Eyuboglu Sinan, Alpsoy Semih, Uversky Vladimir N, Coskuner-Weber Orkid
Turkish-German University, Molecular Biotechnology, Sahinkaya Caddesi, No. 106, Beykoz, Istanbul 34820, Turkey.
USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA.
Comput Biol Chem. 2024 Dec;113:108268. doi: 10.1016/j.compbiolchem.2024.108268. Epub 2024 Oct 24.
Pancreatic ductal adenocarcinoma (PDAC) is recognized for its aggressive nature, dismal prognosis, and a notably low five-year survival rate, underscoring the critical need for early detection methods and more effective therapeutic approaches. This research rigorously investigates the molecular mechanisms underlying PDAC, with a focus on the identification of pivotal genes and pathways that may hold therapeutic relevance and prognostic value. Through the construction of a protein-protein interaction (PPI) network and the examination of differentially expressed genes (DEGs), the study uncovers key hub genes such as CDK1, KIF11, and BUB1, demonstrating their substantial role in the pathogenesis of PDAC. Notably, the dysregulation of these genes is consistent across a spectrum of cancers, positing them as potential targets for wide-ranging cancer therapeutics. This study also brings to the fore significant genes encoding intrinsically disordered proteins, in particular GPRC5A and KRT7, unveiling promising new pathways for therapeutic intervention. Advanced machine learning techniques were harnessed to classify PDAC patients with high accuracy, utilizing the key genetic markers as a dataset. The Support Vector Machine (SVM) model leveraged the hub genes to achieve a sensitivity of 91 % and a specificity of 85 %, while the RandomForest model notched a sensitivity of 91 % and specificity of 92.5 %. Crucially, when the identified genes were cross-referenced with TCGA-PAAD clinical datasets, a tangible correlation with patient survival rates was discovered, reinforcing the potential of these genes as prognostic biomarkers and their viability as targets for therapeutic intervention. This study's findings serve as a potent testament to the value of molecular analysis in enhancing the understanding of PDAC and in advancing the pursuit for more effective diagnostic and treatment strategies.
胰腺导管腺癌（PDAC）因其侵袭性、预后不佳以及显著较低的五年生存率而为人所知，这突出表明迫切需要早期检测方法和更有效的治疗方法。本研究严格调查了PDAC的分子机制，重点是识别可能具有治疗相关性和预后价值的关键基因和通路。通过构建蛋白质-蛋白质相互作用（PPI）网络和检测差异表达基因（DEG），该研究发现了关键的枢纽基因，如CDK1、KIF11和BUB1，证明了它们在PDAC发病机制中的重要作用。值得注意的是，这些基因的失调在一系列癌症中是一致的，使其成为广泛癌症治疗的潜在靶点。本研究还凸显了编码内在无序蛋白的重要基因，特别是GPRC5A和KRT7，揭示了有前景的新治疗干预途径。利用先进的机器学习技术，以关键遗传标记作为数据集，对PDAC患者进行了高精度分类。支持向量机（SVM）模型利用枢纽基因实现了91%的灵敏度和85%的特异性，而随机森林模型的灵敏度为91%，特异性为92.5%。至关重要的是，当将鉴定出的基因与TCGA-PAAD临床数据集进行交叉参考时，发现与患者生存率存在明显相关性，这加强了这些基因作为预后生物标志物的潜力及其作为治疗干预靶点的可行性。本研究结果有力地证明了分子分析在增进对PDAC的理解以及推进寻求更有效的诊断和治疗策略方面的价值。
