» Articles » PMID: 38067341

Inflammation and Immunity Gene Expression Patterns and Machine Learning Approaches in Association with Response to Immune-Checkpoint Inhibitors-Based Treatments in Clear-Cell Renal Carcinoma

Overview
Journal Cancers (Basel)
Publisher MDPI
Specialty Oncology
Date 2023 Dec 9
PMID 38067341
Authors
Affiliations
Soon will be listed here.
Abstract

Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer. Despite the rapid evolution of targeted therapies, immunotherapy with checkpoint inhibition (ICI) as well as combination therapies, the cure of metastatic ccRCC (mccRCC) is infrequent, while the optimal use of the various novel agents has not been fully clarified. With the different treatment options, there is an essential need to identify biomarkers to predict therapeutic efficacy and thus optimize therapeutic approaches. This study seeks to explore the diversity in mRNA expression profiles of inflammation and immunity-related circulating genes for the development of biomarkers that could predict the effectiveness of immunotherapy-based treatments using ICIs for individuals with mccRCC. Gene mRNA expression was tested by the RT2 profiler PCR Array on a human cancer inflammation and immunity crosstalk kit and analyzed for differential gene expression along with a machine learning approach for sample classification. A number of mRNAs were found to be differentially expressed in mccRCC with a clinical benefit from treatment compared to those who progressed. Our results indicate that gene expression can classify these samples with high accuracy and specificity.

Citing Articles

Up-regulation of Cuproptosis-related lncRNAS in Patients Receiving Immunotherapy for Metastatic Clear Cell Renal Cell Carcinoma Indicates Progressive Disease.

Katifelis H, Grammatikaki S, Zakopoulou R, Bamias A, Karamouzis M, Stravodimos K In Vivo. 2024; 39(1):146-151.

PMID: 39740865 PMC: 11705156. DOI: 10.21873/invivo.13812.


Insights into Therapeutic Response Prediction for Ustekinumab in Ulcerative Colitis Using an Ensemble Bioinformatics Approach.

Koustenis K, Dovrolis N, Viazis N, Ioannou A, Bamias G, Karamanolis G Int J Mol Sci. 2024; 25(10).

PMID: 38791570 PMC: 11122545. DOI: 10.3390/ijms25105532.

References
1.
Nagumo Y, Kandori S, Kojima T, Hamada K, Nitta S, Chihara I . Whole-Blood Gene Expression Profiles Correlate with Response to Immune Checkpoint Inhibitors in Patients with Metastatic Renal Cell Carcinoma. Cancers (Basel). 2022; 14(24). PMC: 9776722. DOI: 10.3390/cancers14246207. View

2.
Liu J, Chen Z, Li Y, Zhao W, Wu J, Zhang Z . PD-1/PD-L1 Checkpoint Inhibitors in Tumor Immunotherapy. Front Pharmacol. 2021; 12:731798. PMC: 8440961. DOI: 10.3389/fphar.2021.731798. View

3.
Zeng P, Zhang X, Xiang T, Ling Z, Lin C, Diao H . Secreted phosphoprotein 1 as a potential prognostic and immunotherapy biomarker in multiple human cancers. Bioengineered. 2022; 13(2):3221-3239. PMC: 8973783. DOI: 10.1080/21655979.2021.2020391. View

4.
Wang S, Yu Z, Chai K . Identification of EGFR as a Novel Key Gene in Clear Cell Renal Cell Carcinoma (ccRCC) through Bioinformatics Analysis and Meta-Analysis. Biomed Res Int. 2019; 2019:6480865. PMC: 6393869. DOI: 10.1155/2019/6480865. View

5.
Raulet D, Gasser S, Gowen B, Deng W, Jung H . Regulation of ligands for the NKG2D activating receptor. Annu Rev Immunol. 2013; 31:413-41. PMC: 4244079. DOI: 10.1146/annurev-immunol-032712-095951. View