Dataset Information

A Novel Saliva-Based miRNA Signature for Colorectal Cancer Diagnosis.

ABSTRACT: Salivary microRNAs (miRNAs) are of high interest as diagnostic biomarkers for non-oral cancer. However, little is known about their value for colorectal cancer (CRC) detection. Our study aims to characterize salivary miRNAs in order to identify non-invasive markers for CRC diagnosis. The screening of 754 miRNAs was performed in saliva samples from 14 CRC and 10 healthy controls. The differential expressed miRNAs were validated by RT-qPCR in 51 CRC, 19 adenomas and 37 healthy controls. Receiver operating characteristic (ROC) curves and logistic regression models were performed to analyze the clinical value of these miRNAs. Twenty-two salivary miRNAs were significantly deregulated in CRC patients vs. healthy individuals (P < 0.05) in the discovery phase. From those, five upregulated miRNAs (miR-186-5p, miR-29a-3p, miR-29c-3p, miR-766-3p, and miR-491-5p) were confirmed to be significantly higher in the CRC vs. healthy group (P < 0.05). This five-miRNA signature showed diagnostic value (72% sensitivity, 66.67% specificity, AUC = 0.754) to detect CRC, which was even higher in combination with carcinoembryonic antigen (CEA) levels. Overall, after the first global characterization of salivary miRNAs in CRC, a five-miRNA panel was identified as a promising tool to diagnose this malignancy, representing a novel approach to detect cancer-associated epigenetic alterations using a non-invasive strategy.

SUBMITTER: Rapado-Gonzalez O

PROVIDER: S-EPMC6947363 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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A Novel Saliva-Based miRNA Signature for Colorectal Cancer Diagnosis.

Rapado-González Óscar Ó Majem Blanca B Álvarez-Castro Ana A Díaz-Peña Roberto R Abalo Alicia A Suárez-Cabrera Leticia L Gil-Moreno Antonio A Santamaría Anna A López-López Rafael R Muinelo-Romay Laura L Suarez-Cunqueiro María Mercedes MM

Journal of clinical medicine 20191120 12

Salivary microRNAs (miRNAs) are of high interest as diagnostic biomarkers for non-oral cancer. However, little is known about their value for colorectal cancer (CRC) detection. Our study aims to characterize salivary miRNAs in order to identify non-invasive markers for CRC diagnosis. The screening of 754 miRNAs was performed in saliva samples from 14 CRC and 10 healthy controls. The differential expressed miRNAs were validated by RT-qPCR in 51 CRC, 19 adenomas and 37 healthy controls. Receiver o ...[more]

PMID: 31757017

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Project description:Background: Colorectal cancer (CRC) is a common malignant tumor that affects the large bowel or the rectum. Cuproptosis, recently discovered programmed cell death process, may play an important role in CRC tumorigenesis. Long non-coding RNAs (lncRNAs) can alter the proliferation of colorectal cancer cells through the control and activation of gene expression. To date, cuproptosis-related lncRNAs, have not been investigated as potential predictive biomarkers in colorectal cancer. Methods: The mRNA and lncRNA expression data of colorectal cancer were gathered from The Tumor Genome Atlas (TCGA) database, and Pearson correlation analysis and univariate Cox regression analysis were used to identify the lncRNAs with differential prognosis. Colorectal cancer was classified using consistent clustering, and the clinical significance of different types, tumor heterogeneity, and immune microenvironment differences was investigated. The differential lncRNAs were further screened using LASSO regression to develop a risk scoring model, which was then paired with clinicopathological variables to create a nomogram. Finally, the copy number changes in the high-risk and low-risk groups were compared. Results: Two clusters were formed based on the 28 prognostic cuproptosis-related lncRNAs, and the prognosis of cluster 2 was found to be significantly lower than that of cluster 1. Cluster 1 showed increased immune cell infiltration and immunological score, as well as strong enrichment of immune checkpoint genes. Next, LASSO regression was used to select 11 distinctive lncRNAs, and a risk score model was constructed using the training set to distinguish between high and low-risk groups. Patients in the high-risk group had a lower survival rate than those in the low-risk group, and both the test set and the total set produced consistent results. The AUC value of the ROC curve revealed the scoring model's efficacy in predicting long-term OS in patients. Moreover, the model could be used as an independent predictor when combined with a multivariate analysis of clinicopathological features, and our nomogram could be used intuitively to predict prognosis. Conclusion: Collectively, we developed a risk model using 11 differential lncRNAs and demonstrated that the model has predictive value as well as clinical and therapeutic implications for predicting prognosis in CRC patients.

Project description:To identify the novel, noninvasive biomarkers to assess the outcome and prognosis of breast cancer (BC), patients with high sensitivity and specificity are greatly desired. Herein, the miRNA expression profile and matched clinical features of BC patients were extracted from The Cancer Genome Atlas (TCGA) database. The preliminary candidates were screened out by the univariate Cox regression test. Then, with the help of LASSO Cox regression analysis, the hsa-let-7b, hsa-mir-101-2, hsa-mir-135a-2, hsa-mir-22, hsa-mir-30a, hsa-mir-31, hsa-mir-3130-1, hsa-mir-320b-1, hsa-mir-3678, hsa-mir-4662a, hsa-mir-4772, hsa-mir-493, hsa-mir-556, hsa-mir-652, hsa-mir-6733, hsa-mir-874, and hsa-mir-9-3 were selected to construct the overall survival (OS) predicting signature, while the hsa-mir-130a, hsa-mir-204, hsa-mir-217, hsa-mir-223, hsa-mir-24-2, hsa-mir-29b-1, hsa-mir-363, hsa-mir-5001, hsa-mir-514a-1, hsa-mir-624, hsa-mir-639, hsa-mir-659, and hsa-mir-6892 were adopted to establish the recurrence-free survival (RFS) predicting signature. Referring to the median risk scores generated by the OS and RFS formulas, respectively, subgroup patients with high risk were strongly related to a poor OS and RFS revealed by Kaplan-Meier (K-M) plots. Meanwhile, receiver operating curve (ROC) analysis validated the accuracy and stability of these two signatures. When stratified by clinical features, such as tumor stage, age, and molecular subtypes, we found that the miRNA-based OS and RFS classifiers were still significant in predicting OS/RFS and showed the best predictive values than any other features. Besides, functional prediction analyses showed that these targeted genes of the enrolled miRNAs were enriched in cancer-associated pathways, such as MAPK/RTK, Ras, and PI3K-Akt signaling pathways. In summary, our observations demonstrate that the novel miRNA-based OS and RFS signatures are independent prognostic indicators for BC patients and worthy to be validated by further prospective studies.

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Dataset Information

A Novel Saliva-Based miRNA Signature for Colorectal Cancer Diagnosis.

Publications

A Novel Saliva-Based miRNA Signature for Colorectal Cancer Diagnosis.

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