Oncogenic role and prognostic significance of PIMREG in melanoma

Introduction

Melanoma is a type of tumor that develops from melanocytes and is known for its insidious onset, rapid metastasis, high mortality rate, and resistance to drugs. While it only accounts for about 3% of all tumors, its incidence is increasing at an annual rate of 6–7%, making it one of the fastest-growing tumors worldwide (1). Currently, the therapeutic outcomes for patients with melanoma remain unsatisfactory. Therefore, it is urgent to explore more specific therapeutic targets and accurately predict the prognosis to bring new hope for improving the survival rate of patients.

Phosphatidylinositol binding clathrin assembly protein interacting mitotic regulator (PIMREG), also known as FAM64A, plays a role in the transition from metaphase to anaphase in the cell cycle. PIMREG is primarily expressed in the thymus, spleen, and colon in normal adult tissue, with moderate expression in the small intestine, ovary, and brain. Several studies have shown that PIMREG is highly expressed in various malignancies, such as renal cell carcinoma, breast cancer, and glioma, and it can serve as a prognostic marker for these carcinomas (2-4). PIMREG is potentially upregulated in response to genotoxic agents, and its silencing renders glioblastoma cells sensitive to temozolomide treatment. Studies conducted have shown that PIMREG is involved in the migration, epithelial-mesenchymal transition (EMT), and stemness of breast cancer cells (5,6). However, the molecular mechanism and function of PIMREG in melanoma carcinogenesis are not yet fully understood.

This study examines the expression and function of PIMREG in melanoma. The findings indicate that patients with skin cutaneous melanoma (SKCM) exhibit higher levels of PIMREG expression compared to the normal group. Abnormal expression of PIMREG predicts poor survival outcomes for patients. Furthermore, we analyze the clinical function, immune cell filtration, prognostic values, and drug sensitivity of PIMREG in melanoma. The study demonstrated that PIMREG is a promising diagnostic and prognostic biomarker, and a possible therapeutic target for SKCM. We present this article in accordance with the REMARK reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1861/rc).

Methods

Patients in The Cancer Genome Atlas (TCGA) database

Normalized RNA-seq data and relevant clinical information from 469 SKCM tumor tissues and 813 normal tissues were downloaded from the TCGA database (https://portal.gdc.cancer.gov/) and the Genotype-Tissue Expression (GTEx) Project (https://www.gtexportal.org) (7). Data from GSE3189 and GSE46517 were also added when evaluating PIMREG expression in SKCM tissues. These single-cell transcriptomic data of SKCM are derived from Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) datasets GSE115978 and GSE72056. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

By the median of mRNA expression, we divided the SKCM patients into PIMREG-high and PIMREG-low expression groups. The data were gathered and analyzed via R3.6.3 software. The association between PIMREG mRNA expression and the overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) of patients with SKCM was calculated by using the TCGA-SKCM dataset. Immunostaining images were obtained from the Human Protein Atlas (HPA) (https://www.proteinatlas.org/).

Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) analysis. The enriched pathways were collected using GO (8,9), KEGG (10-12) and GSEA (13,14). PIMREG co-expression genes in GO were analyzed statistically using GO biological process (GO_BP), GO cellular component (GO_CC), and GO molecular function (GO_MF). The predetermined genome was obtained from the Molecular Signatures Database (MSigDB) database (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). An adjusted P value of <0.05 and a false discovery rate (FDR) of <0.25 were considered statistically significant.

Protein-protein interaction (PPI) network analysis

In order to establish the interaction relationship between PIMREG and its related proteins in SKCM, we derived the human PPI network from the STRING database. We also visualized a PPI network with PIMREG-related proteins in Cytoscape accompanied by a requirement of an interaction score reaching at least 0.7. After that, 10 PIMREG-related hub genes were identified through Cytoscape.

Tumor IMmune Estimation Resource (TIMER) analysis

TIMER web server (https://cistrome.shinyapps.io/timer/) is a comprehensive resource for systematic analysis of immune infiltrates across diverse cancer types (15,16). It also allows users to enter function-specific parameters and dynamically display the resulting plots for convenient access to tumor genomic features. The relevance of PIMREG and hub factors was explored by TIMER.

Immune cells infiltration of single-sample GSEA (ssGSEA)

ssGSEA was used to identify immune infiltration in SKCM (14,17). The correlation analysis between PIMREG and the infiltration levels of these 24 immune cell types was quantified by the Spearman correlation test.

Single-cell functional analysis

The Cancer Single-cell State Atlas, commonly referred to as CancerSEA (http://biocc.hrbmu.edu.cn/CancerSEA/), is a comprehensive dataset and analysis tool specifically designed to explore cancer cell functions at the single-cell level (18). In the context of our research, we used this platform to delve deeper into the functionality of PIMREG.

MethSurv analysis

MethSurv (https://biit.cs.ut.ee/methsurv/) used data from the Cancer Genome Map to analyse the survival of cancer patients based on their methylation patterns (19). We used MethSurv to explore the relationship between PIMREG methylation and the prognostic values in a heat map.

Drug sensitivity analysis

GSCALite (http://bioinfo.life.hust.edu.cn/web/GSCALite/) (20) and Cellminer (https://discover.nci.nih.gov/cellminer/home.do) (21) were used for gene expression and drug sensitivity analysis. In this study, PIMREG was considered to be a target for the search of known drugs related to melanoma.

Statistical analysis

Multivariate analysis was used to evaluate the impact of clinical variables on survival. The statistical analysis was performed using R software and SPSS 22.0. A P value of less than 0.05 was considered statistically significant. All reported P values were two-sided.

Results

Diagnostic and prognostic value of PIMREG in SKCM

The design flow chart of the whole analysis process is shown in Figure 1.

Figure 1 Design flow chart of the whole analysis process of this study. TIMER, Tumor IMmune Estimation Resource; IHC, immunohistochemistry; SKCM, skin cutaneous melanoma; PIMREG, phosphatidylinositol binding clathrin assembly protein interacting mitotic regulator; ROC, receiver operating characteristic; GSEA, Gene Set Enrichment Analysis; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

First, we evaluated the expression and prognostic role of PIMREG in SKCM. In the TCGA + GTEx, GSE3189, and GSE46517 datasets, PIMREG was found to be significantly highly expressed in SKCM (see Figure 2A-2C). The receiver operating characteristic (ROC) analysis also revealed that PIMREG had high diagnostic potential, with area under the ROC curve (AUC) values of 0.874 (see Figure 2D). It was observed that the protein expression level of PIMREG was significantly higher in melanoma tissues compared to normal skin tissues (Figure 2E). To investigate the relationship between PIMREG and SKCM, we determined whether PIMREG expression is associated with SKCM outcome. The Kaplan-Meier curves used in survival analysis indicated a correlation between high PIMREG expression and poor OS [hazard ratio (HR) =1.60, P<0.001], DSS (HR =1.73, P<0.001), and PFI (HR =1.35, P=0.03) in SKCM patients (Figure 2F-2H). Multivariate regression analysis confirmed that PIMREG expression was an independent prognostic factor for OS (HR =1.387, P=0.048) (Figure 3A) and DSS (HR =1.434, P=0.04) (Figure 3B) in SKCM patients. These results suggest that PIMREG could be a promising diagnostic and prognostic biomarker in SKCM.

Figure 2 The expression of PIMREG in SKCM tissues and prognostic value of PIMREG for clinical outcomes in SKCM patients. (A-C) Differences of PIMREG expressions between tumor samples and normal samples from TCGA + GTEx (A), GSE3189 (B), GSE46517 (C). (D) The ROC curve for PIMREG show potential discrimination power between normal and SKCM samples. (E) Representative images of PIMREG expression in melanoma from HPA. Normal samples (left), tumor samples (right). Image credit goes to the HPA, the staining method is immunohistochemistry. The links to the normal and tumor tissues of PIMREG protein are provided (https://images.proteinatlas.org/43783/97662_B_5_8.jpg; https://images.proteinatlas.org/43783/97660_B_4_8.jpg). (F-H) Kaplan-Meier analysis of (F) OS, (G) DSS, (H) PFI for patients with SKCM. TCGA, The Cancer Genome Atlas; GTEx, Genotype-Tissue Expression; TPM, transcripts per million; FPR, false positive rate; TPR, true positive rate; AUC, area under the curve; CI, confidence interval; HR, hazard ratio; PIMREG, phosphatidylinositol binding clathrin assembly protein interacting mitotic regulator; SKCM, skin cutaneous melanoma; ROC, receiver operating characteristic; HPA, Human Protein Atlas; OS, overall survival; DSS, disease-specific survival; PFI, progression-free interval.

Figure 3 Multivariate Cox proportional hazards analysis of the correlation between PIMREG expression and OS (A), DSS (B) for SKCM patients. HR, hazard ratio; CI, confidence interval; PIMREG, phosphatidylinositol binding clathrin assembly protein interacting mitotic regulator; OS, overall survival; DSS, disease-specific survival; SKCM, skin cutaneous melanoma.

Single-cell functional analysis of PIMREG

To investigate the role of PIMREG in tumors, we analyzed its function at the single-cell level using CancerSEA. When analyzing the relevance of PIMREG mRNA expression levels to 14 functional states in pan-cancer, we observed strong positive correlations between PIMREG and the functional status of SKCM among multiple types of cancers (Figure 4A). Further, our findings showed a positive correlation between PIMREG and cell cycle (R=0.50, P<0.001), proliferation (R=0.47, P<0.001), DNA damage (R=0.33, P<0.001), invasion (R=0.32, P<0.001), EMT (R=0.23, P<0.01), and DNA repair (R=0.21, P<0.01) in SKCM (Figure 4B).

Figure 4 The function of PIMREG in single-cell functional analysis from the CancerSEA database. (A) Functional status of PIMREG in different human cancers. (B) Correlation analysis between functional status and PIMREG in SKCM. The correlation between PIMREG and functional status: **, P<0.01; ***, P<0.001. EMT, epithelial-mesenchymal transition; CNS, central nervous system; PIMREG, phosphatidylinositol binding clathrin assembly protein interacting mitotic regulator; SKCM, skin cutaneous melanoma.

Establishing protein interaction networks

The PIMREG-binding protein interaction network was established and visualized using text mining and experimental evidence identification, as shown in Figure 5A. Additionally, we identified 10 hub members, including CDC20, CCNB1, CCNA2, BUB1B, UBE2C, NEK2, CDK1, PTTG1, CCNB2, and BUB1, by comparing PIMREG-interacted genes in SKCM, as depicted in Figure 5B. Furthermore, there was a significant positive association between PIMREG mRNA expression and the expression of these genes (P<0.001), as illustrated in Figure 5C.

Figure 5 Function enrichment analysis of PIMREG-interacted genes. (A) The interaction network of PIMREG-binding proteins was obtained from the STRING database. (B) Hub genes were selected by Cytoscape. (C) Correlation analysis between PIMREG expression and screened out hub genes as follows: CDC20, CCNB1, CCNA2, BUB1B, UBE2C, NEK2, CDK1, PTTG1, CCNB2, BUB1. The images were obtained from the website TIMER, the link to these images are provided (https://cistrome.shinyapps.io/timer/). (D) Volcano plot of differentially expressed genes between the high and low PIMREG expression groups. (E) Function enrichment analysis of PIMREG in SKCM using GO and KEGG. (F,G) Enrichment plots from GSEA. (F) Signaling enrichment. (G) Hallmark G2/m checkpoint, Hallmark MYC signaling V1, Hallmark MYC signaling V2, Hallmark epithelial mesenchymal transition. TPM, transcripts per million; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes; GO, Gene Ontology; NES, normalized enrichment score; FDR, false discovery rate; FC, fold change; PIMREG, phosphatidylinositol binding clathrin assembly protein interacting mitotic regulator; SKCM, skin cutaneous melanoma; GSEA, Gene Set Enrichment Analysis.

Function enrichment of PIMREG in SKCM

To clarify the potential cellular mechanisms, we analyzed the data using GO, KEGG, and GSEA. We divided 469 patients with SKCM into two groups based on the median PIMREG expression: high and low. Then, we identified differentially expressed genes (DEGs) in the high-PIMREG group compared to the low-PIMREG group with an absolute log-fold change >1.5 and P<0.05 (as shown in Figure 5D). Next, we functionally annotated the DEGs associated with PIMREG in SKCM patients. Figure 5E demonstrates that the DEGs of PIMREG are primarily involved in the development and structural constituents of the epidermis and skin, as well as keratinization. The KEGG pathway analysis revealed enrichment in primary immunodeficiency, cytokine-cytokine receptor interaction, and staphylococcus aureus infection. Additionally, PIMREG is involved in the Wnt signaling pathway, PI3K/Akt signaling pathway, and olfactory signaling pathway, as well as signaling by interleukins. Meanwhile, the significantly differentially enriched pathways in the PIMREG high expression phenotype were MYC targets, EMT, and G2/M checkpoint (Figure 5F,5G).

DNA methylation sites within PIMREG

DNA methylation also plays a critical role in the regulation of gene expression and affects clinical outcomes. The DNA methylation sites of the PIMREG genes and the prognostic values of each CpG obtained from TCGA database were analyzed by MethSurv (Figure 6). The results showed methylated CpG islands including cg14343091 that had aberrant levels of DNA methylation.

Figure 6 Heat map of correlation between PIMREG mRNA expression level and methylation in SKCM. BMI, body mass index; PIMREG, phosphatidylinositol binding clathrin assembly protein interacting mitotic regulator; SKCM, skin cutaneous melanoma.

The association between PIMREG and immune infiltration

The ESTIMATE algorithm was used to calculate the stromal score, immune score and estimate score, and infers tumour purity from these values. As shown in Figure 7A, although the difference in terms of Stromal score was not statistically significant, PIMREG expression was significantly negatively correlated with Immune score and ESTIMATE score in SKCM. As some of the functional annotation and predicted signaling pathways were related to an immune reaction, we further investigated the correlation between PIMREG expression and the level of immune cell infiltration quantified by ssGSEA in SKCM (Figure 7B). It was observed that PIMREG had a negative correlation with most immune cells, although the correlation coefficients were rather weak (|R|<0.3), except for Th2 cells. The abundance of Th2 cells showed a positive association with PIMREG (R=0.324, P<0.001) (Figure 7C). Additionally, PIMREG expression levels were weakly correlated with the expression levels of immune checkpoint regulators, including CTLA4, PDCD1LG2, and PDCD1, in the SKCM samples (Figure 7D-7F). To further explore the types of cells expressing PIMREG in SKCM tissues, we examined single cell PIMREG expression utilizing Tumor Immune Single-cell Hub (TISCH) database. As demonstrated in Figure 8A, indicating that PIMREG is extensively expressed in a range of immune and malignant cells, mainly in Tprolif cells, fibroblasts cells and malignant cells (Figure 8B,8C).

Figure 7 The correlation of PIMREG and immune infiltration in SKCM. (A,B) A comparison between immune cell infiltration and PIMREG expression. (C) Correlations between PIMREG expression and Th2 cell levels in SKCM. (D-F) Correlations between PIMREG expression level and CTLA4, PDCD1LG2, PDCD1 expression levels in SKCM. The correlation between PIMREG and immune cell infiltration: ns, P>0.05; *, P<0.05; **, P<0.01; ***, P<0.001. PIMREG, phosphatidylinositol binding clathrin assembly protein interacting mitotic regulator; NK, natural killer; TPM, transcripts per million; SKCM, skin cutaneous melanoma.

Figure 8 Single-cell expression analysis of PIMREG in SKCM tissues. (A) Cluster heatmaps of PIMREG expression levels in different cell types in SKCM tissues. (B,C) UMAP plots showing clustering of different cell types and PIMREG expression levels from GSE115978 and GSE72056. PIMREG, phosphatidylinositol binding clathrin assembly protein interacting mitotic regulator; TPM, transcripts per million; NK, natural killer; SKCM, skin cutaneous melanoma; UMAP, Uniform Manifold Approximation and Projection.

Drug sensitivity analysis

GSCALite was used to investigate the drug sensitivity of PIMREG expression in tumors. A positive correlation was found between PIMREG expression and 37 types of drugs, such as BHG712, NG-25, TPCA-1, PHA-793887, THZ-2-49, and I-BET-762 (see Figure 9A). Meanwhile, according to data from Cellminer, PIMREG expression was negatively associated with the IC₅₀ of multiple drugs, including imexon, isotretinoin, artemether, celecoxib, saridegib, acetate, dacarbazine, econazole nitrate, and others dacarbazine, econazole nitrate, palbociclib, masitinib, entinostat, dromostanolone.proplo, rebimastat, and x7.hydroxystauropporin (Figure 9B). These findings suggest that PIMREG levels could be used for anticancer drug selection.

Figure 9 The associations of PIMREG expression and drug sensitivity. The analysis base on (A) GSCALite and (B) Cellminer. GDSC, Genomics of Drug Sensitivity in Cancer; PIMREG, phosphatidylinositol binding clathrin assembly protein interacting mitotic regulator; IC₅₀, half-maximal inhibitory concentration.

Discussion

The objective of this study was to assess the impact of PIMREG on SKCM progression and investigate its potential mechanism. Our findings indicate that PIMREG is significantly overexpressed in SKCM tissues and is associated with worse OS in SKCM patients. Multivariate regression analysis confirmed that high PIMREG expression is an independent risk factor for OS and DSS in SKCM patients. ROC analysis also confirmed the diagnostic value of PIMREG. Additionally, the results indicate that PIMREG may promote the progression of SKCM by regulating cell cycle-related markers and EMT. This is consistent with a previous study that found high expression of PIMREG in hepatocellular carcinoma cells led to a significant increase in cell cycle progression (22). These findings suggest that PIMREG may play a carcinogenic role in SKCM progression and could be a promising target for future SKCM treatment.

According to the KEGG analysis, PIMREG is involved in various cellular processes, such as epidermis development, skin development, and epidermal cell differentiation. Additionally, PIMREG is an essential molecule for intermediate filament cytoskeleton and structural constituent of skin epidermis. Coincidentally, through single-cell function analysis, we observed a positive association between high PIMREG expression and cell cycle, cell proliferation, DNA damage, and invasion. The bioinformatics analyses results above are similar to in vitro experimental results obtained from glioma cells (23). Altogether, these results suggest that PIMREG may have a significant impact on the malignant phenotype of SKCM.

We also noticed that most Hub genes regulated by PIMREG were related with cell cycle regulation, such as CDC20, CCNB1, CCNA2, and BUB1B. CDC20 is highly expressed in mitotic cells of epidermal differentiation layer (24), which was also identified as one of key hub genes involved in the pathogenesis of cutaneous melanoma (25). CCNB1 is required for proper control of the G2-M cell cycle transition. Its overexpression enhances tumor formation and invasion of adjacent tissues (26). CCNA2 binds to and activates CDK2, thereby promoting transformation through G1/S and G2/M. BUB1B involves in spindle-assembly checkpoint, and which was significantly associated with OS and recurrence-free survival in clear cell renal cell carcinoma (27). Our findings indicate that melanoma cells with high PIMREG expression may promote the expression of molecules necessary for cell division, thereby increasing their growth potential.

The role of PIMREG in SKCM progression is currently unknown. PIMREG has been shown to promote breast cancer aggressiveness by disrupting the negative feedback loop of NF-κB/IκBα (28), which also affects interferon signaling pathway in prostate cancer cells (29). Recent study has shown that PIMREG interacts with STAT3 in the nucleus and regulates the binding of STAT3 to the promoters of its target genes, thereby potentiating inflammation-associated cancer (30). Our study found a close relationship between PIMREG and Wnt signaling. In glioma cells, overexpression of PIMREG activated the β-catenin signaling pathway. This was evidenced by the increased total and nuclear expression of β-catenin and the up-regulated expression of its downstream target c-Myc (31). It’s well known that Wnt signaling is one of the key cascades regulating EMT and migration of melanoma cells (32). Additional experiments are necessary to validate the role of Wnt signaling in mediating the effect of PIMREG on the invasive phenotype of SKCM.

A study on melanoma has revealed that upregulation of Wnt signaling may inhibit immune infiltration (33). Next, we focused on the immune system of SKCM. Melanoma is recognized as one of the most immunogenic malignancies due to the abundant infiltration of various immune cells (34). Its correlations with several immunomodulators such as CTLA-4 in breast cancer (3). PIMREG in glioma was also observed to be related to the infiltration of several immune cell types, such as M1 and M2 macrophages, monocytes, and CD8⁺ T cells (4). Although this study demonstrated that PIMREG expression in SKCM was negatively associated with multiple types of immune cell infiltration, the correlation between PIMREG expression and the immune cell as well as immune checkpoint genes was weak, and PIMREG only showed a moderate positive correlation with Th2 cells. So, these results reveal that PIMREG’s participation in the progression of SKCM might not be through regulating the infiltration of immune cells.

Looking for drugs related to PIMREG might provide new insights for SKCM therapy. It was reported that PIMREG is induced by genotoxic agents, and its silencing renders glioblastoma cells sensitive to treatment of temozolomide, an alkylating agent-imidazolium tetrazine derivative (35). Recent study has also shown that PIMREG phosphorylation at S16 activates NF-κB and promotes cisplatin resistance in triple-negative breast cancer (36). Here, the correlation between PIMREG mRNA and anticancer drug sensitivity was examined using GSCALite and Cellminer databases. The results suggest that the expression of PIMREG is correlated with the sensitivity of multiple classical antitumor drugs, providing insights toward a rational basis for therapies for SKCM patients with high PIMREG expression.

The limitation of this study was that it was primarily based on bioinformatics analysis. The data was collected from online platform databases and still needs to be confirmed by in vitro and in vivo experimental validation.

Conclusions

Although PIMREG is highly expressed in multiple other types of cancers (37,38), to date, few studies have explored the role of PIMREG in SKCM. Our results indicated that PIMREG was a reliable prognostic biomarker for SKCM, and could be a promising target for SKCM treatment.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the REMARK reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1861/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1861/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1861/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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