Cancer-associated fibroblast-derived gene signatures predict radiotherapeutic survival in prostate cancer patients

Identification and dissection of prostate cancer grounded on fatty acid metabolism-correlative features for predicting prognosis and assisting immunotherapy

BACKGROUND

Fatty acid metabolism (FAM) plays a critical role in tumor progression and therapeutic resistance by enhancing lipid biosynthesis, storage, and catabolism. Dysregulated FAM is a hallmark of prostate cancer (PCa), enabling cancer cells to adapt to extracellular signals and metabolic changes, with the tumor microenvironment (TME) playing a key role. However, the prognostic significance of FAM in PCa remains unexplored.

METHODS

We analyzed 309 FAM-related genes to develop a prognostic model using least absolute shrinkage and selection operator (LASSO) regression based on The Cancer Genome Atlas (TCGA) database. This model stratified PCa patients into high- and low-risk groups and was validated using the Gene Expression Omnibus (GEO) database. We constructed a nomogram incorporating risk score, clinical variables (T and N stage, Gleason score, age), and assessed its performance with calibration curves. The associations between risk score, tumor mutation burden (TMB), immune checkpoint inhibitors (ICIs), and TME features were also examined. Finally, a hub gene was identified via protein-protein interaction (PPI) networks and validated.

RESULTS

The risk score was an independent prognostic factor for PCa. High-risk patients showed worse survival outcomes but were more responsive to immunotherapy, chemotherapy, and targeted therapies. A core gene with high expression correlated with poor prognosis, unfavorable clinicopathological features, and immune cell infiltration.

CONCLUSION

These findings reveal the prognostic importance of FAM in PCa, providing novel insights into prognosis and potential therapeutic targets for PCa management.

Construction of regulatory T cells specific genes predictive models of prostate cancer patients based on machine learning: a computational analysis and in vitro experiments

BACKGROUND

Diseases are often caused by multiple factors, regulatory T cells specific genes (RTSGs) have been shown to be associated with cancer, however, their role in prostate cancer (PRAD) has not been fully explored.

METHODS

RTSGs associated with PRAD prognosis were identified using Cox regression analysis and LASSO analysis. Furthermore, a prognostic model was constructed in PRAD based on the 4 RTSGs, and its biological function were analyzed. We evaluated the differences in tumor immune microenvironment based on prognostic signature. Finally, cell experiments confirmed the function of synaptonemal complex protein-2 (SYCP2) in PRAD cells.

RESULTS

The prognostic value of RTSGs in PRAD patients has been comprehensively analyzed for the first time and identified four RTSGs with prognostic values. A prognosis risk model was constructed based on four RTSGs and its prognostic value was validated on an independent external PRAD dataset. In PRAD patients, this prognostic feature is an independent risk factor and was significantly correlated with clinical feature information of PRAD patients. This feature is also related to the immune microenvironment of PRAD. Cell experiments have confirmed that SYCP2 regulates the apoptosis and cycle progression of PRAD cells significantly. Therefore, SYCP2 may become an important regulatory factor in the progression of PRAD by participating in intracellular functional regulation.

CONCLUSIONS

This research provides a fundamental theoretical basis for improving the diagnosis and treatment of PRAD in clinical practice.

Immunological characterization and prognostic of colon cancer evaluated by angiogenesis-related features: a computational analysis and in vitro experiments

BACKGROUND

Diseases are often caused by multiple factors, angiogenesis-related genes (ARGs) have been shown to be associated with cancer, however, their role in colon cancer had not been fully explored. This study investigated potential biomarkers based on ARGs to improve prognosis and treatment effect in colon cancer.

METHODS

ARGs associated with colon cancer prognosis were identified using Cox regression analysis and LASSO analysis. Furthermore, a prognostic model was constructed in colon cancer based on the 3 ARGs, and its biological function were analyzed. We evaluated the differences in tumor immune microenvironment based on prognostic signature. Finally, cell experiments confirmed the function of genes in colon cancer.

RESULTS

The prognostic value of ARGs in colon cancer patients has been comprehensively analyzed for the first time and identified 3 ARGs with prognostic values. A prognosis risk model was constructed based on 3 ARGs and its prognostic value was validated on an independent external colon cancer dataset. In colon cancer patients, this prognostic feature was an independent risk factor and was significantly correlated with clinical feature information of colon cancer patients. This feature was also related to the immune microenvironment of colon cancer. Cell experiments showed that high expression of TNF Receptor Superfamily Member 1B (TNFRSF1B) significantly promoted apoptosis and inhibited proliferation of colon cancer cells. Therefore, TNFRSF1B may become an important regulatory factor in the progression of colon cancer by participating in intracellular functional regulation.

CONCLUSIONS

This study constructed a prognostic risk model based on three ARGs and for the first time discovered that TNFRSF1B may become an important regulatory factor in cancer progression by participating in intracellular functional regulation.

Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States

Prostate cancer is a significant health concern and the most commonly diagnosed cancer in men worldwide. Understanding the complex process of prostate tumor evolution and progression is crucial for improved diagnosis, treatments, and patient outcomes. Previous studies have focused on unraveling the dynamics of prostate cancer evolution using phylogenetic or lineage analysis approaches. However, those approaches have limitations in capturing the complete disease process or incorporating genomic and transcriptomic variations comprehensively. In this study, we applied a novel computational approach to derive a prostate cancer progression model using multidimensional data from 497 prostate tumor samples and 52 tumor-adjacent normal samples obtained from The Cancer Genome Atlas study. The model was validated using data from an independent cohort of 545 primary tumor samples. By integrating transcriptomic and genomic data, our model provides a comprehensive view of prostate tumor progression, identifies crucial signaling pathways and genetic events, and uncovers distinct transcription signatures associated with disease progression. Our findings have significant implications for cancer research and hold promise for guiding personalized treatment strategies in prostate cancer.

SIGNIFICANCE

We developed and validated a progression model of prostate cancer using >1,000 tumor and normal tissue samples. The model provided a comprehensive view of prostate tumor evolution and progression.

Integrated machine learning algorithms reveal a bone metastasis-related signature of circulating tumor cells in prostate cancer

Bone metastasis is an essential factor affecting the prognosis of prostate cancer (PCa), and circulating tumor cells (CTCs) are closely related to distant tumor metastasis. Here, the protein-protein interaction (PPI) networks and Cytoscape application were used to identify diagnostic markers for metastatic events in PCa. We screened ten hub genes, eight of which had area under the ROC curve (AUC) values > 0.85. Subsequently, we aim to develop a bone metastasis-related model relying on differentially expressed genes in CTCs for accurate risk stratification. We developed an integrative program based on machine learning algorithm combinations to construct reliable bone metastasis-related genes prognostic index (BMGPI). On the basis of BMGPI, we carefully evaluated the prognostic outcomes, functional status, tumor immune microenvironment, somatic mutation, copy number variation (CNV), response to immunotherapy and drug sensitivity in different subgroups. BMGPI was an independent risk factor for disease-free survival in PCa. The high risk group demonstrated poor survival as well as higher immune scores, higher tumor mutation burden (TMB), more frequent co-occurrence mutation, and worse efficacy of immunotherapy. This study highlights a new prognostic signature, the BMGPI. BMGPI is an independent predictor of prognosis in PCa patients and is closely associated with the immune microenvironment and the efficacy of immunotherapy.

Cancer-associated fibroblasts (CAFs) gene signatures predict outcomes in breast and prostate tumor patients

BACKGROUND

Over the last two decades, tumor-derived RNA expression signatures have been developed for the two most commonly diagnosed tumors worldwide, namely prostate and breast tumors, in order to improve both outcome prediction and treatment decision-making. In this context, molecular signatures gained by main components of the tumor microenvironment, such as cancer-associated fibroblasts (CAFs), have been explored as prognostic and therapeutic tools. Nevertheless, a deeper understanding of the significance of CAFs-related gene signatures in breast and prostate cancers still remains to be disclosed.

METHODS

RNA sequencing technology (RNA-seq) was employed to profile and compare the transcriptome of CAFs isolated from patients affected by breast and prostate tumors. The differentially expressed genes (DEGs) characterizing breast and prostate CAFs were intersected with data from public datasets derived from bulk RNA-seq profiles of breast and prostate tumor patients. Pathway enrichment analyses allowed us to appreciate the biological significance of the DEGs. K-means clustering was applied to construct CAFs-related gene signatures specific for breast and prostate cancer and to stratify independent cohorts of patients into high and low gene expression clusters. Kaplan-Meier survival curves and log-rank tests were employed to predict differences in the outcome parameters of the clusters of patients. Decision-tree analysis was used to validate the clustering results and boosting calculations were then employed to improve the results obtained by the decision-tree algorithm.

RESULTS

Data obtained in breast CAFs allowed us to assess a signature that includes 8 genes (ITGA11, THBS1, FN1, EMP1, ITGA2, FYN, SPP1, and EMP2) belonging to pro-metastatic signaling routes, such as the focal adhesion pathway. Survival analyses indicated that the cluster of breast cancer patients showing a high expression of the aforementioned genes displays worse clinical outcomes. Next, we identified a prostate CAFs-related signature that includes 11 genes (IL13RA2, GDF7, IL33, CXCL1, TNFRSF19, CXCL6, LIFR, CXCL5, IL7, TSLP, and TNFSF15) associated with immune responses. A low expression of these genes was predictive of poor survival rates in prostate cancer patients. The results obtained were significantly validated through a two-step approach, based on unsupervised (clustering) and supervised (classification) learning techniques, showing a high prediction accuracy (≥ 90%) in independent RNA-seq cohorts.

CONCLUSION

We identified a huge heterogeneity in the transcriptional profile of CAFs derived from breast and prostate tumors. Of note, the two novel CAFs-related gene signatures might be considered as reliable prognostic indicators and valuable biomarkers for a better management of breast and prostate cancer patients.

Unlocking the Crucial Role of Cancer-Associated Fibroblasts in Tumor Metastasis: Mechanisms and Therapeutic Prospects

BACKGROUND

Tumor metastasis represents a stepwise progression and stands as a principal determinant of unfavorable prognoses among cancer patients. Consequently, an in-depth exploration of its mechanisms holds paramount clinical significance. Cancer-associated fibroblasts (CAFs), constituting the most abundant stromal cell population within the tumor microenvironment (TME), have garnered robust evidence support for their pivotal regulatory roles in tumor metastasis.

AIM

of Review This review systematically explores the roles of CAFs at eight critical stages of tumorigenic dissemination: 1) extracellular matrix (ECM) remodeling, 2) epithelial-mesenchymal transition (EMT), 3) angiogenesis, 4) tumor metabolism, 5) perivascular migration, 6) immune escape, 7) dormancy, and 8) premetastatic niche (PMN) formation. Additionally, we provide a compendium of extant strategies aimed at targeting CAFs in cancer therapy. Key Scientific Concepts of Review This review delineates a structured framework for the interplay between CAFs and tumor metastasis while furnishing insights for the potential therapeutic developments. It contributes to a deeper understanding of cancer metastasis within the TME, facilitating the utilization of CAF-targeting therapies in anti-metastatic approaches.

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.

Advances in prognostic models for osteosarcoma risk

The risk prognosis model is a statistical model that uses a set of features to predict whether an individual will develop a specific disease or clinical outcome. It can be used in clinical practice to stratify disease severity and assess risk or prognosis. With the advancement of large-scale second-generation sequencing technology, along Prognosis models for osteosarcoma are increasingly being developed as large-scale second-generation sequencing technology advances and clinical and biological data becomes more abundant. This expansion greatly increases the number of prognostic models and candidate genes suitable for clinical use. This article will present the predictive effects and reliability of various prognosis models, serving as a reference for their evaluation and application.

Comprehensive analyses of the cancer-associated fibroblast subtypes and their score system for prediction of outcomes and immunosuppressive microenvironment in prostate cancer

BACKGROUND

Cancer-associated fibroblasts (CAFs) drive cancer progression and treatment failure on one hand, while their tumor-restraining functions are also observed on the other. Recent single cell RNA sequencing (scRNA-seq) analyses demonstrates heterogeneity of CAFs and defines molecular subtypes of CAFs, which help explain their different functions. However, it remains unclear whether these CAF subtypes have the same or different biological/clinical implications in prostate cancer (PCa) or other malignancies.

METHODS

PCa cells were incubated with supernatant from normal fibroblasts and CAFs to assess their effects on cell behaviors. Sequencing, genomic, and clinical data were collected from TCGA, MSKCC, CPGEA and GEO databases. CAF molecular subtypes and total CAF scores were constructed and grouped into low and high groups based on CAF-specific gene expression. Progression free interval (PFI), clinicopathological features, telomere length, immune cell infiltration, drug treatment and somatic mutations were compared among CAF molecular subtypes and low/high score groups.

RESULTS

The PCa CAF-derived supernatant promoted PCa cell proliferation and invasion. Based on differentially expressed genes identified by scRNA-seq analyses, we classified CAFs into 6 molecular subtypes in PCa tumors, and each subtype was then categorized into score-high and low groups according to the subtype-specific gene expression level. Such score models in 6 CAF subtypes all predicted PFI. Telomeres were significantly shorter in high-score tumors. The total CAF score from 6 CAF subtypes was also associated with PFI in PCa patients inversely, which was consistent with results from cellular experiments. Immunosuppressive microenvironment occurred more frequently in tumors with a high CAF score, which was characterized by increased CTLA4 expression and indicated better responses to CTLA4 inhibitors. Moreover, this model can also serve as a useful PFI predictor in pan-cancers.

CONCLUSION

By combining scRNA-seq and bulk RNA-seq data analyses, we develop a CAF subtype score system as a prognostic factor for PCa and other cancer types. This model system also helps distinguish different immune-suppressive mechanisms in PCa, suggesting its implications in predicting response to immunotherapy. Thus, the present findings should contribute to personalized PCa intervention.

Single-cell RNA combined with bulk RNA analysis to explore oxidative stress and energy metabolism factors and found a new prostate cancer oncogene MXRA8

BACKGROUND

Prostate cancer is the most common malignancy among men worldwide, and its diagnosis and treatment are challenging due to its heterogeneity.

METHODS

Integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data, we identified two molecular subtypes of prostate cancer based on dysregulated genes involved in oxidative stress and energy metabolism. We constructed a risk score model (OMR) using common differentially expressed genes, which effectively evaluated prostate cancer prognosis.

RESULTS

Our analysis demonstrated a significant correlation between the risk score model and various factors, including tumor immune microenvironment, genomic variations, chemotherapy resistance, and immune response. Notably, patients with low-risk scores exhibited increased sensitivity to chemotherapy and immunotherapy compared to those with high-risk scores, indicating the model's potential to predict patient response to treatment. Additionally, our investigation of MXRA8 in prostate cancer showed significant upregulation of this gene in the disease as confirmed by PCR and immunohistochemistry. Functional assays including CCK-8, transwell, plate cloning, and ROS generation assay demonstrated that depletion of MXRA8 reduced the proliferative, invasive, migratory capabilities of PC-3 cells, as well as their ROS generation capacity.

CONCLUSIONS

Our study highlights the potential of oxidative stress and energy metabolism-related genes as prognostic markers and therapeutic targets in prostate cancer. The integration of scRNA-seq and bulk RNA-seq data enables a better understanding of prostate cancer heterogeneity and promotes personalized treatment development. Additionally, we identified a novel oncogene MXRA8 in prostate cancer.

Radiation-induced effects on TGF-β and PDGF receptor signaling in cancer-associated fibroblasts

BACKGROUND

Cancer-associated fibroblasts (CAFs) consist of heterogeneous connective tissue cells and are often constituting the most abundant cell type in the tumor stroma. Radiation effects on tumor stromal components like CAFs in the context of radiation treatment is not well-described.

AIM

This study explores potential changes induced by ionizing radiation (IR) on platelet-derived growth factor (PDGF)/PDGFRs and transforming growth factor-beta (TGF-β)/TGFβRs signaling systems in CAFs.

METHODS AND RESULTS

Experiments were carried out by employing primary cultures of human CAFs isolated from freshly resected non-small cell lung carcinoma tumor tissues. CAF cultures from nine donors were treated with one high (1 × 18 Gy) or three fractionated (3 × 6 Gy) radiation doses. Alterations in expression levels of TGFβRII and PDGFRα/β induced by IR were analyzed by western blots and flow cytometry. In the presence or absence of cognate ligands, receptor activation was studied in nonirradiated and irradiated CAFs. Radiation exposure did not exert changes in expression of PDGF or TGF-β receptors in CAFs. Additionally, IR alone was unable to trigger activation of either receptor. The radiation regimens tested did not affect PDGFRβ signaling in the presence of PDGF-BB. In contrast, signaling via pSmad2/3 and pSmad1/5/8 appeared to be down-regulated in irradiated CAFs after stimulation with TGF-β, as compared with controls.

CONCLUSION

Our data demonstrate that IR by itself is insufficient to induce measurable changes in PDGF or TGF-β receptor expression levels or to induce receptor activation in CAFs. However, in the presence of their respective ligands, exposure to radiation at certain doses appear to interfere with TGF-β receptor signaling.

A promising natural killer cell-based model and a nomogram for the prognostic prediction of clear-cell renal cell carcinoma

BACKGROUND

Clear-cell renal cell carcinoma (ccRCC) is one of prevalent kidney malignancies with an unfavorable prognosis. There is a need for a robust model to predict ccRCC patient survival and guide treatment decisions.

METHODS

RNA-seq data and clinical information of ccRCC were obtained from the TCGA and ICGC databases. Expression profiles of genes related to natural killer (NK) cells were collected from the Immunology Database and Analysis Portal database. Key NK cell-related genes were identified using consensus clustering algorithms to classify patients into distinct clusters. A NK cell-related risk model was then developed using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression to predict ccRCC patient prognosis. The relationship between the NK cell-related risk score and overall survival, clinical features, tumor immune characteristics, as well as response to commonly used immunotherapies and chemotherapy, was explored. Finally, the NK cell-related risk score was validated using decision tree and nomogram analyses.

RESULTS

ccRCC patients were stratified into 3 molecular clusters based on expression of NK cell-related genes. Significant differences were observed among the clusters in terms of prognosis, clinical characteristics, immune infiltration, and therapeutic response. Furthermore, six NK cell-related genes (DPYSL3, SLPI, SLC44A4, ZNF521, LIMCH1, and AHR) were identified to construct a prognostic model for ccRCC prediction. The high-risk group exhibited poor survival outcomes, lower immune cell infiltration, and decreased sensitivity to conventional chemotherapies and immunotherapies. Importantly, the quantitative real-time polymerase chain reaction (qRT-PCR) confirmed significantly high DPYSL3 expression and low SLC44A4 expression in ACHN cells. Finally, the decision tree and nomogram consistently show the dramatic prediction performance of the risk score on the survival outcome of the ccRCC patients.

CONCLUSIONS

The six-gene model based on NK cell-related gene expression was validated and found to accurately mirror immune microenvironment and predict clinical outcomes, contributing to enhanced risk stratification and therapy response for ccRCC patients.

Integrating single-cell and bulk RNA sequencing data unveils antigen presentation and process-related CAFS and establishes a predictive signature in prostate cancer

BACKGROUND

Cancer-associated fibroblasts (CAFs) are heterogeneous and can influence the progression of prostate cancer in multiple ways; however, their capacity to present and process antigens in PRAD has not been investigated. In this study, antigen presentation and process-related CAFs (APPCAFs) were identified using bioinformatics, and the clinical implications of APPCAF-related signatures in PRAD were investigated.

METHODS

SMART technology was used to sequence the transcriptome of primary CAFs isolated from patients undergoing different treatments. Differential expression gene (DEG) screening was conducted. A CD4 + T-cell early activation assay was used to assess the activation degree of CD4 + T cells. The datasets of PRAD were obtained from The Cancer Genome Atlas (TCGA) database and NCBI Gene Expression Omnibus (GEO), and the list of 431 antigen presentation and process-related genes was obtained from the InnateDB database. Subsequently, APP-related CAFs were identified by nonnegative matrix factorization (NMF) based on a single-cell seq (scRNA) matrix. GSVA functional enrichment analyses were performed to depict the biological functions. A risk signature based on APPCAF-related genes (APPCAFRS) was developed by least absolute shrinkage and selection operator (LASSO) regression analysis, and the independence of the risk score as a prognostic factor was evaluated by univariate and multivariate Cox regression analyses. Furthermore, a biochemical recurrence-free survival (BCRFS)-related nomogram was established, and immune-related characteristics were assessed using the ssGSEA function. The immune treatment response in PRAD was further analyzed by the Tumor Immune Dysfunction and Exclusion (TIDE) tool. The expression levels of hub genes in APPCAFRS were verified in cell models.

RESULTS

There were 134 upregulated and 147 downregulated genes, totaling 281 differentially expressed genes among the primary CAFs. The functions and pathways of 147 downregulated DEGs were significantly enriched in antigen processing and presentation processes, MHC class II protein complex and transport vesicle, MHC class II protein complex binding, and intestinal immune network for IgA production. Androgen withdrawal diminished the activation effect of CAFs on T cells. NMF clustering of CAFs was performed by APPRGs, and pseudotime analysis yielded the antigen presentation and process-related CAF subtype CTSK + MRC2 + CAF-C1. CTSK + MRC2 + CAF-C1 cells exhibited ligand‒receptor connections with epithelial cells and T cells. Additionally, we found a strong association between CTSK + MRC2 + CAF-C1 cells and inflammatory CAFs. Through differential gene expression analysis of the CTSK + MRC2 + CAF-C1 and NoneAPP-CAF-C2 subgroups, 55 significant DEGs were identified, namely, APPCAFRGs. Based on the expression profiles of APPCAFRGs, we divided the TCGA-PRAD cohort into two clusters using NMF consistent cluster analysis, with the genetic coefficient serving as the evaluation index. Four APPCAFRGs, THBS2, DPT, COL5A1, and MARCKS, were used to develop a prognostic signature capable of predicting BCR occurrence in PRAD patients. Subsequently, a nomogram with stability and accuracy in predicting BCR was constructed based on Gleason grade (p = n.s.), PSA (p < 0.001), T stage (p < 0.05), and risk score (p < 0.01). The analysis of immune infiltration showed a positive correlation between the abundance of resting memory CD4 + T cells, M1 macrophages, resting dendritic cells, and the risk score. In addition, the mRNA expression levels of THBS2, DPT, COL5A1, and MARCKS in the cell models were consistent with the results of the bioinformatics analysis.

CONCLUSIONS

APPCAFRS based on four potential APPCAFRGs was developed, and their interaction with the immune microenvironment may play a crucial role in the progression to castration resistance of PRAD. This novel approach provides valuable insights into the pathogenesis of PRAD and offers unexplored targets for future research.

Cancer-associated fibroblasts in neoadjuvant setting for solid cancers

Therapeutic approaches for cancer have become increasingly diverse in recent times. A comprehensive understanding of the tumor microenvironment (TME) holds great potential for enhancing the precision of tumor therapies. Neoadjuvant therapy offers the possibility of alleviating patient symptoms and improving overall quality of life. Additionally, it may facilitate the reduction of inoperable tumors and prevent potential preoperative micrometastases. Within the TME, cancer-associated fibroblasts (CAFs) play a prominent role as they generate various elements that contribute to tumor progression. Particularly, extracellular matrix (ECM) produced by CAFs prevents immune cell infiltration into the TME, hampers drug penetration, and diminishes therapeutic efficacy. Therefore, this review provides a summary of the heterogeneity and interactions of CAFs within the TME, with a specific focus on the influence of neoadjuvant therapy on the microenvironment, particularly CAFs. Finally, we propose several potential and promising therapeutic strategies targeting CAFs, which may efficiently eliminate CAFs to decrease stroma density and impair their functions.

Identification of cancer-associated fibroblasts signature for predicting the prognosis and immunotherapy response in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignancies globally with poor prognosis. Cancer-associated fibroblasts (CAFs) play multiple functions in the regulation of tumorigenesis, metastasis and therapeutic resistance of cancer. The current study aimed to explore the role of CAFs-related genes in the prognosis and immunotherapy response in HCC. CAFs-related genes were identified by using single-cell RNA-sequencing analysis. Least absolute shrinkage and selection operator (LASSO) analysis was conducted to develop a CAFs-related prognostic signature (FRPS) in TCGA dataset and verified in ICGC, GSE14520 and GSE76427 cohorts. Several tools, including Tumor Immune Dysfunction and Exclusion (TIDE) score, immunophenoscore, and Tumor Mutation Burden (TMB) score were used to evaluate the value of FRPS in predicting immunotherapy benefits. The FRPS constructed based on 10 genes (RGS5, CNN3, PALLD, FLNA, KLHL23, MYC, NDRG2, SERPINE1, CD151 CALU) served as an independent risk factor and showed stable and powerful performance in predicting the overall survival rate of HCC patients with an AUCs of 0. 734, 0.727, and 0.717 in 2-, 3-, and 4-year ROC curve in TCGA cohort. Low risk score indicated a higher abundance of CD8+ T cells and NK, and lower abundance of Treg. Moreover, HCC patients with low risk score had a higher PD1&CTLA4 immunophenoscore, higher TMB score, and lower TIDE score. Moreover, high risk score indicated a lower IC50 value of 5-fluorouracil, camptothecin, cisplatin, docetaxel, gemcitabine, paclitaxel, afatinib, crizotinib, dasatinib, erlotinib, erlotinib, gefitinib, lapatinib, and osimertinib in HCC. Our study develops a novel FRPS HCC. The FRPS acts as a risk factor for the prognosis of HCC patients and it can predict the immunotherapy benefits of HCC patients.

Integrated machine learning identifies epithelial cell marker genes for improving outcomes and immunotherapy in prostate cancer

BACKGROUND

Prostate cancer (PCa), a globally prevalent malignancy, displays intricate heterogeneity within its epithelial cells, closely linked with disease progression and immune modulation. However, the clinical significance of genes and biomarkers associated with these cells remains inadequately explored. To address this gap, this study aimed to comprehensively investigate the roles and clinical value of epithelial cell-related genes in PCa.

METHODS

Leveraging single-cell sequencing data from GSE176031, we conducted an extensive analysis to identify epithelial cell marker genes (ECMGs). Employing consensus clustering analysis, we evaluated the correlations between ECMGs, prognosis, and immune responses in PCa. Subsequently, we developed and validated an optimal prognostic signature, termed the epithelial cell marker gene prognostic signature (ECMGPS), through synergistic analysis from 101 models employing 10 machine learning algorithms across five independent cohorts. Additionally, we collected clinical features and previously published signatures from the literature for comparative analysis. Furthermore, we explored the clinical utility of ECMGPS in immunotherapy and drug selection using multi-omics analysis and the IMvigor cohort. Finally, we investigated the biological functions of the hub gene, transmembrane p24 trafficking protein 3 (TMED3), in PCa using public databases and experiments.

RESULTS

We identified a comprehensive set of 543 ECMGs and established a strong correlation between ECMGs and both the prognostic evaluation and immune classification in PCa. Notably, ECMGPS exhibited robust predictive capability, surpassing traditional clinical features and 80 published signatures in terms of both independence and accuracy across five cohorts. Significantly, ECMGPS demonstrated significant promise in identifying potential PCa patients who might benefit from immunotherapy and personalized medicine, thereby moving us nearer to tailored therapeutic approaches for individuals. Moreover, the role of TMED3 in promoting malignant proliferation of PCa cells was validated.

CONCLUSIONS

Our findings highlight ECMGPS as a powerful tool for improving PCa patient outcomes and supply a robust conceptual framework for in-depth examination of PCa complexities. Simultaneously, our study has the potential to develop a novel alternative for PCa diagnosis and prognostication.

Define cancer-associated fibroblasts (CAFs) in the tumor microenvironment: new opportunities in cancer immunotherapy and advances in clinical trials

Despite centuries since the discovery and study of cancer, cancer is still a lethal and intractable health issue worldwide. Cancer-associated fibroblasts (CAFs) have gained much attention as a pivotal component of the tumor microenvironment. The versatility and sophisticated mechanisms of CAFs in facilitating cancer progression have been elucidated extensively, including promoting cancer angiogenesis and metastasis, inducing drug resistance, reshaping the extracellular matrix, and developing an immunosuppressive microenvironment. Owing to their robust tumor-promoting function, CAFs are considered a promising target for oncotherapy. However, CAFs are a highly heterogeneous group of cells. Some subpopulations exert an inhibitory role in tumor growth, which implies that CAF-targeting approaches must be more precise and individualized. This review comprehensively summarize the origin, phenotypical, and functional heterogeneity of CAFs. More importantly, we underscore advances in strategies and clinical trials to target CAF in various cancers, and we also summarize progressions of CAF in cancer immunotherapy.

Comprehensive Analysis for Anti-Cancer Target-Indication Prioritization of Placental Growth Factor Inhibitor (PGF) by Use of Omics and Patient Survival Data

The expression of the placental growth factor (PGF) in cancer cells and the tumor microenvironment can contribute to the induction of angiogenesis, supporting cancer cell metabolism by ensuring an adequate blood supply. Angiogenesis is a key component of cancer metabolism as it facilitates the delivery of nutrients and oxygen to rapidly growing tumor cells. PGF is recognized as a novel target for anti-cancer treatment due to its ability to overcome resistance to existing angiogenesis inhibitors and its impact on the tumor microenvironment. We aimed to integrate bioinformatics evidence using various data sources and analytic tools for target-indication identification of the PGF target and prioritize the indication across various cancer types as an initial step of drug development. The data analysis included PGF gene function, molecular pathway, protein interaction, gene expression and mutation across cancer type, survival prognosis and tumor immune infiltration association with PGF. The overall evaluation was conducted given the totality of evidence, to target the PGF gene to treat the cancer where the PGF level was highly expressed in a certain tumor type with poor survival prognosis as well as possibly associated with poor tumor infiltration level. PGF showed a significant impact on overall survival in several cancers through univariate or multivariate survival analysis. The cancers considered as target diseases for PGF inhibitors, due to their potential effects on PGF, are adrenocortical carcinoma, kidney cancers, liver hepatocellular carcinoma, stomach adenocarcinoma, and uveal melanoma.

LINC00963-FOSB-mediated transcription activation of UBE3C enhances radioresistance of breast cancer cells by inducing ubiquitination-dependent protein degradation of TP73

BACKGROUND

The ubiquitin protein ligase E3C (UBE3C) has been reported to play an oncogenic role in breast cancer (BRCA). This work further investigates the effect of UBE3C on the radioresistance of BRCA cells.

METHODS

Molecules linking to radioresistance in BRCA were identified by analyzing two GEO datasets, GSE31863 and GSE101920. UBE3C overexpression or knockdown was induced in parental or radioresistant BRCA cells, followed by irradiation treatment. The malignant properties of cells in vitro, and the growth and metastatic activity of cells in nude mice, were analyzed. Downstream target proteins, as well as upstream transcriptional regulators of UBE3C, were predicted by bioinformatics tools. Molecular interactions were confirmed by immunoprecipitation and immunofluorescence assays. Furthermore, artificial alterations of TP73 and FOSB were induced in the BRCA cells for functional rescue assays.

RESULTS

According to bioinformatics analyses, UBE3C expression was linked to radioresistance in BRCA. UBE3C knockdown in radioresistant BRCA cells reduced while its overexpression in parental BRCA cells increased the radioresistance of cells in vitro and in vivo. UBE3C, which induced ubiquitination-dependent protein degradation of TP73, was transcriptionally activated by FOSB. The radioresistance of cancer cells was blocked by TP73 overexpression or FOSB knockdown. Additionally, LINC00963 was found to be responsible for the recruitment of FOSB to the UBE3C promoter for transcription activation.

CONCLUSION

This work demonstrates that LINC00963 induces nuclear translocation of FOSB and the consequent transcription activation of UBE3C, which enhances radioresistance of BRCA cells by inducing ubiquitination-dependent protein degradation of TP73.

Cancer-associated fibroblasts in radiotherapy: Bystanders or protagonists?

BACKGROUND

The primary goal of radiotherapy (RT) is to induce cellular damage on malignant cells; however, it is becoming increasingly recognized the important role played by the tumor microenvironment (TME) in therapy outcomes. Therapeutic irradiation of tumor lesions provokes profound cellular and biological reconfigurations within the TME that ultimately may influence the fate of the therapy.

MAIN CONTENT

Cancer-associated fibroblasts (CAFs) are known to participate in all stages of cancer progression and are increasingly acknowledged to contribute to therapy resistance. Accumulated evidence suggests that, upon radiation, fibroblasts/CAFs avoid cell death but instead enter a permanent senescent state, which in turn may influence the behavior of tumor cells and other components of the TME. Despite the proposed participation of senescent fibroblasts on tumor radioprotection, it is still incompletely understood the impact that RT has on CAFs and the ultimate role that irradiated CAFs have on therapy outcomes. Some of the current controversies may emerge from generalizing observations obtained using normal fibroblasts and CAFs, which are different cell entities that may respond differently to radiation exposure.

CONCLUSION

In this review we present current knowledge on the field of CAFs role in radiotherapy; we discuss the potential tumorigenic functions of radiation-induced senescent fibroblasts and CAFs and we make an effort to integrate the knowledge emerging from preclinical experimentation with observations from the clinics. Video Abstract.