Systematic Meta-Analysis Identifies Co-Expressed Kinases and GPCRs in Ovarian Cancer Tissues Revealing a Potential for Targeted Kinase Inhibitor Delivery

Unveiling G-protein coupled receptors as potential targets for ovarian cancer nanomedicines: from RNA sequencing data analysis to in vitro validation

Genetic heterogeneity in ovarian cancer indicates the need for personalised treatment approaches. Currently, very few G-protein coupled receptors (GPCRs) have been investigated for active targeting with nanomedicines such as antibody-conjugated drugs and drug-loaded nanoparticles, highlighting a neglected potential to develop personalised treatment. To address the genetic heterogeneity of ovarian cancer, a future personalised approach could include the identification of unique GPCRs expressed in cancer biopsies, matched with personalised GPCR-targeted nanomedicines, for the delivery of lethal drugs to tumour tissue before, during and after surgery. Here we report on the systematic analysis of public ribonucleic acid-sequencing (RNA-seq) gene expression data, which led to prioritisation of 13 GPCRs as candidates with frequent overexpression in ovarian cancer tissues. Subsequently, primary ovarian cancer cells derived from ascites and ovarian cancer cell lines were used to confirm frequent gene expression for the selected GPCRs. However, the expression levels showed high variability within our selection of samples, therefore, supporting and emphasising the need for the future development of case-to-case personalised targeting approaches.

SARS-CoV-2 external structures interacting with nanospheres using docking and molecular dynamics

Coronavirus is caused by the SARS-CoV-2 virus has shown rapid proliferation and scarcity of treatments with proven effectiveness. In this way, we simulated the hospitalization of carbon nanospheres, with external active sites of the SARS-CoV-2 virus (M-Pro, S-Gly and E-Pro), which can be adsorbed or inactivated when interacting with the nanospheres. The computational procedures performed in this work were developed with the SwissDock server for molecular docking and the GROMACS software for molecular dynamics, making it possible to extract relevant data on affinity energy, distance between molecules, free Gibbs energy and mean square deviation of atomic positions, surface area accessible to solvents. Molecular docking indicates that all ligands have an affinity for the receptor's active sites. The nanospheres interact favorably with all proteins, showing promising results, especially C60, which presented the best affinity energy and RMSD values ​​for all protein macromolecules investigated. The C60 with E-Pro exhibited the highest affinity energy of -9.361 kcal/mol, demonstrating stability in both molecular docking and molecular dynamics simulations. Our RMSD calculations indicated that the nanospheres remained predominantly stable, fluctuating within a range of 2 to 3 Å. Additionally, the analysis of other structures yielded promising results that hold potential for application in other proteases.Communicated by Ramaswamy H. Sarma.

Using GPCRs as Molecular Beacons to Target Ovarian Cancer with Nanomedicines

The five-year survival rate for women with ovarian cancer is very poor despite radical cytoreductive surgery and chemotherapy. Although most patients initially respond to platinum-based chemotherapy, the majority experience recurrence and ultimately develop chemoresistance, resulting in fatal outcomes. The current administration of cytotoxic compounds is hampered by dose-limiting severe adverse effects. There is an unmet clinical need for targeted drug delivery systems that transport chemotherapeutics selectively to tumor cells while minimizing off-target toxicity. G protein-coupled receptors (GPCRs) are the largest family of membrane receptors, and many are overexpressed in solid tumors, including ovarian cancer. This review summarizes the progress in engineered nanoparticle research for drug delivery for ovarian cancer and discusses the potential use of GPCRs as molecular entry points to deliver anti-cancer compounds into ovarian cancer cells. A newly emerging treatment paradigm could be the personalized design of nanomedicines on a case-by-case basis.

Identification of Dysregulated Expression of G Protein Coupled Receptors in Endocrine Tumors by Bioinformatics Analysis: Potential Drug Targets?

Background: Many studies link G protein-coupled receptors (GPCRs) to cancer. Some endocrine tumors are unresponsive to standard treatment and/or require long-term and poorly tolerated treatment. This study explored, by bioinformatics analysis, the tumoral profiling of the GPCR transcriptome to identify potential targets in these tumors aiming at drug repurposing. Methods: We explored the GPCR differentially expressed genes (DEGs) from public datasets (Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA)). The GEO datasets were available for two medullary thyroid cancers (MTCs), eighty-seven pheochromocytomas (PHEOs), sixty-one paragangliomas (PGLs), forty-seven pituitary adenomas and one-hundred-fifty adrenocortical cancers (ACCs). The TCGA dataset covered 92 ACCs. We identified GPCRs targeted by approved drugs from pharmacological databases (ChEMBL and DrugBank). Results: The profiling of dysregulated GPCRs was tumor specific. In MTC, we found 14 GPCR DEGs, including an upregulation of the dopamine receptor (DRD2) and adenosine receptor (ADORA2B), which were the target of many drugs. In PGL, seven GPCR genes were downregulated, including vasopressin receptor (AVPR1A) and PTH receptor (PTH1R), which were targeted by approved drugs. In ACC, PTH1R was also downregulated in both the GEO and TCGA datasets and was the target of osteoporosis drugs. Conclusions: We highlight specific GPCR signatures across the major endocrine tumors. These data could help to identify new opportunities for drug repurposing.

G Protein-coupled Receptors in Radioiodine-refractory Thyroid Cancer in the Era of Precision Medicine

CONTEXT

Radioiodine-refractory thyroid cancers have poor outcomes and limited therapeutic options (tyrosine kinase inhibitors) due to transient efficacy and toxicity of treatments. Therefore, combinatorial treatments with new therapeutic approaches are needed. Many studies link G protein-coupled receptors (GPCRs) to cancer cell biology.

OBJECTIVE

To perform a specific atlas of GPCR expression in progressive and refractory thyroid cancer to identify potential targets among GPCRs aiming at drug repositioning.

METHODS

We analyzed samples from tumor and normal thyroid tissues from 17 patients with refractory thyroid cancer (12 papillary thyroid cancers [PTCs] and 5 follicular thyroid cancers [FTCs]). We assessed GPCR mRNA expression using NanoString technology with a custom panel of 371 GPCRs. The data were compared with public repositories and pharmacological databases to identify eligible drugs. The analysis of prognostic value of genes was also performed with TCGA datasets.

RESULTS

With our transcriptomic analysis, 4 receptors were found to be downregulated in FTC (VIPR1, ADGRL2/LPHN2, ADGRA3, and ADGRV1). In PTC, 24 receptors were deregulated, 7 of which were also identified by bioinformatics analyses of publicly available datasets on primary thyroid cancers (VIPR1, ADORA1, GPRC5B, P2RY8, GABBR2, CYSLTR2, and LPAR5). Among all the differentially expressed genes, 22 GPCRs are the target of approved drugs and some GPCRs are also associated with prognostic factors.

DISCUSSION

For the first time, we performed GPCR mRNA expression profiling in progressive and refractory thyroid cancers. These findings provide an opportunity to identify potential therapeutic targets for drug repositioning and precision medicine in radioiodine-refractory thyroid cancer.

Inhibition of host Ogr1 enhances effector CD8+ T-cell function by modulating acidic microenvironment

Immunotherapies for cancer, such as immune checkpoint blockade or adoptive T-cell transfer, can lead to a long-lasting clinical response. But the therapeutic response rate remains low on account of many tumors that have evolved sophisticated strategies to evade immune surveillance. Solid tumors are characterized by the highly acidic microenvironment, which may weaken the effectiveness of antitumor immunity. Here, we explored a promising therapeutic development deployed by pH manipulation for avoiding immunoevasion. The highly acidified microenvironment of melanoma induces the expression of G-protein-coupled receptor (Ogr1) in T cells, which weakened their effective function and promote tumor growth. Ogr1 inhibition reactivate CD8⁺ T cells and have a cytotoxic role by reducing the activity of high glycolysis, resulting in comparatively low acidification of the tumor microenvironment, and leads to tumor suppression. In addition, the adoptive transfer of Ogr1^-/--CD8⁺ T cells enhanced the antitumor responses, with the potential for immediate clinical transformation.

A prognosis-predictive nomogram of ovarian cancer with two immune-related genes: CDC20B and PNPLA5

Ovarian carcinoma (OV) is one of the most lethal gynecological malignancies globally, and the overall 5-year survival rate of OV was 47% in 2018 according to American data. To increase the survival rate of patients with OV, many researchers have sought to identify biomarkers that act as both prognosis-predictive markers and therapy targets. However, most of these have not been suitable for clinical application. The present study aimed at constructing a predictive prognostic nomogram of OV using the genes identified by combining The Cancer Genome Atlas (TCGA) dataset for OV with the immune score calculated by the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm. Firstly, the algorithm was used to calculate the immune score of patients with OV in the TCGA-OV dataset. Secondly, differentially expressed genes (DEGs) between low and high immune score tissues were identified, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis was performed to predict the functions of these DEGs. Thirdly, univariate, multivariate and Lasso Cox's regression analyses were carried out step by step, and six prognosis-related DEGs were identified. Then, Kaplan-Myer survival curves were generated for these genes and validated by comparing their expression levels to further narrow the range of DEGs and to calculate the risk score. Two genes were identified, cell division cycle 20B and patatin-like phospholipase domain containing 5, which were both shown to have higher expression levels in OV tissues and to be significantly associated with the prognosis of OV. Next, a nomogram was created using these two genes and age, and using the receiver operating characteristic (ROC) curve and calibration curve, the effectiveness of the nomogram was validated. Finally, an external validation was conducted for this nomogram. The ROC showed that the areas under the curve (AUCs) of the 3- and 5-year overall survival predictions for the nomogram were 0.678 and 0.62, respectively. Moreover, the ROC of the external validation model showed that the AUCs of the 3- and 5-year were 0.699 and 0.643, respectively, demonstrating the effectiveness of the generated nomogram. In conclusion, the present study has identified two immune-related genes as biomarkers that reliably predict overall survival in OV. These biomarkers might also be potential molecular targets of immune therapy to treat patients with OV.