PURPOSE: The S100 gene family, which comprises over 20 members, including S100A1, S100A2, S100A8, S100A9, profilaggrin, and hornerin encodes low molecular weight calcium-binding proteins with physiological and pathological roles in keratinization. Recent studies have suggested a link between S100 proteins and human cancer progression. The purpose of the present study was to determine the expression levels of hornerin, S100A8, and S100A9 and evaluate their roles in the progression of invasive ductal carcinoma (IDC). METHODS: Seventy cases of ductal carcinoma in situ (DCIS), IDC, and metastatic carcinoma in lymph nodes (MCN) were included. Tissue microarrays were constructed from lesions of DCIS, IDC, and MCN from the same patients. Expression of hornerin, S100A8, and S100A9 was analyzed using immunohistochemistry. RESULTS: The expression of hornerin was associated with the estrogen receptor-negative (p=0.003) and the human epidermal growth factor receptor 2-positive (p=0.002) groups. The expression of S100A8 was associated with a higher pT stage (p=0.017). A significant (p<0.001) correlation between the expression of S100A9 and S100A8 was also found. The mean percentages of hornerin-positive tumor cells in DCIS, IDC, and MCN were 1.0%±3.3% (mean±standard deviation), 12.0%±24.0%, and 75.3%± 27.6%, respectively. The expression of hornerin significantly (p<0.001) increased with the progression of carcinoma. The mean levels of S100A8 and S100A9 in DCIS, IDC, and MCN were not significantly (p>0.050) different. The expression of hornerin increased in a stepwise manner (DCIS Breast Neoplasms* ; Breast* ; Calcium-Binding Proteins ; Carcinoma, Ductal ; Carcinoma, Intraductal, Noninfiltrating ; Estrogens ; Humans ; Immunohistochemistry ; Lymph Nodes ; Molecular Weight ; Receptor, Epidermal Growth Factor ; S100 Proteins

PURPOSE: Breast cancer has a high prevalence in Korea. To achieve personalized therapy for breast cancer, long-term follow-up specimens are needed for next-generation sequencing (NGS) and multigene analysis. Formalin-fixed paraffin-embedded (FFPE) samples are easier to store than fresh frozen (FF) samples. The objective of this study was to optimize RNA extraction from FFPE blocks for NGS. METHODS: RNA quality from FF and FFPE tissues (n=5), expected RNA amount per unit area, the relationship between archiving time and quantity/quality of FFPE-extracted RNA (n=14), differences in quantitative real-time polymerase chain reaction (qRT-PCR) and NGS results, and comparisons of both techniques with tissue processing at different institutions (n=96) were determined in this study. RESULTS: The quality of RNA did not show any statistically significant difference between paired FF and FFPE specimens (p=0.49). Analysis of tumor cellularity gave an expected RNA amount of 33.25 ng/mm2. Archiving time affected RNA quality, showing a negative correlation with RNA integrity number and a positive correlation with threshold cycle. However, RNA from samples as old as 10 years showed a 100% success rate in qRT-PCR using short primers, showing that the effect of archiving time can be overcome by proper experiment design. NGS showed a higher success rate than qRT-PCR. Specimens from institution B (n=46), which were often stored in a refrigerator for more than 6 hours and fixed without slicing, showed lower success rates and worse results than specimens from the other institutes. CONCLUSION: Archived FFPE tissues can be used to extract RNA for NGS if they are properly processed before fixation. The expected amount of RNA per unit size calculated in this study will be useful for other researchers.
Academies and Institutes ; Breast Neoplasms ; Estrogens ; Follow-Up Studies ; Humans ; Korea ; Prevalence ; Real-Time Polymerase Chain Reaction ; RNA* ; Sequence Analysis

Purpose: The appropriate evaluation of height and accurate estimation of bone age are crucial for proper assessment of the growth status of a child. We developed a bone age estimation program using a deep learning algorithm and established a model to predict the final adult height of Korean children. Materials and Methods: A total of 1678 radiographs from 866 children, for which the interpretation results were consistent between two pediatric endocrinologists, were used to train and validate the deep learning model. The bone age estimation algorithm was based on the convolutional neural network of the deep learning system. The test set simulation was performed by a deep learning program and two raters using 150 radiographs and final height data for 100 adults. Results: There was a statistically significant correlation between bone age interpreted by the artificial intelligence (AI) program and the reference bone age in the test set simulation (r=0.99, p<0.001). In the test set simulation, the AI program showed a mean absolute error (MAE) of 0.59 years and a root mean squared error (RMSE) of 0.55 years, compared with reference bone age, and showed similar accuracy to that of an experienced pediatric endocrinologist (rater 1). Prediction of final adult height by the AI program showed an MAE of 4.62 cm, compared with the actual final adult height. Conclusion We developed a bone age estimation program based on a deep learning algorithm. The AI-derived program demonstrated high accuracy in estimating bone age and predicting the final adult height of Korean children and adolescents.