Integrated analysis reveals an aspartate metabolism-related gene signature for predicting the overall survival in patients with hepatocellular carcinoma.

BACKGROUND: Deregulating cellular metabolism is one of the prominent hallmarks of malignancy, with a critical role in tumor survival and growth. However, the role of reprogramming aspartate metabolism in hepatocellular carcinoma (HCC) are largely unknown. METHODS: The multi-omics data of HCC patients were downloaded from public databases. Univariate and multivariate stepwise Cox regression were used to establish an aspartate metabolism-related gene signature (AMGS) in HCC. The Kaplan-Meier and receiver operating characteristic curve analyses were performed to evaluate the predictive ability for overall survival (OS) in HCC patients. Gene set enrichment analysis and immune infiltration analysis were operated to determine the potential mechanisms underlying the AMGS. Single-cell RNA sequencing (scRNA-seq) data of liver cancer stem cells were visualized by t-SNE algorithm. In vivo and in vitro experiments were implemented to investigate the biological function of CAD in HCC. In addition, a nomogram based on the AMGS and clinicopathologic characteristics was constructed by univariate and multivariate Cox regression analyses. RESULTS: Patients in the high-AMGS subgroup exerted advanced tumor status and poor prognosis. Mechanistically, the high-AMGS subgroup patients had significantly enhanced proliferation and stemness-related pathways, increased infiltration of regulatory T cells and upregulated expression levels of suppressive immune checkpoints in the tumor immune microenvironment. Notably, scRNA-seq data revealed CAD, one of the aspartate metabolism-related gene, is significantly upregulated in liver cancer stem cells. Silencing CAD inhibited proliferative capacity and stemness properties of HCC cells in vitro and in vivo. Finally, a novel nomogram based on the AMGS showed an accurate prediction in HCC patients. CONCLUSIONS: The AMGS represents a promising prognostic value for HCC patients, providing a perspective for finding novel biomarkers and therapeutic targets for HCC.

Single-cell analysis extracted CAFs-related genes to established online app to predict clinical outcome and radiotherapy prognosis of prostate cancer.

BACKGROUND: Cancer-associated fibroblasts (CAFs) play a significant role in regulating the clinical outcome and radiotherapy prognosis of prostate cancer (PCa). The aim of this study is to identify CAFs-related genes (CAFsRGs) using single-cell analysis and evaluate their potential for predicting the prognosis and radiotherapy prognosis in PCa. METHODS: We acquire transcriptome and single-cell RNA sequencing (scRNA-seq) results of PCa and normal adjacent tissues from The GEO and TCGA databases. The "MCPcounter" and "EPIC" R packages were used to assess the infiltration level of CAFs and examine their correlation with PCa prognosis. ScRNA-seq and differential gene expression analyses were used to extract CAFsRGs. We also applied COX and LASSO analysis to further construct a risk score (CAFsRS) to assess biochemical recurrence-free survival (BRFS) and radiotherapy prognosis of PCa. The predictive efficacy of CAFsRS was evaluated by ROC curves and subgroup analysis. Finally, we integrated the CAFsRS gene signature with relevant clinical features to develop a nomogram, enhancing the predictive accuracy. RESULTS: The abundance of CAFs is associated with a poor prognosis of PCa patients. ScRNA-seq and differential gene expression analysis revealed 323 CAFsRGs. After COX and LASSO analysis, we obtained seven CAFsRGs with prognostic significance (PTGS2, FKBP10, ENG, CDH11, COL5A1, COL5A2, and SRD5A2). Additionally, we established a risk score model based on the training set (n = 257). The ROC curve was used to confirm the performance of CAFsRS (The AUC values for 1, 3 and 5-year survival were determined to be 0.732, 0.773, and 0.775, respectively.). The testing set (n = 129), GSE70770 set (n = 199) and GSE116918 set (n = 248) revealed that the model exhibited exceptional predictive performance. This was also confirmed by clinical subgroup analysis. The violin plot demonstrated a statistically significant disparity in the CAFs infiltrations between the high-risk and low-risk groups of CAFsRS. Further analysis confirmed that both CAFsRS and T stage were independent prognostic factors for PCa. The nomogram was then established and its excellent predictive performance was demonstrated through calibration and ROC curves. Finally, we developed an online prognostic prediction app ( https://sysu-symh-cafsnomogram.streamlit.app/ ) to facilitate the practical application of the nomogram. CONCLUSIONS: The prognostic prediction risk score model we constructed could accurately predict BRFS and radiotherapy prognosis PCa, which can provide new ideas for clinicians to develop personalized PCa treatment and follow-up programs.