1MicroRNA-34a: a key regulator in the hallmarks of renal cell carcinoma [622890]

1MicroRNA-34a: a key regulator in the hallmarks of renal cell carcinoma
Eman A . Toraih1*, Afaf T. Ibrahiem2, Manal S . Fawzy3*, Mohammad H . Hussein4, Saeed
Awad M. Al -Qahtani5, Aly A. M. Shaalan6,7
1 Department of Histology and Cell Biology (Genetics Unit) , Faculty of Medicine, Suez
Canal University, Ismailia, Egypt , P.O. 41522.
2 Department of Pathology, Faculty of Medicine, Mansoura University, Mansoura, Egypt
3 Department of Medical Biochemistry, Faculty of Medicine, Suez Canal University,
Ismailia, Egypt, P.O. 41522.
4 Ministry of Health, Cairo, Egypt
5 Department of Physiology, Faculty of Medicine, Jazan University, Jazan, Saudi Arabia
6 Department of Histology and Cell Biology , Faculty of Medicine, Suez Canal University,
Ismailia, Egypt
7 Department of Anatomy and Histology, Faculty of Medicine, Jazan University, Jazan,
Saudi Arabia
*Correspondence to:
Manal S. Fawzy, e-mail: [anonimizat]
Eman A. Toraih, e-mail : [anonimizat]

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Abstract
Renal cell carcinoma (RCC) incidence has increased over the past two decades with
unpredictable long -term morbidity and mortality. Recent studies reported microRNAs as
promising biomarkers for early cancer detection , accurate prognosis and molecula r targets
for future treatment. This study aimed to evaluate the expression levels of miR -34a and 1 1
of its bioinformatically -selected target genes and proteins to test their potential
dysregulation in RCC. Quantitative real -time PCR for miR -34a and its ta rgets; MET
oncogene, genes regulating apoptosis ( TP53INP2, DFFA ), cell proliferation ( E2F3 ), and cell
differentiation ( SOX2 , TGFB3 ) as well as immunohistochemical assay for VEGFA, TP53,
Bcl2, TGFB1 and Ki67 protein expression have been performed in 85 FFPE RCC tumor
specimens . Clinicopathological parameters correlation and in silico network analysis , have
also implicated . We found RCC tissues displayed significantly higher miR -34a expression
level than their corresponding non-cancer ous tissues, particularl y in chromophobic
subtype. MET and E2F3 were significantly up -regulated, while TP53INP2 and SOX2 were
down -regulated. ROC analysis showed high diagnostic performance of miR -34a
(AUC=0.854), MET (AUC=0.765), and E2F3 (AUC=0.761). The a dvanced pathological
grade was associated with strong expression of TGFB1, VEGFA, and Ki67 protein s and
absent Tp53 staining. These findings indicate m iR-34a along with its putative target genes
could play a role in RCC tumorigenesis and progression .
Keywords: MicroRNA -34a; RC C; MET ; E2F3 ; TP53INP2 ; SOX2 ; DFFA ; TGFB1;
VEGFA; Ki67

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1. Introduction
Renal cell cancer (RCC) accounts for approximately 3% of human malignancies and its
incidence appears to be increas ing globally [1]. RCC is not a single disease ; although it is
derived from cells of the renal tubular epithelium, it has several histolo gical subtypes
which differ in their clinical outcome and biological features . It is classified into c lear cell
RCC accounting for (75 %) of cases, papillary RCC (10 -15%), chromophobe RCC (5%),
collecting duct RCC (<1%), and unclassified subtype [2]. For the refinement of RCC
therapeutic strategies, a better realization of the RCC underlying molecular mechanisms will
be mandatory [3].
Over the past few years, emerging numerous bioinf ormat ics tools have been developed to
identify candidate disease -causing genes [4], including microRNAs (miRNAs) genes . This
class of non -coding RNAs are small, single -stranded , 19–25 nucleotide long that act as
negative regulators involved in post-transcriptio nal silencing of the gene expression [5]. An
aberrant miRNA expression could contribute to cancer development and progression [6, 7],
and could affect t heir target genes that involved in many biological processes, such as cell
differentiation, proliferatio n, apoptosis, metabolism and development [8]. Recently, the
potential therapeutic use of miRNAs has been evaluated due to their dynamic and reversible
properties . This may include oncomirs (oncogenic miRNAs) inhibition , or tumor -suppressor –
miRNAs replaceme nt therapies [6, 9].
MicroRNA -34a (miR -34a) that is located on chromosome 1p36; belongs to one of
evolutionar y conserved miRNA families (miR -34 family) that consists of three members:
miR-34a, miR -34b, and miR -34c [10]. MiR-34a has its own transcript and i s expressed at
higher levels than miR -34b/c in most tissues, and this expression could be dysregulated in
multiple diseases, especially in cancers [11]. It is involved in p53 pathways and is implicated
in cell death/survival signaling , the cell cycle and d ifferentiation, thereby playing a regulatory

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role in carcinogenesis [12]. Previous studies have reported that several key molecules were
identified as targets of miR -34a, including Bcl-2 (B-cell lymphoma 2) [13], TGF B
(transforming growth factor -beta) [14], the t ranscription inducer of cell -cycle progression
E2F3a [15], MET oncogene [16,17] , and vascular en dothelial growth factor ( VEGF ) [18]. In
addition, our bioinformatic analysis that has been discussed in details in the methods section
of the current work, has revealed other miRNA -34a target genes that could be involved in
cancer -related biology , including genes for apoptosis [TP53INP (tumor protei n p53 inducible
nuclear protein ), Tp53 , DFFA (DNA fragmentation factor subunit alpha )], cell proliferation
(Ki67), and cell differentiation SOX2 (Sex Determining Region Y -Box 2) . As miR -34a has
many different targets in regulating different kinds of human cancer, Yu et al. [18] suggested
the role of miR -34a is possibly tumor -specific and highly dependent on its t argets in different
cancer cells.
Whether miR -34a or any one of its selected aforementioned 11 putative target genes or
proteins could be related to RCC pathogenesis and/or progression in our population, still lack
of solid evidence. Therefore, we aimed to investigate the expression level of miR -34a and a
panel of selected putative targets in an attempt to better understand the molecular
mechanisms that underlie the tumorigenesis and progression of RCC. This could represent
potential future therapeutic targ ets in renal cell carcinoma.
2. Materials and Methods
2.1. Study population
Eighty five a rchived formalin -fixed paraffin embedded (FFPE) renal samples that have been
taken from patients who underwent radica l nephrectomy for a primary RCC and dating back
for 3 years, were collected from Pathology laboratory of Mansoura Oncology Center,
Mansoura and Pathology laboratory of the Suez Canal University Hospital, Ismailia, Egyp t.
None of the patients received any neoadjuvant chemotherapy or radiotherapy. Complet e

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clinicopathological data, including (patients’ age, sex, tumor ’s site and size) were obtained
from patient medical records . Sections of cancer –free tissues adjacent to the tumor were cut,
examined, and collected to serve as controls during the genetic p rofiling . Samples that were
not h omogeneous, histologically well -characterized primary renal cancer , nor had cancer -free
adjacent tissue s determined by an experienced pathologist have been excluded. The study
was conducted in accordance with the guidelines in the Declaration of Helsinki and approved
by the Medical Research Ethics Committee of Faculty of Medicine, Suez Canal University.
Written informed consent was obtained from all participants before providing the archived
tissue samples as part of their r outine register in our University Teaching Hospitals.
2.2. Bioinformatic s selection of miRNA -34a and the study molecular targets
Predicted and experimentally validated miRNAs that significantly targeting renal cell
carcinoma KEGG pathway (hsa05211) were id entified by DIANA -mirPath v3.0 web server
via Reverse Search module and TarBase v7.0 pipeline [19]. The most top and highly
significant miRNA involved in this pathway was hsa -miR-34a-5p (p = 1.275767e -88) with 2 8
target genes , including MET oncogene and th ree angiogenesis -related genes ( VEGFA ,
TGFB1 , TGFB3 ). Assessment of miR -34a regulatory roles in cancer biology was performed
by DIANA -mirPath v3.0 online software , Fig. 1 .
The list of all experimentally validated target genes for miR -34a-5p was retrieved from
miRTarBase v20 ( http://mirtarbase.mbc.nctu.edu.tw/ ) [20] and DIANA – TarBase v7.0
(http://diana.imis.athena -innovation.gr/) [21]. A panel of other targets involved in cancer –
related biology was chosen. I t included genes for apoptosis ( Tp53, TP53INP2, DFFA ), anti-
apoptosis ( BCL2), cell proliferation ( E2F3 and Ki67 ), and cell differentiation ( SOX2 ), Fig. 2 .
Structural analysis of MIR-34A gene and transcripts were retrieved from Ensembl.org. Gene
expression of miR -34a across normal human tissues was obtained from BioGPS.org and
Expression Atlas. Complementary base pairing of miR -34a-5p seed region with the selected

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mRNA targets were confirmed by both microRNA.org resource [22] and miRTarBase v20.
Prior public ations demonstrating functional experimental validation of miRNA -target
interactions by different methods (as Luciferase reporter assay, Western blot, Microarray,
qRT-PCR, Immunocytochemistry) are listed in Supplementary Table S1. A functional
interaction network of selected target proteins was implemented using STRING v10 program
(http://string -db.org), inferring protein -protein associations from co -expression data [23].
2.3. MicroRNA -34a and gene expression analysis
Total RNA ; including the small RNAs, was isolated from FFPE tissue sections (5 to 8 -μm-
thick ) using the Qiagen miRNeasy FFPE Kit (Qiagen, 217504) following the protocol
supplied by the manufacturer . Briefly, after the removal of paraffin by xylene and washing
the sample with ethanol several times, p roteins were degraded by incubation with
proteinase K s olution at 45°C for a few hours and later incubation with DNAses for DNA
digestion . To tal RNA quantity and quality were measured by Nanodrop ND -1000
(NanoDrop Technologies, Wilmington, DE). Samples with a 260/280 nm absorbance ratio
less th an 1.8 were discarded and new sections of the corresponding tissue block were cut and
purified, if possible . Subsequent reverse transcription (RT) and amplification of cDNA by
real-time PCR using StepOne™ Real -Time PCR System (Applied Biosystems) were done
as described in details previously [8, 24]. RNUB6 small RNA and GAPDH have been run
for normalization of miRNA -34a and target gene mRNAs expression analysis,
respectively. All the PCR react ions were carried out in accordance with the Minimum
Information for Publication of Quantitative Real -Time PCR Experiments guidelines [25].
Ten percent randomly selected study samples were re-evaluated in separate runs for the
study gene expression s to tes t the reproducibility of the qPCR which showed very close Cq
values results and low standard deviations.

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2.4. Histopathological examination
Sections of 4 μm thickness have been cut from FFPE blocks of RCC tissues for routine H&E
examination and other sections were prepared on charged slides for immunohistochemistry.
Examination of three tumor slides from each specimen was done with an Olympus CX31
light mic roscope. Photoe s were obtained by a PC -driven digital camera (Olympus E -620).
Cases were reviewed to determine the histological type according to the International Society
of Urological Pathology (ISUP ) Vancouver Modification of WHO (2004) Histologic
Class ification of Kidney Tumors [26]. Nuclear grade is assessed according to Fuhrman et al.
[27]. Tumors were staged according to the International Union against Cancer [28].
2.5. Immunohistochemistry examination and analysis
Immunohistochemical analysis for p53 protein, Bcl2 protein, Ki67, TGFb and VEG F with a
labelled streptavidin -biotin -peroxidase complex technique wa s performed on tumor sections.
A commercially availab le monoclonal antibody against p 53 (mou se monoclonal p 53
antibody, isotype IgG2a, Genem ed, dil 1:50), Bcl2 (mouse monoclonal antibody, Ig G, CELL
MARQUE, 226M -98, prediluted), Ki -67 (clone MIB -1, N1633, Dako Corporation,
Carpinteria, CA, USA, RTU, prediluted), TGFb (mouse monoclonal antibody Ig G1, Dako
Corporation, Carpinteria, CA, USA, R TU, dil 1:50) and VEGF (mous e monoclonal antibody,
Ig G, Gene Tex, GTX102643, dil 1:300). A high sensitive kit has been used as d etection kit
(DakoCytomation envision and dual link system peroxidase code K4061) using DAB as a
chromogene. Antigen retrieval required pretreatment with 1 mM ED TA (at pH 8.0) for 20
minutes (p 53, Bcl2, and VEGF) and 60 minutes (Ki67, and TGFb) in microwave oven.
Proper positive and nega tive controls were performed. As a positive control; b reast
carcinoma has been run for p 53, tonsils for Ki67, lymph node for Bcl2 and cells of
proximal and distal convoluted tubu les of nearby tumor free kidney for TGFb . In addition,

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placental tissue was stained for VEGF as a positive control for VEGF antibody. As a negative
control, sections we re stained without the addition of a primary antibody .
For the im munohistochemistry assessment , examination of all prepared slides from each
specimen were done with an Olympus CX31 light microscope. Photos were obtained from a
PC-driven digital camera (Oly mpus E -620) and analyzed by (Cell*, Olympus Soft Imaging
Solution GmbH). Slides were scanned by x40 magnification. Ten cellular areas were selected
(i.e. the so -called hot spots) and evaluated at x 400 magnification . Positive p53 protein
staining was defin ed as nuclear staining, cytoplasmic staining was considered nonspecific
and ignored. The percentage of tumor cell nuclei with positive staining was evaluated in
relation to t he total number of neoplastic nuclei in at least 10 fields observed at magnificat ion
x 400. Scoring of immunostained was categorized as m entioned in previous literature
follows: 3+ = high level (91 -100% of positive cells), 2+ = medium level (11 -90% of positive
cells), 1+ = low level (up to 10% of positive cells), – = negative cells (0 % of positive cells)
[29].
Ki-67 antigen labeling was localized to the nucleus with a fine, strong and homogenous
brown granularity. Staining was considered positive if any nuclear staining was seen Ki67
labeling index was done by calculating the ratio of positive nuclei in relation to the total
number of neoplastic nuclei in 10 HPFs. Ki67 was considered to be abnormal when >10%
tissue positivity was observed. The labeling index (number of positive tumor cells/total
number of tumor cells expressed as a perc entage) w as calculated in every specimen . The
Ki67 proliferation index was considered low if 0 -30% of tumor cells were positive, Moderate
PI if 31 -69% were positive and high if ≥ 70% were positive. Unequivocal nuclear rea ctivity
was considered positive [30, 31] .
The BCL2 positivity was determine d by cytoplasmic staining (brown) of neoplastic cells
which are deep colored. The percentage of positive cells at the whole section after exclusion

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of the areas of reactive T cells was determined. It was scored negative if 5% or less of
neoplastic cells we re stained. The value of BCL2 was considered weak positive if 6 % to less
than 50% were brown stained and strong positive if ≥ 50% of tumor cells were brown stained
[32].
TGF B immunohistochemistry specimens were classified based on the intensity of staining as
follows: weak or absent staining (< 10% of cells), intermediate (10 –25%), focally strong (25 –
50%), and strong (> 50% of cells) [33]. VEGF s ections were considered positive for VEGF if
the membranes or cytoplasm of more than 10% of tumor cells were stained [34].
2.6. Statistical analysis
Data were managed using the R packag e (version 3.3.2 ). Categorical variables were
compared using the Chi-square (2) or Fisher's exact tests where appropriate, while Mann –
Whitney U (MW) and Kruskal -Wallis (KW) tests were used to compare continuous
variables. The correlation between miR -34a level and mRNAs and protein expression was
calculated by Spearman’s rank correlation analysis . A two -tailed p-value of < 0.05 was
considered statistically significant. The receiver operating characteristic (ROC ) curves were
performed to get the best cutoff values of either the miRNA -34a or mRNAs for
discriminating RCC from non -cancer tissues . The fold change of miRNA and mRNA
expression s in each patient cancer tissue relative to the corresponding cancer -free tissue was
calculated via Livak method based on the quantitative cycle (Cq) values with the following
equation: relative quantity = 2−ΔΔCq; where ΔΔ Cq = (Cq miRNA – Cq NBU6) RCC − (Cq
miRNA – Cq NBU6) NAT in case of miRNA -34a analysis , where ΔΔCq = (Cq mRNA – Cq
GAPDH) RCC − (Cq mRNA – Cq GAPDH) NAT in case of study gene target analysis [35].

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3. Results
3.1. Baseline characteristics of the study population
In the current study, 85 patients (32 females and 53 males) were enrolled in the study.
Their age ranged from 20 to 79 years old with mean ± SD of 5 2.23 ± 11.12. Renal c ancer
samples were compared to normal tissues. T here was no significant difference in age and
gender between FFPE tumor samples and normal renal tissues ( p = 0.087 and 0.214,
respectively). Clinico -pathological characteristics of renal cell carcinomas patients are
demonstrated in Table 1. According to the 2004 WHO classification, several histological
RCC subtypes were recognized in the study population. The most frequent histological
subtypes included clear cell renal cell carcinomas (c cRCC), papillary renal cell carcinomas
(pRCC), and chromophobe renal cell carcinomas (crRCC). Most cancer specimens were
moderately or poorly differentiated, nevertheless, low proportions of tumors had high
tumor size (T3), positive lymph node involvement, capsular and pelvic infiltration, and
vascular invasion.
3.2. Gene and protein expression analysis
Using qRT-PCR technology and immunohistochemistry , gene and protein expression
analysis were used to identify differential molecular changes between tumor and normal
renal tissues. Gene expression profiling revealed a significant over-expression of miR-34a
in almost all RCC patients (91.7%) with an overall median and quartile values of 7.97
(2.37 -29.54). In addition, among the 6 genes that have been predicte d to be targeted by
miR-34a via the in silico computational tools, two genes were significantly up-regulated
(MET and E2F3 ) in 87.1% of FFPE samples, while two other s were down -regulated
(TP53INP2 and SOX2 ) in almost all RCC patients compared to non -cancer tissues, Fig. 3.
However, c orrelation analysis revealed no significant relationship of miR -34a with the
tested target genes , Supplementary Table S 2 and Fig. S1.

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Immunohistochemistry of renal tissue samples demonstrated variable staining patterns,
Fig. 4. Ki-67 expression, a cell proliferation marker, was detected in all cases of RCC , but
with variable level of expression. Low level of expression (< 10%) was detected in 28
cases (70 %), while high expression (≥ 10%) was noted in 12 cases (30 %). Similarly, the
angiogenesis -mediated protein (VEGFA) and the anti-apoptotic marker (Bcl2) were
expressed in all cancer tissues. Eighty percent of patients had high expression of VEGFA,
while only 20% demonstrated weak staining. Anti -human Bcl2 antibody was widely
distributed all over the renal cancer tissues. Strong expression was noted in 75% of
samples. For TGFB1 protein, most of cancer tissue attained moderate to strong expression
in the nucleus, but the protein was less intense in fifth of patients and absent in t hree
samples only. In contrast, expression of the tumor suppressor protein (Tp53) was not
detected by immunohistochemistry in less than half of tumor specimens. Unlike tumor
cells , which had nuclear staining, lining cells of the proximal tubules stained th e
cytoplasm only.
ROC curve analysis of all genes and proteins showed significant high diagnostic
performance of miR -34a (AUC = 0.854), MET (AUC = 0.765), and E2F3 (AUC = 0.761)
in differentiating between cancer specimens and non -cancer tissues, Table 2.
3.3. Association of gene and protein signature with clinico -pathological features
The expression of miR -34a was markedly higher in RCC samples with chromophobic
renal cell carcinoma and lower in clear cell type ( p = 0.039), Fig. 5A. In addition, its level
was inversely correlated with the tumor pathological grade (r = -0.301, p = 0.037). Among
the target genes, lower levels of three genes E2F3 , SOX2 , and DFFA were significantly
associated with capsular, pelvic and vascular invasion, respectively, Fig. 5B-D. These
findings were consistent with Spearman's correlation analysis, Supplementary Table S 3.

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Immunohistochemistry photos of the target proteins in renal tissues in relation with
pathological parameters are illustrated in Fig. 6. The a dvanced pathological g rade was
significantly associated with strong expression of Ki67 ( p = 0.001), TGFB1 ( p = 0.034),
VEGFA proteins ( p = 0.001), and absent Tp53 staining ( p = 0.029), Fig. 7A-D. Larger
tumor size and capsular infiltration also showed higher Ki67 expression ( p = 0.027 and
0.014, respectively), Fig. 7E-F. Additionally, there was differential expression of TGFB1
and Tp53 proteins according to the specimen histopathological diagnosis, with stronger
staining in chromophobic renal cell carcinoma type, Fig. 6G-H. Simi larly, correlation
analysis between proteins and clinicopathological characteristics demonstrated moderate
correlation of Ki67 with histopathological diagnosis (r = -0.419, p = 0.007), grade (r =
0.690, p < 0.001), tumor size (r = 0.389, p ≤ 0.001), LN invasion (r = 0.351, p = 0.026),
and capsular infiltration (r = 0.431, p = 0.006). TGFB1 protein showed moderate
correlation with histopathological diagnosis (r = 0.427, p = 0.006) and tumor grade (r =
0.441, p = 0.004). VEGFA protein also sh owed a significant positive correlation with
pathological grade (r = 0.563, p < 0.001). In contrast, there was a negative correlation
between Tp53 and grade (r = -0.403, p = 0.010) , Supplementary Table S 3.
3.4. In silico data analysis
Hsa-miR-34a is encode d by MIR34A gene ( ENSG00000228526 ), mapped at 1p36.22. The
gene has 2 exons. It encodes for 4 transcripts due to alternative splicing. Three of them are
transcribed from two exons , separated by an intron of about 30Kb, forming long intergenic
non-coding RNA of 4211, 529, and 409 bp long. However, the 4th transcript, of 110 bp in
length, is encoded from the second exon only to form the precursor transcript of miR -34a,
Fig. 8A. The first exon contains a p53 -binding site within a CpG island about 30 kb upstream
of the mature MIR34A sequence. The precursor miRNA stem -loop is processed in the
cytoplasm of the cell, with the predominant miR -34a mature sequence excised from the 5'

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arm of the hairpin. Secondary structure of hsa -miR-34a stem loop predicted by
computational programs is illustrated in Fig. 8B. Functional characterization of miR -34a
based on differential expression experiments revealed its control on numerous can cer-related
molecular pathways and cellular processes. It can control up to 115 genes involved in
pathways in cancer (hsa05200), in addition to dozens of genes in particular tumor types . As
shown in the heat map, t he most top five significant pathways targeted by miR -34a, were
microRNAs in cancer, fatty acid biosynthesis, proteoglycans in cancer, adherens junction,
and cell cycle , Fig. 8C and Supplementary Table S4.
Interaction of mature miR -34a-5p with complementary sites of selected experimentally
valida ted targets is shown in Supplementary Fig. S2. Protein -protein interaction between the
targets is shown in Supplementary Fig. S3 . Enrichment analysis of the target panel elucidated
their functional impact on numerous biological processes and cancer KEGG pathways,
Supplementary Table S 5 and S 6.
4. Discussion
A key goal in clinical oncology is the development of therapeutic strategies specific to
pathways that are deregulated in cancer. Understanding the biological pathways
involving candidate disease -causin g genes will provide new therapeutic modalities for renal
cancer.
In the current study , up-regulation of miR -34a was observed in more than 90% of RCC
patients, with median fold change of 7.97 in RCC FFPE tissues compared to non -cancer
tissues. ROC analysi s revealed a high diagnostic performance of miR -34a in discriminating
between cancer and non -cancer tissues (AUC=0.854). However, higher levels showed a
better prognosis; it was moderately correlated with well differentiated tumors. In addition,
expression profiles in chromophobic RCC samples were markedly greater than that of clear
cell and papillary RCC subtypes.

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According to a survey across diverse normal human tissues, miR -34a was down -regulated in
most human normal tissues , including renal cortex and medulla (data source: U133plus2
Affymetrix microarray from BioGPS. org). Consistent with our findings in renal cancer
tissues , Dutta et al. [36] show ed that miRNA -34a supports cell proliferation in oxidative
stress -induced renal carcinogenesis rat model and is overexpressed in various types of human
cancer. It has been shown to be over -expressed in papillary thyroid carcinoma [37], colorectal
carcinoma [38], and uteri ne leiomyoma [39] . In addition to solid tumors , miR -34a was up –
regulated in chronic lymphocy tic leukemia more than 20 -folds during the leukemic phase and
not during the preleukemic stage [40].
Liu et al. reported miR -34a as one of the u p-regulated miRNAs in RCC which function by
down -regulating tumor suppressor genes including secreted frizzled related p rotein 1
(SFRP1) [41]. In addition, miR-34a was identified to be a direct target of the tumor
suppressor Tp53 protein in human and mouse cells, thus can mediate some pro -apoptotic
biological function of Tp53 [42,43] . Similarly, He et al found that deregulation of miR -34a
on response to DNA damage and oncogenic stress depends on p53 in vitro and in vivo [13].
In vitro , miR34a was co -expressed with Tp53 at high levels in colorectal cancer cell lines and
in irradiated mice, but was not expressed in Tp 53-knockout mice [44]. These findings could
explain the good prognosis of higher levels of miR -34a in our samples with low pathological
grade and in those of the chromophobic RCC. Expression of miR -34a promotes apoptosis
[42,44] and mediates suppression fo r cell proliferation [38,45]. Exogenous miR -34a inhibited
cell growth and increased apoptotic activity in retinoblastoma cell lines [46], non -small lung
cancer [47], and prostate cancer cell lines [48]. In addition, miR -34a restoration in pancreatic
cancer cells significantly inhibited tumor growth and invasion in vitro and in vivo , induced
G1 and G2/M arrest in cell cycle, and sensitized the cells to chemotherapy and radiation
[16,49]. Reintroduction of this type of miRNA into three different neuroblastoma cell lines

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caused a dramatic reduction in cell proliferation through the induction of a caspase -dependent
apoptotic pathway [45]. It has been found to cause dramatic reprogramming of gene targets
that regulate apoptosis, DNA repair, cell cycle progression , epithelial -mesenchymal transition
and angiogenesis [42,50].
However, low expression of miR -34a was noted in some other types of cancer, as malignant
mesothelioma [51], retinoblastoma [46], melanoma [52,53], and oral squamous cell
carcinoma [54]. This in consistent expression may suggest miRNA -34a works in a cell type –
specific manner with differential p53 pathway inactivation [36]. MiR -34a down -regulation
was found in glioblastoma and glioma with mutant Tp53 [55], chronic lymphocytic
lymphoma with Tp53 del etion [56], and metastatic hepatocellular carcinoma [57]. In
addition, MIR34A gene is commonly deleted in human cancers. Chang et al observed
frequent absence of MIR-34A gene in pancreatic cancer cells [42]. Wei et al. reported 1p36
loss in neuroblastoma t umors expressing a lower level of miR -34a compared to those with
normal copies of 1p36 [58]. However, no mutations were discovered in sequencing of the
miR-34a locus in 30 neuroblastoma cell lines [59].
Taken our results with the findings of prior studies , we could support the hypothesis that
miR-34a over -expression in the current study is a secondary consequence in cancer cells
elucidated to compete the DNA damage and uncontrolled growth proliferation. Accumulation
of further mutations in higher pathologi cal grade tumors , especially those related to Tp53
gene activity or 1p36 locus itself could account for the fall of miR -34a expression profile in
those patients. Further functional studies are recommended to unravel the molecular
mechanisms underlying chro mophobic RCC which has the best prognosis among all other
subtypes in our cases [60,61, 62].
In silico analysis of miR -34a targets in databases revealed numerous candidate gene targets.
Functional annotation and enrichment analysis showed high linkage of m iR-34a with cancer –

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related pathways. It can influence pathways involved in all cancer hallmarks acquired during
the multistep development of human tumors; sustaining proliferative cell signaling, evading
growth suppressors, resisting apoptosis, inducing an giogenesis, and activating invasion and
metastasis. In the current study, we identified predicted putative miR -34a binding sites within
the 3' UTR, 5'UTR, or coding regions of eleven mRNAs. These selected genes were
functionally validated in prior experime nts listed in Supplementary Table S1. Nevertheless,
our data did not reveal inverse correlations of these targets with miR -34a. This could be
explained by the fact that their gene expressions result from integrated cell responses and
cross -talk between sig naling pathways. Additionally, according to miRNAs databases, there
are multiple -to-multiple relationships between microRNAs and target genes; one miRNA
may regulate transcription of many genes and a single gene could be targeted by multiple
miRNAs simulta neously, thus forming complex genetic circuits in human cancer [63,64]. Liu
et al. [41], in addition, speculated the identified putative targets of miRNA could be
regulated by translation inhibition rather than degradation and subsequently this would leav e
mRNA levels unaffected, but reduce protein levels , and this warrant the need of protei n level
measurement in tumor/normal samples along with the miRNA and mRNA levels for the same
gene to identify such type of regulation.
Of the deregulated target gene s expressed significantly in the current renal cancer specimens,
MET and E2F3 were significantly up -regulated in RCC compared to non -cancer tissues
with high diagnostic performance . The Met proto -oncogene, mapped at 7q31.2, has two
alternative spliced isof orms (genecards.org). We identified two putative miR -34a binding
sites within the 3' UTR and 5' UTR of the human c -Met mRNA. Similarly, Li et al. [16] and
Hu et al . [17], reported that c -Met is directly targeted by miR -34a. The MET gene encodes a
receptor tyrosine kinase that is activated by hepatocyte growth factor (HGF ) [16]. Ligand
binding at the cell surface induces autophosphorylation of carboxyl terminus of MET on its

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intracellular domain that generates docking sites for second messengers, which acti vate
several signaling pathways involving RAS -ERK, mitogen -activated protein kinase (MAPK),
phosphatidylinositol 3 -kinase (PI3K) -AKT, signal transducer and activator of transcription
(STAT), and phospholipase C [65] . Such downstream signaling pathways evok e a variety of
pleiotropic physiological processes , including survival, morphogenesis, differentiation,
epithelial -mesenchymal transition, and regulation of cell migration [66]. During embryonic
development, MET signaling regulates gastrulation, angiogenes is and kidney formation. In
adults, participates in organ regeneration and tissue remodeling [16]. However, improper
activation of c -MET may confer proliferative and invasive/metastatic abilities of cancer cells
[67]. Similar to our findings in RCC patient s, MET over -expression was associated with
multiple human cancers , including lung adenocarcinoma [68], node -positive breast cancer
[69], glioma [70], gastric carcino ma [65] , colorectal cancer [71], pancreatic carcinoma [72],
and papillary thyroid cancer [73]. Aberrant expression of MET may lead to a more aggressive
cancer phenotype and may be a prognostic indicator of poor overall survival and resistance to
therapy [74,75]. Transcriptional deregulation can occur by different mechanis ms [65] .
Activating poi nt mutations in the MET gene were reported in the germline hereditary
papillary RCC patients [76]. Gene amplification was reported in glioblastomas [77] and in
advanced stage colorectal cancer and liver metastases, suggesting a role in the late phases of
malignant progression [78]. Oncogenic deletion leading to constitutive c -Met expression was
documented in lung cancer [79] . Other gene mutations were associated with childhood
hepatocellular carcinoma [80] and various head and neck cancers [81].
The second up-regulated gene in most of RCC samples was the transcription factor E2F3 .
ROC analysis showed high discrimination accuracy for diagnosing RCC. In addition, its
expression profile was associated with capsular infiltration. E2F3 gene lies on chromosome
6p22.3 and has 3 transcript variants encoding 3 proteins of 465, 334 and 128 amino acids

18
long. In contrast to full -length E2F3 protein, which is expressed only at the G1/S boundary,
truncated isoforms are expressed throughout the cell cycle [82,83]. E2F3 rec ognizes a
specific sequence motif in DNA and interacts directly with the tumor suppressor
retinoblastoma protein (pRB) to regulate the expression of genes involved in the G1/S
boundary of cell cycle and DNA replication; hence E2F3 has a critical role in th e control of
cellular proliferation [84]. Acute loss of E2F3 activity affected the expression of genes
encoding DNA replication and mitotic activities [85]. In vitro and in vivo studies showed the
failure of division and proliferation in E2f3 -null retinal progenitor cells [86] and early
embryonic death in E2F3 -null mice [87,88]. Loss of E2F3 function leads to CDKN1A
(Cyclin Dependent Kinase Inhibitor 1A ) protein expression, resulting in a decrease in cyclin –
dependent kinase activity and Rb phosphorylation [87]. Down -regulation of E2F3 resulted in
cell death and delays in cytokinesis in breast cancer [89]. Altered copy number and activity of
this gene have been observed in a number of malignant tumors. Higher levels of E2F3 protein
and mRNA were detected in W ilm's tumors [90]. Cooper et al . [91] and Zeng et al. [92]
demonstrated nuclear over -expression of the E2F3 transcription factor in human lung cancer
and hepatocellular carcino ma, respectively. Huang et al. [93] reported significant up –
regulation of E2F3 i n poor -differentiated bladder cancer tissues a nd cell lines. Oeggerli et al.
[94] reveale d 6p22 amplification in 12 tumo r-derived cell lines with over -expressed E2F3. In
addition, comparative genomic hybridization studies identified consistent amplificatio n of
6p22 in bladder cancer with up -regulated E2F3 [95]. The presence of this amplification was
shown to correlate with pathological grade and tumor cell prolifera tion rate [96] . Increased
E2F3 staining was associated with tumor aggressiveness and poor ove rall survival in prostate
cancer [96,97]. However, t ransgenic mice expressing induca ble E2F3 resulted in hyperplasia,
but not tumor development [98].

19
The expression of proliferation -related Ki67 a ntigen was investigated in the current study. It
is an exce llent marker to determine the growth fraction of a given cell population. The
fraction of Ki67 positive cells is often correlated with the clinical course of tumors. In the
study , this marker of proliferation was detected in all cases of RCC , but with variable level s
of expression. High expression of Ki67 was associated with advanced pathological grade,
large tumor size, lymphatic invasion, and capsular infiltration . In addition, strong staining
was correlated with chromophobic RCC s ubtype. Antigen K I67 is a nuclear protein, encoded
by MKI67 gene that is mapped at 10q26.2. It has five splice variants; only two of them are
translated to synthesize a long form (3256 aa/ 395 kDa) and a short form (2896 aa/ 345 kDa)
proteins that differ only by the presence or a bsence of exon 7 [99]. Antigen KI67 is
associated with cellular proliferation [100] . During interphase, the Ki67 protein can be
exclusively detected within the cell nucleus, whereas in mitosis most of the protein is
relocated to the surface of the chromoso mes [101] . Ki67 protein is present during all active
phases of the cell cycle (G1, S, G2, and mitosis), but is absent from resting cells (G0)
[100,102]. GO annotation identified the gene to play role in the regulation of chromosomal
segregation and organiz ation, nuclear division, cell cycle, organ regeneration, and stress
response (genecards.org). It is also associated with ribosomal RMA transcription [103] .
Inactivation of antigen KI -67 leads to inhibition of rRNA synthesis [104] . Several lines of
evidence implicate the importance of Ki67 index in lymphoma [105] , colorectal carcinoma
[106] , nephroblastoma [107] , prostate carcinoma [108] , brain cancer [109] , endometrial
cancer [110] , and breast cancer [111] . Furthermore, prognostic value for survival and tum or
recurrence of Ki67 expression was also reported. MKI67 expression i n breast cancer, was
associated with tumor grading, overall and disease -free survival [112] , earlier central nervous
system metastases [113] , and could help in distinguishing the benign from the malignant form
[114] . Moreover, a high MKI67 expression was correlated with poorly differentiated

21
carcinomas and vascular invasion in cervical cancer [115, 116]. The MKI67 expression was
found to be associated with poorer overall survival in ovaria n cancer patients [117] ,
malignant gastrointestinal stromal tumors [118] , and glioma [119] .
The TGF B superfamily proteins are known to be implicated in growth and
differentiation . These growth factors bind various TGF -beta receptors , leading to recruitment
and activation of SMAD family transcription factors that regulate expression of downstream
genes , including interferon gamma and tumor necrosis factor alpha [120] . We exam ined the
expression profile of TGFB1 protein and TGFB3 gene. In RCC samples, TGFB3 mRNA did
not show differential expression compared to non -cancer tissues. However, TGFB1 protein
showed moderate to strong nuclear staining in mos t samples. Higher protein level expression
was associated with poor tumor differentiation and chromophobic subt ype. This protein is
involved in embryogenesis and cell differentiation, and may play a role in apoptosis, immune
defense, inflammation, and tissue repair [120,121] . Overexpression or alterations of its active
protein induced by somatic mutations in the TGFB1 gene are frequently observed in tumor
cells [122] , as lung [123] , bladder [124] , prostate [125] , and breast cancers [126] . In addition,
increase TGFB signaling activation enhance s the aggressiveness of tumors, stimulates
invasion, angiogenes is, and met astasis, and inhibits immune surveillance [127] . It could, in
addition, promote epithelial -mesenchymal transformation [122] .
SOX2 is a critical transcription factor for self -renewal and maintenance of undifferentiated
embryonic stem cells [128]. SOX2 gene is mapped at 3q26.33, consists of a single exo n that
enco des a protein of 318 amino acid residues (genecards.org). The SOX2 protein binds to the
minor groove in DNA. It is involved in the regulation of embryonic development and in the
determination of cell fate. SOX2 is one of the four transcription factors whose over
expression can induce reprogramming of somatic cells to acquire pluripotency characteristics
[129,130]. SOX2 is identified as an oncogenic factor and is overexpressed in certain types of

21
cance r, including breast cancer [131], colorectal cancer [132], hepatocellular carcinoma
[133], Ewing’s sarcoma [128], and glioma [134]. Knockdown of SOX2 could inhibit cell
viability and tumorgenesis in vitro and in vivo [128,131]. However, in gastric carcinom a, its
level was significantly down -regulated compared with normal gastric epi thilia. SOX2 was
found to potentiate cell -cycle arrest associated with decreased levels of CCND1 and
phosphoryl ated Rb, and up -regulated p27Kip1 level [135] . In our samples, no differential
expression was observed between cancer an d non -cancer tissues; nevertheless , lower
expression of SOX2 mRNA was correlated with cancer infiltration of renal pelvic tissues.
One anti -apoptotic and three pro -apoptotic miR -34a targets regulating essential cancer –
related pathways were examined. The a poptotic regulator BCL2 gene, mapped at 18q21.33,
has 2 alternative transcripts and encodes an integral outer mitochondrial membrane protein
[136] . It has two protein isoforms; Bcl2a (5.5 -Kb mRNA/239 aa) and Bcl2b (3.5 -Kb/205 aa),
which are identical excep t for the C -terminal portion. The former contains a hydrophobic tail
for membrane anchorage which seems to be necessary for anti -apoptotic ability [137] . Bcl2 is
found on the outer membrane of mitochondria. It functions as an apoptosis inhibitor by
forming complexes with caspase -9 and APAF1, thus prevent them to initiate the protease
cascade and apoptosis through caspase -3 cytochrome C dependent activation [138] . In 2002,
Marsden et al. [139] discovered that Bcl2 can also function independently via other
pathways. Bcl2 constitutively blocks p53 -induced apoptosis and enables the survival of
colorectal cancer cells [140] . Over -expression of Bcl2 blocks TNF -related apoptosis -inducing
ligand (TRAIL) -induced apoptosis in human lung cancer cells [141] . In addition to the anti –
apoptotic function, Bcl2 is known to regulate mitochondrial fusion and fission dynamics
[142] . Bcl2 act as a potent regulator of cell survival in neurons both during development and
throughout adult life [143] . Additionally, in pancreatic beta -cells, Bcl2 is known to be
involved in controlling metabolic activity and insulin secretion [138] . In cancer, over –

22
expression of the anti -apoptotic Bcl2 can result in a distinct cellular growth advantage due to
lack of cell death, a hallmark of cancer. In the present study, Bcl2 protein was expressed in
all cancer tissues. Similarly , in previous studies, Bcl2 up -regulation has been reported in
many types of cancer, including leukemia [144] , lymphomas [145] , ovarian carcinoma [146] ,
and breast cancer [147] . Moreover, it has also been associated with poor clinical outcome
and shorter overall survival in cancer patients [148] . It confers resistance to chemotherapy
and radiotherapy in non-small -cell lung cancer (NSCLC) [149] . In addition, up-regulated
Bcl2 pro tein was associated with cisplatin resistance in urinary bladder cancer [150] .
Targeting Bcl2 by miR -125a, miR -206, and miR -34a inhibited cell proliferation and induced
apoptosis in colon cancer cells [151] , NSCLC [152] , and breast cancer [153] .
The tumo r suppressor protein Tp53, t he guardian of the genome, is essential for the
prevention of cancer formation. In the current study, it was not detected by
immunohistochemistry in less than half of the specimens. Absent staining of Tp53 protein
in tumor cell nuclei was significantly associated with advanced pathological grade, while
positive staining was observed in the chromophobic RCC, known to have the best
prognosis. The transcription factor Tp53 is encoded by TP53 gene mapped at 17p13.1.
This gene has a complex transcriptional expression pattern encoding 28 different mRNA
variants through the use of an internal promoter in intron 4 and alternative splicing
machinery. All variants could be detected in all tissues with only 5 are exclusively
transcribed in t issue -specific manner [154] ; each isoform has distinct biological activity
and subcellular localizations [155] . Normally, Tp53 is expressed at low levels and kept
inactive through the action of MDM2 (Mouse double minute 2 homolog) which promotes
its degrad ation [155] . However, during cellular stresses or DNA damage, activated Tp53
induces cell cycle arrest for DNA repair or forces apoptosis. It binds to DNA and regulates
transcription of target genes that induce cell cycle arrest, apoptosis, and DNA repair [155 –

23
157] . It can trigger cell death independently of its transcriptional activity through
subcellular translocation and activation of pro -apoptotic Bcl -2 family members [158] .
Attenuation of Tp53 activity would render the cells more susceptible to further genetic
damage and therefore to neoplastic transformation and tumor progression.
Another apoptotic gene, Tp53INP2 , is located at 20q11.22 with 4 transcripts, encodes for 3
putative protein variants of 220, 88, and 77 amino acid long. It is thought to be a scaffold
protein that is normally expressed upon induction by the Tp53 protein [159]. The protein
encoded by this gene has two distinct functions dependi ng on its cellular localization [160] . It
is essential for proper autophagy, a self -degradative proce ss that occurs at critical times in
development to recycle unnecessary intracellular components and damaged organelles [161] .
Tp53INP2 protein shuttles between the nucleus and the cytoplasm, depending on cellular
stress conditions, and relocates in the aut ophagosomes during autophagy activation. It
recruits Atg8 -like proteins to the autophagosome membrane by interacting with the
transmembrane protein VMP1 (vacuole membrane protein 1) [159] . Failure of autophagy is
thought to be one of the main reasons for t he accumulation of cell damage and aging [162] . In
addition to its role in autophagy, it serves as a transcriptional co -activator for several nuclear
receptors, such as the glucocorticoid receptor, vitamin D receptor (VDR), and peroxisome
proliferator acti vated receptor gamma [160] , thus possess a tumo r suppressor -like
functionality similar to Tp53 [163, 164]. Tp53INP2 was found to express differently between
papillary thyroid carcinoma and their matching normal tissues [165] . It has also been detected
at un usually low levels in some prostate cancer patients [166]. In addition, there was a
significant down -regulation of TP53INP2 in their urine, with high sensitivity , but low
specificity as it was not able to discriminate between benign and malignant samples 164].
TP53INP2 expression was not induced by Tp53 in some colorectal cancer [167] . Nordgren et
al. [168] detected absence of Tp53INP2 gene caused by a 700 kb deletion of 20q11.22 in

24
hairy cell leukaemia. Furthermore, Moran -Jones et al. [169] proposed that t he occurrence of a
specific splice variant of TP53INP2 might be a necessary event in order for malignant cells to
invade the extracellular matrix. Therefore, our results along with previous data highlight its
putative role in cancer development and progres sion.
Low levels of the apoptotic gene, DFFA , were observed in almost all RCC samples. Lower
expression was associated with vascular infiltration. DFFA gene is located in the same region
of miR -34a at 1p36.22 which is commonly deleted in human tumors. DFFA plays an
essential role in apoptosis. When cleaved by caspase -3, it induces the release of its partner
DFFB, which in turn trigger DNA fragmentation by its nuclease activity [170] . Hence,
absence of this protein could result in aberrant apoptosis, invasiv e growth and metastasis
[171] . Similar to our findings, down -regulated DFFA expression was observed during the
exponential phase of growth in several human colonic cancer cell lines [172] . DFFA( -/-) mice
exerted severe genomic instability and tumor progres sion in colon epithelial cells [173].
Moreover, low DFFA expression was associated with poor prognosis in esophageal cancer
[174] and neuroblastoma tumors [175].
Angiogenesis is of central importance in the growth and metastasis of tumors [176]. We
inves tigated the expression of the angiogenesis -mediated protein, VEGFA , in RCC
compared to non -cancer tissues. VEGFA protein expression was detected in all RCC
tissues, with 80% of samples showing strong staining. Elevated levels were associated
with advanced tumor grade. VEGFA protein is encoded by the VEGFA gene, mapped at
6p21.1, a highly polymorphic region that showed association with cancer susceptibility,
aggressiveness and therapeutic response in various tumor types [177,178]. Alternative
exon splicing c an generate up to 29 transcript variants with different isoforms
(Ensembl.org). There is also evidence for alternative translation initiation codons resulting
in additional isoforms. VEGFA promotes proliferation and migration of vascular

25
endothelial cells both in vitro and in vivo , and is essential for both physiological and
pathological angiogenesis [177]. This pro -survival effect is mediated via PI3 -kinase/Akt
signal transduction pathway [179]. In addition, it induces permeabilization of blood
vessels, th us known as a vascular permeability [180]. It induces endothelial fenestration in
vascular beds [181], and enhances vasodilatation in vitro in a dose -dependent manner
[182]. In addition, VEGFA promotes apoptosis and induces expression of the anti –
apoptotic proteins Bcl -2 [183]. In vivo , VEGFA inhibition results in abnormal embryonic
blood vessel formation and extensive apoptotic changes in the vasculature of
neonatal mice [184,185]. Within tumors, cancer cells and cancer -associated stroma are the
major sour ce of VEGFA [178]. It influences newly formed blood vessels , but not the
established ones. In agreement with our findings, VEGFA was over -expressed in several
different tumor types; including breast cancer [176], lung cancer [186], head and neck
squamous c ell carcinoma [187], and colorectal cancer [188]. Anti -VEGF antibodies
exerted a potent inhibitory effect on tumor growth of prostate xen ografts [189], in addition
to several cancer cell lines in nude mice [190]. Furthermore, VEGFA expression is
correlated with tumor stage and progression. It is found to be associated with high
pathological grade, tumor size, lymph node metastasis, and poor prognosis in breast
cancers [176,177,191]. Positive VEGFA in cancer showed resistance to tamoxifen in
breast cancer pa tients [192]. High VEGFA expression was also associated with high
intratumoral angiogenesis and low survival rate in NSCLC [193]. A meta -analysis study
demonstrated that high level s of VEGFA were associated with poor overall survival and
poor outcomes in h epatocellular carcinoma patients treated with sorafenib [194].
5. Conclusions
The current study does con firm the association of miR -34a overexpression with RCC in our
population, suggesting its potential role in pathogenesis and progression of this type of

26
cancer . Furthermore, c hromophobic RCC subtype has been postulated to attain different
transcriptomics and proteomic s characteristics compared to other subtyp es. It has been
found to have higher MIR-34A, Tp53 , Ki67 and TGFB1 expressions . Hence, the molecul ar
mechanism and genes involved in this parti cular type need to be validated in large scale
multicenter study for better disease outcome and response to treatment prediction s. In
addition, the exact molecular interplay between the potential miR-34a target genes are still
unclear and will warrant further detailed studies. One of the limitations that need to be
considered is that the protein levels of the selected target genes in tumor/normal samples
were not measured along with the mRNA levels . Hence, we cou ld not suggest if the
selected potential targets could be affected by translation inhibition rather than degradation
in light of absence of miRNA34a -selected targets anti -correlation . Another limitation is
the lack of the functional analysis either on tumo r cell lines or RCC rat models to validate
the current findings and explore the detailed biological mecha nisms and the potential
therapeutic role s of miR-34a in RCC . This will be considered the logic next step in our
ongoing research.
Com peting interests
The authors declare that they have no competing interests .
Acknowledgements
The authors thank the Oncology Diagnostic Unit and the Center of Excellence in Molecular
and Cellular Medicine , Suez Canal University, Ismailia, Egypt for provid ing the facilities for
performing the research work
List of abbreviations
AUC: Area under curve; Bcl -2: B -cell lymphoma 2; CDKN1A: Cyclin dependent kinase
inhibitor 1A; crRCC: Chromophobe renal cell carcinomas; ccRCC: Clear cell renal cell
carcinomas; DFFA: DNA fragmentation factor subunit alpha; FFPE: Formalin -fixed paraffin

27
embedded; HGF : Hepatocyte growth factor; ISUP: International society of urological
pathology; KEGG: kyoto encyclopedia of genes and genomes; MDM2: Mouse double
minute 2 homolog ; miR -34a: MicroRNA -34a; miRNAs : MicroRNAs; Ng: Nuclear grade;
NSCLC: Non-small -cell lung cancer; pRCC : Papillary renal cell carcinomas; PI3K :
Phosphatidylinositol 3-kinase; qPCR : Quantitative polymerase chain reaction; Cq:
Quantitative cycle; ROC: Receiver operating characteristic; RCC: Renal cell carcinoma.
pRB: Retinoblastoma protein; RT: Reverse transcription; SFRP1 : Secreted frizzled related
protein 1; STAT : Signal transducer and activator of transcription; SOX2: Sex Determining
Region Y -Box 2; TGFB: Transforming growth factor -beta; TP53INP: Tumor protein p53
inducible nuclear protein; UTR: Untranslated region; VEGF: Vascular endothelial growth
factor; VDR: Vit amin D receptor; VMP1: Vacuole membrane protein 1 .
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Figure legends
Figure 1. Predicted Target Genes of miRNA -34a in Renal cell carcinoma pathway
[KEGG hsa05211]. Hsa-miR-34a-5p targets HIF -1, VEGF, and TGF -β signaling pathways
in clear cell and papillary type II RCC (eosinophilic), as well as MAPK signaling pathway in
papillary RCC type I (basophilic). However, candidate genes and the role of miR -34a in
oncocytoma and chromophobic RCC pathways are still undetermined. Colored box; miRNA –
34a target gene, green color; target on 3 'UTR sequence, bl ue color; t arget on 5 'UTR
sequence , pink color; target on coding sequences, white box; not predicted target.
Figure 2. miR-34a target genes regulating th e hallmarks of cancer. (A) Blue color has
been analyzed by immunohistochemitry , Yellow color ; has been analyzed by quantitative
Real-Time PCR. (B) Classification of the study miRNA -34a target genes and proteins
according to their major role in cancer -related biology .
Figure 3. Gene expression profiling in cancer and normal renal tissues. Data are
represente d as medians. The box defines upper and lower quartiles (25% and 75%,
respectively) and the error bars indicate upper and lower adjacent limits. Expression levels of
miR-34a and targets in cancer and normal tissues were normalized to RNU6B and GAPDH,
respe ctively. Fold change was calculated using the delta -delta CT method [= 2 (-ΔΔCT)] in
comparison to normal renal tissues. The gra y dash line represents the expression level of
normal renal tissues (equivalent to 1.0). P values < 0.05 was considered statisti cal significant.
Mann -Whitney U test was used for comparison. Median and quartile values of patients are
noted in red. Bar above the figure shows the percentage of up (pink) and down -regulated
(green) samples.

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Figure 4. Frequency of I mmunohistochemistry ma rkers of miR-34a putative target
proteins in RCC specimens.
Figure 5. Association of miR -34a and target genes with clinicopathological features in
RCC patients.
Figure 6. Immunohistochemistry images according to type and grade of RCC.
Clear cell RCC (Ng1) weakly express bcl2 (x100), (photo 1), Clear cell RCC (Ng2) with
focal strong cytoplasmic bcl2 (x100) (photo 2), chromophobe RCC (Ng2) with diffuse
moderate expression of with bcl2 (x400) (photo 3), papillary RCC (Ng2) with diffuse strong
expression of bcl2 (x200) (photo 4). Clear cell RCC (Ng1/2) with diffuse strong nuclear
expression of P53 (x100) (photo 5), Clear cell RCC Clear cell RCC (Ng3) showed scarce cell
nuclei express P53 (X200) (photo 6), Chromophobe RCC (Ng2) diffusely and strongly
express P53 (X100) (photo 7), Papillary RCC dosn't express p53 (x100) (photo 8). Clear cell
RCC (Ng2) with weak expression of TGFB1 (x100) (photo 9), RCC (Ng3) wit strong
expression of TGFB1 (x200) (photo 10), Chromophobe RCC with focal strong expression of
TGFB 1 (X200) (photo 11), Papillary with focal strong expression of TGFB1 (X200) (photo
12). Clear cell RCC (Ng1) with diffuse weak expression of VEGFR (X100) (photo 13),
Clear RCC (Ng3) with diffuse moderate expression of VEGFR (X200) (photo 14),
chromophobe RCC with weak expression of VEGF (X100)(photo 15), Papillary RCC with
intermediate expression of VEGFR (X200) (photo 16). RCC (Ng2) with low expression of
Ki67 (X100) (photo 17), RCC (Ng3) with high expression of Ki67 (X200) (photo 18),
Papillary RCC do esn’t express Ki 67 (x100) (photo 19), Chromophobe RCC dosn't express
Ki67 (x200) (photo 20).
Figure 7. Association of immunohistochemistry markers with clinicopathological
features in RCC patients. (A-D) Association with pathological grade, and (E -F) w ith
histopathological type.

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Figure 8. Structural analysis of miRNA -34a gene locus and transcripts. (A) Genomic
coordination of Homo sapiens MIR34A gene (blue) is at the reverse strand of chromosome 1:
9,148,011 -9,196,983. It is non -coding gene with 4 trans cripts : 3 long intergenic non -coding
RNA (in red) and one microRNA (in orange). (B) Homo sapiens hairpin secondary structure
of pre -miR-34a stem -loop. Mature miR -34a-5p highlighted in red [Data source: miRTarBase
v20]. (C) Enrichment pathway analysis for miR-34a. Heat map showing targeted pathways,
diseases, and cancers for both hsa -miR-34a-5p and hsa -miR-34a-3p with p -values < 0.05 and
microT -CDS threshold 0.4. A total of 33 pathways have target genes (in CDS or 3’ -UTR
regions) for miR -34a. The enrichment results of validated target genes showed that most
pathways were related to cancer and cancer -related pathways. KEGG Pathway annotations
related specifically to current RCC wor k had been labeled (red ). Degree of color is based on
the significant p values of the predicted algorithm by DIANA tools; the red has the top
significance estimated by false discovery rate (Data source: DIANA -miRPath v2.0 web –
server).

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Supplementary files
Table S1. Experimentally validated targets of miR -34a-5p in the literature.
Table S2. Correlation analysis of miRNA -34a and target genes and proteins in renal cell
carcinoma patients.
Table S3. Correlation analysis between expression profile and clinicopathological
characteristics in renal cell carcinoma specimens.
Table S4. Experimentally supported miR -34a-5p functional annotation.
Table S5. Enrichment analysis of miRNA -34a targets on numerous biological process .
Table S6. Enrichment analysis of miRNA -34a target s on cancer KEGG pathways .
Figure S1. Correlation analysis of miR -34a with the tested target genes and proteins.
Chromosomal location is shown (numbers), blue line: inverse corr elation, red line: positive
correlation, continuous line: p < 0.01, interrupted line: p < 0.05.
Figure S2. Schematic representation of the target genes and the predicted miR -34
binding sites. Base pairing of miRNA -target interactions by microRNA.org web se rver
(photo A -F) and miRTarBase v20 (photo B, G -J).
Figure S3. Protein -protein interaction using STRING network analysis.
The network is composed of 11 protein nodes and 20 edges representing protein -protein
associations, with the following settings: mini mum required interaction score of medium
confidence (4.0), no extra interactors, and disable structure previews inside network bubbles.
The network clustering coefficient was 0.82 and protein -protein interaction (PPI) enrichment
p value was 4.77e -07. The l ine colors of edges indicate the type of interaction evidence; pink
for experimentally determines, blue for curated database, and green for text mining.

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Table 1. Clinicopathological characteristics of renal cell carcinoma patients (n=85)
Data are presented as N (number), % ( percentage); RCC, renal cell carcinoma; T, tumor size; LN,
lymph node.

Variables N % Age
20y- 5 5.9 40y- 47 55.3 60y- 33 38.8 Gender
Females 34 40.0 Males 51 60.0 Affected side
Right 47 44.7 Left 38 55.3 Histological type
Clear cell RCC 47 55.3 Papillary RCC 15 17.6 Chromophobic RCC 13 15.3
Unclassified 10 11.8 Pathological grade Grade 1 11 12.9
Grade 2 51 60.0
Grade 3 23 27.1
Tumor size
T1 25 29.4
T2 42 49.4
T3 18 21.2
LN involvement
Negative 77 90.6
Positive 8 9.4
Capsular infiltration
Negative 60 70.6
Positive 25 29.4
Vascular infiltration
Negative 71 83.5
Positive 14 16.5
Renal pelvis infiltration
Negative 79 92.9
Positive 6 7.1

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Table 2. ROC Curve of miRNA -34a and target genes in renal cancer and normal tissues
Bold values are statistically significant at p < 0.05.

Variable(s) Area Standard
error p value 95% Confidence Interval
Lower Bound Upper Bound
miR-34a 0.854 0.051 <0.001 0.754 0.954
MET 0.765 0.059 0.005 0.648 0.881
E2F3 0.761 0.063 0.006 0.638 0.884
SOX2 0.571 0.094 0.479 0.388 0.755
TGFB3 0.553 0.081 0.586 0.395 0.711
DFFA 0.118 0.053 0.068 0.014 0.222
TP53INP2 0.587 0.062 0.173 0.466 0.709
Combined first three markers 0.793 0.034 <0.001 0.727 0.859
All combined markers 0.589 0.030 0.014 0.530 0.648

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Figure 8