Clinicopathological significance and prognostic value of cancer-associated fibroblasts in prostate cancer patients

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.

Effects of cancer-associated fibroblasts deletion using HSVtk suicide system in OSCC

OBJECTIVE

To evaluate the biological characteristics of oral cancer cells co-cultured with cancer-associated fibroblasts (CAFs)-HSVtk and to assess the reliability of the CAFs-HSVtk suicide system in a co-culture model.

METHODS

CAFs were lentivirus-transfected with PCDH-HSVtk. Ganciclovir (GCV) was added and the survival rates of the CAFs-HSVtk were measured. In parallel with the selective elimination of CAFs, comparison was made of the effects of CAF-HSVtk on tumor cell proliferation/migration in a CAFs-tumor co-cultural system. Cell death of co-cultured oral cancer cells was evaluated by flow cytometry.

RESULTS

Q-PCR analysis showed that the expression of HSVtk in the CAFs-HSVtk group was significantly higher than in the control group (p < 0.01). The survival rates of CAFs-HSVtk with GCV were significantly reduced (p < 0.01). Following selective depletion of CAFs-HSVtk, the growth and migration rates of oral cancer cells co-cultured with CAFs-HSVtk were reduced in a mixture ratio of 1:2 (p < 0.01, p < 0.01).

CONCLUSIONS

Enhanced proliferation and migration rates of oral cancer cells in co-culture were seriously impaired after deleting CAFs using the HSVtk suicide system, while oral tumor cell death was not affected. Therefore, CAFs-HSVtk can be utilized as a valid model for CAF signature identification.

A comprehensive review of 3D cancer models for drug screening and translational research

The 3D cancer models fill the discovery gap of 2D cancer models and play an important role in cancer research. In addition to cancer cells, a range of other factors include the stroma, density and composition of extracellular matrix, cancer-associated immune cells (e.g., cancer-associated fibroblasts cancer cell-stroma interactions and subsequent interactions, and a number of other factors (e.g., tumor vasculature and tumor-like microenvironment in vivo) has been widely ignored in the 2D concept of culture. Despite this knowledge, the continued use of monolayer cell culture methods has led to the failure of a series of clinical trials. This review discusses the immense importance of tumor microenvironment (TME) recapitulation in cancer research, prioritizing the individual roles of TME elements in cancer histopathology. The TME provided by the 3D model fulfills the requirements of in vivo spatiotemporal arrangement, components, and is helpful in analyzing various different aspects of drug sensitivity in preclinical and clinical trials, some of which are discussed here. Furthermore, it discusses models for the co-assembly of different TME elements in vitro and focuses on their synergistic function and responsiveness as tumors. Furthermore, this review broadly describes of a handful of recently developed 3D models whose main focus is limited to drug development and their screening and/or the impact of this approach in preclinical and translational research.

Establishment of cancer-associated fibroblasts-related subtypes and prognostic index for prostate cancer through single-cell and bulk RNA transcriptome

Current evidence indicate that cancer-associated fibroblasts (CAFs) play an important role in prostate cancer (PCa) development and progression. In this study, we identified CAF-related molecular subtypes and prognostic index for PCa patients undergoing radical prostatectomy through integrating single-cell and bulk RNA sequencing data. We completed analyses using software R 3.6.3 and its suitable packages. Through single-cell and bulk RNA sequencing analysis, NDRG2, TSPAN1, PTN, APOE, OR51E2, P4HB, STEAP1 and ABCC4 were used to construct molecular subtypes and CAF-related gene prognostic index (CRGPI). These genes could clearly divide the PCa patients into two subtypes in TCGA database and the BCR risk of subtype 1 was 13.27 times higher than that of subtype 2 with statistical significance. Similar results were observed in MSKCC2010 and GSE46602 cohorts. In addtion, the molucular subtypes were the independent risk factor of PCa patients. We orchestrated CRGPI based on the above genes and divided 430 PCa patients in TCGA database into high- and low- risk groups according to the median value of this score. We found that high-risk group had significant higher risk of BCR than low-risk group (HR: 5.45). For functional analysis, protein secretion was highly enriched in subtype 2 while snare interactions in vesicular transport was highly enriched in subtype 1. In terms of tumor heterogeneity and stemness, subtype 1 showd higher levels of TMB than subtype 2. In addition, subtype 1 had significant higher activated dendritic cell score than subtype 2. Based on eight CAF-related genes, we developed two prognostic subtypes and constructed a gene prognostic index, which could predict the prognosis of PCa patients very well.

Identification of new co-diagnostic genes for sepsis and metabolic syndrome using single-cell data analysis and machine learning algorithms

Sepsis, a serious inflammatory response that can be fatal, has a poorly understood pathophysiology. The Metabolic syndrome (MetS), however, is associated with many cardiometabolic risk factors, many of which are highly prevalent in adults. It has been suggested that Sepsis may be associated with MetS in several studies. Therefore, this study investigated diagnostic genes and metabolic pathways associated with both diseases. In addition to microarray data for Sepsis, PBMC single cell RNA sequencing data for Sepsis and microarray data for MetS were downloaded from the GEO database. Limma differential analysis identified 122 upregulated genes and 90 downregulated genes in Sepsis and MetS. WGCNA identified brown co-expression modules as Sepsis and MetS core modules. Two machine learning algorithms, RF and LASSO, were used to screen seven candidate genes, namely, STOM, BATF, CASP4, MAP3K14, MT1F, CFLAR and UROD, all with an AUC greater than 0.9. XGBoost assessed the co-diagnostic efficacy of Hub genes in Sepsis and MetS. The immune infiltration results show that Hub genes were expressed at high levels in all immune cells. After performing Seurat analysis on PBMC from normal and Sepsis patients, six immune subpopulations were identified. The metabolic pathways of each cell were scored and visualized using ssGSEA, and the results show that CFLAR plays an important role in the glycolytic pathway. Our study identified seven Hub genes that serve as co-diagnostic markers for Sepsis and MetS and revealed that diagnostic genes play an important role in immune cell metabolic pathway.

SFRP1 induces a stem cell phenotype in prostate cancer cells

Prostate cancer (PCa) ranks second in incidence and sixth in deaths globally. The treatment of patients with castration-resistant prostate cancer (CRPC) continues to be a significant clinical problem. Emerging evidence suggests that prostate cancer progression toward castration resistance is associated with paracrine signals from the stroma. SFRP1 is one of the extracellular proteins that modulate the WNT pathway, and it has been identified as a mediator of stromal epithelium communication. The WNT pathway is involved in processes such as cell proliferation, differentiation, cell anchoring, apoptosis, and cell cycle regulation as well as the regulation of stem cell populations in the prostatic epithelium. In the present study, we explored the role of exogenous SFRP1 on the stem cell phenotype in prostate cancer. The results reveal that cancer stem cell markers are significantly increased by exogenous SFRP1 treatments, as well as the downstream target genes of the Wnt/-catenin pathway. The pluripotent transcription factors SOX2, NANOG, and OCT4 were also up-regulated. Furthermore, SFRP1 promoted prostate cancer stem cell (PCSC) properties in vitro, including tumorsphere formation, migration, bicalutamide resistance, and decreased apoptosis. Taken together, our results indicate that SFRP1 participates in the paracrine signaling of epithelial cells, influencing them and positively regulating the stem cell phenotype through deregulation of the WNT/β-catenin pathway, which could contribute to disease progression and therapeutic failure. This research increases our molecular understanding of how CRPC progresses, which could help us find new ways to diagnose and treat the disease.

Integrative characterisation of secreted factors involved in intercellular communication between prostate epithelial or cancer cells and fibroblasts

Reciprocal interactions between prostate cancer cells and carcinoma-associated fibroblasts (CAFs) mediate cancer development and progression; however, our understanding of the signalling pathways mediating these cellular interactions remains incomplete. To address this, we defined secretome changes upon co-culture of prostate epithelial or cancer cells with fibroblasts that mimic bi-directional communication in tumours. Using antibody arrays, we profiled conditioned media from mono- and co-cultures of prostate fibroblasts, epithelial and cancer cells, identifying secreted proteins that are upregulated in co-culture compared to mono-culture. Six of these (CXCL10, CXCL16, CXCL6, FST, PDGFAA, IL-17B) were functionally screened by siRNA knockdown in prostate cancer cell/fibroblast co-cultures, revealing a key role for follistatin (FST), a secreted glycoprotein that binds and bioneutralises specific members of the TGF-β superfamily, including activin A. Expression of FST by both cell types was required for the fibroblasts to enhance prostate cancer cell proliferation and migration, whereas FST knockdown in co-culture grafts decreased tumour growth in mouse xenografts. This study highlights the complexity of prostate cancer cell-fibroblast communication, demonstrates that co-culture secretomes cannot be predicted from individual cultures, and identifies FST as a tumour-microenvironment-derived secreted factor that represents a candidate therapeutic target.

An autophagy-related gene prognostic index predicting biochemical recurrence, metastasis, and drug resistance for prostate cancer

Given the dual role of autophagy presenting in tumorigenesis and inhibition, we established an autophagy-related gene prognostic index (ARGPI) with validation to well predict the biochemical recurrence (BCR), metastasis, as well as chemoresistance for patients with prostate cancer (PCa) who underwent radical radiotherapy or prostatectomy. Then, Lasso and COX regression was used to develop the ARGPI. We performed the whole analyses through R packages (version 3.6.3). Secreted phosphoprotein 1 (SPP1), single-minded 2 (SIM2), serine protease inhibitor b5 (SERPINB5), aldehyde dehydrogenase 2 (ALDH2), and acyl-CoA synthetase long-chain 3 (ACSL3) were eventually used to establish the ARGPI score. Patients were divided into two different-risk groups based on the median ARGPI score, high-risk patients with a higher risk of BCR than low-risk patients (hazard ratio [HR]: 5.46, 95% confidence interval [CI]: 3.23-9.24). The risk of metastasis of high-risk patients was higher than low-risk patients (HR: 11.31, 95% CI: 4.89-26.12). In The Cancer Genome Atlas (TCGA) dataset, we observed similar prognostic value of ARGPI in terms of BCR-free survival (HR: 1.79, 95% CI: 1.07-2.99) and metastasis-free survival (HR: 1.80, 95% CI: 1.16-2.78). ARGPI score showed a diagnostic accuracy of 0.703 for drug resistance. Analysis of gene set enrichment analysis (GSEA) indicated that patients in the high-risk group were significantly positively related to interleukin (IL)-18 signaling pathway. Moreover, ARGPI score was significantly related to cancer-related fibroblasts (CAFs; r = 0.36), macrophages (r = 0.28), stromal score (r = 0.38), immune score (r = 0.35), estimate score (r = 0.39), as well as tumor purity (r = -0.39; all P < 0.05). Drug analysis showed that PI-103 was the common sensitive drug and cell line analysis indicated that PC3 was the common cell line of PI-103 and the definitive gene. In conclusion, we found that ARGPI could predict BCR, metastasis, and chemoresistance in PCa patients who underwent radical radiotherapy or prostatectomy.

Cellular landscape of tumour microenvironment in prostate cancer

Prostate cancer is still a public health priority in men and the impact of this disease will be more pronounced with the ageing of the world's population. Clinical heterogeneity of prostate cancer is reflected in spatial and clonal genomic diversity. Accumulating evidence demonstrates that the malignant behaviour of cancer is not only attributed to cancer cells but also fundamentally affected by stromal activity and controlled by various mechanisms of the tumour microenvironment. Data on prostate cancer in this study was derived from seven GEO datasets and the TCGA database. We analyzed the tumour microenvironment of prostate cancer in terms of clinical process, T stage and Gleason score using EPIC and xCell algorithms. We also analyzed the common immune checkpoints. In this study, we confirmed remarkable tumour tissue remodelling in the development of prostate cancer and further demonstrated the importance of cancer-related fibroblasts in the biochemical recurrence and metastasis for patients with prostate cancer undergoing radical radiotherapy or prostatectomy. In addition, we found that NRP1, CD200, TNFSF18 and CD80 might be the potential targets for prostate cancer.

Cell-Type-Specific Signalling Networks Impacted by Prostate Epithelial-Stromal Intercellular Communication

Prostate cancer is the second most common cause of cancer death in males. A greater understanding of cell signalling events that occur within the prostate cancer tumour microenvironment (TME), for example, between cancer-associated fibroblasts (CAFs) and prostate epithelial or cancer cells, may identify novel biomarkers and more effective therapeutic strategies for this disease. To address this, we used cell-type-specific labelling with amino acid precursors (CTAP) to define cell-type-specific (phospho)proteomic changes that occur when prostate epithelial cells are co-cultured with normal patient-derived prostate fibroblasts (NPFs) versus matched CAFs. We report significant differences in the response of BPH-1 benign prostate epithelial cells to CAF versus NPF co-culture. Pathway analysis of proteomic changes identified significant upregulation of focal adhesion and cytoskeleton networks, and downregulation of metabolism pathways, in BPH-1 cells cultured with CAFs. In addition, co-cultured CAFs exhibited alterations in stress, DNA damage, and cytoskeletal networks. Functional validation of one of the top differentially-regulated proteins in BPH-1 cells upon CAF co-culture, transglutaminase-2 (TGM2), demonstrated that knockdown of this protein significantly reduced the proliferation and migration of prostate epithelial cells. Overall, this study provides novel insights into intercellular communication in the prostate cancer TME that may be exploited to improve patient management.

Proteomic characterisation of prostate cancer intercellular communication reveals cell type-selective signalling and TMSB4X-dependent fibroblast reprogramming

BACKGROUND

In prostate cancer, the tumour microenvironment (TME) represents an important regulator of disease progression and response to treatment. In the TME, cancer-associated fibroblasts (CAFs) play a key role in tumour progression, however the mechanisms underpinning fibroblast-cancer cell interactions are incompletely resolved. Here, we address this by applying cell type-specific labelling with amino acid precursors (CTAP) and mass spectrometry (MS)-based (phospho)proteomics to prostate cancer for the first time.

METHODS

Reciprocal interactions between PC3 prostate cancer cells co-cultured with WPMY-1 prostatic fibroblasts were characterised using CTAP-MS. Signalling network changes were determined using Metascape and Enrichr and visualised using Cytoscape. Thymosin β4 (TMSB4X) overexpression was achieved via retroviral transduction and assayed by ELISA. Cell motility was determined using Transwell and random cell migration assays and expression of CAF markers by indirect immunofluorescence.

RESULTS

WPMY-1 cells co-cultured with PC3s demonstrated a CAF-like phenotype, characterised by enhanced PDGFRB expression and alterations in signalling pathways regulating epithelial-mesenchymal transition, cytoskeletal organisation and cell polarisation. In contrast, co-cultured PC3 cells exhibited more modest network changes, with alterations in mTORC1 signalling and regulation of the actin cytoskeleton. The expression of the actin binding protein TMSB4X was significantly decreased in co-cultured WPMY-1 fibroblasts, and overexpression of TMSB4X in fibroblasts decreased migration of co-cultured PC3 cells, reduced fibroblast motility, and protected the fibroblasts from being educated to a CAF-like phenotype by prostate cancer cells.

CONCLUSIONS

This study highlights the potential of CTAP-MS to characterise intercellular communication within the prostate TME and identify regulators of cellular crosstalk such as TMSB4X.

Targeting the tumor stroma for cancer therapy

Tumors are comprised of both cancer cells and surrounding stromal components. As an essential part of the tumor microenvironment, the tumor stroma is highly dynamic, heterogeneous and commonly tumor-type specific, and it mainly includes noncellular compositions such as the extracellular matrix and the unique cancer-associated vascular system as well as a wide variety of cellular components including activated cancer-associated fibroblasts, mesenchymal stromal cells, pericytes. All these elements operate with each other in a coordinated fashion and collectively promote cancer initiation, progression, metastasis and therapeutic resistance. Over the past few decades, numerous studies have been conducted to study the interaction and crosstalk between stromal components and neoplastic cells. Meanwhile, we have also witnessed an exponential increase in the investigation and recognition of the critical roles of tumor stroma in solid tumors. A series of clinical trials targeting the tumor stroma have been launched continually. In this review, we introduce and discuss current advances in the understanding of various stromal elements and their roles in cancers. We also elaborate on potential novel approaches for tumor-stroma-based therapeutic targeting, with the aim to promote the leap from bench to bedside.

A Ferroptosis-Related Gene Prognostic Index Associated With Biochemical Recurrence and Radiation Resistance for Patients With Prostate Cancer Undergoing Radical Radiotherapy

Background: Ferroptosis is a new type of programmed cell death which has been reported to be involved in the development of various cancers. In this study, we attempted to explore the possible links between ferroptosis and prostate cancer (PCa), and a novel ferroptosis-related gene prognostic index (FGPI) was constructed to predict biochemical recurrence (BCR) and radiation resistance for PCa patients undergoing radical radiotherapy (RRT). Moreover, the tumor immune microenvironment (TME) of PCa was analyzed.

Methods: We merged four GEO datasets by removing batch effects. All analyses were conducted with R version 3.6.3 and its suitable packages. Cytoscape 3.8.2 was used to establish a network of transcriptional factor and competing endogenous RNA.

Results: We established the FGPI based on ACSL3 and EPAS1. We observed that FGPI was an independent risk factor of BCR for PCa patients (HR: 3.03; 95% CI: 1.68–5.48), consistent with the result of internal validation (HR: 3.44; 95% CI: 1.68–7.05). Furthermore, FGPI showed high ability to identify radiation resistance (AUC: 0.963; 95% CI: 0.882–1.00). LncRNA PART1 was significantly associated with BCR and might modulate the mRNA expression of EPAS1 and ACSL3 through interactions with 60 miRNAs. Gene set enrichment analysis indicated that FGPI was enriched in epithelial–mesenchymal transition, allograft rejection, TGF beta signaling pathway, and ECM receptor interaction. Immune checkpoint and m6A analyses showed that PD-L2, CD96, and METTL14 were differentially expressed between BCR and no BCR groups, among which CD96 was significantly associated with BCR-free survival (HR: 1.79; 95% CI: 1.06–3.03). We observed that cancer-related fibroblasts (CAFs), macrophages, stromal score, immune score, estimate score, and tumor purity were differentially expressed between BCR and no BCR groups and closely related to BCR-free survival (HRs were 2.17, 1.79, 2.20, 1.93, 1.92, and 0.52 for cancer-related fibroblasts, macrophages, stromal score, immune score, estimate score, and tumor purity, respectively). Moreover, cancer-related fibroblasts (coefficient: 0.20), stromal score (coefficient: 0.14), immune score (coefficient: 0.14), estimate score (coefficient: 0.15), and tumor purity (coefficient: −0.15) were significantly related to FGPI, among which higher positive correlation between cancer-related fibroblasts and FGPI was observed.

Conclusion: We found that FGPI based on ACSL3 and EPAS1 might be used to predict BCR and radiation resistance for PCa patients. CD96 and PD-L2 might be a possible target for drug action. Besides, we highlighted the importance of immune evasion in the process of BCR.