Dataset Information

Predictive Value of Circulating miRNAs in Lymph Node Metastasis for Colon Cancer.

ABSTRACT: (1) Background: Lymph node (LN) status is an indubitable prognostic factor for survival among colon cancer patients. MicroRNAs (miRNAs) have been implicated in the development and progression of many cancers and are potential biomarkers for cancer diagnosis and prognosis. Therefore, we validated candidate biomarkers using circulating miRNAs by analyzing the plasma miRNA concentrations from patients with colon cancer to predict LN metastasis. (2) Methods: This study included 79 blood samples from patients diagnosed with colon cancer. The NanoString assay was used for screening, and TaqMan miRNA assays for quantitative real-time polymerase chain reaction (RT-PCR) test was used for validation. In a discovery set, we compared the expression of 800 circulating miRNAs in 24 samples (stage 0/I/IIA versus IIIB/IIIC). For validation, a total 79 samples were tested using quantitative RT-PCR. (3) Results: In the discovery set, 10 candidate circulating miRNAs were detected (4 up-regulated miRNAs: miR-323a-3p, miR-382-5p, miR-29a-3p, and miR-376a-3p; 6 down-regulated miRNAs: miR-26a-5p, let-7g-5p, miR-15b-5p, miR-142-3p, miR-374a-5p, and let-7b-5p). In the validation set, higher expression of three circulating miRNAs (miR-323a-3p, miR-382-5p, and miR-376a-3p) was significantly associated with LN metastasis (p = 0.0063, 0.0107, and 0.0022). (4) Conclusions: High expression of circulating miR-323a-3p, miR-382-5p, and miR-376a-3p was significantly associated with LN metastasis in colon cancer patients. These miRNAs could be circulating biomarker candidates that predict the presence of LN metastasis.

SUBMITTER: Lee IH

PROVIDER: S-EPMC7912296 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predictive Value of Circulating miRNAs in Lymph Node Metastasis for Colon Cancer.

Lee In Hee IH Kim Gyeonghwa G Kwak Sang Gyu SG Baek Dong Won DW Kang Byung Woog BW Kim Hye Jin HJ Park Su Yeon SY Park Jun Seok JS Choi Gyu-Seog GS Hur Keun K Kim Jong Gwang JG

Genes 20210127 2

(1) Background: Lymph node (LN) status is an indubitable prognostic factor for survival among colon cancer patients. MicroRNAs (miRNAs) have been implicated in the development and progression of many cancers and are potential biomarkers for cancer diagnosis and prognosis. Therefore, we validated candidate biomarkers using circulating miRNAs by analyzing the plasma miRNA concentrations from patients with colon cancer to predict LN metastasis. (2) Methods: This study included 79 blood samples from ...[more]

PMID: 33513887

Similar Datasets

Project description:BackgroundPathological complete response (pCR) with neoadjuvant chemotherapy (NAC) has been regarded as a surrogate endpoint for disease-free survival (DFS) and overall survival (OS) of patients with breast cancer. No consensus regarding the definition of pCR has been established; there are several definitions according to a variety of classifications. Eradication of cancer cells in both breast and lymph nodes has been better associated with improved prognosis than in the breast alone. Even in patients diagnosed as having clinically node-negative cancer before NAC, postoperative pathological examination often shows axillary lymph node metastases.Patients and methodsOf the 771 patients with breast cancer who underwent NAC in the Cancer Institute Hospital between January 2000 and May 2009, 146 patients preoperatively diagnosed as having node-negative breast cancer were retrospectively evaluated. We have made the definition of clinically lymph node-negative (N0) as follows: first, ultrasonography before NAC did not show any lymphadenopathy. Second, a cytological procedure confirmed negative study for each patient when ultrasonography suggested lymphadenopathy.ResultsThe median observation period was 79.7 months, and the median age of the subjects was 51 years. Pathological examination at the time of the surgery showed lymph node metastases (ypN+) in 46 patients (31.5%). Histological therapeutic effects revealed ypT0/is in 9 patients (6.2%) and ypTinv in 137 (93.8%). Multivariate analysis demonstrated that younger age (49>), large tumor size, NG3, and ypN+ were significant poor prognostic factors for DFS (p = 0.020, p = 0.008, P = 0.022 and p = 0.010, respectively). Moreover, ypN+ was the only significant poor prognostic factor for OS (p = 0.022). The predictive factors of ypN+ in clinically lymph node-negative breast cancer were ypTinv (p = 0.036) and the luminal type (HR+ and HER2-) (p = 0.029).ConclusionThe prognosis of clinically lymph node negative breast cancer depended on ypN+, which was associated with ypTinv and luminal subtype.

Project description:BACKGROUND:Preoperative detection of lymph node (LN) metastasis is critical for planning treatments in colon cancer (CC). The clinical diagnostic criteria based on the size of the LNs are not sensitive to determine metastasis using CT images. In this retrospective study, we investigated the potential value of CT texture features to diagnose LN metastasis using preoperative CT data and patient characteristics by developing quantitative prediction models. METHODS:A total of 390 CC patients, undergone surgical resection, were enrolled in this monocentric study. 390 histologically validated LNs were collected from patients and randomly separated into training (312 patients, 155 metastatic and 157 normal LNs) and test cohorts (78 patients, 39 metastatic and 39 normal LNs). Six patient characteristics and 146 quantitative CT imaging features were analyzed and key variables were determined using either exhaustive search or least absolute shrinkage algorithm. Two kernel-based support vector machine classifiers (patient-characteristic model and radiomic-derived model), generated with 10-fold cross-validation, were compared with the clinical model that utilizes long-axis diameter for diagnosis of metastatic LN. The performance of the models was evaluated on the test cohort by computing accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC). RESULTS:The clinical model had an overall diagnostic accuracy of 64.87%; specifically, accuracy of 65.38% and 62.82%, sensitivity of 83.87% and 84.62%, and specificity of 47.13% and 41.03% for training and test cohorts, respectively. The patient-demographic model obtained accuracy of 67.31% and 73.08%, the sensitivity of 62.58% and 69.23%, and specificity of 71.97% and 76.23% for training and test cohorts, respectively. Besides, the radiomic-derived model resulted in an accuracy of 81.09% and 79.49%, sensitivity of 83.87% and 74.36%, and specificity of 78.34% and 84.62% for training and test cohorts, respectively. Furthermore, the diagnostic performance of the radiomic-derived model was significantly higher than clinical and patient-demographic models (p?<?0.02) according to the DeLong method. CONCLUSIONS:The texture of the LNs provided characteristic information about the histological status of the LNs. The radiomic-derived model leveraging LN texture provides better preoperative diagnostic accuracy for the detection of metastatic LNs compared to the clinically accepted diagnostic criteria and patient-demographic model.

Dataset Information

Predictive Value of Circulating miRNAs in Lymph Node Metastasis for Colon Cancer.

Publications

Predictive Value of Circulating miRNAs in Lymph Node Metastasis for Colon Cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets