Kwon Min-Seok, Kim Yongkang, Lee Seungyeoun, Namkung Junghyun, Yun Taegyun, Yi Sung Gon, Han Sangjo, Kang Meejoo, Kim Sun Whe, Jang Jin-Young, Park Taesung
BMC Genomics. 2015;16 Suppl 9(Suppl 9):S4. doi: 10.1186/1471-2164-16-S9-S4. Epub 2015 Aug 17.
microRNA (miRNA) expression plays an influential role in cancer classification and malignancy, and miRNAs are feasible as alternative diagnostic markers for pancreatic cancer, a highly aggressive neoplasm with silent early symptoms, high metastatic potential, and resistance to conventional therapies.
In this study, we evaluated the benefits of multi-omics data analysis by integrating miRNA and mRNA expression data in pancreatic cancer. Using support vector machine (SVM) modelling and leave-one-out cross validation (LOOCV), we evaluated the diagnostic performance of single- or multi-markers based on miRNA and mRNA expression profiles from 104 PDAC tissues and 17 benign pancreatic tissues. For selecting even more reliable and robust markers, we performed validation by independent datasets from the Gene Expression Omnibus (GEO) data depository. For validation, miRNA activity was estimated by miRNA-target gene interaction and mRNA expression datasets in pancreatic cancer.
Using a comprehensive identification approach, we successfully identified 705 multi-markers having powerful diagnostic performance for PDAC. In addition, these marker candidates annotated with cancer pathways using gene ontology analysis.
Our prediction models have strong potential for the diagnosis of pancreatic cancer.
微小RNA（miRNA）表达在癌症分类和恶性肿瘤形成中发挥着重要作用，并且miRNA作为胰腺癌的替代诊断标志物是可行的，胰腺癌是一种具有隐匿早期症状、高转移潜能且对传统疗法耐药的高侵袭性肿瘤。
在本研究中，我们通过整合胰腺癌中的miRNA和mRNA表达数据来评估多组学数据分析的益处。使用支持向量机（SVM）建模和留一法交叉验证（LOOCV），我们基于104个胰腺导管腺癌（PDAC）组织和17个良性胰腺组织的miRNA和mRNA表达谱评估了单标记或多标记的诊断性能。为了选择更可靠和稳健的标志物，我们通过来自基因表达综合数据库（GEO）的数据存档中的独立数据集进行验证。为了进行验证，通过胰腺癌中的miRNA-靶基因相互作用和mRNA表达数据集来估计miRNA活性。
使用全面的鉴定方法，我们成功鉴定出705个对PDAC具有强大诊断性能的多标记物。此外，这些候选标志物通过基因本体分析注释了癌症通路。
我们的预测模型在胰腺癌诊断方面具有强大的潜力。
