Document Type : Original Article
Authors
1 Department of urology, Shahid Modarres Hospital, Shahid Beheshti University of Medical Sciences; Tehran, Iran
2 Department of Urology, Zahedan University of Medical Sciences, Zahedan, Iran
3 Urology and Nephrology Research Center, Department of Urology, Shahid Labbafinejad Medical Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4 School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
5 Student Research Committee, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
6 Department of Medical Genetics, Afzalipour Faculty of Medicine, Kerman University of Medical Sciences, Kerman, Iran
Abstract
Graphical Abstract
Highlights
Keywords
Main Subjects
Introduction
Renal cell carcinoma (RCC) is a predominant kind of kidney cancer in which malignant cells form in the kidney's tubules. RCCs account for approximately %2 to 3% of all adult malignancies scenarios. It can be classified into three major subgroups, such as a clear cell (ccRCC), chromophobe (chRCC), and papillary (pRCC) (1). People may not be aware of having cancer at all until the cancer is in its later stages. Most cases are diagnosed incidentally on imaging, ultrasound, CT scan, and MRI (2). Kidney cancer survival rates depend heavily on the tumor stage and the treatment selection. Most cases of RCC are detected between 50 and 70 years of age (3, 4).
The usual symptoms of RCC are haematuria, flank pain, unintentional weight loss, loss of appetite, abdominal mass, and abnormal liver function (5). Kidney cancer survival rates depend heavily on the tumor stage and the treatment selection. Nonetheless, the overall patient survival rate is still low (6). The well-established predisposing factors for renal cell carcinoma are hypertension, obesity, family history, cigarette smoking, and aromatic hydrocarbon (6, 7). MiRNAs (MicroRNAs) are the large class of short non-coding RNAs (ncRNA) that span between 18-25nt. Post-transcriptionally, they regulate the multiple genes expression by binding to the mRNA of the coding genes (8, 9). Early evidence of the role of miRNAs in human cancer was revealed in the pathogenesis of chronic lymphocytic leukaemia (CLL). One of the most common cytogenetic abnormalities observed in more than 80% of B-CLL patients is 13q14.3 deletions.
According to the report, microRNAs have many roles in the biological mechanisms related to tumorigenesis, including apoptosis, stress response, cell-cycle regulation, and Immune response. So, they are promising candidates for biomarker development (10-12). Evidence recommends that miRNAs are abnormally expressed in renal cell carcinoma and have notable parts in malignancies' onset, progression, and metastasis. Any change in miRNA binding sites or their sequences may affect the interaction of mRNA-miRNA target site, thus, leading to a change in the regulation of target gene expression (13).
Several studies have concentrated on the connection between miR-34 family members and tumorigenesis of several malignancies, such as hepatocellular carcinoma (HCC). Xu et al., acknowledged that SNP rs4938723 in the promoter region of pri-miR-34a/b,c may contribute to hepatocellular carcinoma susceptibility (14). In this paper, we investigated whether pri-miR-34b T>C rs4938723 and miR34a A>C rs6577555 polymorphism increase the risk of RCC and affect the outcome of patients with renal cell carcinoma in the Iranian population.
Methods
Study subjects and DNA Extraction
This research was performed based on the declaration of Helsinki (IR. SBMU.UNRC.1397.33). Informed written consent was signed by patients. Generally, 100 renal cell carcinoma patients and 100 controls were enrolled in this case-control study. Samples were collected from RCC patients who were treated in Modares and Labbafinejad hospital from September 2016 to September 2020. A total of Peripheral blood samples were gathered in EDTA containers and stored at -80 C° for further molecular analysis. Genomic DNA was extracted from 5ml peripheral blood samples by the salting-out method. The two miRNA SNPs chosen for this paper were pri-miR-34b T>C rs4938723 and miR34a A>C rs6577555. We used Amplification refractory mutation system polymerase reaction chain (T-ARMS- PCR) assays to analyze these SNPs. We also tested the relationship between these polymorphisms and clinicopathologic data, including tumor stage, grade, tumor type, age, gender, lymph-node involvement, distant metastasis, etc. The role of SNP was measured with the comparison of allele frequency of miRNAs in the patient group to that in control groups.
T-ARMS-PCR primers design and genotyping
For genotyping of 200 human samples, T-ARMS-PCR assays were applied. Two missense SNPs, and four pairs of primers were found and designed following the rules (Table 1). The PCR reactions were performed in a total volume of 25μL, containing 10pmol of each of the inner primers, 10pmol of each of the outer primers, 10mM of Amplicon PCR master mix 1×, and 100ng of DNA. An initial denaturation at 95 °C for 5min is used to perform the PCR amplification profile preceded by denaturation at 95 °C for 1min (32 cycles), annealing at 54 °C (32 cycles), and extension at 72 °C for 45min (32 cycles), preceded by the last extension for 5min. The products were detected by electrophoresis in 1.5% agarose gel stained with ethidium bromide at an onstant voltage (100V) for 40min.
Table 1. Primer sequences and product size for the T-ARMS-PCR assay of miR-34a and pri-miR-34b gene polymorphisms
SNP |
Primer |
Sequence 5´ - 3´ |
PCR product |
rs4938723 T>C |
Forward inner primer (C allele) |
CCTCTGGGAACCTTCTTTGACCTCTC |
202 |
Reverse inner primer (T allele) |
AGAAGGGAGGTCCTCAATGAGAGCTTTA |
142 |
|
Forward outer primer |
TCACAAGATACTGTTTTTCTGGCATCCA |
290 |
|
Reverse outer primer |
TAGTCAAATAGTGAGCCAGGCAGCTTGT |
290 |
|
rs6577555 A>C |
Forward inner primer (A allele) |
ACCAGCCTGGTTAACATAGCCAGATCA |
194 |
Reverse inner primer (C allele) |
CATTTTGTAGAGACAGTTGCTGAAGGTAGG |
266 |
|
Forward outer primer | CGTGCCTGTAGTCCTAGCTACTGGAGAG |
403 |
|
Reverse outer primer | TCAACCACTGTCCTTTTCGAATTTTTTC 640 |
403 |
Statistical analysis
A paired T-test was applied to investigate the differences in levels of miRNAs between patient and control groups. By using the student’s T-test or Chi-square test, differences between the two groups were analyzed. We used Pearson’s correlation coefficient analysis. A P-value<0.05 was considered statistically significant. Many variables were adjusted, including age, gender, drinking status, smoking, and clinicopathological characteristics.
Results
Study subject characteristics
In this study, the 100 RCC cases, including 67(50.4%) males and 33(49.3%) females with an overall mean age of 55 ± 12 years, and 100 cases without renal cancer, including 66(49.6%) males and 34 (50.7%)females with an overall mean age of15±51 years, were compared. No statistically significant difference was found between the two study groups regarding age and gender (P-value=0.54 and 0.5, respectively).
Table 2 shows the comparison of subjects with and without RCC based on weight loss, anorexia, positive personal history, positive family history, myalgia smoking, alcohol consumption, and drug use. We identified that smoking, alcohol consumption, and drug use rates were lower in the control group than in the RCC patients’ group, and 27% of all patients had lost weight symptoms, while this number was 9 in the control group.
Table 2. Association between RCC and baseline information of patients
Variables |
Controls (N:100) |
Patients (N:100) |
Total |
P-value
|
Number (%) |
Number (%) |
Number (%) |
||
Weight loss |
36 |
27 (27%) |
9 (9%) |
0.001 |
Decreased appetite |
30 |
20 (20%) |
10 (10%) |
0.048 |
Myalgia |
31 |
21 (21%) |
10 (10%) |
0.032 |
Positive Personal history |
2 |
2 (2%) |
0 (0%) |
0.156 |
Positive Family history |
1 |
1 (1%) |
0 (0%) |
0.317 |
Smoking |
28 |
21 (21%) |
7 (7%) |
0.004 |
Opium |
11 |
10 (10%) |
1 (1%) |
.005 |
Alcohol consumption |
6 |
6 (6%) |
0 (0%) |
0.013 |
Presence of comorbidities and clinical characteristics
In this study, we compared the number of comorbidities in the patient and control groups with an overall percentage. The prevalence of specific comorbidities in the RCC patients’ group was: diabetes (n=16, 43%), hypertension (n=32, 42.6%), hyperlipidemia (n=12, 34.2%), cardiovascular diseases (n=16, 34.7%) and the prevalence of comorbidities in the control groups was: diabetes (n=21, 57%), hypertension (n=43, 57.3%), hyperlipidemia (n=23, 65.8%), cardiovascular diseases (n=30, 65.3%). It is essential to note that several patients had more than one comorbidity.
Descriptive statistics
We identified differences between groups based on site distribution, clinical/pathological stage, presence of clinical/pathological nodal metastasis, differentiation grade, and lymphatic invasion by histology. The most usual kind of RCC was clear cell type (61%). 61% of the cases were clear cell type, and 13% were mucinous carcinoma. The majority of patients were in the T3 stage (81%), while 27 and 68% showed lymphatic vessel invasion and nodal metastasis, respectively (Table 5).
Data analysis showed that most patients were of clear cell type (61%).40% cases were at T1a stage while 21% of them were at T3a and t1b stage. 59% of cases were right-sided (Table 5). The RCC was located at the lower pole in four out of thirteen patients (30%), at the upper pole in 29% of cases, at the lower-middle and upper-lower-middle in 3% of patients, and in the upper-middle in four cases (4%). The primary tumor location was the middle pole in thirty-one cases (31%) (Table 5). When we analyzed our data, according to Fuhrman nuclear grading system, we found that 19% belongs to grade I, 44% to grade II, 21% to grade III, and 16% to grade IV (Table 3).
Table 3. Clinical characteristics of RCC cancer cases and controls
Feature |
Number |
Percentage |
Stage |
||
T1aNxMx |
34 |
34 |
T1bNxMx |
16 |
16 |
T2NxMx |
11 |
11 |
T3aNxMx |
17 |
17 |
T3bN1Mx |
7 |
7 |
T3bN2Mx |
5 |
5 |
T3bNxMx |
6 |
6 |
T4NxMx |
4 |
4 |
Total |
100 |
100 |
Tumor type |
||
Papillary type1 |
10 |
10 |
Papillary type2 |
6 |
6 |
Choromophob |
12 |
12 |
Clear cell |
60 |
60 |
Rcc oncocytic |
2 |
2 |
Scc |
1 |
1 |
Adenocarcinoma |
0 |
0 |
Rcc unclacified |
0 |
0 |
Sarcomatoeid |
2 |
2 |
Conventional |
3 |
3 |
Celecting duct carcinoma |
2 |
2 |
Metastatic |
1 |
1 |
Metanephric stroma |
1 |
1 |
Total |
100 |
100 |
Tumor location |
||
Upper pole |
29 |
29 |
Lower pole |
30 |
30 |
Middle pole |
31 |
31 |
Upper-middle |
4 |
4 |
Lower-middle |
3 |
3 |
Upper-lower-middle |
3 |
3 |
Laterality |
||
Right |
59 |
59 |
Left |
41 |
41 |
Tumor grade |
||
I |
21 |
21 |
II |
42 |
42 |
III |
21 |
21 |
IV |
16 |
16 |
Association of the rs4938723 SNP and rs6577555 SNP with renal cell carcinoma risk
The distributions of genotypes for the miRNA polymorphisms pri-miR-34b T>C rs4938723 and miR34a A>C rs6577555 in RCC patients and control cases are illustrated in Table 4. We observed that pri-miR-34b CC and miR34a CC genotypes were more frequent in both groups, but no statistically significant difference was in the distribution of the other genotypes between RCC cases and the control group.
Table 4. The distributions of genotypes for the miRNA polymorphisms pri-miR-34b T>C rs4938723 and miR34a A>C rs6577555 in RCC patients and control cases
SNP |
Case/Control |
TT |
CT |
CC |
rs4938723 T>C |
Patients (N: 100) |
33 (33%) |
45 (45%) |
6 (6%) |
Controls (N: 100) |
42 (42%) |
44 (44%) |
14 (14%) |
|
Total (N: 200) |
75 |
89 |
19 |
|
P-value |
0.189 |
0.887 |
0.060 |
|
SNP |
Case/Control |
AA |
CA |
CC |
rs6577555 A>C |
Patients (N: 100) |
36 (36%) |
41 (41%) |
23 (23%) |
Controls (N: 100) |
43 (43%) |
44 (44%) |
13 (13%) |
|
Total |
79 |
85 |
36 |
|
P-value |
0.312 |
0.668 |
0.066 |
Subgroup analysis according to RCC subtype
We examined the association of the two miRNA SNPs with each RCC subtype. We observed that rs6577555 polymorphism revealed a significant association with tumor type (P-value=0.047), but rs4938723 SNP was not related to the classification of tumor subtype (P-value=0.42) (Table 5).
Table 5. Association of the pri-miR-34b T>C rs4938723 and miR34a A
Tumor type |
Rs6577555 |
Rs4938723 |
||||
AA |
AC |
CC |
TT |
CT |
CC |
|
Papillary type1 |
5 |
3 |
2 |
5 |
5 |
0 |
Papillary type2 |
2 |
3 |
1 |
0 |
6 |
0 |
Choromophob |
2 |
8 |
2 |
2 |
9 |
1 |
Clear cell |
17 |
33 |
10 |
27 |
30 |
3 |
RCC oncocytic |
0 |
2 |
0 |
0 |
2 |
0 |
Squamous Cell Carcinoma |
1 |
0 |
0 |
0 |
0 |
1 |
Adenocarcinoma |
0 |
1 |
0 |
0 |
0 |
0 |
RCC unclassified |
0 |
0 |
0 |
0 |
0 |
0 |
Sarcomatoid |
0 |
1 |
1 |
0 |
1 |
1 |
Conventional |
2 |
1 |
0 |
1 |
2 |
0 |
Collecting duct carcinoma |
1 |
1 |
0 |
1 |
1 |
0 |
RCC metastatic |
1 |
0 |
0 |
0 |
1 |
0 |
Metanephric stromal |
1 |
0 |
0 |
1 |
0 |
0 |
Total |
32 |
52 |
16 |
37 |
57 |
6 |
We compared persons with and without RCC based on lymph node involvement, distant metastasis, surgical margin status, perineural invasion, Perivascular invasion, Perirenal Fat Invasion, and adrenal invasion. Rs4938723 polymorphism revealed a remarkable association with perirenal Fat Invasion (P-value=0.020), distant metastasis (P-value=0.03), and lymph node involvement (P-value=0.02) as patients. Rs4938723 polymorphism revealed a significant association with tumor stage (P-value=0.025), but rs6577555 SNP did not relate to the classification of malignant tumors stage (P-value=0.38) (Table 6).
Table 6. Comparison between gene genotypes and pathological stage
SNP |
Pathologic stage |
TT |
CT |
CC |
Total |
Rs4938723(P=0.025) |
T1a |
16 |
16 |
8 |
40 |
T1bNxMx |
5 |
15 |
1 |
21 |
|
T2NxMx |
5 |
1 |
1 |
7 |
|
T3aNxMx |
3 |
11 |
7 |
21 |
|
T3bNxMx |
2 |
1 |
2 |
5 |
|
T3bN1Mx |
0 |
1 |
2 |
3 |
|
T3bN2Mx |
2 |
0 |
0 |
2 |
|
T4NxMx |
0 |
0 |
1 |
1 |
|
Total |
33 |
45 |
22 |
100 |
|
Rs6577555 (p=0.38) |
Pathologic stage |
AA |
AC |
CC |
Total |
T1a |
13 |
19 |
8 |
40 |
|
T1bNxMx |
10 |
7 |
4 |
21 |
|
T2NxMx |
2 |
3 |
2 |
7 |
|
T3aNxMx |
8 |
9 |
4 |
21 |
|
T3bNxMx |
2 |
1 |
2 |
5 |
|
T3bN1Mx |
0 |
1 |
2 |
3 |
|
T3bN2Mx |
1 |
1 |
0 |
2 |
|
T4NxMx |
0 |
0 |
1 |
1 |
|
Total |
36 |
41 |
23 |
100 |
The results showed a significant connection between the rs4938723 polymorphism and tumor grade of RCC patients (P-value=0.018), but no significant correlation was detected between the pri-miR-34b rs4938723 variant and tumor types (P-value=0.42). Rs6577555 polymorphism revealed a significant correlation with tumor types of RCC subjects (P-value=0.047), but rs6577555 SNP did not associate with their tumor grade of them (P-value=0.11) (Table 7).
Table 7. Comparison between gene genotypes and tumor grade with RCC tumor type
Polymorphism |
Tumor Grade |
CC |
CT |
TT |
Total |
Rs4938723 |
1/4 |
3 |
10 |
6 |
19 |
2/4 |
11 |
18 |
15 |
44 |
|
3/4 |
5 |
8 |
8 |
21 |
|
4/4 |
3 |
9 |
4 |
16 |
|
Total |
22 |
45 |
33 |
100 |
|
Rs6577555 |
Tumor Grade |
AA |
AC |
CC |
Total |
1/4 |
6 |
6 |
7 |
19 |
|
2/4 |
9 |
19 |
16 |
44 |
|
3/4 |
4 |
11 |
6 |
21 |
|
4/4 |
4 |
5 |
7 |
16 |
|
Total |
23 |
41 |
36 |
100 |
Discussion
In This paper, we discovered that rs4938723 in pri-miR-34b and rs6577555 in miR34a were linked to the development of RCC in Iranian population. Both variants did not correlate with age and sex in RCC patients, but pri-miR-34b rs4938723 polymorphism is more frequent in RCC grade 2/4. According to the results, CC genotype for both rs4938723 and rs6577555 is correlated with distant metastasis and lymph node involvement.
MicroRNAs are small groups of ncRNAs that are now recognized as the primary regulator of gene activity. Numerous studies have shown the association between cancer risk and miR-SNPs. when SNPs in miRNA genes could possibly affect miRNA biogenesis and change target selection, much attention has been paid to evaluating the assosiation between the cancer risk and variation in microRNAs (15). MiR-34a and miR-34b are a member of the highly conserved miR-34 family. The mir 34 family, like the let7 family, is one of the leading families of TSmirs families. This family includes three memberes: miR-34a, miR-34b, and miR-34c (16).
A study by Zhang S and colleagues showed that miR-34 rs4938723 is a potentially functional variation in the promoter area associated with RCC risk in a Chinese population (17). A meta-analysis study indicated the association between microRNA-34b/c rs4938723 polymorphism and risk for cancer development (18). Another meta-analysis highlighted that hsa-miR-34b/c rs4938723 polymorphism might have a contradictory role in various malignancies (19). A study by Wang X. et al., showed a remarkable connection between the rs4938723 polymorphism and cancer risk in the codominant model of various cancer types, such as osteosarcoma, nasopharyngeal cancer, and kidney cancer (20).
A study by Yan Sun et al., was conducted to investigate the association of SNPs in the hsa-miR-34a regulatory region with diabetes mellitus (DM) or diabetic nephropathy (DN) susceptibility. Three SNPs (rs12128240, rs2666433, rs6577555) in miR-34a were analyzed in Type 2 diabetes mellitus (T2DM) patients with or without DN and normal controls (21). A study by Zhang S and colleagues revealed no significant association between rs12128240 or rs6577555 and ischemic stroke (IS) found in the Chinese population (22).
Several studies indicate the role of hsa-miR-34a polymorphism in different malignancies, both in tumor formation and poor prognosis of patients (23, 24). Several studies indicate the role of hsa-miR-34a polymorphism in different malignancies, both in tumor formation and poor prognosis of patients (24). Both Mutations in the P53 gene and deletion of the short arm of chromosome 1 are connected to lower levels of miR-34a. MiR-34a plays as a TSmir and physically affects the proto-oncogene MET and targets its function In head and neck squamous cell carcinoma (HNSCC) (23).
Conclusion
This study aimed to determine signature of miRNA that could recognize RCC blood serum of patients and healthy controls and confirm distinguished miRNAs as potential biomarkers for RCC. The results of these studies identified that pri-miR-34b T>C rs4938723 and miR34a A>C rs6577555 polymorphisms helped predict the severity of kidney cancer as well as patients’ survival rate and might be used in the future as diagnostic biomarkers or targets for treatment that require observations. To approve our findings, greater sample sizes and various ethnicities are required in future studies.
Declarations
Author’s contributions
All authors contribute equally.
Acknowledgment
Special thanks to the Department of Urology, Zahedan University of Medical Sciences, Zahedan, Iran.
Conflict of interest
The authors have no conflicts of interest to declare.
Funding
There is no funding.
Ethics statement
This research was performed under the declaration of Helsinki (IR. SBMU.UNRC.1397.33). Participants provided informed written consent to participate in the study.
Data availability
Data will be provided on request.
Abbreviations
Arms PCR Amplification refractory mutation system polymerase reaction chain
CLL Chronic lymphocytic leukaemia
DM Diabetes mellitus
DN Diabetic nephropathy
HCC Hepatocellular carcinoma
HNSCC Head and neck squamous cell carcinoma
IS Ischemic stroke
MiRNA Micro RNA
RCC Renal Cell Carcinoma
SNP Single nucleotide polymorphism
T2DM Type 2 diabetes mellitus