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

SIGMAR1 screened by a GPCR-related classifier regulates endoplasmic reticulum stress in bladder cancer

BACKGROUND

Bladder cancer (BC) is one of the most common malignancies worldwide. G protein-coupled receptors (GPCRs) are a large family of transmembrane proteins that are increasingly recognised as key players in cancer biology, affecting cell signalling and the tumour microenvironment. The sigma-1 receptor (SIGMAR1), although not a classical GPCR, has similar functions and is associated with the regulation of ER stress. However, its specific role and mechanism in bladder cancer are still unclear.

METHOD

The data sets pertaining to batch sequencing, single-cell RNA sequencing (scRNA-seq), immunotherapy response and clinical pathological characteristics were obtained from the public database. Thereafter, multiple algorithms were employed for the screening of GPCRs and immune cells related to the prognosis of BC. A GPCR-tumour microenvironment (TME) classifier was constructed and validated using different queues and multi-omics methods. The key biological pathways between GPCR-TME subgroups were identified through the utilisation of methodologies such as Gene Set Enrichment Analysis (GSEA), Weighted Gene Co-expression Network Analysis (WGCNA), and Tumour Immunophenotype Tracking (TIP). The expression of SIGMAR1 in BC cell lines and tissue samples was validated by western blotting. The Gene Ontology (GO) and GSEA were employed for biological process enrichment analysis. The biological role of SIGMAR1 in BC was investigated through functional experiments and subcutaneous tumour-bearing experiments in nude mice. The relationship between SIGMAR1 and immune cell infiltration was explored using the CIBERSORT method.

RESULTS

A total of 15 types of GPCR and 5 types of immune cells were identified and established as a GPCR-TME classifier. Patients in the GPCR-low + TME-high group exhibited the most favourable prognosis, whereas patients in the GPCR-high + TME-low group demonstrated the least favourable prognosis. The scRNA-seq results revealed an increase in GPCR expression in CD8 + T cells, endothelial cells, and NK cells. GPCR-TME was significantly correlated with overall survival (OS) in BC patients and outperformed a range of clinical parameters, making it an independent risk factor affecting the prognosis of BC patients. In comparison to normal tissues, SIGMAR1 was markedly expressed in BC tissues, and was associated with a poor prognosis. Functional experiments demonstrated that SIGMAR1 deficiency impeded the invasive capacity of cancer cells and restrained cellular proliferation. Moreover, in vivo experiments corroborated that SIGMAR1 deficiency curtailed the growth of xenografts in nude mice. Western blotting analysis revealed that SIGMAR1 silencing intensified endoplasmic reticulum (ER) stress in BC cells and promoted cell apoptosis. Additionally, the expression level of SIGMAR1 was correlated with the level of immune cell infiltration and immune-related functions.

CONCLUSION

The construction of a BC-related GPCR-TME classifier enabled the effective prediction of the OS of BC patients and the identification of SIGMAR1, a key factor regulating ER stress in BC. The knockout of SIGMAR1 can destroy its protective effect on ER stress, enhance apoptosis of BC cells, and facilitate further investigation of novel treatment strategies for cancer therapy.

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.