Identification of a novel T cell-related signature to predict prognosis in colorectal cancer via integrating single-cell and bulk RNA sequencing

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Abstract Background: T cells, the key mediators of tumor destruction, have a considerable impact on tumor prognosis. However, the clinical significance of T cell-associated biomarkers in colorectal cancer (CRC) haven’t been well understood. The aim of this study was to investigate the expression profile of T cell marker genes in CRC and develop a prognostic signature based on these genes. Methods: Single-cell RNA-sequencing (scRNA-seq) data were retrieved from the Gene Expression Omnibus (GEO) database. Bulk RNA-sequencing data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and GEO databases. We firstly conducted a comprehensive analysis of scRNA-seq data to investigate the heterogeneity of various cells in the CRC tumor microenvironment (TME). Then, we performed cell-cell communication analysis and cell trajectory analysis to explore the intercellular interactions and functional changes of T cells. By combing the bulk RNA-seq data, a T-cell related gene signature was eventually constructed and its predictive ability was determined by the Kaplan–Meier (K-M), and receiver operating characteristic (ROC) curves in three independent cohorts. Results: ScRNA-seq data obtained from the GEO database were re-integrated and analyzed, resulting in 23 cell clusters. Distinct cell clusters were annotated using extensively reported cell markers. The CellChat algorithm revealed that tumor cells suppress the cellular function of tumor-infiltrating T cells through the MIF/CD74 pathway. The evolutionary trajectory of tumor-infiltrating T cells was elucidated by the CytoTRACE and monocle2 algorithms. Eventually, a prognostic prediction model based on 5 T cell-related genes was constructed using single-cell and bulk RNA sequencing data. The validation results from several independent CRC cohorts indicated that the 5 T cell-related genes prognostic model could accurately predict the survival outcomes of CRC patients, providing new evidence for precision treatment in CRC. Conclusions: Our study not only offers prospects for a better understanding of the cellular heterogeneity of TME, but also provides a useful tool for stratifying patients with different prognoses and facilitating personalized treatment.

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