Identification of a distinct luminal subgroup diagnosing and stratifying early stage prostate cancer by tissue-based single-cell RNA sequencing

A distinct subset of urothelial cells with enhanced EMT features promotes chemotherapy resistance and cancer recurrence by increasing COL4A1-ITGB1 mediated angiogenesis

Drug resistance and tumor recurrence remain clinical challenges in the treatment of urothelial carcinoma (UC). However, the underlying mechanism is not fully understood. Here, we performed single-cell RNA sequencing and identified a subset of urothelial cells with epithelial-mesenchymal transition (EMT) features (EMT-UC), which is significantly correlated with chemotherapy resistance and cancer recurrence. To validate the clinical significance of EMT-UC, we constructed EMT-UC like cells by introducing overexpression of two markers, Zinc Finger E-Box Binding Homeobox 1 (ZEB1) and Desmin (DES), and examined their histological distribution characteristics and malignant phenotypes. EMT-UC like cells were mainly enriched in UC tissues from patients with adverse prognosis and exhibited significantly elevated EMT, migration and gemcitabine tolerance in vitro. However, EMT-UC was not specifically identified from tumorous tissues, certain proportion of them were also identified in adjacent normal tissues. Tumorous EMT-UC highly expressed genes involved in malignant behaviors and exhibited adverse prognosis. Additionally, tumorous EMT-UC was associated with remodeled tumor microenvironment (TME), which exhibited high angiogenic and immunosuppressive potentials compared with the normal counterparts. Furthermore, a specific interaction of COL4A1 and ITGB1 was identified to be highly enriched in tumorous EMT-UC, and in the endothelial component. Targeting the interaction of COL4A1 and ITGB1 with specific antibodies significantly suppressed tumorous angiogenesis and alleviated gemcitabine resistance of UC. Overall, our findings demonstrated that the driven force of chemotherapy resistance and recurrence of UC was EMT-UC mediated COL4A1-ITGB1 interaction, providing a potential target for future UC treatment.

ONECUT2 acts as a lineage plasticity driver in adenocarcinoma as well as neuroendocrine variants of prostate cancer

Androgen receptor- (AR-) indifference is a mechanism of resistance to hormonal therapy in prostate cancer (PC). Here we demonstrate that ONECUT2 (OC2) activates resistance through multiple drivers associated with adenocarcinoma, stem-like and neuroendocrine (NE) variants. Direct OC2 gene targets include the glucocorticoid receptor (GR; NR3C1) and the NE splicing factor SRRM4, which are key drivers of lineage plasticity. Thus, OC2, despite its previously described NEPC driver function, can indirectly activate a portion of the AR cistrome through epigenetic activation of GR. Mechanisms by which OC2 regulates gene expression include promoter binding, enhancement of genome-wide chromatin accessibility, and super-enhancer reprogramming. Pharmacologic inhibition of OC2 suppresses lineage plasticity reprogramming induced by the AR signaling inhibitor enzalutamide. These results demonstrate that OC2 activation promotes a range of drug resistance mechanisms associated with treatment-emergent lineage variation in PC and support enhanced efforts to therapeutically target OC2 as a means of suppressing treatment-resistant disease.

Integrated single-cell analysis defines the epigenetic basis of castration-resistant prostate luminal cells

Understanding prostate response to castration and androgen receptor signaling inhibitors (ARSI) is critical to improving long-term prostate cancer (PCa) patient survival. Here, we use a multi-omics approach on 229,794 single cells to create a mouse single-cell reference atlas for interpreting mouse prostate biology and castration response. Our reference atlas refines single-cell annotations and provides a chromatin context, which, when coupled with mouse lineage tracing, demonstrates that castration-resistant luminal cells are distinct from the pre-existent urethra-proximal stem/progenitor cells. Molecular pathway analysis and therapeutic studies further implicate AP1 (JUN/FOS), WNT/β-catenin, FOXQ1, NF-κB, and JAK/STAT pathways as major drivers of castration-resistant luminal populations with relevance to human PCa. Our datasets, which can be explored through an interactive portal (https://visportal.roswellpark.org/data/tang/), can aid in developing combination treatments with ARSI for advanced PCa patients.

Advances in sequencing and omics studies in prostate cancer: unveiling molecular pathogenesis and clinical applications

Background

Prostate cancer (PCa) is one of the most threatening health problems for the elderly males. However, our understanding of the disease has been limited by the research technology for a long time. Recently, the maturity of sequencing technology and omics studies has been accelerating the studies of PCa, establishing themselves as an essential impetus in this field.

Methods

We assessed Web of Science (WoS) database for publications of sequencing and omics studies in PCa on July 3rd, 2023. Bibliometrix was used to conduct ulterior bibliometric analysis of countries/affiliations, authors, sources, publications, and keywords. Subsequently, purposeful large amounts of literature reading were proceeded to analyze research hotspots in this field.

Results

3325 publications were included in the study. Research associated with sequencing and omics studies in PCa had shown an obvious increase recently. The USA and China were the most productive countries, and harbored close collaboration. CHINNAIYAN AM was identified as the most influential author, and CANCER RESEARCH exhibited huge impact in this field. Highly cited publications and their co-citation relationships were used to filtrate literatures for subsequent literature reading. Based on keyword analysis and large amounts of literature reading, 'the molecular pathogenesis of PCa' and 'the clinical application of sequencing and omics studies in PCa' were summarized as two research hotspots in the field.

Conclusion

Sequencing technology had a deep impact on the studies of PCa. Sequencing and omics studies in PCa helped researchers reveal the molecular pathogenesis, and provided new possibilities for the clinical practice of PCa.

Identification of functional and diverse circulating cancer-associated fibroblasts in metastatic castration-naïve prostate cancer patients

In prostate cancer (PCa), cancer-associated fibroblasts (CAFs) promote tumor progression, drug resistance, and metastasis. Although circulating tumor cells are studied as prognostic and diagnostic markers, little is known about other circulating cells and their association with PCa metastasis. Here, we explored the presence of circulating CAFs (cCAFs) in metastatic castration-naïve prostate cancer (mCNPC) patients. cCAFs were stained with fibroblast activation protein (FAP), epithelial cell adhesion molecule (EpCAM), and receptor-type tyrosine-protein phosphatase C (CD45), then FAP+EpCAM- cCAFs were enumerated and sorted using fluorescence-activated cell sorting. FAP+EpCAM- cCAFs ranged from 60 to 776 (389 mean ± 229 SD) per 2 × 108 mononuclear cells, whereas, in healthy donors, FAP+ EpCAM- cCAFs ranged from 0 to 71 (28 mean ± 22 SD). The mCNPC-derived cCAFs showed positivity for vimentin and intracellular collagen-I. They were viable and functional after sorting, as confirmed by single-cell collagen-I secretion after 48 h of culturing. Two cCAF subpopulations, FAP+CD45- and FAP+CD45+, were identified, both expressing collagen-I and vimentin, but with distinctly different morphologies. Collectively, this study demonstrates the presence of functional and viable circulating CAFs in mCNPC patients, suggesting the role of these cells in prostate cancer.

The implications of single-cell RNA-seq analysis in prostate cancer: unraveling tumor heterogeneity, therapeutic implications and pathways towards personalized therapy

In recent years, advancements in single-cell and spatial transcriptomics, which are highly regarded developments in the current era, particularly the emerging integration of single-cell and spatiotemporal transcriptomics, have enabled a detailed molecular comprehension of the complex regulation of cell fate. The insights obtained from these methodologies are anticipated to significantly contribute to the development of personalized medicine. Currently, single-cell technology is less frequently utilized for prostate cancer compared with other types of tumors. Starting from the perspective of RNA sequencing technology, this review outlined the significance of single-cell RNA sequencing (scRNA-seq) in prostate cancer research, encompassing preclinical medicine and clinical applications. We summarize the differences between mouse and human prostate cancer as revealed by scRNA-seq studies, as well as a combination of multi-omics methods involving scRNA-seq to highlight the key molecular targets for the diagnosis, treatment, and drug resistance characteristics of prostate cancer. These studies are expected to provide novel insights for the development of immunotherapy and other innovative treatment strategies for castration-resistant prostate cancer. Furthermore, we explore the potential clinical applications stemming from other single-cell technologies in this review, paving the way for future research in precision medicine.

Identification of an anaplastic subtype of prostate cancer amenable to therapies targeting SP1 or translation elongation

Histopathological heterogeneity is a hallmark of prostate cancer (PCa). Using spatial and parallel single-nucleus transcriptomics, we report an androgen receptor (AR)-positive but neuroendocrine-null primary PCa subtype with morphologic and molecular characteristics of small cell carcinoma. Such small cell-like PCa (SCLPC) is clinically aggressive with low AR, but high stemness and proliferation, activity. Molecular characterization prioritizes protein translation, represented by up-regulation of many ribosomal protein genes, and SP1, a transcriptional factor that drives SCLPC phenotype and overexpresses in castration-resistant PCa (CRPC), as two potential therapeutic targets in AR-indifferent CRPC. An SP1-specific inhibitor, plicamycin, effectively suppresses CRPC growth in vivo. Homoharringtonine, a Food And Drug Administration-approved translation elongation inhibitor, impedes CRPC progression in preclinical models and patients with CRPC. We construct an SCLPC-specific signature capable of stratifying patients for drug selectivity. Our studies reveal the existence of SCLPC in admixed PCa pathology, which may mediate tumor relapse, and establish SP1 and translation elongation as actionable therapeutic targets for CRPC.

Up-regulation of RAN by MYBL2 maintains osteosarcoma cancer stem-like cells population during heterogeneous tumor generation

Intratumor heterogeneity is one of the major features of cancers, leading to aggressive disease and treatment failure. Cancer stem-like cells (CSCs) are believed to give rise to the heterogeneous cell types within tumors. Hence, understanding the regulatory mechanism underlying the recurrence process of heterogeneous tumor by CSCs could facilitate the development of CSC-targeted therapies. Here, utilizing single-cell transcriptomics, we present the molecular profile of osteosarcoma CSCs-derived heterogeneous tumors consisting of CSC clusters, osteoprogenitor and differentiated cell types, such as pre-osteoblasts, osteoblasts and chondroblasts. Furthermore, by constructing the comprehensive map of modulated genes during CSCs self-renewal and differentiation, we identify RAN exhibiting specific peak expression in osteosarcoma CSCs clusters which is transcriptionally up-regulated by MYBL2. Functionality, MYBL2-RAN pathway promotes the CSCs self-renewal by enhancing the nuclear accumulation of MYC protein, which in turn boosts the overexpression of RAN as a positive feedback. Importantly, blockage of MYBL2-RAN pathway sensitizes CSCs to cisplatin treatment and synergistically enhanced the cisplatin-induced cytotoxicity. Both MYBL2 and RAN are highly expressed in clinical osteosarcoma tissues which indicate poor prognosis. Collectively, our study provides advanced insights into the regeneration process of heterogeneous tumor originating from CSCs and highlights the MYBL2-RAN pathway as a promising target for CSC-based therapy in osteosarcoma.

The G protein-coupled estrogen receptor of the trigeminal ganglion regulates acute and chronic itch in mice

AIMS

Itch is an unpleasant sensation that severely impacts the patient's quality of life. Recent studies revealed that the G protein-coupled estrogen receptor (GPER) may play a crucial role in the regulation of pain and itch perception. However, the contribution of the GPER in primary sensory neurons to the regulation of itch perception remains elusive. This study aimed to investigate whether and how the GPER participates in the regulation of itch perception in the trigeminal ganglion (TG).

METHODS AND RESULTS

Immunofluorescence staining results showed that GPER-positive (GPER+ ) neurons of the TG were activated in both acute and chronic itch. Behavioral data indicated that the chemogenetic activation of GPER+ neurons of the TG of Gper-Cre mice abrogated scratching behaviors evoked by acute and chronic itch. Conversely, the chemogenetic inhibition of GPER+ neurons resulted in increased itch responses. Furthermore, the GPER expression and function were both upregulated in the TG of the dry skin-induced chronic itch mouse model. Pharmacological inhibition of GPER (or Gper deficiency) markedly increased acute and chronic itch-related scratching behaviors in mouse. Calcium imaging assays further revealed that Gper deficiency in TG neurons led to a marked increase in the calcium responses evoked by agonists of the transient receptor potential ankyrin A1 (TRPA1) and transient receptor potential vanilloid V1 (TRPV1).

CONCLUSION

Our findings demonstrated that the GPER of TG neurons is involved in the regulation of acute and chronic itch perception, by modulating the function of TRPA1 and TRPV1. This study provides new insights into peripheral itch sensory signal processing mechanisms and offers new targets for future clinical antipruritic therapy.

A reliable transcriptomic risk-score applicable to formalin-fixed paraffin-embedded biopsies improves outcome prediction in localized prostate cancer

BACKGROUND

Clinical manifestation of prostate cancer (PCa) is highly variable. Aggressive tumors require radical treatment while clinically non-significant ones may be suitable for active surveillance. We previously developed the prognostic ProstaTrend RNA signature based on transcriptome-wide microarray and RNA-sequencing (RNA-Seq) analyses, primarily of prostatectomy specimens. An RNA-Seq study of formalin-fixed paraffin-embedded (FFPE) tumor biopsies has now allowed us to use this test as a basis for the development of a novel test that is applicable to FFPE biopsies as a tool for early routine PCa diagnostics.

METHODS

All patients of the FFPE biopsy cohort were treated by radical prostatectomy and median follow-up for biochemical recurrence (BCR) was 9 years. Based on the transcriptome data of 176 FFPE biopsies, we filtered ProstaTrend for genes susceptible to FFPE-associated degradation via regression analysis. ProstaTrend was additionally restricted to genes with concordant prognostic effects in the RNA-Seq TCGA prostate adenocarcinoma (PRAD) cohort to ensure robust and broad applicability. The prognostic relevance of the refined Transcriptomic Risk Score (TRS) was analyzed by Kaplan-Meier curves and Cox-regression models in our FFPE-biopsy cohort and 9 other public datasets from PCa patients with BCR as primary endpoint. In addition, we developed a prostate single-cell atlas of 41 PCa patients from 5 publicly available studies to analyze gene expression of ProstaTrend genes in different cell compartments.

RESULTS

Validation of the TRS using the original ProstaTrend signature in the cohort of FFPE biopsies revealed a relevant impact of FFPE-associated degradation on gene expression and consequently no significant association with prognosis (Cox-regression, p-value > 0.05) in FFPE tissue. However, the TRS based on the new version of the ProstaTrend-ffpe signature, which included 204 genes (of originally 1396 genes), was significantly associated with BCR in the FFPE biopsy cohort (Cox-regression p-value < 0.001) and retained prognostic relevance when adjusted for Gleason Grade Groups. We confirmed a significant association with BCR in 9 independent cohorts including 1109 patients. Comparison of the prognostic performance of the TRS with 17 other prognostically relevant PCa panels revealed that ProstaTrend-ffpe was among the best-ranked panels. We generated a PCa cell atlas to associate ProstaTrend genes with cell lineages or cell types. Tumor-specific luminal cells have a significantly higher TRS than normal luminal cells in all analyzed datasets. In addition, TRS of epithelial and luminal cells was correlated with increased Gleason score in 3 studies.

CONCLUSIONS

We developed a prognostic gene-expression signature for PCa that can be applied to FFPE biopsies and may be suitable to support clinical decision-making.

Integrated analysis of tumor microenvironment features to establish a diagnostic model for papillary thyroid cancer using bulk and single-cell RNA sequencing technology

BACKGROUND

Characterizing tumor microenvironment using single-cell RNA sequencing has been a promising strategy for cancer diagnosis and treatment. However, a few studies have focused on diagnosing papillary thyroid cancer (PTC) through this technology. Therefore, our study explored tumor microenvironment (TME) features and identified potential biomarkers to establish a diagnostic model for papillary thyroid cancer.

METHODS

The cell types were identified using the markers from the CellMarker database and published research. The CellChat package was conducted to analyze the cell-cell interaction. The SCEVAN package was used to identify malignant thyroid cells. The SCP package was used to perform multiple single-cell downstream analyses, such as GSEA analysis, enrichment analysis, pseudotime trajectory analysis, and differential expression analysis. The diagnostic model of PTC was estimated using the calibration curves, receiver operating characteristic curves, and decision curve analysis. RT-qPCR was performed to validate the expression of candidate genes in human papillary thyroid samples.

RESULTS

Eight cell types were identified in the scRNA-seq dataset by published cell markers. Extensive cell-cell interactions like FN1/ITGB1 existed in PTC tissues. We identified 26 critical genes related to PTC progression. Further, eight subgroups of PTC tumor cells were identified and exhibited high heterogeneity. The MDK/LRP1, MDK/ALK, GAS6/MERTK, and GAS6/AXL were identified as potential ligand-receptor pairs involved in the interactions between fibroblasts/endothelial cells and tumor cells. Eventually, the diagnostic model constructed by TRPC5, TENM1, NELL2, DMD, SLC35F3, and AUTS2 showed a good efficiency for distinguishing the PTC and normal tissues.

CONCLUSIONS

Our study comprehensively characterized the tumor microenvironment in papillary thyroid cancer. Through combined analysis with bulk RNA-seq, six potential diagnostic biomarkers were identified and validated. The diagnostic model we constructed was a promising tool for PTC diagnosis. Our findings provide new insights into the heterogeneity of thyroid cancer and the theoretical basis for diagnosing thyroid cancer.

Identification of a 24-gene panel and a novel marker of PODXL2 essential for the pathological diagnosis of early prostate cancer

Precise diagnosis of early prostate cancer (PCa) is critical for preventing tumor progression. However, the diagnostic outcomes of currently used markers are far from satisfactory due to the low sensitivity or specificity. Here, we identified a diagnostic subpopulation in PCa tissue with the integrating analysis of single-cell and bulk RNA-seq. The representative markers of this subpopulation were extracted to perform intersection analysis with early-PCa-related gene module generated from weighted correlation network analysis (WGCNA). A total of 24 overlapping genes were obtained, the diagnostic roles of which were validated by distinguishing normal and tumorous prostate samples from the public dataset. A least absolute shrinkage and selection operator (LASSO) model was constructed based on these genes and the obtained 24-gene panel showed high sensitivity and specificity for PCa diagnosis, with better identifying capability of PCa than the commercially used gene panel of Oncotype DX. The top two risk factors, TRPM4 and PODXL2, were verified to be highly expressed in early PCa tissues by multiplex immunostaining, and PODXL2 was more sensitive and specific compared to TRPM4 and the pathologically used marker AMACR for early PCa diagnosis, suggesting a novel and promising pathology marker.

Single-cell analysis of castration-resistant prostate cancers to identify potential biomarkers for diagnosis and prognosis of neuroendocrine prostate cancer

The high heterogeneity and low percentage of neuroendocrine cells in prostate cancer limit the utility of traditional bulk RNA sequencing and even single-cell RNA sequencing to find better biomarkers for early diagnosis and stratification. Re-clustering of specific cell-type holds great promise for identification of intra-cell-type heterogeneity. However, this has not yet been used in studying neuroendocrine prostate cancer heterogeneity. Neuroendocrine cluster(s) were individually identified in each castration-resistant prostate cancer specimen and combined for trajectory analysis. Three neuroendocrine states were identified. Neuroendocrine state 2 with the highest AR score was considered the initial starting state of neuroendocrine transdifferentiation. State 1 and state 3 with distinct high neuroendocrine scores and marker genes enriched in N-Myc and REST target genes, respectively, were considered as two different types of neuroendocrine differentiated cancer cells. These two states contained distinct groups of prostate cancer biomarkers and a strong distinguishing ability of normal versus cancerous prostate across different pathological grading was found in the N-Myc-associated state. Our data highlight the central role of N-Myc and REST in mediating lineage plasticity and classifying neuroendocrine phenotypes.

ONECUT2 Activates Diverse Resistance Drivers of Androgen Receptor-Independent Heterogeneity in Prostate Cancer

Androgen receptor- (AR-) indifference is a mechanism of resistance to hormonal therapy in prostate cancer (PC). Here we demonstrate that the HOX/CUT transcription factor ONECUT2 (OC2) activates resistance through multiple drivers associated with adenocarcinoma, stem-like and neuroendocrine (NE) variants. Direct OC2 targets include the glucocorticoid receptor and the NE splicing factor SRRM4, among others. OC2 regulates gene expression by promoter binding, enhancement of chromatin accessibility, and formation of novel super-enhancers. OC2 also activates glucuronidation genes that irreversibly disable androgen, thereby evoking phenotypic heterogeneity indirectly by hormone depletion. Pharmacologic inhibition of OC2 suppresses lineage plasticity reprogramming induced by the AR signaling inhibitor enzalutamide. These results demonstrate that OC2 activation promotes a range of drug resistance mechanisms associated with treatment-emergent lineage variation in PC. Our findings support enhanced efforts to therapeutically target this protein as a means of suppressing treatment-resistant disease.

Identification and validation of obesity related genes signature based on microenvironment phenotypes in prostate adenocarcinoma

BACKGROUND

The role of obesity related genes (ORGs) in the immune checkpoint inhibitors (ICIs) treatment of prostate adenocarcinoma (PRAD) has not yet been proved by research.

METHODS

We comprehensively evaluated the ORGs patterns in PRAD based on tumor microenvironment (TME) phenotypes and immunotherapy efficacies. Then we constructed a ORGs risk score for prognosis and a ORGs signature for accurate prediction of TME phenotype and immunotherapy efficacy in order to evaluate individual patients.

RESULTS

Two distinct ORGs patterns were generated. The two ORGs patterns were consistent with inflammatory and non-inflammatory TME phenotypes. ORGs patterns had an important role for predicting immunotherapy efficacies. Next, we constructed a ORGs risk score for predicting each patient's prognosis with high performance in TCGA-PRAD. The ORGs risk score could be well verified in the external cohorts including GSE70769 and GSE21034. Then, we developed a ORGs signature and found it was significantly positively correlated with tumor-infiltrating lymphocytes in TCGA-PRAD. We found that each patient in the high-risk ORGs signature group represented a non-inflamed TME phenotype on the single cell level. The patients with high ORGs signature had more sensitivity to immunotherapy. And those ORGs were verified.

CONCLUSIONS

ORGs pattern depicts different TME phenotypes in PRAD. The ORGs risk score and ORGs signature have an important role for predicting prognosis and immunotherapy efficacies.

Explainable artificial intelligence to predict and identify prostate cancer tissue by gene expression

BACKGROUND AND OBJECTIVE

Prostate cancer is one of the most prevalent forms of cancer in men worldwide. Traditional screening strategies such as serum PSA levels, which are not necessarily cancer-specific, or digital rectal exams, which are often inconclusive, are still the screening methods used for the disease. Some studies have focused on identifying biomarkers of the disease but none have been reported for diagnosis in routine clinical practice and few studies have provided tools to assist the pathologist in the decision-making process when analyzing prostate tissue. Therefore, a classifier is proposed to predict the occurrence of PCa that provides physicians with accurate predictions and understandable explanations.

METHODS

A selection of 47 genes was made based on differential expression between PCa and normal tissue, GO gene ontology as well as the literature to be used as input predictors for different machine learning methods based on eXplainable Artificial Intelligence. These methods were trained using different class-balancing strategies to build accurate classifiers using gene expression data from 550 samples from 'The Cancer Genome Atlas'. Our model was validated in four external cohorts with different ancestries, totaling 463 samples. In addition, a set of SHapley Additive exPlanations was provided to help clinicians understand the underlying reasons for each decision.

RESULTS

An in-depth analysis showed that the Random Forest algorithm combined with majority class downsampling was the best performing approach with robust statistical significance. Our method achieved an average sensitivity and specificity of 0.90 and 0.8 with an AUC of 0.84 across all databases. The relevance of DLX1, MYL9 and FGFR genes for PCa screening was demonstrated in addition to the important role of novel genes such as CAV2 and MYLK.

CONCLUSIONS

This model has shown good performance in 4 independent external cohorts of different ancestries and the explanations provided are consistent with each other and with the literature, opening a horizon for its application in clinical practice. In the near future, these genes, in combination with our model, could be applied to liquid biopsy to improve PCa screening.

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

Cancer-associated fibroblasts (CAFs), the central players in the tumor microenvironment (TME), can promote tumor progression and metastasis via various functions. However, the properties of CAFs in prostate cancer (PCa) have not been fully assessed. Therefore, we aimed to examine the CAF characteristics in PCa and construct a CAF-derived signature to predict PCa prognosis. CAFs were identified using single-cell RNA sequencing (scRNA-seq) data from 3 studies. We performed the FindAllMarkers function to extract CAF marker genes and constructed a signature to predict the biochemical relapse-free survival (bRFS) of PCa in the Cancer Genome Atlas (TCGA) cohort. Subsequently, different algorithms were applied to reveal the differences of the TME, immune infiltration, treatment responses in the high- and low-risk groups. Additionally, the CAF heterogeneity was assessed in PCa, which were confirmed by the functional enrichment analysis, gene set enrichment analysis (GSEA), and AUCell method. The scRNA-seq analysis identified a CAF cluster with 783 cells and determined 183 CAF marker genes. Cell-cell communication revealed extensive interactions between fibroblasts and immune cells. A CAF-related prognostic model, containing 7 genes (ASPN, AEBP1, ALDH1A1, BGN, COL1A1, PAGE4 and RASD1), was developed to predict bRFS and validated by 4 independent bulk RNA-seq cohorts. Moreover, the high-risk group of the signature score connected with an immunosuppressive TME, such as a higher level of M2 macrophages and lower levels of plasma cells and CD8+ T cells, and a reduced reaction rate for immunotherapy compared with low-risk group. After re-clustering CAFs via unsupervised clustering, we revealed 3 biologically distinct CAF subsets, namely myofibroblast-like CAFs (myCAFs), immune and inflammatory CAFs (iCAFs) and antigen-presenting CAFs (apCAFs). In conclusion, the CAF-derived signature, the first of its kind, can effectively predict PCa prognosis and serve as an indicator for immunotherapy. Furthermore, our study identified 3 CAF subpopulations with distinct functions in PCa.

Targeting intratumor heterogeneity suppresses colorectal cancer chemoresistance and metastasis

Intratumor heterogeneity (ITH) is a barrier to effective therapy. However, it is largely unknown how ITH is established at the onset of tumor progression, such as in colorectal cancer (CRC). Here, we integrate single-cell RNA-seq and functional validation to show that asymmetric division of CRC stem-like cells (CCSC) is critical for early ITH establishment. We find that CCSC-derived xenografts contain seven cell subtypes, including CCSCs, that dynamically change during CRC xenograft progression. Furthermore, three of the subtypes are generated by asymmetric division of CCSCs. They are functionally distinct and appear at the early stage of xenografts. In particular, we identify a chemoresistant and an invasive subtype, and investigate the regulators that control their generation. Finally, we show that targeting the regulators influences cell subtype composition and CRC progression. Our findings demonstrate that asymmetric division of CCSCs contributes to the early establishment of ITH. Targeting asymmetric division may alter ITH and benefit CRC therapy.

Unraveling the heterogeneity of cholangiocarcinoma and identifying biomarkers and therapeutic strategies with single-cell sequencing technology

Cholangiocarcinoma (CCA) is a common malignant tumor of the biliary tract that carries a high burden of morbidity and a poor prognosis. Due to the lack of precise diagnostic methods, many patients are often diagnosed at advanced stages of the disease. The current treatment options available are of varying efficacy, underscoring the urgency for the discovery of more effective biomarkers for early diagnosis and improved treatment. Recently, single-cell sequencing (SCS) technology has gained popularity in cancer research. This technology has the ability to analyze tumor tissues at the single-cell level, thus providing insights into the genomics and epigenetics of tumor cells. It also serves as a practical approach to study the mechanisms of cancer progression and to explore therapeutic strategies. In this review, we aim to assess the heterogeneity of CCA using single-cell sequencing technology, with the ultimate goal of identifying possible biomarkers and potential treatment targets.

A Novel Molecular Classification Method for Glioblastoma Based on Tumor Cell Differentiation Trajectories

The latest 2021 WHO classification redefines glioblastoma (GBM) as the hierarchical reporting standard by eliminating glioblastoma, IDH-mutant and only retaining the tumor entity of "glioblastoma, IDH-wild type." Knowing that subclassification of tumors based on molecular features is supposed to facilitate the therapeutic choice and increase the response rate in cancer patients, it is necessary to carry out molecular classification of the newly defined GBM. Although differentiation trajectory inference based on single-cell sequencing (scRNA-seq) data holds great promise for identifying cell heterogeneity, it has not been used in the study of GBM molecular classification. Single-cell transcriptome sequencing data from 10 GBM samples were used to identify molecular classification based on differentiation trajectories. The expressions of identified features were validated by public bulk RNA-sequencing data. Clinical feasibility of the classification system was examined in tissue samples by immunohistochemical (IHC) staining and immunofluorescence, and their clinical significance was investigated in public cohorts and clinical samples with complete clinical follow-up information. By analyzing scRNA-seq data of 10 GBM samples, four differentiation trajectories from the glioblastoma stem cell-like (GSCL) cluster were identified, based on which malignant cells were classified into five characteristic subclusters. Each cluster exhibited different potential drug sensitivities, pathways, functions, and transcriptional modules. The classification model was further examined in TCGA and CGGA datasets. According to the different abundance of five characteristic cell clusters, the patients were classified into five groups which we named Ac-G, Class-G, Neo-G, Opc-G, and Undiff-G groups. It was found that the Undiff-G group exhibited the worst overall survival (OS) in both TCGA and CGGA cohorts. In addition, the classification model was verified by IHC staining in 137 GBM samples to further clarify the difference in OS between the five groups. Furthermore, the novel biomarkers of glioblastoma stem cells (GSCs) were also described. In summary, we identified five classifications of GBM and found that they exhibited distinct drug sensitivities and different prognoses, suggesting that the new grouping system may be able to provide important prognostic information and have certain guiding significance for the treatment of GBM, and identified the GSCL cluster in GBM tissues and described its characteristic program, which may help develop new potential therapeutic targets for GSCs in GBM.

The Single-Cell Landscape of Intratumoral Heterogeneity and The Immunosuppressive Microenvironment in Liver and Brain Metastases of Breast Cancer

Distant metastasis remains the major cause of morbidity for breast cancer. Individuals with liver or brain metastasis have an extremely poor prognosis and low response rates to anti-PD-1/L1 immune checkpoint therapy compared to those with metastasis at other sites. Therefore, it is urgent to investigate the underlying mechanism of anti-PD-1/L1 resistance and develop more effective immunotherapy strategies for these patients. Using single-cell RNA sequencing, a high-resolution map of the entire tumor ecosystem based on 44 473 cells from breast cancer liver and brain metastases is depicted. Identified by canonical markers and confirmed by multiplex immunofluorescent staining, the metastatic ecosystem features remarkable reprogramming of immunosuppressive cells such as FOXP3+ regulatory T cells, LAMP3+ tolerogenic dendritic cells, CCL18+ M2-like macrophages, RGS5+ cancer-associated fibroblasts, and LGALS1+ microglial cells. In addition, PD-1 and PD-L1/2 are barely expressed in CD8+ T cells and cancer/immune/stromal cells, respectively. Interactions of the immune checkpoint molecules LAG3-LGALS3 and TIGIT-NECTIN2 between CD8+ T cells and cancer/immune/stromal cells are found to play dominant roles in the immune escape. In summary, this study dissects the intratumoral heterogeneity and immunosuppressive microenvironment in liver and brain metastases of breast cancer for the first time, providing insights into the most appropriate immunotherapy strategies for these patients.

Identification of cancer-associated fibroblasts subtypes in prostate cancer

Introduction

Cancer-associated fibroblasts (CAFs) are one of the most abundant cell types in tumor microenvironment. However, the phenotypic and functional heterogeneities among CAFs have not been sufficiently investigated in prostate cancer.

Methods

We obtained and analyzed the single-cell RNA-sequencing data from 26 hormone-sensitive prostate cancer samples and 8 castration-resistant prostate cancer samples, along with the analysis of bulk-sequencing datasets. Furthermore, we performed multicolor immunofluorescence staining to verify the findings from the data analysis.

Results

We identified two major CAFs subtypes with distinct molecular characteristics and biological functions in prostate cancer microenvironment, namely αSMA+ CAV1+ CAFs-C0 and FN1+ FAP+ CAFs-C1. Another single-cell RNA-sequencing dataset containing 7 bone metastatic prostate cancer samples demonstrated that osteoblasts in the bone metastatic lesions comprised two subtypes with molecular characteristics and biological functions similar to CAFs-C0 and CAFs-C1 in the primary tumor sites. In addition, we discovered a transcriptional factor regulatory network depending on CAFs-C1. CAFs-C1, but not CAFs-C0, was associated with castration resistance and poor prognosis. We also found that CAFs-C1 signature was involved in treatment resistance to immune checkpoint inhibitors.

Discussion

In summary, our results identified the presence of heterogeneous CAFs subtypes in prostate cancer microenvironment and the potential of specific CAFs subtype as therapeutic target for castration-resistant prostate cancer.

Current Status of Research on Small Extracellular Vesicles for the Diagnosis and Treatment of Urological Tumors

Extracellular vesicles (EVs) are important mediators of communication between tumor cells and normal cells. These vesicles are rich in a variety of contents such as RNA, DNA, and proteins, and can be involved in angiogenesis, epithelial-mesenchymal transition, the formation of pre-metastatic ecological niches, and the regulation of the tumor microenvironment. Small extracellular vesicles (sEVs) are a type of EVs. Currently, the main treatments for urological tumors are surgery, radiotherapy, and targeted therapy. However, urological tumors are difficult to diagnose and treat due to their high metastatic rate, tendency to develop drug resistance, and the low sensitivity of liquid biopsies. Numerous studies have shown that sEVs offer novel therapeutic options for tumor treatment, such as tumor vaccines and tumor drug carriers. sEVs have attracted a great deal of attention owing to their contribution to in intercellular communication, and as novel biomarkers, and role in the treatment of urological tumors. This article reviews the research and applications of sEVs in the diagnosis and treatment of urological tumors.

SCExecute: custom cell barcode-stratified analyses of scRNA-seq data

MOTIVATION

In single-cell RNA-sequencing (scRNA-seq) data, stratification of sequencing reads by cellular barcode is necessary to study cell-specific features. However, apart from gene expression, the analyses of cell-specific features are not sufficiently supported by available tools designed for high-throughput sequencing data.

RESULTS

We introduce SCExecute, which executes a user-provided command on barcode-stratified, extracted on-the-fly, single-cell binary alignment map (scBAM) files. SCExecute extracts the alignments with each cell barcode from aligned, pooled single-cell sequencing data. Simple commands, monolithic programs, multi-command shell scripts or complex shell-based pipelines are then executed on each scBAM file. scBAM files can be restricted to specific barcodes and/or genomic regions of interest. We demonstrate SCExecute with two popular variant callers-GATK and Strelka2-executed in shell-scripts together with commands for BAM file manipulation and variant filtering, to detect single-cell-specific expressed single nucleotide variants from droplet scRNA-seq data (10X Genomics Chromium System).In conclusion, SCExecute facilitates custom cell-level analyses on barcoded scRNA-seq data using currently available tools and provides an effective solution for studying low (cellular) frequency transcriptome features.

AVAILABILITY AND IMPLEMENTATION

SCExecute is implemented in Python3 using the Pysam package and distributed for Linux, MacOS and Python environments from https://horvathlab.github.io/NGS/SCExecute.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-cell sequencing reveals the landscape of the tumor microenvironment in a skeletal undifferentiated pleomorphic sarcoma patient

Skeletal undifferentiated pleomorphic sarcoma (SUPS) is an invasive pleomorphic soft tissue sarcoma with a high degree of malignancy and poor prognosis. It is prone to recur and metastasize. The tumor microenvironment (TME) and the pathophysiology of SUPS are barely described. Single-cell RNA sequencing (scRNA-seq) provides an opportunity to dissect the landscape of human diseases at an unprecedented resolution, particularly in diseases lacking animal models, such as SUPS. We performed scRNA-seq to analyze tumor tissues and paracancer tissues from a SUPS patient. We identified the cell types and the corresponding marker genes in this SUPS case. We further showed that CD8+ exhausted T cells and Tregs highly expressed PDCD1, CTLA4 and TIGIT. Thus, PDCD1, CTLA4 and TIGIT were identified as potential targets in this case. We applied copy number karyotyping of aneuploid tumors (CopyKAT) to distinguish malignant cells from normal cells in fibroblasts. Our study identified eight malignant fibroblast subsets in SUPS with distinct gene expression profiles. C1-malignant Fibroblast and C6-malignant Fibroblast in the TME play crucial roles in tumor growth, angiogenesis, metastasis and immune response. Hence, targeting malignant fibroblasts could represent a potential strategy for this SUPS therapy. Intervention via tirelizumab enabled disease control, and immune checkpoint inhibitors (ICIs) of PD-1 may be considered as the first-line option in patients with SUPS. Taken together, scRNA-seq analyses provided a powerful basis for this SUPS treatment, improved our understanding of complex human diseases, and may afforded an alternative approach for personalized medicine in the future.

Single-cell transcriptomics reveals cell type diversity of human prostate

Extensive studies have been performed to describe the phenotypic changes occurring during malignant transformation of the prostate. However, the cell types and associated changes that contribute to the development of prostate diseases and cancer remain elusive, largely due to the heterogeneous composition of prostatic tissues. Here, we conduct a comprehensive evaluation of four human prostate tissues by single-cell RNA sequencing (scRNA-seq) to analyze their cellular compositions. We identify 18 clusters of cell types, each with distinct gene expression profiles and unique features; of these, one cluster of epithelial cells (Ep) is found to be associated with immune function. In addition, we characterize a special cluster of fibroblasts and aberrant signaling changes associated with prostate cancer (PCa). Moreover, we provide insights into the epithelial changes that occur during the cellular senescence and aging. These results expand our understanding of the unique functional associations between the diverse prostatic cell types and the contributions of specific cell clusters to the malignant transformation of prostate tissues and PCa development.

Integrated Tissue and Blood miRNA Expression Profiles Identify Novel Biomarkers for Accurate Non-Invasive Diagnosis of Breast Cancer: Preliminary Results and Future Clinical Implications

We aimed to identify miRNAs that were closely related to breast cancer (BRCA). By integrating several methods including significance analysis of microarrays, fold change, Pearson’s correlation analysis, t test, and receiver operating characteristic analysis, we developed a decision-tree-based scoring algorithm, called Optimized Scoring Mechanism for Primary Synergy MicroRNAs (O-PSM). Five synergy miRNAs (hsa-miR-139-5p, hsa-miR-331-3p, hsa-miR-342-5p, hsa-miR-486-5p, and hsa-miR-654-3p) were identified using O-PSM, which were used to distinguish normal samples from pathological ones, and showed good results in blood data and in multiple sets of tissue data. These five miRNAs showed accurate categorization efficiency in BRCA typing and staging and had better categorization efficiency than experimentally verified miRNAs. In the Protein-Protein Interaction (PPI) network, the target genes of hsa-miR-342-5p have the most regulatory relationships, which regulate carcinogenesis proliferation and metastasis by regulating Glycosaminoglycan biosynthesis and the Rap1 signaling pathway. Moreover, hsa-miR-342-5p showed potential clinical application in survival analysis. We also used O-PSM to generate an R package uploaded on github (SuFei-lab/OPSM accessed on 22 October 2021). We believe that miRNAs included in O-PSM could have clinical implications for diagnosis, prognostic stratification and treatment of BRCA, proposing potential significant biomarkers that could be utilized to design personalized treatment plans in BRCA patients in the future.

Single-cell RNA-sequencing technology demonstrates the heterogeneity between aged prostate peripheral and transitional zone

BACKGROUND

Identifying cellular and functional heterogeneity within aged prostate is critical for understanding the spatial distribution of prostate diseases.

METHODS

Aged human prostate peripheral zone (PZ) and transitional zone (TZ) tissues were used for single-cell RNA-sequencing. Results were validated by immunofluorescence staining.

RESULTS

We found that club/hillock epithelial cells, compared with other epithelial cells, had significantly higher NOTCH signaling activity and expressed higher levels of neuro-stems but lower androgen-related genes. These cells were primarily found in the TZ and provided a stem-like niche around the proximal prostate ducts. Significant heterogeneity was observed in the aged luminal population. A novel TFF3+ luminal subtype with elevated MYC and E2F pathway activities was observed, primarily in the PZ. Further analysis revealed that epithelial cells in the TZ had higher levels of stem- and inflammation-related pathway activities but lower androgen/lineage-related pathway activities than those in the PZ. Notably, the activation of MYC, E2F and DNA repair pathways significantly increased in PZ luminal cells. In the immune landscape, we found that the immune microenvironment in the TZ is more complex and disordered with more infiltration of NK and Treg cells. CD8 T cell and macrophage in the TZ exhibit both inflammation activation and suppression phenotypes. In the stroma, the TZ had a higher fibroblast density, and fibroblasts in the TZ exhibited stronger transcriptome activity in immunity and proliferation. Ligand-receptor interaction analysis revealed that fibroblasts could contribute to a NOTCH signaling niche for club/hillock cells in the TZ and balance the prostate immune microenvironment. The activation of stem properties, inflammatory infiltration and loss of androgen/lineage activity are prominent features distinguishing the TZ from PZ.

CONCLUSIONS

Our study explains the heterogeneity between the TZ and PZ of aged prostate, which may help understand the spatial distribution of prostate diseases and establish a foundation for novel target discovery.

Defining mast cell differentiation and heterogeneity through single-cell transcriptomics analysis

Mast cells (MCs) are widely recognized as central effector cells during type 2 inflammatory reactions and thought to also play a role in innate immune responses, wound healing, and potentially cancer. Circulating progenitor cells mature to MCs in peripheral tissues, where they exhibit phenotypic and functional heterogeneity. This diversity likely originates from differences in MC development imprinted by microenvironmental signals. The advent of single-cell transcriptomics reveals MC diversity beyond differences in proteases that were classically used to identify MC phenotypes. Here, we provide an overview of the current knowledge on MC progenitor differentiation and characteristics, and MC heterogeneity seen in health versus disease, that are drastically advanced through single-cell profiling technologies. This powerful approach can provide detailed cellular maps of tissues to decipher the complex cellular functions and interactions that may lead to identifying candidate factors to target in therapies.

Histone lysine methylation patterns in prostate cancer microenvironment infiltration: Integrated bioinformatic analysis and histological validation

Background

Epigenetic reprogramming through dysregulated histone lysine methylation (HLM) plays a crucial role in prostate cancer (PCa) progression. This study aimed to comprehensively evaluate HLM modification patterns in PCa microenvironment infiltration.

Materials and methods

Ninety-one HLM regulators in The Cancer Genome Atlas (TCGA) dataset were analyzed using bioinformatics. Differentially expressed genes (DEGs) and survival analyses were performed using TCGA-PRAD clinicopathologic and follow-up information. Consensus clustering analysis divided patients into subgroups. Gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the DEGs. Tumor mutation burden (TMB) and tumor microenvironment (TME) cell infiltration were evaluated in different HLM clusters. Quantitative real-time PCR (qPCR) analysis assessed HLM regulators in clinical PCa tissues.

Results

The tumor vs. normal (TN), Gleason score (GS) > 7 vs. GS < 7, pathological T stage (pT) = 2 vs. pT = 3, and TP53 mutation vs. wild-type comparisons using TCGA-PRAD dataset revealed 3 intersecting HLM regulators (EZH2, NSD2, and KMT5C) that were consistently upregulated in advanced PCa (GS > 7, pT3, HR > 1, and TP53 mutation) (P < 0.05) and verified in clinical PCa tissues. Consensus clustering analysis revealed three distinct HLM modification patterns (HLMclusters). However, no significant differences in recurrence-free survival (RFS) rates were found among the groups (P > 0.05). We screened 189 HLM phenotype-related genes that overlapped in the pairwise comparisons of HLMclusters and P < 0.01 in the Cox regression analysis. Three distinct subgroups (geneClusters) were revealed based on the 189 genes, in which cluster A involved the most advanced PCa (PSA > 10, T3-4, GS8-10, and biochemical recurrence) and the poorest RFS. The HLM score (HLMscore) was calculated by principal component analysis (PCA) of HLM phenotype-related genes that have positive predictive value for RFS (P < 0.001) and immune therapy responses (in the CTLA4-positive and -negative responses accompanied by a PD1-negative response).

Conclusion

We comprehensively evaluated HLM regulators in the PCa microenvironment using TCGA-PRAD, revealing a nonnegligible role of HLM patterns in PCa complexity and heterogeneity. Elucidating the effects of HLM regulators in PCa may enhance prognostics, aggressiveness assessments, and immunotherapy strategies.

Prostate Cancer Secretome and Membrane Proteome from Pten Conditional Knockout Mice Identify Potential Biomarkers for Disease Progression

Prostate cancer (PCa) is the second most common cause of mortality among men. Tumor secretome is a promising strategy for understanding the biology of tumor cells and providing markers for disease progression and patient outcomes. Here, transcriptomic-based secretome analysis was performed on the PCa tumor transcriptome of Genetically Engineered Mouse Model (GEMM) Pb-Cre4/Pten^f/f mice to identify potentially secreted and membrane proteins—PSPs and PMPs. We combined a selection of transcripts from the GSE 94574 dataset and a list of protein-coding genes of the secretome and membrane proteome datasets using the Human Protein Atlas Secretome. Notably, nine deregulated PMPs and PSPs were identified in PCa (DMPK, PLN, KCNQ5, KCNQ4, MYOC, WIF1, BMP7, F3, and MUC1). We verified the gene expression patterns of Differentially Expressed Genes (DEGs) in normal and tumoral human samples using the GEPIA tool. DMPK, KCNQ4, and WIF1 targets were downregulated in PCa samples and in the GSE dataset. A significant association between shorter survival and KCNQ4, PLN, WIF1, and F3 expression was detected in the MSKCC dataset. We further identified six validated miRNAs (mmu-miR-6962-3p, mmu-miR- 6989-3p, mmu-miR-6998-3p, mmu-miR-5627-5p, mmu-miR-15a-3p, and mmu-miR-6922-3p) interactions that target MYOC, KCNQ5, MUC1, and F3. We have characterized the PCa secretome and membrane proteome and have spotted new dysregulated target candidates in PCa.

Deciphering cell-cell interactions and communication in the tumor microenvironment and unraveling intratumoral genetic heterogeneity via single-cell genomic sequencing

A tumor's heterogeneity has important implications in terms of its clonal origin, progression, stemness, and drug resistance. Therefore, because of its significance in treatment, it is important to understand the gene expression pattern of a single cell, track gene expression or mutation in heterogeneous cells, evaluate the clonal origin of cancer cells, and determine the selective evolution of different subpopulations of cancer cells. Researchers are able to trace a cell's mutation and identify different types of tumor cells by measuring the whole transcriptome with single-cell sequencing (scRNA-seq). This technology provides a better understanding of the molecular mechanisms driving tumor growth than that offered by traditional RNA sequencing methods. In addition, it has revealed changes in the mutations and functions of somatic cells as a tumor evolves; it has also clarified immune cell infiltration and activation. Research on scRNA-seq technology has recently advanced significantly, suggesting new strategies for the treatment of cancer. In short, cancer researchers have become increasingly dependent on scRNA-seq. This paper reviews the development, detection principles, and processes of scRNA-seq technology and their application in tumor research. It also considers potential clinical applications.

Review: RNA-based diagnostic markers discovery and therapeutic targets development in cancer

The present review aimed to outline different types of RNAs in cancer diagnostics and treatment, and to provide novel insights into their clinical applications. RNAs, including mRNA, long non-coding (lnc)RNA, circular (circ)RNA and micro (mi)RNA, are now increasingly utilized in the diagnosis and treatment of various cancers. Each aforementioned type of RNA possess their own unique characteristics and could be aberrantly expressed as diagnostic markers or therapeutic targets in different cancers. In addition to mRNAs, which have become a promising alternative in cancer diagnostics and therapy, the uses of lncRNA, circRNA and miRNA in predictive tumor diagnostics and therapy has rapidly increased in recent years. In the present review, the mechanisms of mRNA, lncRNA, circRNA and miRNA in regulating and participating in the development of different cancers were determined, and their potential capacity in cancer diagnostics and therapy were investigated. In addition, the present review analyzed the assoaciations between different RNAs and their subsequent potential in cancer prediction and treatment.

Single-Cell RNA Sequencing of Metastatic Testicular Seminoma Reveals the Cellular and Molecular Characteristics of Metastatic Cell Lineage

Background

Testicular cancer is the most common solid malignancy in young men. Given the many histological classifications of testicular tumors, seminoma is one of the most treatable cancers. The survival rate in early-stage disease was more than 90%. Thus, seminoma at the cellular and molecular levels, especially at the single-cell level, is worth studying.

Methods

We performed a single-cell RNA sequencing (scRNA-seq) study on a patient who was diagnosed with testicular seminoma with lymph node metastasis. This study presented tumor tissue, PBMC, pelvic and renal hilus lymph node in a total of 18,206 high-quality single-cell transcriptome information. The characteristics of metastatic cell lineage were revealed by the comparison between different tumor cell subtypes at the scRNA level.

Results

A single-cell map of testicular seminoma with lymph node metastasis was constructed by scRNA-seq. We discovered the gene expression characteristics of the tumor cells in testicular seminoma, especially metastatic tumor cells. KRT8 and KRT18 were commonly expressed in the three tumor cell subtypes. However, TCF7L1, SCG3 and SV2C were the specifically expressed genes of tumor cell subtypes in primary tumor sites. Some molecular markers specifically expressed by the metastatic cell lineage, such as POU5F1, were identified.

Conclusions

We revealed the molecular characteristics of testicular seminoma at the single-cell level, especially the metastatic tumor cells. This study could provide new insights into the diagnosis and treatment of testicular seminoma.

Transcriptome Analysis on Key Metabolic Pathways in Rhodotorula mucilaginosa Under Pb(II) Stress

Rhodotorula mucilaginosa shows adaption to a broad range of Pb²⁺ stress. In this study, three key pathways, i.e., glycolysis (EMP), the tricarboxylic acid (TCA) cycle, and oxidative phosphorylation (OXPHOS), were investigated under 0-2,500 mg · L^-1 Pb stress, primarily based on biochemical analysis and RNA sequencing. R. mucilaginosa cells showed similar metabolic response to low/medium (500/1000 mg · L^-1) Pb²⁺ stress. High (2,500 mg · L^-1) Pb²⁺ stress exerted severe cytotoxicity to R. mucilaginosa. The downregulation of HK under low-medium Pb²⁺ suggested a correlation with the low hexokinase enzymatic activity in vivo. However, IDH3, regulating a key step of circulation in TCA, was upregulated to promote ATP feedstock for downstream OXPHOS. Then, through activation of complex I & IV in the electron transport chain (ETC) and ATP synthase, ATP production was finally enhanced. This mechanism enabled fungal cells to compensate for ATP consumption under low-medium Pb²⁺ toxicity. Hence, R. mucilaginosa tolerance to such a broad range of Pb²⁺ concentrations can be attributed to energy adaption. In contrast, high Pb²⁺ stress caused ATP deficiency. Then, the subsequent degradation of intracellular defense systems further intensified Pb toxicity. This study correlated responses of EMP, TCA, and OXPHOS pathways in R. mucilaginosa under Pb stress, hence providing new insights into the fungal resistance to heavy metal stress. IMPORTANCE Glycolysis (EMP), the tricarboxylic acid (TCA) cycle, and oxidative phosphorylation (OXPHOS) are critical metabolism pathways for microorganisms to obtain energy during the resistance to heavy metal (HM) stress. However, these pathways at the genetic level have not been elucidated to evaluate their cytoprotective functions for Rhodotorula mucilaginosa under Pb stress. In this study, we investigated these three pathways based on biochemical analysis and RNA sequencing. Under low-medium (500-1,000 mg · L^-1) Pb²⁺ stress, ATP production was stimulated mainly due to the upregulation of genes associated with the TCA cycle and the electron transport chain (ETC). Such an energy compensatory mechanism could allow R. mucilaginosa acclimation to a broad range of Pb²⁺ concentrations (up to 1000 mg · L^-1). In contrast, high (2500 mg · L^-1) Pb²⁺ stress exerted its excessive toxicity by provoking ATP deficiency and damage to intracellular resistance systems. This study provided new insights into R. mucilaginosa resistance to HM stress from the perspective of metabolism.

The POR rs10954732 polymorphism decreases susceptibility to hepatocellular carcinoma and hepsin as a prognostic biomarker correlated with immune infiltration based on proteomics

The effect of the cytochrome P450 oxidoreductase (POR) rs10954732 (G > A) polymorphism on hepatocellular carcinoma (HCC) susceptibility is unknown. Here we found that A allele carriers showed a 69% decrease in susceptibility to HCC with overall survival (OS) prolonged to 199%, accompanied by lower activity for cytochrome P450 2E1. A total of 222 differentially expressed proteins were mainly enriched in neutrophil and T cell activation and involved in the immune and inflammatory responses, constituting the altered immune tumor microenvironment related with A allele by proteomics analysis. Hepsin (HPN) showed significant down-regulation in HCC and up-regulation in A allele carriers. A lower HPN level was associated with increased susceptibility to HCC and a worse prognosis. Moreover, HPN is a potential independent prognostic biomarker for HCC and is strongly associated with clinicopathological features, tumor-infiltrating status of immune cells both in our discovery cohort and database surveys. Our findings provide a new potential mechanism by which HPN may play an important role in the susceptibility of rs10954732 A allele carriers to HCC and their prognosis through tumor immune infiltration, thus offering potential insights for future studies on tumor immunotherapy.

Single-Cell Transcriptome Profiling Reveals Neutrophil Heterogeneity and Functional Multiplicity in the Early Stage of Severe Burn Patients

The pathophysiological mechanisms, especially the roles of immune cells, underlying early stages of severe burn injury have not yet been fully clarified. Here, we analyzed circulating neutrophils (PMNs) in healthy donors and early burned patients by single-cell RNA sequencing to provide a comprehensive transcriptional landscape of PMNs in heterogeneity and functional multiplicity. Circulating PMNs in the healthy donors and burned groups were divided into five subgroups (G3, G4, G5a, G5b, G5c) with different functions. The dominant subsets of PMNs in homeostasis and burn injury significantly differed between groups. In addition, cells in the same subpopulation had the same core identity markers but performed different functions in healthy and burned states. Under burned conditions, PMN activation was very evident and accompanied by clear degranulation and metabolic abnormalities. Interestingly, was found that PMN activation, degranulation, chemotaxis, phagocytosis and reactive oxygen species (ROS) production in burned patients significantly differed between day 1 and days 2 or 3, thus providing a theoretical basis for PMN interventions in early burn stages. Significantly, previously undescribed transcription factors were also identified, including ZNF-787, ZNF-467, ZNF-189, ZNF-770, ZNF-262. In conclusion, this study conducted for the first time a detailed analysis of the heterogeneity and functional multiplicity of PMNs in early stages of severe burn injuries. Our findings attempted to clarify the influence of PMN heterogeneity on the pathophysiology and related mechanisms of burn injuries, which can provide new ideas for further research in burn intervention.

Classification of pyroptosis patterns and construction of a novel prognostic model for prostate cancer based on bulk and single-cell RNA sequencing

BACKGROUND

Emerging evidence suggests an important role for pyroptosis in tumorigenesis and recurrence, but it remains to be elucidated in prostate cancer (PCa). Considering the low accuracy of common clinical predictors of PCa recurrence, we aimed to develop a novel pyroptosis-related signature to predict the prognosis of PCa patients based on integrative analyses of bulk and single-cell RNA sequencing (RNA-seq) profiling.

METHODS

The RNA-seq data of PCa patients was downloaded from several online databases. PCa patients were stratified into two Classes by unsupervised clustering. A novel signature was constructed by Cox and the Least Absolute Shrinkage and Selection Operator (LASSO) regression. The Kaplan-Meier curve was employed to evaluate the prognostic value of this signature and the single sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to analysis tumor-infiltrating immune cells. At single-cell level, we also classified the malignant cells into two Classes and constructed cell developmental trajectories and cell-cell interaction networks. Furthermore, RT-qPCR and immunofluorescence were used to validate the expression of core pyroptosis-related genes.

RESULTS

Twelve prognostic pyroptosis-related genes were identified and used to classify PCa patients into two prognostic Classes. We constructed a signature that identified PCa patients with different risks of recurrence and the risk score was proven to be an independent predictor of the recurrence free survival (RFS). Patients in the high-risk group had a significantly lower RFS (P<0.001). The expression of various immune cells differed between the two Classes. At the single-cell level, we classified the malignant cells into two Classes and described the heterogeneity. In addition, we observed that malignant cells may shift from Class1 to Class2 and thus have a worse prognosis.

CONCLUSION

We have constructed a robust pyroptosis-related signature to predict the RFS of PCa patients and described the heterogeneity of prostate cancer cells in terms of pyroptosis.

Pre-existing Castration-resistant Prostate Cancer–like Cells in Primary Prostate Cancer Promote Resistance to Hormonal Therapy

BACKGROUND

Hormonal therapy targeting the androgen receptor inhibits prostate cancer (PCa), but the tumor eventually recurs as castration-resistant prostate cancer (CRPC).

OBJECTIVE

To understand the mechanisms by which subclones within early PCa develop into CRPC.

DESIGN, SETTING, AND PARTICIPANTS

We isolated epithelial cells from fresh human PCa cases, including primary adenocarcinoma, locally recurrent CRPC, and metastatic CRPC, and utilized single-cell RNA sequencing to identify subpopulations destined to become either CRPC-adeno or small cell neuroendocrine carcinoma (SCNC).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

We revealed dynamic transcriptional reprogramming that promotes disease progression among 23226 epithelial cells using single-cell RNA sequencing, and validated subset-specific progression using immunohistochemistry and large cohorts of publically available genomic data.

RESULTS AND LIMITATIONS

We identified a small fraction of highly plastic CRPC-like cells in hormone-naïve early PCa and demonstrated its correlation with biochemical recurrence and distant metastasis, independent of clinical characteristics. We show that progression toward castration resistance was initiated from subtype-specific lineage plasticity and clonal expansion of pre-existing neuroendocrine and CRPC-like cells in early PCa.

CONCLUSIONS

CRPC-like cells are present early in the development of PCa and are not exclusively the result of acquired evolutionary selection during androgen deprivation therapy. The lethal CRPC and SCNC phenotypes should be targeted earlier in the disease course of patients with PCa.

PATIENT SUMMARY

Here, we report the presence of pre-existing castration-resistant prostate cancer (CRPC)-like cells in primary prostate cancer, which represents a novel castration-resistant mechanism different from the adaptation mechanism after androgen deprivation therapy (ADT). Patients whose tumors harbor increased pre-existing neuroendocrine and CRPC-like cells may become rapidly resistant to ADT and may require aggressive early intervention.

Understanding and targeting prostate cancer cell heterogeneity and plasticity

Prostate cancer (PCa) is a prevalent malignancy that occurs primarily in old males. Prostate tumors in different patients manifest significant inter-patient heterogeneity with respect to histo-morphological presentations and molecular architecture. An individual patient tumor also harbors genetically distinct clones in which PCa cells display intra-tumor heterogeneity in molecular features and phenotypic marker expression. This inherent PCa cell heterogeneity, e.g., in the expression of androgen receptor (AR), constitutes a barrier to the long-term therapeutic efficacy of AR-targeting therapies. Furthermore, tumor progression as well as therapeutic treatments induce PCa cell plasticity such that AR-positive PCa cells may turn into AR-negative cells and prostate tumors may switch lineage identity from adenocarcinomas to neuroendocrine-like tumors. This induced PCa cell plasticity similarly confers resistance to AR-targeting and other therapies. In this review, I first discuss PCa from the perspective of an abnormal organ development and deregulated cellular differentiation, and discuss the luminal progenitor cells as the likely cells of origin for PCa. I then focus on intrinsic PCa cell heterogeneity in treatment-naïve tumors with the presence of prostate cancer stem cells (PCSCs). I further elaborate on PCa cell plasticity induced by genetic alterations and therapeutic interventions, and present potential strategies to therapeutically tackle PCa cell heterogeneity and plasticity. My discussions will make it clear that, to achieve enduring clinical efficacy, both intrinsic PCa cell heterogeneity and induced PCa cell plasticity need to be targeted with novel combinatorial approaches.

FUCA2 Is a Prognostic Biomarker and Correlated With an Immunosuppressive Microenvironment in Pan-Cancer

Background

The expression of Fucosidase, alpha-L-2 (FUCA2) varies across tumors. However, its role in various tumor types and relationship with the tumor immune microenvironment (TIME) is poorly defined.

Methods

We analyzed profiles of FUCA2 expression using datasets from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Next, gene alteration, clinical characteristics and prognostic values of FUCA2 were elucidated based on TCGA pan-cancer data. This was followed by gene set enrichment analysis by R software. Relationships between FUCA2 expression and immune infiltration and immune-related genes were also evaluated. Moreover, the association of immune cell infiltration with FUCA2 expression was evaluated across three different sources of immune cell infiltration data, namely the TIMER online, ImmuCellAI databases, as well as a published study. In addition, MTT assays was also conducted to validate the oncogene role of FUCA2 in lung cancer cells.

Results

FUCA2 was upregulated in most tumors, and this was significantly associated with poor survival rates. Gene set enrichment analysis uncovered that FUCA2 correlated with immune pathways in different tumor types. FUCA2 expression was positively related to tumor associated macrophages (TAMs), especially M2-like TAMs. Moreover, FUCA2 level showed a positive relationship with most immunosuppression genes, including programmed death-ligand 1 (PD-L1), transforming growth factor beta 1 (TGFB1), and interleukin-10 (IL10) in most cancer types. FUCA2 knockdown inhibited the cell viability in lung cancer cells.

Conclusions

Our study reveals that FUCA2 is a potential oncogene and is indicative biomarker of a worse prognosis in pan-cancer. High FUCA2 expression may contribute to increased infiltration of TAMs and associates with an immunosuppressive microenvironment, providing a potential target for tumor therapy.

SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data

BACKGROUND

Recent studies have demonstrated the utility of scRNA-seq SNVs to distinguish tumor from normal cells, characterize intra-tumoral heterogeneity, and define mutation-associated expression signatures. In addition to cancer studies, SNVs from single cells have been useful in studies of transcriptional burst kinetics, allelic expression, chromosome X inactivation, ploidy estimations, and haplotype inference.

RESULTS

To aid these types of studies, we have developed a tool, SCReadCounts, for cell-level tabulation of the sequencing read counts bearing SNV reference and variant alleles from barcoded scRNA-seq alignments. Provided genomic loci and expected alleles, SCReadCounts generates cell-SNV matrices with the absolute variant- and reference-harboring read counts, as well as cell-SNV matrices of expressed Variant Allele Fraction (VAFRNA) suitable for a variety of downstream applications. We demonstrate three different SCReadCounts applications on 59,884 cells from seven neuroblastoma samples: (1) estimation of cell-level expression of known somatic mutations and RNA-editing sites, (2) estimation of cell- level allele expression of biallelic SNVs, and (3) a discovery mode assessment of the reference and each of the three alternative nucleotides at genomic positions of interest that does not require prior SNV information. For the later, we applied SCReadCounts on the coding regions of KRAS, where it identified known and novel somatic mutations in a low-to-moderate proportion of cells. The SCReadCounts read counts module is benchmarked against the analogous modules of GATK and Samtools. SCReadCounts is freely available ( https://github.com/HorvathLab/NGS ) as 64-bit self-contained binary distributions for Linux and MacOS, in addition to Python source.

CONCLUSIONS

SCReadCounts supplies a fast and efficient solution for estimation of cell-level SNV expression from scRNA-seq data. SCReadCounts enables distinguishing cells with monoallelic reference expression from those with no gene expression and is applicable to assess SNVs present in only a small proportion of the cells, such as somatic mutations in cancer.

The Role of Somatic Mutations on the Immune Response of the Tumor Microenvironment in Prostate Cancer

Immunotherapy has improved patient survival in many types of cancer, but for prostate cancer, initial results with immunotherapy have been disappointing. Prostate cancer is considered an immunologically excluded or cold tumor, unable to generate an effective T-cell response against cancer cells. However, a small but significant percentage of patients do respond to immunotherapy, suggesting that some specific molecular subtypes of this tumor may have a better response to checkpoint inhibitors. Recent findings suggest that, in addition to their function as cancer genes, somatic mutations of PTEN, TP53, RB1, CDK12, and DNA repair, or specific activation of regulatory pathways, such as ETS or MYC, may also facilitate immune evasion of the host response against cancer. This review presents an update of recent discoveries about the role that the common somatic mutations can play in changing the tumor microenvironment and immune response against prostate cancer. We describe how detailed molecular genetic analyses of the tumor microenvironment of prostate cancer using mouse models and human tumors are providing new insights into the cell types and pathways mediating immune responses. These analyses are helping researchers to design drug combinations that are more likely to target the molecular and immunological pathways that underlie treatment failure.

Single-cell RNA sequencing in cancer: Applications, advances, and emerging challenges

Cancer has become one of the greatest threats to human health, and new technologies are urgently needed to further clarify the mechanisms of cancer so that better detection and treatment strategies can be developed. At present, extensive genomic analysis and testing of clinical specimens shape the insights into carcinoma. Nevertheless, carcinoma of humans is a complex ecosystem of cells, including carcinoma cells and immunity-related and stroma-related subsets, with accurate characteristics obscured by extensive genome-related approaches. A growing body of research shows that sequencing of single-cell RNA (scRNA-seq) is emerging to be an effective way for dissecting human tumor tissue at single-cell resolution, presenting one prominent way for explaining carcinoma biology. This review summarizes the research progress of scRNA-seq in the field of tumors, focusing on the application of scRNA-seq in tumor circulating cells, tumor stem cells, tumor drug resistance, the tumor microenvironment, and so on, which provides a new perspective for tumor research.