro-tumoral activities. These biomarkers might appear as both preventive targets and biomarkers for risk evaluation. As a result, this research proposes novel molecular markers that might be alternative to endoscopic approaches for diagnosis of adenomatous polyps.
结肠腺瘤性息肉是最常见的肿瘤性息肉。尽管大多数腺瘤性息肉不会发生恶性转化，但大多数结直肠癌起源于肿瘤性息肉。因此，了解这种转化过程将有助于预防性治疗和评估恶性风险。本研究通过整合几种基于网络的方法，揭示了基因表达变化作为潜在的生物标志物。对包含 150 个正常结肠和腺瘤性息肉样本的统一微阵列队列进行了计算机模拟分析。通过加权基因共表达网络分析获得了显著的基因模块。具有相似图谱的基因模块被映射到结肠组织特异性功能相互作用网络上。对结肠特异性网络和聚类之间的最显著子模块进行了几种聚类算法的运行。通过筛选涉及重要生物学过程和途径的差异表达基因来选择生物标志物。根据其差异基因表达，生物标志物也在两个独立数据集上进行了验证。据我们所知，这种级联网络分析管道首次在大量正常结肠和息肉样本上实施。我们发现 TLR4 和 MSX1 的表达显著增加，以及大多数具有促肿瘤活性的趋化因子谱的减少。这些生物标志物可能作为预防靶点和风险评估的生物标志物出现。因此，本研究提出了新的分子标志物，可能替代内镜方法诊断腺瘤性息肉。
