Pan-Cancer Analysis and Validation of Opioid-Related Receptors Reveals the Immunotherapeutic Value of Toll-Like Receptor 4

Abstract

Introduction

The relationship between the expression of opioid-associated receptors and cancer outcomes is complex and varies among studies.

Methods

This study focused on six opioid-related receptors (OPRM1, OPRD1, OPRK1, OPRL1, OGFR, and TLR4) and their impact on cancer patient survival. Bioinformatics analysis was conducted on 33 cancer types from The Cancer Genome Atlas database to examine their expression, clinical correlations, mechanisms in the tumor microenvironment, and potential for immunotherapy. Due to significantly lower expression of OPRM1, OPRD1, and OPRK1 compared to OGFR and TLR4, the analysis concentrated on the latter two genes.

Results

OGFR was highly expressed in 16 tumor types, while TLR4 showed low expression in 13. Validation from external samples, the Gene Expression Omnibus, and the Human Protein Atlas supported these findings. The diagnostic value of these two genes was demonstrated using the Genotype-Tissue Expression database. Univariate Cox regression models and Kaplan-Meier curves confirmed OGFR’s impact on prognosis in a cancer type-specific manner, while high TLR4 expression was associated with a favorable prognosis. Analysis of the tumor microenvironment using a deconvolution algorithm linked OGFR to CD8+ T cells and TLR4 to macrophages. Single-cell datasets further validated this correlation. In 25 immune checkpoint blockade treatment cohorts, TLR4 expression showed promise as an immunotherapy efficacy predictor in non-small cell lung cancer, urothelial carcinoma, and melanoma.

Conclusion

In a pan-cancer analysis of 33 tissues, OGFR was consistently highly expressed, while TLR4 had low expression. Both genes have diagnostic and prognostic significance and are linked to immune cells in the tumor microenvironment. TLR4 has potential as an immunotherapeutic response marker.

Keywords:

• opioid-related receptors
• pan-cancer analysis
• immune checkpoint blockade therapy
• TLR4
• OGFR
• prognosis
• macrophage
• CD8+ T cell

Abbreviations

OPRL1, opioid receptor-like 1; NK, natural killer; TCGA, The Cancer Genome Atlas; GTEx, Genotype-Tissue Expression; GEO, Gene Expression Omnibus; COAD, colon adenocarcinoma; ESCA, esophageal squamous cell cancer; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; HPA, Human Protein Atlas; ROC, receiver operating characteristic; AUC, areas under the curve; HR, hazard rate; K-M, Kaplan-Meier; DFS, disease-free survival; DSS, disease-specific survival; PFS, progression-free survival; GSEA, gene set enrichment analysis; FDR, false discovery rate; GO, gene ontology; KEGG, Kyoto encyclopedia of genes and genome; NES, normalized enrichment score; TMB, tumor mutation burden; MSI, microsatellite instability; CI, confidence interval; RB, retinoblastoma; DCs, dendritic cells; ICB, immune checkpoint blockade.

Data Sharing Statement

Publicly available datasets were analyzed in this study. TCGA and GTEx data can be found here: (UCSC) Xena browser (https://xena.ucsc.edu/,) and the Genotype-Tissue Expression (GTEx) database (https://www.gtexportal.org/home/-index.html). GSE10927, GSE13507, GSE93601, GSE26566, GSE68468, GSE53625, GSE108474, GSE13601, GSE40435, GSE53757, GSE26574, GSE89377, GSE75037, GSE67061, GSE18520, GSE32688, GSE87211, and GSE26899 datasets were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo). The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.

Ethics Approval and Consent to Participate

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Research Ethics Committee of the Sixth Affiliated Hospital of Sun Yat-sen University (No. 2021ZSLYEC-064). Written informed consent was obtained from all participants.

Acknowledgments

We thank Sangerbox tools and Xiantao Academic Tools for providing online analysis tools for our bioinformatics analysis. We would like to express our sincere gratitude to Dr Haosheng Zheng for his bioinformatics analysis and chart production in the revised manuscript.

Disclosure

The authors declare that they have no competing interests.

Additional information

Funding

This work was supported by a grant from the Natural Science Foundation of Guangdong Province (No.2018A030313069). The funding agency was not involved in the study design, data collection, analysis and interpretation, or manuscript writing.