Xiao Jun, Cao Yangkun, Li Xuan, Xu Long, Wang Zhihang, Huang Zhenyu, Mu Xuechen, Qu Yinwei, Xu Ying
College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Systems Biology Laboratory for Metabolic Reprogramming, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China.
Int J Mol Sci. 2024 Dec 31;26(1):275. doi: 10.3390/ijms26010275.
Cancer occurrence rates exhibit diverse age-related patterns, and understanding them may shed new and important light on the drivers of cancer evolution. This study systematically analyzes the age-dependent occurrence rates of 23 carcinoma types, focusing on their age-dependent distribution patterns, the determinants of peak occurrence ages, and the significant difference between the two genders. According to the SEER reports, these cancer types have two types of age-dependent occurrence rate (ADOR) distributions, with most having a unimodal distribution and a few having a bimodal distribution. Our modeling analyses have revealed that (1) the first type can be naturally and simply explained using two age-dependent parameters: the total number of stem cell divisions in an organ from birth to the current age and the availability levels of bloodborne growth factors specifically needed by the cancer (sub)type, and (2) for the second type, the first peak is due to viral infection, while the second peak can be explained as in (1) for each cancer type. Further analyses indicate that (i) the iron level in an organ makes the difference between the male and female cancer occurrence rates, and (ii) the levels of sex hormones are the key determinants in the onset age of multiple cancer types. This analysis deepens our understanding of the dynamics of cancer evolution shared by diverse cancer types and provides new insights that are useful for cancer prevention and therapeutic strategies, thereby addressing critical gaps in the current paradigm of oncological research.
癌症发生率呈现出与年龄相关的多种模式，了解这些模式可能会为癌症演变的驱动因素带来新的重要启示。本研究系统分析了23种癌症类型的年龄依赖性发生率，重点关注其年龄依赖性分布模式、发病高峰年龄的决定因素以及两性之间的显著差异。根据监测、流行病学和最终结果（SEER）报告，这些癌症类型有两种年龄依赖性发生率（ADOR）分布，大多数呈单峰分布，少数呈双峰分布。我们的模型分析表明：（1）第一种类型可以用两个与年龄相关的参数自然而简单地解释：从出生到当前年龄器官中干细胞分裂的总数以及癌症（亚）类型特别需要的血源性生长因子的可利用水平；（2）对于第二种类型，第一个高峰是由于病毒感染，而第二个高峰对于每种癌症类型都可以如（1）中那样解释。进一步分析表明：（i）器官中的铁水平导致了男性和女性癌症发生率的差异；（ii）性激素水平是多种癌症类型发病年龄的关键决定因素。这项分析加深了我们对不同癌症类型共同的癌症演变动态的理解，并提供了对癌症预防和治疗策略有用的新见解，从而填补了当前肿瘤学研究范式中的关键空白。
