SLC2A1 plays a pivotal role in cancer glycometabolism. SLC2A1 has been proposed as a putative driver gene in various cancers. However, a pan-cancer analysis of SLC2A1 has not yet been performed. In this study, we explored the expression and prognosis of SLC2A1 in pan-cancer across multiple databases. We conducted genetic alteration, epigenetic, and functional enrichment analyses of SLC2A. We calculated the correlation between SLC2A1 and tumor microenvironment using the TCGA pan-cancer dataset. We observed high expression levels of SLC2A1 with poor prognosis in most cancers. The overall genetic alteration frequency of SLC2A1 was 1.8% in pan-cancer, and the SLC2A1 promoter was hypomethylation in several cancers. Most m6A-methylation-related genes positively correlated with the expression of SLC2A1 in 33 TCGA cancers. Moreover, SLC2A1 was mainly related to the functions including epithelial–mesenchymal transition, glycolysis, hypoxia, cell-cycle regulation, and DNA repair. Finally, SLC2A1 positively associated with neutrophils and cancer-associated fibroblasts in the tumor microenvironment of most cancers and significantly correlated with TMB and MSI in various cancers. Notably, SLC2A1 was remarkably positively correlated with PD-L1 and CTLA4 in most cancers. SLC2A1 might serve as an attractive pan-cancer biomarker for providing new insights into cancer therapeutics.

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Background: The role of pyroptosis in lung squamous cell carcinoma (LUSC) remains unclear. This study aimed to screen pyroptosis-related genes (PRGs) and construct a model to investigate the immune infiltration, gene mutations, and immune response of patients of LUSC. Methods: We conducted a comprehensive evaluation of pyroptosis patterns in patients with LUSC with 51 PRGs. Pyroptosis-related clusters were identified using consistency clustering algorithm. Differences in the biologic and clinical characteristics between the clusters were analyzed. Cox regression analysis was performed to screen for differentially expressed genes (DEGs) related to prognosis, and a principal component analysis (PCA) algorithm was used to construct a model based on these genes. The pyroptosis score was calculated for each tumor sample, and the samples were classified into high- and low-score groups based on the score. The disparities in survival, single-nucleotide variation (SNV), copy number variation (CNV), and immunotherapy response between high-score and low-score groups were analyzed. Results: A total of 51 PRGs were used to classify LUSC samples into three pyroptosis clusters with significant differences in survival (P=0.005). Based on the 390 DEGs between the three clusters, two distinct pyroptosis gene clusters were identified by secondary clustering, with significant differences in prognosis (P=0.005). A pyroptosis scoring model was established to evaluate the regulatory patterns of PRGs, and patients were stratified into two groups with high and low scores, using the median pyroptosis score as the cutoff. The survival analyses indicated that patients with high scores had worse prognoses in The Cancer Genome Atlas (TCGA)-LUSC cohort (P=0.002), which was further supported by the analysis of the GSE37745 (P=0.006) and GSE135222 datasets (P=0.02). Conclusions: The quantification of pyroptosis patterns was found to be important in predicting prognosis and devising personalized treatment strategies in patients with LUSC.