Comprehensive assessment of cellular senescence in the tumor microenvironment
Xiaoman Wang 1, Lifei Ma 1, Xiaoya Pei 1,
Heping Wang 1, Xiaoqiang Tang 2, Jian-Fei Pei 1, Yang-Nan Ding 1, Siyao Qu 1,
Zi-Yu Wei 1, Hui-Yu Wang 1, Xiaoyue Wang 1, Gong-Hong Wei 3, De-Pei Liu 1,
Hou-Zao Chen 1
Brief Bioinform. 2022 Apr 13;bbac118. doi:
10.1093/bib/bbac118.
PMID: 35419596 DOI: 10.1093/bib/bbac118
Abstract
Cellular senescence (CS), a state of
permanent growth arrest, is intertwined with tumorigenesis. Due to the absence
of specific markers, characterizing senescence levels and senescence-related
phenotypes across cancer types remain unexplored. Here, we defined computational
metrics of senescence levels as CS scores to delineate CS landscape across 33
cancer types and 29 normal tissues and explored CS-associated phenotypes by
integrating multiplatform data from ~20 000 patients and ~212 000 single-cell
profiles. CS scores showed cancer type-specific associations with genomic and
immune characteristics and significantly predicted immunotherapy responses and
patient prognosis in multiple cancers. Single-cell CS quantification revealed
intra-tumor heterogeneity and activated immune microenvironment in senescent
prostate cancer. Using machine learning algorithms, we identified three CS
genes as potential prognostic predictors in prostate cancer and verified them
by immunohistochemical assays in 72 patients. Our study provides a comprehensive
framework for evaluating senescence levels and clinical relevance, gaining
insights into CS roles in cancer- and senescence-related biomarker discovery.