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

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Abstract

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.

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Data availability

The scRNA-seq dataset, expression profiles and clinical information involved in this article were downloaded from TCGA Database and GEO Database. Please contact the author if you want to access the codes.

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Acknowledgements

We would like to thank TCGA and ENA databases for their contributions.

Funding

This study was supported by Key Areas Research and Development Program of Guangdong (2023B1111030006), National Natural Science Foundation of China (82372766 and 82072841), Natural Science Foundation of Guangdong Province (2021A1515010199), Key Areas Research and Development Program of Guangdong (2020B111114002), Guangdong Provincial Clinical Research Center for Urological Diseases (2020B1111170006) and Guangdong Science and Technology Department (2020B1212060018).

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Author notes

  1. Cong Lai, Zhikai Wu, Zhuohang Li and Xin Huang contributed equally to this study.

Authors and Affiliations

  1. Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510000, Guangdong, China

    Cong Lai, Zhikai Wu, Zhuohang Li, Xin Huang, Hao Yu, Zhihan Yuan, Juanyi Shi, Jintao Hu, Yelisudan Mulati, Cheng Liu & Kewei Xu

  2. Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510000, Guangdong, China

    Cong Lai, Zhikai Wu, Xin Huang, Zhihan Yuan, Jintao Hu & Yelisudan Mulati

  3. Guangdong Provincial Clinical Research Center for Urological Diseases, Guangzhou, 510000, Guangdong, China

    Zhuohang Li, Hao Yu, Juanyi Shi, Cheng Liu & Kewei Xu

  4. Department of Medical Informatics, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510000, Guangdong, China

    Zhensheng Hu

  5. Sun Yat-Sen College of Medical Science, Sun Yat-Sen University, Shenzhen, 518000, Guangdong, China

    Kewei Xu

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Contributions

CL: data curation, formal analysis, writing—original draft, writing—review and editing. ZW: data curation, formal analysis, writing—original draft, writing—review and editing. ZL: formal analysis, writing—original draft, writing—review and editing. XH: data curation, writing—original draft, writing—review and editing. ZH: formal analysis, writing—review and editing. HY: formal analysis, writing—review and editing. ZY: formal analysis, writing—original draft, writing—review and editing. JS: formal analysis, writing—review and editing. JH: writing—review and editing. YM: writing—review and editing. CL: conceptualization, funding acquisition, supervision, writing—review and editing. KX: conceptualization, funding acquisition, supervision, writing—review and editing.

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Correspondence to Cheng Liu or Kewei Xu.

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Lai, C., Wu, Z., Li, Z. et al. Single-cell analysis extracted CAFs-related genes to established online app to predict clinical outcome and radiotherapy prognosis of prostate cancer. Clin Transl Oncol 26, 1240–1255 (2024). https://doi.org/10.1007/s12094-023-03348-6

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