










































Novel Expanding Renal Cell Carcinoma Biomarkers
Francesco Claps,1,2 M. Carmen Mir1

1 Department of Urology, Fundacion Instituto Valenciano Oncología, Valencia, Spain 2  Department of Urology, University of Trieste, Hospital of Cattinara, Trieste, Italy

Abstract

Identification of reliable molecular biomarkers that can complement clinical practice represents a fascinating 
challenge in any cancer field. Renal tumors are usually asymptomatic and incidentally identified during imaging 
studies undertaken for unrelated causes. However, in 25% to 30% of patients the first diagnosis is accompanied by 
symptoms and associated with distant metastasis. Thus, early diagnosis may reduce the risk of disease progression 
also avoiding side effects of inadequate treatments. Moreover, the ability to categorize patients' risk of recurrence after 
radical treatment, or even predict benefit from a target therapy, represents a compelling challenge. Here we review the 
current state-of-the-art on RCC biomarkers, particularly focusing on the new approaches of genomics, liquid biopsy, 
proteomics, and metabolomics.

Introduction

Renal cell carcinoma (RCC) is the third most common urological cancer in the United States, with an estimated 
44 120 new cases in 2019[1]. Clear-cell renal cell carcinoma (ccRCC) is the most frequent subtype, accounting for 
approximately 75% to 80% of these tumors, and is responsible for the majority of kidney cancer deaths[2]. In this 
narrative review we present the current state-of-the-art on diagnostic and prognostic RCC biomarkers, particularly 
focusing on the new approaches of genomics, liquid biopsy, proteomics, and metabolomics. A MEDLINE/PubMed 
search was performed using individual or/and different combinations of terms including “renal cell carcinoma,” 
“biomarker,” “diagnosis,” “prognosis,” and “survival.” Only papers with the title and abstract in the English language 
were screened for eligibility. The full text of included papers was analyzed.

Biomarkers in Early Detection and Diagnosis 
Recent advances in diagnostic techniques have increased early ccRCC detection. Mortality rates, however, remain 
steady[3]. Imaging studies are still unable to differentiate histology, and renal mass biopsy has a 10% to 20% non-
diagnostic rate[4]. Therefore, it is highly desirable to have novel and reliable biomarkers suitable for RCC screening 
and early detection, ensuring that the benefits of new technologies are fully realised (Table 1).

Circulating cell-free DNA
Liquid biopsy assays, such as circulating tumor cells (CTCs) or circulating cell-free DNA (cf DNA), constitute 
promising and less invasive techniques that can overcome the limits related to conventional diagnostic methods[5]. 
cfDNA consist mostly of double-stranded molecules that circulate as nucleoprotein complexes[6].

Hauser et al. evaluated cfDNA from patients with RCC and from healthy individuals using quantitative real-time 
polymerase chain reaction (PCR). Two primer sets amplifying a sequence of the actin-beta gene (ACTB) were used: 
ACTB-106 detects fragmented cf DNA that results from apoptosis, and ACTB-384 detects long DNA fragments 
released by necrosis. In this analysis, DNA fragments were significantly increased in RCC patients compared to 
healthy controls[7]. Lu et al. evaluated cfDNA extracted from plasma of healthy controls and 229 ccRCC patients at 
stages M0 and M1. The 306 base pairs fragment was lower in RCC patients than in controls. Since cfDNA fragment 
sizes are indicators of the integrity of cfDNA molecules, the authors showed that the ratio of longer to shorter cfDNA 

Key Words Competing Interests Article Information

Renal cell carcinoma, biomarker, diagnosis, 
prognosis, survival

None declared. Received on July 16, 2020 
Accepted on September 8, 2020

Soc Int Urol J. 2021;2(1):32–42

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fragments was significantly improved in patients staged 
as M0 compared with those as M1 subgroup[8]. On 
plasma cf DNA, Yamamoto et al. showed that median 
levels of cf DNA and median size of fragments from 
RCC patients were significantly greater than those from 
controls. An optimal cut-off value of 2876 copies/mL 
was identified[9].

Nuzzo et al. performed cell-free methylated DNA 
immunoprecipitation and high-throughput sequencing 
(cf MeDIP-seq). cf MeDIP-seq is an enrichment-
based method to comprehensively interrogate cf DNA 
methylation profile extracted from plasma and urine. 
The authors identified differentially methylated regions 
and selected the top rated between case and control 
samples. Samples from RCC patients were assigned 
a higher median methylation score than those from 
controls. Furthermore, the lowest methylation scores 
in RCC patients came from patients with small tumors. 
Thus the authors reported an accurate classification of 
patients across all stages of RCC in plasma cfDNA (AUC 
0.99) and demonstrated the validity of this assay using 
urine cfDNA (AUC 0.86)[10].

The abundance and relative fragmentation of cfDNA 
in blood can be a universal marker for RCC7 yet the 
precise cfDNA metrics that are most clinically relevant 
remain controversial. Study results reported to date are 
limited by heterogeneity with respect to clinical stage, 
tumor pathology, blood sample processing, and methods 
of cfDNA analysis.

Circulating tumor cells 
CTCs are cells that have been shed into the vasculature 
or lymphatics from a primary tumor. The detection 
and analysis of CTCs can assist in determining patient 
prognosis and personalized treatments, as well as initial 
diagnostic and monitoring procedures. Moreover, 
CTCs are particularly suited to interrogate functional 
heterogeneity by combining genetic and transcriptomic 
assessment of single CTC[11] or by transcriptome and 
epigenome analysis[12].

It is difficult to assess the diagnostic value of CTCs 
in RCC due to the use of different methods of CTC 
collection and identification across studies[13]. The 
different techniques include epithelial or non-epithelial 
marker-dependent isolation, reverse transcription PCR-
based methods, and morphological- and cell size-based 
methods[14]. Moreover, RCC cancer cells are inclined to 
the loss of their epithelial antigens through epithelial-
to-mesenchymal transition, in which morphological 
transformation leads to acquisition of mesenchymal 
features[15]. Other surface markers have been developed 
to select RCC cancer cells in the blood (eg, CAIX). 
Adding this new set of cell surface markers including 
CAIX and CD147 to the conventional detection of 
CTCs through epithelial markers, such as the epithelial 
cell adhesion molecule (EpCAM), has shown better 
results[16].

The role of microRNAs
MicroRNAs (miRs) are implicated in the regulation of 
processes such as proliferation, migration, invasion, 
and apoptosis, and are readily detectable in tissues and 
bodily fluids[17].

Wulfken et al. reported that the level of miR-1233 
was significantly increased in patients with RCC 
compared with healthy controls. Thus, miR-1233 levels 
were investigated in an independent cohort confirming 
a higher mean value in RCC patients[18]. Zhao et al. 
found that miR-210 levels were higher in primary RCC 
tissues than in normal tissue. Furthermore, the serum 
level of miR-210 was significantly decreased in patients 
7 days after nephrectomy; consequently, a potential 
combined role in early detection and monitoring after 
radical treatment could be proposed[19]. Iwamoto et 
al. confirmed at the serum level that the expression of 
miR-210 was significantly higher in RCC patients than 
in healthy controls[20]. In addition, a meta-analysis 
conducted by Chen et al. that included 7 studies, 570 
RCC patients, and 415 healthy controls showed pooled 
sensitivity, specificity, and diagnostic OR to predict RCC 
of 74%, 76%, and 8.81, respectively[21].

Chen et al. evaluated the expression levels of miR-
129-3p and miR-129-5p in 69 cases of paired renal 
tumors, healthy tissues, and conventional RCC cell lines. 
MiR-129-3p and miR-129-5p are 2 mature products of 
miR-129-2 known for its anti-tumor effects in various 
malignancies. They showed that miR-129-3p, but not 
miR-129-5p, was widely attenuated in human ccRCC, 
and chRCC, yielding a 73.5% accuracy in discriminating 
ccRCC from normal tissues. The relative miR-129-3p 
expression significantly differed between malignant and 
benign kidney tumors[22].

In a prospective cohort, Yadav et al. found that use 
of serum miR-34a, miR-141, and miR-1233 was able 

Abbreviations 
ACTB actin-beta gene
ccRCC clear-cell renal cell carcinoma
CSS cancer-specific survival
CTCs circulating tumor cells
cfDNA circulating cell-free DNA
miRs microRNAs
OS overall survival
PCR polymerase chain reaction
PFS progression-free survival
RCC renal cell carcinoma

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to diagnose ccRCC with a sensitivity of 80.76%, 75%, 
and 93.33%, and specificity of 80%, 73.33%, and 100%, 
respectively, when tumor pathologic was used as the 
reference. Moreover, a combined approach using a panel 
of 2 serum miRs (miR-141 and miR-1233), allowed a 
diagnosis of ccRCC with 100% sensitivity and 73.3% 
specificity[23].

Recently, Zhang et al. investigated whether miRNAs 
in serum exosomes can serve as biomarkers in ccRCC. 
Their findings showed that the expression levels of 
exosomal miR-210 and miR-1233 were significantly 
higher in RCC patients than in healthy individuals (both 
P < 0.01). ROC analysis demonstrated that exosomal 
expression levels distinguished RCC patients from 
healthy individuals with 70% sensitivity and 62.2% 
specificity for miR-210, and 81% sensitivity and 76% 
specificity for miR-1233[24].

Metabolites as novel biomarkers of RCC
Metabolomic approaches have shown promising 
results in oncology, with the recognition of metabolic 
reprogramming as a hallmark of cancer. Globally, RCC 
metabolic signature of the tumor microenvironment is 
characterized by alterations in metabolites associated 
with energy metabolism, especially those involved 
in glycolysis, amino acid metabolism, and fatty acid 
catabolism pathways, which are essential for cell growth 
and proliferation[25].

Kim et al. first evaluated the utility of urine meta-
bolomics analysis for metabolomic profiling. The authors 
identified a total of 212 molecules able to differentiate 
RCC presence. The rate of correct classification was 
88%[26]. Ganti et al. showed differential urinary 
concentrations of several acylcarnitines as a surrogate of 
RCC status and grade, with most acylcarnitines being 
increased in RCC patients’ urine. Furthermore, urinary 
acylcarnitines were increased in a grade-dependent 
fashion in RCC patients and likely emanated from the 
tumor tissues. Acylcarnitines have both cytotoxicity 
and immune modulatory properties and thus may play 
a role in decreasing the inf lammatory response and 
providing a mechanism by which these cells are able 
to evade immune surveillance[27]. In the same field, 
Niziol et al. showed that hydroxybutyrylcarnitine, 
decanoylcarnitine, propanoylcarnitine, carnitine, 
dodecanoylcarnitine, and norepinephrine sulfate were 
found in much higher concentrations in both RCC 
tissues (compared with the paired normal tissue) and 
urine of cancer patients (compared with urine of control 
subjects)[28].

Proteomics analysis
Proteomics offers a useful platform to study the complex 
molecular events of tumorigenesis. Upregulation in 
the glycolytic f lux is a common pathway in cancer. 

Therefore, using isobaric tags for relative and absolute 
quantitation (iTRAQ) White et al. identified 55 proteins 
significantly dysregulated in RCC. Dysregulation 
of alpha-enolase (ENO1), L-lactate dehydrogenase 
A chain (LDHA), heatshock protein beta-1, known 
as Hsp27, mitochondrial (HSPE1) was confirmed in 
2 independent sets of patients by western blot and 
immu nohistochemist r y (IHC). The ex pressions 
of AHNAK, ENO1, and Hsp27 were found to be 
significantly elevated in ccRCC compared with matched 
normal tissues. Whereas HSPE1 was significantly 
downregulated in RCC patients[29]. Zhang et al. recently 
found 16 significantly upregulated and 14 significantly 
downregulated in early-stage RCC compared with 
healthy controls. Serum heat shock cognate 71 (HSC71) 
was highly elevated in the RCC group compared 
with control group[30]. Kim et al. showed that RCC 
upregulated proteins were nicotinamide-N-methyl-
transferase (NNMT), secretagogin (SCGN), L-plastin, 
human neuron specific enolase (hNSE), nonmetastatic 
cell 1 (NM23A), ferritin light chain (FTL), and 
thioredoxin peroxidase (KIM2010). NNMT was the 
most commonly upregulated protein over all types of 
RCC, especially in comparison with normal tissues. 
SCGN was elevated in ccRCC samples but not in 
papillary, chromophobe, or normal tissue, while NM23A 
showed the same behavior, although the magnitude of 
changes was smaller than in the first 2 molecules[31].

Prognostic Biomarkers
Although most biomarkers for early detection and 
diagnosis remain at an early stage, more advances have 
been made with prognostic biomarkers for RCC. To 
date, few biomarkers have been taken beyond single 
studies, thus none are yet ready for routine clinical 
practice. Furthermore, emerging and promising 
approaches can serve as new platform in which novel 
potential biomarkers can be found. Following any type 
of surgical treatment of RCC, there is a need for risk 
stratification aiming to enable personalized outcome 
prediction. The major endpoints evaluated and predicted 
using prognostic biomarkers across the studies referred 
to in the following sections of this paper are disease/
progression/recurrence-free survival (D/P/RFS), overall 
and cancer-specific survival (OS, CSS), and correlations 
with clinicopathological features that might influence 
the prognosis among these patients[32] (Table 2). 

cfDNA
One of the most promising uses of liquid biopsy is to 
determine the risk of recurrence after curative treatment. 
Wan et al. measured plasma levels of cfDNA before and 
after surgery for localized disease. Mean preoperative 
level of plasma cf DNA in patients who developed 
recurrent disease was significantly higher than in those 

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with localized disease or controls[33]. Analyzing the 
genomic and mitochondrial cf DNA concentrations, 
Lu et a l. developed 2 models that incorporated 
clinicopathological features to specific expression 

patterns among cf DNA fragments. Particularly, APP 
gene, the Alu sequences, and the mitochondrial DNA 
fragments showing significant correlation in terms of 
OS and RFS[8]. In terms of quantitative measurement, 

TABLE 1. 

Novel potential candidates biomarkers in diagnosis and early detection of renal cell carcinoma

Biomarker Source Trend Correlation/Use Reference

cfDNA
plasma
serum increased 

RCC and mRCC detection, association 
with histotype, monitoring after 

curative surgery

Hauser et al. (2010)[7] 
de Martino et al. (2012)[51] 

Lu et al. (2016)[8] 
Yamamoto et al. (2018)[9] 

Nuzzo et al. (2020)[10]

CTCs blood increased  RCC detection and monitoring

Allard et al. (2004)[52] 
Li et al. (2005)[53] 
Liu et al. (2016)[16] 

Broncy et al. (2018)[54]

miRNA

miR-1233 serum increased  RCC detection
Wulfken et al. (2011)[18] 

Zhang et al (2018)[24] 
Yadav et al. (2017)[23]

miR-451 serum decreased  RCC detection Redova et al. (2012)[55]

miR-378 serum increased  RCC detection Redova et al. (2012)[55]

miR-21 tissue increased 
RCC detection, differential diagnosis 

between ccRCC, pRCC and chRCC  
and oncocytoma

Faragalla et al. (2012)[56]

miR-15a
tissue
urine

both 
increased 

RCC detection, differential diagnosis 
between malignant and benign renal 

tumors

von Brandestain et al. 
(2012)[57]

miR-210
tissue 
urine 
serum

increased 
increased 
increased 

RCC detection and disease monitoring 
after local treatment

Zhao et al. (2013)[19] 
Iwamoto et al. (2014)[20] 

Zhang et al. (2018)[24] 
Chen et al. (2018)[21]

miR-129-3p tissue decreased 
RCC detection, differential diagnosis 
between malignant and benign renal 

tumors
Chen et al. (2014)[22]

miR-34a serum decreased  RCC detection Yadav et al. (2017)[23]

miR-141 serum decreased  RCC detection Yadav et al. (2017)[23]

Abbreviations: cfDNA, cell-free DNA; ccRCC, clear-cell renal cell carcinoma; chRCC, chromophobe renal cell carcinoma; CTCs, circulating tumor cells; 
ENO1, alpha-enolase; FINC, fibronectin-1; HSC71, heat shock cognate 71; HSPE1, heat shock protein family E member 1; miRNA, microRNA; mRCC, 
metastatic renal cell carcinoma; NNMT, nicotinamide N-methyltransferase; pRCC, papillary renal cell carcinoma; RCC, renal cell carcinoma; S100A8, 
S100 calcium-binding protein A8; S100A9, S100 calcium-binding protein A9; Tu M2-PK, tumor M2-PK.

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Yamamoto et al. divided their cohort into 2 subgroups 
according to the length of cf DNA fragments. Their 
resu lts showed t hat cf DNA f ragment size was 
significantly associated with progression-free survival 
(PFS). Although cf DNA fragmentation correlated 
with poorer outcomes, cf DNA plasma levels were not 
associated with any of survival outcomes[9]. Evaluating 
plasma circulating tumor DNA (ctDNA) as a subset of 
cfDNA, Bacon et al. reported that only 33% of patients 
had detectable ctDNA. Among ctDNA-positive patients 
the most commonly mutated genes were VHL, BAP1, 
and PBRM1. Moreover, ctDNA-positive patients had 
shorter OS and PFS on first-line therapy[34].

CTCs identification
An initial experience using a RT-PCR assay to detect 
CTCs in peripheral blood of patients at different stages 
of RCC reported a different rate of positivity on localized 
and metastatic RCC (mRCC)[35]. Developing a new set 

of cell surface markers including CAIX and CD147, Liu 
et al. showed a significant association of CTC number/
CTC expression status of vimentin, with disease 
progression[16]. Wang et al. investigated the relationship 
of dynamic changes of CTCs and Beclin-1 expression 
of CTCs and RCC prognosis. CTCs were divided into 
epithelial, mesenchymal, and mixed phenotype-based 
surface biomarkers. For the metastatic group, the 
number of mixed CTCs at 12 months was significantly 
higher than mixed preoperatively and 6 months CTCs. 
Of note, the number of preoperative Beclin-1 positive 
CTCs was significantly higher in the metastatic group. 
Thus, variation trend of CTCs and Beclin-1 expressive 
CTCs was significantly associated with the onset of 
metastatic disease[36]. Moreover, in a prospective cohort 
of 60 patients who underwent surgical treatment with 
curative intent, Haga et al. evaluated CTCs drawn 
from a peripheral artery collected just before and 
immediately after surgery. The authors showed that open 

TABLE 1. 

Novel potential candidates biomarkers in diagnosis and early detection of renal cell carcinoma

Biomarker Source Trend Correlation/Use Reference

Metabolomics and Proteomics

Acetylcarnitines
urine 
tissue increased 

RCC detection, grade-dependent 
behavior

Ganti et al. (2012)[27] 
Niziol et al. (2018)[28]

Tu M2-PK plasma increased  RCC and mRCC detection
Roigas et al. (2001)[58]

Weinberger et al. (2007)[59]

AHNAK tissue increased  RCC detection White et al. (2014)[29] 

ENO1 tissue increased  RCC detection White et al. (2014)[29]

HSPE1 tissue decreased  RCC detection White et al. (2014)[29]

NNMT tissue increased  RCC detection Kim et al. (2010)[31]

HSC71 serum increased  RCC detection Zhang Y et al. (2015)[30]

S100A8 serum increased  RCC detection
Zhang L et al. (2015)[60]
Zhang L et al. (2016)[61]

S100A9 serum increased  RCC detection
Zhang L et al. (2015)[60]
Zhang L et al. (2016)[61]

FINC plasma increased  RCC detection Yokomizo et al. (2011)[62]

Abbreviations: cfDNA, cell-free DNA; ccRCC, clear-cell renal cell carcinoma; chRCC, chromophobe renal cell carcinoma; CTCs, circulating tumor cells; 
ENO1, alpha-enolase; FINC, fibronectin-1; HSC71, heat shock cognate 71; HSPE1, heat shock protein family E member 1; miRNA, microRNA; mRCC, 
metastatic renal cell carcinoma; NNMT, nicotinamide N-methyltransferase; pRCC, papillary renal cell carcinoma; RCC, renal cell carcinoma; S100A8, 
S100 calcium-binding protein A8; S100A9, S100 calcium-binding protein A9; Tu M2-PK, tumor M2-PK.

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TABLE 2. 

Novel potential candidate biomarkers in prognosis of renal cell carcinoma

Biomarker Source Outcomes correlated Reference

CTCs peripheral blood RFS, OS
Bluemke et al. (2009) [63] 

Liu et al. (2016) [16] 
Wang et al. (2019) [36]

Beclin-1-positive CTCs peripheral blood RFS Wang et al. (2019) [36]

cfDNA plasma RFS, OS

de Martino et al. (2011) [51] 
Wan et al. (2013) [33] 

Lu et al. (2016) [8] 
Yamamoto et al. (2018) [9] 

Bacon et al. (2020) [34]

miRNA

miR-378 serum DFS, clinical stage Fedorko et al. (2015) [64]

miR-221 serum
OS, CSM, 

lymphovascular invasion
Teixeira et al. (2014) [65] 
Vergo et al. (2014) [66]

miR-150 serum DSS, clinical stage Chanudet et al. (2017) [67]

miR-451 serum clinical stage Redova et al. (2012) [68]

miR-21 tumor tissue
CSS, OS, DFS, clinical 

stage, tumor grade, tumor 
size

Faragalla et al. (2012) [56]
Tang et al. (2015) [38]

Vergho et al. (2014) [69]
Vergho et al. (2014) [66]

miR-126 tumor tissue DFS, CSS, OS
Vergho et al. (2014) [69]
Vergho et al. (2014) [66]
Khella et al. (2015) [70]

miR-106b tumor tissue PFS Slaby et al. (2010) [71]

miR-27a-3p tumor tissue PFS Nakata et al. (2015) [72]

miR-210 tumor tissue CSS Tang et al. (2015) [38]

miR-141 tumor tissue CSS Tang et al. (2015) [38]

miR-200c tumor tissue CSS Tang et al. (2015) [38]

miR-429 tumor tissue CSS Tang et al. (2015) [38]

miR-486 tumor tissue CSM, clinical stage Goto et al. (2013) [73]

miR-23b tumor tissue OS Ishihara et al. (2014)[74]

Abbreviations: cfDNA, cell-free DNA; CSM, cancer-specific mortality; CSS, cancer-specific survival; CTCs, circulating tumor cells; DFS, disease-free 
survival; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; OS, overall survival; PFS, progression-free survival; PKM2, pyruvate kinase-muscle-2; 
RCC, renal cell carcinoma; RFS, recurrence-free survival; S100A8, S100 calcium-binding protein A8; TK1, thymidine kinase 1; Tu M2-PK, tumor M2-P

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nephrectomy resulted in a significantly greater number 
of postoperative CTCs. At multivariate level that the 
surgical approach was significantly correlated with 
the number of postoperative CTCs (P = 0.016) and the 
perioperative change in CTCs (P = 0.01). Thus, especially 
after open surgery more cancer cells can be expelled into 
the bloodstream, suggesting a careful follow-up for these 
patients[37].

miRNA
In a comprehensive meta-analysis of 29 published 
studies reporting miRNA signatures in RCC, Tang 
et al. identified a robust meta-signature of miRNAs 
as a prognostic biomarker. They reported that high 
expression of miR-21, miR-210, and low expression 
of miR-141, miR-200c, and miR-429 were associated 
with worse CSS following RCC resection[38]. Similarly, 

TABLE 2. 

Novel potential candidate biomarkers in prognosis of renal cell carcinoma

Biomarker Source Outcomes correlated Reference

miR-27b tumor tissue OS Ishihara et al. (2014)[74]

lnc-ZNF180-2 tumor tissue
PFS, CSS, OS, 
clinical stage

Ellinger et al. (2015)[40]

lnc-NBAT-1 tumor tissue OS Xue et al. (2015) [75]

Metabolomics

Creatine tumor tissue
Advanced Tumor Stages 

(T3-4)
Gato et al. (2012) [42]

Glutamate tumor tissue
Advanced Tumor Stages 

(T3-4)
Gato et al. (2012) [42]

Glutamine tumor tissue
Advanced Tumor Stages 

(T3-4)
Gato et al. (2012) [42]

GAPDH tumor tissue High Grade Wettersten et al. (2015) [43]

Enolase-2 tumor tissue High Grade Wettersten et al. (2015) [43]

PKM2 tumor tissue High Grade Wettersten et al. (2015) [43]

L-Lactate tumor tissue High Grade Wettersten et al. (2015) [43]

Glutathione tumor tissue Advanced Stages Hakimi et al. (2016) [44]

TuM2-PK serum RFS
Nisman et al. (2010) [76] 
Gayed et al. (2015) [77]

TK1 serum RFS Nisman et al. (2010) [76]

cathepsin D urine OS Vasudev et al. (2009) [78]

S100A8 tissue DFS, tumor grade, stage An et al. (2019) [79]

Abbreviations: cfDNA, cell-free DNA; CSM, cancer-specific mortality; CSS, cancer-specific survival; CTCs, circulating tumor cells; DFS, disease-free 
survival; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; OS, overall survival; PFS, progression-free survival; PKM2, pyruvate kinase-muscle-2; 
RCC, renal cell carcinoma; RFS, recurrence-free survival; S100A8, S100 calcium-binding protein A8; TK1, thymidine kinase 1; Tu M2-PK, tumor M2-P

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Gu et al. conducted a meta-analysis of 27 published 
studies and found that elevated expression of miR-21, 
miR-1260b, miR-210, miR-100, miR-125b, miR-221, miR-
630, and miR-497 was associated with a poor prognosis 
in RCC patients. Conversely, decreased expression of 
miR-106b, miR-99a, miR-1826, miR-215, miR-217, miR-
187, miR-129–3p, miR-23b, miR-27b, and miR-126 was 
associated with a worse prognosis. Importantly, the 
results from this meta-analysis confirmed that elevated 
miR-21 expression was associated with shorter OS, CSS, 
and DFS. The decreased expression of miR-126 was 
associated with shorter CSS, OS, and DFS[39].

Also, results were promising in a study by Ellinger 
et al. regarding specific circulating long non-coding 
(lnc) RNAs, defined as RNA transcripts longer than 
200 nucleotides that are not transcribed into a protein. 
The authors next validated the expression profile of 6 
lncRNAs transcripts (lnc-ACO1625, lnc-CYP4A22-2/3, 
lnc-PEAK1.1-1, lnc-PCYOX1L , lnc-VCAN-1, lnc-
ZNF180-2) with potential prognostic interest. A 
significant increase of lnc-ZNF180-2 expression in 
advanced RCC tissue compared with localized RCC 
was observed. Furthermore, lnc-ZNF180-2 expression 
levels were an independent predictor of PFS, CSS, and 
OS[40]. Qu et al. built a model named RCClnc4 based on 
4 lncRNAs to improve postoperative risk stratification 
after radical treatment. Stratifying patients into high-
risk versus low-risk groups in terms of clinical outcomes, 
RCClnc4 remained as an independent prognostic 
factor, achieving a higher accuracy than clinical staging 
systems like TNM and SSIGN score[41].

Prognostic value of metabolomic 
approaches 
Analyzing tumors and their matched tissue, Gao et 
al. studied the metabolomic RCC profile. Creatine, 
glutamate, and glutamine were found at higher 
concentrations in tissues of tumors at T3-4 stages[42]. 
The glycolysis-relevant metabolites are significantly 
increased in high-grade disease, suggesting that glucose 
metabolism is more prominent with increasing tumor 
grade. Consequently, glyceraldehyde 3-phosphate 
dehydrogenase, enolase 2, and py ruvate k inase-
muscle-2 are increased in tumor tissue as compared 
with normal tissues. L-lactate follows the same tendency 
in a grade-dependent manner. Also levels of carnitine 
and acyl/acetyl-carnitines were associated with grade, 
suggesting how the combination of these metabolites 
can predict the biological aggressiveness of RCC and 
thus influence its prognosis[43]. A study by Hakemi et al. 
showed increased levels of glutathione were also grade- 
and stage-dependent[44]. Thus, the upregulation of 
antioxidant capacity in adaptation to intrinsic oxidative 
stress is indeed a common event in RCC, especially in 
the advanced stages[45].

Epigenetic and DNA methylation 
biomarkers 
Epigenetic variations play an important role in renal 
carcinogenesis and progression. DNA methylation 
is defined as a covalent addition of a methyl group to 
cytosines that precede a guanosine which are mainly 
clustered as CpG islands in the promoter region of genes 
bringing a functional silencing[46]. Furthermore, DNA 
methylation alterations are often shown to be associated 
with clinicopathological features and RCC patient 
survival or both[47]. CpG island methylation markers 
ref lect tumor biology, allowing the identification of 
patients with “high epigenetic risk” who can benefit 
from tailored management to improve sur v iva l 
outcomes.

In a recent systematic review, Joosten et al. described 
9 genes (SFRP1, BNC1, GREM1, RASSF1A, PCDH8, 
SCUBE3, GATA5, LAD1, and NEFH), associated with patient 
survival. Their prognostic value was indepen dently 
validated in other studies[48]. To develop a 5-CpG-
based assay for ccRCC prognosis, a panel composed 
by methylation of PITX1, FOXE3, TWF2, RIN1, and 
EHBP1L, was validated in 3 independent sets from 
China, the United States, and the Cancer Genome Atlas 
(TCGA) database. Stratifying patients into 2 groups 
from this 5-CpG panel, Wei et al. defined low- and high-
risk categories. An important correlation between the 
high-risk group and poorer OS[49] was demonstrated. 
With the same endpoint, Chen et al. identified 7 specific 
prognosis-subgroups based on the DNA methylation 
spectrum of RCC from the TCGA database. The specific 
DNA methylation patterns ref lected differentially 
in the clinical index, including TNM classification, 
pathological grade, clinical stage, and age. In addition, 
437 CpGs corresponding to 477 genes of 151 samples 
were identified as specific hyper/hypomethylation sites 
for each specific subgroup. The authors then constructed 
a Bayesian classifier to determine the function of the 
prognosis prediction model, with 437 specific CpG sites 
as characters (AUC 0.95)[50].

Conclusions

Ca ncer biomarkers have shif ted treat ment a nd 
management of patients with many cancer types. 
A lt hough “persona lized ” medicine is becoming 
more common in our daily practice, none of the RCC 
biomarkers discussed are in routine clinical use. 
Metabolomics and proteomics studies have shown 
excellent potential in terms of diagnostic accuracy, but 
research in these areas still appears to be hypothesis-
generating. Most of publications mentioned above 
aimed to understand tumor biology due to the high 
heterogenicity of RCC.

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Circulating biomarkers have attracted a lot of interest; 
however, the great diversity of techniques precludes any 
further conclusions. The growing use of liquid biopsy, 
popularized by the easy accessibility of samples, and the 
accompanying standardization of methods of analysis 
and quantification of CTCs, cfDNA and miRNAs will 
continue to provide promising results. Particularly, 
NGS cfDNA is a novel technology that can complement 

tumor tissue biopsy. It has demonstrated its potential 
role across the diagnostic and prognostic fields of 
both localized and metastatic RCC. Single molecule 
validations are being replaced by multipanel biomarkers 
to provide improved validation results. It also reflects the 
role of molecular biology in current clinical nomograms 
as a transition tool from bench to bedside.

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