1. Introduction
Melanoma is one of the most malignant skin tumors, which mainly includes four histopathological types, superficial spreading melanoma, nodular melanoma, lentigo maligna melanoma, and acral lentiginous melanoma [
1]. About 25% of melanoma cases occur on pre-existing moles. Melanocytes are neural crest-derived cells that are mainly distributed in the basal epidermis, hair follicles, mucosal surfaces, meninges, and choroidal layers of the eye [
2]. Approximately 1–8% of patients with a history of melanoma will develop multiple primary melanomas [
3]. The transformation of melanocytes into malignant melanoma involves interactions among genetic factors, ultraviolet radiation (UV), and the tumor microenvironment. UV radiation mediates direct DNA damage by forming photoproducts and generating reactive oxygen species (ROS) [
4]. Most melanomas develop through an initial phase known as the radial growth phase (RGP), including in situ and minimally invasive tumors, with a near 100% chance of cure. Despite the current shift toward early identification of melanoma, approximately 70% of melanomas have evolved by diagnosis to a point known as the vertical growth phase (VGP) or tumorigenic melanoma [
5]. Pathogenic features of melanoma include heterotypic melanocyte growth and expansion, self-sufficient growth factors, insensitivity to growth inhibitors, evasion of apoptosis, potential for unlimited replication, ongoing angiogenesis, and tissue invasion and transfer [
6].
Accurate classification helps patients get early diagnosis and the best possible treatment. The diagnosis of melanoma is mainly based on its clinical features. The ABCD rule is used to identify pigmented lesions suspected of early melanoma: asymmetry, border irregular, color inhomogeneous, and diameter greater than 5 mm [
7]. Through a deep neural learning network, combined with the location of the disease and the classification characteristics of benign and malignant skin lesions, an algorithm can be used to identify benign and malignant skin lesions of epidermis and melanocyte origin from images of skin lesions [
8]. Types of melanomas include desmoplastic melanoma, polypoid melanoma, primary dermal melanoma, verrucous malignant melanoma, pigmented epithelioid melanocytoma, mucosal melanoma, follicular melanoma, and melanoma with nonmelanocytic differentiation. Two subtypes of melanoma that are difficult to diagnose are nevoid melanoma and amelanotic melanoma [
9]. Oral melanoma is extremely rare, accounting for 0.2% to 8.0% of all malignant melanomas, with the most common sites being the hard palate and gums [
10]. Primary intestinal melanomas are rare, whereas small intestinal metastatic melanomas are common because cutaneous melanomas tend to metastasize to the gastrointestinal tract [
11].
Proteins for clinical pathology of melanoma are related to epithelial–mesenchymal transition (SPARC and N-cadherin), cell adhesion and migration (ALCAM and ADAM-10), regulation of mitosis (PLK1), cell survival (FOXP1), and the function of the Golgi apparatus (GOLPH3 and GP73) [
12]. A single gene mutation in the hair color gene
STX17G causes an unbalanced behavior of melanocytes, leading to gray tendencies and the development of vitiligo and melanoma. The hair color genes
ASIP and
MC1RE can be based on black > jujube > sorrel, increasing the possible risk of melanoma [
13]. The interactions between melanoma cells and their interactions with cell types in the microenvironment occur through endocrine and paracrine communication, direct cell–cell and cell–matrix contacts, and gap junctional intercell communication (GJIC) [
14].
The positive rate of immunohistochemistry-related antigens of malignant melanoma can be queried through the DaKa pathological diagnostic tool (
http://dia.dakapath.com/wap (accessed on 13 May 2021)). Among them, the almost always positive (≥95%) category includes CD146, IRF-4, MLH1, MSH-2, Myosin, ProExC, S100, SOX10, VERSICAN, and Vimentin; the usually positive (≥75%, <95%) category includes α-1-anti-trypsin, BCL-2, BCL-x, Cathepsin-K, CD63, HMB45, iNOS, KBA62, melan-A, Nestin, P16, PTEN, topo2A, and Tyrosinase; the frequently positive (≥55%, <75%) category includes α-1-anti-chymotrypsin, CD45RO, CD68, E-cadherin, MITF, MSE, PNL2, SOX2, TGF-β1, and XRCC6; the sometimes positive (≥35%, <55%) category includes BRAF-V600E, CD10, CD117, CD133, CD271, CD7, CEA-P, MAGE-1, N-Cadherin, Osteonectin, P21, P27, P504s, P53, PGP9.5, VEGF, WT-1, and XRCC5; the occasionally positive (≥15%, <35%) category includes CD138, CD5, CD64, CD99, Cyclin-D1, Inhibin, Iysozyme, MAGE-A4, mdm2, and VEGFr2; the rarely positive (≥5%, <15%) category includes Calretinin, CD56, CD57, EpCAM, FKBP12, FL1-1, GAP43, HBME-1, Hep-Par1, and Synaptophysin; the almost always negative (<5%) category includes 34bE12, actin-HHF-35, AE1, AE1/AE3, Arginase, Brachyuny, CA15-3, CA19-9, CAM5.2, CD15, CD163, CD19, CD1a, CD2, CD20, CD3, CD30, CD31, CD34, CD4, CD45, CD74, CD75, CD79a, CD8, CDX-2, CEA-M, CgA, CK20, CD7, CK8, CK-HMW, CK-LMW, GCDFP-15, GFAP, HCAD, HCG-α, HER2, Laminin, MAC387, Mesothelin, Myogenin, NY-ESO-1, OCT4, Osteocalcin, P63, PAX5, PAX8, PKK1, PSA, SMA, TAG72, TDT, and TFE3. Different patients have different clinical manifestations and pathological manifestations of melanoma, and the corresponding antigen markers should be selected for diagnosis and classification according to the medical history and antigen-related pathological characteristics.
Clark invasive grade and Breslow thickness grade are the current methods to judge the prognosis of melanoma. Breslow’s original study found that melanomas less than 0.76 mm thick had a good prognosis and no metastases [
15]. Surgery remains the current gold standard. Extensive resection is performed according to the Breslow depth of the lesion and sentinel lymph nodes. Medium-depth melanomas (1.0 to 4.0 mm) should be considered for sentinel lymph node biopsy, and melanomas 0.75 to 1.0 mm in depth with high-risk lesions (ulcers and high mitotic index) should be considered for nodule biopsy [
16]. For the mitotic rate (MR), the seventh edition of the TNM staging uses ≥1/mm
2 as the main criterion for defining pT1b stage melanoma [
17]. The surgical margins with Breslow thickness ≤2 mm are expanded to 1 cm beyond the normal skin lesions, and the margins with Breslow thickness >2 mm and ≤4 mm are expanded to 2 cm. Thicker skin lesions should also be expanded to at least 2 cm [
18]. Prophylactic adjuvant regional perfusion for primary melanoma is currently in clinical trials. Postoperative radiotherapy, molecular targeted therapy, and other adjuvant methods should be considered in multiple melanoma and metastatic melanoma. Radiation therapy can be used for palliative treatment of inoperable patients combined with palliative adjuvant therapy such as hyperthermia, or nonradical resection of skin lesions can be performed after surgery [
18]. Multivariate analysis indicated that the most important factors for patients with stage I and II melanoma were tumor thickness, the presence of ulcers, and the anatomical location of the primary tumor (head and neck lesions had a worse prognosis than extremity lesions). For patients with stage III melanoma, all of the above factors plus the degree of lymph node disease have prognostic significance. For patients with stage IV melanoma, the main prognostic factors are the number of metastatic lesions and the site of metastatic involvement (visceral lesions have a poorer prognosis) [
19].
For molecular targeted therapy, melanoma-related target sites need to be sought. A recent study evaluated anti-CTLA-4 and anti-PD-1/PD-L1 blockade therapy in mucosal melanoma [
20].
KIT-mutant melanomas exhibit clinical responses to inhibitors of type III transmembrane receptor tyrosine kinases [
21]. Activating mutations in
c-KIT are frequently found in mucosal melanoma, and this immunotherapy has emerged as a potential treatment. Both acral and mucosal melanomas have
KIT mutations in approximately 15% of cases.
BRAF or
NRAS mutations are found in approximately 10–15% of acral melanomas, but the incidence of these mutations is lower in mucosal melanomas. Concomitant use of BRAF and MEK inhibitors is one of the standards of care for patients with advanced
BRAF-mutant melanoma [
22]. Combination strategies of BRAF inhibitors with ipilimumab and anti-CTLA-4 antibodies with MEK inhibitors or metformin are now in clinical trials [
4]. Some important signaling pathways involved in the pathogenesis of melanoma are the mitogen-activated protein kinase (MAPK) pathway, the phosphoinositide 3-kinase (PI3K/PTEN/AKT) pathway, and the MITF signaling pathway. Currently important targeted therapies include vemurafenib, dabrafenib, and trametinib, as well as immunotherapies such as pembrolizumab, nivolumab, ipilimumab [
6]. Dacarbazine chemotherapy remains the preferred drug for disseminated melanoma, but remissions are usually transient [
23].
The above classification of melanoma, pathological features, diagnostic staging criteria, and surgical indications are used to improve the research on the diagnosis rate and therapeutic targets of melanoma. In this paper, by collecting skin melanoma-related data in TCGA database, we analyzed the changes and differential expression of melanoma-related genes. According to the data analyzed in TCGA database, the occurrence of melanoma involves many verified mutated genes, resulting in the occurrence of tumor-promoting activity, loss of tumor-suppressive function, and epigenetic modifications such as methylation to regulate gene expression [
24]. TCGA database shows a high mutation rate of
KYNU in melanoma. Studies have shown that PEG-KYNUase drugs can deplete the accumulation of the immunosuppressive product kynurenine (KYN) induced by its upstream metabolic enzyme indoleamine 2,3-dioxygenase (IDO) to treat B16-F10 melanoma in mice [
25]. KYNU, as a hydrolytic enzyme in tryptophan metabolic pathway, is expressed in different cell types and diseases throughout the body. By verifying the expression of KYNU in melanoma, the role of KYNU in abnormal proliferative diseases was further reflected. We determined whether the expression pattern of KYNU in melanocyte-derived heterotypic tumor cells is the same as that in epidermal keratinocytes, whether the effects of KYNU on proliferation, differentiation, and metabolism are involved in the process of malignant transformation in melanoma, such as the process of melanogenesis, and whether mutations in the active site of the KYNU enzyme can serve as screening targets in the pathogenesis of melanoma. Thus, this paper takes KYNU as the research target to explore the expression changes of melanoma tumor cells in the functions of proliferation, differentiation, apoptosis, invasion, and migration.
2. Materials and Methods
2.1. Data Download and Analysis
TCGA is a cancer genomics program that provides publicly available data contributing to cutting-edge cancer research (
https://portal.gdc.cancer.gov (accessed on 10 September 2020)). The copy numbers of gene transcriptomes in 472 samples of nevi and melanoma in the Exploration-Melanoma TCGA-SKCM project were collected, including three solid tissue normal samples (paracancerous), 100 primary melanoma samples, and the remaining 367 metastatic melanoma samples. The distribution of most frequently mutated genes is automatically generated by TCGA analysis website. The seq.counts.gv file of the gene expression data of the case was downloaded, and the R language and python language were used to splicing the count file into a genome-wide multi-sample matrix file with the gene symbol as the row and the clinical sample as the column. The merged matrix python file script can be found at
https://github.com/wmin-debug/TCGA-melanoma-merge-matrix-python-file.git (accessed on 19 May 2022). Case-specific clinical data were also downloaded. The gene symbol and corresponding gene name were used to obtain the gene expression files of each sample. The gene expression averages of normal samples and melanoma samples were obtained through Excel and sorted, and the changes in differentially expressed genes were screened out.
The genome ID was converted to gene symbol through the Ensembl database, and then the differential gene expression levels of adjacent normal tissues and tumor tissues were compared. Since multiple gene variants were present, the expression changes of target genes were screened according to the type of disease. According to the melanoma-related tumor factors reported in the literature and the melanoma-related signaling pathway map05218 melanoma file in the KEGG database (
https://www.genome.jp/kegg-bin/show_pathway?hsa05218+H00038 (accessed on 15 September 2020)), we searched for its upstream and downstream mechanisms and detected their relative expression abundance in normal tissues and tumors. By searching the melanoma-related genes in TCGA database, it was found that the proportion of copy number variation (CNV) among samples varied at different levels according to the upregulation and downregulation of copy number.
GCDA correlation analysis software (
http://gdac.broadinstitute.org/runs/info/Mutation_Significance.svg (accessed on 6 February 2021)) was used to identify melanoma data of SKCM in TCGA database. Using the CopyNumberLowPass_Gistic2.Level and Correlate_Methylation_vs_mRNA.mage-tab functions, common gene mutation sites associated with melanoma could be obtained, in addition to the strength of the association between melanoma gene methylation and the impact on gene expression.
The GSE dataset was downloaded from NCBI GEO DataSets (
https://www.ncbi.nlm.nih.gov/gds/ (accessed on 17 November 2020)), and the corrected FPKM data of the genomic expression levels in the GSE152699 and GSE152722 datasets were collected. GSE152699 is a melanoma cell sample, with
n = 6 in the untreated group and vemurafenib-treated group; the mouse tissue samples in the GSE152722 dataset have
n = 8 in the primary group,
n = 13 in the repressed BRAF group, and
n = 8 in the recurrent group. The expression level of KYNU was selected, and the expression difference of KYNU in melanoma was analyzed by
t-test or one-way ANOVA.
2.2. Immunohistochemistry
All specimens were fixed with 10% paraformaldehyde solution for 24–48 h. Then, the tissue samples were dehydrated and embedded in paraffin. Here, 10 μm was selected as the target slice thickness for the wax block, and it was quickly adsorbed onto the glass. The sections were dewaxed by xylene twice and were soaked in a gradient concentration of ethanol for rehydration. Next, 3% H2O2 and 0.01 mol/L citrate buffer were used for antigen retrieval. After boiling the slices and then cooling down naturally, normal goat serum blocking solution was added dropwise. Then, 50 µL of the primary antibody with a dilution of 1:100–1:1000 was added at 4 °C overnight. Isotype IgG staining of the target antibody was set up, whereby 40–50 μL of biotinylated secondary antibody was added dropwise at 4 °C for 1 h. The sample was washed three times with PBS for 5 min. The DAB color was developed, and hematoxylin was counterstained. Ethanol along a concentration gradient and xylene were used to dehydrate and seal the slices, respectively.
KYNU staining was performed by immunohistochemistry in the same manner as described above. The intradermal nevus and melanoma tissue staining antibody was obtained from Sigma (WH0008942M2, 1:1000, 1 μg/mL dilution); the staining antibody of KYNU in psoriasis, melanoma, squamous cell carcinoma, and other diseases was obtained from GeneTex (#GTX33291, 1:200 dilution).
The semiquantitative analysis method of immunohistochemical results involved randomly selecting five high-power fields with a scale bar = 90 µm for each tissue sample of melanoma and intradermal nevus, followed by counting 100 cells in each field. According to the 1–4 grade classification, the staining intensity and the rate of positive cytoplasmic staining in each visual field were evaluated, and the scores of each group were classified into melanoma and intradermal nevus. The unpaired t-test was used to compare the statistical differences in the staining intensity and the rate of positive staining between the two groups.
2.3. Cell Culture and Transfection
Immortalized keratinocyte cell lines HaCaT and HEKα and human melanoma cell lines A375 and H1205-lu were obtained from an ATCC agent (Shanghai, China) or donated by the laboratory research group. Keratinocyte cell lines and melanoma cell lines were cultured and passaged in Dulbecco’s modified Eagle’s high-glucose medium (H-DMEM) containing 10% fetal bovine serum and 1% penicillin/streptomycin, before incubating at 37 °C with 5% CO2.
For KYNU overexpression, 1 μg of kynuORF-Pcmv66-Entry recombinant plasmid (OriGene, #RC214932) was mixed with 3 μL of PEI transfection reagent, and incubated with opti-MEM for 20 min for cell transfection. When the cells grew well and the cell fusion rate reached 50–60%, the cells were replaced with serum-free opti-MEM and incubated at 37 °C for 1 h in advance. Then, 200 μL/well of the mixed solution was evenly added dropwise into the six-well plate and incubated at 37 °C after mixing.
2.4. Western Blotting
When KYNU was overexpressed, the protein expressions of melanoma-related signaling pathways AKT and ERK1/2, metabolism-related signaling pathway AMPK, invasion and migration-related factors MMP2 and MMP9, and apoptosis-related factor BCL2 were detected by Western blot. The experimental method was as previously described [
26]. Antibodies used in this paper were as follows: KYNU (GeneTex, #33291), AKT antibody (CST, #4691), ERK1/2 antibody (Santa Cruz Biotechnology, Shanghai, China, sc-514302), AMPK antibody (sc-398861; CST, #5832), p -AMPK (CST, #50081), MMP2 (CST, #4022), MMP9 (sc-21733), BCL-2 (CST, #15071), and β-actin (CST, #8H10D10).
2.5. qRT-PCR
The mRNA expression levels of E-cadherin, AKT and ERK1/2 were examined in keratinocytes (HaCaT and HEKα) and melanoma cells (H1205-lu and A375) by qRT-PCR. The cells were stimulated with IL-10 (40 ng/mL) and IFN-γ (40 ng/mL) in keratinocytes (HaCaT and HEKα) and melanoma cells (H1205-lu and A375) for 24 h, respectively. Cells were collected to extract total cell mRNA, and qRT-PCR was used to detect the mRNA expression of cell-cycle-dependent proteins and inhibitors. The relevant primers’ information of detected targets is shown in
Table 1.
2.6. CFDA-SE Method to Detect Cell Proliferation
In keratinocytes (HaCaT and HEKα) and melanoma cells (H1205-lu and A375), the fluorescence intensity of cell proliferation was detected by fluorescent staining according to the protocol of the CFDA-SE staining kit (Beyotime, Shanghai, China, C0051). According to the cell proliferation state and density, the cells were evenly spread in six-well plates, and the cell confluence rate reached 50% after 8 h of adherence. Then, 20 mL of 10× CFDA-SE reagent cell labeling solution was diluted with PBS to 200 mL of 1× labeling solution. Next, 1 mL of CFDA-SE dye was dissolved at 1000× in the dark and stored at −80 °C. Then, 100 µL of 1000× dye was diluted to 10 mL of 10× dye, which was subsequently diluted to 1×, 2×, 5× and 10× dye concentrations, and then the cells were pre-stained to detect dye activity and toxicity toward the cells. The cell culture medium was removed, and then 1 mL of CFDA-SE dye was added to the cells for labeling for 10–15 min, followed by 1 mL of normal cell culture medium, before incubating for 15 min to continue staining. After removing the dye, the medium was replaced with normal medium, and the staining was observed with a green fluorescence microscope excited at 485 nm. It was found that the cells were marked by an increase in the staining concentration, and the cell viability decreased slightly at 10×. Finally, 5× was selected as the concentration for cell staining. The cells were cultured for 48–72 h, during which the dye overflowed and decayed, and the change in medium led to a change in the dye concentration. After 62 h of cell culture, the medium was removed, and the staining images of cell proliferation were collected under a fluorescence microscope. The cells were collected and analyzed by flow cytometry for the staining fluorescence intensity excited at 488 nm in different cell lines and treatment groups.
2.7. Annexin V–PI Double Staining to Detect Cell Apoptosis
Cells were stained in keratinocytes (HaCaT and HEKα) and melanoma cells (A375) using the Annexin V–PI double-staining detection kit (BD pharmingen, Shanghai, China, #556547). At least 106 cells were digested, collected by centrifugation, and resuspended in 100 µL of medium. First, 5 µL of Annexin V was added to incubate for 10 min, and then 5 µL of PI was added for staining, before storing on ice or at 4 °C in the dark; the apoptosis rate was detected within 1 h.
Similarly, the above cells were stimulated with IL-10 (40 ng/mL) for 24 h, and the cell-cycle changes were detected using a PI single-staining kit (solarbio, CA1510). At least 106 cells were collected and incubated with 500 µL of precooled 70% ethanol at 4 °C for 2 h to fix the cells; after centrifugation, the cells were washed with PBS or medium, resuspended with 100 µL of RnaseA solution, and incubated at 37 °C for 30 min. PI staining solution was added and incubated in the dark at 4 °C for 30 min for flow analysis of cell-cycle changes.
4. Discussion
In this paper, the genome-wide analysis of melanoma tumor samples in TCGA database found that signaling pathways in melanoma mainly involved the EGF/EGFR–RAS–BRAF–MEK–ERK–CyclinD1/CDK4, Ras–PI3K–PTEN–PKB/AKT, cell-cycle pathway p14/p16 (CDKN2A)–MDM2–p53–p21–cyclinD1/CDK4/6–Rb/E2F, melanogenesis-related MITF, KIT, cell adhesion CDH1, and other genes. The basal expression of TP53, AKT1, EGFR, KIT, and CDK4 was high in cells and increased in melanoma cells. Furthermore, the expression levels of genes such as PTEN, cAMP, and BCL2 were generally decreased in melanoma cells. According to the copy number variation of genes with higher expression abundance in melanoma, oncogenes BRAF and NRAS showed more upregulation than downregulation, while tumor suppressor genes TP53 and PTEN showed a greater downregulation ratio. Through the study of SNP gene mutation sites in melanoma, it was found that BRAFV600E had a mutation rate of more than 50%, while 10% of patients had a T > C missense mutation of chromosome 1 NRASQ61. Chr3p13 enriched on chromosomes showed an increased copy number of MITF, while chr12q15 showed an increased copy number of MDM2. On the other hand, chr9p21.3 showed a decrease in the copy number of CDKN2A, while chr10q23.21 showed a decrease in the copy number of PTEN. For the negative regulation of gene expression by DNA methylation, KRT18, CDK2, JAK3, BCL2, MITF, MET, CXCL10, EGF, SOX10, SOCS3, and KIT could be seen in melanoma. In summary, we analyzed the changes in gene expression levels in melanoma, the roles of genes in melanoma signaling pathways, mutation sites, and DNA methylation modifications. The related gene expression was measured at the genome-wide level, and the genes that played a role in the transformation of melanoma from benign moles to malignant were identified, thus providing predictive targets for disease classification and treatment.
The pathogenesis of KYNU in melanoma was explored through the expression changes of KYNU in melanoma. As a therapeutic target for melanoma, BRAF can regulate the expression of KYNU. The expression level of KYNU in melanoma tissues and cells was quantitatively analyzed by immunohistochemistry and Western blot, and it was found that the expression level of KYNU in melanoma was lower. Overexpression of KYNU could promote changes in apoptosis-, metabolism-, invasion-, and migration-related proteins in melanoma. In the quickly proliferating melanoma A375 cell line, the expression levels of apoptotic protein BCL-2 and invasion-related MMP9 were significantly increased, while KYNU could promote the increased expression of MMP9 and AMPK. However, in the H1205-lu cell line with a slower proliferation rate, the expression levels of KYNU and other related proteins were lower, and the overexpression of KYNU significantly increased the expression levels of BCL-2 and AMPK. The expression of KYNU was significantly increased in HEKα and A375, but lower in H1205-lu. The expression of endogenous KYNU and overexpression of KYNU were observed through CFDA-SE fluorescent staining of cells. Overexpression of KYNU showed that the number of H1205-lu and HEKα proliferating cells was significantly reduced, the number of apoptotic cells was significantly increased, and the apoptosis of A375 was obvious. According to the change in fluorescence intensity between cells by flow analysis, a slower cell proliferation rate resulted in a stronger fluorescence intensity. Therefore, the proliferation rate of HaCaT was fastest, followed by A375 and HEKα, while that of H1205-lu was slowest. In conclusion, when the expression level of KYNU in tumor cells is high, the overexpression of KYNU has no significant effect. Knockout of KYNU can accelerate the cell cycle and promote tumorigenesis. In melanomas with low expression of KYNU, overexpression of KYNU can promote tumor cell apoptosis. This is considered to be related to the decreased expression of its upstream immunosuppressive product KYN and the increased downstream expression of 3-HAA. On the other hand, immunomodulatory therapy in melanoma is currently an important tumor therapy method, e.g., anti-CD4 and anti-PD-L1 therapy, which are used for clinical validation. Previous studies found that IFN-γ can induce the expression of IDO and promote tumorigenesis, while IL-10 plays an anti-inflammatory role in inflammation. In this experiment, the change in IL-10-induced melanoma protein expression was used to test whether it could be used as a tumor suppressor of melanoma. In melanoma cells (A375 and H1205-lu), the expression of MMP9 and AMPK decreased in A375 cells after stimulation with IL-10 (20 ng/mL), while BCL-2 did not change significantly. The expression of BCL-2 was significantly decreased in H1205-lu. When stimulated with IL-10 (40 ng/mL), CDK1 was significantly downregulated in HaCaT and HEKα. The expressions of CDKs and CDKIs in H1205-lu were lower, but the changes were not obvious. In A375, the expressions of CDK2 and CDK4 were downregulated, and their inhibitors P21 and P27 were also downregulated, resulting in cell-cycle arrest. When stimulated with IL-10 (40 ng/mL), the cell cycle of HEKα cells shifted to the right by PI staining, and the proportion of cells in the S–G2 phase was significantly higher than that of cells in the G1 phase. In A375 cells, however, the cell cycle shifted to the left, and a large number of cells were in the S phase rather than the G2 phase. The abovementioned regulation of cell-cycle-dependent proteins by IL-10 showed that IL-10 could slow down the cell cycle of melanoma A375 cells. For the differentiation and migration of melanoma cells, a change in calcium concentration inside and outside the cell in a short time may be involved in the process of cell differentiation, and its concentration varies in the different layers of keratinocytes. E-cadherin is expressed on the surface of cell membrane and mediates the adhesion between cells and the extracellular matrix. The expression of E-cadherin in melanoma cells is significantly lower than that in keratinocytes, and it can participate in the invasion and migration of tumor cells.
In this paper, by exploring the relevant indicators and cell changes in the process of melanoma proliferation, differentiation, apoptosis, invasion, and migration, the gene mutation types commonly involved in the abnormal differentiation of melanoma from melanocytes into primary melanoma and recurrent and metastatic melanoma were identified. The results of this article can be used reduce the occurrence and recurrence of melanoma cells by (1) reducing the heterotypic differentiation rate of tumor cells, (2) establishing directional differentiation to benign organized tissues, (3) enhancing tumor cell apoptosis, (4) slowing down the cell cycle, and (5) reducing the possibility of invasion and migration.