Background: The type 2 mannose receptor C (MRC2) is involved in tumor biological processes and plays a new role in the remodeling of the extracellular matrix turnover. Previous studies have demonstrated MRC2 expression profiling and prognostic relevance in some tumor types. However, the clinical and immunotherapeutic value of MRC2 in pan-cancers remains controversial. Our study aimed to evaluate MRC2 expression pattern, clinical characteristics and prognostic significance in 33 cancers, explore the relationship between MRC2 and immune-related characteristics, and assess the prediction of MRC2 for the immunotherapeutic response. Methods: Transcriptional and clinical data of 33 cancers were downloaded from The Cancer Genome Atlas database (TCGA) database and two independent immunotherapeutic cohorts were obtained from GSE67501 and the IMvigor210 study. Next, patients stratified by MRC2 expression levels were displayed by Kaplan-Meier plot to compare prognosis-related indexes. Meanwhile, immune infiltrates of different cancers were estimated by tumor immune estimation resources (TIMER) and CIBERSORT. The ESTIMATE algorithm was used to estimate the immune and stromal scores in tumor tissues. MRC2 expression and immunological modulators, including immune inhibitors, immune stimulators, and MHC molecules, were screened through the TISIDB portal. Gene-set enrichment analysis analyses were performed to explore the underlying biological process of MRC2 across different cancers. The immunotherapeutic response prediction was performed in two independent cohorts (GSE78220: metastatic melanoma with pembrolizumab treatment and IMvigor210: advanced urothelial cancer with atezolizumab intervention). Results: MRC2 is expressed differently in many cancers and has been shown to have potential prognostic predicting significance. MRC2 was significantly associated with immune cell infiltration, immune modulators, and immunotherapeutic markers. Notably, the immunotherapeutic response group was associated with lower MRC2 expression in metastatic melanoma and advanced urothelial carcinoma cohort. Conclusion: This study demonstrated that MRC2 could be a prognostic indicator for certain cancer and is critical for tumor immune microenvironments. MRC2 expression level may influence and predict immune checkpoint blockade response as a potential indicator.

Abstract Background Colorectal cancer (CRC) is a common malignant cancer with a poor prognosis. Liver metastasis is the dominant cause of death in CRC patients, and it often involves changes in various gene expression profiling. This study proposed to construct and validate a risk model based on differentially expressed genes between primary and liver metastatic tumors from CRC for prognostic prediction. Methods Transcriptomic and clinical data of CRC were downloaded from The Cancer Genome Atlas database (TCGA) and Gene Expression Omnibus database (GEO). Identification and screening of candidate differentially expressed genes (DEGs) between liver metastatic tissues and corresponding primary tumors were conducted by R package “limma” and univariate Cox analysis in the GSE50760 and TCGA cohort. Last, absolute shrinkage and selection operator (LASSO) Cox regression was carried out to shrink DEGs and develop the risk model. CRC patients from the GSE161158 cohort were utilized for validation. Functional enrichment, CIBERSORT algorithm, and ESTIMATE algorithm for further analysis. Results An 8-gene signature risk model, including HPD, C8G, CDO1, FGL1, SLC2A2, ALDOB, SPINK4, and ITLN1, was developed and classified the CRC patients from TCGA and GEO cohorts into high and low-risk groups. The high-risk group has a worse prognosis compared with the low-risk group. The model was verified as an independent indicator for prognosis. Moreover, tumor immune infiltration analyses demonstrated that monocytes (P = 0.006), macrophage M0 (P < 0.001), and macrophage M1 (P < 0.001) were enriched in the high-risk group, while plasma cells (P = 0.010), T cells CD4 memory resting (P < 0.001) and dendritic cells activated (P = 0.006) were increased in the low-risk group. Conclusions We developed and validated a risk predictive model for the DEGs between liver metastases and primary tumor of CRC, which can be utilized for the clinical prognostic indicator in CRC.