A senescence-based prognostic gene signature for colorectal cancer and identification of the role of SPP1-positive macrophages in tumor senescence

BACKGROUND: Senescence is significantly associated with cancer prognosis. This study aimed to construct a senescence-related prognostic model for colorectal cancer (CRC) and to investigate the influence of senescence on the tumor microenvironment. METHODS: Transcriptome and clinical data of CRC cases were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Senescence-related prognostic genes detected by univariate Cox regression were included in Least Absolute Shrinkage and Selection Operator (LASSO) analysis to construct a model. The efficacy of the model was validated using the receiver operating characteristic (ROC) curve and survival analysis. Differentially expressed genes (DEGs) were identified and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed. CIBERSORT and Immuno-Oncology Biological Research (IOBR) were used to investigate the features of the tumor microenvironment. Single-cell RNA-seq data were used to investigate the expression levels of model genes in various cell types. Immunofluorescence staining for p21, SPP1, and CD68 was performed with human colon tissues. RESULTS: A seven-gene (PTGER2, FGF2, IGFBP3, ANGPTL4, DKK1, WNT16 and SPP1) model was finally constructed. Patients were classified as high- or low-risk using the median score as the threshold. The area under the ROC curve (AUC) for the 1-, 2-, and 3-year disease-specific survival (DSS) were 0.731, 0.651, and 0.643, respectively. Survival analysis showed a better 5-year DSS in low-risk patients in the construction and validation cohorts. GO and KEGG analyses revealed that DEGs were enriched in extracellular matrix (ECM)-receptor interactions, focal adhesion, and protein digestion and absorption. CIBERSORT and IOBR analyses revealed an abundance of macrophages and an immunosuppressive environment in the high-risk subgroup. Low-risk patients had higher response rates to immunotherapy than high-risk patients. ScRNA-seq data revealed high expression of SPP1 in a subset of macrophages with strong senescence-associated secretory phenotype (SASP) features. Using CRC tumor tissues, we discovered that SPP1(+) macrophages were surrounded by a large number of senescent tumor cells in high-grade tumors. CONCLUSION: Our study presents a novel model based on senescence-related genes that can identify CRC patients with a poor prognosis and an immunosuppressive tumor microenvironment. SPP1(+) macrophages may correlate with cell senescence leading to poor prognosis.