Skip to main content
Download PDF

CENPE and LDHA were potential prognostic biomarkers of chromophobe renal cell carcinoma

European Journal of Medical Research volume 28, Article number: 481 (2023) Cite this article

Abstract

Background

Most sarcomatoid differentiated renal cell carcinoma was differentiated from Chromophobe renal cell carcinoma (KICH) and related to a bad prognosis. Thus, finding biomarkers is important for the therapy of KICH.

Methods

The UCSC was used for determining the expression of mRNA and miRNA and clinical data in KICH and normal samples. KEGG and GO were used for predicting potential function of differently expressed genes (DEGs). Optimal prognostic markers were determined by Lasso regression. Kaplan–Meier survival, ROC, and cox regression were used for assessing prognosis value. GSEA was used for predicting potential function of markers. The relations between markers and immune cell infiltration were determined by Pearson method. The upstream miRNA of markers was predicted in TargetScan and DIANA.

Results

The 6162 upregulated and 13,903 downregulated DEGs were identified in KICH. Further CENPE and LDHA were screened out as optimal prognostic risk signatures. CENPE was highly expressed while LDHA was lowly expressed in KICH samples, and the high expressions of 2 genes contributed to bad prognosis. The functions of CENPE and LDHA were mainly enriched in proliferation related pathways such as cell cycle and DNA replication. In addition, the correlation of 2 genes with immune infiltrates in KICH was also observed. Finally, we found that has-miR-577 was the common upstream of 2 genes and the binding sites can be predicted.

Conclusion

CENPE and LDHA were identified as the important prognostic biomarkers in KICH, and they might be involved in the proliferation of cancer cell.

Introduction

Chromophobe renal cell carcinoma (KICH) is a subtype of renal cell carcinoma (RCC) [1], which is first described in 1985 and accounts for 5% of RCC [2, 3]. The lack of rich vascular networks, which usually could be observed in clear-cell carcinomas, is a characteristic of KICH [4]. Numerous studies revealed that the prognosis of KICH is better than other subtypes of RCC [5]. In addition, the prognosis of most KICH was not related to the pathological stage, while the other subtypes were associated with the pathological stage [6]. However, KICH is the main kind of primary subtype of the sarcomatoid differentiated RCC, which is generally related to a bad prognosis [7]. Therefore, it is of critical importance to identify prognosis-related biomarkers, which are helpful for the diagnosis and treatment strategy selection of KICH.

Centrosome-associated protein E (CENPE) is a plus end-directed kinetochore motor protein, which accumulates at the G2 phase of the cell cycle and plays a vital role in mitosis [8, 9]. Previous studies confirmed that CENPE promoted the proliferation of multiple cancers, such as ovarian cancer, lung adenocarcinoma and lung adenocarcinoma [8, 10, 11]. Besides, Zhu et al. claimed that CENPE could be a biomarker of esophageal adenocarcinoma [9]. Moreover, the overexpressed CENPE was closely related to the poor prognosis in breast cancer (BC), which indicated that CENPE could be a potential prognosis biomarker of BC [12]. Although Wang et al. determined that CENPE could promote development and metastasis through the Wnt/β-catenin signal pathway in clear cell renal cell carcinoma [13], the function of CENPE in KICH remains unclear.

Additionally, lactate dehydrogenase A (LDHA) is one of the key enzymes in glycolysis progress which have crucial effects on cancer cell growth [14]. Meanwhile, the overproduction of lactate, which depends on the function of LDHA, in glycolysis causes triggering immune escape and promotes the development of tumors [15]. It has been identified that LDHA was overexpressed in various cancers and involved in tumorigenesis and tumor growth [16]. Furthermore, Huo et al. unraveled that the LDHA-mediated glycolysis was inhibited by LINC00671 in papillary thyroid cancer cells, which contributed to the suppression of tumor cell growth and metastasis [14]. Similarly, the effect of LDHA on KICH is also not reported, while it has been found that LDHA was involved in clear cell renal cell carcinoma [17].

Thus, in this study, we tried to explore the expression, function and regulatory axis of CENPE and LDHA in the development of KICH.

Methods

The collection of RNA expression and clinical information

The expression of mRNA and miRNA and corresponding clinical information (age, gender, race, M stage, N stage, T stage) were obtained from the University of California Santa Cruz (UCSC) Xena database (https://xenabrowser.net/). After removing missing data, 64 KICH samples and 24 normal samples were obtained and used for subsequent analysis. Besides, mRNA and miRNA were considered not expressed if the expressions were not detected in at least 10% samples.

The differently expressed genes (DEGs) analysis

After normalization of the raw count data with transcripts per million (TPM) method and undergoing a log2 transformation, 58,939 genes were annotated. Then, we used the R package t.test function to evaluate the significance of each gene in the tumor group and the normal group, and used the p.adjust function to calculate the false discovery rate (FDR) of each gene, and 14,565 DEGs with an absolute log2 fold change (FC) > 1 and p-value < 0.05 were obtained.

Gene Ontology (GO) term, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis

Then we identified the function of DEGs in KICH progression using GO and KEGG enrichment analyses. For gene set functional enrichment analysis, we used Kegg REST API (https://www.Kegg.jp/Kegg/rest/KeggAPI.html) and org.Hs.eg.db (version 3.1.0) to obtain the latest gene annotation of KEGG and GO Pathway, which was used as the background, and then mapped the genes into the background set, and used R software package clusterProfiler (version 3.14.3) for enrichment analysis. The p < 0.05 and FDR < 0.25 were considered statistically significant.

The identification of hub gene

Least absolute shrinkage and selection operator (LASSO) regression was a kind of method used for selecting candidate genes which were closely related to prognosis. After we integrate the data of overall survival (OS) time, survival status and gene expression, the lasso-cox regression was performed using R software package glmnet with fivefold cross-validation (CV). After optimal lambda value was determined, hub genes with nonzero coefficients were screened out. Meanwhile, the risk scores and risk model were determined according to the gene expression value and LASSO coefficients. After the lasso-cox method, we further evaluated the prognosis value of hub genes and clinical characteristics in KICH through multivariate cox regression in SPSS 25.

Survival analysis

The Kaplan–Meier (K–M) survival curve was used for assessing the effect of mRNA, miRNA and risk score on prognosis in KICH. Before survival analysis, the KICH samples were divided into two groups according to the optimal truncation value determined using the maxstat of the R package. Then, we evaluated the difference between the two groups in the OS time through the survfit of the R package.

Gene set enrichment analysis (GSEA)

We explored the function of hub genes in KICH using GSEA. At first, we obtained GSEA software (version 3.0) from the website of GSEA. Then, GESA was performed by the GSEA software using a gene set database (http://www.gsea-msigdb.org/gsea/downloads.jsp) downloaded from the molecular signatures database. The p < 0.05 and FDR < 0.25 were considered statistically significant.

Immune infiltrate analysis

The tumor Immune Estimation Resource (TIMER) database (https://cistrome.shinyapps.io/timer/) server is a comprehensive resource for the systematical analysis of immune infiltrates across diverse cancer types. We analyzed the association between hub genes and 6 types of immune genes, such as B cell, CD8 + T cell, CD4 + T cell, macrophage, neutrophil and dendritic cell (DC) using TIMER. P-values of less than 0.05 were considered statistically significant.

The prediction of miRNA and construction of a miRNA-mRNA regulatory network

To understand the regulatory network of hub genes in KICH, miRNAs, which could target these genes, were predicted in TargetScan (https://www.targetscan.org/vert_72/) and DIANA (http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=tarbase/index). TargetScan predicts targets of mRNAs by searching for the presence of conserved 8mer, 7mer, and 6mer sites that match the seed region of each mRNA, and DIANA-TarBase provides the experimentally validated interactions of miRNA and gene.

Statistical analysis

In this study, all experiments were repeated three times. The data were analyzed using SPSS 25 and presented as mean ± standard deviation. The comparison between the two groups was evaluated by independent sample t-test, while the comparison among multiple groups was conducted by one-way analysis of variance (ANOVA) followed by post-hoc comparisons. The survival difference between the 2 groups was determined by log-rank test, and the correlation analysis was identified by the Pearson test. p < 0.05 was considered a significant difference.

Results

Identification and enrichment analysis of DEGs in the KICH based on the TCGA database

According to the TCGA database, we found that 6162 genes were upregulated, while 13,903 genes were downregulated in KICH samples compared with normal samples (Fig. 1A). Besides, the heat maps exhibited that KICH samples can be obviously distinguished from the normal samples according to the expression of DEGs (Fig. 1B).

Fig. 1
figure 1

The expression profile of DEGs in KICH and normal samples. A The volcano plot of DEGs. The red triangle indicated upregulated genes, while the green triangle indicated downregulated genes, and the black triangle indicated genes without significantly differential expression in volcano plots. B The heat map of DEGs. The red dots presented upregulated genes, and the blue dots presented downregulated genes

Full size image

Then, GO (CC) enrichment analysis showed that DEGs were mainly enriched in intrinsic component of the membrane, extracellular region and plasma membrane part (Fig. 2A). In the biological process, the DEGs primarily play a role in system development, cellular development process and cell differentiation (Fig. 2B). Additionally, the molecular functions of DEGs were enriched in molecular function regulator, signaling receptor binding and transporter activity (Fig. 2C). The results of KEGG enrichment analysis revealed that the DEGs were mainly involved in the metabolic pathway, neuroactive ligand-receptor interaction, pathways in cancer, cyclic adenosine 3’, 5’-monophosphate (cAMP) signaling pathway, cytokine-cytokine receptor interaction, phosphoinositide 3-kinase (PI3K)—protein kinase B (AKT) signaling pathway, mitogen-activated protein kinase (MAPK) signaling pathway and ras signaling pathway (Fig. 2D).

Fig. 2
figure 2

The GO and KEGG enrichment analysis of DEGs. The A CC, B BP and C MF of DEGs were analyzed by GO enrichment analysis. D The main functions of DEGs were performed by KEGG enrichment analysis. The X-axis was the value of the gene ratio and the Y-axis was the name of the corresponding terms. The size of the circles indicated the number of genes enriched in the term, and the color indicated the P-value

Full size image

Establishment and verification of the risk model for KICH

Then we further selected the genes strongly related to the OS time using Lasso-regression analysis according to the DEGs expression profile. The results revealed that two DEGs with nonzero coefficients namely CENPE and LDHA were finally identified when the lambda coefficients were 0.2218 (Fig. 3A, B). Additionally, the LASSO coefficient of CNEPE and LDHA was 0.0179 and 0.0014, respectively. Moreover, we established the risk score model based on the gene coefficients of CENPE and LDHA. The risk score model was constructed as follows: RiskScore = 0.0179*CENPE + 0.0014*LDHA.

Fig. 3
figure 3

Identification and screening of the prognosis-related genes. A Least absolute shrinkage and selection operator (LASSO) coefficient profiles of DEGs. B Partial likelihood deviance for LASSO coefficient profiles. The red dots represent the partial likelihood values, the gray lines represent the standard error (SE). C ROC curve based on the risk model at 3 year and 5 year. D The K–M curve based on the risk score. E The expression of CENEP in tumor and normal samples. F The expression of LDHA in tumor and normal samples. G The correlation of CENPE and LDHA expressions in KICH. H The effect of CENPE expression level on OS time. I The effect of LDHA expression level on OS time. L indicated low expression of CENPE or LDHA low risk score, while H indicated high expression of CENPE or LDHA or high risk score. **p < 0.01; ***p < 0.001

Full size image

Subsequently, we generated the ROC curve of the risk model. The AUC of 3 years and 5 years were 0.93 and 0.97, respectively (Fig. 3C), which suggested the 3-year and 5-year survival rates could be favorably predicted by the risk model. The survival analysis further indicated that high risk score was related to a bad prognosis compared to the low risk score (Fig. 3D). Then, according to the mRNA expression, we found CENPE was significantly upregulated (Fig. 3E), while LDHA was downregulated in KICH samples compared to the normal samples (Fig. 3F). However, there was a positive correlation between the expression of CENPE and LDHA (Fig. 3G). Moreover, as shown in Fig. 3H, I, the high expressions of CENPE and LDHA were both related to bad prognosis in KICH samples.

Besides, the multivariate cox regression analysis revealed that age, race, T stage, M stage, N stage, CENPE expression and LDHA expression were independenly associated with OS time (Fig. 4), which indicated that the CENPE and LDHA were potential biomarkers of KICH.

Fig. 4
figure 4

The results of multivariate cox regression analysis

Full size image

The GSEA enrichment analysis of CENPE and LDHA in KICH

To further explore the role of CENPE and LDHA in the development of KICH, we analyze their function using Gene Set Enrichment Analysis (GSEA) enrichment analysis. GSEA analysis results showed that 143 pathways were positively regulated by CENPE and 35 pathways were negatively regulated by CENPE in KICH. As can be seen from Fig. 5A–I, the main pathway positively regulated by CENPE contained that cell cycle, p53 signaling pathway, DNA replication, RNA degradation, basal transcription factors, receptor interaction, GNRH signaling pathway, pathways in cancer and T cell receptor signaling pathway. In addition, LDHA promoted 134 pathways but inhibited 30 pathways in KICH. As shown in Fig. 6A–I, LDHA positively regulated the cell cycle, DNA replication, mismatch repair, protein export, base excision repair, RNA degradation, pathways in cancer and retinoic acid-inducible gene I (RIG-I) like receptor signaling pathway, but negatively regulated oxidative phosphorylation.

Fig. 5
figure 5

The GSEA enrichment analysis results of CENPE. A Cell cycle, B p53 signaling pathway, C DNA replication, D RNA degradation, E basal transcription factors, F receptor interaction, G GNRH signaling pathway, H pathways in cancer, I T cell receptor signaling pathway

Full size image
Fig. 6
figure 6

The GSEA enrichment analysis results of LDHA. A Cell cycle, B DNA replication, C mismatch repair, D protein export, E base excision repair, F RNA degradation, G pathways in cancer, H RIG-I like receptor signaling pathway, I oxidative phosphorylation

Full size image

The immune cell infiltration of CENPE and LDHA

Because some enriched pathways, including T cell receptor signaling pathway and RIG-I like receptor signaling pathway, were closely related to the immune response, we further determined the correlation between hub genes and six immune-related cells, including B cell, CD8 + T cell, CD4 + T cell, macrophage, DC and neutrophil. From Fig. 7A–F, CENPE was positively and significantly related to CD8 + T cell and macrophage. Besides, there was a positive and significant relation between LDHA and four immune cells, namely B cell, CD8 + T cell, macrophage and DC (Fig. 8A–F).

Fig. 7
figure 7

The correlation between CENPE and immune cells. A B cell, B CD8 + T cell, C macrophage, D CD4 + T cell, E DC, F neutrophil

Full size image
Fig. 8
figure 8

The correlation between LDHA and immune cells. A B cell, B CD8 + T cell, C CD4 + T cell, D macrophage, E neutrophil, F DC

Full size image

The expression and prognosis value of hsa-miR-577 in KICH

To explore the regulatory pathway of CENPE and LDHA, we tried to validate the miRNA which could both target the two genes in KICH. According to the prediction of DIANA and TargetScan, we found three miRNAs in the intersection of four databases (Fig. 9A), namely hsa-miR-577, has-miR-4307 and has-miR-4470. We found that hsa-miR-577 expression was upregulated in KICH samples compared with normal samples (Fig. 9B). The other 2 miRNAs were not expressed in KICH and normal samples according to the miRNA expression profile from TCGA. Additionally, prediction results showed the has-miR-577 may bind to CENPE in the transcript position 55–85, while it may bind to LDHA in the transcript position 123–138 using DIANA (Fig. 9C). Moreover, we analyzed the prognosis value of hsa-miR-577 through the K–M curve. The results revealed that high expression of hsa-miR-577 was closely associated with a bad prognosis (Fig. 9D).

Fig. 9
figure 9

The validation, expression level and prognosis value of hsa-miR-577. A The miRNAs were screened out from the intersection of four databases. B The expression of hsa-miR-577 was based on the miRNA expression profile. C The combine sites between has-miR-577 and CENPE or LDHA. D The correlation between hsa-miR-577 expression and OS time. L indicated a low expression of hsa-miR-577, and H indicated a high expression of hsa-miR-577. *p < 0.05

Full size image

Discussion

In this study, according to the TCGA dataset, we identified 20,065 differentially expressed genes, including 6162 upregulated genes and 13,903 downregulated genes, in KICH samples compared with normal samples. Besides, the results of GO and KEGG enrichment analysis suggested that the DEGs were involved in the development of tumors and immune [18,19,20,21]. Then, LASSO regression analysis was applied to screen out 2 hub genes, namely CENPE and LDHA. Besides, we established a risk score model based on these 2 genes, and the results of ROC curves indicated that the model could accurately predict the prognosis for patients with KICH, with the AUC of 0.93 and 0.97 for the 3-year and 5-year survival time, respectively. Furthermore, the results of the K–M plot showed that the high risk score was associated with a bad prognosis of patients diagnosed with KICH, which suggested that high expressions of CENPE and LDHA may be involved in the development of KICH.

CENPE had been regarded as a potential biomarker of diverse cancers, such as invasive ductal carcinoma and non-small cell lung cancer [11, 22]. As a mitotic cell cycle-associated gene, CENPE has an essential and positive function in the process of mitotic cytoplasmic separation, which was related to the cell cycle [9, 23], and the rapid proliferation is a common characteristic of tumor cells [24, 25]. Shi et al. certificated that the silencing CENPE led to the inhibition of cell proliferation and promotion of apoptosis in acute myeloid leukemia cells [26]. In addition, Wang et al. found that overexpressing CENPE promoted the cell viability, migration, and invasion of neuroblastoma [27]. These results suggested that the high expression of CENPE could promote the development of tumors. In our study, we found that CENPE was upregulated in KICH samples compared to the normal samples. Besides, the high expression of CENPE could independently predict a bad prognosis in KICH samples according to the K–M plot and multivariate cox regression analysis. These results indicated that CENPE was a potential biomarker of KICH with cancer promoting activity.

RCC is essentially a metabolic disease characterized by a reprogramming of energetic metabolism [28,29,30,31]. In particular the metabolic flux through glycolysis is partitioned [32,33,34], and mitochondrial bioenergetics and OxPhox are impaired, as well as lipid metabolism [32, 35,36,37]. As a kind of lactate dehydrogenase enzyme, LDHA played a vital role in glucose metabolism and many signal pathways related to cancer development [38, 39], and it has been regarded as a biomarker for prognosis of many cancers [40,41,42]. Additionally, the development of tumors could be modulated through regulating glucose metabolism [43]. Although oxidative phosphorylation was the preferred energy production process, cancer cells including clear cell renal carcinoma (ccRCC) usually acquired energy from glycolysis [44], and further studies proved that LDHA could also promote tumor progression through glycolysis [45]. However, in direct contrast to ccRCC which is the most common subtype of RCC, KICH have an irregular metabolic program [46]. Previous studies identified that the increased oxidative phosphorylation was observed in KICH [47]. In this study, we found that LDHA expression downregulated in KICH samples compared to the normal samples, but it was high-expressed in most cancers. In addition, the K–M plot and multivariate cox regression analysis verified that high expression of LDHA also could independently predict bad prognosis in KICH samples, which suggested that LDHA was a potential biomarker of KICH. Moreover, the GSEA results showed that LDHA negatively regulated oxidative phosphorylation. It meant that the LDHA provided the energy to promote the KICH growth through glycolysis and aggravate the KICH prognosis through oxidative phosphorylation.

In addition, RCC is one of the most immune-infiltrated tumors [48, 49]. Emerging evidence suggests that the activation of specific metabolic pathway have a role in regulating angiogenesis and inflammatory signatures [50, 51]. Features of the tumor microenvironment heavily affect disease biology and may affect responses to systemic therapy [52,53,54,55]. Previous studies demonstrated that LDHA was involved in the development of cancer through multiple signal pathways, including AKT/ mammalian target of rapamycin (mTOR), the c-Jun NH (2)-terminal kinase (JNK) and RIG-I like receptor signal pathway [56,57,58]. Besides, we found LDHA also promoted cancer progression through being involved in the cell cycle and DNA replication. Furthermore, RIG-I like the receptor signaling pathway was related to the immune response [59], which suggested that LDHA may be also involved in the immune response. Our results of immune infiltration analysis demonstrated that LDHA was significantly and positively related to the B cell, CD8 + T cell, macrophage and DC. Thus, combined with the expression and prognosis value of LDHA in KICH, we inferred that the downregulation of LDHA might inhibit the anti-tumor immune response and promote the initiation of KICH. Besides, the GSEA enrichment analysis revealed that the CENPE played a positive role in the cell cycle, p53 signaling pathway and DNA replication, which cloud promote tumor cell proliferation [60,61,62]. It was worth noting that the function of highly expressed CENPE was also enriched in the T cell receptor signaling pathway. It had been reported that high expression of CENPE promoted immune cell infiltration, especially B cells [63]. Similarly, we also found that CENPE was significantly and positively related to CD8 + T cell and macrophage. However, it seems to be against the function of CENPE as a carcinogenic factor, because the CD8 + T cell and macrophage could inhibit the development of cancer [64, 65].

It had been reported that CNEPE and LDHA could be targeted by miRNA in some cancers [66, 67]. What’s more, there was a wide regulatory spectrum between mRNA and miRNA, because the mRNA could bind to several miRNAs and the miRNA could bind to hundreds of mRNAs [68]. According to the four prediction datasets and expression datasets, we found hsa-miR-577 was the common target of CENPE and LDHA. We speculated that hsa-miR-577 may target CENPE and LDHA and influence the progression of KICH. It had been reported that hsa-miR-577 was involved in the progression of renal cancer except for KICH [69, 70]. Our study demonstrated that hsa-miR-577 was upregulated in KICH and might promote KICH progression through targeting CENPE and LDHA. The detailed regulation of hsa-miR-577 in KICH needs further investigation.

Though this study provided 2 important biomarkers for KICH, several limitations should be acknowledged. For example, the factors contributed to the LDHA downregulated in tumor samples remains unclear, and the mechanism of CENPE as a carcinogenic factor positively regulating the immune cells was not clarified. These problems might be the direction of the future research.

Conclusions

This study identified 2 prognosis-related biomarkers in KICH, namely CENPE and LDHA. The CENPE was upregulated while LDHA was downregulated in tumor tissues. Enrichment analysis predicted that both CENPE and LDHA were involved in proliferation-related pathways such as cell cycle and DNA replication. The 2 genes were also associated with immune infiltrates, implying that they may participate in the immune response in KICH. The hsa-miR-577 was further found to be the common upstream marker of the 2 genes. Our study provided 2 important biomarkers for KICH and potential regulatory mechanism, and detailed function needs to be verified in vitro experiments.

Availability of data and materials

The dataset used and/or analyzed during the current study is available from the corresponding author on reasonable request.

References

  1. Roldan-Romero JM, Santos M, Lanillos J, Caleiras E, Anguera G, Maroto P, et al. Molecular characterization of chromophobe renal cell carcinoma reveals mTOR pathway alterations in patients with poor outcome. Mod Pathol. 2020;33(12):2580–90.

    Article  CAS  PubMed  Google Scholar 

  2. Alaghehbandan R, Trpkov K, Tretiakova M, Luis AS, Rogala JD, Hes O. Comprehensive review of numerical chromosomal aberrations in chromophobe renal cell carcinoma including its variant morphologies. Adv Anat Pathol. 2021;28(1):8–20.

    Article  CAS  PubMed  Google Scholar 

  3. Linehan WM, Ricketts CJ. The Cancer Genome Atlas of renal cell carcinoma: findings and clinical implications. Nat Rev Urol. 2019;16(9):539–52.

    Article  CAS  PubMed  Google Scholar 

  4. Paule B, Brion N. Temsirolimus in metastatic chromophobe renal cell carcinoma after interferon and sorafenib therapy. Anticancer Res. 2011;31(1):331–3.

    CAS  PubMed  Google Scholar 

  5. Agrawal R, Kaushal K. Attention and short-term memory in normal children, aggressive children, and non-aggressive children with attention-deficit disorder. J Gen Psychol. 1987;114(4):335–44.

    Article  CAS  PubMed  Google Scholar 

  6. Li X, Ma C. Alpha-2-Heremans-Schmid-glycoprotein (AHSG) a potential biomarker associated with prognosis of chromophobe renal cell carcinoma: the PROPOLIS study. Health Sci Rep. 2022;5(6):e878.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Klatte T, Han KR, Said JW, Bohm M, Allhoff EP, Kabbinavar FF, et al. Pathobiology and prognosis of chromophobe renal cell carcinoma. Urol Oncol. 2008;26(6):604–9.

    Article  PubMed  Google Scholar 

  8. Shan L, Zhao M, Lu Y, Ning H, Yang S, Song Y, et al. CENPE promotes lung adenocarcinoma proliferation and is directly regulated by FOXM1. Int J Oncol. 2019;55(1):257–66.

    CAS  PubMed  Google Scholar 

  9. Zhu X, Luo X, Feng G, Huang H, He Y, Ma W, et al. CENPE expression is associated with its DNA methylation status in esophageal adenocarcinoma and independently predicts unfavorable overall survival. PLoS ONE. 2019;14(2):e0207341.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Li Y, Shi Y, Wang X, Yu X, Wu C, Ding S. Silencing of CHFR sensitizes gastric carcinoma to PARP inhibitor treatment. Transl Oncol. 2020;13(1):113–21.

    Article  PubMed  Google Scholar 

  11. Ma C, Wang J, Zhou J, Liao K, Yang M, Li F, et al. CENPE promotes glioblastomas proliferation by directly binding to WEE1. Transl Cancer Res. 2020;9(2):717–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Li TF, Zeng HJ, Shan Z, Ye RY, Cheang TY, Zhang YJ, et al. Overexpression of kinesin superfamily members as prognostic biomarkers of breast cancer. Cancer Cell Int. 2020;20:123.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Wang Q, Xu J, Xiong Z, Xu T, Liu J, Liu Y, et al. CENPA promotes clear cell renal cell carcinoma progression and metastasis via Wnt/beta-catenin signaling pathway. J Transl Med. 2021;19(1):417.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Huo N, Cong R, Sun ZJ, Li WC, Zhu X, Xue CY, et al. STAT3/LINC00671 axis regulates papillary thyroid tumor growth and metastasis via LDHA-mediated glycolysis. Cell Death Dis. 2021;12(9):799.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Feng Y, Xiong Y, Qiao T, Li X, Jia L, Han Y. Lactate dehydrogenase A: a key player in carcinogenesis and potential target in cancer therapy. Cancer Med. 2018;7(12):6124–36.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Wang C, Li Y, Yan S, Wang H, Shao X, Xiao M, et al. Interactome analysis reveals that lncRNA HULC promotes aerobic glycolysis through LDHA and PKM2. Nat Commun. 2020;11(1):3162.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Shi L, An S, Liu Y, Liu J, Wang F. PCK1 regulates glycolysis and tumor progression in clear cell renal cell carcinoma through LDHA. Onco Targets Ther. 2020;13:2613–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Lei T, Qian H, Lei P, Hu Y. Ferroptosis-related gene signature associates with immunity and predicts prognosis accurately in patients with osteosarcoma. Cancer Sci. 2021;112(11):4785–98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zhang C, Zhang M, Ge S, Huang W, Lin X, Gao J, et al. Reduced m6A modification predicts malignant phenotypes and augmented Wnt/PI3K-Akt signaling in gastric cancer. Cancer Med. 2019;8(10):4766–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Dai X, Lu L, Deng S, Meng J, Wan C, Huang J, et al. USP7 targeting modulates anti-tumor immune response by reprogramming tumor-associated macrophages in lung cancer. Theranostics. 2020;10(20):9332–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Han J, Liu Y, Yang S, Wu X, Li H, Wang Q. MEK inhibitors for the treatment of non-small cell lung cancer. J Hematol Oncol. 2021;14(1):1.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Hao X, Qu T. Expression of CENPE and its prognostic role in non-small cell lung cancer. Open Med (Wars). 2019;14:497–502.

    Article  CAS  PubMed  Google Scholar 

  23. Scaife RM. G2 cell cycle arrest, down-regulation of cyclin B, and induction of mitotic catastrophe by the flavoprotein inhibitor diphenyleneiodonium. Mol Cancer Ther. 2004;3(10):1229–37.

    Article  CAS  PubMed  Google Scholar 

  24. Otto T, Sicinski P. Cell cycle proteins as promising targets in cancer therapy. Nat Rev Cancer. 2017;17(2):93–115.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Goncalves MD, Hopkins BD, Cantley LC. Phosphatidylinositol 3-kinase, growth disorders, and cancer. N Engl J Med. 2018;379(21):2052–62.

    Article  CAS  PubMed  Google Scholar 

  26. Shi M, Niu J, Niu X, Guo H, Bai Y, Shi J, et al. Lin28A/CENPE promoting the proliferation and chemoresistance of acute myeloid leukemia. Front Oncol. 2021;11: 763232.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Wang J, Dai W, Zhang M. E2F1 induced neuroblastoma cell migration and invasion via activation of CENPE/FOXM1 signaling pathway. Int J Neurosci. 2022;1–13.

  28. di Meo NA, Lasorsa F, Rutigliano M, Milella M, Ferro M, Battaglia M, et al. The dark side of lipid metabolism in prostate and renal carcinoma: novel insights into molecular diagnostic and biomarker discovery. Expert Rev Mol Diagn. 2023;23(4):297–313.

    Article  PubMed  Google Scholar 

  29. Lucarelli G, Loizzo D, Franzin R, Battaglia S, Ferro M, Cantiello F, et al. Metabolomic insights into pathophysiological mechanisms and biomarker discovery in clear cell renal cell carcinoma. Expert Rev Mol Diagn. 2019;19(5):397–407.

    Article  CAS  PubMed  Google Scholar 

  30. di Meo NA, Lasorsa F, Rutigliano M, Loizzo D, Ferro M, Stella A, et al. Renal cell carcinoma as a metabolic disease: an update on main pathways, potential biomarkers, and therapeutic targets. Int J Mol Sci. 2022;23(22):14360.

    Article  PubMed  PubMed Central  Google Scholar 

  31. De Marco S, Torsello B, Minutiello E, Morabito I, Grasselli C, Bombelli S, et al. The cross-talk between Abl2 tyrosine kinase and TGFbeta1 signalling modulates the invasion of clear cell renal cell carcinoma cells. FEBS Lett. 2023;597(8):1098–113.

    Article  PubMed  Google Scholar 

  32. Bianchi C, Meregalli C, Bombelli S, Di Stefano V, Salerno F, Torsello B, et al. The glucose and lipid metabolism reprogramming is grade-dependent in clear cell renal cell carcinoma primary cultures and is targetable to modulate cell viability and proliferation. Oncotarget. 2017;8(69):113502–15.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Ragone R, Sallustio F, Piccinonna S, Rutigliano M, Vanessa G, Palazzo S, et al. Renal cell carcinoma: a study through NMR-based metabolomics combined with transcriptomics. Diseases. 2016;4(1):7.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Lucarelli G, Galleggiante V, Rutigliano M, Sanguedolce F, Cagiano S, Bufo P, et al. Metabolomic profile of glycolysis and the pentose phosphate pathway identifies the central role of glucose-6-phosphate dehydrogenase in clear cell-renal cell carcinoma. Oncotarget. 2015;6(15):13371–86.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Lucarelli G, Rutigliano M, Sallustio F, Ribatti D, Giglio A, Lepore Signorile M, et al. Integrated multi-omics characterization reveals a distinctive metabolic signature and the role of NDUFA4L2 in promoting angiogenesis, chemoresistance, and mitochondrial dysfunction in clear cell renal cell carcinoma. Aging (Albany NY). 2018;10(12):3957–85.

    Article  CAS  PubMed  Google Scholar 

  36. Bombelli S, Torsello B, De Marco S, Lucarelli G, Cifola I, Grasselli C, et al. 36-kDa annexin A3 isoform negatively modulates lipid storage in clear cell renal cell carcinoma cells. Am J Pathol. 2020;190(11):2317–26.

    Article  CAS  PubMed  Google Scholar 

  37. Lucarelli G, Rutigliano M, Loizzo D, di Meo NA, Lasorsa F, Mastropasqua M, et al. MUC1 tissue expression and its soluble form CA15-3 identify a clear cell renal cell carcinoma with distinct metabolic profile and poor clinical outcome. Int J Mol Sci. 2022;23(22):13968.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Pathria G, Scott DA, Feng Y, Sang Lee J, Fujita Y, Zhang G, et al. Targeting the Warburg effect via LDHA inhibition engages ATF4 signaling for cancer cell survival. EMBO J. 2018;37(20):e99735.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Li G, Li Y, Wang DY. Overexpression of miR-329-3p sensitizes osteosarcoma cells to cisplatin through suppression of glucose metabolism by targeting LDHA. Cell Biol Int. 2021;45(4):766–74.

    Article  CAS  PubMed  Google Scholar 

  40. Yu C, Hou L, Cui H, Zhang L, Tan X, Leng X, et al. LDHA upregulation independently predicts poor survival in lung adenocarcinoma, but not in lung squamous cell carcinoma. Fut Oncol. 2018;14(24):2483–92.

    Article  CAS  Google Scholar 

  41. Ban EJ, Kim D, Kim JK, Kang SW, Lee J, Jeong JJ, et al. Lactate dehydrogenase A as a potential new biomarker for thyroid cancer. Endocrinol Metab (Seoul). 2021;36(1):96–105.

    Article  CAS  PubMed  Google Scholar 

  42. Huang X, Li X, Xie X, Ye F, Chen B, Song C, et al. High expressions of LDHA and AMPK as prognostic biomarkers for breast cancer. Breast. 2016;30:39–46.

    Article  PubMed  Google Scholar 

  43. Bose S, Le A. Glucose metabolism in cancer. Adv Exp Med Biol. 2018;1063:3–12.

    Article  CAS  PubMed  Google Scholar 

  44. Sancho P, Barneda D, Heeschen C. Hallmarks of cancer stem cell metabolism. Br J Cancer. 2016;114(12):1305–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Cai H, Li J, Zhang Y, Liao Y, Zhu Y, Wang C, et al. LDHA promotes oral squamous cell carcinoma progression through facilitating glycolysis and epithelial-mesenchymal transition. Front Oncol. 2019;9:1446.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Rathmell KW, Chen F, Creighton CJ. Genomics of chromophobe renal cell carcinoma: implications from a rare tumor for pan-cancer studies. Oncoscience. 2015;2(2):81–90.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Davis CF, Ricketts CJ, Wang M, Yang L, Cherniack AD, Shen H, et al. The somatic genomic landscape of chromophobe renal cell carcinoma. Cancer Cell. 2014;26(3):319–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Vuong L, Kotecha RR, Voss MH, Hakimi AA. Tumor microenvironment dynamics in clear-cell renal cell carcinoma. Cancer Discov. 2019;9(10):1349–57.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Tamma R, Rutigliano M, Lucarelli G, Annese T, Ruggieri S, Cascardi E, et al. Microvascular density, macrophages, and mast cells in human clear cell renal carcinoma with and without bevacizumab treatment. Urol Oncol. 2019;37(6):355e11-359.

    Article  Google Scholar 

  50. Netti GS, Lucarelli G, Spadaccino F, Castellano G, Gigante M, Divella C, et al. PTX3 modulates the immunoflogosis in tumor microenvironment and is a prognostic factor for patients with clear cell renal cell carcinoma. Aging (Albany NY). 2020;12(8):7585–602.

    Article  PubMed  Google Scholar 

  51. Lucarelli G, Rutigliano M, Ferro M, Giglio A, Intini A, Triggiano F, et al. Activation of the kynurenine pathway predicts poor outcome in patients with clear cell renal cell carcinoma. Urol Oncol. 2017;35(7):461e15-527.

    Article  Google Scholar 

  52. Lasorsa F, di Meo NA, Rutigliano M, Milella M, Ferro M, Pandolfo SD, et al. Immune checkpoint inhibitors in renal cell carcinoma: molecular basis and rationale for their use in clinical practice. Biomedicines. 2023;11(4):1071.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Ghini V, Laera L, Fantechi B, Monte FD, Benelli M, McCartney A, et al. Metabolomics to assess response to immune checkpoint inhibitors in patients with non-small-cell lung cancer. Cancers (Basel). 2020;12(12):3574.

    Article  CAS  PubMed  Google Scholar 

  54. Lucarelli G, Netti GS, Rutigliano M, Lasorsa F, Loizzo D, Milella M, et al. MUC1 expression affects the immunoflogosis in renal cell carcinoma microenvironment through complement system activation and immune infiltrate modulation. Int J Mol Sci. 2023;24(5):4814.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Lasorsa F, Rutigliano M, Milella M, Ferro M, Pandolfo SD, Crocetto F, et al. Cellular and molecular players in the tumor microenvironment of renal cell carcinoma. J Clin Med. 2023;12(12):3888.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Luan Y, Zhang W, Xie J, Mao J. CDKN2A inhibits cell proliferation and invasion in cervical cancer through LDHA-mediated AKT/mTOR pathway. Clin Transl Oncol. 2021;23(2):222–8.

    Article  CAS  PubMed  Google Scholar 

  57. Chauhan AS, Zhuang L, Gan B. Antagonism between antiviral signaling and glycolysis. Trends Endocrinol Metab. 2019;30(9):571–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Zhang XB, Shu WB, Li AB, Lan GH. The anion gap and mortality in critically ill patients with hip fractures. Contrast Media Mol Imaging. 2022;2022:1591507.

    PubMed  PubMed Central  Google Scholar 

  59. Loo YM, Gale M Jr. Immune signaling by RIG-I-like receptors. Immunity. 2011;34(5):680–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Wei GH, Wang X. lncRNA MEG3 inhibit proliferation and metastasis of gastric cancer via p53 signaling pathway. Eur Rev Med Pharmacol Sci. 2017;21(17):3850–6.

    PubMed  Google Scholar 

  61. Yao Y, Liu C, Wang B, Guan X, Fang L, Zhan F, et al. HOXB9 blocks cell cycle progression to inhibit pancreatic cancer cell proliferation through the DNMT1/RBL2/c-Myc axis. Cancer Lett. 2022;533:215595.

    Article  CAS  PubMed  Google Scholar 

  62. Prendergast L, McClurg UL, Hristova R, Berlinguer-Palmini R, Greener S, Veitch K, et al. Resolution of R-loops by INO80 promotes DNA replication and maintains cancer cell proliferation and viability. Nat Commun. 2020;11(1):4534.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Shi K, Zhu X, Wu J, Chen Y, Zhang J, Sun X. Centromere protein E as a novel biomarker and potential therapeutic target for retinoblastoma. Bioengineered. 2021;12(1):5950–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. St Paul M, Ohashi PS. The roles of CD8(+) T cell subsets in antitumor immunity. Trends Cell Biol. 2020;30(9):695–704.

    Article  CAS  PubMed  Google Scholar 

  65. Anderson NR, Minutolo NG, Gill S, Klichinsky M. Macrophage-based approaches for cancer immunotherapy. Cancer Res. 2021;81(5):1201–8.

    Article  CAS  PubMed  Google Scholar 

  66. Liang ML, Hsieh TH, Ng KH, Tsai YN, Tsai CF, Chao ME, et al. Downregulation of miR-137 and miR-6500-3p promotes cell proliferation in pediatric high-grade gliomas. Oncotarget. 2016;7(15):19723–37.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Shao X, Zheng X, Ma D, Liu Y, Liu G. Inhibition of lncRNA-NEAT1 sensitizes 5-Fu resistant cervical cancer cells through de-repressing the microRNA-34a/LDHA axis. Biosci Rep. 2021;41(7):BSR20200533.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Guo Z, Lu X, Yang F, Qin L, Yang N, Cai P, et al. The expression of miR-205 in prostate carcinoma and the relationship with prognosis in patients. Comput Math Methods Med. 2022;2022:1784791.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Niu L, Wang XF, Wang XR. Crocin suppresses cell proliferation and migration by regulating miR-577/NFIB in renal cell carcinoma. J Biol Regul Homeost Agents. 2020;34(4):1523–7.

    CAS  PubMed  Google Scholar 

  70. Dong X, Fu X, Yu M, Li Z. Long intergenic non-protein coding RNA 1094 promotes initiation and progression of glioblastoma by promoting microRNA-577-regulated stabilization of brain-derived neurotrophic factor. Cancer Manag Res. 2020;12:5619–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This work was supported by the following grants: the Medical Health Science and Technology Project of Zhejiang Provincial Health Commission [Grant No. 2021RC005]; Zhejiang Provincial Natural Science Foundation Public Welfare Projects of China [Grant No. LGF21H050005]; Zhejiang Provincial Natural Science Foundation Public Welfare Projects of China [Grant No. LTGC23H050001].

Author information

Authors and Affiliations

  1. Department of Urology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No. 88 Jiefang Road, Shangcheng District, Hangzhou, 310009, Zhejiang, China

    Hui-feng Wu, Hao Liu, Zhe-wei Zhang & Ji-min Chen

Authors
  1. Hui-feng Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhe-wei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ji-min Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

(I) Conception and design: HW, HL. (II) Collection and assembly of data: HW, ZZ. (III) Data analysis and interpretation: HL, JC. (IV) Manuscript writing: All authors. (V) Final approval of manuscript: All authors.

Corresponding author

Correspondence to Hao Liu.

Ethics declarations

Ethics approval and consent to participate

This study does not involve ethical approval, as the data in this study are from public databases.

Consent for publication

Not applicable.

Competing interests

The authors have no conflicts of interest to declare.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, Hf., Liu, H., Zhang, Zw. et al. CENPE and LDHA were potential prognostic biomarkers of chromophobe renal cell carcinoma. Eur J Med Res 28, 481 (2023). https://doi.org/10.1186/s40001-023-01449-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40001-023-01449-0

Keywords

European Journal of Medical Research

ISSN: 2047-783X

Contact us