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陳中興助理教授

陳中興 助理教授 Chung-Hsing Chen
chen

最高學歷：國立中央大學數學所博士

最高學歷: 國立中央大學數學所博士

學術專長: 生物統計、生物資訊 (詳細如下所示)

開課名稱: Python數據科學導論
          教育統計方法
          Matlab程式設計

Office Hour: 三 6-8

教師研究室：T408B(靠校門)

連絡電話：(02)2311-3040 轉分機 1902

E-mail信箱：chchen@uTaipei.edu.tw

Last updated：2026 /07 /02

 

【國科會研究計畫(腫瘤學門)】

  1. 探討TMB型大腸直腸癌之不良預後的關鍵基因組與訊號路徑(延續計畫)(Investigation of Key Gene Sets and Signaling Pathways Underlying the Poor Prognosis of TMB-Type Colorectal Cancer (Continued))(115.08- 116.07) 主持人
  2. 探討TMB型大腸直腸癌之不良預後的關鍵基因組與訊號路徑(Investigation of Key Gene Sets and Signaling Pathways Underlying the Poor Prognosis of TMB-Type Colorectal Cancer)(114.08- 115.07) 主持人
  3. 鑑定並探討一組導致胰腺癌組織間質增生的基因組供臨床應用(Identification and Investigation of a Desmoplasigenic Gene Set for Clinical Applications in Pancreatic Adenocarcinoma)(114.08- 115.07) 共同主持人

【專長簡介】

    作為一名生物統計學家，我專注於利用各種生物醫學數據開發具有臨床應用價值的生物標記，包括遺傳1,17、轉錄/基因表達2-12 (例如，微陣列、蛋白質體學、核糖核酸定序或單細胞核糖核酸定序)、DNA甲基化13-15和RNA干擾16等數據。
    這些生物標記的出現使得醫生可以根據每位病患的生物標記特性，精準地選擇合適的藥物。相較於傳統治療方法，「精準醫療」提供了病人更好的治療策略。而在這種「精準醫療」的趨勢下，對於分析這類數據所得到的應用性，提供了巨大的潛力。然而，如何選擇適當的統計方法來分析數據並合理的解釋結果仍存在相當大的不確定性，這使得這些研究的再現性變得困難。為了減少這種不確定性，我將生物醫學數據的分析流程系統地分為四個部分：數據預處理、統計推斷、生物學解釋和考慮在公共衛生方面的潛在影響。使用此流程將能夠更嚴謹的分析數據，提高對這些生物標記的臨床意義的理解，同時也為其他研究者在探索可能的機制時提供更強而有力的證據支持。這些流程不僅需要對統計推論和生物學知識有一定程度的理解，更重要的是與其他領域研究者一起合作的團隊意識。
    目前研究的主題是關於癌症治療相關的生物標記，包括急性骨髓性白血病7,8,11、大腸直腸癌3,5,10,15,18與胰臟癌。
    此外，我也關注於流行病學中已知與未知風險因子對大眾健康的影響。散佈於環境中的有害物質，例如塑化劑4,13、全氟/多氟烷基物質（PFAS）14與無機砷19等，對健康造成可能的潛在危害。這些研究將著重於如何揭示這些有害物質對健康的影響機制，並將研究成果轉化為對社會大眾有益的資訊，進而作為政府制定公共政策的重要依據。
    大二或大三同學歡迎旁聽我們Meeting，了解數據分析在真實世界中所扮演的腳色，有興趣請寫信讓我知道。
 
 

【Brief Summary of Professional Background】

     I am a biostatistician specializing in the development of biomarkers based on various biomedical big data, including genetic data1,17, transcript/gene expression data2-12 (for example, microarray, proteomics, RNA-seq or single cell RNA-seq), DNA methylation data13-15, and RNAi data16.
       These data provide the opportunity to identify biomarkers for precision medicine. However, the selection of an appropriate statistical model for analyzing this data remains challenging, making result reproducibility difficult. Specifically, I have systematically divided the analysis pipeline of bio-medical big data into four parts: i) data preprocessing, ii) statistical inferences, iii) biological interpretation, and iv) considerations for public health implications. This pipeline could improve the clinical significance of biomarkers and provide evidence-based support when investigating underlying mechanisms. All of these require a sophisticated understanding of statistical modeling, compu-tational inference, and biological knowledge.
       I am interested in identifying and characterizing biomarkers for the diagnosis, prognosis, and therapeutic response in cancer treatments, including chemotherapy, radiation therapy, and immune-therapy, with a specific focus on acute myeloid leukemia7,8,11, colorectal cancer3,5,10,15,18, and pancreatic cancer.
       Additionally, I am interested in investigating risk factors in epidemiologic studies. Environmental contaminants—such as plasticizers4,13, perfluoroalkyl substances (PFAS)14, and inorganic arsenic19—may pose potential effects to human health. These researches focus on the biological mechanisms underlying these exposures and their associated health effects. These studies would have a significant impact on public health and provide valuable evidence for government policies.
 
 
Selected non-MDPI references (**Co-first author; *Co-corresponding author)

  1. Batel Blechter*, Xiaoyu Wang, Juncheng Dai, …, Chung-Hsing Chen, et al. (2026) Stratifying lung adenocarcinoma risk with multi-ancestry polygenic risk scores in East Asian never-smokers. Journal of the National Cancer Institute, 118(2), 231–249. [link]

  2. Chun-Chao Wang*, Sabareeswaran Krishnan, Yen-Hsun Wang, Kai-Chen Hsu, Tzu-Sheng Hsu, Chung-Hsing Chen, Shang-Hung Chen, Ren-Hua Chung* (2026) Identification and functional validation of EPS15L1 as a key driver of triple-negative breast cancer. npj Breast Cancer, https://doi.org/10.1038/s41523-026-00954-9. [link]
  3. Shang-Yin Wu, Po-Chuan Chen, Ren-Hao Chan, Chung-Hsing Chen, Yi-Hsuan Huang, Yi-Jing Huang, Bing-Syuan Chung, Che-Hung Shen, Hsin-Yu Kuo, Jui-Wen Kang, Chung-Ta Lee, Hui-Ju Tsai, Yu-Chen Fang, Peng-Chan Lin, Yu-Min Yeh*, Shang-Hung Chen* (2026) Single-cell analysis reveals crosstalk between TREM1-positive myeloid cells and cancer-associated fibroblasts in colorectal cancer progression. Journal of Gastroenterology, https://doi.org/10.1007/s00535-026-02430-4. [link]
  4. Yi-Ju Wu**, Hui-Ju Wen**, Chung-Hsing Chen, Shin-Fen Tsai, Shih Sheng Jiang*, Shu-Li Wang*, TMICS group (2025) Gene expressions in newborns and the association with prenatal exposure to phthalate plasticizers – The Taiwan Maternal and Infant Cohort Study (TMICS). Journal of Hazardous Materials, https://doi.org/10.1016/j.jhazmat.2025.140355. [link]
  5. Sina Taheri Baghmisheh, Chung-Hsing Chen, Yu‐Min Yeh, Peng‐Chan Lin, Po‐Chuan Chen, Ren‐Hao Chan, Jui-Wen Kang, Chung‐Ta Lee, Hui-Ju Tsai, Yu-Chen Fang, Chun Hei Antonio Cheung, Kwang-Yu Chang, Jang-Yang Chang, Shang‐Hung Chen* (2025) Cathepsin S regulates antitumor immunity through autophagic degradation of PD-L1 in colorectal cancer cells. Cancer Immunology, Immunotherapy, 74: 287. [link]
  6. Yu-Lin Chen*, Amir Yousif, Chung-Hsing Chen, Ava Lowin, Abbey A. Saadey, SsuHan Wang, Shih Sheng Jiang, Ya-Wen Chen*, Hazem E. Ghoneim* (2025) Chronic ISG15 exposure accelerates CD8+ T cell dysfunction while increasing PD-1 blockade sensitivity in oral squamous cell carcinoma. Cancer Immunology Research, 13 (11): 1829-1844. [link]
  7. Ting-Shuan Wu**, Tzu-Hung Hsiao**, Chung-Hsing Chen, Hsin-Ni Li, Miao-Neng Hung, Pei-Pei Jhan, Jia-Rung Tsai, Chieh-Lin Jerry Teng* (2025) ARTN and CCL23 predicted chemosensitivity in acute myeloid leukemia: an Olink® proteomics approach. Clinical Proteomics, 22: 3. [link]
  8. Chung-Hsing Chen**, Tsung-Chih Chen**, Ting-Shuan Wu, Tzu-Hung Hsiao, Chi-Ying F. Huang, Po-Liang Cheng, Jia-Rung Tsai, Chieh‐Lin Jerry Teng* (2024) Myeloperoxidase and thyrotropin-releasing hormone within leukemia stem cells increased chemosensitivity in acute myeloid leukemia. Journal of Cellular and Molecular Medicine, 28(24): e70306. [link] [SCP2316]
  9. Yen-Liang Li**, Chung-Hsing Chen**, You-Syuan Lai, Mei-Ren Pan, Wen-Chun Hung* (2024) Increase of blood CSF3R+ myeloid-derived suppressor cell predicts breast cancer recurrence. American Journal of Cancer Research, 14(6): 3171-3185. [link] [SCP1868]
  10. Yu-Ting Su, Chung-Hsing Chen, Jui-Wen Kang, Hsin-Yu Kuo, Ching-Chieh Yang, Yu-Feng Tian, Cheng-Fa Yeh, Chia-Lin Chou, and Shang-Hung Chen* (2024) Predictive value of FCGBP expression for treatment response and survival in rectal cancer patients undergoing chemoradiotherapy. Aging‑US, 16(9): 7889-7901. [link]
  11. Po-Liang Cheng**, Tzu-Hung Hsiao**, Chung-Hsing Chen, Miao-Neng Hung, Pei-Pei Jhan, Li-Wen Lee, Jia-Rung Tsai, Chieh-Lin Jerry Teng* (2023) Chemoresistance in acute myeloid leukemia: An alternative single-cell RNA sequencing approach. Hematological oncology, 41(3): 499-509. [link]
  12. Yen-Liang Li**, Chung-Hsing Chen**, Jing-Yi Chen, You-Syuan Lai, Shao-Chun Wang, Shih Sheng Jiang*, Wen-Chun Hung* (2020) Single-cell analysis reveals immune modulation and metabolic switch in tumor-draining lymph node. Oncoimmunology, 9(1): 1830513. [link]
  13. Chung-Hsing Chen, Shih Sheng Jiang*, I-Shou Chang, Hui-Ju Wen, Chien-Wen Sun, Shu-Li Wang* (2018) Association between fetal exposure to phthalate endocrine disruptor and genome-wide DNA methylation at birth. Environmental Research, 162: 261-270. [link]
  14. Ryu Miura, Atsuko Araki, Chihiro Miyashita, Sumitaka Kobayashi, Sachiko Kobayashi, Shu-Li Wang, Chung-Hsing Chen, Kunio Miyake, Mayumi Ishizuka, Yusuke Iwasaki, Yoichi M Ito, Takeo Kubota, Reiko Kishia* (2018) An epigenome-wide study of cord blood DNA methylations in relation to prenatal perfluoroalkyl substance exposure: The Hokkaido study. Environment International, 115: 21-28. [link]
  15. Chung-Hsing Chen, Shih Sheng Jiang, Ling-Ling Hsieh, Reiping Tang, Chao A. Hsiung, Hui-Ju Tsai*, I-Shou Chang* (2016) DNA methylation identifies loci distinguishing hereditary nonpolyposis colorectal cancer without germline MLH1/MSH2 mutation from sporadic colorectal cancer. Clinical and Translational Gastroenterology, 7(12): e208. [link]
  16. Chung-Hsing Chen, Wen-Chi Su, Chih-Yu Chen, Jing-Ying Huang, Fang-Yu Tsai, Wen-Chang Wang, Chao A. Hsiung, King-Song Jeng, I-Shou Chang* (2012) A Bayesian measurement error model for two-channel cell-based RNAi data with replicates. Annals of Applied Statistics, 6(1): 356-382. [link]
  17. Li-Hsin Chien, Chung-Hsing Chen, Tzu-Yu Chen, Gee-Chen Chang, Ying-Huang Tsai, Chin-Fu Hsiao, Kuan-Yu Chen, Wu-Chou Su, Wen-Chang Wang, Ming-Shyan Huang, Yuh-Min Chen, Chih-Yi Chen, Sheng-Kai Liang, Chung-Yu Chen, Chih-Liang Wang, Mei-Hsuan Lee, Ren-Hua Chung, Fang-Yu Tsai, Jia-Wei Hu, Hormuzd A. Katki, Nilanjan Chatterjee, Stephen J. Chanock, Nathaniel Rothman, Qing Lan, Pan-Chyr Yang, Chien-Jen Chen, I-Shou Chang*, Chao A. Hsiung* (2020) Predicting lung cancer occurrence in never-smoking females in Asia: TNSF-SQ, a prediction model. Cancer Epidemiology, Biomarkers & Prevention, 29(2): 452-459. [link]

Selected MDPI references (**Co-first author; *Co-corresponding author) 

  1. Chia-Ming Wu, Chung-Hsing Chen, Kuo-Wang Tsai, Mei-Chen Tan, Fang-Yu Tsai, Shih-Sheng Jiang, Shang-Hung Chen, Wei-Shone Chen, Horng-Dar Wang*, Tze-Sing Huang* (2024) TCF12 and lncRNA MALAT1 cooperatively harness high cyclin D1 but low β-Catenin gene expression to exacerbate colorectal cancer prognosis independently of metastasis. Cells, 13(24):10.3390/cells13242035. [link] 

Selected conference poster presentation 

  1. Chung-Hsing Chen, Tsung-Lin Tsai, Yi-Ju Wu, Shin-Fen Tsai, Shu-Li Wang, TMICS study group (2026) Prenatal exposure to inorganic arsenic and immune cell abundances in newborns, 2026 Annual Meeting and ToxExpo, March 23, 2026, San Diego, CA, USA, Abstract #3436. (Presented by Shu-Li Wang)