Comprehensive bioinformatic analysis constructs a CXCL model for predicting survival and immunotherapy effectiveness in ovarian cancer

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Abstract

Background: Immunotherapy has limited effectiveness in ovarian cancer (OC) patients, highlighting the need for reliable biomarkers to predict the effectiveness of these treatments. The C-X-C motif chemokine ligands (CXCLs) have been shown to be associated with survival outcomes and immunotherapy efficacy in cancer patients. In this study, we aimed to evaluate the predictive value of 16 CXCLs in OC patients.

Methods: We analyzed RNA-seq data from The Cancer Genome Atlas, Gene Expression Omnibus, and UCSC Xena database and conducted survival analysis. Consensus cluster analysis was used to group patients into distinct clusters based on their expression patterns. Biological pathway alterations and immune infiltration patterns were examined across these clusters using gene set variation analysis and single-sample gene set enrichment analysis. We also developed a CXCL scoring model using principal component analysis and evaluated its effectiveness in predicting immunotherapy response by assessing tumor microenvironment cell infiltration, tumor mutational burden estimation, PD-L1/CTLA4 expression, and immunophenoscore analysis (IPS).

Results: Most CXCL family genes were overexpressed in OC tissues compared to normal ovarian tissues. Patients were grouped into three distinct CXCL clusters based on their CXCL expression pattern. Additionally, using differentially expressed genes among the CXCL clusters, patients could also be grouped into three gene clusters. The CXCL and gene subtypes effectively predicted survival and immune cell infiltration levels for OC patients. Furthermore, patients with high CXCL scores had significantly better survival outcomes, higher levels of immune cell infiltration, higher IPS, and higher expression of PD-L1/CTLA4 than those with low CXCL scores.

Conclusion: The CXCL score has the potential to be a promising biomarker to guide immunotherapy in individual OC patients and predict their clinical outcomes and immunotherapy responses.

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Most cited references 55

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Gene Ontology: tool for the unification of biology

Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)

Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.

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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

A. Subramanian, P Tamayo, V K Mootha … (2005)

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

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KEGG: kyoto encyclopedia of genes and genomes.

M Kanehisa (2000)

KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).

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Author and article information

Contributors

Shuang Li: URI : https://loop.frontiersin.org/people/1640184/overview

Dawei Zou: URI : https://loop.frontiersin.org/people/1696329/overview

Zhaoqian Liu: URI : https://loop.frontiersin.org/people/671505/overview

Journal

Journal ID (nlm-ta): Front Pharmacol

Journal ID (iso-abbrev): Front Pharmacol

Journal ID (publisher-id): Front. Pharmacol.

Title: Frontiers in Pharmacology

Publisher: Frontiers Media S.A.

ISSN (Electronic): 1663-9812

Publication date (Electronic): 09 March 2023

Publication date Collection: 2023

Volume: 14

Electronic Location Identifier: 1127557

Affiliations

[1] ¹ Hunan Key Laboratory of Pharmacogenetics , Department of Clinical Pharmacology , National Clinical Research Center for Geriatric Disorders , Xiangya Hospital , Central South University , Changsha, China

[2] ² Institute of Clinical Pharmacology , Central South University , Changsha, China

[3] ³ Department of Surgery , Immunobiology and Transplant Science Center , Houston Methodist Research Institute and Institute for Academic Medicine , Houston Methodist Hospital , Houston, TX, United States

Author notes

Edited by: Wen Liu, Xiamen University, China

Reviewed by: Huichang Bi, Southern Medical University, China

Hongbo Wang, Yantai University, China

*Correspondence: Zhaoqian Liu, zqliu@ 123456csu.edu.cn ; Dawei Zou, dzou@ 123456houstonmethodist.org

This article was submitted to Pharmacology of Anti-Cancer Drugs, a section of the journal Frontiers in Pharmacology

Article

Publisher ID: 1127557

DOI: 10.3389/fphar.2023.1127557

PMC ID: 10034089

SO-VID: 1dde2e49-e6a4-4204-9414-e24d1036fa34

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

History

Date received : 19 December 2022

Date accepted : 27 February 2023

Funding

This work was supported by grants from the National Natural Science Foundation of China (81874327, 82173901), the Project Program of the National Clinical Research Center for Geriatric Disorders (Xiangya Hospital, 2020LNJJ02) of China, and the Science and Technology Program of Changsha (kh2003010) of China.

Comprehensive bioinformatic analysis constructs a CXCL model for predicting survival and immunotherapy effectiveness in ovarian cancer

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Cancer Immunotherapy

Most cited references 55

Gene Ontology: tool for the unification of biology

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

KEGG: kyoto encyclopedia of genes and genomes.

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