Senescence‐based colorectal cancer subtyping reveals distinct molecular characteristics and therapeutic strategies

Abstract

Cellular senescence has been listed as a hallmark of cancer, but its role in colorectal cancer (CRC) remains unclear. We comprehensively evaluated the transcriptome, genome, digital pathology, and clinical data from multiple datasets of CRC patients and proposed a novel senescence subtype for CRC. Multi‐omics data was used to analyze the biological features, tumor microenvironment, and mutation landscape of senescence subtypes, as well as drug sensitivity and immunotherapy response. The senescence score was constructed to better quantify senescence in each patient for clinical use. Unsupervised learning revealed three transcriptome‐based senescence subtypes. Cluster 1, characterized by low senescence and activated proliferative pathways, was sensitive to chemotherapeutic drugs. Cluster 2, characterized by intermediate senescence and high immune infiltration, exhibited significant immunotherapeutic advantages. Cluster 3, characterized by high senescence, high immune, and stroma infiltration, had a worse prognosis and maybe benefit from targeted therapy. We further constructed a senescence scoring system based on seven senescent genes through machine learning. Lower senescence scores were highly predictive of longer disease‐free survival, and patients with low senescence scores may benefit from immunotherapy. We proposed the senescence subtypes of CRC and our findings provide potential treatment interventions for each CRC senescence subtype to promote precision treatment.

Senescence risk score: a multifaceted prognostic tool predicting outcomes, stemness, and immune responses in colorectal cancer

“…Additionally, the receiver operating characteristic (ROC) curves demonstrated a high degree of accuracy of SRRS in predicting overall survival (OS) in the training and test sets (Figure 2B; Supplemental Figure 1C). Furthermore, we have identified three cellular senescencerelated signatures for CRC as proposed by Dai et al, Lv et al, and Yue et al (44)(45)(46). Using the TCGA-CRC database, we compared the timedependent AUC values of SRRS with these signatures, and found our signature to exhibit remarkable performance (Supplemental Figure 1F).…”