FUT10 is related to the poor prognosis and immune infiltration in clear cell renal cell carcinoma

Introduction

Renal cell carcinoma (RCC), a heterogeneous group of cancers derived from renal tubular epithelial cells, contributes to >90% of kidney neoplasm and is among the 10 most common cancers worldwide (1). Clear cell RCC (ccRCC), the predominant histology of RCC, is of over 5% incidence among all cancers, representing 75% of all cases of RCC and the majority of cancer-associated deaths (1,2). It has distinct immunological features and is characterized by high malignancy and insensitivity to chemotherapy or radiotherapy (3,4). Despite nephrectomy with curative intent, about 30% of ccRCC patients with localized disease eventually develop metastases (5,6). At present, the 5-year survival rate of localized ccRCC is 65%, but once metastasis occurs, it is reduced to 10–20% (7). Molecular-targeted drugs, tyrosine kinase inhibitors (TKIs), and immune checkpoint inhibitors (ICIs) have been increasingly recommended and investigated for ccRCC (8). In recent years, although the application of targeted therapy and immunotherapy have been extensively and successfully applied to ccRCC, a large number of patients have not responded to treatment due to immunological heterogeneity. There is still a long way to go for accurate ccRCC treatment. Therefore, it is critical to identify the specific molecular markers and therapeutic targets for the early diagnosis and treatment of ccRCC.

Aberrant glycosylation is not only considered a common aspect of cancer, but also often a hallmark of cancer (9,10). Fucosylation is a process in which fucose in guanosine diphosphate-fucose is transferred to its substrates (including N- and O-linked glycans in certain proteins, glycoproteins, or glycolipids) by fucosyltransferases (FUTs) in all mammalian cells (11). Recently, numerous studies have confirmed that increased fucosylation is a marker of malignant cell transformation and contributes to many abnormal events in the development and progression of cancer, such as uncontrolled cell proliferation, tumor cell invasion, cell-matrix interaction, angiogenesis, metastasis, immune escape, therapeutic resistance, and cancer cell signaling pathways (10-12). The main reason for altered glycosylation is the change in the expression of FUTs, which are the responsible enzymes for glycosylation. FUTs3-7 and FUTs9-11 have α-1,3-fucosyltransferase activity, whereas FUT10 has been reported to be involved in a unique α1,3-fucosyltransferase activity with stringent substrate specificity, which is necessary to maintain stem cells in an undifferentiated state (13,14). However, FUT10 in tumors has been rarely studied.

In this study, we investigated the expression, clinical-pathological features, immune infiltrates, and survival probability of FUT10 in ccRCC patients using The Cancer Genome Atlas (TCGA) datasets. In addition, the biological function and mechanism of FUT10 in ccRCC patients were investigated. Our results noted that FUT10 was a potential prognostic biomarker and target for immune treatment of ccRCC. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-449/rc).

Methods

Gene expression and clinical characteristics in TCGA

To investigate the expression pattern of FUT10 on ccRCC, TCGA kidney renal cell carcinoma (KIRC) L3 high-throughput sequencing (HTSeq)-fragments per kilobase per million (FPKM) RNA sequencing (RNA-seq) data format was obtained from TCGA (https://portal.gdc.cancer.gov/; accessed on 12 March 2022). Then, it was converted to transcripts per million (TPM) formats for further analysis. RNA-seq data in TPM format were also obtained from TCGA and Genotype-Tissue Expression (GTEx) databases and processed by UCSC Xena’s Toil process (https://xenabrowser.net/datapages/; accessed on 12 March 2022) (15). The log₂(TPM +1) transformed expression data were applied for the heatmap and box plots to compare the messenger RNA (mRNA) expression of FUT10 between different groups. Moreover, we used GSE126964 (7) datasets from the Gene Expression Omnibus (GEO) database to further testify the expression of FUT10 in ccRCC. In addition, the mRNA expression level of FUT10 was analyzed via the University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN) website. The Human Protein Atlas (HPA) website was used to acquire the expression of FUT10 protein in ccRCC. All procedures performed in this study were in accordance with the Declaration of Helsinki (as revised in 2013).

Function enrichment and gene set enrichment analysis (GSEA) for FUT10

We analyzed the correlations between FUT10 and differential genes by the DESeq2 package of R (v.3.6.3) (7). Then, Gene Ontology (GO) and GSEA were performed to investigate possible pathways by the ClusterProfiler package in R (16). Adjusted P<0.05 and false discovery rate (FDR) <0.25 were considered significant enrichment. The gene set database was from MSigDB Collections (https://www.gsea-msigdb.org/gsea/index.jsp), and the reference gene collection was C2.CP.V7.2.symbols.fmt (Curated).

Co-expression genes analysis

We used R to excavate genes that correlated with FUT10 and then used the threshold |Cor| >0.69 and P<0.05 for heatmap analysis. The Ensembl 101 library was used to annotate the molecular ID (http://ftp.ensembl.org/pub/release-101/gtf/homo_sapiens/).

Immunocyte infiltration analysis

The relationship of FUT10 and 24 types of immune cells was detected by single sample GSEA (ssGSEA) using the gene set variation analysis (GSVA) package and the data of normal groups were filtered out (17,18). The whole correlation analysis adopted the Spearman method.

Quantitative real-time polymerase chain reaction (qRT-PCR)

Adjacent kidney (n=10) and ccRCC tissue (n=13) were purchased from Shanghai Outdo Biotech Company, Shanghai, China. According to the manufacturer’s instructions, total RNA was extracted using TRIzol reagent (Takara, Shiga, Japan), complementary DNA (cDNA) was synthesized, and the qRT-PCR was conducted and measured using 2^−ΔΔCt methods. Related primers were displayed as follows: FUT10: 5'-CAGCCAGCGTGTGAGAAA-3' (forward); 5'-TCCAAGACCAGCCCAATC-3' (reverse); β-actin: 5'-GAAGAGCTACGAGCTGCCTGA-3' (forward); 5'-CAGACAGCACTGTGTTGGCG-3' (reverse).

Statistical analysis

The R program (R Foundation for Statstical Computing, Vienna, Austria) was used to conduct all statistical analyses. We analyzed the expression of FUT10 in ccRCC from the TCGA database using Welch’s t-test, Wilcoxon signed-rank test, and Dunn’s test. The receiver operating characteristic (ROC) curve was generated using the pROC package. Kaplan-Meier analysis and multivariate Cox analysis were viewed using the survival package. P<0.05 was considered significantly different.

Results

Downregulated expression levels of FUT10 in patients with ccRCC

The gene expression data and clinical data of 539 primary tumors and 72 normal samples were downloaded from the TCGA database, including patients’ gender, age, histological grade, pathologic stage, tumor, node, metastasis (TNM) stage, and survival data. Based on the medium levels of FUT10, we divided them into high or low groups. Unpaired sample analysis showed that mRNA expression levels of FUT10 in ccRCC tissues were significantly lower than those in adjacent tissues (Figure 1A,1B). GSE126964 also showed that FUT10 was lowly expressed in tumor groups (Figure 1C; P<0.001). Moreover, the result of the paired sample analysis was consistent with the result of the unpaired sample analysis (Figure 1D; P<0.001). We also confirmed that the FUT10 expression level was significantly decreased in the tumor tissue of ccRCC via the UALCAN website (Figure 1E). In addition, according to the results of the HPA database, the expression level of FUT10 protein in normal tissue was significantly higher than that in tumor tissue (Figure 1F). These results verified the downregulated expression levels of FUT10 in patients with ccRCC.

Figure 1 FUT10 mRNA expression in ccRCC patients. (A) FUT10 mRNA expression in ccRCC tissues and unpaired normal tissues of TCGA. (B) FUT10 mRNA expression in ccRCC tissues of TCGA and normal tissues of GTEx combined with TCGA. (C) FUT10 mRNA expression in ccRCC tissues and normal tissues based on the GSE126964 dataset. (D) FUT10 mRNA expression in ccRCC tissues and paired adjacent tissues of TCGA. (E) FUT10 mRNA expression in ccRCC tissues and adjacent tissues based on the UALCAN website. (F) FUT10 protein expression in ccRCC tissues and adjacent tissues based on the HPA website (https://www.proteinatlas.org/ENSG00000172728-FUT10) (immunohistochemical staining). FUT10, fucosyltransferase 10; TPM, transcripts per million; TCGA, The Cancer Genome Atlas; GTEx, Genotype-Tissue Expression; KIRC, kidney renal cell carcinoma; mRNA, messenger RNA; ccRCC, clear cell renal cell carcinoma; UALCAN, University of Alabama at Birmingham Cancer Data Analysis Portal; HPA, Human Protein Atlas.

Relationships between clinical pathological characteristics of patients with ccRCC and FUT10 mRNA level

As shown in Table 1 and Figure 2, lower expression levels of FUT10 were associated with histological grade (P<0.001), pathologic stage (P<0.001), T stage (P<0.001), M stage (P=0.01), overall survival (OS) (P<0.001), disease-specific survival (DSS) (P<0.001), and progression-free interval (PFI) (P<0.001), respectively. Other clinicopathological features, including gender (P=0.28), age (P=0.07), and N stage P=0.39), were not statistically significant.

Figure 2 Correlation between expression of FUT10 and clinicopathological parameters according to TCGA data analysis. (A) Gender. (B) Histologic grade. (C) Pathologic stage. (D) T classification. (E) N classification. (F) M classification. (G) OS event. (H) DSS event. (I) PFI event. FUT10, fucosyltransferase 10; TPM, transcripts per million; OS, overall survival; DSS, disease-specific survival; PFI, progression-free interval; TCGA, The Cancer Genome Atlas.

Diagnostic value of FUT10 mRNA expression in ccRCC

The value for FUT10 to distinguish ccRCC samples from normal samples was assessed by ROC curve analysis. The results showed a diagnostic value with an area under the curve (AUC) of 0.787 for FUT10 [Figure 3A; 95% confidence interval (CI): 0.738–0.836]. When the cut-off value was 2.364, the sensitivity, specificity, and accuracy of FUT10 were 75%, 67.3%, and 68.5%, respectively. The positive predictive value was 23.5% and the negative predictive value was 95.3%, indicating that FUT10 was accurate in distinguishing ccRCC tissues from normal tissues.

Figure 3 ROC curve and Kaplan-Meier analysis with FUT10 mRNA expression according to TCGA data analysis. (A) ROC curve of FUT10 mRNA expression in normal and tumor. (B) Kaplan-Meier analysis of OS in patients with ccRCC. (C) Kaplan-Meier analysis of PFS in patients with ccRCC. (D) Kaplan-Meier analysis of DSS in patients with ccRCC. TPR, true positive rate; FPR, false positive rate; FUT10, fucosyltransferase 10; AUC, area under the curve; CI, confidence interval; HR, hazard ratio; ROC, receiver operating characteristic; mRNA, messenger RNA; TCGA, The Cancer Genome Atlas; OS, overall survival; ccRCC, clear cell renal cell carcinoma; PFS, progression-free survival; DSS, disease-specific survival.

FUT10 mRNA expression is an independent risk factor for survival in patients with ccRCC

According to the median expression of FUT10, Kaplan-Meier analysis exhibited that lower FUT10 expression had a much worse OS, DSS, and progression-free survival (PFS) than those in high-FUT10 groups (Figure 3B-3D; P<0.001). The univariate analysis showed that higher FUT10 mRNA expression, pathological stage, and TNM stage were related to OS (Table 2). Moreover, multivariate analysis was revealed that FUT10 was an independent prognostic variable of OS in ccRCC (Table 2; P=0.003). From the nomogram chart, we can see that the lower the FUT10 expression, the less the probability of survival for 1, 3, and 5 years (Figure 4).

Figure 4 Prediction model of nomogram construction according to the results of TCGA data analysis. FUT10, fucosyltransferase 10; TCGA, The Cancer Genome Atlas.

Functional enrichment and GSEA dug FUT10-related mechanism

We conducted GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to construct functional annotations. As shown in Table 3 and Figure 5A, changes in the biological process of FUT10 were correlated with acute inflammatory response, epidermis development, epidermal cell differentiation, humoral immune response, regulation of immune effector process, lymphocyte-mediated immunity, and so on. Moreover, we identified the signaling pathways that played important roles in ccRCC by GSEA. Figure 5B-5D shows that oxidative phosphorylation (OXPHOS), arachidonic acid (AA) metabolism, and primary immunodeficiency (PID) pathways were significantly enriched in the negative correlation with FUT10 mRNA expression phenotype.

Figure 5 GO and KEGG were used to construct functional annotations based on TCGA data. (A) The biological function of FUT10 was plotted. (B-D) OXPHOS, AA metabolism, and PID pathways were differentially enriched in the negatively correlated with FUT10 expression. KEGG, Kyoto Encyclopedia of Genes and Genomes; NES, normalized enrichment score; FDR, false discovery rate; GO, Gene Ontology; TCGA, The Cancer Genome Atlas; FUT10, fucosyltransferase 10; AA, arachidonic acid; OXPHOS, oxidative phosphorylation; PID, primary immunodeficiency.

Analysis of co-expression genes

It has been shown that ARL8B, STAM2, APPL1, ERLIN2, ANKFY1, and BAG4 play roles in tumor cell proliferation, migration, adhesion, cell cycle and survival, and immune response. Thus, to examine possible mechanisms of FUT10 expression that affect ccRCC, the correlation of FUT10 with these molecules was analyzed (Figure 6A-6G). The results showed that FUT10 was positively associated with STAM2, APPL1, ERLIN2, ANKFY1, BAG4, and so on, which can influence tumor progression in multiple aspects, including cell proliferation, migration, cell cycle, and survival. The value of ARL8B was 0.590, which was related to natural killer (NK)-mediated cytotoxicity and was also listed in our results.

Figure 6 Analysis of possible correlation genes with FUT10 expression in ccRCC from TCGA database. (A) The heat map exhibited FUT10 possible correlation molecules. (B-G) FUT10 was positively associated with ARL8B, STAM2, APPL1, ERLIN2, ANKFY1, and BAG4. ***, P<0.001. FUT10, fucosyltransferase 10; TPM, transcripts per million; ccRCC, clear cell renal cell carcinoma; TCGA, The Cancer Genome Atlas.

The relationship of FUT10 expression with immune infiltration

The relationship of FUT10 expression with 24 different immunocytes was analyzed by ssGSEA with Spearman correlation analysis. It was shown that higher FUT10 expression was positively related to central memory T (Tcm) cells and negatively associated with cytotoxic cells and NK CD56bright cells (P<0.001; Figure 7A-7C). Moreover, in higher FUT10 expression groups, NK CD56bright cells showed lower enrichment scores, but the enrichment scores of Tcm were higher (Figure 7D,7E).

Figure 7 The correlation between FUT10 expression and immune infiltration in ccRCC based on TCGA data. (A) the association of FUT10 expression with 24 immune cell types. (B) FUT10 expression was positively related to Tcm cells. (C) FUT10 expression was negatively related to NK CD56bright cells. (D) Tcm cells were highly enriched with higher FUT10 expression. (E) NK CD56bright cells were lowly enriched with higher FUT10 expression. NK, natural killer; TReg, regulatory T cell; pDC, plasmacytoid dendritic cell; aDC, activated dendritic cell; Th, T helper; TFH, follicular helper T cell; DC, dendritic cell; iDC, immature dendritic cell; Tgd, gamma delta T cell; Tem, effector memory T cell; Tcm, central memory T; FUT10, fucosyltransferase 10; TPM, transcripts per million; ccRCC, clear cell renal cell carcinoma; TCGA, The Cancer Genome Atlas.

The verification of FUT10 expression in ccRCC tissues

We used qRT-PCR to verify the expression of FUT10 mRNA in ccRCC tissues, and observed that FUT10 mRNA was down-regulated in ccRCC tumors (n=10) compared with normal renal tissues (Figure 8; P=0.004).

Figure 8 Testified the expression of FUT10 in adjacent kidney and ccRCC tissues by qRT-PCR (P=0.004). FUT10, fucosyltransferase 10; ccRCC, clear cell renal cell carcinoma; qRT-PCR, quantitative real-time polymerase chain reaction.

Discussion

Glycosylation is one of the important post-translational modifications, which is regulated by the role glycosyltransferases (GTs) and glycosidases in glycoproteins and/or lipids. Abnormal glycosylation is not only a result of cancer, but also a driver of malignant phenotypes. Numerous findings have shown that the altered glycosylation of tumor cell proteins directly affects the growth, differentiation, transformation, adhesion, metastasis, and immune surveillance. At the same time, abnormal glycoproteins play an important role in the malignant transition and migration of tumors (19,20). FUTs can be used to identify tumors with well-differentiated cells and also could be used as a marker for early tumorigenesis (21,22). FUT10 is located in Golgi apparatus, endoplasmic reticulum, and nucleoplasm, and has α-1,3-fucosyltransferase activity. FUT10 is predicted to act upstream of or inside the cerebral cortex to maintain directed cell migration and the function of neural stem cell populations (23). However, there is limited maintenance research on the function of FUT10 in tumors.

RCC is a malignant tumor characterized by energy metabolism reprogramming (24-27). In particular, the metabolic flux through glycolysis is partitioned (28-30), mitochondrial bioenergetics, OXPHOS, and lipid metabolism are impaired (28,31-34). In this scenario, to examine possible mechanisms of FUT10 expression affecting ccRCC, FUTs are important regulators of the metabolic activity of mucins (such as MUC1) in ccRCC (33-35). In our study, we used TCGA and GEO databases to analyze the relationship between FUT10 and the prognosis of ccRCC patients. We found that FUT10 was lowly expressed in ccRCC tumors compared with normal kidney tissues, particularly in the later clinical stages III and IV. The results of logistic regression analysis suggested that FUT10 was related to T stage, M stage, and pathological stage. Moreover, decreased FUT10 expression was linked to poor OS, DSS, and PFI, showing a moderate ability for tumor diagnosis and prediction. Besides, the Cox regression analysis showed that FUT10 was an independent prognostic variable in ccRCC. The nomogram based on FUT10 suggested that the lower the expression of FUT10, the lower the survival probability of patients, and showed a downward trend year by year. Overall, these findings revealed that FUT10 might be a protective molecular and prognostic factor.

Regarding functional studies, we found that FUT10 has significantly negative correlations with OXPHOS, AA metabolism, and PID pathways. Firstly, OXPHOS, mitochondrial adenosine triphosphate (ATP) generation coupled to oxygen consumption, is widely recognized as upregulated in tumors and associated with malignancies and tumor cell expansion (36). Currently, more and more chemotherapies aim at directly or indirectly suppressing OXPHOS levels in various tumors, such as triple-negative breast cancer (37), non-small cell lung cancer (38), prostate cancer (39), and so on. Secondly, it is the AA metabolic process that plays a key role in carcinogenesis, and AA has been considered a novel preventive and therapeutic target in cancer (40,41). For example, AA and metabolic prostaglandin E2 (PGE2) as immune regulators modulate tissue homeostasis and pathological processes in squamous cell carcinoma (42). In hepatocellular carcinoma, berberine elevates the ratio of AA to PGE2 by inhibiting the AA metabolic pathway to induce apoptosis (43). Lastly, PID is correlated with recurrent infections, autoimmunity, and cancers. To our knowledge, there have been numerous previous reports about PID in tumors. It is viewed that the normal and deficient immune system needs to focus on the structure and dynamic function of the immune system as a whole rather than on its isolated components alone (44).

A highlight of this work is the prediction of the effects of FUT10 on immune infiltration and possible mechanisms in ccRCC cells. Aberrant glycosylation is usually regarded as one of the hallmarks of cancer and is involved in processes from cell signaling pathways, tumor invasion, and immune regulation (10). Co-expression gene analysis suggested that FUT10 was positively relevant to ARL8B, STAM2, APPL1, ERLIN2, ANKFY1, BAG4, and so on. Those genes play roles in tumor cell proliferation, migration, adhesion, cell cycle and survival, immune response, and so on. Remarkably, ARL8B, a related adenosine diphosphate (ADP)-ribosylation factor-like (ARL) family member, has become an important regulator that helps lysosomal transport to the periphery (45). It has also been reported that ARL8B may be a key factor in NK-mediated cytotoxicity by transferring polarization of dissolved particles toward immune synapses (46). In addition, we also found an interesting molecule, STAM2, which contains a single SH3 domain and an immune receptor tyrosine activation motif (ITAM) (47). Meanwhile, STAM2 has an indispensable role during T cell development and survival, as well as STAM1 (48).

The immune system plays a vital role in tumor progression. Innate immune cells and adaptive immune cells often reside in the tumor microenvironment and determine the growth rate of tumor (49). RCC is one of the malignant tumors with the strongest immune infiltration (50-52). Previous studies have shown that activation of specific metabolic pathways can regulate angiogenesis and inflammatory signals (53,54). Tumor microenvironment can affect the response of anti-tumor therapy (35,55-58). FUT10 can modulate immune cell infiltration and regulate immunoflogosis. We explored whether FUT10 was related to immune regulation through GO and ssGSEA analysis in ccRCC. Genes related to acute inflammatory response, humoral immune response, regulation of inflammatory response, and lymphocyte-mediated immunity were enriched. In addition, NK CD56bright cells and NK CD56dim cells were negatively correlated with FUT10 expression, and those were less enriched in the FUT10 high expression groups. Thus, FUT10 may positively regulate NK-mediated immune response by co-expressing with ARL8B to affect the progression of ccRCC further. However, this inference still needs further exploration at the cellular level.

An increasing body of literature mainly reports the effect of FUTs 1–8 on various tumors. Our previous research mainly focused on the function and action mechanism of FUT2 on the proliferation, migration, invasion, apoptosis, and autophagy in lung adenocarcinoma (59-61). Our work is the first exploration of FUT10 in ccRCC, which is the vanguard in pursuing further biological processing and molecular mechanism research.

Nevertheless, our study still has some limitations. First, our research is based on bioinformatics analysis and lacks comprehensive cell and animal experimental verification, which should be improved in subsequent studies. Second, only PCR experiments were performed, and the lack of independent validation with additional functional wet lab experiments using cell lines or clinical specimens weakens this study. Therefore, in the follow-up study, we will further verify the role and mechanism of FUT in ccRCC at the level of cells, animals, and clinical samples.

Conclusions

In summary, we observed decreasing FUT10 in ccRCC, which had extraordinary accuracy in the diagnosis and prediction of the primary clinical stage, as well as correlation with the primary clinical stage and correlation with the survival of ccRCC patients. Besides, FUT10 might participate in the development and progression of ccRCC through multi-signaling pathways and the impact on the immune infiltrating cells, particularly Tcm and NK CD56bright cells. In particular, we found a related molecule of NK-mediated cytotoxicity—ARL8B—which may co-express with FUT10, but its specific mechanism needs further investigation and clarification. This study revealed the role of FUT10 in ccRCC and provided a potential biomarker for the diagnosis and prognosis of ccRCC.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-449/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-449/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-449/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-449/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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