Integrating single-cell and bulk RNA sequencing to predict prognosis and immunotherapy response in prostate cancer.

Prostate cancer (PCa) stands as a prominent contributor to morbidity and mortality among males on a global scale. Cancer-associated fibroblasts (CAFs) are considered to be closely connected to tumour growth, invasion, and metastasis. We explored the role and characteristics of CAFs in PCa through bioinformatics analysis and built a CAFs-based risk model to predict prognostic treatment and treatment response in PCa patients. First, we downloaded the scRNA-seq data for PCa from the GEO. We extracted bulk RNA-seq data for PCa from the TCGA and GEO and adopted "ComBat" to remove batch effects. Then, we created a Seurat object for the scRNA-seq data using the package "Seurat" in R and identified CAF clusters based on the CAF-related genes (CAFRGs). Based on CAFRGs, a prognostic model was constructed by univariate Cox, LASSO, and multivariate Cox analyses. And the model was validated internally and externally by Kaplan-Meier analysis, respectively. We further performed GO and KEGG analyses of DEGs between risk groups. Besides, we investigated differences in somatic mutations between different risk groups. We explored differences in the immune microenvironment landscape and ICG expression levels in the different groups. Finally, we predicted the response to immunotherapy and the sensitivity of antitumour drugs between the different groups. We screened 4 CAF clusters and identified 463 CAFRGs in PCa scRNA-seq. We constructed a model containing 10 prognostic CAFRGs by univariate Cox, LASSO, and multivariate Cox analysis. Somatic mutation analysis revealed that TTN and TP53 were significantly more mutated in the high-risk group. Finally, we screened 31 chemotherapeutic drugs and targeted therapeutic drugs for PCa. In conclusion, we identified four clusters based on CAFs and constructed a new CAFs-based prognostic signature that could predict PCa patient prognosis and response to immunotherapy and might suggest meaningful clinical options for the treatment of PCa.

Wen XY ，Wang RY ，Yu B ，Yang Y ，Yang J ，Zhang HC ... -  《Scientific Reports》

Identification and validation of cancer-associated fibroblast-related subtypes and the prognosis model of biochemical recurrence in prostate cancer based on single-cell and bulk RNA sequencing.

Cancer-associated fibroblasts (CAFs) are an essential component of the tumor immune microenvironment that are involved in extracellular matrix (ECM) remodeling. We aim to investigate the characteristics of CAFs in prostate cancer and develop a biochemical recurrence (BCR)-related CAF signature for predicting the prognosis of PCa patients. The bulk RNA-seq and relevant clinical information were obtained from the TCGA and GEO databases, respectively. The infiltration scores of CAFs in prostate cancer patients were calculated using the MCP counter and EPIC algorithms. The single-cell RNA sequencing (scRNA-seq) was downloaded from the GEO database. Subsequently, univariate Cox regression analysis was employed to identify prognostic genes associated with CAFs. We identified two subtypes (C1 and C2) of prostate cancer that were associated with CAFs via non-negative matrix factorization (NMF) clustering. In addition, the BCR-related CAF signatures were constructed using Lasso regression analysis. Finally, a nomogram model was established based on the risk score and clinical characteristics of the patients. Initially, we found that patients with high CAF infiltration scores had shorter biochemical recurrence-free survival (BCRFS) times. Subsequently, CAFs in four pairs of tumors and paracancerous tissues were identified. We discovered 253 significantly differentially expressed genes, of which 13 had prognostic significance. Using NMF clustering, we divided PCa patients into C1 and C2 subgroups, with the C1 subgroup having a worse prognosis and substantially enriched cell cycle, homologous recombination, and mismatch repair pathways. Furthermore, a BCR-related CAFs signature was established. Multivariate COX regression analysis confirmed that the BCR-related CAFs signature was an independent prognostic factor for BCR in PCa. In addition, the nomogram was based on the clinical characteristics and risk scores of the patient and demonstrated high accuracy and reliability for predicting BCR. Lastly, our findings indicate that the risk score may be a useful tool for predicting PCa patients' sensitivity to immunotherapy and drug treatment. NMF clustering based on CAF-related genes revealed distinct TME immune characteristics between groups. The BCR-related CAF signature accurately predicted prognosis and immunotherapy response in prostate cancer patients, offering a promising new approach to cancer treatment.

Li T ，Zhou Z ，Xie Z ，Fan X ，Zhang Y ，Zhang Y ，Song X ，Ruan Y ... -  《-》

Single-cell and bulk RNA sequencing reveal cancer-associated fibroblast heterogeneity and a prognostic signature in prostate cancer.

Liu W ，Wang M ，Wang M ，Liu M ... -  《-》

Characterization of cancer-related fibroblasts (CAF) in hepatocellular carcinoma and construction of CAF-based risk signature based on single-cell RNA-seq and bulk RNA-seq data.

Cancer-associated fibroblasts (CAFs) are involved in tumor growth, angiogenesis, metastasis, and resistance to therapy. We sought to explore the CAFs characteristics in hepatocellular carcinoma (HCC) and establish a CAF-based risk signature for predicting the prognosis of HCC patients. The signal-cell RNA sequencing (scRNA-seq) data was obtained from the GEO database. Bulk RNA-seq data and microarray data of HCC were obtained from the TCGA and GEO databases respectively. Seurat R package was applied to process scRNA-seq data and identify CAF clusters according to the CAF markers. Differential expression analysis was performed to screen differentially expressed genes (DEGs) between normal and tumor samples in TCGA dataset. Then Pearson correlation analysis was used to determine the DEGs associated with CAF clusters, followed by the univariate Cox regression analysis to identify CAF-related prognostic genes. Lasso regression was implemented to construct a risk signature based on CAF-related prognostic genes. Finally, a nomogram model based on the risk signature and clinicopathological characteristics was developed. Based on scRNA-seq data, we identified 4 CAF clusters in HCC, 3 of which were associated with prognosis in HCC. A total of 423 genes were identified from 2811 DEGs to be significantly correlated with CAF clusters, and were narrowed down to generate a risk signature with 6 genes. These six genes were primarily connected with 39 pathways, such as angiogenesis, apoptosis, and hypoxia. Meanwhile, the risk signature was significantly associated with stromal and immune scores, as well as some immune cells. Multivariate analysis revealed that risk signature was an independent prognostic factor for HCC, and its value in predicting immunotherapeutic outcomes was confirmed. A novel nomogram integrating the stage and CAF-based risk signature was constructed, which exhibited favorable predictability and reliability in the prognosis prediction of HCC. CAF-based risk signatures can effectively predict the prognosis of HCC, and comprehensive characterization of the CAF signature of HCC may help to interpret the response of HCC to immunotherapy and provide new strategies for cancer treatment.

Yu L ，Shen N ，Shi Y ，Shi X ，Fu X ，Li S ，Zhu B ，Yu W ，Zhang Y ... -  《Frontiers in Immunology》

Revealing a cancer-associated fibroblast-based risk signature for pancreatic adenocarcinoma through single-cell and bulk RNA-seq analysis.

Ma J ，Chen Z ，Hou L 《Aging-US》