Silva Camila Meirelles S, Barros-Filho Mateus C, Wong Deysi Viviana T, Mello Julia Bette H, Nobre Livia Maria S, Wanderley Carlos Wagner S, Lucetti Larisse T, Muniz Heitor A, Paiva Igor Kenned D, Kuasne Hellen, Ferreira Daniel Paula P, Cunha Maria Perpétuo S S, Hirth Carlos G, Silva Paulo Goberlânio B, Sant'Ana Rosane O, Souza Marcellus Henrique L P, Quetz Josiane S, Rogatto Silvia R, Lima-Junior Roberto César P
Department of Physiology and Pharmacology, Faculty of Medicine, Federal University of Ceará, Fortaleza 60430-270, Brazil.
International Research Center-CIPE, A.C. Camargo Cancer Center, Sao Paulo 01525-001, Brazil.
Cancers (Basel). 2021 Mar 24;13(7):1493. doi: 10.3390/cancers13071493.
Colorectal cancer (CRC) is a disease with high incidence and mortality. Colonoscopy is a gold standard among tests used for CRC traceability. However, serious complications, such as colon perforation, may occur. Non-invasive diagnostic procedures are an unmet need. We aimed to identify a plasma microRNA (miRNA) signature for CRC detection. Plasma samples were obtained from subjects ( = 109) at different stages of colorectal carcinogenesis. The patients were stratified into a non-cancer (27 healthy volunteers, 17 patients with hyperplastic polyps, 24 with adenomas), and a cancer group (20 CRC and 21 metastatic CRC). miRNAs (381) were screened by TaqMan Low-Density Array. A classifier based on four differentially expressed miRNAs (miR-28-3p, let-7e-5p, miR-106a-5p, and miR-542-5p) was able to discriminate cancer versus non-cancer cases. The overexpression of these miRNAs was confirmed by RT-qPCR, and a cross-study validation step was implemented using eight data series retrieved from Gene Expression Omnibus (GEO). In addition, another external data validation using CRC surgical specimens from The Cancer Genome Atlas (TCGA) was carried out. The predictive model's performance in the validation set was 76.5% accuracy, 59.4% sensitivity, and 86.8% specificity (area under the curve, AUC = 0.716). The employment of our model in the independent publicly available datasets confirmed a good discrimination performance in five of eight datasets (median AUC = 0.823). Applying this algorithm to the TCGA cohort, we found 99.5% accuracy, 99.7% sensitivity, and 90.9% specificity (AUC = 0.998) when the model was applied to solid colorectal tissues. Overall, we suggest a novel signature of four circulating miRNAs, i.e., miR-28-3p, let-7e-5p, miR-106a-5p, and miR-542-5p, as a predictive tool for the detection of CRC.
结直肠癌（CRC）是一种发病率和死亡率都很高的疾病。结肠镜检查是用于CRC溯源的检测方法中的金标准。然而，可能会出现严重并发症，如结肠穿孔。非侵入性诊断程序是尚未满足的需求。我们旨在确定一种用于CRC检测的血浆微小RNA（miRNA）特征。从处于结直肠癌发生不同阶段的受试者（n = 109）获取血浆样本。患者被分为非癌症组（27名健康志愿者、17名增生性息肉患者、24名腺瘤患者）和癌症组（20名CRC患者和21名转移性CRC患者）。通过TaqMan低密度阵列筛选miRNA（381种）。基于四种差异表达的miRNA（miR - 28 - 3p、let - 7e - 5p、miR - 106a - 5p和miR - 542 - 5p）构建的分类器能够区分癌症与非癌症病例。通过RT - qPCR证实了这些miRNA的过表达，并使用从基因表达综合数据库（GEO）检索到的八个数据系列进行了跨研究验证步骤。此外，还使用来自癌症基因组图谱（TCGA）的CRC手术标本进行了另一项外部数据验证。预测模型在验证集中的表现为准确率76.5%、灵敏度59.4%和特异性86.8%（曲线下面积，AUC = 0.716）。在独立的公开可用数据集中应用我们的模型，证实了在八个数据集中的五个数据集具有良好的区分性能（中位AUC = 0.823）。将该算法应用于TCGA队列时，当模型应用于实体结直肠组织时，我们发现准确率为99.5%、灵敏度为99.7%、特异性为90.9%（AUC = 0.998）。总体而言，我们提出一种由四种循环miRNA组成的新型特征，即miR - 28 - 3p、let - 7e - 5p、miR - 106a - 5p和miR - 542 - 5p，作为检测CRC的预测工具。
