GPRASP1 is a candidate anti-oncogene and correlates with immune microenvironment and immunotherapeutic efficiency in head and neck cancer

Prediction of survival and immunotherapy response by the combined classifier of G protein-coupled receptors and tumor microenvironment in melanoma

BACKGROUND

Melanoma is the deadliest form of skin tumor, and G protein-coupled receptors (GPCRs) play crucial roles in its carcinogenesis. Furthermore, the tumor microenvironment (TME) affects the overall survival (OS) and the response to immunotherapy. The combination of GPCRs and TME from a multi-omics perspective may help to predict the survival of the melanoma patients and their response to immunotherapy.

METHODS

Bulk-seq, single-cell RNA sequencing (scRNA-seq), gene mutations, immunotherapy responses, and clinicopathologic feature data were downloaded from public databases, and prognostic GPCRs and immune cells were screened using multiple machine learning algorithms. The expression levels of GPCRs were detected using real-time quantitative polymerase chain reaction (qPCR) in A375 and HaCaT cell lines. The GPCR-TME classifier was constructed and verified using different cohorts and multi-omics. Gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), and tracking tumor immunophenotype (TIP) were used to identify the key biological pathways among the GPCR-TME subgroups. Then, tumor mutational burden (TMB), vital mutant genes, antigen presentation genes, and immune checkpoints were compared among the subgroups. Finally, the differences in immunotherapy response rates among the GPCR-TME subgroups were investigated.

RESULTS

A total of 12 GPCRs and five immune cell types were screened to establish the GPCR-TME classifier. No significant differences in the expression levels of the 12 GPCRs were found in the two cell lines. Patients with high GPCR score or low TME score had a poor OS; thus, the GPCRlow/TMEhigh subgroup had the most favorable OS. The scRNA-seq result revealed that immune cells had a higher GPCR score than tumor and stromal cells. The GPCR-TME classifier acted as an independent prognostic factor for melanoma. GSEA, WGCNA, and TIP demonstrated that the GPCRlow/TMEhigh subgroup was related to the activation and recruitment of anti-tumor immune cells and the positive regulation of the immune response. From a genomic perspective, the GPCRlow/TMEhigh subgroup had higher TMB, and different mutant genes. Ultimately, higher expression levels of antigen presentation genes and immune checkpoints were observed in the GPCRlow/TMEhigh subgroup, and the melanoma immunotherapy cohorts confirmed that the response rate was highest in the GPCRlow/TMEhigh cohort.

CONCLUSIONS

We have developed a GPCR-TME classifier that could predict the OS and immunotherapy response of patients with melanoma highly effectively based on multi-omics analysis.

Comprehensive pan-cancer analysis of role of GPRASP1, associated with clinical outcomes, immune microenvironment, and immunotherapeutic efficiency in pancreatic cancer

BACKGROUND

GPRASP1 (G-protein-coupled receptor-associated sorting protein 1) plays an important role in tumorigenesis. However, GPRASP1 specific role has not been clearly clarified in cancer, particularly in pancreatic cancer(PC).

METHODS

Firstly, we utilized pan-cancer analysis based on RNA sequencing data from TCGA (The Cancer Genome Atlas) to evaluate the expression pattern and immunological role of GPRASP1. Then, through multiple transcriptome datasets (TCGA and Gene Expression Omnibus (GEO)) and multi-omics (RNA-seq, DNA methylation, copy number variations (CNV), somatic mutation data) in-depth analysis, we comprehensively explore the relationship of GPRASP1 expression with clinicopathologic characteristics, clinical outcomes, CNV, and DNA methylation in pancreatic cancer. Additionally, we employed immunohistochemistry (IHC) to further confirm GPRASP1 expression pattern between PC tissues and paracancerous tissues. Lastly, we systematically associated the GPRASP1 with immunological properties from numerous perspectives, such as immune cell infiltration, immune-related pathways, immune checkpoint inhibitors, immunomodulators, immunogenicity, and immunotherapy.

RESULTS

Through pan-cancer analysis, we identified that GPRASP1 plays a critical role in the occurrence and prognosis of PC, and is closely related to immunological characteristics in PC. IHC analysis confirmed that GPRASP1 is significantly down-regulated in PC compared with normal tissues. The expression of GPRASP1 is significantly negatively correlated with clinical features (histologic grade, T stage, and TNM stage), and is an independent predictor of favorable prognosis, regardless of other clinicopathological features (HR: 0.69, 95% CI 0.54-0.92, p= 0.011). The etiological investigation found that the abnormal expression of GPRASP1 was related to DNA methylation and CNV frequency. Subsequently, the high expression of GPRASP1 was significantly correlated with immune cell infiltration (CD8 + T cell, tumor-infiltrating lymphocyte(TIL)), immune-related pathways(cytolytic activity, check-point, human leukocyte antigen (HLA)), immune checkpoint inhibitors (CTLA4, HAVCR2, LAG3, PDCD1 and TIGIT), immunomodulators ( CCR4/5/6, CXCL9, CXCR4/5), and immunogenicity(immune score, neoantigen, TMB(tumor mutation burden)). Finally, IPS (immunophenoscore) and TIDE (tumor immune dysfunction and exclusion) analysis demonstrated that GPRASP1 expression levels can accurately predict the immunotherapeutic response.

CONCLUSION

GPRASP1 is a promising candidate biomarker that plays a role in the occurrence, development, and prognosis of PC. Evaluating GPRASP1 expression will aid in the characterization of tumor microenvironment (TME) infiltration and orient more efficient immunotherapy strategies.