










































Promising Biomarkers in Renal Cell Carcinoma
Jada Kapoor,1 Francesco Claps,2,3 M. Carmen Mir,2 Joseph Ischia,1,4

1 Department of Urology, Austin Health, Melbourne, Australia, 2 Department of Urology, Fundacion Instituto Valenciano Oncologia, Valencia, Spain,  
3 Department of Urology, University of Trieste, Hospital of Cattinara, Trieste, Italy, 4 Department of Surgery, University of Melbourne, Australia

Abstract

Renal cell carcinoma (RCC) incidence has been increasing in recent years, and it now represents the sixth most 
common cancer diagnosis in men and the tenth in women. Although this is partly due to increased detection of 
incidental small renal masses on unrelated imaging, advanced RCC continues to be diagnosed in a significant portion 
of patients, with more than 15% presenting with distant metastases. Biomarkers can be a cost-effective tool to identify 
high-risk patients and institute appropriate individualised therapies. While the literature in this field is nascent, this 
paper focuses on several biomarkers that have been extensively investigated in the diagnosis and prognosis of RCC, as 
well as in predicting its response to treatments, particularly the newer immuno-oncology drugs.

Introduction

Renal cell carcinoma (RCC) is a heterogeneous disease with a relatively unpredictable natural history. There are few 
reliable markers to distinguish between indolent and aggressive lesions at the time of diagnosis, predict relapse, and 
guide treatment decisions in the management of RCC.

Biomarkers have long been anticipated to deliver on the promise of precision medicine, and thus lead to better 
patient care and lower health care costs[1]. They are defined as “objectively measurable indicators of normal biological 
processes, pathogenic processes or pharmacologic responses to therapeutic intervention”[2]. Diagnostic biomarkers 
can allow for an early detection and classification of cancer. Prognostic biomarkers can inform clinicians about 
the natural course of an individual cancer and guide their decision of whom to treat, and how aggressively to treat. 
Predictive biomarkers assess the probability of a patient benefiting from a particular treatment.

Despite significant advances in our knowledge of RCC at a molecular level, there are no validated biomarkers 
for this disease. An array of serum, urine, and tissue-based biomarkers have been described, but each has its own 
practical limitations. Profiling complex fluids, such as serum and urine, requires awareness of the effect of other 
circulating proteases and nucleases on marker signals, as well as pre-fractionation strategies, given the vast difference 
in orders of magnitude in protein concentrations[3]. On the other hand, tumour heterogeneity may limit the utility of 
tissue-based markers[4].

This paper summarises the current status of the most widely studied molecular and genetic biomarkers in RCC. It 
is only a broad overview, and detailed description of individual markers should be sought in the referenced literature. 
While the recent advances in proteomics and metabolomics are likely to provide a more nuanced understanding of 
this disease in the future, their discussion is outside the scope of this paper.

Key Words Competing Interests Article Information

Biomarkers, kidney cancer, diagnosis, 
prognosis

None declared. Received on June 13, 2020 
Accepted on December 6, 2020

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Diagnostic Biomarkers

Improved characterisation of small renal masses is 
required to avoid surgical intervention in those with 
benign or indolent lesions and treat those with high 
metastatic potential in a timely manner. Given the high 
level of discordance in pathological subtyping as seen in 
non-clear cell RCC cases[5,6] or biopsy specimens[7], a 
diagnostic biomarker would be particularly useful.

Carbonic anhydrase IX (CAIX)
CAIX is a downstream effector of HIF-1a and is thought 
to play a role in regulating intracellular and extracellular 
pH in tumour cells. It is highly expressed in 95% of clear 
cell RCC (ccRCC), compared with minimal expression 
in oncocytomas, chromophobe, and papillary RCC[8–10]. 
CAIX is also expressed in other tumours, including 
carcinomas of lung, breast, uterus, oesophagus, and 
brain, as well as in normal gastric mucosa[11]. While this 
somewhat limits the use of CAIX as a diagnostic marker 
for metastatic disease, there is ongoing enthusiasm for 
its utility in characterisation of the small renal mass. 
Several clinical trials have demonstrated the possibility 
of improving the performance of positron emission 
tomography/ computed tomography (PET/CT) by using 
radio-labelled girentuximab, a chimeric monoclonal 
antibody against CAIX. REDECT, a phase III open-label 
multi-centre trial, assessed the diagnostic accuracy of 
124I-girentuximab PET/CT in 195 patients undergoing 
nephrectomy and reported a sensitivity of 86.2% and 
specificity of 85.9% in non-invasively identifying ccRCC. 
Sensitivity was higher in tumours >2cm, and the overall 
positive predictive value was 94.4%, obviating the need 
for an invasive biopsy in these cases[12].

Gene expression profiling
Gene expression arrays have been created to differentiate 
between RCC subtypes and identify the aggressiveness 
and metastatic potential of tumours. Multiple studies 
have correlated the genetic expression profile of different 
types and grades of RCC with their morphological 
classification[13–15]. Analysis of these signatures from 
early stage ccRCC have also informed us of additional 
pathways in tumourigenesis, including the down-
regulation of transcription factors required for normal 
renal development, such as GATA3, TFCP2L1, TFAP2B, 

and DMRT2. Other studies have identified a panel of up 
to 34 genes that is predictive of tumour aggression, and 
may function as a biomarker in the future[16].

Urinary biomarkers
Urine is an easily accessed source for biomarkers. 
Profiling studies have identified 2 promising proteins 
originating from the proximal tubule, aquaporin 1 
and adipophilin, that may be shed in urine and have 
diagnostic potential. Initial results indicate that both 
proteins are significantly elevated in urine from patients 
with RCC compared with healthy controls, declining 
to control levels following nephrectomy[17,18]. Nuclear 
matrix protein 22 (NMP22), an accepted urothelial 
cancer marker, was found to also be significantly 
elevated in urine samples from patients with RCC in a 
few studies conducted more than 15 years ago; however, 
there have been no further reports since[19–21]. 
Other markers, eg, kidney injury molecule-1 (KIM-1), 
neutrophil gelatinise-associated lipocalin (NGAL), 
and matrix metalloproteinases (MMPs) have been 
inadequate in differentiating renal malignancy[22,23].

Tissue biomarkers
There is a wide panel of antibodies that are currently used 
for diagnostic purposes, including CK7, CD10, Pax 2, 
Pax 8, vimentin, and alpha-methylacyl-CoA (AMACR)
[24]. Because of the particular difficulty in differentiating 
between benign and malignant eosinophilic tumours, a 
number of additional biomarkers have been studied to 
better characterise chromophobe RCC and oncocytoma 
such as Hale’s colloidal iron, several cadherins, and 
BCA2[25]. Additional analysis of distinct chromosomal 
aberrations, such as TFE3 and TFEB, is now established 
for translocation-associated RCCs.

Composite biomarkers
The optimal future biomarker will likely be a panel of 
biomarkers utilising the strengths of those mentioned 
above. One such biomarker is the composite 3-marker 
panel of nicotinamide N-methyltransferase (NNMT), 
L-plastin (LCP1), and non-metastatic cell 1 protein 
(NM23A) that was evaluated in a cohort study of 189 
patients, and further validated in 100 patients. Plasma 
levels of NNMT, LCP1, and NM23A were significantly 
elevated in patients with kidney cancer. This composite 
assay had a positive predictive value of 87.2%, and a 
negative predictive value of 97% for diagnosis of renal 
cell carcinoma[26]. A recent validation of this assay 
was conducted in 9 centres with 1042 individuals, 
resulting in similar findings: the diagnostic sensitivity 
and specificity were 0.871 and 0.894, respectively, and 
the resulting area under curve of receiver operating 
characteristic was 0.917[27].

Abbreviations 
CAIX carbonic anhydrase IX
CRP C-reactive protein
RCC renal cell carcinoma
VEGF vascular endothelial growth factor

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Prognostic Biomarkers 

Accurate prognostic markers are the cornerstone of 
cancer management, and are indispensable in patient 
counselling, determining need for adjuvant therapies, 
and developing appropriate surveillance strategies. 
Currently, at least 8 prognostic nomograms are 
frequently utilised for predicting the risk of relapse and 
survival in RCC[28–32]. However, recent prospective 
validation of these models, using data from the ASSURE 
trial, showed a more substantial decrease in each of 
their prognostic abilities than previously reported in 
retrospective external validation studies[33]. Most 
models only marginally outperformed standard staging, 
and all had a C-index below 0.7[34]. The search for more 
precise prognostic markers therefore continues.

Routine blood markers
Serum LDH, calcium, and haemoglobin have been 
widely reported to have independent prognostic 
significance in metastatic disease and are included in 
several nomograms[35,36].

Several studies have reported that C-reactive protein 
(CRP) is a strong predictor of metastasis and overall 
mortality after nephrectomy for localised RCC[37–41]. 
Immunohistochemical studies have also demonstrated 
significant intratumoural production of CRP which can 
be correlated with survival outcomes[42,43]. Interestingly, 
after adjusting for tumour staining, preoperative serum 
CRP was not a significant predictor of overall survival 
(OS) (P = 0.741) in one of these studies[43].

Thrombocytosis, another marker of the inflammatory 
milieu, is also an adverse prognostic factor in many 
cancers, including RCC [44–46]. However, in a predictive 
model comprising TNM stage, age, Fuhrman grade, 
histological subtype, and preoperative haemoglobin, 
thrombocytosis did not add any meaningful value, with 
a predictive accuracy gain of 0.3% only[47].

I nc re a sed per ipher a l blood or i nt r at u mou r a l 
neutrophils is also associated with poor survival[48–52]. 
Furthermore, a number of studies have demonstrated 
that a higher blood neutrophil/lymphocyte ratio (NLR) 
portends a poorer prognosis[53–55]. NLR is emerging 
as a prognostic factor in several other cancers, and is 
thought to represent an impaired cell-mediated immune 
response due to systemic inflammation[56].

Changes in coagulation pathways are also well-
recognised in malignancy. Cohort studies have reported 
significantly higher concentrations of plasma fibrinogen 
and D-dimer in patients with metastatic disease, and 
identified independent association with reduced cancer-
specific survival (CSS) and OS[57–59].

The VHL, HIF, and VEGF axis
Mutation of the VHL gene has been associated with 
a longer progression-free survival (PFS) and CSS in 
ccRCC in some studies[60,61]; however, this was not 
reproduced in other analyses[62–64]. Similarly, analyses 
of elevated HIF-1a levels and survival have varied, with 
some studies demonstrating favourable prognosis, and 
others associating it with worse outcomes[65,66].

Increased vascular endothelial growth factor (VEGF) 
concentration has consistently been associated with 
worse tumour stage and grade, necrosis, microvessel 
invasion, and CSS[67,68]. However, given that VEGF 
is contained within platelets and released on clotting, 
falsely elevated results due to contamination of plasma 
with platelets or coagulation due to delays in processing 
the sample can occur, compromising its clinical 
applicability[69].

CAIX is one of the HIF target genes, and is associated 
with tumour growth, aggressive phenotype, and poor 
prognosis in most cancers[70–72]. In contrast, high 
CAIX expression is associated with a better prognosis in 
RCC in several studies[73–76]. In a larger study, however, 
CAIX expression was not an independent prognostic 
factor, after adjusting for the effect of nuclear grade, 
sarcomatoid differentiation, and tumour necrosis[11]. 
These findings were further validated at the 5-year 
follow-up of this study[77].

Immunologic markers
The B7 family of immune regulatory ligands produce 
co-stimulatory or co-inhibitory T-cell signals, and 
therefore have been identified as promising prognostic 
biomarkers. B7-H1 functions as a negative regulator 
of immunity, and its over-expression is independently 
associated with significantly increased progression 
to metastatic disease (RR 3.46; P < 0.001) and cancer-
specific mortality (RR 3.92; P < 0.001)[78, 79]. B7-H4 
and, less strongly, B7-H3 have also been implicated 
as adverse prognostic factors[80, 81]. Non-invasive 
immunoassays for the soluble forms of the B7 family are 
being developed with promising early results[82].

Given the immunogenic nature of RCC, pathologic 
specimens harbour a high number of tumour-
infiltrating lymphocy tes (TILs). Their prognostic 
significance is not established because of inconsistent 
findings on various multivariate analyses to date[83–85].

Markers of cell proliferation and apoptosis
Various nuclear proteins that regulate cell growth, 
proliferation, and apoptosis are established as prognostic 
markers in other cancers. Some of these are also very 
promising in RCC, as summarised in Table 1[86–99].

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The cell cycle progression (CCP) score is an RNA 
expression assay that measures the activity of 31 genes 
involved in cellular proliferation, which has been widely 
validated for use in prostate cancer. Most recently, 
its prognostic utility has also been demonstrated in 
predicting recurrence and mortality in a cohort of 
565 RCC patients undergoing nephrectomy[100]. In 
another study from the same authors, a higher CCP 
score on renal mass biopsy was correlated with adverse 
pathology on surgery, suggesting its clinical value in 
risk-stratifying patients being considered for active 
surveillance of small renal masses[101].

Utility of biomarkers in prognostic models
Incorporation of molecular markers into existing 
prognostic models, as well as combining markers to 
create molecular signatures of the disease, will certainly 
be of greater utility than any single marker. A prognostic 
model using p53, CAIX, gelsolin, and vimentin, 
combined with metastatic status, T-stage and ECOG 
(Eastern Cooperative Oncology Group) performance 
status was 79% accurate in a cohort of 318 patients[102]. 
In another study of 634 patients, the integration of 
BioScore, which is based on expression of Ki-67, survivin 
and B7-H1, with the UISS and SSIGN models improved 
the prognostic accuracy of the models by 4.5% and 1.6% 
respectively. Furthermore, patients with high BioScores 

were noted to be 5 times more likely to die from RCC 
(HR 5.03; P <0.001)[103].

Lastly, the prognostic value of multi-gene assays, such as 
ClearCode-34 and 16-gene signature, has been reported 
to be greater than the established predictive models, and 
has now been validated in at least one prospective cohort. 
There are certainly caveats around tumour heterogeneity 
and misclassification due to sample bias; however, the 
results so far have been encouraging[104–107].

Predictive Biomarkers
The therapeutic landscape in metastatic RCC has 
transformed in the past decade with the introduction of 
targeted and immuno-oncology treatments. Identifying 
markers that can reliably predict the response to specific 
treatments is essential to optimise patient management. 
This section focuses on predictive markers for these 
contemporary treatments.

Immune checkpoint inhibitors
Immunohistochemical expression of PD-L1 is the 
most studied marker in this realm. Studies to date 
have not established its independent predictive value. 
In all prospective trials, PD-L1 expression has been 
associated with worse prognosis, but not with response 
to checkpoint inhibitors[108–110]. Biological and 

TABLE 1. 

Markers of cell proliferation and apoptosis as prognostic biomarkers

Prognostic 
biomarker

Role Supporting literature Refence

Insulin-like growth 
factor II mRNA-
binding protein 3 
(IMP3)

Oncofoetal RNA-
binding protein; cell 
proliferation and 
invasion

Expression associated with significantly worse outcomes, >1400 patients 
total in 3 major studies
• 5-year OS 27% vs 82%, P < 0.0001
• OS HR 1.42, P = 0.024; risk of distant metastases HR 4.71, P < 0.001
• Risk of metastases specifically in chromophobe or papillary RCC - HR 

13.45, P < 0.001

86 

87
88
89

Ki-67 Cell proliferation 
marker

Poor prognosis in many cancers, including RCC
HR 2.18 and 2.50 for CSS in 741 tumours with and without necrosis 
respectively
Expression correlates with increasing stage and grade
Only independent predictor of RCC recurrence, in a study of several markers 
(CAIX, CRP, HIF) in 216 patients; HR 3.73

90 
91 
92
93
94

Survivin Member of the 
inhibitor of apoptosis 
protein (IAP) family

Overexpressed in almost all human cancers, including RCC
Worse CSS, HR 2.4, P < 0.001; and PFS, HR 1.9, P 0.02
Confirmed in several retrospective studies

95–98

p53 Induces apoptosis 
when DNA damaged

Over-expression is noted in 70% of papillary, 27% of chromophobe and 12% 
of clear cell tumours. Prognostic significance not yet established.

99

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logistical challenges in using PD-L1 as a biomarker 
have been well described, including intratumoural 
heterogeneity, discordant expression in primary and 
secondary sites, dynamic expression, and variation in 
immunohistochemical assays[111].

Biomarker analysis from trial JAVELIN-101 was recently 
published, and included a 26-gene expression signature 
and mutations and polymorphisms based on whole 
exome sequencing, in addition to PD-L1 expression[112]. 
PD-L1 expression was not predictive of response to 
avelumab. As in previous studies, tumour mutational 
burden did not demonstrate any significant predictive 
value[113]. Several genetic mutations and the 26-gene 
signature were implicated in predicting treatment 
response; however, these findings need to be further 
validated.

Likewise, the phase II IMmotion-150 trial demonstrated 
the utility of gene expression signatures, ref lecting 
angiogenesis and effector T-cell response, in predicting 
response to atezolizumab. A high angiogenic signature 
was associated with improved response rate and PFS in 
patients treated with sunitinib, and patients with high 
effector T-cell signature had better responses to ICB. 
These findings were subsequently confirmed by the 
phase III IMmotion-151 trial[114].

Other predictive markers under investigation include 
tumour-infiltrating lymphocytes, mutation signatures 
and microsatellite instability, HLA classification, TGF-b 
expression, PD-L2, CTLA-4, mutational or neoantigen 
burden, and commensal gut microbiome[115,116].

VEGF-related therapies
VHL mutation status failed to show any predictive 
value in various studies[117,118]. However, downstream 
effectors of angiogenesis have shown some promise. 
High IL-6 concentration is associated with improved 

PFS benefit from pazopanib compared with placebo, 
as well as improved OS benefit from bevacizumab plus 
IFN-a compared with IFN-a alone[119,120]. The results 
for baseline levels of VEGF-A, VEGF receptor 2 and 3, 
HIF-1a and 2a and CAIX have been variable and incon-
sistent. Similar limitations were seen in analysis of other 
markers such as osteopontin, MMP, tissue inhibitor of 
metalloproteinase I (TIMP-I), TNF-related apoptosis-
inducing ligand (TRAIL), and NLR[121,122].

The role of PD-L1 status was evaluated in 2 recent trials 
comparing the efficacy of cabozantinib to everolimus 
(METEOR), and sunitinib (CABOSUN). Although PD-
L1 expression was associated with shorter PFS and OS in 
both studies, it was not predictive for response to either 
treatment[123,124].

Limitations
A major barrier to translating research findings to 
clinically applicable tools has been a lack of stand-
ardisation in study methodologies and small sample 
sizes lacking statistical power to demonstrate any 
meaningful correlation. A shift to collaborative efforts of 
large research networks involving industry and scientific 
experts in prospective trials, instead of the traditional 
model of small laboratory-based retrospective studies, 
is hoped to yield higher quality data and provide us 
with an exciting and unprecedented opportunity for the 
discovery and large-scale validation of reliable, precise 
and cost-effective RCC biomarkers.

Conclusion
Volumes of literature have been published on numerous 
promising diagnostic, prognostic and predictive RCC 
biomarkers. None of these have yet been established 
for routine clinical use in management of this hetero-
geneous disease.

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