Cancer biology deciphered by single-cell transcriptomic sequencing

Integrating single-cell with transcriptome-proteome Mendelian randomization reveals colorectal cancer targets

BACKGROUND

Colorectal carcinogenesis involves dynamic interactions between genetic susceptibility and cellular heterogeneity, yet current studies rarely disentangle causal genes from passive associations. While GWAS have mapped numerous risk loci, only a minority colocalize with eQTL/pQTL. A multi-omics framework combining single-cell transcriptomics, transcriptomics, proteomics, and MR is urgently needed to resolve cell-type-specific drivers of colorectal cancer pathogenesis.

METHODS

We integrated GWAS data, eQTL data, pQTL data, and single-cell RNA sequencing differential gene expression profiles from public databases. Subsequent batch Two-sample Mendelian randomization and further SMR analysis aimed to identify key genes in the pathogenesis of colorectal cancer.

RESULTS

Cluster analysis identified 4909 DEGs across various cell types. We discovered that 428 DEGs had a causal association with colorectal cancer through eQTL, of which 38 genes met the FDR statistical standards, and four of these genes (CTSF, PCSK7, LYZ, LMAN2L) also had causal associations through pQTL. SMR analysis confirmed the reliability of PCSK7 as a disease target.

CONCLUSION

By integrating single-cell data, transcriptomic data, proteomic data and GWAS data for MR analysis, we identified CTSF, PCSK7, LYZ, LMAN2L as potential targets for colorectal cancer.

Inflammatory Cell Interactions in the Rotator Cuff Microenvironment: Insights From Single-Cell Sequencing

Rotator cuff injuries are a common cause of shoulder pain and dysfunction, with chronic inflammation complicating recovery. Recent advances in single-cell RNA sequencing (scRNA-seq) have provided new insights into the immune cell interactions within the rotator cuff microenvironment during injury and healing. This review focuses on the application of scRNA-seq to explore the roles of immune and nonimmune cells, including macrophages, T-cells, fibroblasts, and myofibroblasts, in driving inflammation, tissue repair, and fibrosis. We discuss how immune cell crosstalk and interactions with the extracellular matrix influence the progression of healing or pathology. Single-cell analyses have identified distinct molecular signatures associated with chronic inflammation, which may contribute to persistent tissue damage. Additionally, we highlight the therapeutic potential of targeting inflammation in rotator cuff repair, emphasizing personalized medicine approaches. Overall, the integration of scRNA-seq in studying rotator cuff injuries enhances our understanding of the cellular mechanisms involved and offers new perspectives for developing targeted treatments in regenerative medicine.

Integrating single-cell sequencing and clinical insights to explore malignant transformation in odontogenic keratocyst

The malignant transformation of odontogenic keratocysts (OKC) into cancerous odontogenic keratocysts (COKC) is exceedingly rare, and its mechanisms remain poorly understood. Studies exploring the cellular heterogeneity, molecular pathways, and clinical features of COKC are limited. In this study, we performed single-cell RNA sequencing (scRNA-seq) on three COKC samples and integrated the data with a public OKC dataset, identifying 22,509 single cells. Two COKC-specific epithelial subpopulations, Basal-C0-EXT1 and Basal-C3-HIST1H3B, were identified. These subpopulations exhibited enhanced stemness and invasive potential, respectively, suggesting their roles as key drivers of OKC carcinogenesis. Fibroblasts underwent phenotypic transitions, particularly from inflammation-associated fibroblasts (IFBs) to myofibroblasts (MFBs). Similarly, macrophage phenotypic transformation may also play a role in OKC carcinogenesis. Clinical observations of severe lesion-area pain in COKC patients suggest potential neuroinvasiveness, Supported by single-cell transcriptomic data, imaging findings, and histopathological evidence. A review of clinical data revealed that none of the COKC patients exhibited cervical lymph node metastasis. Single-cell transcriptomics suggests that this phenomenon may be associated with an active immune microenvironment in COKC, reduced epithelial-mesenchymal transition (EMT) activity, lower VEGFC expression, and upregulated MAST4 expression as a potential regulator of lymphatic metastasis. In conclusion, COKC exhibits distinct molecular, cellular, and clinical characteristics compared to OKC, featuring potent neuroinvasiveness and low lymph node metastatic potential. These findings provide important insights into the mechanisms underlying COKC development and may guide novel diagnostic and therapeutic strategies.

Heterogeneous cellular responses to hyperthermia support combined intraperitoneal hyperthermic immunotherapy for ovarian cancer mouse models

The benefit of hyperthermic intraperitoneal chemotherapy (HIPEC) in ovarian cancer remains controversial, hindering the development of rational combination therapies based on hyperthermia (HT). This study reports the preliminary results of the neoadjuvant HIPEC (NHIPEC) trial (ChiCTR2000038173), demonstrating enhanced tumor response in high-grade serous ovarian cancer with NHIPEC. Through single-cell RNA sequencing analysis, we identified both homogeneous and heterogeneous cellular responses to HT within the tumor and microenvironment. Epithelial-mesenchymal transition-activated tumor cells and matrix metallopeptidase 11 (MMP-11)+ cancer-associated fibroblasts (CAFs) exhibited greater reductions and higher sensitivity to HT. CUT&Tag and RNA sequencing integration unveiled the differential binding programs and transcriptional regulatory mechanisms of HSF1 under normothermia (NT) and HT in tumor cells and CAFs. Furthermore, HT ameliorated the immunosuppressive tumor microenvironment, and in vivo mouse models confirmed the combined antitumor effects of HT and programmed cell death ligand 1 blockade. These findings provide an innovative strategy for rational combination therapy with HT in ovarian cancer.

Morphomolecular Pathology and Genomic Insights into the Cells of Origin of Cholangiocarcinoma and Combined Hepatocellular-Cholangiocarcinoma

Cholangiocarcinomas are a highly heterogeneous group of malignancies that, despite recent progress in the understanding of their molecular pathogenesis and clinical management, continue to pose a major challenge to public health. The traditional view posits that cholangiocarcinomas derive from the neoplastic transformation of cholangiocytes lining the biliary tree. However, increasing genetic and experimental evidence has recently pointed to a more complex, and nuanced, scenario for the potential cell of origin of cholangiocarcinomas. Hepatocytes as well as hepatic stem/progenitor cells are being considered as additional potential sources, depending on microenvironmental contexts, including liver injury. The hypothesis of potentially diverse cells of origin for cholangiocarcinoma, albeit controversial, is certainly not surprising given the plasticity of the cells populating the liver as well as the existence of liver cancer subtypes with mixed histologic and molecular features. This review carefully examines the current pathologic, genomic, and experimental evidence supporting the existence of multiple cells of origin of liver and biliary tract cancers, with particular focus on cholangiocarcinoma and combined hepatocellular-cholangiocarcinoma.

Differential expression of the MYC-Notch axis drives divergent responses to the front-line therapy in central and peripheral extensive-stage small-cell lung cancer

Central and peripheral extensive-stage small-cell lung cancer (ES-SCLC) are reported to be two distinct tumor entities, but their responses to the front-line therapies and underlying biological mechanisms remain elusive. In this study, we first compared the outcomes of central and peripheral ES-SCLC receiving front-line chemotherapy or chemo-immunotherapy with a cohort of 265 patients. Then we performed single-cell RNA sequencing (scRNA-seq) on nine treatment-naïve ES-SCLC samples to investigate potential mechanisms underlying the response differences. Under chemotherapy, the peripheral type had a lower objective response rate (44.8% vs. 71.2%, p = 0.008) and shorter progression-free survival (median 3.4 vs. 5.1 months, p = 0.001) than the central type. When comparing chemo-immunotherapy with chemotherapy, the peripheral type showed a greater potential to reduce progression (HR, 0.18 and 0.52, respectively) and death (HR, 0.44 and 0.91 respectively) risks than the central type. Concerning the scRNA-seq data, the peripheral type was associated with chemo-resistant and immune-responsive tumoral and microenvironmental features, including a higher expression level of MYC-Notch-non-neuroendocrine (MYC-Notch-non-NE) axis and a more potent antigen presentation and immune activation status. Our results revealed that central and peripheral ES-SCLC had distinct responses to front-line treatments, potentially due to differential activation statuses of the MYC-Notch-non-NE axis.

Advancements in pathology: Digital transformation, precision medicine, and beyond

Pathology, a cornerstone of medical diagnostics and research, is undergoing a revolutionary transformation fueled by digital technology, molecular biology advancements, and big data analytics. Digital pathology converts conventional glass slides into high-resolution digital images, enhancing collaboration and efficiency among pathologists worldwide. Integrating artificial intelligence (AI) and machine learning (ML) algorithms with digital pathology improves diagnostic accuracy, particularly in complex diseases like cancer. Molecular pathology, facilitated by next-generation sequencing (NGS), provides comprehensive genomic, transcriptomic, and proteomic insights into disease mechanisms, guiding personalized therapies. Immunohistochemistry (IHC) plays a pivotal role in biomarker discovery, refining disease classification and prognostication. Precision medicine integrates pathology's molecular findings with individual genetic, environmental, and lifestyle factors to customize treatment strategies, optimizing patient outcomes. Telepathology extends diagnostic services to underserved areas through remote digital pathology. Pathomics leverages big data analytics to extract meaningful insights from pathology images, advancing our understanding of disease pathology and therapeutic targets. Virtual autopsies employ non-invasive imaging technologies to revolutionize forensic pathology. These innovations promise earlier diagnoses, tailored treatments, and enhanced patient care. Collaboration across disciplines is essential to fully realize the transformative potential of these advancements in medical practice and research.

Spatial transcriptomics reveals unique metabolic profile and key oncogenic regulators of cervical squamous cell carcinoma

BACKGROUND

As a prevalent and deadly malignant tumor, the treatment outcomes for late-stage patients with cervical squamous cell carcinoma (CSCC) are often suboptimal. Previous studies have shown that tumor progression is closely related with tumor metabolism and microenvironment reshaping, with disruptions in energy metabolism playing a critical role in this process. To delve deeper into the understanding of CSCC development, our research focused on analyzing the tumor microenvironment and metabolic characteristics across different regions of tumor tissue.

METHODS

Utilizing spatial transcriptomics (ST) sequencing technology, we conducted a study on FFPE (formalin-fixed paraffin-embedded) tumor samples from CSCC patients. Coupled with single-cell RNA sequencing (scRNA-seq) data after deconvolution, we described spatial distribution maps of tumor leading edge and core regions in detail. Tumor tissues were classified into hypermetabolic and hypometabolic regions to analyze the metabolism profiles and tumor differentiation degree across different spatial areas. We also employed The Cancer Genome Atlas (TCGA) database to examine the analysis results of ST data.

RESULTS

Our findings indicated a more complex tumor microenvironment in hypermetabolic regions. Cell-cell communication analysis showed that various cells in tumor microenvironment were influenced by the signalling molecule APP released by cancer cells and higher expression of APP was observed in hypermetabolic regions. Furthermore, our results revealed the correlation between APP and the transcription factor TRPS1. Both APP and TRPS1 demonstrated significant effects on cancer cell proliferation, migration, and invasion, potentially contributing to tumor progression.

CONCLUSIONS

Utilizing ST, scRNA-seq, and TCGA database, we examined the spatial metabolic profiles of CSCC tissues, including metabolism distribution, metabolic variations, and the relationship between metabolism and tumor differentiation degree. Additionally, potential cancer-promoting factors were proposed, offering a valuable foundation for the development of more effective treatment strategies for CSCC.

Comprehensive analysis of single-cell and bulk RNA sequencing reveals postoperative progression markers for non-muscle invasive bladder cancer and predicts responses to immunotherapy

BACKGROUND

Non-muscle-invasive bladder cancer (NMIBC) is renowned for its high recurrence, invasiveness, and poor prognosis. Consequently, developing new biomarkers for risk assessment and investigating innovative therapeutic targets postoperative in NMIBC patients are crucial to aid in treatment planning.

APPROACHES

Differential gene expression analysis was performed using multiple Gene Expression Omnibus (GEO) datasets to identify differentially expressed genes (DEGs) between NMIBC and normal tissue, as well as between NMIBC and muscle-invasive bladder cancer (MIBC). Functional enrichment analysis was conducted based on the DEGs identified. Subsequently, prognosis-related genes were selected using Kaplan-Meier (KM) analysis and Cox regression analysis. The Boruta algorithm was utilized to further screen for core DEGs related to postoperative progression in NMIBC based on the aforementioned prognosis-related genes. Single-cell and clinical correlation studies were performed to verify their expression across various stages of bladder cancer. To investigate the link between core genes and the immune microenvironment, single-sample gene set enrichment analysis (ssGSEA) was utilized, and Receiver Operating Characteristic (ROC) and KM analyses were performed to confirm predictive power for immune therapy outcomes. Machine learning (ML) models were constructed using the DepMap dataset to predict the efficacy of core gene inhibitors in treating bladder cancers. The prognostic performance of the core genes was evaluated using ROC curve analysis. An online prediction tool was developed based on the core genes to provide prognostic predictions. Finally, RT-qPCR, CCK-8, and Transwell assays were used to verify the pro-tumor effects of the GINS2 in bladder cancer.

RESULTS

A total of 70 DEGs were identified, among which 11 prognostic genes were obtained through KM analysis, and an additional 8 prognostic genes were obtained through COX analysis. The Boruta algorithm selected AURKB, GINS2, and UHRF1 as the three core DEGs. Single-cell and clinical variable correlation analyses indicated that the core genes promoted the progression of bladder cancer. The analysis of immune infiltration revealed a strong positive association between the core genes and both activated CD4 T cells and Type 2 helper T cells. Two random forest (RF) models were constructed to effectively predict the treatment effect of bladder cancer after targeted inhibition of AURKB and GINS2. In addition, an online nomogram tool was developed to effectively predict the risk of postoperative progression in NMIBC patients undergoing TURBT. Finally, RT-qPCR, CCK8, and Transwell assays showed that GINS2 promoted the growth and progression of bladder cancer.

CONCLUSION

AURKB, GINS2, and UHRF1 have the potential to enhance postoperative management of NMIBC patients undergoing transurethral resection of bladder tumor (TURBT) and can predict immunotherapy response, establishing them as promising therapeutic targets.

The role of NOP58 in prostate cancer progression through SUMOylation regulation and drug response

Background

Prostate cancer is one of the leading causes of cancer-related deaths in men. Its molecular pathogenesis is closely linked to various genetic and epigenetic alterations, including posttranslational modifications like SUMOylation. Identifying biomarkers that predict outcomes and specific therapeutic targets depends on a comprehensive understanding of these processes. With growing interest in SUMOylation as a mechanism affecting prostate cancer-related genes, this study aimed to investigate the central role of SUMOylation in prostate cancer prognostics, focusing on the significance of NOP58.

Methods

We conducted a comprehensive bioinformatics analysis, integrating differential expression analysis, survival analysis, gene set enrichment analysis (GSEA), and single-cell transcriptomic analyses using data from The Cancer Genome Atlas (TCGA). Key genes were identified through intersections of Venn diagrams, Boralta algorithm signatures, and machine learning models. These signaling mechanisms were validated through experimental studies, including immunohistochemical staining and gene ontology analyses.

Results

The dual-gene molecular subtype analysis with SUMO1, SUMO2, and XPO1 genes revealed significant differences in survival outcomes across molecular subtypes, further emphasizing the potential impact of NOP58 on SUMOylation, a key post-translational modification, in prostate cancer. NOP58 overexpression was strongly associated with shorter overall survival (OS), progression-free interval (PFI), and disease-specific death in prostate cancer patients. Immunohistochemical analysis confirmed that NOP58 was significantly overexpressed in prostate cancer tissues compared to normal tissues. ROC curve analysis demonstrated that NOP58 could distinguish prostate cancer from control samples with high diagnostic accuracy. Gene Ontology analysis, along with GSVA and GSEA, suggested that NOP58 may be involved in cell cycle regulation and DNA repair pathways. Moreover, NOP58 knockdown led to increased BCL2 expression and decreased Ki67 levels, promoting apoptosis and inhibiting cell proliferation. Colony formation assays further showed that NOP58 knockdown inhibited, while its overexpression promoted, colony formation, highlighting the critical role of NOP58 in prostate cancer cell growth and survival. Additionally, NOP58 was linked to drug responses, including Methotrexate, Rapamycin, Sorafenib, and Vorinostat.

Conclusion

NOP58 is a key regulator of prostate cancer progression through its mediation of the SUMOylation pathway. Its expression level serves as a reliable prognostic biomarker and an actionable therapeutic target, advancing precision medicine for prostate cancer. Targeting NOP58 may enhance therapeutic efficacy and improve outcomes in oncology.

KNSTRN Is a Prognostic Biomarker That Is Correlated with Immune Infiltration in Breast Cancer and Promotes Cell Cycle and Proliferation

Kinetochore-localized astrin/SPAG5-binding protein (KNSTRN) promotes the progression of bladder cancer and lung adenocarcinoma. However, its expression and biological function in breast cancer remain largely unknown. Therefore, this study aimed to analyze KNSTRN expression, prognoses, correlation with immune infiltration, expression-associated genes, and regulated signaling pathways to characterize its role in regulating the cell cycle using both bioinformatics and in vitro functional experiments. Analyses of The Cancer Genome Atlas, Gene Expression Omnibus, TIMER, and The Human Protein Atlas databases revealed a significant upregulation of KNSTRN transcript and protein levels in breast cancer. Kaplan-Meier survival analyses demonstrated a significant association between high expression of KNSTRN and poor overall survival, relapse-free survival, post-progression survival, and distant metastases-free survival in patients with breast cancer. Furthermore, multivariate Cox regression analyses confirmed that KNSTRN is an independent prognostic factor for breast cancer. Immune infiltration analysis indicated a positive correlation between KNSTRN expression and T regulatory cell infiltration while showing a negative correlation with Tgd and natural killer cell infiltration. Gene set enrichment analysis along with single-cell transcriptome data analysis suggested that KNSTRN promoted cell cycle progression by regulating the expression of key cell cycle proteins. The overexpression and silencing of KNSTRN in vitro, respectively, promoted and inhibited the proliferation of breast cancer cells. The overexpression of KNSTRN enhanced the expression of key cell cycle regulators, including CDK4, CDK6, and cyclin D3, thereby accelerating the G1/S phase transition and leading to aberrant proliferation of breast cancer cells. In conclusion, our study demonstrates that KNSTRN functions as an oncogene in breast cancer by regulating immune response, promoting G1/S transition, and facilitating breast cancer cell proliferation. Moreover, KNSTRN has potential as a molecular biomarker for diagnostic and prognostic prediction in breast cancer.

Prognostic aging gene-based score for colorectal cancer: unveiling links to drug resistance, mutation burden, and personalized treatment strategies

OBJECTIVE

Colorectal cancer (CRC) is characterized by high incidence and mortality rates worldwide. In this study, we present a novel aging-related gene-based risk scoring system (Aging score) as a predictive tool for CRC prognosis.

METHOD

We identified prognostic aging-related genes using univariate Cox regression analysis, revealing key biological processes in CRC progression. We then constructed a robust prognostic model using LASSO and multivariate Cox regression analyses, including four critical genes: CAV1, FOXM1, MAD2L1, and WT1.

RESULT

The Aging score demonstrated high prognostic performance across the training, testing, and entire TCGA-CRC datasets, proving its reliability. High-risk patients identified by the Aging score had significantly shorter overall survival times than low-risk patients, indicating its potential for patient stratification and personalized treatment. The Aging score remained an independent prognostic factor compared to age, gender, and tumor stage. Additionally, the score was linked to tumor mutation burden and microsatellite instability, indicators of immune checkpoint inhibitor response. High-risk patients also showed higher estimated IC50 values for common chemotherapeutic drugs, suggesting possible treatment resistance.

CONCLUSION

Our findings highlight the Aging score's potential to enhance clinical decision-making and pave the way for personalized CRC management.

Study on the mechanism of heterogeneous tumor-associated macrophages in three subtypes of breast cancer through the integration of single-cell RNA sequencing and in vitro experiments

BACKGROUND

Tumor-associated macrophages (TAM) exert a significant influence on the progression and heterogeneity of various subtypes of breast cancer (BRCA). However, the roles of heterogeneous TAM within BRCA subtypes remain unclear. Therefore, this study sought to elucidate the role of TAM across the following three BRCA subtypes: triple-negative breast cancer, luminal, and HER2.

MATERIALS AND METHODS

This investigation aimed to delineate the variations in marker genes, drug sensitivity, and cellular communication among TAM across the three BRCA subtypes. We identified specific ligand-receptor (L-R) pairs and downstream mechanisms regulated by VEGFA-VEGFR1, SPP1-CD44, and SPP1-ITGB1 L-R pairs. Experimental verification of these pairs was conducted by co-culturing macrophages with three subtypes of BRCA cells.

RESULTS

Our findings reveal the heterogeneity of macrophages within the three BRCA subtypes, evidenced by variations in marker gene expression, composition, and functional characteristics. Notably, heterogeneous TAM were found to promote invasive migration and epithelial-mesenchymal transition (EMT) in MDA-MB-231, MCF-7, and SKBR3 cells, activating NF-κB pathway via P38 MAPK, TGF-β1, and AKT, respectively, through distinct VEGFA-VEGFR1, SPP1-CD44, and SPP1-ITGB1 L-R pairs. Inhibition of these specific L-R pairs effectively reversed EMT, migration, and invasion of each cancer cells. Furthermore, we observed a correlation between ligand gene expression and TAM sensitivity to anticancer drugs, suggesting a potential strategy for optimizing personalized treatment guidance.

CONCLUSION

Our study highlights the capacity of heterogeneous TAM to modulate biological functions via distinct pathways mediated by specific L-R pairs within diverse BRCA subtypes. This study might provide insights into precision immunotherapy of different subtypes of BRCA.

CXCR6-positive circulating mucosal-associated invariant T cells can identify patients with non-small cell lung cancer responding to anti-PD-1 immunotherapy

BACKGROUND

Mucosal-associated invariant T (MAIT) cells have been reported to regulate tumor immunity. However, the immune characteristics of MAIT cells in non-small cell lung cancer (NSCLC) and their correlation with the treatment efficacy of immune checkpoint inhibitors (ICIs) remain unclear.

PATIENTS AND METHODS

In this study, we performed single-cell RNA sequencing (scRNA-seq), flow cytometry, and multiplex immunofluorescence assays to determine the proportion and characteristics of CD8+MAIT cells in patients with metastatic NSCLC who did and did not respond to anti-PD-1 therapy. Survival analyses were employed to determine the effects of MAIT proportion and C-X-C chemokine receptor 6 (CXCR6) expression on the prognosis of patients with advanced NSCLC.

RESULTS

The proportion of activated and proliferating CD8+MAIT cells were significantly higher in responders-derived peripheral blood mononuclear cells (PBMCs) and lung tissues before anti-PD-1 therapy, with enhanced expression of cytotoxicity-related genes including CCL4, KLRG1, PRF1, NCR3, NKG7, GZMB, and KLRK1. The responders' peripheral and tumor-infiltrating CD8+MAIT cells showed an upregulated CXCR6 expression. Similarly, CXCR6+CD8+MAIT cells from responders showed higher expression of cytotoxicity-related genes, such as CST7, GNLY, KLRG1, NKG7, and PRF1. Patients with ≥15.1% CD8+MAIT cells to CD8+T cells ratio and ≥35.9% CXCR6+CD8+MAIT cells to CD8+MAIT cells ratio in peripheral blood showed better progression-free survival (PFS) after immunotherapy. The role of CD8+MAIT cells in lung cancer immunotherapy was potentially mediated by classical/non-classical monocytes through the CXCL16-CXCR6 axis.

CONCLUSION

CD8+MAIT cells are a potential predictive biomarker for patients with NSCLC responding to anti-PD-1 therapy. The correlation between CD8+MAIT cells and immunotherapy sensitivity may be ascribed to high CXCR6 expression.

GTADC: A Graph-Based Method for Inferring Cell Spatial Distribution in Cancer Tissues

The heterogeneity of tumors poses a challenge for understanding cell interactions and constructing complex ecosystems within cancer tissues. Current research strategies integrate spatial transcriptomics (ST) and single-cell sequencing (scRNA-seq) data to thoroughly analyze this intricate system. However, traditional deep learning methods using scRNA-seq data tend to filter differentially expressed genes through statistical methods. In the context of cancer tissues, where cancer cells exhibit significant differences in gene expression compared to normal cells, this heterogeneity renders traditional analysis methods incapable of accurately capturing differences between cell types. Therefore, we propose a graph-based deep learning method, GTADC, which utilizes Silhouette scores to precisely capture genes with significant expression differences within each cell type, enhancing the accuracy of gene selection. Compared to traditional methods, GTADC not only considers the expression similarity of genes within their respective clusters but also comprehensively leverages information from the overall clustering structure. The introduction of graph structure effectively captures spatial relationships and topological structures between the two types of data, enabling GTADC to more accurately and comprehensively resolve the spatial composition of different cell types within tissues. This refinement allows GTADC to intricately reconstruct the cellular spatial composition, offering a precise solution for inferring cell spatial composition. This method allows for early detection of potential cancer cell regions within tissues, assessing their quantity and spatial information in cell populations. We aim to achieve a preliminary estimation of cancer occurrence and development, contributing to a deeper understanding of early-stage cancer and providing potential support for early cancer diagnosis.

Histone 3 lysine 9 acetylation-specific reprogramming regulates esophageal squamous cell carcinoma progression and metastasis

Dysregulation of histone acetylation is widely implicated in tumorigenesis, yet its specific roles in the progression and metastasis of esophageal squamous cell carcinoma (ESCC) remain unclear. Here, we profiled the genome-wide landscapes of H3K9ac for paired adjacent normal (Nor), primary ESCC (EC) and metastatic lymph node (LNC) esophageal tissues from three ESCC patients. Compared to H3K27ac, we identified a distinct epigenetic reprogramming specific to H3K9ac in EC and LNC samples relative to Nor samples. This H3K9ac-related reprogramming contributed to the transcriptomic aberration of targeting genes, which were functionally associated with tumorigenesis and metastasis. Notably, genes with gained H3K9ac signals in both primary and metastatic lymph node samples (common-gained gene) were significantly enriched in oncogenes. Single-cell RNA-seq analysis further revealed that the corresponding top 15 common-gained genes preferred to be enriched in mesenchymal cells with high metastatic potential. Additionally, in vitro experiment demonstrated that the removal of H3K9ac from the common-gained gene MSI1 significantly downregulated its transcription, resulting in deficiencies in ESCC cell proliferation and migration. Together, our findings revealed the distinct characteristics of H3K9ac in esophageal squamous cell carcinogenesis and metastasis, and highlighted the potential therapeutic avenue for intervening ESCC through epigenetic modulation via H3K9ac.

scMGCN: A Multi-View Graph Convolutional Network for Cell Type Identification in scRNA-seq Data

Single-cell RNA sequencing (scRNA-seq) data reveal the complexity and diversity of cellular ecosystems and molecular interactions in various biomedical research. Hence, identifying cell types from large-scale scRNA-seq data using existing annotations is challenging and requires stable and interpretable methods. However, the current cell type identification methods have limited performance, mainly due to the intrinsic heterogeneity among cell populations and extrinsic differences between datasets. Here, we present a robust graph artificial intelligence model, a multi-view graph convolutional network model (scMGCN) that integrates multiple graph structures from raw scRNA-seq data and applies graph convolutional networks with attention mechanisms to learn cell embeddings and predict cell labels. We evaluate our model on single-dataset, cross-species, and cross-platform experiments and compare it with other state-of-the-art methods. Our results show that scMGCN outperforms the other methods regarding stability, accuracy, and robustness to batch effects. Our main contributions are as follows: Firstly, we introduce multi-view learning and multiple graph construction methods to capture comprehensive cellular information from scRNA-seq data. Secondly, we construct a scMGCN that combines graph convolutional networks with attention mechanisms to extract shared, high-order information from cells. Finally, we demonstrate the effectiveness and superiority of the scMGCN on various datasets.

Single-cell RNA-sequencing atlas reveals an FABP1-dependent immunosuppressive environment in hepatocellular carcinoma

BACKGROUND

Single-cell RNA sequencing, also known as scRNA-seq, is a method profiling cell populations on an individual cell basis. It is particularly useful for more deeply understanding cell behavior in a complicated tumor microenvironment. Although several previous studies have examined scRNA-seq for hepatocellular carcinoma (HCC) tissues, no one has tested and analyzed HCC with different stages.

METHODS

In this investigation, immune cells isolated from surrounding normal tissues and cancer tissues from 3 II-stage and 4 III-stage HCC cases were subjected to deep scRNA-seq. The analysis included 15 samples. We distinguished developmentally relevant trajectories, unique immune cell subtypes, and enriched pathways regarding differential genes. Western blot and co-immunoprecipitation were performed to demonstrate the interaction between fatty acid binding protein 1 (FABP1) and peroxisome proliferator-activated receptor gamma(PPARG). In vivo experiments were performed in a C57BL/6 mouse model of HCC established via subcutaneous injection.

RESULTS

FABP1 was discovered to be overexpressed in tumor-associated macrophages (TAMs) with III-stage HCC tissues compared with II-stage HCC tissues. This finding was fully supported by immunofluorescence detection in significant amounts of HCC human samples. FABP1 deficiency in TAMs inhibited HCC progression in vitro. Mechanistically, FABP1 interacted with PPARG/CD36 in TAMs to increase fatty acid oxidation in HCC. When compared with C57BL/6 mice of the wild type, tumors in FABP1-/- mice consistently showed attenuation. The FABP1-/- group's relative proportion of regulatory T cells and natural killer cells showed a downward trend, while dendritic cells, M1 macrophages, and B cells showed an upward trend, according to the results of mass cytometry. In further clinical translation, we found that orlistat significantly inhibited FABP1 activity, while the combination of anti-programmed cell death 1(PD-1) could synergistically treat HCC progression. Liposomes loaded with orlistat and connected with IR780 probe could further enhance the therapeutic effect of orlistat and visualize drug metabolism in vivo.

CONCLUSIONS

ScRNA-seq atlas revealed an FABP1-dependent immunosuppressive environment in HCC. Orlistat significantly inhibited FABP1 activity, while the combination of anti-PD-1 could synergistically treat HCC progression. This study identified new treatment targets and strategies for HCC progression, contributing to patients with advanced HCC from new perspectives.

Revolutionizing cancer immunotherapy in solid tumor: CAR engineering and single-cell sequencing insights

The global increase in cancer incidence presents significant economic and societal challenges. While chimeric antigen receptor-modified T cell (CAR-T) therapy has demonstrated remarkable success in hematologic malignancies and has earned FDA approval, its translation to solid tumors encounters faces significant obstacles, primarily centered around identifying reliable tumor-associated antigens and navigating the complexities of the tumor microenvironment. Recent developments in single-cell RNA sequencing (scRNA-seq) have greatly enhanced our understanding of tumors by offering high-resolution, unbiased analysis of cellular heterogeneity and molecular patterns. These technologies have revolutionized our comprehension of tumor immunology and have led to notable progress in cancer immunotherapy. This mini-review explores the progress of chimeric antigen receptor (CAR) cell therapy in solid tumor treatment and the application of scRNA-seq at various stages following the administration of CAR cell products into the body. The advantages of scRNA-seq are poised to further advance the investigation of the biological characteristics of CAR cells in vivo, tumor immune evasion, the impact of different cellular components on clinical efficacy, the development of clinically relevant biomarkers, and the creation of new targeted drugs and combination therapy approaches. The integration of scRNA-seq with CAR therapy represents a promising avenue for future innovations in cancer immunotherapy. This synergy holds the potential to enhance the precision and efficacy of CAR cell therapies while expanding their applications to a broader range of malignancies.

Pan-cancer spatially resolved single-cell analysis reveals the crosstalk between cancer-associated fibroblasts and tumor microenvironment

Cancer-associated fibroblasts (CAFs) are a heterogeneous cell population that plays a crucial role in remodeling the tumor microenvironment (TME). Here, through the integrated analysis of spatial and single-cell transcriptomics data across six common cancer types, we identified four distinct functional subgroups of CAFs and described their spatial distribution characteristics. Additionally, the analysis of single-cell RNA sequencing (scRNA-seq) data from three additional common cancer types and two newly generated scRNA-seq datasets of rare cancer types, namely epithelial-myoepithelial carcinoma (EMC) and mucoepidermoid carcinoma (MEC), expanded our understanding of CAF heterogeneity. Cell-cell interaction analysis conducted within the spatial context highlighted the pivotal roles of matrix CAFs (mCAFs) in tumor angiogenesis and inflammatory CAFs (iCAFs) in shaping the immunosuppressive microenvironment. In patients with breast cancer (BRCA) undergoing anti-PD-1 immunotherapy, iCAFs demonstrated heightened capacity in facilitating cancer cell proliferation, promoting epithelial-mesenchymal transition (EMT), and contributing to the establishment of an immunosuppressive microenvironment. Furthermore, a scoring system based on iCAFs showed a significant correlation with immune therapy response in melanoma patients. Lastly, we provided a web interface ( https://chenxisd.shinyapps.io/pancaf/ ) for the research community to investigate CAFs in the context of pan-cancer.

Single-cell dissection of tumor microenvironmental response and resistance to cancer therapy

Cancer treatment strategies have evolved significantly over the years, with chemotherapy, targeted therapy, and immunotherapy as major pillars. Each modality leads to unique treatment outcomes by interacting with the tumor microenvironment (TME), which imposes a fundamental selective pressure on cancer progression. The advent of single-cell profiling technologies has revolutionized our understanding of the intricate and heterogeneous nature of the TME at an unprecedented resolution. This review delves into the commonalities and differential manifestations of how cancer therapies reshape the microenvironment in diverse cancer types. We highlight how groundbreaking immune checkpoint blockade (ICB) strategies alone or in combination with tumor-targeting treatments are endowed with comprehensive mechanistic insights when decoded at the single-cell level, aiming to drive forward future research directions on personalized treatments.

Single neurons on microelectrode array chip: manipulation and analyses

Chips-based platforms intended for single-cell manipulation are considered powerful tools to analyze intercellular interactions and cellular functions. Although the conventional cell co-culture models could investigate cell communication to some extent, the role of a single cell requires further analysis. In this study, a precise intercellular interaction model was built using a microelectrode array [microelectrode array (MEA)]-based and dielectrophoresis-driven single-cell manipulation chip. The integrated platform enabled precise manipulation of single cells, which were either trapped on or transferred between electrodes. Each electrode was controlled independently to record the corresponding cellular electrophysiology. Multiple parameters were explored to investigate their effects on cell manipulation including the diameter and depth of microwells, the geometry of cells, and the voltage amplitude of the control signal. Under the optimized microenvironment, the chip was further evaluated using 293T and neural cells to investigate the influence of electric field on cells. An examination of the inappropriate use of electric fields on cells revealed the occurrence of oncosis. In the end of the study, electrophysiology of single neurons and network of neurons, both differentiated from human induced pluripotent stem cells (iPSC), was recorded and compared to demonstrate the functionality of the chip. The obtained preliminary results extended the nature growing model to the controllable level, satisfying the expectation of introducing more elaborated intercellular interaction models.

Identification of stromal cell proportion-related genes in the breast cancer tumor microenvironment using CorDelSFS feature selection: implications for tumor progression and prognosis

Background: The tumor microenvironment (TME) of breast cancer (BRCA) is a complex and dynamic micro-ecosystem that influences BRCA occurrence, progression, and prognosis through its cellular and molecular components. However, as the tumor progresses, the dynamic changes of stromal and immune cells in TME become unclear. Objective: The aim of this study was to identify differentially co-expressed genes (DCGs) associated with the proportion of stromal cells in TME of BRCA, to explore the patterns of cell proportion changes, and ultimately, their impact on prognosis. Methods: A new heuristic feature selection strategy (CorDelSFS) was combined with differential co-expression analysis to identify TME-key DCGs. The expression pattern and co-expression network of TME-key DCGs were analyzed across different TMEs. A prognostic model was constructed using six TME-key DCGs, and the correlation between the risk score and the proportion of stromal cells and immune cells in TME was evaluated. Results: TME-key DCGs mimicked the dynamic trend of BRCA TME and formed cell type-specific subnetworks. The IG gene-related subnetwork, plasmablast-specific expression, played a vital role in the BRCA TME through its adaptive immune function and tumor progression inhibition. The prognostic model showed that the risk score was significantly correlated with the proportion of stromal cells and immune cells in TME, and low-risk patients had stronger adaptive immune function. IGKV1D-39 was identified as a novel BRCA prognostic marker specifically expressed in plasmablasts and involved in adaptive immune responses. Conclusions: This study explores the role of proportionate-related genes in the tumor microenvironment using a machine learning approach and provides new insights for discovering the key biological processes in tumor progression and clinical prognosis.

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.

Cancer-associated fibroblasts-derived FMO2 as a biomarker of macrophage infiltration and prognosis in epithelial ovarian cancer

OBJECTIVE

Recent molecular profiling revealed that cancer-associated fibroblasts (CAFs) are essential for matrix remodeling and tumor progression. Our study aimed to investigate the role of flavin-containing monooxygenase 2 (FMO2) in epithelial ovarian cancer (EOC) as a novel CAF-derived prognostic biomarker.

METHODS

Primary fibroblasts were isolated from EOC samples. Microdissection and single-cell RNA sequencing (scRNA-seq) datasets (including TCGA, GSE9891, GSE63885, GSE118828 and GSE178913) were retrieved to determine the expression profiles. Gene set enrichment analysis (GSEA) was used to explore the correlation between FMO2 and stromal activation as well as immune infiltration. The predictive value of FMO2 and combined macrophage infiltration level was verified in an independent EOC cohort (n = 113).

RESULTS

We demonstrated that FMO2 was upregulated in tumor stroma and correlated with fibroblast activation. Besides, FMO2 had the predictive power for worse clinical outcome of EOC patients. In the mesenchymal subtype of EOC, the FMO2-defined signature revealed that FMO2 contributed to infiltration of tumor-infiltrating lymphocytes. Moreover, we confirmed the positive correlation between FMO2 and CD163+ cell infiltration level in EOC tissues, and showed that combination of FMO2 expression with CD163+ cell infiltration level in the tumor stroma could predict poor overall survival (HR = 3.63, 95% CI = 1.93-6.84, p = 0.0008). Additionally, FMO2 also predicted the prognosis of patients with ovarian cancer based on the expression of immune checkpoints (such as PD-L1 and PD1).

CONCLUSION

Our results address the tumor-supporting role of FMO2 in EOC and its association with immune components, and it might be a prospective target for stroma-oriented therapies against EOC.

Pan-Cancer Analysis on the Oncogenic Role of Programmed Cell Death 10

Purpose

Programmed cell death factor 10 (PDCD10) is associated with intercellular junction, cytoskeleton organization, cell proliferation, apoptosis, exocytosis, and angiogenesis. However, the role of PDCD10 in human cancer is unclear. This study aims to explore the role of PDCD10 in various tumors and its possible mechanism through bioinformatics analysis.

Methods

We verified the expression of the PDCD10 gene based on data from the ONCOMINE, TIMER2.0, and TISDB databases. The correlation of PDCD10 with prognosis of patients with different tumors was analyzed using data from the GEPIA2 database. Proteins bound to PDCD10 were analyzed from the STRING database. PDCD10, PDCD10-binding proteins, and associated candidate genes were analyzed in DAVID for functional and pathway analyses. We also evaluated the immunological, clinical, and genetic aspects of distinct cancers by using TIMER2.0 and the connection between PDCD10 expression and tumor immune subtypes by using TISDB. Single-cell sequencing data from the CancerSEA database were used to characterize cancer cell functional states and generate heat maps.

Results

PDCD10 overexpression is linked to certain molecular subtypes of human cancer. Low PDCD10 expression in patients with bladder urothelial carcinoma (BLCA), lung adenocarcinoma (LUAD), liver hepatocellular carcinoma (LIHC), adrenocortical carcinoma (ACC), head and neck squamous cell carcinoma (HNSC), kidney chromophobe carcinoma (KICH), brain lower grade glioma (LGG), pancreatic adenocarcinoma (PAAD), uterine corpus endometrial carcinoma (UCEC), oral squamous cell carcinoma (OSCC), and esophageal adenocarcinoma (ESAD) was correlated with favorable OS, whereas high PDCD10 expression in patients with LUSC, KIRC, READ, SKCM, and THYM was correlated with good prognosis. STRING network prediction results showed that 20 proteins, namely, paxillin (PXN), CCM2 scaffold protein (CCM2), TRAF3 interacting protein 3 (TRAF3IP3), FGFR1 oncogene partner 2 (FGFR1OP2), chromosome 4 open reading frame 19 (C4orf19), suppressor of IKBKE 1 (SIKE1), serine/threonine kinase 25 (STK25), striatin (STRN), protein phosphatase 2 catalytic subunit alpha (PPP2CA), mammalian sterile-20-like kinase 4 (MST4), MOB family member 4 (MOB4), protein phosphatase 2 scaffold subunit Abeta (PPP2R1B), sarcolemma-associated protein (SLMAP), serine/threonine kinase 24 (STK24), striatin 4 (STRN4), STRN3, protein phosphatase 2 scaffold subunit A alpha (PPP2R1A), striatin interacting protein 1 (STRIP1), CTTNBP2 N-terminal like (CTTNBP2NL), and cortactin binding protein 2 (CTTNBP2), can bind to PDCD10. Gene enrichment analysis suggested that PDCD10 is involved in the occurrence of different tumors through the Hippo signalling pathway, RNA transport, mRNA monitoring pathway, endocytosis, and T cell receptor signalling pathway. An inverse relationship was found between PDCD10 expression and cancer-associated fibroblasts in LUSC and TGCT, and PDCD10 expression was strongly connected with immunological subtypes, such as C1 (wound healing), C2 (interferon-gamma dominant), C3 (inflammation), C4 (lymphocyte depletion), C5 (immune silenced), and C6 (TGF-beta dominant). Finally, analysis of single-cell sequencing data revealed that PDCD10 expression is linked to epigenetic reprogramming, DNA repair, cell cycle progression, cell differentiation, inflammation, cell proliferation, cell differentiation, cell invasion, and angiogenesis.

Conclusion

The results of our investigation demonstrate that PDCD10 has an oncogenic function in many cancer types. This study provides a reference for future research on antitumor therapeutic targets.

NETest: serial liquid biopsies in gastroenteropancreatic NET surveillance

Objective

Up to now, serial NETest measurements in individuals assessing the disease course of gastroenteropancreatic neuroendocrine tumors (GEPNETs) at long-term follow-up and treatment response were not studied.

Design

The study was a longitudinal validation study of serial NETest measurements - a blood-based gene expression signature - in 132 patients with GEPNETs on therapy or watch-and-wait strategy.

Methods

Serial samples were collected during 46 (range: 6-71) months of follow-up. NETest scores were compared with Response Evaluation Criteria in Solid Tumors version 1.1-defined treatment response (e.g. no evidence of disease (NED), stable disease (SD) or progressive disease (PD)).

Results

Consecutive NETest scores fluctuated substantially (range: 0-100) over time in individuals with SD (n = 28) and NED (n = 30). Follow-up samples were significantly higher in SD (samples 3-5) and NED subgroups (samples 2-5) compared with baseline results, without changes in imaging. In 82% of untreated patients with PD, consecutive NETest scores consistently remained high. In patients undergoing systemic treatment, the median pre-treatment NETest score in treatment-responders was 76.5 (n = 22) vs 33 (n = 12) in non-responders (P = 0.001). Patients with low pre-treatment scores had 21 months reduced progression-free survival (10 vs 31 months; P = 0.01). The accuracy of the NETest for treatment response prediction was 0.73 (P = 0.009).

Conclusion

In patients not undergoing treatment, consecutive low NETest scores are associated with indolent behavior. Patients who develop PD exhibit elevated scores. Elevated results have important predictive value for treatment responsiveness and could be used for individualizing decisions on systemic therapy. The clinical value of follow-up NETest scores for patients who choose to watch and wait requires further study.

Pan-cancer analysis of oncogenic role of Programmed Cell Death 2 Like (PDCD2L) and validation in colorectal cancer

Background

Programmed Cell Death 2 Like (PDCD2L) correlates with cell proliferation, apoptosis and mouse embryonic development. However, the role of PDCD2L in human cancers is unclear.

Methods

Multiple bioinformatic methods, in vitro function experiments and validation were performed to clarify the oncogenic role of PDCD2L in human cancers.

Results

Our study found that PDCD2L was aberrantly expressed in multiple types of human cancers, and associated with clinical stage and molecular subtype. Furthermore, overexpression of PDCD2L predicted poor overall survival in adrenocortical carcinoma(ACC), kidney chromophobe(KICH), acute myeloid leukemia(LAML), brain lower grade glioma(LGG),liver hepatocellular carcinoma(LIHC), mesothelioma(MESO), uveal melanoma(UVM) and poor diseases free survival in ACC, bladder urothelial carcinoma(BLCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), kidney renal clear cell carcinoma(KIRC), kidney renal papillary cell carcinoma(KIRP), LGG, LIHC, and UVM. PDCD2L expression was negatively associated with cancer associated fibroblast in breast invasive carcinoma (BRCA), lung squamous cell carcinoma (LUSC), sarcoma (SARC), stomach adenocarcinoma (STAD) and testicular germ cell tumors (TGCT). Mechanically, we found that PDCD2L expression was associated with apoptosis, invasion and cell cycle by investigating single cell sequencing data. For further validation, PDCD2Lwas highly expressed in colorectal cancer (CRC) cell lines and tissue samples compared with the normal colon cell line and non-tumor adjacent colorectal mucosa tissues. PDCD2L knockdown induced the apoptosis and proliferation of CRC cells.

Conclusions

Our study shows that the oncogenic role of PDCD2L in various cancers and PDCD2L could be served as a biomarker of CRC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02525-x.