PANoptosis-related signature in melanoma: Transcriptomic mapping and clinical prognostication

Machine learning-based prognostic modeling integrating PANoptosis in head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous and aggressive cancer, posing challenges for prognosis and treatment. The role of PANoptosis in HNSCC remains unclear, despite its potential impact on the tumor immune microenvironment.

METHODS

We obtained the single-cell RNA sequencing dataset GSE181919 from the GEO database and used the AUCell R package to analyze PANoptosis enrichment and heterogeneity, identifying key PANoptosis-related genes at the single-cell level. A prognostic model was constructed using 101 machine learning algorithms and their combinations, with TCGA-HNSCC as the training set and GSE41613 and GSE65858 as validation sets. Model performance was assessed using Kaplan-Meier survival analysis, ROC curves, and PCA. Glutathione S-transferase omega 1 (GSTO1) was identified as a key model gene, and its expression was validated using PCR in clinical samples.

RESULTS

We integrated single-cell sequencing, bulk transcriptomic sequencing, and machine learning algorithms to construct a StepCox[backward] + RSF prognostic model for HNSCC. This model stratified HNSCC patients into high-risk and low-risk groups, with the high-risk group exhibiting worse prognosis. ROC and PCA analyses confirmed the model's robustness. Additionally, the key gene GSTO1 was identified and further validated to be upregulated in HNSCC and associated with poor prognosis.

CONCLUSIONS

The PANoptosis-based prognostic model offers strong predictive value for HNSCC and has potential applications in personalized treatment approaches. GSTO1 stands out as a promising biomarker and therapeutic target. Future research should focus on experimental validation and the development of therapeutic strategies that modulate PANoptosis to improve outcomes for HNSCC patients.

Deciphering cutaneous melanoma prognosis through LDL metabolism: Single-cell transcriptomics analysis via 101 machine learning algorithms

Cutaneous melanoma poses a formidable challenge within the field of oncology, marked by its aggressive nature and capacity for metastasis. Despite extensive research uncovering numerous genetic and molecular contributors to cutaneous melanoma development, there remains a critical knowledge gap concerning the role of lipids, notably low-density lipoprotein (LDL), in this lethal skin cancer. This article endeavours to bridge this knowledge gap by delving into the intricate interplay between LDL metabolism and cutaneous melanoma, shedding light on how lipids influence tumour progression, immune responses and potential therapeutic avenues. Genes associated with LDL metabolism were extracted from the GSEA database. We acquired and analysed single-cell sequencing data (GSE215120) and bulk-RNA sequencing data, including the TCGA data set, GSE19234, GSE22153 and GSE65904. Our analysis unveiled the heterogeneity of LDL across various cell types at the single-cell sequencing level. Additionally, we constructed an LDL-related signature (LRS) using machine learning algorithms, incorporating differentially expressed genes and highly correlated genes. The LRS serves as a valuable tool for assessing the prognosis, immunity and mutation status of patients with cutaneous melanoma. Furthermore, we conducted experiments on A375 and WM-115 cells to validate the function of PPP2R1A, a pivotal gene within the LRS. Our comprehensive approach, combining advanced bioinformatics analyses with an extensive review of current literature, presents compelling evidence regarding the significance of LDL within the cutaneous melanoma microenvironment.