Cryptorchidism refers to undescended testis, is one of the most frequent urogenital abnormalities in newborn boys (15), and it is the best-characterized risk factor for reduced fertility in males and testicular neoplasia (6). In human, the testosterone and insulin-like factor 3 (INSL3) are the main regulators for testicular descenting from the abdominal cavity to the bottom of the scrotum (16, 17). Mutations in the genes encodes for INSL3 and its receptor and androgen receptors were considered to be the main cause of some forms of cryptorchidism (18–20). Although some cases of cryptorchidism in humans can be attributed to known genetic defects and several pathogenetic mechanisms for cryptorchidism have been described, the exact cause of cryptorchidism in most patients remains unknown.
PFKM gene maps on chromosome 12q13.11 and its coding region consist of 2340 bp nucleotides, which encodes approximately 780 amino acids (21). A key glycolytic regulatory enzyme, PFKM, which is concerned as an energy activator of muscle glycolysis, is encoded by the PFKM gene. Recently, there have been several researches focused on the association between PFKM mutations and different types of cancers, such as bladder cancer (22), breast cancer (23), and ovarian cancer (24), etc. The funny fact that most of these cancers are also highly correlated with hormones (25–27). The occurrence of cryptorchidism was also thought to be influenced by the hormones coincidentally. What is even more interesting is that earlier research in rat embryos indicated that growth retardation and congenital defects could be caused by interfering with glycolysis, which is an important source of ATP production (28). As is known to us, PFKM is a pivotal regulator of cellular glycolysis by catalyzing the phosphorylation of fructose-6-phosphate to fructose-1,6-bisphosphate. Taken together, there may be a significant role of the PFKM gene to play in the pathogenesis of cryptorchidism. Among the 3 tag SNPs we have chosen in PFKM, one SNP is located in the intron region and was observed to be associated with cryptorchidism risk, and the other two SNPs are located in the synonymous codon regions and both of them have no significant correlation with cryptorchidism risk. Julia S et al.(29) identified the muscle patterning defection is association with cryptorchidism in the rat fetal. Therefore, we suspected that the tag SNP rs2228500 may be affecting protein structure by influencing coding amino acids. In addition, it may affect the energy metabolism in muscle of glycolysis pathway, which may be a key factor in the lack of testicular descent motivation.
In summary, we investigated the impact of the PFKM gene polymorphism on cryptorchidism, and we observed significant differences in the frequency of alleles and genotypes at 1 tag SNP between patients and controls. We have offered primary evidence that the G allele and the G/G genotype of rs2228500 SNP in the PFKM gene are more frequent in patients with cryptorchidism than healthy controls. It implies that the polymorphism of the PFKM gene locus (rs2228500) may be a new genetic marker for cryptorchidism susceptibility and these alleles and genotypes may be risk factors for this disease.
Although we detected the association between the PFKM SNPs and cryptorchidism, there were certain limitations in this study. The sample size and ethic types of this study were small and these results needs to be further confirmed in a larger cohort. Moreover, the function and the underlying signal transduction mechanisms of PFKM gene in cryptorchidism development need to be clarified.
Table 3
Distributions of PFKM SNPs among cryptorchidism patients and controls as well as their associations with cryptorchidism susceptibility.
Genetic model | Genotype | Patients | Controls | Logistic regression | |
N = 140(%) | N = 227(%) | OR(95%CI) | p |
rs2228500 | |
Codominant | G/G | 85(60.7%) | 105(46.3%) | 1.00 | 0.025 |
| A/G | 47(33.6%) | 102(44.9%) | 1.76(1.12–2.75) | |
| A/A | 8(5.7%) | 20(8.8%) | 2.02(0.85–4.82) | |
Dominant | G/G | 85(60.7%) | 105(46.3%) | 1.00 | 0.0069 |
| A/G-A/A | 55(39.3%) | 122(53.7%) | 1.80(1.17–2.75) | |
Recessive | G/G-A/G | 132(94.3%) | 207(91.2%) | 1.00 | 0.27 |
| A/A | 8(5.7%) | 20(8.8%) | 1.59(0.68–3.72) | |
Overdominant | G/G-A/A | 93(66.4%) | 125(55.1%) | 1.00 | 0.03 |
| A/G | 47(33.6%) | 102(44.9%) | 1.61(1.04–2.50) | |
Log-additive | | | | 1.58(1.11–2.24) | 0.0089 |
rs4075913 | |
Codominant | A/A | 65(47.1%) | 87(39.5%) | 1.00 | 0.21 |
| A/G | 60(43.5%) | 101(45.9%) | 1.26(0.80–1.98) | |
| G/G | 13(9.4%) | 32(14.6%) | 1.84(0.89–3.78) | |
Dominant | A/A | 65(47.1%) | 87(39.5%) | 1.00 | 0.16 |
| A/G-G/G | 73(52.9%) | 133(60.5%) | 1.36(0.89–2.09) | |
Recessive | A/A-A/G | 125(90.6%) | 188(85.5%) | 1.00 | 0.15 |
| G/G | 13(9.4%) | 32(14.6%) | 1.64(0.83–3.24) | |
Overdominant | A/A-G/G | 78(56.5%) | 119(54.1%) | 1.00 | 0.65 |
| A/G | 60(43.5%) | 101(45.9%) | 1.10(0.72–1.69) | |
Log-additive | | | | 1.32(0.96–1.82) | 0.084 |
rs11168417 | |
Codominant | C/C | 111 (79.3%) | 194 (85.5%) | 1.00 | 0.21 |
| C/T | 28 (20%) | 30 (13.2%) | 0.61 (0.35–1.08) | |
| T/T | 1 (0.7%) | 3 (1.3%) | 1.72 (0.18–16.70) | |
Dominant | C/C | 111 (79.3%) | 194 (85.5%) | 1.00 | 0.13 |
| C/T-T/T | 29 (20.7%) | 33 (14.5%) | 0.65 (0.38–1.13) | |
Recessive | C/C-C/T | 139 (99.3%) | 224 (98.7%) | 1.00 | 0.57 |
| T/T | 1 (0.7%) | 3 (1.3%) | 1.86 (0.19–18.07) | |
Overdominant | C/C-T/T | 112 (80%) | 197 (86.8%) | 1.00 | 0.087 |
| C/T | 28(20%) | 30(13.2%) | 0.61(0.35–1.07) | |
Log-additive | | | | 0.78(0.46–1.30) | 0.21 |
CI: confidence interval; OR: odds ratio. Bold Values indicate a significant difference at the 5% level. |