Exploring new mechanisms in cancer molecular pathways and pathogenic cell transformation: PIP4K2A as a prognostic marker and therapeutic target in cutaneous malignant melanoma

Abstract

Background: Cutaneous malignant melanoma is a very aggressive and metastatic form of skin cancer, typically linked with poor outcomes. Advances in genomic analysis have underscored the crucial role of T cells in tumor immunity. Immune checkpoint inhibitors have notably transformed melanoma treatment by boosting T cell activity. Studies of gene expression have found that the phosphatidylinositol-4-phosphate kinase 2A (PIP4K2A) gene is abnormally expressed in various tumors, indicating its potential role in tumor progression. Utilizing single-cell sequencing and machine learning, researchers can now explore the complex interactions between T cells and melanoma cells at a genomic level. This study aimed to investigate the role of the PIP4K2A gene in cutaneous malignant melanoma, with a focus on its influence on T cell-mediated immune responses.

Methods: Samples from cutaneous melanoma patients were analysed by single-cell transcriptome for differentially expressed genes and signalling pathways associated with cutaneous melanoma. Then, genes were identified and predictive models were built based on the transcriptomic data using machine learning models to assess whether the expression level of PIP4K2A could effectively predict the malignancy and prognosis of cutaneous melanoma. In addition, we also performed drug therapy predictive analysis and immunotherapy analysis.Finally, the critical role of PIP4K2A in cutaneous melanoma was further confirmed by immunohistochemistry.

Results: The PIP4K2A gene exhibited a significantly elevated expression level in cutaneous malignant melanoma, showing a strong correlation with the clinical stage and patient prognosis. At the therapeutic level, high PIP4K2A expression is less responsive to immunotherapy, and this gene is a risk factor for drug therapy in cutaneous malignant melanoma. Additionally, our experimental outcomes validated this observation.

Conclusions: The PIP4K2A gene could be a crucial prognostic marker for cutaneous malignant melanoma, as it significantly affects T cell activity within the tumor microenvironment. This study offers essential insights into melanoma pathogenesis and assists in pinpointing new early diagnostic markers and therapeutic targets. Utilizing advanced genomic tools and computational techniques, the research enhances our understanding of T cell dynamics in melanoma, facilitating the development of personalized medicine and more effective immunotherapy strategies.

Keywords: Machine learning; PIP4K2A; ScRNA-seq; Single-cell transcriptome; Skin cutaneous melanoma (SKCM); T cell.

Fig. 1

Flowchart of multi-step screening strategy for bioinformatics data

Fig. 2

Single-cell sequencing analysis. A 7 cell types identified based on marker gene expression. B Classification of cells. A total of 7 cell types can be found, such as B cells, NK cells, Melanoma cells and T cell. C Violin plots displaying expression of marker genes for the 7 cell types across cells. D Proportional representation of different cell types. E GSVA investigation of different cell types. F Network diagram of the frequency of ligand-receptor interactions between different cell types

Fig. 3

Gene expression characterization of SKCM-infiltrating tregs and exhausted CD8 + T cells A t-SNE plot showing T lymphocyte subtypes. B Volcano plot showing differentially expressed genes in SKCM -infiltrating Tregs. C GSEA analysis results in GSE15605 D The same volcano plot as in B in the GSE46517 dataset. E GSEA analysis results in GSE46517 F The Venn graph showing the overlap of the two data sets with T cell genes

Fig. 4

Enrichment analysis and machine learning. A Based on GO enrichment analysis, these genes are mainly involved in CD4-positive, alpha–beta T cell activation and carbohydrate transmembrane transport in biological processes, and in terms of cellular components are mainly focused on tertiary granule and secretory granule membranes, while in terms of molecular functions, they mainly exhibit carbohydrate transmembrane transport activity. B KEGG-enriched analyzed genes were mainly focused on pathways related to Ubiquitin mediated proteolysis, Cell cycle, and DNA replication. C Enrichment analysis of the Hallmark gene set showed that common genes were mainly concentrated in pathways related to Myc targets v1, Protein secretion, G2/M checkpoint, etc. D Machine learning model comparison using 7 different machine learning algorithms. E Receiver operating characteristics (ROC) curves for multiple machine learning models. F The “logreg” machine learning algorithm model exhibits the highest AUC of 0.995

Fig. 5

Identification of key genes strongly associated with disease pathogenicity and survival analysis. A Binomial deviation of overall survival (OS) for LASSO coefficient curve. B LASSO coefficient profiles of genes. C Patients in the PIP4K2A high-expression group in cutaneous malignant melanoma have a somewhat shorter survival time. D Meta-survival analysis results of PIP4K2A expression. E Higher expression of PIP4K2A in elderly patients > 65 years of age. F Higher expression of PIP4K2A in patients with clinical manifestations of ulceration. G, H PIP4K2A is more highly expressed in cutaneous malignant melanoma tissue samples. I Higher PIP4K2A expression at higher T stage. J The expression of PIP4K2A showed differences in the performance of individual staging

Fig. 6

Immunoinfiltration analysis of PIP4K2A expression

Fig. 7

Immunotherapy predictive analytics and immunohistochemistry. A–D Prediction of immunotherapy sensitivity in different cohorts. (E)The expression of PIP4K2A was significantly higher in cutaneous melanoma tissues than in normal skin tissues