Maohao Li

Abstract Despite its strong regenerative capacity, liver aging paradoxically increases susceptibility to fibrosis and metabolic dysfunction-associated steatotic liver disease through dysregulated inflammation, senescence-associated secretory phenotypes, and immune-metabolic crosstalk. To systematically characterize these processes, we integrated longitudinal transcriptomics, single-cell RNA sequencing, and machine-learning approaches.

Transcriptomic data used in this study were sourced from two publicly available repositories: Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) [23] and Genotype-Tissue Expression (GTEx) (http://commonfund.nih.gov/GTEx/) [24]. The primary dataset analyzed was GSE132040 [8], which offers a comprehensive spatiotemporal transcriptomic atlas of 16 mouse organs across the lifespan, ranging from 1 to 27 months of age (n = 891).

1 INTRODUCTION Cancer remains a major public health challenge worldwide, responsible for nearly 10 million deaths in 2020.1 In 2023, there is an estimated 1,958,310 new cancer cases and 609,820 cancer deaths in the United States.2 Therefore, it is still an urgent need to identify novel diagnostic biomarkers and therapeutic targets for cancer.