RESUMO
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.
RESUMO
The immunonutritional status has important effects on outcomes for cancer patients. Albumin-to-globulin ratio (AGR) and the prognostic nutrition index (PNI) are often used to assess the immunonutritional status of cancer patients. However, the clinical significance of these factors in colorectal cancer (CRC) remains unclear. We aimed to evaluate the clinical significance of the AGR and PNI in CRC. We reviewed the clinical data of 511 patients with CRC in two hospitals. Data from one institution was used as the training cohort. The optimal cutoff values for AGR and PNI in the training cohort were 1.4 and 48.65, respectively. Patients in both the low AGR and low PNI groups had poor overall survival (OS) and progression-free survival (PFS), while those in the low AGR-low PNI group had the lowest OS and PFS. Multivariate analysis revealed that preoperative AGR, preoperative PNI, gross type, and TNM stage were independent prognostic factors influencing OS in patients with CRC. Preoperative AGR, preoperative PNI, and TNM stage were independently associated with PFS in patients with CRC. According to the results of multivariate analysis in the training cohort, we developed the nomograms for OS and PFS and performed internal and external validation, which showed good prediction ability of the nomograms. In conclusion, preoperative AGR and PNI can be used as effective indicators to predict survival for patients with CRC. AGR and PNI may help develop effective adjuvant-therapy schedules.