https://dspace.ewha.ac.kr/handle/2015.oak/171700 Fri, 10 Apr 2026 08:58:29 GMT 2026-04-10T08:58:29Z https://dspace.ewha.ac.kr/handle/2015.oak/275067 Title: Immune Microenvironment Signatures Predict Response and Survival in Rectal Cancer Patients After Neoadjuvant Chemoradiation Ewha Authors: 이동환 Abstract: Background/Aim: Response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer varies. Recent studies have highlighted the role of the tumor immune microenvironment in influencing tumor behavior. Herein, we aimed to assess immune-related gene expression in rectal cancer following nCRT and to investigate their potential as predictive and prognostic biomarkers. Materials and Methods: Expression profiling of 730 immune-related genes was conducted in 48 post-nCRT rectal cancer using the NanoString nCounter platform and the PanCancer Immune Profiling panel. Differentially expressed genes were compared between good and poor responders, and gene set enrichment analysis was conducted. The prognostic significance of these genes was analyzed. A genetic model was generated to predict nCRT responses. Results: We identified 24 immune-associated genes that were differentially expressed between good and poor responders, among which S100A8, SPINK5, ANXA1, FOXJ1, and CLEC7A showed high expression levels in good responders (Log2 fold change >1, p<0.05). Pathway analysis revealed that these genes were mainly involved in biological process associated with natural killer cell-mediated cytotoxicity. S100A8 and SPINK5 expression levels were associated with relapse-free survival (p=0.001 and 0.036, respectively), and these findings were validated in a publicly available dataset (S100A8; p=0.015, and SPINK5; p=0.024). The accuracy of the predictive model comprising TLR4, CCND3, TCF7, CREB5, TNFRSF10B, DPP4, PBK, DUSP4, and MUC1 was 85.7%. Conclusion: Immune-related gene expression patterns are associated with response to nCRT in rectal cancer. High expression levels of S100A8, SPINK5, ANXA1, FOXJ1, and CLEC7A were indicative of favorable treatment response, and S100A8 and SPINK5 were associated with prognosis. A machine learning-based model composed of immune-related genes showed strong predictive potential. Our results support the use of immune gene signatures to guide personalized therapy in rectal cancer. © 2026 The Author(s). Thu, 01 Jan 2026 00:00:00 GMT https://dspace.ewha.ac.kr/handle/2015.oak/275067 2026-01-01T00:00:00Z https://dspace.ewha.ac.kr/handle/2015.oak/274955 Title: Causal analysis of traditional and environmental risk factors for long-term development of type 2 diabetes using a conditional survival Bayesian network: evidence from the Korean Genome and Epidemiology Study Ewha Authors: 오만숙 Abstract: Background: Over the past decade, traditional demographic, lifestyle, and metabolic factors, along with air pollutants, have increasingly been recognized as key contributors to type 2 diabetes (T2D). However, the comprehensive causal structure among these factors and their individual and interacting interventional effects have seldom been characterized in long-term population studies. Methods: Using 11-year follow-up data from 2,102 adults without T2D in the Ansan cohort of the Korean Genome and Epidemiology Study (KoGES), we investigated causal pathways among demographic, lifestyle, metabolic factors, and multiple ambient air pollutants leading to long-term T2D incidence. We employed a Conditional Survival Bayesian Network (CSBN), which integrates survival analysis with Bayesian network modeling to accommodate censored and incomplete data, to visualize the causal structure among risk factors, and to estimate both individual and joint (interaction) interventional effects. Results: The CSBN depicted a holistic causal structure showing how multiple risk factors jointly shape T2D development over the 11-year follow-up and helped distinguish putative direct/indirect pathways from associations likely reflecting confounding. Interventional analysis quantified each factor’s causal contribution to the 11-year T2D incidence. Obesity produced the largest individual effect: setting BMI to the obese category approximately doubled 11-year T2D risk compared with normal weight. High alanine aminotransferase (ALT) and older age increased risk by about 40–50%, while family history of T2D, dyslipidemia, overweight,, and gaseous pollutants had intermediate effects. Furthermore, the CSBN uncovered synergistic interactions mainly among metabolic factors. In particular, ALT with family history, dyslipidemia, or obesity displayed strong additive interactions. By contrast, air pollutants were found to influence T2D independently rather than through interactions with other risk factors. Conclusion: These findings underscore the importance of integrated public health strategies targeting multiple risk factors to effectively curb T2D incidence. The CSBN’s capability to explicitly model complex causal interactions highlights the necessity for advanced epidemiological analyses to inform targeted preventive measures and efficient resource allocation. © The Author(s) 2026. Thu, 01 Jan 2026 00:00:00 GMT https://dspace.ewha.ac.kr/handle/2015.oak/274955 2026-01-01T00:00:00Z https://dspace.ewha.ac.kr/handle/2015.oak/274849 Title: Individual and interaction effects of health determinants on health-related quality of life in Korean adults aged 50–81 years: A causal Bayesian network analysis Ewha Authors: 오만숙 Abstract: Health-related quality of life (HRQoL) reflects physical and mental well-being and is increasingly important in aging populations, yet traditional approaches often fail to capture the complex causal pathways among its determinants. We analyzed 2,566 adults aged 50–81 years from the Korean Genome and Epidemiology Study using the Short Form-12 (Physical Component Summary [PCS] and Mental Component Summary [MCS]). A causal Bayesian network was learned using the PC algorithm of Spirtes and Glymour with hierarchical constraints to ensure causal interpretability. We then estimated the causal effects of each variable on tail-defined outcomes—poor (bottom quartile) and good (top quartile) PCS and MCS—and quantified pairwise interaction effects. The network revealed how upstream factors propagate through direct and indirect pathways to shape HRQoL. Notably, PCS and MCS shared common upstream causes but showed no direct causal connection. Quantifying these causal pathways through relative risk (RR) estimates revealed the magnitude of individual factor effects. For poor PCS, severe insomnia (RR = 1.98), high stress (RR = 1.45), low physical activity (RR = 1.39), and multimorbidity (RR = 1.36) were the principal risk factors. For poor MCS, high stress (RR = 3.28) and severe insomnia (RR = 2.72) dominated. Notably, low BMI increased poor MCS risk (RR = 1.20), consistent with frailty pathways. The patterns for good outcomes largely mirrored these findings, with favorable levels showing protective effects. Interaction analyses revealed substantial synergistic effects: severe insomnia with high stress increased poor MCS probability by 6.44 percentage points (pp) beyond additivity, while high stress with physical inactivity added 4.77 pp. For good MCS, low insomnia with low stress (+4.72 pp) and low BMI with exercise (+4.21 pp) showed synergy, whereas stress with inactivity exhibited antagonism (–4.00 pp). These results support integrated interventions that combine sleep improvement, stress reduction, physical activity promotion, and multimorbidity management to improve HRQoL in aging populations. © 2026 Lee, Oh. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Thu, 01 Jan 2026 00:00:00 GMT https://dspace.ewha.ac.kr/handle/2015.oak/274849 2026-01-01T00:00:00Z https://dspace.ewha.ac.kr/handle/2015.oak/274747 Title: New continuous bivariate distributions developed based on general shock models Ewha Authors: 차지환 Abstract: In this study, we develop novel classes of continuous bivariate distributions based on general shock models. One class is that of absolutely continuous bivariate distributions, whereas the other one is that of non-absolutely continuous bivariate distributions. These classes are versatile in the sense that they can generate numerous families of distributions. We explore the distributional characteristics of the proposed classes, examining the bivariate ageing property and the dependence structure. Under some conditions, the proposed class of distributions satisfy certain kind of dependence property, called conditional PQD. Our result also reveals that a well-defined subclass of the proposed class satisfies the bivariate lack of memory property. Finally, we generate particular distribution families and apply them to two real-world datasets to illustrate their usefulness. © 2026 Elsevier B.V. Thu, 01 Jan 2026 00:00:00 GMT https://dspace.ewha.ac.kr/handle/2015.oak/274747 2026-01-01T00:00:00Z