1. Introduction
Melanoma ranks among the most aggressive human cancers. It develops from the uncontrolled proliferation of melanocytes and most frequently linked to UV radiation exposure, which causes genetic mutations and promotes tumor formation. Annually, approximately 320,000 new cases of cutaneous melanoma are diagnosed worldwide, resulting in about 57,000 deaths. In Iran, it accounts for 5.2% of all malignancies. Unlike squamous and basal cell cancers, melanomas can spread unpredictably and potentially affect any organ in the body. Moreover, melanoma demonstrates significant resistance to current therapies, especially during its metastatic stage. There are three main subtypes of melanoma: Superficial Spreading Melanoma (SSM), which comprises approximately 70% of cases and is linked to UV exposure or arises from naevi; Lentigo Maligna Melanoma (LMM), responsible for 4 to 15% of cases and associated with UV exposure and older age; and Nodular Melanoma (NMM), making up 5% of cases and characterized by rapid progression and a high rate of metastasis. Other variants are uncommon (1-5). 
MicroRNAs (miRNAs) are a class of small noncoding RNAs (containing 21 to 23 nucleotides) that regulate gene expression by interacting with the 3′-UTR of specific mRNA targets, resulting in either degradation of the transcript or inhibition of translation. MiRNAs are derived from coding and non-coding transcriptional units found in introns, exons, or intergenic regions. Their generation involves a complex, multistep process that requires the coordinated action of proteins in both the nucleus and cytoplasm (6-8). MiRNAs are involved in oncogenic processes through their ability to regulate the expression of various tumor suppressor genes and oncogenes, as well as by triggering pathways associated with cancer progression. Over the past few years, profiling and sequencing analyses have provided evidence that miRNA signatures can be utilized in cancer for classification, diagnostic purposes, and prognosis assessment. Researchers have evaluated the diverse patterns of miRNA expression and their functions across various types of cancer. MiRNAs may function either as oncogenes or tumor suppressors depending on the cancerous context. Oncogenic miRNAs enhance tumor development by inhibiting tumor suppressor genes or apoptosis-related genes, while tumor suppressor miRNAs suppress cancer progression by targeting oncogenes or genes that regulate cell cycle progression and promote apoptosis (9-11).
Melanoma miRNA profiles revealed that various miRNAs modulate gene translation or stability depending on the disease stage, highlighting their crucial role in regulating processes linked to melanoma progression. Recent findings have revealed that certain types of miRNAs, such as miR-98, miR-200c, miRNA-106b, miR-33a, and miR-524-5p, are implicated in melanoma progression. Considering previous research, it indicates that miR-425 may perform multiple roles by modulating various downstream targets across different types of cancer. This microRNA functions as a tumor suppressor by targeting IGF1 in melanoma (12-14).
Another type of functional noncoding RNA is long noncoding RNA (lncRNA), which is longer than 200 nucleotides and is involved in the regulation of many tumor suppressor and oncogene expressions (15,16). Their role has been reported in various tumors, including melanoma.
MHENCR (melanoma highly expressed noncoding RNA), as a noncoding RNA, plays a crucial role in tumorigenesis. Studies have shown that it is upregulated in melanoma and promotes melanoma progression. This lncRNA specifically binds to miR-425 and miR-489, leading to increased expression of their target genes, namely IGF-1 and SPIN1, thereby activating the PI3K-Akt signaling pathway.
This pathway plays a crucial role in the coordinated regulation of metabolism, as well as cell growth and proliferation. In many types of cancer, components of this pathway become dysregulated, ultimately, resulting in the inhibition of apoptosis and an increase in cell proliferation and growth (17-21).
Therefore, given that investigating the MHENCR/miR‑425/PI3K axis in melanoma may offer new insights into the molecular mechanisms of tumorigenesis and metastasis, this study examined the expression of these genes and evaluated how alterations in their expression correlate with patients’ clinical characteristics compared to healthy controls. Considering the growing burden of melanoma and limitations of tissue‑based diagnostics, there is also a particular need for non‑invasive biomarkers in Iranian populations to facilitate early detection, risk stratification, and disease monitoring through accessible and minimally invasive approaches.
2. Materials and Methods
2.1 Sample Collection
In this study, 34 samples were collected from Razi Specialized Dermatology Hospital in Tehran and Vali-e-Asr Specialized Hospital in Zanjan. These included 17 melanoma samples and 17 normal samples from healthy individuals. All samples were obtained with the patients’ informed consent and ethical approval from the ethics committee (IR.IAU.Z.REC.1396.101). Healthy individuals and patients were between 20 and 80 years old, and blood samples were collected in EDTA-containing tubes and stored at 20°C until use. Clinical characteristics of melanoma patients are shown in the Table 1.
2.2 RNA Extraction and cDNA Synthesis
RNA was extracted using the Roche RNA extraction kit following the manufacturer’s protocol.  RNA quality was assessed using both NanoDrop spectrophotometry and agarose gel electrophoresis. cDNA was synthesized using the Takara PrimeScript RT reagent kit. During cDNA synthesis, appropriate volumes of buffer, easy dilution solution, oligo dT primers, random 6-mer primers, reverse transcriptase enzyme, total RNA, and master mix were combined and incubated in a PCR machine under the following temperature conditions: 37°C for 15 minutes, 85°C for 5 seconds, and 4°C for 5 minutes. Upon completion, the samples were stored at -20 °C. Stem-loop primers targeting mir33a   and the control mir199 were employed during cDNA synthesis from miRNA. The sequences of these primers are listed in Table 2. Primers were designed and validated for specificity using Primer-BLAST (NCBI) and for secondary structure using OligoAnalyzer.
2.3 Quantitative Real-Time PCR (qRT-PCR)
The real-time PCR reaction was carried out in a total volume of 25 µl, containing 1 µl of forward primer, 1 µl of reverse primer, 12.5 µl of SYBR Green, 8.5 µl of water, and 2 µl of cDNA. The samples were then placed in the real-time PCR machine and run for 45 cycles with a total reaction volume of 25 µl, using the annealing temperatures specific to each primer, as indicated in Table 2.  Primer sequences used in the study are listed in Table 2. The melting curve was analyzed at the end of each reaction. Experiments were performed in triplicate to ensure reproducibility and accuracy of the results (22).
2.4 Statistical Analysis
Statistical analysis was performed using SPSS software. Expression changes between tumor and normal samples were assessed using the 2^-ΔΔCT method. The significance of the results was evaluated by the t-test, with p-values less than 0.05 considered statistically significant.