GSE73002

• In the present study, the expression profiles of serum miRNA in several large cohorts have been examined to identify novel miRNA that can be used to detect early stage breast cancer.

• A total of 1280 serum samples of breast cancer patients stored in the National Cancer Center Biobank were used. In addition, 2836 serum samples were obtained from non-cancer controls, 451 from patients with other types of cancers, and 63 from patients with non-breast benign diseases.

• miRNA expressions were compared between patients with breast cancer and non-breast cancer, and a combination of five miRNA was found to be able to detect breast cancer. 
  1. miR-1246
  2. miR-1307-3p
  3. miR-4634
  4. miR-6861-5p
  5. miR-6875-5p
• This combination had a sensitivity of 97.3%, specificity of 82.9% and accuracy of 89.7% for breast cancer in the test cohort.

• A total of 400 patients with other cancers (100, 98, 50, 50, 50 and 52 cases of pancreatic, biliary tract, colon, gastric, esophageal and hepatic cancers, respectively) and 21 patients with non-malignant pancreatic ⁄ biliary tract disease who were registered in the National Cancer Center (NCC) Biobank were included to determine the circulating miRNA in control subjects. 
• Whole miRNA expression profiles of these serum samples are present in the Gene Expression Omnibus (GEO) database (GSE59856), and the registered data and clinical information(21) were used in the present study.

• To normalize the signals across the different microarrays tested, three pre-selected internal control miRNA (miR-149-3p, miR-2861 and miR-4463), which had been stably detected in more than 500 serum samples, were used.
• Each miRNA signal value was standardized with the ratio of the average signal value of the three internal control miRNA to the pre-set value.

• The diagnostic index showed high performance for all cancer stages (stage 0, 98.0%; stage 1, 98.1%; stage 2, 95.7%; stage 3, 100%; stage 4, 96.2%), and TNM grades (T: Tis, 97.7%; T1, 98.2%; T2, 95.8%; T3, 90.9%; T4, 97.1%; N: N0, 97.2%; N1, 98.2%; N2, 92.9%; N3, 100%; and M: M0, 97.3%; M1, 96.2%).

• A problem with the developed diagnostic index is that it cannot distinguish benign breast diseases from breast cancer.
• With this diagnostic index, benign breast diseases were identified as breast cancer. Therefore, it might be used for breast hyperplasia.
• However, as one aim of the present study was to identify an initial screening technique that provides good reasoning and motivation for more women to undergo mammography, we believe that the problem of false-positive results of the diagnostic index caused by benign breast diseases is acceptable at the present time. 
• However, studies should be performed in the future to improve the diagnostic index so that it can distinguish benign breast diseases from breast cancer.

• Here, we compared breast benign disease cases with non-cancer cases.

• A classification model has been built using Trainset. The selected probe was:
  1. hsa-mir-1307-3p

• The model has been tested using Testset.