Recent studies have found that 5-methylcytosine (m5C) modulators are associated with the prognosis and treatment of cancer. However, the relevance of m5C modulators in sarcoma prognosis and the tumour microenvironment is unclear.
We selected 15 m5C regulators and performed unsupervised clustering to identify m5C modification patterns and differentially expressed genes associated with the m5C phenotype in The Cancer Genome Atlas (TCGA) sarcomas. The extent of immune cell infiltration in different clustering groups was explored using single-sample gene set enrichment analysis and estimation algorithms. A principal component analysis algorithm-based m5C scoring protocol was performed to assess the m5C modification patterns of individual tumors.
We identified two distinct m5C modification patterns in the TCGA sarcoma cohort, which possess different clinical outcomes and biological processes. Tumour microenvironment analysis revealed two groups of immune infiltration patterns highly consistent with m5C modification patterns, classified as immune inflammatory and immune desert types. We constructed m5C scores and found that high m5C scores were closely associated with leiomyosarcoma and other subtypes, and were associated with poorer prognosis, lower PD-L1 expression, and poorer immunotherapy outcomes. The best application was validated against the m5C database.
We constructed an m5C score for sarcoma based on the TCGA database and identified a poorer prognosis in the high m5c score group. The stability and good prognostic predictive power of the m5C score was verified by an external database. We found that sarcomas in the low m5C score group may have a better response to immunotherapy.