A robust primary liver cancer subtype related to prognosis and drug response based on a multiple combined classifying strategy
Jielian Deng,
Guichuan Lai,
Cong Zhang,
Kangjie Li,
Wenyan Zhu,
Biao Xie,
Xiaoni Zhong
Affiliations
Jielian Deng
Department of Epidemiology and Health Statistics, School of Public Health, Chongqing Medical University, Chongqing, China; Medical Department, Yidu Cloud (Beijing) Technology Co., Beijing, China
Guichuan Lai
Department of Epidemiology and Health Statistics, School of Public Health, Chongqing Medical University, Chongqing, China
Cong Zhang
Department of Epidemiology and Health Statistics, School of Public Health, Chongqing Medical University, Chongqing, China
Kangjie Li
Department of Epidemiology and Health Statistics, School of Public Health, Chongqing Medical University, Chongqing, China
Wenyan Zhu
Chongqing Engineering Research Center of Pharmaceutical Sciences, Chongqing Medical and Pharmaceutical College, Chongqing, China; College of Pharmacy, Chongqing Medical University, Chongqing, China; Medical Department, Yidu Cloud (Beijing) Technology Co., Beijing, China; Corresponding author. Chongqing Engineering Research Center of Pharmaceutical Sciences, Chongqing Medical and Pharmaceutical College, Chongqing, China.
Biao Xie
Department of Epidemiology and Health Statistics, School of Public Health, Chongqing Medical University, Chongqing, China; Corresponding author.
Xiaoni Zhong
Department of Epidemiology and Health Statistics, School of Public Health, Chongqing Medical University, Chongqing, China; Corresponding author.
The recurrence or resistance to treatment of primary liver cancer (PLL) is significantly related to the heterogeneity present within the tumor. In this study, we integrated prognosis risk score, mRNAsi index, and immune characteristics clustering to classify patients. The four subtypes obtained from the combined classification are associated with PLC's prognosis and drug response. In these subtypes, we observed mRNAsiH_ICCA subtype, the intersection between high mRNAsi and immune characteristics clustering A, had the worst prognosis. Specifically, immune characteristics clustering B (ICC_B) had high drug sensitivity in most drugs regardless of the value of mRNAsi. On the other hand, patients with low mRNAsi responded better to ten drugs including KU-55933 and NU7441, while patients with high mRNAsi might benefit from drugs like Leflunomide. By matching the specific characteristics of each combined subtype with the drug-induced cell line expression profile, we identified a group of potential therapeutic drugs that might regulate the expression of disease signature genes. We developed a feasible multiple combined typing strategy, hoping to guide therapeutic selection and promote the development of precision medicine.
