loading page

Developing a Prognostic Signature with Cancer-Associated Fibroblasts for Predicting the Prognosis and Immune Landscape of Prostate Cancer
  • +3
  • Xubo Kong,
  • Qin Liu,
  • Jin Yang,
  • Ruyi Wang,
  • Jiangshu He,
  • hanchao Zhang
Xubo Kong
Clinical Medical College & Affiliated Hospital of Chengdu University
Author Profile
Qin Liu
Southwest Medical University
Author Profile
Jin Yang
Clinical Medical College & Affiliated Hospital of Chengdu University
Author Profile
Ruyi Wang
Clinical Medical College & Affiliated Hospital of Chengdu University
Author Profile
Jiangshu He
Clinical Medical College & Affiliated Hospital of Chengdu University
Author Profile
hanchao Zhang
Clinical Medical College & Affiliated Hospital of Chengdu University

Corresponding Author:[email protected]

Author Profile

Abstract

Background: Prostate cancer (PCa) is the common malignant disease in older men. Cancer-associated fibroblasts (CAFs) are a vital components of the TME and the major subpopulation of cells that promote tumor heterogeneity. However, there are still few studies on the correlation between PCa and CAFs. Thus, we predicted the prognosis of PCa by investigating CAFs characteristics in PCa and constructing a prognostic model with CAFs-related features. Methods: Firstly, we obtained the scRNA-seq and clinical data on PCa from the GEO and TCGA databases. We adopted a survival analysis to evaluate the impact of three distinct CAFs subtypes on the prognosis of PCa patients. Besides, we identified different CAFs by integrated univariate Cox regression analysis, LASSO analysis, and multivariate Cox regression analysis. Based on the CAFRGs, we built a prognostic model to exhibit PCa prognostic relevance and validated the prognostic signature. We also screened the drugs for PCa. Furthermore, we explored the correlation between malignant features and PCa. Results: We revealed that apCAFs and myCAFs were significantly correlated with PCa patient prognosis. 5 prognostic CAFRGs (SYNM, NR4A1, MSMB, HOPX, and GJC1) were screened by integrated analysis. We found that the low-risk group patients had significantly higher survival rates. And validation analyses targeting the prognostic model indicated that the high-risk group patients were more to exhibit higher BCR across external validation sets. The ssGSEA algorithm indicted that the majority of the immune cells had increased levels of infiltration and higher immune function scores in the high-risk group. Furthermore, the z-score algorithm results showed that CAFRG was closely connected angiogenesis, EMT, and cell cycle scores. Conclusion: In conclusion, we build a prognostic model with CAFs prognostic characteristics for PCa to offer further prediction of PCa prognosis and immunotherapy response, which ultimately guides the clinical management of PCa.