The function of chemokines and their receptors in the ovarian cancer microenvironment and research on their prognostic significance

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Methods We constructed a prognostic correlation model via statistical analysis, consensus clustering analysis, and other analyses, relying on the transcriptome and corresponding clinical data of ovarian cancer samples taken from the TCGA and GEO databases. We further put the model into application for drug sensitivity analysis, tumor microenvironment analysis, and clinical correlation analysis. We again utilized the model to carry out drug sensitivity analysis, tumor microenvironment analysis, and clinical correlation analysis. Results Based on the expression of chemokines and their receptors in the samples, consensus clustering analysis was used to group the samples into subtypes I and II. Both subtypes displayed notable distinctions. We identified prognostic differentially expressed genes (DEGs), which are mostly involved in cell signaling, viral interactions, and immunological and inflammatory responses. We conducted a second consistent cluster analysis based on DEGs using COX analysis, which revealed two isoforms, A and B, and 709 DEGs between them. We developed and verified the prognosis risk score model based on the prognostic DEGs, where (-CXCL9*0.20-MMP9*0.11 + GFPT2*0.15 + CXCL12*0.11-MSX1*0.06 + HIF3A*0.11 + B3GNT3*0.16-FOXJ1*0.08-AADAC*0.14-AGR2*0.09) indicates the risk score. Despite having a negative correlation with stem cells and greater immune cell and lower stromal cell scores, patients with ovarian cancer in the low-risk category were projected to live longer. Conclusion It includes the chemokines CXCL9, MMP9, GFPT2, CXCL12, MSX1, HIF3A, B3GNT3, FOXJ1, AADAC, and AGR2. The characteristic scores of these chemokines and their receptors can be used as independent prognostic factors for patients with ovarian cancer, which will further support the need for more clinical and functional research on chemokines and their receptors in it. chemokines chemokine receptors immune infiltration ovarian cancer tumor microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction A gynecological cancer that has garnered a lot of attention because of its high disease burden is ovarian cancer. A significant percentage of gynecologic cancers worldwide, particularly in affluent nations, are ovarian cancers [1] . Due to atypical early symptoms, ovarian cancer is frequently difficult to identify at an early stage, meaning that most patients receive a diagnosis at an advanced stage with a dismal prognosis [2] . Patients with stage I and stage II ovarian cancer have a 90% 5-year survival rate, however those with stage III and IV only have a 30% 5-year survival rate. Unfortunately, many patients will relapse because 75% of patients are diagnosed with stage III and IV ovarian cancer [3] . The local environment for tumor cell growth and survival is known as the tumor microenvironment (TME) [4] . This environment includes both cellular (such as immune cells, stromal cells, endothelial cells, etc.) and non-cellular (such as extracellular matrix, growth hormones, chemokines, etc.) components [5] . The occurrence, development, metastasis, and medication resistance of ovarian cancer are all significantly influenced by TME. In TME, cytokines, chemokines, and immunosuppressive cells cooperate to support tumor immune escape and progression [6] . Important chemicals in TME are chemokines and chemokine receptors (CCRs). Through certain signaling pathways, they participate in physiological activities like immune cell recruitment and cell migration during tissue repair [7] . Based on their structure and ligand specificity, the family of G protein-coupled receptors known as chemokines and associated receptors is further subdivided into subfamilies [8] . In addition to being engaged in the developmental processes of tissue differentiation, hematopoiesis, inflammation, and immunological control, chemokines and their receptors are also crucial for the development of tumors [9] . A strong association is found between the expression levels of chemokines and their receptors and the prognosis, metastasis, and aggressiveness of tumors. Studies show that the presence of CXCR4/CXCL12 in breast cancers is associated with a poor breast - cancer outcome, increased metastasis, resistance to standard treatment drugs, and poor pathogenesis results [4] . In detail, the activation of chemokines and receptors can improve immune evasion and angiogenesis of tumor cells. In contrast, their inhibition may hold up tumor growth. As studies suggest, CXCL8 is up - regulated at the border of where tumors invade in a number of human cancer cases. It is indicated that the interaction of the tumor with its surroundings strengthens angiogenesis, tumor genetic diversity, survival, proliferation, immune evasion, metastasis, and multidrug resistance, all factors for tumor progression [10] . In spite of reports on chemokines, their receptors, and ovarian cancer, the role of chemokines and their receptors in ovarian cancer has not been thoroughly examined. This study will try to find the possible biological roles and traits of chemokines and their receptors to improve the prognostic evaluation of ovarian cancer. Materials and Method Data sources related to ovarian cancer samples The TCGA database gave out clinical, transcriptome, and gene mutation information of samples from ovarian cancer. The TCGA database gave out clinical, transcriptome, and gene mutation information from ovarian cancer samples [11] . Additionally, the GEO database contains clinical and transcriptomic data from GSE32062 and GSE32063 [11] . Background correction and quantile normalization were applied to the original GEO and TCGA files, respectively. Transcriptome data for TCGA were combined with transcriptome data from GSE32062 and GSE32063 after being transformed from FPKM to TPM. Analysis of mutation burden, copy number variation and differential expression of chemokines and their receptors at OC Examination of Chemokine and Receptor Differential Expression, Copy Number Variation, and Mutational Burden in 0C Catherine E. Hughes revealed the 67 chemokines and their receptors that were part of this investigation [12] . To show the frequency and copy of mutations in chemokines and receptors in ovarian cancer, a mutation heat map and copy bar chart of related chemokine and receptor genes in ovarian cancer samples were put together using the ovarian cancer gene mutation data in TCGA. To look into the role of chemokines and their receptors in ovarian cancer, we performed the Kaplan-Meier survival assay, univariate COX analysis, and differential expression analysis of chemokines and their receptors. Consensus cluster analysis of chemokines and their receptor subtypes, survival analysis and functional analysis between subtypes The subtypes of chemokines and their receptors were identified using consensus cluster analysis, which was followed by principal component analysis (PCA) and Kaplan-Meier survival analysis. The differences between the chemokine and its receptor clustered isoforms were examined using the limma program, and log FC filter = 1 adj. P. Val. Filter = 0.05. GSVA and ssGSEA were examined for the clustering isoforms of chemokines and their receptors using the GSVA and GSEABase programs. The acquired differential genes (EDGs) were subjected to KEGG and GO analyses based on the clusterProfiler program. Construction and validation of prognostic models According to the results of one-way COX analysis of EDGs, consensus cluster analysis was carried out again. Kaplan-Meier survival analysis and differential expression analysis of chemokines and their receptors were performed for the consensus cluster analysis type. The samples from the combined TCGA and GEO were randomly separated into a learning group and a test group. A prognostic model was then created through lasso regression analysis according to one - way COX analysis results of EDGs. The high - risk and low - risk groups had analyses done on chemokine and its receptor subtypes, chemokine and its receptor - related gene subtypes, differential gene analysis between groups, survival analysis, prognostic R ovarian cancer analysis, and nomogram display. TME analysis According to the risk score obtained from the prognosis model, the comprehensive scores of stem cells, immune cells, and the microenvironment in the medium- and low-risk OC groups and the mutational burden were studied. We did stem cell correlation analysis and drug sensitivity analysis on the core genes in order to construct the prognostic model. Drug susceptibility analysis Drug susceptibility was analyzed in GSCA [13] . From 860 cell lines, GSCA gathered 265 small molecule IC50s and the mRNA gene expressions that corresponded to them from Cancer Drug Sensitivity Genomics (GDSC). Furthermore, via the Genomics of Therapeutics Response Portal (CTRP), GSCA gathered 481 small chemical IC50s and the matching mRNA gene expressions from 1001 cell lines. Integrate data on antimicrobial susceptibility and mRNA expression. To determine the relationship between medication IC50 and gene mRNA expression, do Pearson correlation analysis. FDR is used to alter the P-value. Single-cell sequencing analysis Ovarian cancer single-cell sequencing GSE118828 files can be downloaded from the GEO database. Single-cell data can then be processed and analyzed to find different cell clusters. ceRNA network construction We got miRNA-related data with the help of the miRanda, miRDB, miRWalk, and TargetScan databases. Perl software was used to forecast the miRNA binding of related prognostic genes. We got lncRNA data from the spongeScan database and then used Perl software to construct miRNA-binding lncRNA. Finally, Cytoscape 3.10.1 software was used to create the ceRNA regulation network diagram. Outcome Analysis of mutational burden and copy number variation of chemokines and their receptors in ovarian cancer The mutational burden of chemokines and their receptors in ovarian cancer was examined using the TCGA mutation data. They were all zero, with the exception of 1% alterations in CCR3, CXCR3, CCL21, CCR6, CCR8, and XCR1. Furthermore, missignificance mutations in C > T are the primary way that the mutational burden of chemokines and their receptors in ovarian cancer is displayed (Fig. 1 a). The copy number variation analysis results indicated that the copy number of the other chemokines and their receptors decreases in addition to CCL22 and CX3CL1, as well as CXCL8, CXCL1, CXCL2, CXCL6, CXCL1, CXCL5, CXCL3, CXCL2, CXCL9, CXCL10, CXCL11, CCL28, CCL27, CCL19, CCL21, CXCL13, CCL24, CCL26, CXCR5, CXCL16, CXCL14, CXCL12, CXCR3, and CCL17 (Fig. 1 c). Survival analysis, functional analysis between subtypes, and consensus cluster analysis of chemokines and their receptor subtypes The subtypes were comparatively independent when the chemokines and their receptors were grouped into I and II isoforms (Fig. 2 a). There was a difference in survival between the I and II subtypes, according to the Kaplan-Meier survival analysis (Fig. 2 b). I and II isoforms could be clearly identified using principal component analysis (PCA) (Fig. 2 c). Significant variations between I and II subtypes in immune response, cell adhesion, extracellular matrix composition, and disease-specific pathways were shown by the GSVA, KEGG, and GO findings. (Fig. 2 d, e). Immune cell differential analyses for I and II subtypes were conducted in accordance with ssGSEA. With statistical differences compared to type II, the great majority of immune cells were enriched in type I (Fig. 2 f). Construction of prognostic models The outcomes of the COX analysis on genes came from the differential analysis of the I and II isoforms. Subtypes A and B were generated by re - doing the consensus cluster analysis (Fig. 3 a). Subtypes A and B had different survival rates, according to the Kaplan-Meier survival analysis (Fig. 3 c). Figure 3 d shows the heatmap of isoforms A and B. The findings of the COX analysis of genes were derived from the examination of the differences between I and II isoforms. The learning set and test set were randomly allocated by integrating samples, and lasso regression analysis and multivariate COX regression were carried out (Fig. 3 e,f).Finally, design the model:Risk score=(-CXCL9*0.20-MMP9*0.11 + GFPT2*0.15 + CXCL12*0.11-MSX1*0.06 + HIF3A*0.11 + B3GNT3*0.16-FOXJ1*0.08-AADAC*0.14-AGR2*0.09). Validation of prognostic models The risk score was used to split the combined TCGA and GEO samples into high - risk and low - risk groups. The results of a differential expression analysis done between high - and low - risk groups in the integrated sample showed that both low - and low - risk groups of the ovarian cancer sample had notably different chemokines and their receptors (Fig. 4 a).By means of the model score, the sample risk distribution map, test set, and learning set of the integrated sample were analyzed separately (Fig. 4 b, c, d).The results showed that when the patient's risk score went up, their survival time got longer, the death toll rose, and the expression of CXCL9, MMP9, FOXJ1, and AACAC declined. All these were low - risk factors. By way of contrast, GFPT2, CXCL12, B3GNT3, and HIF3A were high - risk factors. Clinical relevance was analyzed by means of Kaplan - Meier survival analysis (Fig. 4 e, f, g) and then comes prognostic ROC analysis (Fig. 4 h, i, j), which brought together the sample, test, and learning sets. The results of survival analysis showed that patients in the high - risk group had a shorter survival time than patients in the low - risk group. Prognostic ROC analysis found an AUC greater than 0. 6, meaning that the model had a fairly good fitting effect and could precisely prognose ovarian cancer. Given a value of 6, it means the model had a good fitting and can precisely predict the prognosis of ovarian cancer. TME analysis In the samples that integrate TCGA and GEO data, patients in the high - risk group had higher stem cell scores than those in the low - risk group. But they had lower immune cell and composite scores (Fig. 5 a). We looked at the relationship between the model's core genes and immune cells. The correlation diagram of this look - over is presented as a heat map in the annex (Fig. 5 b).From the results of immune cell correlation analysis, it was found that the core genes of the model were associated with CD8T lymphocyte - activated memory T cells (CD4), plasma cells, activated natural killer cells, monocytes, activated mast cells, M0, M1, and M2 macrophages, eosinophils, quiescent and activated dendritic cells, memory B cells, and other immune cells. Through a cumulative mutation burden analysis of chemokines and their receptors, it was found that the low - risk and high - risk groups did not have a significant difference in the number of chemokine and receptor mutations (Fig. 5 c, d, e).There was an appreciable difference and a negative correlation between risk ratings and stem cells (Fig. 5 f). Drug susceptibility analysis These genes were linked to a range of anticancer medications, according to drug sensitivity analysis based on GDSC and CTRP (Fig. 6 a, b). After GDSC and CTRP drug susceptibility analysis results crossed, 20 medications were ultimately linked to the prognostic model's key genes (Fig. 6 c). Single-cell UMAP cluster analysis The GSE118828 dataset in the GEO dataset was subjected to UMAP cluster analysis, yielding 12 clustered cell populations. The cell populations of various classes were labeled with varying colors (Fig. 7 a), signifying primary cells, normal ovarian cells, and metastatic cells, respectively. Six cell types—fibroblasts, epithelial cells, malignant cells, myofibroblasts, monocytes/macrophages, and CD4T cells—were identified from the 12 cell subsets (Fig. 7 d). Following UMAP cluster analysis of the malignant cells, pie charts (Fig. 7 e) and percentage stacked histograms (Fig. 7 f) were displayed, and three cell types—immune cells, malignant cells, and stem cells—were annotated (Fig. 7 d). UMAP cluster analysis of prognostic genes FOXJ1 and AGR2 were highly aggregated in malignant cells, CXCL12 was strongly aggregated in stem cells, and other prognostic genes were not significantly aggregated, according to UAMP cluster analysis of prognostic genes (Fig. 8 a-h). Construction of ceRNA network of prognostic genes MiRanda, miRDB, miRWalk and Targetscan database were used to acquire miR-related data; Perl was used to predict the gene binding of the miRNA binding pathway; spongeScan database was used to acquire lncRNA data; Perl was used to construct miRNA-binding lncRNA; ceRNA network was constructed.Finally, Cytoscape software was used to map the ceRNA regulation network.. Discussion Ovarian cancer is the eighth most common cancer in women and has the highest mortality rate of any gynecological cancer. It is clinically difficult to diagnose ovarian cancer early, and nearly 70% of patients are diagnosed with stage III–IV metastatic disease. Additionally, there are no reliable diagnostic or prognostic biomarkers, and ovarian cancer recurrence and chemotherapy resistance are serious problems that can have negative outcomes [14] . TME is essential for the growth and recurrence of tumors in ovarian cancer, and inflammation is also a significant factor in TME [15, 16] . Furthermore, there is significant anticancer activity in the tumor immunological microenvironment [17] . By controlling the inflammatory response and immune cell infiltration, chemokines and their receptors can affect the condition of the tumor immunological microenvironment [18, 19] . Thus, a methodical investigation of the function of chemokines and their receptors in GBM would advance knowledge of the immune response to tumors and the prognosis of GBM. We began this investigation by examining the CNV and mutation frequency of chemokines and their receptors in ovarian cancer. Consensus cluster analysis was used to investigate the biological roles and underlying processes of the majority of chemokines and their receptors, which we identified to have the most diverse expression across ovarian cancer tissues. GBM is classified into I and II subtypes by consensus clustering, and the differences between the two categories are obvious. The extracellular matrix composition, immunological response, cell adhesion, and disease-specific pathways all showed a substantial enrichment of the distinct genes between I and II isoforms. Elevated levels of CRP, an acute-phase protein, have been linked to a poor prognosis for women with ovarian cancer and may also be connected to inflammation related to the tumor [20] . By enlisting immune cells, Jan Korbecki et al. methodically examined how chemokines and their receptors affect the TME of ovarian cancer [21] . We also conducted one-way COX analysis based on the EDGs between I and II isoforms obtained from consensus cluster analysis of 67 chemokines and their receptors in order to better investigate the mechanism of action of chemokines and their receptors in GBM. A consensus clustering analysis was conducted once more in accordance with the EDGs' one-way COX results. Two separate subtypes, A and B, were identified by repeating the consensus cluster analysis. This suggests that there is some association between EDGs and chemokines and their receptors. Following a random division of the combined TCGA and GEO samples into learning and training sets, lasso regression and COX analyses were conducted on EDGs, the clinical relevance was confirmed, and ultimately the risk prognosis model was effectively built. Based on the predictive model of ovarian cancer centered on chemokines and their receptor - related genes, the scientists found that the high - risk group in this study had lower immune cell scores and higher stromal cell scores relative to the low - risk group.Elevated stromal scores mirror increased extracellular matrix (ECM) deposition and cancer-associated fibroblast (CAF) activation [22] . Generally speaking, a high immune cell score would suggest a more active immune system, which could be connected to immune cell aggregation and the immunological response against tumors [23] . The negative link between risk scores and stem cells suggests that high-risk tumors are less responsive to medicines that target stem cells, like ALDH inhibitors, and may gain an edge in metastasis but lose the potential to initiate tumors. [24] . The high-risk group's stromal cells scored higher than the low-risk group's, suggesting that the TME is more complex and that there may be a greater quantity or activity of immune cells and stem cells in the high-risk group. Generally speaking, a high immune cell score would suggest a more active immune system, which could be connected to immune cell aggregation and the immunological response against tumors. GFPT2 (glutamine - fructose − 6 - phosphate transaminase 2) serves as the first step and rate - controlling enzyme of the hexosamine biosynthesis pathway (HBP), a part of the glucose metabolism system that offers substrates for glycosylation modifications and has a broad influence on cellular function [25] . In a number of human cancers, GFPT2 expression levels were heightened, and it was negatively related to the survival prognosis of ovarian cancer patients [26] . There exists a link between GFPT2 and immune cells in the ovarian cancer's microenvironment.Investigations have pointed out a substantial connection between GFPT2 and immunomodulatory substances, immune - related biomarkers, and immune infiltration.Obviously, a well - defined correlation was identified between the effectiveness of immunotherapy and GFPT2 expression.Evidently, a strong correlation occurred between the efficiency of immunotherapy and GFPT2 expression [27] . HIF3A (Hypoxia - inducible factor 3 - alpha) is the gene that encodes the hypoxia - inducible factor HIF − 3α, vital for governing the cellular response to hypoxia.HIF3A regulates the expression of downstream genes by binding to particular hypoxia response elements (HREs) and generating heterodimers with HIF - β subunits [28] . The analysis of follow - up data from ovarian cancer patients brought to light a negative correlation between HIF3A expression levels and patient prognosis. In essence, patients with higher HIF3A expression levels had a poorer outcome.The ability of ovarian cancer cells to migrate and clone, suppressed by LINC01342 knockdown, was reversed when HIF3A was overexpressed, suggesting HIF3A is crucial for ovarian cancer migration and cloning [29] . One chemokine that is crucial in the interaction between immune cells and the milieu in ovarian cancer is CXCL9 (C-X-C motif chemokine ligand 9). Patients with ovarian cancer who had higher levels of CXCL9 mRNA expression had a significantly better prognosis. According to this research, CXCL9 might be crucial for the prognosis of ovarian cancer [30] . CXCL9 was identified as a critical marker gene for predicting the effectiveness of immune checkpoint inhibitors (ICIs) through multi-cohort analysis and single-cell RNA sequencing. It was positively connected with M1 macrophage expression and associated with immune-related pathways and immune cell infiltration [31] . According to studies, overexpression of CXCL9 can improve T cell infiltration, stop tumor growth, and increase the effectiveness of anti-PD-L1 therapy for ovarian cancer [32] . A transcription factor called FOXJ1 (Forkhead Box J1) controls differentiation, cell motility, and ciliation. Recent research has revealed that ovarian cancer is among the many tumors where FOXJ1 is aberrantly expressed. FOXJ1 can increase the invasion and metastasis of ovarian cancer cells by downregulating E-cadherin and upregulating N-cadherin and vitementin. FOXJ1 is a crucial regulator of cilia formation, and defects in cilia can activate pathways including WNT and Hedgehog that promote cancer. By using PI3K/AKT or DNA repair pathways, FOXJ1 may lessen the ovarian cancer cells' susceptibility to platinum medications. FOXJ1 has been demonstrated to be overexpressed in metastatic and advanced (stage III/IV) ovarian cancer. According to a study on high-grade serous ovarian cancer (HGSOC), patients with high FOXJ1 expression had significantly shorter PFS and OS. By encouraging immunosuppression of the tumor microenvironment (such as CAFs and TAMs), FOXJ1 may have an impact on prognosis. Despite preliminary research on the involvement of FOXJ1 in ovarian cancer, further research is still required to determine whether OXJ1 is a viable independent prognostic marker. Limitations This study has several limitations. First, the analysis was based on publicly available datasets (TCGA and GEO), which, although comprehensive, may not fully capture the heterogeneity of ovarian cancer across diverse populations. Second, the prognostic model was constructed and validated using retrospective data, and prospective clinical validation is required to confirm its generalizability. Third, the functional roles of the identified chemokines and their receptors in ovarian cancer progression warrant further experimental investigation. Finally, the sample size for single-cell sequencing analysis (GSE118828) was limited, and larger cohorts are needed to validate the cellular distribution of prognostic genes. Conclusion We developed a new predictive model for patients with ovarian cancer and thoroughly analyzed the biological role and prognostic significance of chemokines and their receptors in ovarian cancer. We discovered that the immune microenvironment state of ovarian cancer patients can be reflected in the risk score of this model. This offers new information on the mechanism of action of chemokines and their receptors in ovarian cancer, as well as possible biomarkers for prognosis and treatment. Declarations Data Availability The datasets analyzed during the current study are publicly available as follows: The ovarian cancer transcriptome and clinical data from The Cancer Genome Atlas (TCGA) can be accessed at: (https://www.cancer.gov/ccg/research/genome-sequencing/tcga) (TCGA-OV). Gene Expression Omnibus (GEO) datasets GSE32062, GSE32063, and GSE118828 are available at: ( https://www.ncbi.nlm.nih.gov/geo/ ) with accession numbers GSE32062, GSE32063, and GSE118828. Drug sensitivity data were obtained from the GSCA platform ( http://bioinfo.life.hust.edu.cn/GSCA ), which integrates GDSC and CTRP databases. miRNA-related data were retrieved from miRanda(miRanda (microRNA.org) | miRToolsGallery),miRDB(https://mirdb.org/), miRWalk(http://mirwalk.umm.uni-heidelberg.de/),and TargetScan (https://www.targetscan.org/vert_80/)databases. lncRNA data were obtained from the spongeScan database(spongeScan | miRToolsGallery). All data sources are publicly accessible, and no new datasets were generated in this study Consent to publish Not applicable. Contributions Xinyi Ma: Writing – Original Draft Preparation; Faming Tian: revised the work critically for important intellectual content; Yimin Liu: Made a significant contribution to the conception or design of the work; Hu Gang: Writing – Review & Editing. Acknowledgements Thank you to the collaborators of this study for their time and effort in our research. Funding No funding was received to assist with the preparation of this manuscript. Consent Statements required This comment letter does not include any new patient data, and no informed consent is required. Ethics This study is not applicable. References SIEGEL R L, MILLER K D, JEMAL A. Cancer statistics, 2020 [J]. CA Cancer J Clin, 2020, 70(1): 7-30. TORRE L A, TRABERT B, DESANTIS C E, et al. Ovarian cancer statistics, 2018 [J]. CA Cancer J Clin, 2018, 68(4): 284-96. MIN Y, PARK H B, BAEK K H, et al. Cellular Functions of Deubiquitinating Enzymes in Ovarian Adenocarcinoma [J]. Genes (Basel), 2023, 14(4). HINTON C V, AVRAHAM S, AVRAHAM H K. Role of the CXCR4/CXCL12 signaling axis in breast cancer metastasis to the brain [J]. Clin Exp Metastasis, 2010, 27(2): 97-105. HANAHAN D, WEINBERG R A. Hallmarks of cancer: the next generation [J]. Cell, 2011, 144(5): 646-74. QUAIL D F, JOYCE J A. Microenvironmental regulation of tumor progression and metastasis [J]. Nat Med, 2013, 19(11): 1423-37. ZLOTNIK A, YOSHIE O. Chemokines: a new classification system and their role in immunity [J]. Immunity, 2000, 12(2): 121-7. CHARO I F, RANSOHOFF R M. The many roles of chemokines and chemokine receptors in inflammation [J]. N Engl J Med, 2006, 354(6): 610-21. LIU H, YANG Z, LU W, et al. Chemokines and chemokine receptors: A new strategy for breast cancer therapy [J]. Cancer Med, 2020, 9(11): 3786-99. ASOKAN S, BANDAPALLI O R. CXCL8 Signaling in the Tumor Microenvironment [J]. Adv Exp Med Biol, 2021, 1302: 25-39. KAWAGUCHI A, YAJIMA N, TSUCHIYA N, et al. Gene expression signature-based prognostic risk score in patients with glioblastoma [J]. Cancer Sci, 2013, 104(9): 1205-10. HUGHES C E, NIBBS R J B. A guide to chemokines and their receptors [J]. Febs j, 2018, 285(16): 2944-71. LIU C J, HU F F, XIE G Y, et al. GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels [J]. Brief Bioinform, 2023, 24(1). FRĄSZCZAK K, BARCZYŃSKI B. The Role of Cancer Stem Cell Markers in Ovarian Cancer [J]. Cancers (Basel), 2023, 16(1). ZHANG X, LI M, YIN N, et al. The Expression Regulation and Biological Function of Autotaxin [J]. Cells, 2021, 10(4). JIANG Y, WANG C, ZHOU S. Targeting tumor microenvironment in ovarian cancer: Premise and promise [J]. Biochim Biophys Acta Rev Cancer, 2020, 1873(2): 188361. COLOTTA F, ALLAVENA P, SICA A, et al. Cancer-related inflammation, the seventh hallmark of cancer: links to genetic instability [J]. Carcinogenesis, 2009, 30(7): 1073-81. GRIFFITH J W, SOKOL C L, LUSTER A D. Chemokines and chemokine receptors: positioning cells for host defense and immunity [J]. Annu Rev Immunol, 2014, 32: 659-702. MANTOVANI A, BONECCHI R, LOCATI M. Tuning inflammation and immunity by chemokine sequestration: decoys and more [J]. Nat Rev Immunol, 2006, 6(12): 907-18. PEIGNé M, DECANTER C. Serum AMH level as a marker of acute and long-term effects of chemotherapy on the ovarian follicular content: a systematic review [J]. Reprod Biol Endocrinol, 2014, 12: 26. KORBECKI J, BOSIACKI M, BARCZAK K, et al. Involvement in Tumorigenesis and Clinical Significance of CXCL1 in Reproductive Cancers: Breast Cancer, Cervical Cancer, Endometrial Cancer, Ovarian Cancer and Prostate Cancer [J]. Int J Mol Sci, 2023, 24(8). SAHAI E, ASTSATUROV I, CUKIERMAN E, et al. A framework for advancing our understanding of cancer-associated fibroblasts [J]. Nat Rev Cancer, 2020, 20(3): 174-86. CHEN L, FAN Z, CHANG J, et al. Sequence-based drug design as a concept in computational drug design [J]. Nat Commun, 2023, 14(1): 4217. ARATANI Y, UEMURA T, HAGIHARA T, et al. Green leaf volatile sensory calcium transduction in Arabidopsis [J]. Nat Commun, 2023, 14(1): 6236. ZHANG H R, LI T J, YU X J, et al. The GFPT2-O-GlcNAcylation-YBX1 axis promotes IL-18 secretion to regulate the tumor immune microenvironment in pancreatic cancer [J]. Cell Death Dis, 2024, 15(4): 244. ZHOU L, LUO M, CHENG L J, et al. Glutamine-fructose-6-phosphate transaminase 2 (GFPT2) promotes the EMT of serous ovarian cancer by activating the hexosamine biosynthetic pathway to increase the nuclear location of β-catenin [J]. Pathol Res Pract, 2019, 215(12): 152681. ZHOU Y, DONG Y. The Prognostic Value and Immunotherapeutic Characteristics of GFPT2 in Pan-cancer [J]. Comb Chem High Throughput Screen, 2024. TOLONEN J P, HEIKKILä M, MALINEN M, et al. A long hypoxia-inducible factor 3 isoform 2 is a transcription activator that regulates erythropoietin [J]. Cell Mol Life Sci, 2020, 77(18): 3627-42. ZHANG C, LIU J, ZHANG Y, et al. LINC01342 promotes the progression of ovarian cancer by absorbing microRNA-30c-2-3p to upregulate HIF3A [J]. J Cell Physiol, 2020, 235(4): 3939-49. 高境泽, 吴霞. 卵巢肿瘤组织中CXCL9 mRNA表达与患者的预后、免疫微环境 特征的相关性研究 [J]. 上海交通大学学报(医学版), 2020, 40(04): 457-63. YU Y, CHEN H, OUYANG W, et al. Unraveling the role of M1 macrophage and CXCL9 in predicting immune checkpoint inhibitor efficacy through multicohort analysis and single-cell RNA sequencing [J]. MedComm (2020), 2024, 5(3): e471. SEITZ S, DREYER T F, STANGE C, et al. CXCL9 inhibits tumour growth and drives anti-PD-L1 therapy in ovarian cancer [J]. Br J Cancer, 2022, 126(10): 1470-80. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8096911","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":553610808,"identity":"21f5ecd1-db75-43ba-a815-5938429a39d6","order_by":0,"name":"Xinyi Ma","email":"","orcid":"","institution":"The Affiliated Hospital of North China University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xinyi","middleName":"","lastName":"Ma","suffix":""},{"id":553610809,"identity":"fd4372f2-c31d-429c-b013-95e33722fe74","order_by":1,"name":"Faming Tian","email":"","orcid":"","institution":"North China University of 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1","display":"","copyAsset":false,"role":"figure","size":105646,"visible":true,"origin":"","legend":"\u003cp\u003eWaterfall chart of abrupt load load. (a) copy the number of laps. (b) Copy number variation bar plot. (c).\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8096911/v1/9afa339a6e2d933c6b159646.png"},{"id":97299529,"identity":"0838f382-4b8e-400c-8c9d-56f8abc9a2ae","added_by":"auto","created_at":"2025-12-03 00:51:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":98515,"visible":true,"origin":"","legend":"\u003cp\u003eConsensus cluster analysis diagram. (a) Subtype Kaplan-Meier survival curve. (b)Subtype PCA diagram. (c)Subtype GSVA heatmap. (d) Subtype KEGG and GO bar charts. (e) Differential analysis of subtype immune cells. (f).\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8096911/v1/79c4c75e25ffd93504ece17b.png"},{"id":97299535,"identity":"d32f28b8-f4b3-49c3-b99e-6324063bd84d","added_by":"auto","created_at":"2025-12-03 00:51:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":171670,"visible":true,"origin":"","legend":"\u003cp\u003eConsensus cluster analysis diagram. (a) Sankey diagram of the process of constructing the model. (b) Survival chart of subtype. (c) Kaplan-Meier. (d) Subtype heat map. (e). Prognostic LASSO coefficient screening chart. (f).\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8096911/v1/c176f13de0dcdbd635a6d447.png"},{"id":97368950,"identity":"59aa558a-a4e2-49cf-b271-d8b7b2c86e8c","added_by":"auto","created_at":"2025-12-03 16:23:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":137736,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of the difference between high and low risk. (a). Sample risk profile of the integrated sample. (b). A sample risk profile of the test set. (c). A sample risk distribution map of the learning set. (d). Kaplan-Meier survival curve plot of the integrated sample. (e). Kaplan-Meier survival curve plot of the test set. (f). Kaplan-Meier survival curve plot for a learning set. (g). Prognostic ROC analysis of integrated samples. (h). Prognostic ROC analysis of the test set. (i). Prognostic ROC analysis of the learning set. (j).\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8096911/v1/0d594cc3e1379d4a39c3c0a8.png"},{"id":97299531,"identity":"2acdb271-a735-44ff-8677-a80cc0588c27","added_by":"auto","created_at":"2025-12-03 00:51:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":109419,"visible":true,"origin":"","legend":"\u003cp\u003eTME score. (a). Immune cell correlation heat map. (b). Cumulative waterfall plot of abrupt burden for high-risk groups. (c). Cumulative waterfall plot of mutation burden for low-risk groups. (d). Analysis of differences in mutational burden between risk scores. (e). Risk score correlation analysis with stem cells.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8096911/v1/9b947f1c1eb7037c722f30b1.png"},{"id":97368943,"identity":"2312a851-a0a0-44a6-9b5c-e3380645e031","added_by":"auto","created_at":"2025-12-03 16:23:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":104075,"visible":true,"origin":"","legend":"\u003cp\u003eGDSC-based drug susceptibility analysis. (a). CTRP-based drug susceptibility analysis. (b). drug Venn Diagram. (c).\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8096911/v1/dfc61b7d39702ea58adf4024.png"},{"id":97299538,"identity":"a93eadb0-e261-451d-92a1-2b3aecf8428f","added_by":"auto","created_at":"2025-12-03 00:51:20","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":51287,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-cell sequencing data of cell subsets after dimensionality reduction by UMAP. (a). Single-cell sequencing data: Cell types identified after dimensionality reduction by UMAP. (b). Single-cell sequencing data for cell types identified after dimensionality reduction of the major cell lines identified by UMAP. (c). Types of malignant cell lines identified by UMAP dimensionality reduction from single-cell sequencing data. (d). Pie chart of cell types for major cell lines. (e). Stacked histogram of cell type percentages for major cell lines. (f).\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8096911/v1/51da0f6812e8c4cfcd02f426.png"},{"id":97299540,"identity":"f8b4e039-c9c0-4198-af33-ab6a229aaa27","added_by":"auto","created_at":"2025-12-03 00:51:20","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":46804,"visible":true,"origin":"","legend":"\u003cp\u003eThe prognostic gene HIF3A in samples was analyzed using UMAP clustering. (a). UMAP cluster analysis of prognostic gene GFPT2 in samples. (b). UMAP cluster analysis of prognostic gene FOXJ1 in samples. (c). UMAP cluster analysis of prognostic gene CXCL12 in samples. (d). UMAP cluster analysis of the prognostic gene CXCL9 in samples. (e). UMAP cluster analysis of the prognostic gene AADAC in samples. (f). UMAP cluster analysis of AGR2 prognostic gene in samples. (g). \u0026nbsp;UMAP cluster analysis of H prognostic gene B3GNT3 in samples. (h).\u003c/p\u003e","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8096911/v1/2ea7a91d5cbd2e8054d22590.png"},{"id":97299549,"identity":"82fd52de-4f1d-4201-96b0-aa83d1ff2977","added_by":"auto","created_at":"2025-12-03 00:51:20","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":131837,"visible":true,"origin":"","legend":"\u003cp\u003eThe prognostic gene HIF3A in samples was analyzed using UMAP clustering.\u003c/p\u003e","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8096911/v1/560f47bbc4f32a033b7f88c7.png"},{"id":98924018,"identity":"daa09f35-eddc-468c-95d8-08b2c665d1dd","added_by":"auto","created_at":"2025-12-24 07:09:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2189379,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8096911/v1/b491943c-083d-4653-b53a-697633968fdd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The function of chemokines and their receptors in the ovarian cancer microenvironment and research on their prognostic significance","fulltext":[{"header":"Introduction","content":"\u003cp\u003eA gynecological cancer that has garnered a lot of attention because of its high disease burden is ovarian cancer. A significant percentage of gynecologic cancers worldwide, particularly in affluent nations, are ovarian cancers \u003csup\u003e[1]\u003c/sup\u003e. Due to atypical early symptoms, ovarian cancer is frequently difficult to identify at an early stage, meaning that most patients receive a diagnosis at an advanced stage with a dismal prognosis \u003csup\u003e[2]\u003c/sup\u003e. Patients with stage I and stage II ovarian cancer have a 90% 5-year survival rate, however those with stage III and IV only have a 30% 5-year survival rate. Unfortunately, many patients will relapse because 75% of patients are diagnosed with stage III and IV ovarian cancer \u003csup\u003e[3]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe local environment for tumor cell growth and survival is known as the tumor microenvironment (TME) \u003csup\u003e[4]\u003c/sup\u003e. This environment includes both cellular (such as immune cells, stromal cells, endothelial cells, etc.) and non-cellular (such as extracellular matrix, growth hormones, chemokines, etc.) components \u003csup\u003e[5]\u003c/sup\u003e. The occurrence, development, metastasis, and medication resistance of ovarian cancer are all significantly influenced by TME. In TME, cytokines, chemokines, and immunosuppressive cells cooperate to support tumor immune escape and progression \u003csup\u003e[6]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eImportant chemicals in TME are chemokines and chemokine receptors (CCRs). Through certain signaling pathways, they participate in physiological activities like immune cell recruitment and cell migration during tissue repair \u003csup\u003e[7]\u003c/sup\u003e. Based on their structure and ligand specificity, the family of G protein-coupled receptors known as chemokines and associated receptors is further subdivided into subfamilies \u003csup\u003e[8]\u003c/sup\u003e. In addition to being engaged in the developmental processes of tissue differentiation, hematopoiesis, inflammation, and immunological control, chemokines and their receptors are also crucial for the development of tumors \u003csup\u003e[9]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA strong association is found between the expression levels of chemokines and their receptors and the prognosis, metastasis, and aggressiveness of tumors. Studies show that the presence of CXCR4/CXCL12 in breast cancers is associated with a poor breast - cancer outcome, increased metastasis, resistance to standard treatment drugs, and poor pathogenesis results \u003csup\u003e[4]\u003c/sup\u003e. In detail, the activation of chemokines and receptors can improve immune evasion and angiogenesis of tumor cells. In contrast, their inhibition may hold up tumor growth. As studies suggest, CXCL8 is up - regulated at the border of where tumors invade in a number of human cancer cases. It is indicated that the interaction of the tumor with its surroundings strengthens angiogenesis, tumor genetic diversity, survival, proliferation, immune evasion, metastasis, and multidrug resistance, all factors for tumor progression \u003csup\u003e[10]\u003c/sup\u003e. In spite of reports on chemokines, their receptors, and ovarian cancer, the role of chemokines and their receptors in ovarian cancer has not been thoroughly examined. This study will try to find the possible biological roles and traits of chemokines and their receptors to improve the prognostic evaluation of ovarian cancer.\u003c/p\u003e"},{"header":"Materials and Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData sources related to ovarian cancer samples\u003c/h2\u003e\u003cp\u003eThe TCGA database gave out clinical, transcriptome, and gene mutation information of samples from ovarian cancer. The TCGA database gave out clinical, transcriptome, and gene mutation information from ovarian cancer samples\u003csup\u003e[11]\u003c/sup\u003e. Additionally, the GEO database contains clinical and transcriptomic data from GSE32062 and GSE32063 \u003csup\u003e[11]\u003c/sup\u003e. Background correction and quantile normalization were applied to the original GEO and TCGA files, respectively. Transcriptome data for TCGA were combined with transcriptome data from GSE32062 and GSE32063 after being transformed from FPKM to TPM.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalysis of mutation burden, copy number variation and differential expression of chemokines and their receptors at OC\u003c/b\u003e\u003c/p\u003e\u003cp\u003eExamination of Chemokine and Receptor Differential Expression, Copy Number Variation, and Mutational Burden in 0C Catherine E. Hughes revealed the 67 chemokines and their receptors that were part of this investigation \u003csup\u003e[12]\u003c/sup\u003e. To show the frequency and copy of mutations in chemokines and receptors in ovarian cancer, a mutation heat map and copy bar chart of related chemokine and receptor genes in ovarian cancer samples were put together using the ovarian cancer gene mutation data in TCGA. To look into the role of chemokines and their receptors in ovarian cancer, we performed the Kaplan-Meier survival assay, univariate COX analysis, and differential expression analysis of chemokines and their receptors.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConsensus cluster analysis of chemokines and their receptor subtypes, survival analysis and functional analysis between subtypes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe subtypes of chemokines and their receptors were identified using consensus cluster analysis, which was followed by principal component analysis (PCA) and Kaplan-Meier survival analysis. The differences between the chemokine and its receptor clustered isoforms were examined using the limma program, and log FC filter\u0026thinsp;=\u0026thinsp;1 adj. P. Val. Filter\u0026thinsp;=\u0026thinsp;0.05. GSVA and ssGSEA were examined for the clustering isoforms of chemokines and their receptors using the GSVA and GSEABase programs. The acquired differential genes (EDGs) were subjected to KEGG and GO analyses based on the clusterProfiler program.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eConstruction and validation of prognostic models\u003c/h3\u003e\n\u003cp\u003eAccording to the results of one-way COX analysis of EDGs, consensus cluster analysis was carried out again. Kaplan-Meier survival analysis and differential expression analysis of chemokines and their receptors were performed for the consensus cluster analysis type. The samples from the combined TCGA and GEO were randomly separated into a learning group and a test group. A prognostic model was then created through lasso regression analysis according to one - way COX analysis results of EDGs. The high - risk and low - risk groups had analyses done on chemokine and its receptor subtypes, chemokine and its receptor - related gene subtypes, differential gene analysis between groups, survival analysis, prognostic R ovarian cancer analysis, and nomogram display.\u003c/p\u003e\n\u003ch3\u003eTME analysis\u003c/h3\u003e\n\u003cp\u003eAccording to the risk score obtained from the prognosis model, the comprehensive scores of stem cells, immune cells, and the microenvironment in the medium- and low-risk OC groups and the mutational burden were studied. We did stem cell correlation analysis and drug sensitivity analysis on the core genes in order to construct the prognostic model.\u003c/p\u003e\n\u003ch3\u003eDrug susceptibility analysis\u003c/h3\u003e\n\u003cp\u003eDrug susceptibility was analyzed in GSCA \u003csup\u003e[13]\u003c/sup\u003e. From 860 cell lines, GSCA gathered 265 small molecule IC50s and the mRNA gene expressions that corresponded to them from Cancer Drug Sensitivity Genomics (GDSC). Furthermore, via the Genomics of Therapeutics Response Portal (CTRP), GSCA gathered 481 small chemical IC50s and the matching mRNA gene expressions from 1001 cell lines. Integrate data on antimicrobial susceptibility and mRNA expression. To determine the relationship between medication IC50 and gene mRNA expression, do Pearson correlation analysis. FDR is used to alter the P-value.\u003c/p\u003e\n\u003ch3\u003eSingle-cell sequencing analysis\u003c/h3\u003e\n\u003cp\u003eOvarian cancer single-cell sequencing GSE118828 files can be downloaded from the GEO database. Single-cell data can then be processed and analyzed to find different cell clusters.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eceRNA network construction\u003c/h2\u003e\u003cp\u003eWe got miRNA-related data with the help of the miRanda, miRDB, miRWalk, and TargetScan databases. Perl software was used to forecast the miRNA binding of related prognostic genes. We got lncRNA data from the spongeScan database and then used Perl software to construct miRNA-binding lncRNA. Finally, Cytoscape 3.10.1 software was used to create the ceRNA regulation network diagram.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eOutcome\u003c/h3\u003e\n\u003cp\u003e\u003cb\u003eAnalysis of mutational burden and copy number variation of chemokines and their receptors in ovarian cancer\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe mutational burden of chemokines and their receptors in ovarian cancer was examined using the TCGA mutation data. They were all zero, with the exception of 1% alterations in CCR3, CXCR3, CCL21, CCR6, CCR8, and XCR1. Furthermore, missignificance mutations in C\u0026thinsp;\u0026gt;\u0026thinsp;T are the primary way that the mutational burden of chemokines and their receptors in ovarian cancer is displayed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The copy number variation analysis results indicated that the copy number of the other chemokines and their receptors decreases in addition to CCL22 and CX3CL1, as well as CXCL8, CXCL1, CXCL2, CXCL6, CXCL1, CXCL5, CXCL3, CXCL2, CXCL9, CXCL10, CXCL11, CCL28, CCL27, CCL19, CCL21, CXCL13, CCL24, CCL26, CXCR5, CXCL16, CXCL14, CXCL12, CXCR3, and CCL17 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSurvival analysis, functional analysis between subtypes, and consensus cluster analysis of chemokines and their receptor subtypes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe subtypes were comparatively independent when the chemokines and their receptors were grouped into I and II isoforms (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). There was a difference in survival between the I and II subtypes, according to the Kaplan-Meier survival analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). I and II isoforms could be clearly identified using principal component analysis (PCA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Significant variations between I and II subtypes in immune response, cell adhesion, extracellular matrix composition, and disease-specific pathways were shown by the GSVA, KEGG, and GO findings. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, e). Immune cell differential analyses for I and II subtypes were conducted in accordance with ssGSEA. With statistical differences compared to type II, the great majority of immune cells were enriched in type I (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eConstruction of prognostic models\u003c/h3\u003e\n\u003cp\u003eThe outcomes of the COX analysis on genes came from the differential analysis of the I and II isoforms. Subtypes A and B were generated by re - doing the consensus cluster analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Subtypes A and B had different survival rates, according to the Kaplan-Meier survival analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed shows the heatmap of isoforms A and B. The findings of the COX analysis of genes were derived from the examination of the differences between I and II isoforms. The learning set and test set were randomly allocated by integrating samples, and lasso regression analysis and multivariate COX regression were carried out (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee,f).Finally, design the model:Risk score=(-CXCL9*0.20-MMP9*0.11\u0026thinsp;+\u0026thinsp;GFPT2*0.15\u0026thinsp;+\u0026thinsp;CXCL12*0.11-MSX1*0.06\u0026thinsp;+\u0026thinsp;HIF3A*0.11\u0026thinsp;+\u0026thinsp;B3GNT3*0.16-FOXJ1*0.08-AADAC*0.14-AGR2*0.09).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eValidation of prognostic models\u003c/h2\u003e\u003cp\u003eThe risk score was used to split the combined TCGA and GEO samples into high - risk and low - risk groups. The results of a differential expression analysis done between high - and low - risk groups in the integrated sample showed that both low - and low - risk groups of the ovarian cancer sample had notably different chemokines and their receptors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea).By means of the model score, the sample risk distribution map, test set, and learning set of the integrated sample were analyzed separately (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, c, d).The results showed that when the patient's risk score went up, their survival time got longer, the death toll rose, and the expression of CXCL9, MMP9, FOXJ1, and AACAC declined. All these were low - risk factors. By way of contrast, GFPT2, CXCL12, B3GNT3, and HIF3A were high - risk factors. Clinical relevance was analyzed by means of Kaplan - Meier survival analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee, f, g) and then comes prognostic ROC analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh, i, j), which brought together the sample, test, and learning sets. The results of survival analysis showed that patients in the high - risk group had a shorter survival time than patients in the low - risk group. Prognostic ROC analysis found an AUC greater than 0. 6, meaning that the model had a fairly good fitting effect and could precisely prognose ovarian cancer. Given a value of 6, it means the model had a good fitting and can precisely predict the prognosis of ovarian cancer.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eTME analysis\u003c/h2\u003e\u003cp\u003eIn the samples that integrate TCGA and GEO data, patients in the high - risk group had higher stem cell scores than those in the low - risk group. But they had lower immune cell and composite scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). We looked at the relationship between the model's core genes and immune cells. The correlation diagram of this look - over is presented as a heat map in the annex (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).From the results of immune cell correlation analysis, it was found that the core genes of the model were associated with CD8T lymphocyte - activated memory T cells (CD4), plasma cells, activated natural killer cells, monocytes, activated mast cells, M0, M1, and M2 macrophages, eosinophils, quiescent and activated dendritic cells, memory B cells, and other immune cells. Through a cumulative mutation burden analysis of chemokines and their receptors, it was found that the low - risk and high - risk groups did not have a significant difference in the number of chemokine and receptor mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec, d, e).There was an appreciable difference and a negative correlation between risk ratings and stem cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eDrug susceptibility analysis\u003c/h2\u003e\u003cp\u003eThese genes were linked to a range of anticancer medications, according to drug sensitivity analysis based on GDSC and CTRP (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, b). After GDSC and CTRP drug susceptibility analysis results crossed, 20 medications were ultimately linked to the prognostic model's key genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSingle-cell UMAP cluster analysis\u003c/h2\u003e\u003cp\u003eThe GSE118828 dataset in the GEO dataset was subjected to UMAP cluster analysis, yielding 12 clustered cell populations. The cell populations of various classes were labeled with varying colors (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea), signifying primary cells, normal ovarian cells, and metastatic cells, respectively. Six cell types\u0026mdash;fibroblasts, epithelial cells, malignant cells, myofibroblasts, monocytes/macrophages, and CD4T cells\u0026mdash;were identified from the 12 cell subsets (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed). Following UMAP cluster analysis of the malignant cells, pie charts (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee) and percentage stacked histograms (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ef) were displayed, and three cell types\u0026mdash;immune cells, malignant cells, and stem cells\u0026mdash;were annotated (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eUMAP cluster analysis of prognostic genes\u003c/h2\u003e\u003cp\u003eFOXJ1 and AGR2 were highly aggregated in malignant cells, CXCL12 was strongly aggregated in stem cells, and other prognostic genes were not significantly aggregated, according to UAMP cluster analysis of prognostic genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea-h).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eConstruction of ceRNA network of prognostic genes\u003c/h2\u003e\u003cp\u003eMiRanda, miRDB, miRWalk and Targetscan database were used to acquire miR-related data; Perl was used to predict the gene binding of the miRNA binding pathway; spongeScan database was used to acquire lncRNA data; Perl was used to construct miRNA-binding lncRNA; ceRNA network was constructed.Finally, Cytoscape software was used to map the ceRNA regulation network..\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOvarian cancer is the eighth most common cancer in women and has the highest mortality rate of any gynecological cancer. It is clinically difficult to diagnose ovarian cancer early, and nearly 70% of patients are diagnosed with stage III\u0026ndash;IV metastatic disease. Additionally, there are no reliable diagnostic or prognostic biomarkers, and ovarian cancer recurrence and chemotherapy resistance are serious problems that can have negative outcomes \u003csup\u003e[14]\u003c/sup\u003e. TME is essential for the growth and recurrence of tumors in ovarian cancer, and inflammation is also a significant factor in TME \u003csup\u003e[15, 16]\u003c/sup\u003e. Furthermore, there is significant anticancer activity in the tumor immunological microenvironment \u003csup\u003e[17]\u003c/sup\u003e. By controlling the inflammatory response and immune cell infiltration, chemokines and their receptors can affect the condition of the tumor immunological microenvironment \u003csup\u003e[18, 19]\u003c/sup\u003e. Thus, a methodical investigation of the function of chemokines and their receptors in GBM would advance knowledge of the immune response to tumors and the prognosis of GBM.\u003c/p\u003e\u003cp\u003eWe began this investigation by examining the CNV and mutation frequency of chemokines and their receptors in ovarian cancer. Consensus cluster analysis was used to investigate the biological roles and underlying processes of the majority of chemokines and their receptors, which we identified to have the most diverse expression across ovarian cancer tissues. GBM is classified into I and II subtypes by consensus clustering, and the differences between the two categories are obvious. The extracellular matrix composition, immunological response, cell adhesion, and disease-specific pathways all showed a substantial enrichment of the distinct genes between I and II isoforms. Elevated levels of CRP, an acute-phase protein, have been linked to a poor prognosis for women with ovarian cancer and may also be connected to inflammation related to the tumor\u003csup\u003e[20]\u003c/sup\u003e. By enlisting immune cells, Jan Korbecki et al. methodically examined how chemokines and their receptors affect the TME of ovarian cancer \u003csup\u003e[21]\u003c/sup\u003e. We also conducted one-way COX analysis based on the EDGs between I and II isoforms obtained from consensus cluster analysis of 67 chemokines and their receptors in order to better investigate the mechanism of action of chemokines and their receptors in GBM. A consensus clustering analysis was conducted once more in accordance with the EDGs' one-way COX results. Two separate subtypes, A and B, were identified by repeating the consensus cluster analysis. This suggests that there is some association between EDGs and chemokines and their receptors. Following a random division of the combined TCGA and GEO samples into learning and training sets, lasso regression and COX analyses were conducted on EDGs, the clinical relevance was confirmed, and ultimately the risk prognosis model was effectively built.\u003c/p\u003e\u003cp\u003eBased on the predictive model of ovarian cancer centered on chemokines and their receptor - related genes, the scientists found that the high - risk group in this study had lower immune cell scores and higher stromal cell scores relative to the low - risk group.Elevated stromal scores mirror increased extracellular matrix (ECM) deposition and cancer-associated fibroblast (CAF) activation \u003csup\u003e[22]\u003c/sup\u003e. Generally speaking, a high immune cell score would suggest a more active immune system, which could be connected to immune cell aggregation and the immunological response against tumors \u003csup\u003e[23]\u003c/sup\u003e. The negative link between risk scores and stem cells suggests that high-risk tumors are less responsive to medicines that target stem cells, like ALDH inhibitors, and may gain an edge in metastasis but lose the potential to initiate tumors. \u003csup\u003e[24]\u003c/sup\u003e. The high-risk group's stromal cells scored higher than the low-risk group's, suggesting that the TME is more complex and that there may be a greater quantity or activity of immune cells and stem cells in the high-risk group. Generally speaking, a high immune cell score would suggest a more active immune system, which could be connected to immune cell aggregation and the immunological response against tumors.\u003c/p\u003e\u003cp\u003eGFPT2 (glutamine - fructose \u0026minus;\u0026thinsp;6 - phosphate transaminase 2) serves as the first step and rate - controlling enzyme of the hexosamine biosynthesis pathway (HBP), a part of the glucose metabolism system that offers substrates for glycosylation modifications and has a broad influence on cellular function \u003csup\u003e[25]\u003c/sup\u003e. In a number of human cancers, GFPT2 expression levels were heightened, and it was negatively related to the survival prognosis of ovarian cancer patients \u003csup\u003e[26]\u003c/sup\u003e. There exists a link between GFPT2 and immune cells in the ovarian cancer's microenvironment.Investigations have pointed out a substantial connection between GFPT2 and immunomodulatory substances, immune - related biomarkers, and immune infiltration.Obviously, a well - defined correlation was identified between the effectiveness of immunotherapy and GFPT2 expression.Evidently, a strong correlation occurred between the efficiency of immunotherapy and GFPT2 expression\u003csup\u003e[27]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHIF3A (Hypoxia - inducible factor 3 - alpha) is the gene that encodes the hypoxia - inducible factor HIF \u0026minus;\u0026thinsp;3α, vital for governing the cellular response to hypoxia.HIF3A regulates the expression of downstream genes by binding to particular hypoxia response elements (HREs) and generating heterodimers with HIF - β subunits \u003csup\u003e[28]\u003c/sup\u003e. The analysis of follow - up data from ovarian cancer patients brought to light a negative correlation between HIF3A expression levels and patient prognosis. In essence, patients with higher HIF3A expression levels had a poorer outcome.The ability of ovarian cancer cells to migrate and clone, suppressed by LINC01342 knockdown, was reversed when HIF3A was overexpressed, suggesting HIF3A is crucial for ovarian cancer migration and cloning \u003csup\u003e[29]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOne chemokine that is crucial in the interaction between immune cells and the milieu in ovarian cancer is CXCL9 (C-X-C motif chemokine ligand 9). Patients with ovarian cancer who had higher levels of CXCL9 mRNA expression had a significantly better prognosis. According to this research, CXCL9 might be crucial for the prognosis of ovarian cancer \u003csup\u003e[30]\u003c/sup\u003e. CXCL9 was identified as a critical marker gene for predicting the effectiveness of immune checkpoint inhibitors (ICIs) through multi-cohort analysis and single-cell RNA sequencing. It was positively connected with M1 macrophage expression and associated with immune-related pathways and immune cell infiltration \u003csup\u003e[31]\u003c/sup\u003e. According to studies, overexpression of CXCL9 can improve T cell infiltration, stop tumor growth, and increase the effectiveness of anti-PD-L1 therapy for ovarian cancer \u003csup\u003e[32]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA transcription factor called FOXJ1 (Forkhead Box J1) controls differentiation, cell motility, and ciliation. Recent research has revealed that ovarian cancer is among the many tumors where FOXJ1 is aberrantly expressed. FOXJ1 can increase the invasion and metastasis of ovarian cancer cells by downregulating E-cadherin and upregulating N-cadherin and vitementin. FOXJ1 is a crucial regulator of cilia formation, and defects in cilia can activate pathways including WNT and Hedgehog that promote cancer. By using PI3K/AKT or DNA repair pathways, FOXJ1 may lessen the ovarian cancer cells' susceptibility to platinum medications. FOXJ1 has been demonstrated to be overexpressed in metastatic and advanced (stage III/IV) ovarian cancer. According to a study on high-grade serous ovarian cancer (HGSOC), patients with high FOXJ1 expression had significantly shorter PFS and OS. By encouraging immunosuppression of the tumor microenvironment (such as CAFs and TAMs), FOXJ1 may have an impact on prognosis. Despite preliminary research on the involvement of FOXJ1 in ovarian cancer, further research is still required to determine whether OXJ1 is a viable independent prognostic marker.\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThis study has several limitations. First, the analysis was based on publicly available datasets (TCGA and GEO), which, although comprehensive, may not fully capture the heterogeneity of ovarian cancer across diverse populations. Second, the prognostic model was constructed and validated using retrospective data, and prospective clinical validation is required to confirm its generalizability. Third, the functional roles of the identified chemokines and their receptors in ovarian cancer progression warrant further experimental investigation. Finally, the sample size for single-cell sequencing analysis (GSE118828) was limited, and larger cohorts are needed to validate the cellular distribution of prognostic genes.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe developed a new predictive model for patients with ovarian cancer and thoroughly analyzed the biological role and prognostic significance of chemokines and their receptors in ovarian cancer. We discovered that the immune microenvironment state of ovarian cancer patients can be reflected in the risk score of this model. This offers new information on the mechanism of action of chemokines and their receptors in ovarian cancer, as well as possible biomarkers for prognosis and treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are publicly available as follows:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eThe ovarian cancer transcriptome and clinical data from The Cancer Genome Atlas (TCGA) can be accessed at: (https://www.cancer.gov/ccg/research/genome-sequencing/tcga) (TCGA-OV).\u003c/li\u003e\n \u003cli\u003eGene Expression Omnibus (GEO) datasets GSE32062, GSE32063, and GSE118828 are available at:\u0026nbsp;(\u003cstrong\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/strong\u003e)\u0026nbsp;with accession numbers GSE32062, GSE32063, and GSE118828.\u003c/li\u003e\n \u003cli\u003eDrug sensitivity data were obtained from the GSCA platform (\u003cstrong\u003ehttp://bioinfo.life.hust.edu.cn/GSCA\u003c/strong\u003e), which integrates GDSC and CTRP databases.\u003c/li\u003e\n \u003cli\u003emiRNA-related data were retrieved from miRanda(miRanda (microRNA.org) | miRToolsGallery),miRDB(https://mirdb.org/), miRWalk(http://mirwalk.umm.uni-heidelberg.de/),and TargetScan (https://www.targetscan.org/vert_80/)databases.\u003c/li\u003e\n \u003cli\u003elncRNA data were obtained from the spongeScan database(spongeScan | miRToolsGallery).\u003cbr\u003e\u0026nbsp;All data sources are publicly accessible, and no new datasets were generated in this study\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003cbr\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXinyi Ma: Writing \u0026ndash; Original Draft Preparation; Faming Tian: revised the work critically for important intellectual content; Yimin Liu: Made a significant contribution to the conception or design of the work; Hu Gang: Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThank you to the collaborators of this study for their time and effort in our research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received to assist with the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent Statements required\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis comment letter does not include any new patient data, and no informed consent is required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSIEGEL R L, MILLER K D, JEMAL A. Cancer statistics, 2020 [J]. CA Cancer J Clin, 2020, 70(1): 7-30.\u003c/li\u003e\n\u003cli\u003eTORRE L A, TRABERT B, DESANTIS C E, et al. Ovarian cancer statistics, 2018 [J]. CA Cancer J Clin, 2018, 68(4): 284-96.\u003c/li\u003e\n\u003cli\u003eMIN Y, PARK H B, BAEK K H, et al. Cellular Functions of Deubiquitinating Enzymes in Ovarian Adenocarcinoma [J]. Genes (Basel), 2023, 14(4).\u003c/li\u003e\n\u003cli\u003eHINTON C V, AVRAHAM S, AVRAHAM H K. Role of the CXCR4/CXCL12 signaling axis in breast cancer metastasis to the brain [J]. Clin Exp Metastasis, 2010, 27(2): 97-105.\u003c/li\u003e\n\u003cli\u003eHANAHAN D, WEINBERG R A. Hallmarks of cancer: the next generation [J]. Cell, 2011, 144(5): 646-74.\u003c/li\u003e\n\u003cli\u003eQUAIL D F, JOYCE J A. Microenvironmental regulation of tumor progression and metastasis [J]. Nat Med, 2013, 19(11): 1423-37.\u003c/li\u003e\n\u003cli\u003eZLOTNIK A, YOSHIE O. Chemokines: a new classification system and their role in immunity [J]. Immunity, 2000, 12(2): 121-7.\u003c/li\u003e\n\u003cli\u003eCHARO I F, RANSOHOFF R M. The many roles of chemokines and chemokine receptors in inflammation [J]. N Engl J Med, 2006, 354(6): 610-21.\u003c/li\u003e\n\u003cli\u003eLIU H, YANG Z, LU W, et al. Chemokines and chemokine receptors: A new strategy for breast cancer therapy [J]. Cancer Med, 2020, 9(11): 3786-99.\u003c/li\u003e\n\u003cli\u003eASOKAN S, BANDAPALLI O R. CXCL8 Signaling in the Tumor Microenvironment [J]. Adv Exp Med Biol, 2021, 1302: 25-39.\u003c/li\u003e\n\u003cli\u003eKAWAGUCHI A, YAJIMA N, TSUCHIYA N, et al. Gene expression signature-based prognostic risk score in patients with glioblastoma [J]. Cancer Sci, 2013, 104(9): 1205-10.\u003c/li\u003e\n\u003cli\u003eHUGHES C E, NIBBS R J B. A guide to chemokines and their receptors [J]. Febs j, 2018, 285(16): 2944-71.\u003c/li\u003e\n\u003cli\u003eLIU C J, HU F F, XIE G Y, et al. GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels [J]. Brief Bioinform, 2023, 24(1).\u003c/li\u003e\n\u003cli\u003eFRĄSZCZAK K, BARCZYŃSKI B. The Role of Cancer Stem Cell Markers in Ovarian Cancer [J]. Cancers (Basel), 2023, 16(1).\u003c/li\u003e\n\u003cli\u003eZHANG X, LI M, YIN N, et al. The Expression Regulation and Biological Function of Autotaxin [J]. Cells, 2021, 10(4).\u003c/li\u003e\n\u003cli\u003eJIANG Y, WANG C, ZHOU S. Targeting tumor microenvironment in ovarian cancer: Premise and promise [J]. Biochim Biophys Acta Rev Cancer, 2020, 1873(2): 188361.\u003c/li\u003e\n\u003cli\u003eCOLOTTA F, ALLAVENA P, SICA A, et al. Cancer-related inflammation, the seventh hallmark of cancer: links to genetic instability [J]. Carcinogenesis, 2009, 30(7): 1073-81.\u003c/li\u003e\n\u003cli\u003eGRIFFITH J W, SOKOL C L, LUSTER A D. Chemokines and chemokine receptors: positioning cells for host defense and immunity [J]. Annu Rev Immunol, 2014, 32: 659-702.\u003c/li\u003e\n\u003cli\u003eMANTOVANI A, BONECCHI R, LOCATI M. Tuning inflammation and immunity by chemokine sequestration: decoys and more [J]. Nat Rev Immunol, 2006, 6(12): 907-18.\u003c/li\u003e\n\u003cli\u003ePEIGN\u0026eacute; M, DECANTER C. Serum AMH level as a marker of acute and long-term effects of chemotherapy on the ovarian follicular content: a systematic review [J]. Reprod Biol Endocrinol, 2014, 12: 26.\u003c/li\u003e\n\u003cli\u003eKORBECKI J, BOSIACKI M, BARCZAK K, et al. Involvement in Tumorigenesis and Clinical Significance of CXCL1 in Reproductive Cancers: Breast Cancer, Cervical Cancer, Endometrial Cancer, Ovarian Cancer and Prostate Cancer [J]. Int J Mol Sci, 2023, 24(8).\u003c/li\u003e\n\u003cli\u003eSAHAI E, ASTSATUROV I, CUKIERMAN E, et al. A framework for advancing our understanding of cancer-associated fibroblasts [J]. Nat Rev Cancer, 2020, 20(3): 174-86.\u003c/li\u003e\n\u003cli\u003eCHEN L, FAN Z, CHANG J, et al. Sequence-based drug design as a concept in computational drug design [J]. Nat Commun, 2023, 14(1): 4217.\u003c/li\u003e\n\u003cli\u003eARATANI Y, UEMURA T, HAGIHARA T, et al. Green leaf volatile sensory calcium transduction in Arabidopsis [J]. Nat Commun, 2023, 14(1): 6236.\u003c/li\u003e\n\u003cli\u003eZHANG H R, LI T J, YU X J, et al. The GFPT2-O-GlcNAcylation-YBX1 axis promotes IL-18 secretion to regulate the tumor immune microenvironment in pancreatic cancer [J]. Cell Death Dis, 2024, 15(4): 244.\u003c/li\u003e\n\u003cli\u003eZHOU L, LUO M, CHENG L J, et al. Glutamine-fructose-6-phosphate transaminase 2 (GFPT2) promotes the EMT of serous ovarian cancer by activating the hexosamine biosynthetic pathway to increase the nuclear location of \u0026beta;-catenin [J]. Pathol Res Pract, 2019, 215(12): 152681.\u003c/li\u003e\n\u003cli\u003eZHOU Y, DONG Y. The Prognostic Value and Immunotherapeutic Characteristics of GFPT2 in Pan-cancer [J]. Comb Chem High Throughput Screen, 2024.\u003c/li\u003e\n\u003cli\u003eTOLONEN J P, HEIKKIL\u0026auml; M, MALINEN M, et al. A long hypoxia-inducible factor 3 isoform 2 is a transcription activator that regulates erythropoietin [J]. Cell Mol Life Sci, 2020, 77(18): 3627-42.\u003c/li\u003e\n\u003cli\u003eZHANG C, LIU J, ZHANG Y, et al. LINC01342 promotes the progression of ovarian cancer by absorbing microRNA-30c-2-3p to upregulate HIF3A [J]. J Cell Physiol, 2020, 235(4): 3939-49.\u003c/li\u003e\n\u003cli\u003e高境泽, 吴霞. 卵巢肿瘤组织中CXCL9 mRNA表达与患者的预后、免疫微环境 特征的相关性研究 [J]. 上海交通大学学报(医学版), 2020, 40(04): 457-63.\u003c/li\u003e\n\u003cli\u003eYU Y, CHEN H, OUYANG W, et al. Unraveling the role of M1 macrophage and CXCL9 in predicting immune checkpoint inhibitor efficacy through multicohort analysis and single-cell RNA sequencing [J]. MedComm (2020), 2024, 5(3): e471.\u003c/li\u003e\n\u003cli\u003eSEITZ S, DREYER T F, STANGE C, et al. CXCL9 inhibits tumour growth and drives anti-PD-L1 therapy in ovarian cancer [J]. Br J Cancer, 2022, 126(10): 1470-80.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"chemokines, chemokine receptors, immune infiltration, ovarian cancer, tumor microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-8096911/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8096911/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAlthough chemokines and their receptors (CCRs) are important in the microenvironment of ovarian cancer (OC), little is known about their prognostic significance.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe constructed a prognostic correlation model via statistical analysis, consensus clustering analysis, and other analyses, relying on the transcriptome and corresponding clinical data of ovarian cancer samples taken from the TCGA and GEO databases. We further put the model into application for drug sensitivity analysis, tumor microenvironment analysis, and clinical correlation analysis. We again utilized the model to carry out drug sensitivity analysis, tumor microenvironment analysis, and clinical correlation analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eBased on the expression of chemokines and their receptors in the samples, consensus clustering analysis was used to group the samples into subtypes I and II. Both subtypes displayed notable distinctions. We identified prognostic differentially expressed genes (DEGs), which are mostly involved in cell signaling, viral interactions, and immunological and inflammatory responses. We conducted a second consistent cluster analysis based on DEGs using COX analysis, which revealed two isoforms, A and B, and 709 DEGs between them. We developed and verified the prognosis risk score model based on the prognostic DEGs, where (-CXCL9*0.20-MMP9*0.11\u0026thinsp;+\u0026thinsp;GFPT2*0.15\u0026thinsp;+\u0026thinsp;CXCL12*0.11-MSX1*0.06\u0026thinsp;+\u0026thinsp;HIF3A*0.11\u0026thinsp;+\u0026thinsp;B3GNT3*0.16-FOXJ1*0.08-AADAC*0.14-AGR2*0.09) indicates the risk score. Despite having a negative correlation with stem cells and greater immune cell and lower stromal cell scores, patients with ovarian cancer in the low-risk category were projected to live longer.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eIt includes the chemokines CXCL9, MMP9, GFPT2, CXCL12, MSX1, HIF3A, B3GNT3, FOXJ1, AADAC, and AGR2. The characteristic scores of these chemokines and their receptors can be used as independent prognostic factors for patients with ovarian cancer, which will further support the need for more clinical and functional research on chemokines and their receptors in it.\u003c/p\u003e","manuscriptTitle":"The function of chemokines and their receptors in the ovarian cancer microenvironment and research on their prognostic significance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-03 00:51:15","doi":"10.21203/rs.3.rs-8096911/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e844a8a4-fe9e-4cb2-82ef-ca608437ba6a","owner":[],"postedDate":"December 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-24T07:09:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-03 00:51:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8096911","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8096911","identity":"rs-8096911","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}