FIGURE 1.

Identification of senescence-related genes (SRGs) based on machine learning algorithms and pan-cancer analysis. (A) LASSO coefficient profiles of the 279 SRGs. (B) LASSO cross‐validation for selecting optimal tuning parameter (λ). (C) The error rate of SVM-RFE modeling across varying feature counts. (D) The accuracy rate of SVM-RFE modeling across varying feature counts. (E) The overlapping of selected SRGs based on two algorithms. (F) The somatic copy number variance (SCNV) of SRGs across various cancers. (G) Comparative analysis for expressing levels of SRGs in tumor tissues with corresponding normal tissues across various cancers. (H) The frequency of SRG mutations across various cancers. (I) The prognostic roles of SRGs across various cancers. The use of red and blue colors corresponds to factors associated with poor and favorable prognosis, respectively. (J) The correlation analysis for the expressing levels of SRGs and SCNV across various cancers.