Exploring the prognostic significance and therapeutic potential of SUCLG2 in prostate cancer

Abstract

Background: Prostate cancer (PCa), a highly heterogeneous cancer with a complex molecular pathogenesis, is a leading cause of cancer-related mortality among men globally. The present study presents a lipid metabolism-based risk model for PCa and explores the role of succinyl-CoA ligase GDP-forming subunit beta (SUCLG2), a potential marker and therapeutic target in PCa involved in lipid metabolism and cancer progression, from the perspective of developing effective diagnostic and therapeutic strategies.

Methods: High-throughput RNA sequencing and single-cell RNA sequencing were used to investigate the expression and functional relevance of SUCLG2 in PCa. We analyzed 497 PCa samples from The Cancer Genome Atlas and conducted a comprehensive bioinformatics analysis, including univariate Cox proportional hazards regression, least absolute shrinkage and selection operator regression, and gene set enrichment analysis. Furthermore, quantitative real-time polymerase chain reaction and immunofluorescence assays were performed to validate SUCLG2 expression in clinical samples and the prostate carcinoma epithelial cell line 22Rv1.

Results: Our findings revealed that lipid metabolism-related genes, including SUCLG2, have significant prognostic value, based on a 16-gene risk model constructed that accurately predicted PCa prognosis. In particular, SUCLG2 was significantly enriched in luminal and basal/intermediate cell subsets, highlighting its potential role in tumor progression and therapy resistance. Drug sensitivity analysis indicated that SUCLG2 expression is correlated with the efficacy of several chemotherapeutic agents, based on which strategies for personalized therapy in PCa treatment could be devised.

Conclusion: SUCLG2 plays a pivotal role in the metabolic reprogramming of PCa, thus offering new insights into its progression and potential therapeutic targets. Our study underscores the importance of metabolic pathways in PCa pathogenesis and paves the way for the development of targeted therapies, thus contributing to personalized medicine in PCa management.

Keywords: SUCLG2; lipid metabolism; personalized therapy; prostate cancer; single-cell RNA sequencing.

FIGURE 1

Construction and Validation of a Lipid Metabolism Gene Risk Model. (A) A LASSO regression model was constructed to identify 16 lipid metabolism-related genes associated with high risk in PCa. (B) Gene coefficients of genes related to the risk of PCa progression. (C) Risk score and survival time in high- and low-risk groups, (D) Overall survival analysis in high- and low-risk groups. (E) Disease-free survival analysis in high- and low-risk groups. (F) Progression-free survival analysis in high- and low-risk groups. (G) AUC curve for prediction accuracy.

FIGURE 2

Clinical correlation and expression heatmap of the risk model comprising 16 lipid metabolism-related genes. *p < 0.05 and **p < 0.01.

FIGURE 3

Cellular Heterogeneity based on Single-Cell RNA Sequencing. (A) A t-distributed stochastic neighbor embedding (tSNE) view of 35,405 single cells, color-coded by assigned cell clusters. (B) tSNE view of 7 cell subsets. (C) Uniform Manifold Approximation and Projection (UMAP) plot of 7 cell clusters. (D) tSNE view of all cells, color-coded by the number of genes detected in each cell. (E) Expression of marker genes for each cell type, where dot size and color represent the percentage of marker gene expression (pct. exp) and the averaged scaled expression (avg. exp. scale) value, respectively. (F) tSNE view of the expression of 10 lipid metabolism-related risk genes in 7 cell subsets. (G) Violin plot of SUCLG2 expression in 7 cell subsets.

FIGURE 4

Role of SUCLG2 in PCa based on Functional Enrichment Analysis. (A) Gene Ontology (GO) enrichment for 41 lipid metabolism genes with significant prognostic value. (B) Gene Set Enrichment Analysis (GSEA) results related to high SUCLG2 expression. (C) GSEA results related to low expression of SUCLG2. NES, normalized enrichment score; FDR q, false discovery rate q value.

FIGURE 5

Validation of SUCLG2 Expression in PCa. (A) Quantitative real-time PCR analysis of 10 matched pairs of tumor and adjacent normal tissues. (B) Immunohistochemical data from the Human Protein Atlas dataset. (C) Immunofluorescence assay of SUCLG2 expression in the prostate carcinoma epithelial cell line 22Rv1.

FIGURE 6

Results of Drug Sensitivity Analysis. (A) Correlation between gene expression and drug sensitivity for the top 9 drugs used for PCa treatment. (B) Relationship between gene expression and drug sensitivity in groups with high and low SUCLG2 expression. Cor, correlation coefficient; NS, not significant; **p < 0.01; ***p < 0.001.