Single-cell multiomics analysis reveals regulatory programs in clear cell renal cell carcinoma

The implications for urological malignancies of non-coding RNAs in the the tumor microenvironment

Urological malignancies are a major global health issue because of their complexity and the wide range of ways they affect patients. There's a growing need for in-depth research into these cancers, especially at the molecular level. Recent studies have highlighted the importance of non-coding RNAs (ncRNAs) – these don't code for proteins but are crucial in controlling genes – and the tumor microenvironment (TME), which is no longer seen as just a background factor but as an active player in cancer progression. Understanding how ncRNAs and the TME interact is key for finding new ways to diagnose and predict outcomes in urological cancers, and for developing new treatments. This article reviews the basic features of ncRNAs and goes into detail about their various roles in the TME, focusing specifically on how different ncRNAs function and act in urological malignancies.

The role of macrophages in fibrosis of chronic kidney disease

Macrophages are widely distributed throughout various tissues of the body, and mounting evidence suggests their involvement in regulating the tissue microenvironment, thereby influencing disease onset and progression through direct or indirect actions. In chronic kidney disease (CKD), disturbances in renal functional homeostasis lead to inflammatory cell infiltration, tubular expansion, glomerular atrophy, and subsequent renal fibrosis. Macrophages play a pivotal role in this pathological process. Therefore, understanding their role is imperative for investigating CKD progression, mitigating its advancement, and offering novel research perspectives for fibrosis treatment from an immunological standpoint. This review primarily delves into the intrinsic characteristics of macrophages, their origins, diverse subtypes, and their associations with renal fibrosis. Particular emphasis is placed on the transition between M1 and M2 phenotypes. In late-stage CKD, there is a shift from the M1 to the M2 phenotype, accompanied by an increased prevalence of M2 macrophages. This transition is governed by the activation of the TGF-β1/SMAD3 and JAK/STAT pathways, which facilitate macrophage-to-myofibroblast transition (MMT). The tyrosine kinase Src is involved in both signaling cascades. By thoroughly elucidating macrophage functions and comprehending the modes and molecular mechanisms of macrophage-fibroblast interaction in the kidney, novel, tailored therapeutic strategies for preventing or attenuating the progression of CKD can be developed.

Single-cell transcriptional profiling of clear cell renal cell carcinoma reveals a tumor-associated endothelial tip cell phenotype

Clear cell renal cell carcinoma (ccRCC) is the most prevalent form of renal cancer, accounting for over 75% of cases. The asymptomatic nature of the disease contributes to late-stage diagnoses and poor survival. Highly vascularized and immune infiltrated microenvironment are prominent features of ccRCC, yet the interplay between vasculature and immune cells, disease progression and response to therapy remains poorly understood. Using droplet-based single-cell RNA sequencing we profile 50,236 transcriptomes from paired tumor and healthy adjacent kidney tissues. Our analysis reveals significant heterogeneity and inter-patient variability of the tumor microenvironment. Notably, we discover a previously uncharacterized vasculature subpopulation associated with epithelial-mesenchymal transition. The cell-cell communication analysis reveals multiple modes of immunosuppressive interactions within the tumor microenvironment, including clinically relevant interactions between tumor vasculature and stromal cells with immune cells. The upregulation of the genes involved in these interactions is associated with worse survival in the TCGA KIRC cohort. Our findings demonstrate the role of tumor vasculature and stromal cell populations in shaping the ccRCC microenvironment and uncover a subpopulation of cells within the tumor vasculature that is associated with an angiogenic phenotype.

Unfolding the mysteries of heterogeneity from a high-resolution perspective: integration analysis of single-cell multi-omics and spatial omics revealed functionally heterogeneous cancer cells in ccRCC

The genomic landscape of clear cell renal cell carcinoma (ccRCC) has a considerable intra-tumor heterogeneity, which is a significant obstacle in the field of precision oncology and plays a pivotal role in metastasis, recurrence, and therapeutic resistance of cancer. The mechanisms of intra-tumor heterogeneity in ccRCC have yet to be fully established. We integrated single-cell RNA sequencing (scRNA-seq) and transposase-accessible chromatin sequencing (scATAC-seq) data from a single-cell multi-omics perspective. Based on consensus non-negative matrix factorization (cNMF) algorithm, functionally heterogeneous cancer cells were classified into metabolism, inflammatory, and EMT meta programs, with spatial transcriptomics sequencing (stRNA-seq) providing spatial information of such disparate meta programs of cancer cells. The bulk RNA sequencing (RNA-seq) data revealed high clinical prognostic values of functionally heterogeneous cancer cells of three meta programs, with transcription factor regulatory network and motif activities revealing the key transcription factors that regulate functionally heterogeneous ccRCC cells. The interactions between varying meta programs and other cell subpopulations in the microenvironment were investigated. Finally, we assessed the sensitivity of cancer cells of disparate meta programs to different anti-cancer agents. Our findings inform on the intra-tumor heterogeneity of ccRCC and its regulatory networks and offers new perspectives to facilitate the designs of rational therapeutic strategies.

A Single-Cell Transcriptome Profiling of Triptolide-Induced Nephrotoxicity in Mice

Triptolide (TP), an active component isolated from the traditional Chinese herb Tripterygium wilfordii Hook F (TWHF), shows great promise for treating inflammation-related diseases. However, its potential nephrotoxic effects remain concerning. The mechanism underlying TP-induced nephrotoxicity is inadequately elucidated, particularly at single-cell resolution. Hence, single-cell RNA sequencing (scRNA-seq) of kidney tissues from control and TP-treated mice is performed to generate a thorough description of the renal cell atlas upon TP treatment. Heterogeneous responses of nephron epithelial cells are observed after TP exposure, attributing differential susceptibility of cell subtypes to excessive reactive oxygen species and increased inflammatory responses. Moreover, TP disrupts vascular function by activating endothelial cell immunity and damaging fibroblasts. Severe immune cell damage and the activation of pro-inflammatory Macro_C1 cells are also observed with TP treatment. Additionally, ligand-receptor crosstalk analysis reveals that the SPP1 (osteopontin) signaling pathway targeting Macro_C1 cells is triggered by TP treatment, which may promote the infiltration of Macro_C1 cells to exacerbate renal toxicity. Overall, this study provides comprehensive information on the transcriptomic profiles and cellular composition of TP-associated nephrotoxicity at single-cell resolution, which can strengthen the understanding of the pathogenesis of TP-induced nephrotoxicity and provide valuable clues for the discovery of new therapeutic targets to ameliorate TP-associated nephrotoxicity.

SifiNet: a robust and accurate method to identify feature gene sets and annotate cells

SifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods. SifiNet can analyze both single-cell RNA and ATAC sequencing data, thereby rendering comprehensive multi-omic cellular profiles. It is conveniently available as an open-source R package.

Boosting Clear Cell Renal Carcinoma-Specific Drug Discovery Using a Deep Learning Algorithm and Single-Cell Analysis

Clear cell renal carcinoma (ccRCC), the most common subtype of renal cell carcinoma, has the high heterogeneity of a highly complex tumor microenvironment. Existing clinical intervention strategies, such as target therapy and immunotherapy, have failed to achieve good therapeutic effects. In this article, single-cell transcriptome sequencing (scRNA-seq) data from six patients downloaded from the GEO database were adopted to describe the tumor microenvironment (TME) of ccRCC, including its T cells, tumor-associated macrophages (TAMs), endothelial cells (ECs), and cancer-associated fibroblasts (CAFs). Based on the differential typing of the TME, we identified tumor cell-specific regulatory programs that are mediated by three key transcription factors (TFs), whilst the TF EPAS1/HIF-2α was identified via drug virtual screening through our analysis of ccRCC's protein structure. Then, a combined deep graph neural network and machine learning algorithm were used to select anti-ccRCC compounds from bioactive compound libraries, including the FDA-approved drug library, natural product library, and human endogenous metabolite compound library. Finally, five compounds were obtained, including two FDA-approved drugs (flufenamic acid and fludarabine), one endogenous metabolite, one immunology/inflammation-related compound, and one inhibitor of DNA methyltransferase (N4-methylcytidine, a cytosine nucleoside analogue that, like zebularine, has the mechanism of inhibiting DNA methyltransferase). Based on the tumor microenvironment characteristics of ccRCC, five ccRCC-specific compounds were identified, which would give direction of the clinical treatment for ccRCC patients.

SifiNet: A robust and accurate method to identify feature gene sets and annotate cells

SifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods. SifiNet can analyze both single-cell RNA and ATAC sequencing data, thereby rendering comprehensive multiomic cellular profiles. It is conveniently available as an open-source R package.

Explore & actuate: the future of personalized medicine in oncology through emerging technologies

PURPOSE OF REVIEW

The future of medicine is aimed to equip the physician with tools to assess the individual health of the patient for the uniqueness of the disease that separates it from the rest. The integration of omics technologies into clinical practice, reviewed here, would open new avenues for addressing the spatial and temporal heterogeneity of cancer. The rising cancer burden patiently awaits the advent of such an approach to personalized medicine for routine clinical settings.

RECENT FINDINGS

To weigh the translational potential, multiple technologies were categorized based on the extractable information from the different types of samples used, to the various omic-levels of molecular information that each technology has been able to advance over the last 2 years. This review uses a multifaceted classification that helps to assess translational potential in a meaningful way toward clinical adaptation.

SUMMARY

The importance of distinguishing technologies based on the flow of information from exploration to actuation puts forth a framework that allows the clinicians to better adapt a chosen technology or use them in combination to enhance their goals toward personalized medicine.

Development and implementation of a prognostic model for clear cell renal cell carcinoma based on heterogeneous TLR4 expression

Objective

Clear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cell carcinomas and has the worst prognosis, originating from renal tubular epithelial cells. Toll-like receptor 4 (TLR4) plays a crucial role in ccRCC proliferation, infiltration, and metastasis. The aim of this study was to construct a prognostic scoring model for ccRCC based on TLR4 expression heterogeneity and to explore its association with immune infiltration, thereby providing insights for the treatment and prognostic evaluation of ccRCC.

Methods

Using R software, a differential analysis was conducted on normal samples and ccRCC samples, and in conjunction with the KEGG database, a correlation analysis for the clear cell renal cell carcinoma pathway (hsa05211) was carried out. We observed the expression heterogeneity of TLR4 in the TCGA-KIRC cohort and identified its related differential genes (TRGs). Based on the expression levels of TRGs, consensus clustering was employed to identify TLR4-related subtypes, and further clustering heatmaps, principal component, and single-sample gene set enrichment analyses were conducted. Overlapping differential genes (ODEGs) between subtypes were analysed, and combined with survival data, univariate Cox regression, LASSO, and multivariate Cox regression were used to establish a prognostic risk model for ccRCC. This model was subsequently evaluated through ROC analysis, risk factor correlation analysis, independent prognostic factor analysis, and intergroup differential analysis. The ssGSEA model was employed to explore immune heterogeneity in ccRCC, and the performance of the model in predicting patient prognosis was evaluated using box plots and the oncoPredict software package.

Results

In the TCGA-KIRC cohort, TLR4 expression was notably elevated in ccRCC samples compared to normal samples, correlating with improved survival in the high-expression group. The study identified distinct TLR4-related differential genes and categorized ccRCC into three subtypes with varied survival outcomes. A risk prognosis model based on overlapping differential genes was established, showing significant associations with immune cell infiltration and key immune checkpoints (PD-1, PD-L1, CTLA4). Additionally, drug sensitivity differences were observed between risk groups.

Conclusion

In the TCGA-KIRC cohort, the expression of TLR4 in ccRCC samples exhibited significant heterogeneity. Through clustering analysis, we identified that the primary immune cells across subtypes are myeloid-derived suppressor cells, central memory CD4 T cells, and regulatory T cells. Furthermore, we successfully constructed a prognostic risk model for ccRCC composed of 17 genes. This model provides valuable references for the prognosis prediction and treatment of ccRCC patients.

Metabolic heterogeneity in clear cell renal cell carcinoma revealed by single-cell RNA sequencing and spatial transcriptomics

BACKGROUND

Clear cell renal cell carcinoma is a prototypical tumor characterized by metabolic reprogramming, which extends beyond tumor cells to encompass diverse cell types within the tumor microenvironment. Nonetheless, current research on metabolic reprogramming in renal cell carcinoma mostly focuses on either tumor cells alone or conducts analyses of all cells within the tumor microenvironment as a mixture, thereby failing to precisely identify metabolic changes in different cell types within the tumor microenvironment.

METHODS

Gathering 9 major single-cell RNA sequencing databases of clear cell renal cell carcinoma, encompassing 195 samples. Spatial transcriptomics data were selected to conduct metabolic activity analysis with spatial localization. Developing scMet program to convert RNA-seq data into scRNA-seq data for downstream analysis.

RESULTS

Diverse cellular entities within the tumor microenvironment exhibit distinct infiltration preferences across varying histological grades and tissue origins. Higher-grade tumors manifest pronounced immunosuppressive traits. The identification of tumor cells in the RNA splicing state reveals an association between the enrichment of this particular cellular population and an unfavorable prognostic outcome. The energy metabolism of CD8+ T cells is pivotal not only for their cytotoxic effector functions but also as a marker of impending cellular exhaustion. Sphingolipid metabolism evinces a correlation with diverse macrophage-specific traits, particularly M2 polarization. The tumor epicenter is characterized by heightened metabolic activity, prominently marked by elevated tricarboxylic acid cycle and glycolysis while the pericapsular milieu showcases a conspicuous enrichment of attributes associated with vasculogenesis, inflammatory responses, and epithelial-mesenchymal transition. The scMet facilitates the transformation of RNA sequencing datasets sourced from TCGA into scRNA sequencing data, maintaining a substantial degree of correlation.

CONCLUSIONS

The tumor microenvironment of clear cell renal cell carcinoma demonstrates significant metabolic heterogeneity across various cell types and spatial dimensions. scMet exhibits a notable capability to transform RNA sequencing data into scRNA sequencing data with a high degree of correlation.

moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud

Single-cell omics sequencing has rapidly advanced, enabling the quantification of diverse omics profiles at a single-cell resolution. To facilitate comprehensive biological insights, such as cellular differentiation trajectories, precise annotation of cell subtypes is essential. Conventional methods involve clustering cells and manually assigning subtypes based on canonical markers, a labor-intensive and expert-dependent process. Hence, an automated computational prediction framework is crucial. While several classification frameworks for predicting cell subtypes from single-cell RNA sequencing datasets exist, these methods solely rely on single-omics data, offering insights at a single molecular level. They often miss inter-omic correlations and a holistic understanding of cellular processes. To address this, the integration of multi-omics datasets from individual cells is essential for accurate subtype annotation. This article introduces moSCminer, a novel framework for classifying cell subtypes that harnesses the power of single-cell multi-omics sequencing datasets through an attention-based neural network operating at the omics level. By integrating three distinct omics datasets-gene expression, DNA methylation, and DNA accessibility-while accounting for their biological relationships, moSCminer excels at learning the relative significance of each omics feature. It then transforms this knowledge into a novel representation for cell subtype classification. Comparative evaluations against standard machine learning-based classifiers demonstrate moSCminer's superior performance, consistently achieving the highest average performance on real datasets. The efficacy of multi-omics integration is further corroborated through an in-depth analysis of the omics-level attention module, which identifies potential markers for cell subtype annotation. To enhance accessibility and scalability, moSCminer is accessible as a user-friendly web-based platform seamlessly connected to a cloud system, publicly accessible at http://203.252.206.118:5568. Notably, this study marks the pioneering integration of three single-cell multi-omics datasets for cell subtype identification.

Novel tumor-associated macrophage populations and subpopulations by single cell RNA sequencing

Tumor-associated macrophages (TAMs) are present in almost all solid tumor tissues. 16They play critical roles in immune regulation, tumor angiogenesis, tumor stem cell activation, tumor invasion and metastasis, and resistance to therapy. However, it is unclear how TAMs perform these functions. With the application of single-cell RNA sequencing (scRNA-seq), it has become possible to identify TAM subpopulations associated with distinct functions. In this review, we discuss four novel TAM subpopulations in distinct solid tumors based on core gene signatures by scRNA-seq, including FCN1 +, SPP1 +, C1Q + and CCL18 + TAMs. Functional enrichment and gene expression in scRNA-seq data from different solid tumor tissues found that FCN1 + TAMs may induce inflammation; SPP1 + TAMs are potentially involved in metastasis, angiogenesis, and cancer cell stem cell activation, whereas C1Q + TAMs participate in immune regulation and suppression; And CCL18 + cells are terminal immunosuppressive macrophages that not only have a stronger immunosuppressive function but also enhance tumor metastasis. SPP1 + and C1Q + TAM subpopulations can be further divided into distinct populations with different functions. Meanwhile, we will also present emerging evidence highlighting the separating macrophage subpopulations associated with distinct functions. However, there exist the potential disconnects between cell types and subpopulations identified by scRNA-seq and their actual function.

Unveiling the hidden AP-1: revealing the crucial role of AP-1 in ccRCC at single-cell resolution

Clear cell renal cell carcinoma (ccRCC), as the most common histological subtype of kidney cancer, has been reported to originate primarily from proximal tubule (PT) cells in the kidney. However, the current research on its associated molecular mechanisms remains relatively limited. In our study, we analyzed multiple single-cell multi-omics datasets obtained from various research teams, revealing the significant role of the activator protein 1 (AP-1) in ccRCC tumorigenesis. The motif activity analysis of transcription factors (TFs) showed a predominant activation of AP-1 in ccRCC cancer cells compared to PT cells. Furthermore, our findings at single-cell resolution revealed a notable absence of AP-1 expression in PT cells when compared to ccRCC cancer cells. In bulk-RNA of discovery cohort, no differential expression of AP-1 was detected in normal kidney and ccRCC samples, which may be attributed to confounding effects in bulk-RNA sequencing. Meanwhile, spatial transcriptomics analysis demonstrated a broader expression range of the AP-1 compared to the ccRCC marker CA9. Moreover, we observed chromatin accessibility of the AP-1 in various cell-types, including PT cells, suggesting that the transcriptional expression of AP-1 in PT cells may be influenced by subsequent transcriptional modifications, reflecting the complex regulatory mechanism of AP-1 transcription. These findings provide important insights for a deeper understanding of the function and regulatory mechanisms of AP-1 in ccRCC, thereby establishing a theoretical foundation for future clinical research and the development of treatment strategies.

Large-scale analysis of cell-cell communication reveals angiogenin-dependent tumor progression in clear cell renal cell carcinoma

Cellular crosstalk in the tumor microenvironment (TME) is still largely uncharacterized, while it plays an essential role in shaping immunosuppression or anti-tumor response. Large-scale analyses are needed to better decipher cell-cell communication in cancer. In this work, we used original and publicly available single-cell RNA sequencing (scRNAseq) data to characterize in-depth the communication networks in human clear cell renal cell carcinoma (ccRCC). We identified 50 putative communication channels specifically used by cancer cells to interact with other cells, including two novel angiogenin-mediated interactions. Expression of angiogenin and its receptors was validated at the protein level in primary ccRCC. Mechanistically, angiogenin enhanced ccRCC cell line proliferation and down-regulated secretion of IL-6, IL-8, and MCP-1 proinflammatory molecules. This study provides novel biological insights into molecular mechanisms of ccRCC, and suggests angiogenin and its receptors as potential therapeutic targets in clear cell renal cancer.

Delineating the early dissemination mechanisms of acral melanoma by integrating single-cell and spatial transcriptomic analyses

Acral melanoma (AM) is a rare subtype of melanoma characterized by a high incidence of lymph node (LN) metastasis, a critical factor in tumor dissemination and therapeutic decision-making. Here, we employ single-cell and spatial transcriptomic analyses to investigate the dynamic evolution of early AM dissemination. Our findings reveal substantial inter- and intra-tumor heterogeneity in AM, alongside a highly immunosuppressive tumor microenvironment and complex intercellular communication networks, particularly in patients with LN metastasis. Notably, we identify a strong association between MYC+ Melanoma (MYC+MEL) and FGFBP2+NKT cells with LN metastasis. Furthermore, we demonstrate that LN metastasis requires a metabolic shift towards fatty acid oxidation (FAO) induced by MITF in MYC+MEL cells. Etomoxir, a clinically approved FAO inhibitor, can effectively suppress MITF-mediated LN metastasis. This comprehensive dataset enhances our understanding of LN metastasis in AM, and provides insights into the potential therapeutic targeting for the management of early AM dissemination.

Single cell RNA-sequencing analysis reveals that N-acetylcysteine partially reverses hepatic immune dysfunction in biliary atresia

Background & Aims

Our previous study indicated that CD177+ neutrophil activation has a vital role in the pathogenesis of biliary atresia (BA), which is partially ameliorated by N-acetylcysteine (NAC) treatment. Here, we evaluated the clinical efficacy of NAC treatment and profiled liver-resident immune cells via single cell RNA-sequencing (scRNA-seq) analysis to provide a comprehensive immune landscape of NAC-derived immune regulation.

Methods

A pilot clinical study was conducted to evaluate the potential effects of intravenous NAC treatment on infants with BA, and a 3-month follow-up was carried out to assess treatment efficacy. scRNA-seq analysis of liver CD45+ immune cells in the control (n = 4), BA (n = 6), and BA + NAC (n = 6) groups was performed and the effects on innate cells, including neutrophil and monocyte-macrophage subsets, and lymphoid cells were evaluated.

Results

Intravenous NAC treatment demonstrated beneficial efficacy for infants with BA by improving bilirubin metabolism and bile acid flow. Two hepatic neutrophil subsets of innate cells were identified by scRNA-seq analysis. NAC treatment suppressed oxidative phosphorylation and reactive oxygen species production in immature neutrophils, which were transcriptionally and functionally similar to CD177+ neutrophils. We also observed the suppression of hepatic monocyte-mediated inflammation, decreased levels of oxidative phosphorylation, and M1 polarisation in Kupffer-like macrophages by NAC. In lymphoid cells, enhancement of humoral immune responses and attenuation of cellular immune responses were observed after NAC treatment. Moreover, cell-cell interaction analysis showed that innate/adaptive proinflammatory responses were downregulated by NAC.

Conclusions

Our clinical and scRNA-seq data demonstrated that intravenous NAC treatment partially reversed liver immune dysfunction, alleviated the proinflammatory responses in BA by targeting innate cells, and exhibited beneficial clinical efficacy.

Impact and implications

BA is a serious liver disease that affects newborns and has no effective drug treatment. In this study, scRNA-seq showed that NAC treatment can partially reverse the immune dysfunction of neutrophil extracellular trap-releasing CD177+ neutrophils and Kupffer cells, and lower the inflammatory responses of other innate immune cells in BA. In consequence, intravenous NAC treatment improved the clinical outcomes of patients with BA in term of bilirubin metabolism.

Epigenetic regulation during cancer transitions across 11 tumour types

Chromatin accessibility is essential in regulating gene expression and cellular identity, and alterations in accessibility have been implicated in driving cancer initiation, progression and metastasis1-4. Although the genetic contributions to oncogenic transitions have been investigated, epigenetic drivers remain less understood. Here we constructed a pan-cancer epigenetic and transcriptomic atlas using single-nucleus chromatin accessibility data (using single-nucleus assay for transposase-accessible chromatin) from 225 samples and matched single-cell or single-nucleus RNA-sequencing expression data from 206 samples. With over 1 million cells from each platform analysed through the enrichment of accessible chromatin regions, transcription factor motifs and regulons, we identified epigenetic drivers associated with cancer transitions. Some epigenetic drivers appeared in multiple cancers (for example, regulatory regions of ABCC1 and VEGFA; GATA6 and FOX-family motifs), whereas others were cancer specific (for example, regulatory regions of FGF19, ASAP2 and EN1, and the PBX3 motif). Among epigenetically altered pathways, TP53, hypoxia and TNF signalling were linked to cancer initiation, whereas oestrogen response, epithelial-mesenchymal transition and apical junction were tied to metastatic transition. Furthermore, we revealed a marked correlation between enhancer accessibility and gene expression and uncovered cooperation between epigenetic and genetic drivers. This atlas provides a foundation for further investigation of epigenetic dynamics in cancer transitions.

Single-cell sequencing technology applied to epigenetics for the study of tumor heterogeneity

BACKGROUND

Previous studies have traditionally attributed the initiation of cancer cells to genetic mutations, considering them as the fundamental drivers of carcinogenesis. However, recent research has shed light on the crucial role of epigenomic alterations in various cell types present within the tumor microenvironment, suggesting their potential contribution to tumor formation and progression. Despite these significant findings, the progress in understanding the epigenetic mechanisms regulating tumor heterogeneity has been impeded over the past few years due to the lack of appropriate technical tools and methodologies.

RESULTS

The emergence of single-cell sequencing has enhanced our understanding of the epigenetic mechanisms governing tumor heterogeneity by revealing the distinct epigenetic layers of individual cells (chromatin accessibility, DNA/RNA methylation, histone modifications, nucleosome localization) and the diverse omics (transcriptomics, genomics, multi-omics) at the single-cell level. These technologies provide us with new insights into the molecular basis of intratumoral heterogeneity and help uncover key molecular events and driving mechanisms in tumor development.

CONCLUSION

This paper provides a comprehensive review of the emerging analytical and experimental approaches of single-cell sequencing in various omics, focusing specifically on epigenomics. These approaches have the potential to capture and integrate multiple dimensions of individual cancer cells, thereby revealing tumor heterogeneity and epigenetic features. Additionally, this paper outlines the future trends of these technologies and their current technical limitations.

Recent Advances in Single-Cell RNA-Sequencing of Primary and Metastatic Clear Cell Renal Cell Carcinoma

Over the past two decades, significant progress has been made in the treatment of clear cell renal cell carcinoma (ccRCC), with a shift towards adopting new treatment approaches ranging from monotherapy to triple-combination therapy. This progress has been spearheaded by fundamental technological advancements that have allowed a deeper understanding of the various biological components of this cancer. In particular, the rapid commercialization of transcriptomics technologies, such as single-cell RNA-sequencing (scRNA-seq) methodologies, has played a crucial role in accelerating this understanding. Through precise measurements facilitated by these technologies, the research community has successfully identified and characterized diverse tumor, immune, and stromal cell populations, uncovering their interactions and pathways involved in disease progression. In localized ccRCC, patients have shown impressive response rates to treatment. However, despite the emerging findings and new knowledge provided in the field, there are still patients that do not respond to treatment, especially in advanced disease stages. One of the key challenges lies in the limited study of ccRCC metastases compared to localized cases. This knowledge gap may contribute to the relatively low survival rates and response rates observed in patients with metastatic ccRCC. To bridge this gap, we here delve into recent research utilizing scRNA-seq technologies in both primary and metastatic ccRCC. The goal of this review is to shed light on the current state of knowledge in the field, present existing treatment options, and emphasize the crucial steps needed to improve survival rates, particularly in cases of metastatic ccRCC.

S100A5 Attenuates Efficiency of Anti-PD-L1/PD-1 Immunotherapy by Inhibiting CD8+ T Cell-Mediated Anti-Cancer Immunity in Bladder Carcinoma

Although immune checkpoint blockade (ICB) therapies have been approved for bladder cancer (BLCA), only a minority of patients respond to these therapies, and there is an urgent need to explore combined therapies. Systematic multi-omics analysis identified S100A5 as a novel immunosuppressive target for BLCA. The expression of S100A5 in malignant cells inhibited CD8+ T cell recruitment by decreasing pro-inflammatory chemokine secretion. Furthermore, S100A5 attenuated effector T cell killing of cancer cells by inhibiting CD8+ T cell proliferation and cytotoxicity. In addition, S100A5 acted as an oncogene, thereby promoting tumor proliferation and invasion. Targeting S100A5 synergized with the efficacy of anti-PD-1 treatment by enhancing infiltration and cytotoxicity of CD8+ T cells in vivo. Clinically, there was a spatially exclusive relationship between S100A5+ tumor cells and CD8+ T cells in tissue microarrays. Moreover, S100A5 negatively correlated with immunotherapy efficacy in our real-world and several public immunotherapy cohorts. In summary, S100A5 shapes a non-inflamed tumor microenvironment in BLCA by inhibiting the secretion of pro-inflammatory chemokines and the recruitment and cytotoxicity of CD8+ T cells. Targeting S100A5 converts cold tumors into hot tumors, thus enhancing the efficacy of ICB therapy in BLCA.

Multi-omics analysis uncovers clinical, immunological, and pharmacogenomic implications of cuproptosis in clear cell renal cell carcinoma

OBJECTIVE

The latest research proposed a novel copper-dependent programmed cell death named cuproptosis. We aimed to elucidate the influence of cuproptosis in clear cell renal cell carcinoma (ccRCC) from a multi-omic perspective.

METHODS

This study systematically assessed mRNA expression, methylation, and genetic alterations of cuproptosis genes in TCGA ccRCC samples. Through unsupervised clustering analysis, the samples were classified as different cuproptosis subtypes, which were verified through NTP method in the E-MTAB-1980 dataset. Next, the cuproptosis score (Cuscore) was computed based on cuproptosis-related genes via PCA. We also evaluated clinical and immunogenomic features, drug sensitivity, immunotherapeutic response, and post-transcriptional regulation.

RESULTS

Cuproptosis genes presented multi-layer alterations in ccRCC, and were linked with patients' survival and immune microenvironment. We defined three cuproptosis subtypes [C1 (moderate cuproptosis), C2 (low cuproptosis), and C3 (high cuproptosis)], and the robustness and reproducibility of this classification was further proven. Overall survival was best in C3, moderate in C1, and worst in C2. C1 had the highest sensitivity to pazopanib, and sorafenib, while C2 was most sensitive to sunitinib. Furthermore, C1 patients benefited more from anti-PD-1 immunotherapy. Patients with high Cuscore presented the notable survival advantage. Cuscore was highly linked with immunogenomic features, and post-transcriptional events that contributed to ccRCC development. Finally, several potential compounds and druggable targets (NMU, RARRES1) were selected for low Cuscore group.

CONCLUSION

Overall, our study revealed the non-negligible role of cuproptosis in ccRCC development. Evaluation of the cuproptosis subtypes improves our cognition of immunogenomic features and better guides personalized prognostication and precision therapy.

Identification and validation of a novel HOX-related classifier signature for predicting prognosis and immune microenvironment in pediatric gliomas

Background: Pediatric gliomas (PGs) are highly aggressive and predominantly occur in young children. In pediatric gliomas, abnormal expression of Homeobox (HOX) family genes (HFGs) has been observed and is associated with the development and progression of the disease. Studies have found that overexpression or underexpression of certain HOX genes is linked to the occurrence and prognosis of gliomas. This aberrant expression may contribute to the dysregulation of important pathological processes such as cell proliferation, differentiation, and metastasis. This study aimed to propose a novel HOX-related signature to predict patients' prognosis and immune infiltrate characteristics in PGs. Methods: The data of PGs obtained from publicly available databases were utilized to reveal the relationship among abnormal expression of HOX family genes (HFGs), prognosis, tumor immune infiltration, clinical features, and genomic features in PGs. The HFGs were utilized to identify heterogeneous subtypes using consensus clustering. Then random forest-supervised classification algorithm and nearest shrunken centroid algorithm were performed to develop a prognostic signature in the training set. Finally, the signature was validated in an internal testing set and an external independent cohort. Results: Firstly, we identified HFGs significantly differentially expressed in PGs compared to normal tissues. The individuals with PGs were then divided into two heterogeneous subtypes (HOX-SI and HOX-SII) based on HFGs expression profiles. HOX-SII showed higher total mutation counts, lower immune infiltration, and worse prognosis than HOX-SI. Then, we constructed a HOX-related gene signature (including HOXA6, HOXC4, HOXC5, HOXC6, and HOXA-AS3) based on the cluster for subtype prediction utilizing random forest supervised classification and nearest shrunken centroid algorithm. The signature was revealed to be an independent prognostic factor for patients with PGs by multivariable Cox regression analysis. Conclusion: Our study provides a novel method for the prognosis classification of PGs. The findings also suggest that the HOX-related signature is a new biomarker for the diagnosis and prognosis of patients with PGs, allowing for more accurate survival prediction.

Mapping the tumor microenvironment in clear cell renal carcinoma by single-cell transcriptome analysis

Introduction: Clear cell renal cell carcinoma (ccRCC) is associated with unfavorable clinical outcomes. To identify viable therapeutic targets, a comprehensive understanding of intratumoral heterogeneity is crucial. In this study, we conducted bioinformatic analysis to scrutinize single-cell RNA sequencing data of ccRCC tumor and para-tumor samples, aiming to elucidate the intratumoral heterogeneity in the ccRCC tumor microenvironment (TME). Methods: A total of 51,780 single cells from seven ccRCC tumors and five para-tumor samples were identified and grouped into 11 cell lineages using bioinformatic analysis. These lineages included tumor cells, myeloid cells, T-cells, fibroblasts, and endothelial cells, indicating a high degree of heterogeneity in the TME. Copy number variation (CNV) analysis was performed to compare CNV frequencies between tumor and normal cells. The myeloid cell population was further re-clustered into three major subgroups: monocytes, macrophages, and dendritic cells. Differential expression analysis, gene ontology, and gene set enrichment analysis were employed to assess inter-cluster and intra-cluster functional heterogeneity within the ccRCC TME. Results: Our findings revealed that immune cells in the TME predominantly adopted an inflammatory suppression state, promoting tumor cell growth and immune evasion. Additionally, tumor cells exhibited higher CNV frequencies compared to normal cells. The myeloid cell subgroups demonstrated distinct functional properties, with monocytes, macrophages, and dendritic cells displaying diverse roles in the TME. Certain immune cells exhibited pro-tumor and immunosuppressive effects, while others demonstrated antitumor and immunostimulatory properties. Conclusion: This study contributes to the understanding of intratumoral heterogeneity in the ccRCC TME and provides potential therapeutic targets for ccRCC treatment. The findings emphasize the importance of considering the diverse functional roles of immune cells in the TME for effective therapeutic interventions.

Pan-cancer survey of lncRNA rewiring and functional alternation in tumor-infiltrating T cell by scLNC

Long non-coding RNAs (lncRNAs) have been reported to involve in diverse biological processes, including tumor immunity. Since lncRNAs are expressed with high cell-type specificity, investigation of lncRNAs at the single-cell level will unveil the cell-type-specific functions of lncRNAs. However, at the single-cell level, a systematic pan-cancer analysis of lncRNA functions in tumor immune microenvironments (TIMEs) remains lacking. Here, we performed pan-cancer single-cell profiling of lncRNA functions in TIMEs and developed a tool, scLNC, tailored for lncRNA functional characterization at the single-cell level. scLNC enabled the comparison of lncRNA function from the levels of lncRNA-mRNA pairs, lncRNA regulatory unit activity and unit function in a cell-type-specific manner. Applying scLNC, our analysis depicted the cross-tumor and tumor-specific lncRNA regulatory profiles in the T cell subtypes and revealed the new regulatory units that lncRNAs established in tumor-infiltrating T cells, particularly in the tumor-enriched T cells. We further characterized the activity and functional alternations of lncRNAs through their regulatory units. Overall, our findings suggested that lncRNAs played an important role in the regulation of cytokine production, cell activation and migration in tumor-enriched T cells and further in immunotherapy.

An integrative multi-omics analysis based on disulfidptosis-related prognostic signature and distinct subtypes of clear cell renal cell carcinoma

Background

The association between clear cell renal cell carcinoma (ccRCC) and disulfidoptosis remains to be thoroughly investigated.

Methods

We conducted multiple bioinformatics analyses, including prognostic analysis and cluster analysis, using R software. Additionally, we utilized Quantitative Real-time PCR to measure RNA levels of specific genes. The proliferation of ccRCC was assessed through CCK8 and colony formation assays, while the invasion and migration of ccRCC cells were evaluated using the transwell assay.

Results

In this study, utilizing data from multiple ccRCC cohorts, we identified molecules that contribute to disulfidoptosis. We conducted a comprehensive investigation into the prognostic and immunological roles of these molecules. Among the disulfidoptosis-related metabolism genes (DMGs), LRPPRC, OXSM, GYS1, and SLC7A11 exhibited significant correlations with ccRCC patient prognosis. Based on our signature, patients in different groups displayed varying levels of immune infiltration and different mutation profiles. Furthermore, we classified patients into two clusters and identified multiple functional pathways that play important roles in the occurrence and development of ccRCC. Given its critical role in disulfidoptosis, we conducted further analysis on SLC7A11. Our results demonstrated that ccRCC cells with high expression of SLC7A11 exhibited a malignant phenotype.

Conclusions

These findings enhanced our understanding of the underlying function of DMGs in ccRCC.

Comprehensive analysis of single-cell RNA-seq and bulk RNA-seq revealed the heterogeneity and convergence of the immune microenvironment in renal cell carcinoma

Substantial progress has been made in cancer biology and treatment in recent years, but the clinical outcome of patients with renal cell carcinoma (RCC) remains unsatisfactory. The tumor microenvironment (TME) is a potential target. By analyzing single-cell RNA sequencing (sc-RNAseq) data from six RCC tumor samples, this study identified 11 different cell types in the RCC cellular microenvironment, indicating a high degree of intratumoral heterogeneity. Through re-dimensionality reduction clustering of epithelial cells, neutrophils, macrophages, and T cells, we deeply reveal differences in the RCC tumor microenvironment. By analyzing differentially expressed genes in normal epithelial cells and malignant epithelial cells, we identify RNASET2 and GATM as potential prognostic biomarkers in RCC. In addition, by transcriptional factor analysis, we found significant differences in the expression of GZMK-CD8 T cell and B cell transcription factors between cancer tissues and normal tissues. By cell correlation analysis, we found significant correlations between neutrophils and macrophages and between IL7R-CD4 T cells and T regulatory (Treg) cells in RCC, which may be involved in the formation of immune TMEs. By cell developmental trajectory analysis, we showed that macrophages may be derived from neutrophils, whereas Treg cells may be derived from IL7R-CD4 T cells. By cell communication analysis, we found a clear interaction between macrophages and endothelial cells, neutrophils, and GZMK-CD8 T cells. In addition, we found that ADGRE5 signaling was mainly derived from mast cells and GZMK-CD8 T cells, and had a significant communication effect with neutrophils. The COLLAGEN signaling pathway is mainly derived from fibroblasts and has a significant communication effect with mast cells. Finally, we verified that RNASET2, which is highly expressed in epithelial cells, promotes proliferation and migration of RCC in vitro. RNASET2 is likely to be a potential target for renal cell carcinoma therapy. The results based on sc-RNAseq data analysis help to further elucidate the cellular microenvironment of RCC and provide help for cancer heterogeneity studies. This will help to provide more accurate personalized treatment for patients in clinical diagnosis.

Cell-cell communications shape tumor microenvironment and predict clinical outcomes in clear cell renal carcinoma

BACKGROUND

Cell-cell communications of various cell populations within tumor microenvironment play an essential role in primary tumor growth, metastasis evolution, and immune escape. Nevertheless, comprehensive investigation of cell-cell communications in the ccRCC (Clear cell renal carcinoma) microenvironment and how this interplay affects prognosis still remains limited.

METHODS

Intercellular communications were characterized by single-cell data. Firstly, we employed "CellChat" package to characterize intercellular communications across all types of cells in microenvironment in VHL mutated and non-mutated samples from 8 patients, respectively. And pseudotime trajectory analyses were performed with monocle analyses. Finally clinical prognosis and immunotherapy efficacy with different landscapes of intercellular interplay are evaluated by TCGA-KIRC and immunotherapy cohort.

RESULTS

Firstly, the VHL phenotype may be related to the intercellular communication landscape. And trajectory analysis reveals the potential relationship of cell-cell communication molecules with T cells and Myeloid cells differentiation. Furthermore, those molecules also correlate with the infiltration of T cells and Myeloid cells. A tumor cluster with highly expressed ligands was defined by quantitative analysis and transcription factor enrichment analysis, which was identified to be pivotal for intercellular communications in tumor microenvironment. Finally, bulk data indicates bulk that different clusters with different intercellular communications have significant predictive value for prognosis and distinguished immunotherapy efficiency.

CONCLUSIONS

The intercellular communication landscapes of VHL wild and VHL mutant ccRCC vary. Intercellular communications within the tumor microenvironment also influence T cell and myeloid cell development and infiltration, as well as predict clinical prognosis and immunotherapy efficacy in ccRCC.

Dissection of tumor antigens and immune landscape in clear cell renal cell carcinoma: Preconditions for development and precision medicine of mRNA vaccine

Accumulating evidence reveals that mRNA-type cancer vaccines could be exploited as cancer immunotherapies in various solid tumors. However, the use of mRNA-type cancer vaccines in clear cell renal cell carcinoma (ccRCC) remains unclear. This study aimed to identify potential tumor antigens for the development of an anti-ccRCC mRNA vaccine. In addition, this study aimed to determine immune subtypes of ccRCC to guide the selection of patients to receive the vaccine. Raw sequencing and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. Further, the cBioPortal website was used to visualize and compare genetic alterations. GEPIA2 was employed to evaluate the prognostic value of preliminary tumor antigens. Moreover, the TIMER web server was used to evaluate correlations between the expression of specific antigens and the abundance of infiltrated antigen-presenting cells (APCs). Single-cell RNA sequencing data of ccRCC was used to explore the expression of potential tumor antigens at single-cell resolution. The immune subtypes of patients were analyzed by the consensus clustering algorithm. Furthermore, the clinical and molecular discrepancies were further explored for a deep understanding of the immune subtypes. Weighted gene co-expression network analysis (WGCNA) was used to cluster the genes according to the immune subtypes. Finally, the sensitivity of drugs commonly used in ccRCC with diverse immune subtypes was investigated. The results revealed that the tumor antigen, LRP2, was associated with a good prognosis and enhanced the infiltration of APCs. ccRCC could be divided into two immune subtypes (IS1 and IS2) with distinct clinical and molecular characteristics. The IS1 group showed a poorer overall survival with an immune-suppressive phenotype than the IS2 group. Additionally, a large spectrum of differences in the expression of immune checkpoints and immunogenic cell death modulators were observed between the two subtypes. Lastly, the genes correlated with the immune subtypes were involved in multiple immune-related processes. Therefore, LRP2 is a potential tumor antigen that could be used to develop an mRNA-type cancer vaccine in ccRCC. Furthermore, patients in the IS2 group were more suitable for vaccination than those in the IS1 group.

Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications

The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.