Next Article in Journal
Interactions between COVID-19 and Lung Cancer: Lessons Learned during the Pandemic
Next Article in Special Issue
Insights into Circulating Tumor Cell Clusters: A Barometer for Treatment Effects and Prognosis for Prostate Cancer Patients
Previous Article in Journal
The Diagnostic Value of the Added MR Imaging of the Scrotum in the Preoperative Workup of Sonographically Indeterminate Testicular Lesions—A Retrospective Multicenter Analysis
Previous Article in Special Issue
Urine Cellular DNA Point Mutation and Methylation for Identifying Upper Tract Urinary Carcinoma
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Cancers
Volume 14
Issue 15
10.3390/cancers14153596
cancers-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

FXYD3 Expression Predicts Poor Prognosis in Renal Cell Carcinoma with Immunosuppressive Tumor Microenvironment

by
Satoru Yonekura
1,*,† and
Kosuke Ueda
1,2,†
1
Gustave Roussy Cancer Campus (GRCC), 94800 Villejuif, France
2
Department of Urology, School of Medicine, Kurume University, Kurume 830-0011, Japan
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Cancers 2022, 14(15), 3596; https://doi.org/10.3390/cancers14153596
Submission received: 26 June 2022 / Revised: 15 July 2022 / Accepted: 17 July 2022 / Published: 23 July 2022
(This article belongs to the Special Issue Advances and Updates in Prognostic and Predictive Biomarkers of Urologic Malignancy)
Browse Figures
Versions Notes

Abstract

:

Simple Summary

FXYD3 belongs to the protein-coding gene family associated with Na+/K+-ATPase enzymes and chloride ion channels. Recently, the biological role of FXYD3 has been reported in multiple cancers. Nevertheless, the prognostic value of FXYD3 expression has been undiscovered in clear renal cell carcinoma (KIRC). In this study, we assessed the datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset (GSE29609). We found the FXYD3 high KIRC patients had distinct clinical characteristics, including hypoxia and poor overall survival. Furthermore, the algorithms discovered that FXYD3 mRNA levels were associated with tumor purity, multiple types of the tumor infiltrating lymphocytes (TILs) and several genes related to T cell exhaustion. In conclusion, FXYD3 predicts a poor prognosis associated with hypoxia, pro-tumor TILs, and T cell exhaustion in KIRC.

Abstract

FXYD3 is a protein-coding gene, belonging to the FXYD protein family associated with Na+/K+-ATPase enzymes and chloride ion channels. Accumulating evidence suggests the biological role of FXYD3 in multiple cancers. However, the prognostic value of FXYD3 expression in clear renal cell carcinoma (KIRC) is unclear. Therefore, we evaluated the clinical data with tumor-infiltrating lymphocytes (TILs) and immunoinhibitory gene expression data using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset (GSE29609). First, the FXYD3 high KIRC patients had distinct clinical characteristics, including age, sex, disease stage, histological grade, and hypoxia-related gene expressions. Next, FXYD3 gene expression was correlated with poor overall survival in both TCGA and GSE29609 cohorts. The ESTIMATE algorithm revealed that higher FXYD3 mRNA levels were associated with increased infiltration of immune cells and tumor purity. Moreover, the FXYD3 high KIRC tissue harbored increased TILs such as B cells, CD8+ T cells, and M1 macrophage, whereas NK cells and neutrophils were decreased. In addition, we showed FXYD3 was co-expressed with several immunoinhibitory genes related to T cell exhaustion such as LGALS9, CTLA4, BTLA, PDCD1, and LAG3. In conclusion, FXYD3 is an unfavorable prognostic biomarker associated with hypoxia, pro-tumor TILs, and T cell exhaustion.

1. Introduction

Renal cell carcinoma (RCC) is the most common type of kidney cancer in adults, of which clear cell RCC (KIRC) accounts for most cancer-related deaths [1]. Clinical challenges in advanced KIRC lie in managing a poor prognosis caused by resistance to radiotherapy and chemotherapy [2]. KIRC harbors an immunogenic tumor microenvironment (TME) containing various tumor-infiltrating T lymphocytes (TILs) [3]. Patients with advanced and metastatic RCC have been treated with molecularly targeted agent monotherapy such as tyrosine kinase inhibitors (TKIs). However, since the advent of immune checkpoint blockade (ICB), the treatment of RCC has reached a significant turning point. ICBs that target programmed cell death1 (PD-1), programmed cell death-ligand 1 (PD-L1), and cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) or ICB plus TKIs are now standard treatment options for RCC [4,5]. In the era of the ICB, an understanding of the immunogenic TME would be useful to find a new therapeutic strategy in KIRC management.
FXYD3 is a protein-coding gene, belonging to the FXYD protein family in association with Na+/K+-ATPase enzymes and chloride ion channels [6]. FXYD3 is expressed in various organs such as the liver, pancreas, stomach, colon, prostate, lungs, skeletal muscles, and kidneys [7]. In addition, evidence shows that FXYD3 expression can be a poor prognostic biomarker in multiple solid tumors [8,9,10,11,12,13]. However, the prognostic role of FXYD3 in KIRC has not been clarified so far. Therefore, we hypothesized FXYD3 could predict the outcome in KIRC patients. Moreover, if FXYD3 is associated with the TME of KIRC, FXYD3 could be a potential target to modulate in order to increase the response of ICB in KIRC.
Herein, we investigated the prognostic value of FXYD3 gene expressions in KIRC using The Cancer Genome Atlas (TCGA) datasets and Gene Expression Omnibus (GEO) datasets. Moreover, we investigated the tumor microenvironment (TME) by evaluating the estimated TILs [14] and immune-inhibitory gene expressions [15] in association with FXYD3 expression.

2. Materials and Methods

2.1. Survival Analysis with FXYD3 mRNA Levels in the Public Database

Clinical data of patients with KIRC on TCGA datasets and GSE29609 [16] were obtained from cBioPortal (TCGA PanCancer Atlas; https://www.cbioportal.org/, accessed on 16 December 2020) [17,18], and GEO database, respectively. The patients with an upper 50% expression of FXYD3 mRNA in each dataset were classified as FXYD3 high, and the rest were classified as FXYD3 low in all the following analyses. Kaplan-Meier curves were plotted for overall survival (OS). Data of tumor purity (ESTIMATE score), the level of stromal cells present (stroma score), and the infiltration level of immune cells (immunity score) based on expression data were obtained from ESTIMATE (https://bioinformatics.mdanderson.org/estimate/disease.html, accessed on 17 July 2022) [19]. FXYD3 expression levels with tumor grade and disease stage were achieved on TISIDB [20]. The multivariate Cox regression analysis was used on Gene Expression Profiling Interactive Analysis 2 (GEPIA2), a web tool for comprehensive analyses with TIL data on TCGA [21,22], to evaluate the prognostic significance of FXYD3 gene expression as a continuous variable with other parameters such as age, sex (male vs. female), and disease stage at initial diagnosis.

2.2. Estimated TIL Fraction

The TIL fraction data estimated using quanTIseq [14] was downloaded (http://timer.comp-genomics.org/, accessed on 17 July 2022). The composition of TILs was visualized by principal component analysis. The estimated TIL fractions between FXYD3 high vs. low patients were compared by the Mann–Whitney test. A heatmap of TIL fractions estimated using quanTIseq [14] with clinical information was generated by the R package ‘ComplexHeatmap’. Each fraction of TILs was converted to a z-score. We performed hierarchical clustering to characterize the TILs, and the calculating methods of the distance for rows and columns were ‘canberra’ and ‘euclidean’, respectively. The clustering methods for rows and columns were ‘ward.D2’ and ‘complete’, respectively.

2.3. Gene Co-Expression Network Analysis

We used the R package ‘igraph’ to visualize the co-expression network of FXYD3 and the immune-inhibitory genes [15]. Spearman’s correlation coefficients were used to generate the network graph. The nodes represented the genes and the edges corresponded to the interaction between genes. Subgraphs that show densely connected genes in the graph were created by calculating the leading non-negative eigenvector.

2.4. Statistical Analysis

For graphs and statistical analyses (principal component analysis, Mann-Whitney test), we used the R freeware (http://www.r-project.org, accessed on 17 July 2022) and GraphPad Prism (v. 9.4.0) software (GraphPad Software Inc., San Diego, CA, USA). All p-values were two-sided, and a p-value of ≤ 0.05 was considered statistically significant.

3. Results

3.1. Clinical Characteristics by FXYD3 Expression Level in KIRC Patients

Table 1 shows the clinical characteristics between FXYD3 high and low patients (N = 255, n = 255, respectively). Significant differences of age, sex, disease stage, and histologic grade were detected. In the high FXYD3 group, the median of age and the % of male patients were significantly higher. Moreover, the FXYD3 high patients harbored more hypoxia-related gene expression by Buffa hypoxia score [23]. In contrast, there was no significant difference in mutation counts.

3.2. Prognostic Impact of FXYD3 Gene Expression in KIRC

We investigated the OS using Kaplan-Meier curves in TCGA dataset (Figure 1A). Patients with higher FXYD3 gene expressions had shorter OS on TCGA dataset (Hazard ratio (HR) (95% confidence interval (CI)): 1.64 (1.09–2.47), p = 0.019, Figure 1A). Similarly, in the other cohort (GSE29609 [16]), higher FXYD3 gene expression was correlated with poor OS (HR (95% CI): 2.91 (1.12–7.57), p = 0.034, Figure 1B). Multivariate Cox regression analysis showed that higher FXYD3 gene expression was significantly correlated with poor OS (HR (95% CI): 1.117 (1.013–1.232), p = 0.027, Table 2) on TCGA cohort. In addition, the patients with higher FXYD3 expression had higher histologic grade of tumor and stage of disease (Spearman’s rho = 0.158, p = 2.86 × 10−4; Spearman’s rho = 0.17, p = 8.4 × 10−5, respectively, Figure 1C,D).

3.3. Infiltrating Immune Cells and FXYD3 mRNA Levels

To understand the poor outcome in FXYD3 high KIRC patients, we examined the characteristics of the TME between FXYD3 high and low patients in terms of TILs, tumor purity, and abundance of stromal cells using the ESTIMATE algorithm [19]. We found FXYD3 high KIRC patients had higher levels of infiltration of immune cells and tumor purity (Figure 2A,B) whereas there was no difference of stromal cells in the TME (Figure 2C). TILs are key factors in regulating tumor progression in KIRC [24]. Next, we compared the TILs estimated using the quanTIseq method [14] between FXYD3 high and low groups. The TIL population composition did not strongly overlap based on FXYD3 expression levels (Figure 3A). Next, the FXYD3 high group had increased B cells, CD8+ T cells, and M1 macrophages, and decreased NK cells and neutrophils (Figure 3B,C,E,G,H). Regulatory T cells (Treg) and M2 macrophages were not significantly different (Figure 3D,F).
The heatmap of TILs and FXYD3 mRNA levels is shown in Figure 4. Three clusters were identified. In clusters 1 and 3, most patients were FXYD3 high, while most of the cluster 2 patients had low FXYD3 gene expression. The cluster 3 patients had high fractions of TILs, including M1/M2 macrophages, CD8+ T cells, B cells, and Treg, with fewer NK cells and neutrophils. The cluster 1 patients had similar TIL patterns to cluster 3, though all those TILs (i.e., M1/M2 macrophage, CD8+ T cells, B cells, and Treg) were relatively fewer than cluster 3. Contrary to clusters 1 and 3, the cluster 2 patients harbored increased NK cells and neutrophils and fewer M1/M2 macrophages, CD8+ T cells, B cells, and Treg.

3.4. Expression of Immunoinhibitory Genes and FXYD3 mRNA Levels

To discover which immunosuppressive gene signals contribute to a worse prognosis in FXYD3 high patients, we screened the expression of the immunoinhibitory gene set [15]. The heatmap of FXYD3 mRNA levels and immunoinhibitory gene expression data is shown in Figure 5. Three clusters were identified. In column clusters 2 and 3, the majority of patients had higher FXYD3 expression, while column cluster 1 was composed of more FXYD3 low patients. The expression levels of row cluster 1 genes (CD160, KIR2DL1, KIR2DL3, KDR, ADORA2A, IDO1, HAVCR2, NECTIN2, TGFB1, TGFBR1, VTCN1, and IL10RB) were not distinctly different across all column clusters. In contrast, the row cluster 2 gene expressions (CSF1R, IL10, LGALS9, CD244, CTLA4, BTLA, TIGIT, CD96, PDCD1, and LAG3) were higher in column clusters 2 and 3, whereas column cluster 1 rarely expressed those genes. In addition, we evaluated the correlation of FXYD3 gene expression with the immunosuppressive gene set in KIRC patients (Figure 6A). FXYD3 had positive correlations with most of the gene set: VTCN1, TIGIT, TGFBR1, TGFB1, NECTIN2, PDCD1, LGALS9, LAG3, IL10RB, IL10, CTLA4, CSF1R, CD96, CD244, and BTLA. Only KDR was negatively correlated with FXYD3. Next, we performed a network analysis based on gene correlations to visualize the gene interactions (Figure 6B). Figure 6B shows gene correlation networks with Spearman’s rho values larger than 0.2. Three clusters were identified. Cluster 1 (yellow in Figure 6B) included FXYD3, TIGIT, PDCD1, LGALS9, LAG3, CTLA4, CSF1R, CD96, BTLA, IL10RB, IL10, HAVCR2, and TGFBR1. Cluster 2 (blue in Figure 6B) had TGFB1, IDO1, NECTIN2, and VTCN1. The cluster 3 (green in Figure 6B) genes were KDR, ADORA2A, KIR2DL3, CD244, CD160, and KIR2DL1. FXYD3 had 9 edges and nodes. The network analysis identified the genes interacting closely with FXYD3, such as TGFB1, LGALS9, LAG3, CTLA4, KDR, BTLA, and PDCD1.

4. Discussion

The present study shows the FXYD3 mRNA level is a prognostic factor in KIRC. In the TME of KIRC, we observed the different infiltration of multiple TILs associated with FXYD3 expression levels. Moreover, KIRC patients with higher FXYD3 gene expression had distinct expression patterns of immunoinhibitory genes, suggesting T cell exhaustion was associated with FXYD3.
FXYD families are expressed widely in mammalian tissues. The role of FXYD3 in malignancy has been investigated in a variety of cancers, including esophageal squamous cell carcinoma [25], gastric cancer [26], endometrial cancer [8], pancreatic cancer [27], breast cancer [28,29,30], hepatocellular carcinoma [13], glioma [31], lung cancer [32], and colorectal carcinoma [10,11,12,33,34,35]. FXYD3 expression in tumor tissue revealed by immunohistochemistry was an unfavorable prognostic marker in hepatocellular carcinoma [10] and colorectal cancer [10,12], though Jin et al. reported a contradictory prognostic role in colorectal cancer using TCGA database [11]. Strong FXYD3 expression was observed in the infiltrative type in gastric [26] and colon cancer [12]. The in vitro experiments suggest FXYD3 can be involved in tumor proliferation [27,28,32]. In total, FXYD3 could be involved in tumor cell behavior contributing to poor prognosis in multiple cancers. Further studies using KIRC cell lines would be warranted to investigate the biological role of FXYD3 in KIRC.
Our study provides a potential explanation for the poor prognosis in FXYD3 high KIRC. First, increased hypoxia-related signaling was suggested in FXYD3 high groups. Hypoxia is one of the hallmarks in various solid tumors with a critical association with tumor genetic instability and prognosis [36]. In KIRC, most tumors harbor somatic inactivation of both VHL alleles with loss of function of the VHL tumor suppressor protein (pVHL) [36]. The loss of function of pVHL leads to activation of hypoxia-inducible factor (HIF), leading to tumor progression [36]. The potential relevance of FXYD3 to hypoxia-related genes should be explored using animal models in further studies. Second, we observed that the TIL pattern in FXYD3 high patients was characteristic of an unfavorable TME containing increased CD8+ T cells [4,37] in KIRC. In the TME of KIRC, highly infiltrated CD8+ T cells paradoxically could not contribute to a better prognosis, likely due to exhaustion of CD8+ T cells [4,38]. Our results showed that disease stage and histological grade were correlated with increased FXYD3 expression. Moreover, we found increased expression of multiple inhibitory genes involved in T cell exhaustion (e.g., PDCD1, CTLA4, TIGIT, LAG3 [39]) in FXYD3 high patients. The results of the network analysis also support the association of FXYD3 with several genes related to T cell exhaustion [39,40] in KIRC. In contrast, the FXYD3 low patients had increased NK cells and neutrophils with fewer T cell subsets such as CD8+ T cells or Treg. High NK cell infiltration in RCC is correlated with a better prognosis [41,42]. Our results suggest that the NK cells inside the TME could contribute to a better prognosis in FXYD3 low patients. A higher neutrophil–lymphocyte ratio is prognostic of poor prognosis in RCC [43]. Neutrophils in the TME have subtypes: antitumor (N1) and pro-tumor (N2) phenotypes [44]. N2 neutrophils can be induced by exposure of the neutrophils to regulatory factors like TGF-β [44]. The present study showed the low expression of TGF-β in FXYD3 low patients, suggesting that infiltrated neutrophils might not gain a pro-tumor phenotype. Taken together, FXYD3 is associated with poor prognosis in KIRC in relation to pro-tumor TME, potentially with hypoxia-related signaling, less NK cells, and exhausted CD8+ T cells.
The acknowledged limitations are that the present study was based only on the public database. In addition, this study did not examine the association between FXYD3 expression and the effects of antitumor agents, including ICBs. A preclinical model study would reveal the causative biological role of FXYD3 signaling in KIRC. Further study is necessary to demonstrate the mechanisms involving FXYD3-related signaling pathways on poor prognosis.

5. Conclusions

The present study demonstrates that FXYD3 is an unfavorable prognostic biomarker in KIRC with hypoxia, pro-tumor TILs, and multiple genes related to T cell exhaustion.

Author Contributions

Conceptualization, S.Y. and K.U.; methodology, S.Y.; software, S.Y.; validation, K.U.; formal analysis, S.Y.; investigation, S.Y.; data curation, S.Y.; writing—original draft preparation, S.Y.; writing—review and editing, K.U.; visualization, S.Y.; supervision, K.U.; project administration, S.Y. and K.U.; funding acquisition, K.U. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by JSPS KAKENHI (Grant No. 18007279 and 20K18128).

Institutional Review Board Statement

Ethical approval was waived since we used only publicly available data.

Informed Consent Statement

Informed consent statement was waived since we used only publicly available data.

Data Availability Statement

The authors downloaded the TCGA gene expression data (https://www.cbioportal.org/) and GSE29609 (https://www.ncbi.nlm.nih.gov/geo/).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Hsieh, J.J.; Purdue, M.P.; Signoretti, S.; Swanton, C.; Albiges, L.; Schmidinger, M.; Heng, D.Y.; Larkin, J.; Ficarra, V. Renal cell carcinoma. Nat. Rev. Dis. Prim. 2017, 3, 17009. [Google Scholar] [CrossRef]
  2. Makhov, P.; Joshi, S.; Ghatalia, P.; Kutikov, A.; Uzzo, R.G.; Kolenko, V.M. Resistance to Systemic Therapies in Clear Cell Renal Cell Carcinoma: Mechanisms and Management Strategies. Mol. Cancer Ther. 2018, 17, 1355–1364. [Google Scholar] [CrossRef] [Green Version]
  3. Nukui, A.; Masuda, A.; Abe, H.; Arai, K.; Yoshida, K.-I.; Kamai, T. Increased serum level of soluble interleukin-2 receptor is associated with a worse response of metastatic clear cell renal cell carcinoma to interferon alpha and sequential VEGF-targeting therapy. BMC Cancer 2017, 17, 372. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Ueda, K.; Suekane, S.; Kurose, H.; Chikui, K.; Nakiri, M.; Nishihara, K.; Matsuo, M.; Kawahara, A.; Yano, H.; Igawa, T. Prognostic value of PD-1 and PD-L1 expression in patients with metastatic clear cell renal cell carcinoma. Urol. Oncol. 2018, 36, 499.e9–499.e16. [Google Scholar] [CrossRef] [PubMed]
  5. Mollica, V.; Santoni, M.; Matrana, M.R.; Basso, U.; De Giorgi, U.; Rizzo, A.; Maruzzo, M.; Marchetti, A.; Rosellini, M.; Bleve, S.; et al. Concomitant Proton Pump Inhibitors and Outcome of Patients Treated with Nivolumab Alone or Plus Ipilimumab for Advanced Renal Cell Carcinoma. Target. Oncol. 2022, 17, 61–68. [Google Scholar] [CrossRef]
  6. Geering, K.; Béguin, P.; Garty, H.; Karlish, S.; Füzesi, M.; Horisberger, J.-D.; Crambert, G. FXYD Proteins: New Tissue- and Isoform-Specific Regulators of Na,K-ATPase. Ann. N. Y. Acad. Sci. 2003, 986, 388–394. [Google Scholar] [CrossRef] [PubMed]
  7. Franzin, C.M.; Gong, X.M.; Teriete, P.; Marassi, F.M. Structures of the FXYD regulatory proteins in lipid micelles and membranes. J. Bioenerg. Biomembr. 2007, 39, 379–383. [Google Scholar] [CrossRef] [Green Version]
  8. Li, Y.; Zhang, X.; Xu, S.; Ge, J.; Liu, J.; Li, L.; Fang, G.; Meng, Y.; Zhang, H.; Sun, X. Expression and clinical significance of FXYD3 in endometrial cancer. Oncol. Lett. 2014, 8, 517–522. [Google Scholar] [CrossRef] [Green Version]
  9. Gordon, G.J.; Richards, W.G.; Sugarbaker, D.J.; Jaklitsch, M.T.; Bueno, R. A prognostic test for adenocarcinoma of the lung from gene expression profiling data. Cancer Epidemiol. Biomark. Prev. 2003, 12, 905–910. [Google Scholar]
  10. Widegren, E.; Önnesjö, S.; Arbman, G.; Kayed, H.; Zentgraf, H.; Kleeff, J.; Zhang, H.; Sun, X.-F. Expression of FXYD3 Protein in Relation to Biological and Clinicopathological Variables in Colorectal Cancers. Chemotherapy 2009, 55, 407–413. [Google Scholar] [CrossRef]
  11. Jin, M.; Zhang, H.; Yang, J.; Zheng, Z.; Liu, K. Expression mode and prognostic value of FXYD family members in colon cancer. Aging 2021, 13, 18404–18422. [Google Scholar] [CrossRef] [PubMed]
  12. Loftås, P.; Önnesjö, S.; Widegren, E.; Adell, G.; Kayed, H.; Kleeff, J.; Zentgraf, H.; Sun, X.-F. Expression of FXYD-3 is an Independent Prognostic Factor in Rectal Cancer Patients With Preoperative Radiotherapy. Int. J. Radiat. Oncol. Biol. Phys. 2009, 75, 137–142. [Google Scholar] [CrossRef] [PubMed]
  13. Wang, L.J.; Li, Q.J.; Le, Y.; Ouyang, H.Y.; He, M.K.; Yu, Z.S.; Zhang, Y.F.; Shi, M. Prognostic significance of sodium-potassium ATPase regulator, FXYD3, in human hepatocellular carcinoma. Oncol. Lett. 2018, 15, 3024–3030. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Finotello, F.; Mayer, C.; Plattner, C.; Laschober, G.; Rieder, D.; Hackl, H.; Krogsdam, A.; Loncova, Z.; Posch, W.; Wilflingseder, D.; et al. Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. Genome Med. 2019, 11, 34. [Google Scholar] [CrossRef] [Green Version]
  15. Charoentong, P.; Finotello, F.; Angelova, M.; Mayer, C.; Efremova, M.; Rieder, D.; Hackl, H.; Trajanoski, Z. Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Rep. 2017, 18, 248–262. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Edeline, J.; Mottier, S.; Vigneau, C.; Jouan, F.; Perrin, C.; Zerrouki, S.; Fergelot, P.; Patard, J.-J.; Rioux-Leclercq, N. Description of 2 angiogenic phenotypes in clear cell renal cell carcinoma. Hum. Pathol. 2012, 43, 1982–1990. [Google Scholar] [CrossRef]
  17. Cerami, E.; Gao, J.; Dogrusoz, U.; Gross, B.E.; Sumer, S.O.; Aksoy, B.A.; Jacobsen, A.; Byrne, C.J.; Heuer, M.L.; Larsson, E.; et al. The cBio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012, 2, 401–404. [Google Scholar] [CrossRef] [Green Version]
  18. Gao, J.; Aksoy, B.A.; Dogrusoz, U.; Dresdner, G.; Gross, B.E.; Sumer, S.O.; Sun, Y.; Jacobsen, A.; Sinha, R.; Larsson, E.; et al. Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal. Sci. Signal. 2013, 6, l1. [Google Scholar] [CrossRef] [Green Version]
  19. Yoshihara, K.; Shahmoradgoli, M.; Martínez, E.; Vegesna, R.; Kim, H.; Torres-Garcia, W.; Trevino, V.; Shen, H.; Laird, P.W.; Levine, D.A.; et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 2013, 4, 2612. [Google Scholar] [CrossRef]
  20. Ru, B.; Wong, C.N.; Tong, Y.; Zhong, J.Y.; Zhong, S.S.W.; Wu, W.C.; Chu, K.C.; Wong, C.Y.; Lau, C.Y.; Chen, I.; et al. TISIDB: An integrated repository portal for tumor–immune system interactions. Bioinformatics 2019, 35, 4200–4202. [Google Scholar] [CrossRef]
  21. Li, T.; Fan, J.; Wang, B.; Traugh, N.; Chen, Q.; Liu, J.S.; Li, B.; Liu, X.S. TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 2017, 77, e108–e110. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Li, T.; Fu, J.; Zeng, Z.; Cohen, D.; Li, J.; Chen, Q.; Li, B.; Liu, X.S. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020, 48, W509–W514. [Google Scholar] [CrossRef] [PubMed]
  23. Buffa, F.M.; Harris, A.L.; West, C.M.; Miller, C.J. Large meta-analysis of multiple cancers reveals a common, compact and highly prognostic hypoxia metagene. Br. J. Cancer 2010, 102, 428–435. [Google Scholar] [CrossRef] [PubMed]
  24. Zhang, S.; Zhang, E.; Long, J.; Hu, Z.; Peng, J.; Liu, L.; Tang, F.; Li, L.; Ouyang, Y.; Zeng, Z. Immune infiltration in renal cell carcinoma. Cancer Sci. 2019, 110, 1564–1572. [Google Scholar] [CrossRef] [Green Version]
  25. Zhu, Z.L.; Yan, B.-Y.; Zhang, Y.; Yang, Y.-H.; Wang, M.-W.; Zentgraf, H.; Zhang, X.-H.; Sun, X.-F. Overexpression of FXYD-3 Is Involved in the Tumorigenesis and Development of Esophageal Squamous Cell Carcinoma. Dis. Markers 2013, 35, 195–202. [Google Scholar] [CrossRef] [Green Version]
  26. Zhu, Z.L.; Zhao, Z.-R.; Zhang, Y.; Yang, Y.-H.; Wang, Z.-M.; Cui, D.-S.; Wang, M.-W.; Kleeff, J.; Kayed, H.; Yan, B.-Y.; et al. Expression and significance of FXYD-3 protein in gastric adenocarcinoma. Dis. Markers 2010, 28, 63–69. [Google Scholar] [CrossRef]
  27. Kayed, H.; Kleeff, J.; Kolb, A.; Ketterer, K.; Keleg, S.; Felix, K.; Giese, T.; Penzel, R.; Zentgraf, H.; Büchler, M.W.; et al. FXYD3 is overexpressed in pancreatic ductal adenocarcinoma and influences pancreatic cancer cell growth. Int. J. Cancer 2006, 118, 43–54. [Google Scholar] [CrossRef]
  28. Yamamoto, H.; Okumura, K.; Toshima, S.; Mukaisho, K.-I.; Sugihara, H.; Hattori, T.; Kato, M.; Asano, S. FXYD3 Protein Involved in Tumor Cell Proliferation Is Overproduced in Human Breast Cancer Tissues. Biol. Pharm. Bull. 2009, 32, 1148–1154. [Google Scholar] [CrossRef] [Green Version]
  29. Herrmann, P.; Aronica, S.M. Estrogen and tamoxifen up-regulate FXYD3 on breast cancer cells: Assessing the differential roles of ER α and ZEB1. SpringerPlus 2015, 4, 245. [Google Scholar] [CrossRef] [Green Version]
  30. Liu, C.C.; Teh, R.; Mozar, C.A.; Baxter, R.C.; Rasmussen, H.H. Silencing overexpression of FXYD3 protein in breast cancer cells amplifies effects of doxorubicin and γ-radiation on Na+/K+-ATPase and cell survival. Breast Cancer Res. Treat. 2016, 155, 203–213. [Google Scholar] [CrossRef]
  31. Wang, M.W.; Gu, P.; Zhang, Z.-Y.; Zhu, Z.-L.; Geng, Y.; Kayed, H.; Zentgraf, H.; Sun, X.-F. FXYD3 Expression in Gliomas and its Clinicopathological Significance. Oncol. Res. Featur. Preclin. Clin. Cancer Ther. 2009, 18, 133–139. [Google Scholar] [CrossRef] [PubMed]
  32. Okudela, K.; Yazawa, T.; Ishii, J.; Woo, T.; Mitsui, H.; Bunai, T.; Sakaeda, M.; Shimoyamada, H.; Sato, H.; Tajiri, M.; et al. Down-Regulation of FXYD3 Expression in Human Lung Cancers: Its Mechanism and Potential Role in Carcinogenesis. Am. J. Pathol. 2009, 175, 2646–2656. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Arimochi, J.; Ohashi-Kobayashi, A.; Maeda, M. Interaction of Mat-8 (FXYD-3) with Na+/K+-ATPase in Colorectal Cancer Cells. Biol. Pharm. Bull. 2007, 30, 648–654. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Loftas, P.; Arbman, G.; Sun, X.-F.; Edler, D.; Syk, E.; Hallböök, O. FXYD-3 expression in relation to local recurrence of rectal cancer. Radiat. Oncol. J. 2016, 34, 52–58. [Google Scholar] [CrossRef]
  35. Meding, S.; Balluff, B.; Elsner, M.; Schöne, C.; Rauser, S.; Nitsche, U.; Maak, M.; Schäfer, A.; Hauck, S.; Ueffing, M.; et al. Tissue-based proteomics reveals FXYD3, S100A11 and GSTM3 as novel markers for regional lymph node metastasis in colon cancer. J. Pathol. 2012, 228, 459–470. [Google Scholar] [CrossRef]
  36. Schödel, J.; Grampp, S.; Maher, E.R.; Moch, H.; Ratcliffe, P.J.; Russo, P.; Mole, D.R. Hypoxia, Hypoxia-inducible Transcription Factors, and Renal Cancer. Eur. Urol. 2016, 69, 646–657. [Google Scholar] [CrossRef] [Green Version]
  37. Drake, C.G.; Stein, M.N. The Immunobiology of Kidney Cancer. J. Clin. Oncol. 2018, 36, 3547–3552. [Google Scholar] [CrossRef]
  38. Kim, M.C.; Jin, Z.; Kolb, R.; Borcherding, N.; Chatzkel, J.A.; Falzarano, S.M.; Zhang, W. Updates on Immunotherapy and Immune Landscape in Renal Clear Cell Carcinoma. Cancers 2021, 13, 5856. [Google Scholar] [CrossRef]
  39. Zarour, H.M. Reversing T-cell Dysfunction and Exhaustion in Cancer. Clin. Cancer Res. 2016, 22, 1856–1864. [Google Scholar] [CrossRef] [Green Version]
  40. Okoye, I.; Xu, L.; Motamedi, M.; Parashar, P.; Walker, J.W.; Elahi, S. Galectin-9 expression defines exhausted T cells and impaired cytotoxic NK cells in patients with virus-associated solid tumors. J. Immunother. Cancer 2020, 8, e001849. [Google Scholar] [CrossRef]
  41. Remark, R.; Alifano, M.; Cremer, I.; Lupo, A.; Dieu-Nosjean, M.-C.; Riquet, M.; Crozet, L.; Ouakrim, H.; Goc, J.; Cazes, A.; et al. Characteristics and Clinical Impacts of the Immune Environments in Colorectal and Renal Cell Carcinoma Lung Metastases: Influence of Tumor Origin. Clin. Cancer Res. 2013, 19, 4079–4091. [Google Scholar] [CrossRef] [Green Version]
  42. Donskov, F.; von der Maase, H. Impact of Immune Parameters on Long-Term Survival in Metastatic Renal Cell Carcinoma. J. Clin. Oncol. 2006, 24, 1997–2005. [Google Scholar] [CrossRef] [PubMed]
  43. Hu, K.; Lou, L.; Ye, J.; Zhang, S. Prognostic role of the neutrophil-lymphocyte ratio in renal cell carcinoma: A meta-analysis. BMJ Open 2015, 5, e006404. [Google Scholar] [CrossRef] [PubMed]
  44. Zhang, L.; Huang, Y.; Lindstrom, A.R.; Lin, T.-Y.; Lam, K.S.; Li, Y. Peptide-based materials for cancer immunotherapy. Theranostics 2019, 9, 7807–7825. [Google Scholar] [CrossRef] [PubMed]
Cancers 14 03596 g001 550
Figure 1. (A,B) Kaplan-Meier survival curves in FXYD3 high vs. low patients with KIRC in TCGA (A) and GSE29609 (B), respectively. The outcome is overall survival. The log-rank test calculated p-value. HR, hazard ratio. (C,D) Box plots of FXYD3 gene expression in each histological grade (C) and disease stage (D). KIRC, clear renal cell carcinoma. CPM, counts per million.
Figure 1. (A,B) Kaplan-Meier survival curves in FXYD3 high vs. low patients with KIRC in TCGA (A) and GSE29609 (B), respectively. The outcome is overall survival. The log-rank test calculated p-value. HR, hazard ratio. (C,D) Box plots of FXYD3 gene expression in each histological grade (C) and disease stage (D). KIRC, clear renal cell carcinoma. CPM, counts per million.
Cancers 14 03596 g001
Cancers 14 03596 g002 550
Figure 2. (A–C) Boxplots of immunity score (A), ESTIMATE score (B), and stroma score (C) between FXYD3 expression low vs. high groups. Immunity score corresponds to the infiltration level of immune cells in tumor tissue. ESTIMATE score infers tumor purity based on expression data. Stroma score infers the level of stromal cells present. ***: p < 0.001 by the Mann–Whitney test. ****: p < 0.0001 by the Mann–Whitney test. ns, not significant.
Figure 2. (A–C) Boxplots of immunity score (A), ESTIMATE score (B), and stroma score (C) between FXYD3 expression low vs. high groups. Immunity score corresponds to the infiltration level of immune cells in tumor tissue. ESTIMATE score infers tumor purity based on expression data. Stroma score infers the level of stromal cells present. ***: p < 0.001 by the Mann–Whitney test. ****: p < 0.0001 by the Mann–Whitney test. ns, not significant.
Cancers 14 03596 g002
Cancers 14 03596 g003 550
Figure 3. (A) Biplot of the principal component analysis of fractions of tumor-infiltrating lymphocytes (TILs) estimated by quanTIseq algorithm. Red dots correspond to FXYD3 high, and blue ones correspond to FXYD3 low patients’ data. (B–H) Boxplots of comparison of the estimated TILs fractions between low vs. high FXYD3 groups: (B) B cells; (C) CD8+ T cells; (D) Regulatory T cells (Treg); (E) M1 macrophages; (F) M2 macrophages; (G) Natural killer (NK) cells; (H) Neutrophils. All statistical tests used in B-H were the Mann–Whitney test. *: p < 0.05. **: p < 0.01. ****: p < 0.0001. ns, not significant.
Figure 3. (A) Biplot of the principal component analysis of fractions of tumor-infiltrating lymphocytes (TILs) estimated by quanTIseq algorithm. Red dots correspond to FXYD3 high, and blue ones correspond to FXYD3 low patients’ data. (B–H) Boxplots of comparison of the estimated TILs fractions between low vs. high FXYD3 groups: (B) B cells; (C) CD8+ T cells; (D) Regulatory T cells (Treg); (E) M1 macrophages; (F) M2 macrophages; (G) Natural killer (NK) cells; (H) Neutrophils. All statistical tests used in B-H were the Mann–Whitney test. *: p < 0.05. **: p < 0.01. ****: p < 0.0001. ns, not significant.
Cancers 14 03596 g003
Cancers 14 03596 g004 550
Figure 4. Heatmap of FXYD3 mRNA levels, clinical information (age, sex, stage, overall survival (OS) status), and TIL fractions. Hierarchical clustering for rows and columns was performed. TIL fractions were estimated using the quanTIseq method. Treg, regulatory T cells. NK cells, natural killer cells.
Figure 4. Heatmap of FXYD3 mRNA levels, clinical information (age, sex, stage, overall survival (OS) status), and TIL fractions. Hierarchical clustering for rows and columns was performed. TIL fractions were estimated using the quanTIseq method. Treg, regulatory T cells. NK cells, natural killer cells.
Cancers 14 03596 g004
Cancers 14 03596 g005 550
Figure 5. Heatmap of FXYD3 mRNA levels, clinical information (age, sex, stage, overall survival [OS] status, histology grade, Buffa hypoxia score), and immunoinhibitory gene set. Hierarchical clustering for rows and columns was performed.
Figure 5. Heatmap of FXYD3 mRNA levels, clinical information (age, sex, stage, overall survival [OS] status, histology grade, Buffa hypoxia score), and immunoinhibitory gene set. Hierarchical clustering for rows and columns was performed.
Cancers 14 03596 g005
Cancers 14 03596 g006 550
Figure 6. (A) Heatmap of correlation matrix of FXYD3 mRNA levels with the immunoinhibitory gene set in KIRC patients. The colored scale shows the values of Spearman correlation coefficients (rho). (B) Gene interaction network of the genes in KIRC. The nodes represent the genes and the edges indicate the interaction between genes. Densely connected subgraphs within the graph are shown using different colored nodes by calculating the leading non-negative eigenvector.
Figure 6. (A) Heatmap of correlation matrix of FXYD3 mRNA levels with the immunoinhibitory gene set in KIRC patients. The colored scale shows the values of Spearman correlation coefficients (rho). (B) Gene interaction network of the genes in KIRC. The nodes represent the genes and the edges indicate the interaction between genes. Densely connected subgraphs within the graph are shown using different colored nodes by calculating the leading non-negative eigenvector.
Cancers 14 03596 g006
Table
Table 1. Subject characteristics by FXYD3 expression levels.
Table 1. Subject characteristics by FXYD3 expression levels.
CharacteristicsFXYD3 Low n = 255FXYD3 High n = 255p †
Age (median [range])60.0 (29–90)61.0 (32–90)0.047
Sex
Male1501750.021
Female10580
Stage
I1461080.0026
II3432
III71108
IV47
Histologic grade
G1121< 0.0001
G212193
G395105
G42154
GX41
Buffa hypoxia score (median [range])−1 (−33–35)5 (−19–43)< 0.001
Mutation count (median [range])49 (8–426)55 (10–591)0.272
Continuous values are expressed as median (range). † Continuous and categorical variables were subjected to the Mann–Whitney U test and Chi-square test, respectively.
Table
Table 2. Multivariate Cox regression analysis on overall survival in TCGA dataset.
Table 2. Multivariate Cox regression analysis on overall survival in TCGA dataset.
VariableHR95% CIp-Value
Age at diagnosis1.0331.018–1.048<0.0001
Male (ref: Female)0.9030.656–1.2440.532
Stage at initial diagnosis (ref: Stage 0/I)
II1.3110.704–2.4400.394
III2.5191.675–3.788<0.0001
IV7.0814.818–10.41<0.0001
FXYD3 mRNA level1.1171.013–1.2320.027
n = 530. TCGA, The Cancer Genome Atlas; HR, hazard ratio; CI, confidence interval; ref, reference.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Yonekura, S.; Ueda, K. FXYD3 Expression Predicts Poor Prognosis in Renal Cell Carcinoma with Immunosuppressive Tumor Microenvironment. Cancers 2022, 14, 3596. https://doi.org/10.3390/cancers14153596

AMA Style

Yonekura S, Ueda K. FXYD3 Expression Predicts Poor Prognosis in Renal Cell Carcinoma with Immunosuppressive Tumor Microenvironment. Cancers. 2022; 14(15):3596. https://doi.org/10.3390/cancers14153596

Chicago/Turabian Style

Yonekura, Satoru, and Kosuke Ueda. 2022. "FXYD3 Expression Predicts Poor Prognosis in Renal Cell Carcinoma with Immunosuppressive Tumor Microenvironment" Cancers 14, no. 15: 3596. https://doi.org/10.3390/cancers14153596

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop