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

Sep 21, 2026

Uploaded by Liam

Genetic heterogeneity in ovarian cancer calls for personalised treatment strategies. Very few G-protein coupled receptors (GPCRs) have been evaluated for active targeting by nanomedicines such as antibody-conjugated drugs or drug-loaded nanoparticles, leaving an underused opportunity for personalised therapy. A future approach could match unique GPCRs from cancer biopsies with personalised GPCR-targeted nanomedicines to deliver lethal payloads to tumour tissue before, during and after surgery. The study reports a systematic analysis of public RNA-seq data and experimental verification in ovarian cancer cells, prioritising 13 GPCR candidates with frequent overexpression and highlighting sample-to-sample expression variability.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Abstract

Introduction

Rationale for personalised targeted nanomedicines

Limits of current precision medicines

Methods and workflow

RNA-seq gene expression analysis

In vitro validation in ovarian cancer cells

Results and implications

Prioritisation of GPCR candidates

Expression variability and need for case-to-case targeting

Khetan et al. Journal of Ovarian Research (2024) 17:156 Journal of Ovarian Research [https://doi.org/10.1186/s13048-024-01479-0](https://doi.org/10.1186/s13048-024-01479-0) RESEARCH Open Access Unveiling G-protein coupled receptors as potential targets for ovarian cancer nanomedicines: from RNA sequencing data analysis to in vitro validation Riya Khetan 1, Preethi Eldi2, Noor A. Lokman3, Carmela Ricciardel li3, Martin K. Oehler3,4, Anton Blencowe5, Sanjay Garg 1, Katherine Pillman6* and Hugo Albrecht 1* Abstract 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 GPCRtargeted nanomedicines, for the delivery of lethal drugs to tumour tissue before, during and after surgery. Here wereport 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. Keywords Ovarian cancer, GPCR, Nanomedicine, Personalised medicine, Drug delivery, RNA-seq *Correspondence: Katherine Pillman [katherine.pillman@unisa.edu.au](katherine.pillman@unisa.edu.au)[ ](katherine.pillman@unisa.edu.au)Hugo Albrecht [hugo.albrecht@unisa.edu.au](hugo.albrecht@unisa.edu.au) 1Centre of Pharmaceutical Innovation, UniSA Clinical and Health Sciences, University of South Australia, Adelaide, South Australia 5000, Australia 2UniSA Clinical and Health Sciences, University of South Australia, Adelaide, South Australia 5000, Australia 3Discipline of Obstetrics and Gynaecology, Adelaide Medical School, Robinson Research Institute, University of Adelaide, Adelaide, South Australia 5000, Australia 4Department of Gynaecological Oncology, Royal Adelaide Hospital, Adelaide, South Australia 5000, Australia 5Applied Chemistry and Translational Biomaterials Group, Centre of Pharmaceutical Innovation, UniSA Clinical and Health Sciences, University of South Australia, Adelaide, South Australia 5000, Australia 6Centre for Cancer Biology, UniSA Clinical and Health Sciences, University of South Australia, Adelaide, South Australia 5000, Australia © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/l](http://creativecommons.org/l)icenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ([http://creativecommons.org/publicdomain/zero/1.0/](http://creativecommons

FAQs

What problem does the study address in ovarian cancer treatment?

Ovarian cancer shows genetic heterogeneity and aggressive behavior, leading to poor responses, resistance, and relapse. This motivates personalised treatment strategies rather than one-size-fits-all approaches.

How did the researchers identify candidate GPCR targets?

They performed a systematic analysis of public RNA-seq gene expression data from ovarian cancer, prioritising 13 GPCRs with frequent overexpression in tumour tissues.

How was the RNA-seq prioritisation validated?

Primary ovarian cancer cells from ascites and established ovarian cancer cell lines were used to confirm frequent expression of the selected GPCRs.

Why is case-to-case targeting considered necessary?

Expression levels of the selected GPCRs varied highly across the tested samples, supporting the need for future personalised targeting on a case-by-case basis.

Sep 21, 2026

Uploaded by Liam