MM-related gene expression data were retrieved from the Gene Expression Omnibus database of the National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/geo/) using ‘melanoma’ as the search term. The GSE98394(11) dataset (platform GPL16791) containing 27 commonly acquired nevus (normal control) and 51 primary melanoma samples was obtained and used for analysis. Differential analysis was conducted using the R package ‘limma’ v3.50.0 (https://bioinf.wehi.edu.au/limma/). Statistical significance of the differences between normal and tumor samples was analyzed via unpaired t-test (for normally distributed data) or Wilcoxon rank-sum test (for non-normally distributed data), with adjusted P<1x10^-10 and |fold change (FC)|>4 as thresholds. The R package ‘pheatmap’ v1.0.12 (https://CRAN.R-project.org/package=pheatmap) was used to generate a heatmap with hierarchical clustering, and a scatter plot was constructed to observe the expression levels and trends of DEGs. DEGs identified by limma were subjected to functional enrichment analysis to identify over-represented Gene Ontology categories (biological processes, cellular components and molecular functions) and Kyoto Encyclopedia of Genes and Genomes pathways (12,13). Enrichment results were summarized and visualized using the R package ‘ggplot2’ v4.0.1 (https://CRAN.R-project.org/package=ggplot2).

The mRNA-sequencing and gene mutation data of 469 skin cutaneous melanoma (SKCM) and 558 normal tissue samples were obtained from TCGA-SKCM dataset (https://tcga-data.nci.nih.gov/). Statistical analyses were conducted using Gene Expression Profiling Interactive Analysis 2 (http://gepia2.cancer-pku.cn), with adjusted P<1x10^-8 and |FC|>2 as thresholds. Gene expression values were presented as transcripts per million. Significantly upregulated and downregulated genes in the GSE98394 and TCGA-SKCM datasets were separately intersected to obtain the final lists of consistently upregulated and downregulated genes. Kaplan-Meier survival curves were generated to compare survival outcomes between groups, and statistical significance was assessed using the log-rank (Mantel-Cox) test. Hazard ratios (HRs) and corresponding 95% confidence intervals were calculated using Cox proportional hazards regression models. Genes significantly associated with patient survival were identified as candidates for further investigation.

To validate TPX2 expression and its association with melanoma progression, two independent datasets (GSE3189 and GSE46517) were retrieved from the Gene Expression Omnibus database. The GSE3189(14) and GSE46517(15) datasets were used to validate differential TPX2 expression between melanoma and nevus tissues. Statistical significance was assessed via unpaired t-tests for comparisons between two groups. For prognostic validation, the GSE65904 dataset was used to analyze the association between gene expression and disease-specific survival. Optimal cut-off points for separating high and low expression groups were determined using the survive_cutpoint function in the R package ‘survminer’. Kaplan-Meier survival curves were generated, and differences were assessed using the log-rank test. Additionally, multivariate Cox proportional hazards regression models were constructed to evaluate the independent prognostic value of TPX2 and AURKA, adjusting for clinical covariates including age, sex and tumor stage.

Human MM cells (A375 and C32) and immortalized human melanocytes (PIG1) originally from LMAI were provided by another laboratory at Guangdong Medical University (Zhanjiang, China). All cells were cultured in RPMI-1640 medium (Gibco; Thermo Fisher Scientific, Inc.) with 10% FBS (Shanghai ExCell Biology, Inc.) and 1% penicillin/streptomycin (Gibco; Thermo Fisher Scientific, Inc.) at 37˚C in a constant temperature incubator with 5% CO₂.

RNA was extracted from cells using TRIzol^® (Invitrogen; Thermo Fisher Scientific, Inc.) and reverse-transcribed into cDNA (incubation at 50˚C for 15 min followed by 85˚C for 2 min) using HiScript II Q RT SuperMix for qPCR (Vazyme Biotech Co., Ltd.). RT-qPCR was performed using ChamQ Universal SYBR qPCR Master Mix (Vazyme Biotech Co., Ltd.) on the Bio-Rad CFX Opus 96 system (Bio-Rad Laboratories, Inc.). The thermocycling conditions were as follows: Initiation with a hot-start activation at 95˚C for 30 sec, followed by 40 cycles of a two-step amplification program consisting of denaturation at 95˚C for 10 sec and a combined annealing/extension step at 60˚C for 30 sec. Fluorescence signal acquisition was performed at the end of each 60˚C phase. To confirm the specificity of the amplified products and the absence of primer-dimers, a melting curve analysis was conducted immediately following the final cycle (60-95˚C). Relative gene expression was calculated using the 2^-ΔΔCq method (16), with GAPDH as the endogenous control for normalization. All experiments were independently repeated three times. The following primer sequences were used in the present study: TPX2 forward, 5'-GAGGGCCTTTCTGGTTCTCT-3'; TPX2 reverse, 5'-CTCCTGTAGTCTGGCCTCCT-3'; GAPDH forward, 5'-GTCTCCTCTGACTTCAACAGCG-3'; and GAPDH reverse, 5'-ACCACCCTGTTGCTGTAGCCAA-3'.

Proteins were extracted using RIPA buffer (Beyotime Biotechnology) with a protease inhibitor (P6730; Beijing Solarbio Science & Technology Co., Ltd.). The BCA assay (Beyotime Biotechnology) was used for protein quantification, and 20 µg total protein was loaded per lane. SDS-PAGE was used to separate proteins, which were transferred to PVDF membranes (MilliporeSigma). After blocking with 5% skimmed milk (Beyotime Biotechnology) for 1 h at room temperature, the membranes were incubated overnight with primary antibodies, including anti-TPX2 (dilution, 1:5,000; cat. no. 11741-1-AP; Proteintech Group, Inc.), anti-AURKA (dilution, 1:2,000; cat. no. 66757-1-Ig; Proteintech Group, Inc.) and anti-GAPDH (dilution, 1:50,000; cat. no. 60004-1-Ig; Proteintech Group, Inc.) antibodies, at 4˚C. The membranes were further washed with 1X TBS with 0.05% Tween-20 (Beyotime Biotechnology) and incubated with goat anti-rabbit secondary antibodies (dilution, 1:5,000; cat. no. RGAR001; Proteintech Group, Inc.) or goat anti-mouse secondary antibodies (dilution, 1:5,000; cat. no. RGAM001; Proteintech Group, Inc.) at room temperature for 1 h. Protein bands were visualized using an enhanced chemiluminescence detection reagent (Thermo Fisher Scientific, Inc.). Band intensities were semi-quantified via densitometric analysis using ImageJ software v1.53 (National Institutes of Health). All experiments were independently repeated three times.

Cells were seeded in a 96-well plate (Thermo Fisher Scientific, Inc.) at a density of 1x10³ cells/well and cultured for 0, 12, 24, 36 and 48 h. An MTT solution (Beyotime Biotechnology) was used to assess cell viability. Formazan crystals were dissolved in DMSO (Thermo Fisher Scientific, Inc.), and the absorbance [optical density (OD)] was measured at 490 nm using a microplate reader. All experiments were independently repeated three times.

The wound healing assay was performed on cells grown to 90% confluence in a 6-well plate (Thermo Fisher Scientific, Inc.). Cells were cultured in RPMI-1640 medium (Gibco; Thermo Fisher Scientific, Inc.) with 10% FBS (Shanghai ExCell Biology, Inc.) and 1% penicillin/streptomycin (Gibco; Thermo Fisher Scientific, Inc.) at 37˚C in a constant temperature incubator with 5% CO₂. To establish a uniform wound, the cells were scraped with a 200-µl micropipette tip. After washing with phosphate-buffered saline, the cells were cultured with 2% FBS RPMI-1640 medium. Cell migration was observed using an inverted fluorescence microscope at 0 and 24 h. Wound closure was quantitatively assessed by measuring the wound area at 0 and 24 h using ImageJ software v1.53 (National Institutes of Health). The percentage of wound closure was calculated as follows: Healing rates (%)=[(initial wound width-wound width at 24 h)/initial wound width] x100. All measurements were performed in at least three randomly selected fields per well, and the mean value was used for statistical analysis. All experiments were independently repeated three times.

A serum-free cell suspension containing 1x10⁵ cells was seeded in the upper chamber of a Transwell system (8.0 µm; Corning, Inc.), and culture medium with 10% fetal bovine serum was added to the lower chamber. After incubation at 37˚C in a constant temperature incubator with 5% CO₂ for 24 h, non-migrated cells on the upper surface of the membrane were gently removed, and the cells that had migrated to the lower surface of the filter were fixed with 4% paraformaldehyde for 30 min at room temperature, stained with crystal violet for 10 min at room temperature (Beyotime Biotechnology), washed with phosphate-buffered saline and allowed to dry. Then, the number of migrating cells was determined using an inverted microscope. All experiments were independently repeated three times.

A total of 5x10⁵ A375 and C32 cells were seeded in a 6-well plate at a density of 70-90%. TPX2 siRNA (sense, 5'-AUUAUUAGCCUUAGUAAUGUA-3' and antisense, 5'-UACAUUACUAAGGCUAAUAAU-3') and siNC (sense, 5'-UUCUCCGAACGUGUCACGU-3' and antisense, 5'-ACGUGACACGUUCGGAGAA-3') were obtained from Changzhou Ruibo Bio-Technology Co., Ltd. siRNA (50 pmol) was transfected into the cells using the Lipofectamine^® RNAiMAX reagent (Invitrogen; Thermo Fisher Scientific, Inc.). siRNA-lipid complexes were prepared in serum-free medium and added to cells, which were then incubated at 37˚C in a humidified atmosphere with 5% CO₂ for 6 h. Subsequently, the transfection medium was replaced with complete culture medium. Cells were harvested for RT-qPCR and western blot analyses 48 h after transfection. Functional assays were performed at the following intervals after transfection unless otherwise stated: MTT assays at 0, 12, 24, 36 and 48 h, and wound healing and Transwell migration assays at 24 h. Cells transfected with non-targeting siNC were used as negative controls for all siRNA transfection experiments. Untransfected parental A375 and C32 cells were included as blank controls where indicated.

Data were analyzed using SPSS v22.0 (IBM, Corp.) and are presented as the mean ± standard deviation. GraphPad Prism v8.0 (Dotmatics) was used for data visualization. Statistical analyses of quantitative data were conducted using one-way ANOVA. Levene's test and F-test were applied to test for unequal variances, and Welch's ANOVA was used for analysis when variances were unequal. Tukey's honestly significant difference test was used when Levene's test indicated equal variances. If Levene's test indicated unequal variances and Welch's ANOVA was applied, pairwise comparisons were performed using the Games-Howell post hoc test. P<0.05 was considered to indicate a statistically significant difference.

Differential expression analysis of the GSE98394 dataset identified 878 significantly downregulated and 812 significantly upregulated genes based on the thresholds of |FC|>4 and adjusted P<1x10^-¹⁰ (Fig. 1A and B). Functional enrichment analysis of these DEGs revealed significant involvement in pathways such as the ‘cytokine-cytokine receptor interaction’ and ‘chemokine signaling pathway’. The enriched biological processes included ‘leukocyte-mediated immunity’, ‘lymphocyte mediated immunity’ and ‘regulation of lymphocyte activation’ (Fig. 1C and D). Similarly, analysis of TCGA-SKCM dataset using cut-offs of |(FC)|>2 and adjusted P<1x10^-8 identified 357 significantly downregulated and 67 significantly upregulated genes (Fig. 1E). Intersecting the DEGs from both datasets revealed eight potentially upregulated genes [anti-silencing function 1B histone chaperone, TPX2, transmembrane protein 132A, PDZ-binding kinase, von Willebrand factor, protein kinase membrane-associated tyrosine/threonine 1 (PKMYT1), ubiquitin-conjugating enzyme E2 C (UBE2C) and insulin-like growth factor-2] and five potentially downregulated genes [adhesion G protein-coupled receptor V1, phytanoyl-CoA 2-hydroxylase-interacting protein, complement factor H (CFH), troponin C1 and family with sequence similarity 153 member B]. Moreover, TPX2 mRNA levels were significantly higher in melanoma than in nevi (P<0.001) in the GSE3189 cohort (Fig. 1F). These findings were consistent with those obtained from the GSE46517 dataset, in which TPX2 levels were also significantly upregulated in malignant tissues (P<0.001; Fig. 1G). Across both validation cohorts, TPX2 expression demonstrated an ~2.8-fold increase in melanoma compared to that in benign lesions.

To evaluate the prognostic significance of candidate genes in melanoma, Kaplan-Meier survival analyses were conducted using OS data from TCGA melanoma cohort. Patients were divided into high and low expression groups based on median expression levels. As shown in Fig. 2A, patients with low expression of TPX2 exhibited significantly longer OS compared with those with high expression (log-rank P=0.0074; HR=1.4), suggesting a potential oncogene function of TPX2 in melanoma. To further validate the prognostic value of the identified candidate genes, an independent survival analysis was performed using the GSE65904 dataset. Patients were stratified into high and low expression groups based on the optimal cut-off values for TPX2 and AURKA. Kaplan-Meier survival analysis revealed that high expression levels of both TPX2 and AURKA were significantly associated with poor disease-specific survival (log-rank P<0.001 for both; Fig. 2B and C). Specifically, patients in the high TPX2 expression group exhibited significantly lower survival probability than those in the low TPX2 expression group. Similarly, elevated AURKA expression was an indicator of unfavorable prognosis. The univariate and multivariate Cox regression results are shown in Table I. These results from an independent cohort further confirm that TPX2 and AURKA are reliable biomarkers for predicting survival outcomes in patients with melanoma.

To evaluate the differential expression of TPX2 between normal melanocytes and melanoma cells, TPX2 mRNA levels were assessed in PIG1 and A375 cells. RT-qPCR analysis revealed that TPX2 mRNA expression was significantly higher in A375 cells than in PIG1 cells (P<0.05; Fig. 3A). This upregulation was further confirmed at the protein level via western blotting, which showed greater TPX2 protein expression in A375 cells compared with PIG1 cells (P<0.05; Fig. 3B). To investigate whether elevated TPX2 expression is associated with functional alterations in melanoma cells, the migration and proliferation of A375 and PIG1 cells were evaluated. MTT proliferation assays indicated that A375 cells exhibited significantly higher OD values from 12 to 48 h, indicating greater proliferation compared with PIG1 cells (P<0.05; Fig. 3C). Additionally, Transwell migration assays demonstrated that A375 cells exhibited significantly enhanced migration, as indicated by a higher number of A375 cells traversing the membrane compared with PIG1 cells (P<0.05; Fig. 3D). Consistently, wound healing assays revealed that A375 cells migrated more rapidly, with a noticeably smaller wound area at 24 h (P<0.05; Fig. 3E). Collectively, these findings suggested that TPX2 is highly expressed in A375 melanoma cells and may be associated with melanoma progression by promoting cell proliferation and migration.

To further investigate the functional roles of TPX2 in melanoma cells, siRNA-mediated knockdown of TPX2 in melanoma cells was performed. RT-qPCR analysis confirmed a significant reduction in TPX2 mRNA levels after transfection with TPX2-specific siRNA compared with those in control A375 cells (P<0.05; Fig. 4A). Western blotting analysis further validated the decrease in TPX2 protein levels in A375-siTPX2 and C32-siTPX2 cells and revealed a concomitant reduction in AURKA protein levels (P<0.05; Fig. 4B). Functionally, MTT assays demonstrated that TPX2 knockdown significantly inhibited A375 cell proliferation. The A375-siTPX2 group exhibited markedly lower OD values than the control A375 group at all time points (P<0.05; Fig. 4C). Transwell migration assays showed that the number of migrated cells was significantly reduced in the A375-siTPX2 and C32-siTPX2 groups, indicating impaired migration (P<0.05; Fig. 4D). Consistently, wound healing assays revealed that the scratch area remained largely open in TPX2-silenced cells after 24 h, whereas A375 and C32 cells exhibited notable wound closure (P<0.05 for A375 cells; Fig. 4E). Collectively, these results suggested that TPX2 promotes melanoma cell proliferation and migration, which could be related to AURKA upregulation.