br mour stage and proficient mismatch repair pMMR status Fig
mour stage and proficient mismatch repair (pMMR) status (Fig. 1e and Supplemental Table S5). No significant distribution difference was found in terms of the CpG island methylator phenotype, chromosome instability status, and genetic mutations (KRAS, BRAF, and P53). With regards to biological behaviour (Fig.1e), pathways involved in DNA rep-lication and repair, oxidative phosphorylation (OXPHOS), cell cycle, and one carbon metabolism were activated in TMEC1 (favourable survival), whereas TMEC2 (worse survival), as expected, showed enrichment of pathways related to stromal activation and cancer progression, such as angiogenesis and epithelial-mesenchymal transition (EMT). As con-
firmed by subsequent cell infiltration analysis, TMEC2 patients showed an obvious increase in infiltration by stromal cells, including neutro-phils, endothelial cells, and fibroblasts (Fig. 1f). Interestingly, it was also observed that in TMEC2 patients, the enrichment of immune rele-vant pathways was accompanied by an increase in the AT13387 of im-mune checkpoint genes. Finally, the distribution of TMEC relative to that of other established colon cancer molecular subtypes was com-pared. The result demonstrated that TMEC2 patients were mainly
Fig. 1. Consensus clustering of tumour microenvironment (TME) genes in colon cancer. (a) Consensus matrices of colon cancer patients for k = 2; (b) Colon cancer cases are divided into two subtypes based on unsupervised analysis and hierarchical clustering of 797 robust prognostic genes. Months of relapse-free survival and relapse status (relapse, red; censor, light yellow) are indicated above the heatmap; (c–d) Differences in patient overall survival (c)and relapse-free survival (d) with two clusters; (e) Heatmap showing the activation status of the biological processes in different TME-relevant clusters; (f) Violin plot of the comparison of immune and stromal cell infiltration between the different TME-relevant clusters;
(g) Sankey chart displaying the distribution of the TME-relevant clusters in C1–C6 subtypes and CMS subtypes. TMEC, tumour microenvironment cluster;MMR, mismatch repair; dMMR, deficient mismatch repair; pMMR, proficient mismatch repair; CIMP, CpG island methylator phenotype; CIN, chromosome instability; MT, mutant type; WT, wild type; IC, immune check point; EMT, epithelial-mesenchymal transition; CTL, cytotoxic lymphocyte; NK, natural killer; DC, dendritic cell; EC, endothelial cells;CMS, consensus molecular subtypes.
Fig. 2. TMRS panel is a prognostic marker. (a–b) Kaplan–Meier curves (left) and ROC curves (right) of relapse-free survival according to TMRS-RFS groups in the training cohort (a) and validation cohort (b); (c) Kaplan–Meier curves (upper) and ROC curves (down) of overall survival according to TMRS-OS groups; (d–e) Forest plots of the associations between TMRS-RFS and relapse-free survival (d) and the associations between TMRS-OS and overall survival (e) in various subgroups. Unadjusted HRs (boxes) and 95% confidence intervals (horizontal lines) are depicted. TMRS, tumour microenvironment risk score; RFS, relapse-free survival; OS, overall survival; HR, hazard ratio; CI, confidence interval; CMS, consensus molecular subtypes.
concentrated in the C4, C6, and CMS4 subtypes, mostly representing mesenchymal phenotypes, while TMEC1 patients were mainly concen-trated in the C1, C3, and CMS2 subtypes, mostly displaying epithelial phenotype characteristics (Fig. 1g, Supplemental Table S5) [33,34].
3.3. Construction of prognostically relevant TMRS gene panel
In order to develop a gene panel practical for clinical use, we applied the LASSO Cox regression model to the 797 robust prognostic genes for dimension reduction. Patients were randomly regrouped into training and validation cohorts for prognostic analyses as described in the “Mate-rials and methods” section. Comparison of patient characteristics be-tween the two groups showed no significant differences (Supplemental Table S2). Through the LASSO model (Supplemental Fig. S2), we gener-ated a TMRS gene panel consisting of 100 genes (Supplemental Table S6) and built two prognostic models using Cox analysis based on RFS (TMRS-RFS) and OS (TMRS-OS) information separately. Patients were stratified into two groups based on TMRS-RFS and TMRS-OS values, respectively, using a cut-off value calculated in the entire cohort (2·26 for TMRS-RFS and 3·02 for TMRS-OS). In both training and validation sets, Kaplan–Meier curves indicated that patients in the high-TMRS-RFS group had a significantly higher risk of relapse (Fig. 2a–b). In ROC (Fig. 2a–b) and c-index analyses (Table 1), the TMRS-RFS model showed