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Wang S, Li H, Zhang K, Wu H, Pang S, Wu W, Ye L, Su J, Zhang Y. scSID: A lightweight algorithm for identifying rare cell types by capturing differential expression from single-cell sequencing data. Comput Struct Biotechnol J 2024; 23:589-600. [PMID: 38274993 PMCID: PMC10809081 DOI: 10.1016/j.csbj.2023.12.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 12/27/2023] [Accepted: 12/27/2023] [Indexed: 01/27/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) is currently an important technology for identifying cell types and studying diseases at the genetic level. Identifying rare cell types is biologically important as one of the downstream data analyses of single-cell RNA sequencing. Although rare cell identification methods have been developed, most of these suffer from insufficient mining of intercellular similarities, low scalability, and being time-consuming. In this paper, we propose a single-cell similarity division algorithm (scSID) for identifying rare cells. It takes cell-to-cell similarity into consideration by analyzing both inter-cluster and intra-cluster similarities, and discovers rare cell types based on the similarity differences. We show that scSID outperforms other existing methods by benchmarking it on different experimental datasets. Application of scSID to multiple datasets, including 68K PBMC and intestine, highlights its exceptional scalability and remarkable ability to identify rare cell populations.
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Affiliation(s)
- Shudong Wang
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China
| | - Hengxiao Li
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China
| | - Kuijie Zhang
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China
| | - Hao Wu
- College of Information Engineering, Northwest A&F University, 712100, Yangling, China
- School of Software, Shandong University, 250100, Jinan, China
| | - Shanchen Pang
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China
| | - Wenhao Wu
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China
| | - Lan Ye
- Cancer Center, the Second Hospital of Shandong University, Jinan, 250033, China
| | - Jionglong Su
- School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi'an Jiaotong-Liverpool University, Suzhou, 215123, Jiangsu, China
| | - Yulin Zhang
- College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, 266590, China
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2
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Zhou Q, Guo D, Shen T, Jiang H. Identification of KLHL17 as a prognostic marker for prostate cancer based on single-cell sequencing and in vitro/in vivo experiments. Discov Oncol 2024; 15:524. [PMID: 39365488 PMCID: PMC11452370 DOI: 10.1007/s12672-024-01406-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2024] [Accepted: 09/30/2024] [Indexed: 10/05/2024] Open
Abstract
BACKGROUND Prostate cancer (PCa) is a leading cause of cancer-related mortality among men, characterized by significant heterogeneity that complicates diagnosis and treatment. METHODS AND RESULTS To enhance our understanding of PCa, we utilized single-cell RNA sequencing (scRNA-seq) data to identify distinct malignant epithelial cell subpopulations and their molecular characteristics. By integrating scRNA-seq data with bulk RNA-seq data, we constructed a prognostic risk score model. The influence of key genes identified in the risk score on PCa was validated through both in vitro and in vivo experiments. Our study revealed eight unique malignant epithelial cell clusters, each exhibiting distinct molecular characteristics and biological functions. KEGG and GO enrichment analyses highlighted their involvement in various pathways. The prognostic risk score model demonstrated strong predictive power for patient outcomes, particularly in predicting progression-free survival (PFS). Notably, KLHL17, identified as a high-risk gene, was found to significantly impact PCa cell proliferation, migration, invasion, and apoptosis upon knockdown. This finding was further validated in vivo using a subcutaneous xenograft tumor model, where reduced KLHL17 expression led to decreased tumor growth. CONCLUSION Our research provides a comprehensive analysis of PCa heterogeneity and highlights KLHL17 as a potential therapeutic target.
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Affiliation(s)
- Qingyu Zhou
- Department of urology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, 121000, Liaoning, China
| | - Dan Guo
- Department of urology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, 121000, Liaoning, China
| | - Tong Shen
- Department of urology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, 121000, Liaoning, China
| | - Huamao Jiang
- Department of urology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, 121000, Liaoning, China.
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Yang J, Xu T, Wang H, Wang L, Cheng Y. Mechanisms of Berberine in anti-pancreatic ductal adenocarcinoma revealed by integrated multi-omics profiling. Sci Rep 2024; 14:22929. [PMID: 39358545 PMCID: PMC11446930 DOI: 10.1038/s41598-024-74943-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 09/30/2024] [Indexed: 10/04/2024] Open
Abstract
This study integrates pharmacology databases with bulk RNA-seq and scRNA-seq to reveal the latent anti-PDAC capacities of BBR. Target genes of BBR were sifted through TargetNet, CTD, SwissTargetPrediction, and Binding Database. Based on the GSE183795 dataset, DEG analysis, GSEA, and WGCNA were sequentially run to build a disease network. Through sub-network filtration acquired PDAC-related hub genes. A PPI network was established using the shared genes. Degree algorithm from cytoHubba screened the key cluster in the network. Analysis of differential mRNA expression and ROC curves gauged the diagnostic performance of clustered genes. CYBERSORT uncovered the potential role of the key cluster on PDAC immunomodulation. ScRNA-seq analysis evaluated the distribution and expression profile of the key cluster at the single-cell level, assessing enrichment within annotated cell subpopulations to delineate the target distribution of BBR in PDAC. We identified 425 drug target genes and 771 disease target genes, using 57 intersecting genes to construct the PPI network. CytoHubba anchored the top 10 highest contributing genes to be the key cluster. mRNA expression levels and ROC curves confirmed that these genes showed good robustness for PDAC. CYBERSORT revealed that the key cluster influenced immune pathways predominantly associated with Macrophages M0, CD8 T cells, and naïve B cells. ScRNA-seq analysis clarified that BBR mainly acted on epithelial cells and macrophages in PDAC tissues. BBR potentially targets CDK1, CCNB1, CTNNB1, CDK2, TOP2A, MCM2, RUNX2, MYC, PLK1, and AURKA to exert therapeutic effects on PDAC. The mechanisms of action appear to significantly involve macrophage polarization-related immunological responses.
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Affiliation(s)
- Jia Yang
- Department of Gastroenterology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Tingting Xu
- Department of Gastroenterology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Hongwei Wang
- Department of Gastroenterology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Lei Wang
- Shanghai Putuo District People's Hospital, Shanghai, China
| | - Yanmei Cheng
- Department of Gastroenterology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
- Shanghai University of Traditional Chinese Medicine, Shanghai, China.
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4
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Zeng S, Chen L, Tian J, Liu Z, Liu X, Tang H, Wu H, Liu C. Integrative analysis of pan-cancer single-cell data reveals a tumor ecosystem subtype predicting immunotherapy response. NPJ Precis Oncol 2024; 8:205. [PMID: 39277681 PMCID: PMC11401940 DOI: 10.1038/s41698-024-00703-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Accepted: 09/04/2024] [Indexed: 09/17/2024] Open
Abstract
Tumor ecosystem shapes cancer biology and potentially influence the response to immunotherapy, but there is a lack of direct clinical evidence. In this study, we utilized EcoTyper and publicly available scRNA-Seq cohorts from ICI-treated patients. We found a ecosystem subtype (ecotype) was linked to improved responses to immunotherapy. Then, a novel immunotherapy-responsive ecotype signature (IRE.Sig) was established and validated through the analysis of pan-cancer data. Utilizing IRE.Sig, machine learning models successfully predicted ICI responses in both validation and testing cohorts, achieving area under the curve (AUC) values of 0.72 and 0.71, respectively. Furthermore, using 5 CRISPR screening cohorts, we identified several potential drugs that may augment the efficacy of ICI. We also elucidated the candidate cellular biomarkers of response to the combined treatment of pembrolizumab plus eribulin in breast cancer. This signature has the potential to serve as a valuable tool for patients in selecting appropriate immunotherapy treatments.
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Affiliation(s)
- Shengjie Zeng
- Department of Urology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Liuxun Chen
- Department of Urology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jinyu Tian
- Department of Urology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhengxin Liu
- Department of Urology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xudong Liu
- Department of Cardiothoracic Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Haibin Tang
- Department of Urology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hao Wu
- Department of Urology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Chuan Liu
- Department of Urology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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Peng J, Zhang Q, Zhu X, Yan Z, Zhu M. Single-cell profiling uncovers proliferative cells as key determinants of survival outcomes in lower-grade glioma patients. Discov Oncol 2024; 15:445. [PMID: 39276278 PMCID: PMC11401832 DOI: 10.1007/s12672-024-01302-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2024] [Accepted: 09/02/2024] [Indexed: 09/16/2024] Open
Abstract
Lower-grade gliomas (LGGs), despite their generally indolent clinical course, are characterized by invasive growth patterns and genetic heterogeneity, which can lead to malignant transformation, underscoring the need for improved prognostic markers and therapeutic strategies. This study utilized single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq to identify a novel cell type, referred to as "Prol," characterized by increased proliferation and linked to a poor prognosis in patients with LGG, particularly under the context of immunotherapy interventions. A signature, termed the Prol signature, was constructed based on marker genes specific to the Prol cell type, utilizing an artificial intelligence (AI) network that integrates traditional regression, machine learning, and deep learning algorithms. This signature demonstrated enhanced predictive accuracy for LGG prognosis compared to existing models and showed pan-cancer prognostic potential. The mRNA expression of the key gene PTTG1 from the Prol signature was further validated through quantitative reverse transcription polymerase chain reaction (qRT-PCR). Our findings not only provide novel insights into the molecular and cellular mechanisms of LGG but also offer a promising avenue for the development of targeted biomarkers and therapeutic interventions.
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Affiliation(s)
- Jianming Peng
- School of Medicine, Yangzhou Polytechnic College, Yangzhou, China
| | - Qing Zhang
- Department of Hepatology, Huai'an No.4 People's Hospital, Huai'an, China
| | - Xiaofeng Zhu
- Department of Neurology, Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China
| | - Zhu Yan
- Emergency Medicine Department, Huai'an Hospital Affiliated to Yangzhou University (The Fifth People's Hospital of Huai'an), Huaian, China.
| | - Meng Zhu
- Department of Geriatrics, Affiliated Huai'an No.2 Hospital of Xuzhou Medical University, Huai'an, Jiangsu, China.
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Luo E, Hao M, Wei L, Zhang X. scDiffusion: conditional generation of high-quality single-cell data using diffusion model. Bioinformatics 2024; 40:btae518. [PMID: 39171840 PMCID: PMC11368386 DOI: 10.1093/bioinformatics/btae518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 08/10/2024] [Accepted: 08/20/2024] [Indexed: 08/23/2024] Open
Abstract
MOTIVATION Single-cell RNA sequencing (scRNA-seq) data are important for studying the laws of life at single-cell level. However, it is still challenging to obtain enough high-quality scRNA-seq data. To mitigate the limited availability of data, generative models have been proposed to computationally generate synthetic scRNA-seq data. Nevertheless, the data generated with current models are not very realistic yet, especially when we need to generate data with controlled conditions. In the meantime, diffusion models have shown their power in generating data with high fidelity, providing a new opportunity for scRNA-seq generation. RESULTS In this study, we developed scDiffusion, a generative model combining the diffusion model and foundation model to generate high-quality scRNA-seq data with controlled conditions. We designed multiple classifiers to guide the diffusion process simultaneously, enabling scDiffusion to generate data under multiple condition combinations. We also proposed a new control strategy called Gradient Interpolation. This strategy allows the model to generate continuous trajectories of cell development from a given cell state. Experiments showed that scDiffusion could generate single-cell gene expression data closely resembling real scRNA-seq data. Also, scDiffusion can conditionally produce data on specific cell types including rare cell types. Furthermore, we could use the multiple-condition generation of scDiffusion to generate cell type that was out of the training data. Leveraging the Gradient Interpolation strategy, we generated a continuous developmental trajectory of mouse embryonic cells. These experiments demonstrate that scDiffusion is a powerful tool for augmenting the real scRNA-seq data and can provide insights into cell fate research. AVAILABILITY AND IMPLEMENTATION scDiffusion is openly available at the GitHub repository https://github.com/EperLuo/scDiffusion or Zenodo https://zenodo.org/doi/10.5281/zenodo.13268742.
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Affiliation(s)
- Erpai Luo
- MOE Key Lab of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Minsheng Hao
- MOE Key Lab of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Lei Wei
- MOE Key Lab of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xuegong Zhang
- MOE Key Lab of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
- School of Life Sciences and School of Medicine, Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China
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de Back TR, van Hooff SR, Sommeijer DW, Vermeulen L. Transcriptomic subtyping of gastrointestinal malignancies. Trends Cancer 2024; 10:842-856. [PMID: 39019673 DOI: 10.1016/j.trecan.2024.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 06/17/2024] [Accepted: 06/20/2024] [Indexed: 07/19/2024]
Abstract
Gastrointestinal (GI) cancers are highly heterogeneous at multiple levels. Tumor heterogeneity can be captured by molecular profiling, such as genetic, epigenetic, proteomic, and transcriptomic classification. Transcriptomic subtyping has the advantage of combining genetic and epigenetic information, cancer cell-intrinsic properties, and the tumor microenvironment (TME). Unsupervised transcriptomic subtyping systems of different GI malignancies have gained interest because they reveal shared biological features across cancers and bear prognostic and predictive value. Importantly, transcriptomic subtypes accurately reflect complex phenotypic states varying not only per tumor region, but also throughout disease progression, with consequences for clinical management. Here, we discuss methodologies of transcriptomic subtyping, proposed taxonomies for GI malignancies, and the challenges posed to clinical implementation, highlighting opportunities for future transcriptomic profiling efforts to optimize clinical impact.
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Affiliation(s)
- Tim R de Back
- Cancer Center Amsterdam, Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands; Amsterdam Gastroenterology Endocrinology Metabolism, Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands; Oncode Institute, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Sander R van Hooff
- Cancer Center Amsterdam, Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands; Amsterdam Gastroenterology Endocrinology Metabolism, Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands; Oncode Institute, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Dirkje W Sommeijer
- Flevohospital, Department of Internal Medicine, Hospitaalweg 1, 1315 RA, Almere, The Netherlands
| | - Louis Vermeulen
- Cancer Center Amsterdam, Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands; Amsterdam Gastroenterology Endocrinology Metabolism, Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands; Oncode Institute, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
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8
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Li X, Lin Z, Zhao F, Huang T, Fan W, Cen L, Ma J. Unveiling the cellular landscape: insights from single-cell RNA sequencing in multiple myeloma. Front Immunol 2024; 15:1458638. [PMID: 39281682 PMCID: PMC11392786 DOI: 10.3389/fimmu.2024.1458638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2024] [Accepted: 08/13/2024] [Indexed: 09/18/2024] Open
Abstract
Objective The aim of this research was to gain a thorough understanding of the processes involved in cell communication and discover potential indicators for treating multiple myeloma (MM) through the use of single-cell RNA sequencing (scRNA-seq). And explored the expression of multiple myeloma-related subgroups on metal ion-related pathways to explore the relationship between MM and metal ions. Methods We performed a fair examination using single-cell RNA sequencing on 32 bone marrow specimens collected from 22 individuals at different points of MM advancement and 9 individuals without any health issues. To analyze the scRNA-seq data, we employed advanced computational algorithms, including Slingshot, Monocle2, and other methodologies. Specifically, Slingshot and Monocle2 enabled us to simulate the biological functionalities of different cell populations and map trajectories of cell developmental pathways. Additionally, we utilized the UMAP algorithm, a powerful dimension reduction technique, to cluster cells and identify genes that were differentially expressed across clusters. Results Our study revealed distinct gene expression patterns and molecular pathways within each patient, which exhibited associations with disease progression. The analysis provided insights into the tumor microenvironment (TME), intra- and inter-patient heterogeneity, and cell-cell interactions mediated by ligand-receptor signaling. And found that multiple myeloma-related subgroups were expressed higher levels in MMP and TIMP pathways, there were some associations. Conclusion Our study presents a fresh perspective for future research endeavors and clinical interventions in the field of MM. The identified gene expression patterns and molecular pathways hold immense potential as therapeutic targets for the treatment of multiple myeloma. The utilization of scRNA-seq technology has significantly contributed to a more precise understanding of the complex cellular processes and interactions within MM. Through these advancements, we are now better equipped to unravel the underlying mechanisms driving the development and progression of this complex disease.
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Affiliation(s)
- Xinhan Li
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Zhiheng Lin
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Fu Zhao
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Tianjiao Huang
- The First School of Clinical Medicine, Heilongjiang University of Traditional Chinese Medicine, Harbin, China
| | - Weisen Fan
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Lijun Cen
- Key Laboratory of Molecular Pathology in Tumors of Guangxi, Department of Transfusion Medicine, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, China
| | - Jun Ma
- Department of Cardiology, Yantai Hospital of Traditional Chinese Medicine, Yantai, China
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Liu B, Hu S, Wang X. Applications of single-cell technologies in drug discovery for tumor treatment. iScience 2024; 27:110486. [PMID: 39171294 PMCID: PMC11338156 DOI: 10.1016/j.isci.2024.110486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/23/2024] Open
Abstract
Single-cell technologies have been known as advanced and powerful tools to study tumor biological systems at the single-cell resolution and are playing increasingly critical roles in multiple stages of drug discovery and development. Specifically, single-cell technologies can promote the discovery of drug targets, help high-throughput screening at single-cell level, and contribute to pharmacokinetic studies of anti-tumor drugs. Emerging single-cell analysis technologies have been developed to further integrating multidimensional single-cell molecular features, expanding the scale of single-cell data, profiling phenotypic impact of genes in single cell, and providing full-length coverage single-cell sequencing. In this review, we systematically summarized the applications of single-cell technologies in various sections of drug discovery for tumor treatment, including target identification, high-throughput drug screening, and pharmacokinetic evaluation and highlighted emerging single-cell technologies in providing in-depth understanding of tumor biology. Single-cell-technology-based drug discovery is expected to further optimize therapeutic strategies and improve clinical outcomes of tumor patients.
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Affiliation(s)
- Bingyu Liu
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
| | - Shunfeng Hu
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Xin Wang
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
- Taishan Scholars Program of Shandong Province, Jinan, Shandong 250021, China
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Meng X, Zheng Y, Zhang L, Liu P, Liu Z, He Y. Single-Cell Analyses Reveal the Metabolic Heterogeneity and Plasticity of the Tumor Microenvironment during Head and Neck Squamous Cell Carcinoma Progression. Cancer Res 2024; 84:2468-2483. [PMID: 38718319 DOI: 10.1158/0008-5472.can-23-1344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 10/31/2023] [Accepted: 04/29/2024] [Indexed: 08/02/2024]
Abstract
Metabolic reprogramming is a hallmark of cancer. In addition to metabolic alterations in the tumor cells, multiple other metabolically active cell types in the tumor microenvironment (TME) contribute to the emergence of a tumor-specific metabolic milieu. Here, we defined the metabolic landscape of the TME during the progression of head and neck squamous cell carcinoma (HNSCC) by performing single-cell RNA sequencing on 26 human patient specimens, including normal tissue, precancerous lesions, early stage cancer, advanced-stage cancer, lymph node metastases, and recurrent tumors. The analysis revealed substantial heterogeneity at the transcriptional, developmental, metabolic, and functional levels in different cell types. SPP1+ macrophages were identified as a protumor and prometastatic macrophage subtype with high fructose and mannose metabolism, which was further substantiated by integrative analysis and validation experiments. An inhibitor of fructose metabolism reduced the proportion of SPP1+ macrophages, reshaped the immunosuppressive TME, and suppressed tumor growth. In conclusion, this work delineated the metabolic landscape of HNSCC at a single-cell resolution and identified fructose metabolism as a key metabolic feature of a protumor macrophage subpopulation. Significance: Fructose and mannose metabolism is a metabolic feature of a protumor and prometastasis macrophage subtype and can be targeted to reprogram macrophages and the microenvironment of head and neck squamous cell carcinoma.
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Affiliation(s)
- Xiaoyan Meng
- Department of Oral Maxillofacial & Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, P.R. China
- National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology Shanghai, Shanghai, P.R. China
| | - Yang Zheng
- Department of Oral Maxillofacial & Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, P.R. China
- National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology Shanghai, Shanghai, P.R. China
| | - Lingfang Zhang
- Suzhou Lingdian Biotechnology Co., Ltd., Suzhou, P.R. China
| | - Peipei Liu
- Suzhou Lingdian Biotechnology Co., Ltd., Suzhou, P.R. China
| | - Zhonglong Liu
- Department of Oral Maxillofacial & Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, P.R. China
- National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology Shanghai, Shanghai, P.R. China
| | - Yue He
- Department of Oral Maxillofacial & Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, P.R. China
- National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology Shanghai, Shanghai, P.R. China
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11
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Liu J, Xia B, Jiang X, Cao L, Xi Z, Liang L, Zhang S, Zhang H, Li W. Single-cell landscape reveals the immune heterogeneity of bone marrow involvement in peripheral T-cell lymphoma. Cancer Sci 2024; 115:2540-2552. [PMID: 38845192 PMCID: PMC11309951 DOI: 10.1111/cas.16227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/03/2024] [Accepted: 05/02/2024] [Indexed: 08/10/2024] Open
Abstract
The prognosis of patients with peripheral T-cell lymphoma (PTCL) depends on bone marrow involvement (BMI). The bone marrow (BM) tumor microenvironment in PTCL remains unclear. We performed single-cell RNA sequencing (scRNA-seq) on 11 fresh BM samples from patients with BMI to reveal the associations of immune landscape and genetic variations with the prognosis of PTCL patients. Compared with PTCL not otherwise specified (NOS), angioimmunoblastic T-cell lymphoma (AITL) had a higher number of T cells, lower number of lymphocytes, and greater inflammation. Immune heterogeneity in AITL is associated with prognosis. In particular, specific T-cell receptor (TCR) T cells are enriched in patients with good response to anti-CD30 therapy. We observed RhoA mutation-associated neoantigens. Chidamide-treated patients had a higher number of CD4+ regulatory cells and a better treatment response compared with other patients. In the nonresponder group, T-cell enrichment progressed to secondary B-cell enrichment and subsequently diffuse large B-cell lymphoma. Moreover, AITL patients with lymphoma-associated hemophagocytic syndrome had more T follicular helper (Tfh) cells with copy number variations in CHR5. To our knowledge, this study is the first to reveal the single-cell landscape of BM microenvironment heterogeneity in PTCL patients with BMI. scRNA-seq can be used to investigate the immune heterogeneity and genetic variations in AITL associated with prognosis.
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Affiliation(s)
- Jun Liu
- Medical Research institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
- Department of Precision Medicine, Shenzhen HospitalSouthern Medical UniversityShenzhenChina
| | - Baijing Xia
- Medical Research institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
- Guangdong Cardiovascular Institute, Guangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhouChina
| | - Xinmiao Jiang
- Department of Lymphoma, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Lixue Cao
- Medical Research institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Zhihui Xi
- Medical Research institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Liting Liang
- Institute of Human Virology, Key Laboratory of Tropical Disease Control of Ministry of Education, Guangdong Engineering Research Center for Antimicrobial Agent and Immunotechnology, Zhongshan School of MedicineSun Yat‐sen UniversityGuangzhouChina
| | - Shaojun Zhang
- Medical Research institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Hui Zhang
- Medical Research institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
- Institute of Human Virology, Key Laboratory of Tropical Disease Control of Ministry of Education, Guangdong Engineering Research Center for Antimicrobial Agent and Immunotechnology, Zhongshan School of MedicineSun Yat‐sen UniversityGuangzhouChina
| | - Wenyu Li
- Medical Research institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
- Department of Lymphoma, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
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12
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Lin G, Lin L, Chen X, Chen L, Yang J, Chen Y, Qian D, Zeng Y, Xu Y. PPAR-γ/NF-kB/AQP3 axis in M2 macrophage orchestrates lung adenocarcinoma progression by upregulating IL-6. Cell Death Dis 2024; 15:532. [PMID: 39060229 PMCID: PMC11282095 DOI: 10.1038/s41419-024-06919-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 07/12/2024] [Accepted: 07/17/2024] [Indexed: 07/28/2024]
Abstract
Aquaporin 3 (AQP3), which is mostly expressed in pulmonary epithelial cells, was linked to lung adenocarcinoma (LUAD). However, the underlying functions and mechanisms of AQP3 in the tumor microenvironment (TME) of LUAD have not been elucidated. Single-cell RNA sequencing (scRNA-seq) was used to study the composition, lineage, and functional states of TME-infiltrating immune cells and discover AQP3-expressing subpopulations in five LUAD patients. Then the identifications of its function on TME were examined in vitro and in vivo. AQP3 was associated with TNM stages and lymph node metastasis of LUAD patients. We classified inter- and intra-tumor diversity of LUAD into twelve subpopulations using scRNA-seq analyses. The analysis showed AQP3 was mainly enriched in subpopulations of M2 macrophages. Importantly, mechanistic investigations indicated that AQP3 promoted M2 macrophage polarization by the PPAR-γ/NF-κB axis, which affected tumor growth and migration via modulating IL-6 production. Mixed subcutaneous transplanted tumor mice and Aqp3 knockout mice models were further utilized, and revealed that AQP3 played a critical role in mediating M2 macrophage polarization, modulating glucose metabolism in tumors, and regulating both upstream and downstream pathways. Overall, our study demonstrated that AQP3 could regulate the proliferation, migration, and glycometabolism of tumor cells by modulating M2 macrophages polarization through the PPAR-γ/NF-κB axis and IL-6/IL-6R signaling pathway, providing new insight into the early detection and potential therapeutic target of LUAD.
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Affiliation(s)
- Guofu Lin
- Fujian Provincial Clinical Research Center of Interventional Respirology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, 362000, China
- Department of Pulmonary and Critical Care Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, 362000, China
- Fujian Provincial Key Laboratory of Lung Stem Cells, Ouanzhou, Fujian Province, 362000, China
| | - Lanlan Lin
- Fujian Provincial Clinical Research Center of Interventional Respirology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, 362000, China
- Department of Pulmonary and Critical Care Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, 362000, China
- Fujian Provincial Key Laboratory of Lung Stem Cells, Ouanzhou, Fujian Province, 362000, China
| | - Xiaohui Chen
- Fujian Provincial Clinical Research Center of Interventional Respirology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, 362000, China
- Department of Pulmonary and Critical Care Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, 362000, China
- Fujian Provincial Key Laboratory of Lung Stem Cells, Ouanzhou, Fujian Province, 362000, China
| | - Luyang Chen
- Fujian Provincial Clinical Research Center of Interventional Respirology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, 362000, China
- Department of Pulmonary and Critical Care Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, 362000, China
- Fujian Provincial Key Laboratory of Lung Stem Cells, Ouanzhou, Fujian Province, 362000, China
| | - Jiansheng Yang
- Department of Thoracic Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian province, 362000, China
| | - Yanling Chen
- Clinical Research Center, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, 362000, China
| | - Danwen Qian
- The Tumor Immunogenomics and Immunosurveillance (TIGI) Lab, UCL Cancer Institute, London, UK
| | - Yiming Zeng
- Fujian Provincial Clinical Research Center of Interventional Respirology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, 362000, China.
- Department of Pulmonary and Critical Care Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, 362000, China.
- Fujian Provincial Key Laboratory of Lung Stem Cells, Ouanzhou, Fujian Province, 362000, China.
| | - Yuan Xu
- Fujian Provincial Clinical Research Center of Interventional Respirology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, 362000, China.
- Clinical Research Center, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, 362000, China.
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13
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Ye B, Ji H, Zhu M, Wang A, Tang J, Liang Y, Zhang Q. Single-cell sequencing reveals novel proliferative cell type: a key player in renal cell carcinoma prognosis and therapeutic response. Clin Exp Med 2024; 24:167. [PMID: 39052149 PMCID: PMC11272756 DOI: 10.1007/s10238-024-01424-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 07/02/2024] [Indexed: 07/27/2024]
Abstract
Renal cell carcinoma (RCC) is characterized by a variety of subtypes, each defined by unique genetic and morphological features. This study utilizes single-cell RNA sequencing to explore the molecular heterogeneity of RCC. A highly proliferative cell subset, termed as "Prol," was discovered within RCC tumors, and its increased presence was linked to poorer patient outcomes. An artificial intelligence network, encompassing traditional regression, machine learning, and deep learning algorithms, was employed to develop a Prol signature capable of predicting prognosis. The signature demonstrated superior performance in predicting RCC prognosis compared to other signatures and exhibited pan-cancer prognostic capabilities. RCC patients with high Prol signature scores exhibited resistance to targeted therapies and immunotherapies. Furthermore, the key gene CEP55 from the Prol signature was validated by both proteinomics and quantitative real time polymerase chain reaction. Our findings may provide new insights into the molecular and cellular mechanisms of RCC and facilitate the development of novel biomarkers and therapeutic targets.
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Affiliation(s)
- Bicheng Ye
- School of Clinical Medicine, Yangzhou Polytechnic College, Yangzhou, China
- Department of Urology, Changhai Hospital, Naval Medical University (Second Military Medical University), Shanghai, China
| | - Hongsheng Ji
- Department of Urology, Lianshui People's Hospital of Kangda College Affiliated to Nanjing Medical University, Huai'an, China
| | - Meng Zhu
- Department of Geriatrics, The Affiliated Huaian Hospital of Xuzhou Medical University, Huaian Second People's Hospital, Huaian, China
| | - Anbang Wang
- Department of Urology, Changhai Hospital, Naval Medical University (Second Military Medical University), Shanghai, China
| | - Jingsong Tang
- Department of General Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China.
| | - Yong Liang
- Department of Medical Laboratory, Huai'an Second People's Hospital Affiliated to Xuzhou Medical Universit, Huaian, China.
| | - Qing Zhang
- Department of Hepatology, Huai'an No. 4 People's Hospital, Huai'an, China.
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14
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Um JH, Zheng Y, Mao Q, Nam C, Zhao H, Koh YW, Shin SJ, Park YM, Lin DC. Genomic and single-cell characterization of patient-derived tumor organoid models of head and neck squamous cell carcinoma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.28.601068. [PMID: 39005427 PMCID: PMC11244938 DOI: 10.1101/2024.06.28.601068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Head and Neck Squamous Cell Carcinoma (HNSCC) remains a significant health burden due to tumor heterogeneity and treatment resistance, emphasizing the need for improved biological understanding and tailored therapies. This study enrolled 31 HNSCC patients for the establishment of patient-derived tumor organoids (PDOs), which faithfully maintained genomic features and histopathological traits of primary tumors. Long-term culture preserved key characteristics, affirming PDOs as robust representative models. PDOs demonstrated predictive capability for cisplatin treatment responses, correlating ex vivo drug sensitivity with patient outcomes. Bulk and single-cell RNA sequencing unveiled molecular subtypes and intratumor heterogeneity (ITH) in PDOs, paralleling patient tumors. Notably, a hybrid epithelial-mesenchymal transition (hEMT)-like ITH program is associated with cisplatin resistance and poor patient survival. Functional analyses identified amphiregulin (AREG) as a potential regulator of the hybrid epithelial/mesenchymal state. Moreover, AREG contributes to cisplatin resistance via EGFR pathway activation, corroborated by clinical samples. In summary, HNSCC PDOs serve as reliable and versatile models, offer predictive insights into ITH programs and treatment responses, and uncover potential therapeutic targets for personalized medicine.
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Affiliation(s)
- Jung Hyun Um
- Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, Korea
| | - Yueyuan Zheng
- Clinical Big Data Research Center, Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China
| | - Qiong Mao
- Clinical Big Data Research Center, Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, P.R. China
- Guangzhou Municipal and Guangdong Provincial Key Laboratory of Protein Modification and Degradation, Guangzhou Medical University, Guangzhou, 510120, P.R. China
| | - Chehyun Nam
- Center for Craniofacial Molecular Biology, Herman Ostrow School of Dentistry, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, USA
| | - Hua Zhao
- Center for Craniofacial Molecular Biology, Herman Ostrow School of Dentistry, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, USA
| | - Yoon Woo Koh
- Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, Korea
| | - Su-Jin Shin
- Department of Pathology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Young Min Park
- Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, Korea
| | - De-Chen Lin
- Center for Craniofacial Molecular Biology, Herman Ostrow School of Dentistry, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, USA
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15
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Liu B, Li S, Cheng Y, Song P, Xu M, Li Z, Shao W, Xin J, Fu Z, Gu D, Du M, Zhang Z, Wang M. Distinctive multicellular immunosuppressive hubs confer different intervention strategies for left- and right-sided colon cancers. Cell Rep Med 2024; 5:101589. [PMID: 38806057 PMCID: PMC11228667 DOI: 10.1016/j.xcrm.2024.101589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 03/11/2024] [Accepted: 05/02/2024] [Indexed: 05/30/2024]
Abstract
Primary colon cancers arising from the left and right sides exhibit distinct clinical and molecular characteristics. Sidedness-associated heterogeneity relies intricately on the oncogenic properties of cancer cells and multicellular interactions in tumor microenvironments. Here, combining transcriptomic profiling of 426,863 single cells from 105 colon cancer patients and validation with spatial transcriptomics and large-scale histological analysis, we capture common transcriptional heterogeneity patterns between left- and right-sided malignant epithelia through delineating two side-specific expression meta-programs. The proliferation stemness meta-program is notably enriched in left-sided malignant epithelia that colocalize with Mph-PLTP cells, activated regulatory T cells (Tregs), and exhausted CD8-LAYN cells, constituting the glucose metabolism reprogramming niche. The immune secretory (IS) meta-program exhibits specific enrichment in right-sided malignant epithelia, especially in smoking patients with right-sided colon cancer. The IShigh malignant epithelia spatially localize in hypoxic regions and facilitate immune evasion through attenuating Mph-SPP1 cell antigen presentation and recruiting innate-like cytotoxicity-reduced CD8-CD161 cells.
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Affiliation(s)
- Bingxin Liu
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Shuwei Li
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yifei Cheng
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Peng Song
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Gastroenterology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Menghuan Xu
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Zhengyi Li
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Wei Shao
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Junyi Xin
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Zan Fu
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China
| | - Dongying Gu
- Department of Oncology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Mulong Du
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Zhengdong Zhang
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Meilin Wang
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China.
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16
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Fang C, Selega A, Campbell KR. Beyond benchmarking and towards predictive models of dataset-specific single-cell RNA-seq pipeline performance. Genome Biol 2024; 25:159. [PMID: 38886757 PMCID: PMC11184819 DOI: 10.1186/s13059-024-03304-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 06/06/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND The advent of single-cell RNA-sequencing (scRNA-seq) has driven significant computational methods development for all steps in the scRNA-seq data analysis pipeline, including filtering, normalization, and clustering. The large number of methods and their resulting parameter combinations has created a combinatorial set of possible pipelines to analyze scRNA-seq data, which leads to the obvious question: which is best? Several benchmarking studies compare methods but frequently find variable performance depending on dataset and pipeline characteristics. Alternatively, the large number of scRNA-seq datasets along with advances in supervised machine learning raise a tantalizing possibility: could the optimal pipeline be predicted for a given dataset? RESULTS Here, we begin to answer this question by applying 288 scRNA-seq analysis pipelines to 86 datasets and quantifying pipeline success via a range of measures evaluating cluster purity and biological plausibility. We build supervised machine learning models to predict pipeline success given a range of dataset and pipeline characteristics. We find that prediction performance is significantly better than random and that in many cases pipelines predicted to perform well provide clustering outputs similar to expert-annotated cell type labels. We identify characteristics of datasets that correlate with strong prediction performance that could guide when such prediction models may be useful. CONCLUSIONS Supervised machine learning models have utility for recommending analysis pipelines and therefore the potential to alleviate the burden of choosing from the near-infinite number of possibilities. Different aspects of datasets influence the predictive performance of such models which will further guide users.
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Affiliation(s)
- Cindy Fang
- Lunenfeld-Tanenbaum Research Institute, Toronto, Canada
- Program in Bioinformatics and Computational Biology, University of Toronto, Toronto, Canada
- Present address: Department of Biostatistics, Johns Hopkins University, Baltimore, USA
| | - Alina Selega
- Lunenfeld-Tanenbaum Research Institute, Toronto, Canada
- Vector Institute, Toronto, Canada
| | - Kieran R Campbell
- Lunenfeld-Tanenbaum Research Institute, Toronto, Canada.
- Vector Institute, Toronto, Canada.
- Departments of Molecular Genetics, Statistical Sciences, Computer Science, University of Toronto, Toronto, Canada.
- Ontario Institute for Cancer Research, Toronto, Canada.
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17
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Zhou Q, Tao C, Ge Y, Yuan J, Pan F, Lin X, Wang R. A novel single-cell model reveals ferroptosis-associated biomarkers for individualized therapy and prognostic prediction in hepatocellular carcinoma. BMC Biol 2024; 22:133. [PMID: 38853238 PMCID: PMC11163722 DOI: 10.1186/s12915-024-01931-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 06/04/2024] [Indexed: 06/11/2024] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is a prevalent malignancy with a pressing need for improved therapeutic response and prognosis prediction. This study delves into a novel predictive model related to ferroptosis, a regulated cell death mechanism disrupting metabolic processes. RESULTS Single-cell sequencing data analysis identified subpopulations of HCC cells exhibiting activated ferroptosis and distinct gene expression patterns compared to normal tissues. Utilizing the LASSO-Cox algorithm, we constructed a model with 10 single-cell biomarkers associated with ferroptosis, namely STMN1, S100A10, FABP5, CAPG, RGCC, ENO1, ANXA5, UTRN, CXCR3, and ITM2A. Comprehensive analyses using these biomarkers revealed variations in immune infiltration, tumor mutation burden, drug sensitivity, and biological functional profiles between risk groups. Specific associations were established between particular immune cell subtypes and certain gene expression patterns. Treatment response analyses indicated potential benefits from anti-tumor immune therapy for the low-risk group and chemotherapy advantages for the high-risk group. CONCLUSIONS The integration of this single-cell level model with clinicopathological features enabled accurate overall survival prediction and effective risk stratification in HCC patients. Our findings illuminate the potential of ferroptosis-related genes in tailoring therapy and prognosis prediction for HCC, offering novel insights into the intricate interplay among ferroptosis, immune response, and HCC progression.
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Affiliation(s)
- Qiong Zhou
- Department of Medical Oncology, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu Province, 210093, PR China
| | - Chunyu Tao
- Department of Medical Oncology, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu Province, 210093, PR China
| | - Yuli Ge
- Department of Medical Oncology, Jinling Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, 210023, PR China
| | - Jiakai Yuan
- Department of Medical Oncology, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu Province, 210093, PR China
| | - Fan Pan
- Department of Medical Oncology, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu Province, 210093, PR China
| | - Xinrong Lin
- Department of Medical Oncology, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu Province, 210093, PR China
| | - Rui Wang
- Department of Medical Oncology, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu Province, 210093, PR China.
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18
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Liu Y, Carbonetto P, Willwerscheid J, Oakes SA, Macleod KF, Stephens M. Dissecting tumor transcriptional heterogeneity from single-cell RNA-seq data by generalized binary covariance decomposition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.15.553436. [PMID: 37645713 PMCID: PMC10462040 DOI: 10.1101/2023.08.15.553436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Profiling tumors with single-cell RNA sequencing (scRNA-seq) has the potential to identify recurrent patterns of transcription variation related to cancer progression, and produce new therapeutically relevant insights. However, the presence of strong inter-tumor heterogeneity often obscures more subtle patterns that are shared across tumors, some of which may characterize clinically relevant disease subtypes. Here we introduce a new statistical method, generalized binary covariance decomposition (GBCD), to address this problem. We show that GBCD can help decompose transcriptional heterogeneity into interpretable components - including patient-specific, dataset-specific and shared components relevant to disease subtypes - and that, in the presence of strong inter-tumor heterogeneity, it can produce more interpretable results than existing methods. Applied to data from three studies on pancreatic cancer adenocarcinoma (PDAC), GBCD produces a refined characterization of existing tumor subtypes (e.g., classical vs. basal), and identifies a new gene expression program (GEP) that is prognostic of poor survival independent of established prognostic factors such as tumor stage and subtype. The new GEP is enriched for genes involved in a variety of stress responses, and suggests a potentially important role for the integrated stress response in PDAC development and prognosis.
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19
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Ruan X, Cheng Y, Ye Y, Wang Y, Chen X, Yang Y, Liu T, Yan F. PIPET: predicting relevant subpopulations in single-cell data using phenotypic information from bulk data. Brief Bioinform 2024; 25:bbae260. [PMID: 38819254 PMCID: PMC11141296 DOI: 10.1093/bib/bbae260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 05/11/2024] [Accepted: 05/15/2024] [Indexed: 06/01/2024] Open
Abstract
Single-cell RNA sequencing has revealed cellular heterogeneity in complex tissues, notably benefiting research on diseases such as cancer. However, the integration of single-cell data from small samples with extensive clinical features in bulk data remains underexplored. In this study, we introduce PIPET, an algorithmic method for predicting relevant subpopulations in single-cell data based on multivariate phenotypic information from bulk data. PIPET generates feature vectors for each phenotype from differentially expressed genes in bulk data and then identifies relevant cellular subpopulations by assessing the similarity between single-cell data and these vectors. Subsequently, phenotype-related cell states can be analyzed based on these subpopulations. In simulated datasets, PIPET showed robust performance in predicting multiclassification cellular subpopulations. Application of PIPET to lung adenocarcinoma single-cell RNA sequencing data revealed cellular subpopulations with poor survival and associations with TP53 mutations. Similarly, in breast cancer single-cell data, PIPET identified cellular subpopulations associated with the PAM50 clinical subtypes and triple-negative breast cancer subtypes. Overall, PIPET effectively identified relevant cellular subpopulations in single-cell data, guided by phenotypic information from bulk data. This approach comprehensively delineates the molecular characteristics of each cellular subpopulation, offering insights into disease-related subpopulations and guiding personalized treatment strategies.
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Affiliation(s)
- Xinjia Ruan
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Yu Cheng
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Yuqing Ye
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Yuhang Wang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Xinyi Chen
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Yuqing Yang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Tiantian Liu
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
| | - Fangrong Yan
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 211198, P.R. China
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20
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Beigi YZ, Lanjanian H, Fayazi R, Salimi M, Hoseyni BHM, Noroozizadeh MH, Masoudi-Nejad A. Heterogeneity and molecular landscape of melanoma: implications for targeted therapy. MOLECULAR BIOMEDICINE 2024; 5:17. [PMID: 38724687 PMCID: PMC11082128 DOI: 10.1186/s43556-024-00182-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 04/08/2024] [Indexed: 05/12/2024] Open
Abstract
Uveal cancer (UM) offers a complex molecular landscape characterized by substantial heterogeneity, both on the genetic and epigenetic levels. This heterogeneity plays a critical position in shaping the behavior and response to therapy for this uncommon ocular malignancy. Targeted treatments with gene-specific therapeutic molecules may prove useful in overcoming radiation resistance, however, the diverse molecular makeups of UM call for a patient-specific approach in therapy procedures. We need to understand the intricate molecular landscape of UM to develop targeted treatments customized to each patient's specific genetic mutations. One of the promising approaches is using liquid biopsies, such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), for detecting and monitoring the disease at the early stages. These non-invasive methods can help us identify the most effective treatment strategies for each patient. Single-cellular is a brand-new analysis platform that gives treasured insights into diagnosis, prognosis, and remedy. The incorporation of this data with known clinical and genomics information will give a better understanding of the complicated molecular mechanisms that UM diseases exploit. In this review, we focused on the heterogeneity and molecular panorama of UM, and to achieve this goal, the authors conducted an exhaustive literature evaluation spanning 1998 to 2023, using keywords like "uveal melanoma, "heterogeneity". "Targeted therapies"," "CTCs," and "single-cellular analysis".
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Affiliation(s)
- Yasaman Zohrab Beigi
- Laboratory of System Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Hossein Lanjanian
- Software Engineering Department, Engineering Faculty, Istanbul Topkapi University, Istanbul, Turkey
| | - Reyhane Fayazi
- Laboratory of System Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Mahdieh Salimi
- Department of Medical Genetics, Institute of Medical Biotechnology, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
| | - Behnaz Haji Molla Hoseyni
- Laboratory of System Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | | | - Ali Masoudi-Nejad
- Laboratory of System Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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21
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Yabo YA, Heiland DH. Understanding glioblastoma at the single-cell level: Recent advances and future challenges. PLoS Biol 2024; 22:e3002640. [PMID: 38814900 PMCID: PMC11139343 DOI: 10.1371/journal.pbio.3002640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024] Open
Abstract
Glioblastoma, the most aggressive and prevalent form of primary brain tumor, is characterized by rapid growth, diffuse infiltration, and resistance to therapies. Intrinsic heterogeneity and cellular plasticity contribute to its rapid progression under therapy; therefore, there is a need to fully understand these tumors at a single-cell level. Over the past decade, single-cell transcriptomics has enabled the molecular characterization of individual cells within glioblastomas, providing previously unattainable insights into the genetic and molecular features that drive tumorigenesis, disease progression, and therapy resistance. However, despite advances in single-cell technologies, challenges such as high costs, complex data analysis and interpretation, and difficulties in translating findings into clinical practice persist. As single-cell technologies are developed further, more insights into the cellular and molecular heterogeneity of glioblastomas are expected, which will help guide the development of personalized and effective therapies, thereby improving prognosis and quality of life for patients.
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Affiliation(s)
- Yahaya A Yabo
- Translational Neurosurgery, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Microenvironment and Immunology Research Laboratory, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Dieter Henrik Heiland
- Translational Neurosurgery, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Microenvironment and Immunology Research Laboratory, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Department of Neurosurgery, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Department of Neurosurgery, Faculty of Medicine, Medical Center University of Freiburg, Freiburg, Germany
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- German Cancer Consortium (DKTK) partner site, Freiburg, Germany
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22
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Chen Q, Luo J, Liu J, Yu H, Zhou M, Yu L, Chen Y, Zhang S, Mo Z. Integrating single-cell and spatial transcriptomics to elucidate the crosstalk between cancer-associated fibroblasts and cancer cells in hepatocellular carcinoma with spleen-deficiency syndrome. J Tradit Complement Med 2024; 14:321-334. [PMID: 38707923 PMCID: PMC11068993 DOI: 10.1016/j.jtcme.2023.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 09/24/2023] [Accepted: 11/20/2023] [Indexed: 05/07/2024] Open
Abstract
Background and aim Most patients with hepatocellular carcinoma (HCC) in China have been diagnosed with spleen deficiency syndrome (SDS), which accelerates the progression of HCC by disrupting the tumor microenvironment homeostasis. This study aimed to investigate the intercellular crosstalk in HCC with SDS. Experimental procedure An HCC-SDS mouse model was established using orthotopic HCC transplantation based on reserpine-induced SDS. Single-cell data analysis and cancer cell prediction were conducted using Seurat and CopyKAT package, respectively. Intercellular interactions were explored using CellPhoneDB and CellChat and subsequently validated using co-culture assays, ELISA and histological staining. We performed pathway activity analysis using gene set variation analysis and the Seurat package. The extracellular matrix (ECM) remodeling was assessed using a gel contraction assay, atomic force microscopy, and Sirius red staining. The deconvolution of the spatial transcriptomics data using the "CARD" package based on single-cell data. Results and conclusion We successfully established the HCC-SDS mouse model. Twenty-nine clusters were identified. The interactions between cancer cells and cancer-associated fibroblasts (CAFs) were significantly enhanced via platelet-derived growth factor (PDGF) signaling in HCC-SDS. CAFs recruited in HCC-SDS lead to ECM remodeling and the activation of TGF-β signaling pathway. Deconvolution of the spatial transcriptome data revealed that CAFs physically surround cancer cells in HCC-SDS. This study reveals that the crosstalk of CAFs-cancer cells is crucial for the tumor-promoting effect of SDS. CAFs recruited by HCC via PDGFA may lead to ECM remodeling through activation of the TGF-β pathway, thereby forming a physical barrier to block immune cell infiltration under SDS.
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Affiliation(s)
- Qiuxia Chen
- Department of Traditional Chinese Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, 510080, China
| | - Jin Luo
- Department of Traditional Chinese Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, 510080, China
| | - Jiahui Liu
- Department of Traditional Chinese Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, 510080, China
| | - He Yu
- Department of Traditional Chinese Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, 510080, China
| | - Meiling Zhou
- Department of Traditional Chinese Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, 510080, China
| | - Ling Yu
- Department of Critical Care Medicine, The Second Clinical College to Guangzhou University of Chinese Medicine, Guangzhou, 510120, China
| | - Yan Chen
- Department of Traditional Chinese Medicine, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, 510630, China
| | - Shijun Zhang
- Department of Traditional Chinese Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, 510080, China
| | - Zhuomao Mo
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, Zhejiang Province, 311113, China
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23
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Zhou H, Shen Y, Zheng G, Zhang B, Wang A, Zhang J, Hu H, Lin J, Liu S, Luan X, Zhang W. Integrating single-cell and spatial analysis reveals MUC1-mediated cellular crosstalk in mucinous colorectal adenocarcinoma. Clin Transl Med 2024; 14:e1701. [PMID: 38778448 PMCID: PMC11111627 DOI: 10.1002/ctm2.1701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 04/26/2024] [Accepted: 04/29/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Mucinous colorectal adenocarcinoma (MCA) is a distinct subtype of colorectal cancer (CRC) with the most aggressive pattern, but effective treatment of MCA remains a challenge due to its vague pathological characteristics. An in-depth understanding of transcriptional dynamics at the cellular level is critical for developing specialised MCA treatment strategies. METHODS We integrated single-cell RNA sequencing and spatial transcriptomics data to systematically profile the MCA tumor microenvironment (TME), particularly the interactome of stromal and immune cells. In addition, a three-dimensional bioprinting technique, canonical ex vivo co-culture system, and immunofluorescence staining were further applied to validate the cellular communication networks within the TME. RESULTS This study identified the crucial intercellular interactions that engaged in MCA pathogenesis. We found the increased infiltration of FGF7+/THBS1+ myofibroblasts in MCA tissues with decreased expression of genes associated with leukocyte-mediated immunity and T cell activation, suggesting a crucial role of these cells in regulating the immunosuppressive TME. In addition, MS4A4A+ macrophages that exhibit M2-phenotype were enriched in the tumoral niche and high expression of MS4A4A+ was associated with poor prognosis in the cohort data. The ligand-receptor-based intercellular communication analysis revealed the tight interaction of MUC1+ malignant cells and ZEB1+ endothelial cells, providing mechanistic information for MCA angiogenesis and molecular targets for subsequent translational applications. CONCLUSIONS Our study provides novel insights into communications among tumour cells with stromal and immune cells that are significantly enriched in the TME during MCA progression, presenting potential prognostic biomarkers and therapeutic strategies for MCA. KEY POINTS Tumour microenvironment profiling of MCA is developed. MUC1+ tumour cells interplay with FGF7+/THBS1+ myofibroblasts to promote MCA development. MS4A4A+ macrophages exhibit M2 phenotype in MCA. ZEB1+ endotheliocytes engage in EndMT process in MCA.
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Affiliation(s)
- Haiyang Zhou
- Shanghai Frontiers Science Center of TCM Chemical BiologyInstitute of Interdisciplinary Integrative Medicine ResearchShanghai University of Traditional Chinese MedicineShanghaiChina
- Department of Colorectal SurgeryChangzheng HospitalNaval Medical UniversityShanghaiChina
| | - Yiwen Shen
- Shanghai Frontiers Science Center of TCM Chemical BiologyInstitute of Interdisciplinary Integrative Medicine ResearchShanghai University of Traditional Chinese MedicineShanghaiChina
| | - Guangyong Zheng
- Shanghai Frontiers Science Center of TCM Chemical BiologyInstitute of Interdisciplinary Integrative Medicine ResearchShanghai University of Traditional Chinese MedicineShanghaiChina
| | - Beibei Zhang
- Department of DermatologyTongren HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Anqi Wang
- Department of Colorectal SurgeryChangzheng HospitalNaval Medical UniversityShanghaiChina
| | - Jing Zhang
- Department of PathologyChangzheng HospitalNaval Medical UniversityShanghaiChina
| | - Hao Hu
- Department of PathologyChanghai HospitalNaval Medical UniversityShanghaiChina
| | - Jiayi Lin
- Shanghai Frontiers Science Center of TCM Chemical BiologyInstitute of Interdisciplinary Integrative Medicine ResearchShanghai University of Traditional Chinese MedicineShanghaiChina
| | - Sanhong Liu
- Shanghai Frontiers Science Center of TCM Chemical BiologyInstitute of Interdisciplinary Integrative Medicine ResearchShanghai University of Traditional Chinese MedicineShanghaiChina
| | - Xin Luan
- Shanghai Frontiers Science Center of TCM Chemical BiologyInstitute of Interdisciplinary Integrative Medicine ResearchShanghai University of Traditional Chinese MedicineShanghaiChina
| | - Weidong Zhang
- Shanghai Frontiers Science Center of TCM Chemical BiologyInstitute of Interdisciplinary Integrative Medicine ResearchShanghai University of Traditional Chinese MedicineShanghaiChina
- Institute of Medicinal Plant DevelopmentChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
- School of PharmacyNaval Medical UniversityShanghaiChina
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24
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Kypraios A, Bennour J, Imbert V, David L, Calvo J, Pflumio F, Bonnet R, Couralet M, Magnone V, Lebrigand K, Barbry P, Rohrlich PS, Peyron JF. Identifying Candidate Gene Drivers Associated with Relapse in Pediatric T-Cell Acute Lymphoblastic Leukemia Using a Gene Co-Expression Network Approach. Cancers (Basel) 2024; 16:1667. [PMID: 38730619 PMCID: PMC11083586 DOI: 10.3390/cancers16091667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 04/22/2024] [Accepted: 04/23/2024] [Indexed: 05/13/2024] Open
Abstract
Pediatric T-cell Acute Lymphoblastic Leukemia (T-ALL) relapses are still associated with a dismal outcome, justifying the search for new therapeutic targets and relapse biomarkers. Using single-cell RNA sequencing (scRNAseq) data from three paired samples of pediatric T-ALL at diagnosis and relapse, we first conducted a high-dimensional weighted gene co-expression network analysis (hdWGCNA). This analysis highlighted several gene co-expression networks (GCNs) and identified relapse-associated hub genes, which are considered potential driver genes. Shared relapse-expressed genes were found to be related to antigen presentation (HLA, B2M), cytoskeleton remodeling (TUBB, TUBA1B), translation (ribosomal proteins, EIF1, EEF1B2), immune responses (MIF, EMP3), stress responses (UBC, HSP90AB1/AA1), metabolism (FTH1, NME1/2, ARCL4C), and transcriptional remodeling (NF-κB family genes, FOS-JUN, KLF2, or KLF6). We then utilized sparse partial least squares discriminant analysis to select from a pool of 481 unique leukemic hub genes, which are the genes most discriminant between diagnosis and relapse states (comprising 44, 35, and 31 genes, respectively, for each patient). Applying a Cox regression method to these patient-specific genes, along with transcriptomic and clinical data from the TARGET-ALL AALL0434 cohort, we generated three model gene signatures that efficiently identified relapsed patients within the cohort. Overall, our approach identified new potential relapse-associated genes and proposed three model gene signatures associated with lower survival rates for high-score patients.
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Affiliation(s)
- Anthony Kypraios
- Université Côte d’Azur, Inserm C3M, 06200 Nice, France (V.I.); (L.D.); (R.B.); (P.S.R.)
- Team#4: “Fundamental to Translational Research on Dysregulated Hematopoiesis—DysHema”, Centre Méditerranéen de Médecine Moléculaire-C3M-Inserm U1065, Bâtiment Universitaire ARCHIMED, 151 Route Saint Antoine de Ginestière, BP 2 3194, CEDEX 3, 06204 Nice, France
| | - Juba Bennour
- Université Côte d’Azur, Inserm C3M, 06200 Nice, France (V.I.); (L.D.); (R.B.); (P.S.R.)
- Team#4: “Fundamental to Translational Research on Dysregulated Hematopoiesis—DysHema”, Centre Méditerranéen de Médecine Moléculaire-C3M-Inserm U1065, Bâtiment Universitaire ARCHIMED, 151 Route Saint Antoine de Ginestière, BP 2 3194, CEDEX 3, 06204 Nice, France
| | - Véronique Imbert
- Université Côte d’Azur, Inserm C3M, 06200 Nice, France (V.I.); (L.D.); (R.B.); (P.S.R.)
- Team#4: “Fundamental to Translational Research on Dysregulated Hematopoiesis—DysHema”, Centre Méditerranéen de Médecine Moléculaire-C3M-Inserm U1065, Bâtiment Universitaire ARCHIMED, 151 Route Saint Antoine de Ginestière, BP 2 3194, CEDEX 3, 06204 Nice, France
| | - Léa David
- Université Côte d’Azur, Inserm C3M, 06200 Nice, France (V.I.); (L.D.); (R.B.); (P.S.R.)
- Team#4: “Fundamental to Translational Research on Dysregulated Hematopoiesis—DysHema”, Centre Méditerranéen de Médecine Moléculaire-C3M-Inserm U1065, Bâtiment Universitaire ARCHIMED, 151 Route Saint Antoine de Ginestière, BP 2 3194, CEDEX 3, 06204 Nice, France
| | - Julien Calvo
- Team#4: “Fundamental to Translational Research on Dysregulated Hematopoiesis—DysHema”, Centre Méditerranéen de Médecine Moléculaire-C3M-Inserm U1065, Bâtiment Universitaire ARCHIMED, 151 Route Saint Antoine de Ginestière, BP 2 3194, CEDEX 3, 06204 Nice, France
| | - Françoise Pflumio
- Team#4: “Fundamental to Translational Research on Dysregulated Hematopoiesis—DysHema”, Centre Méditerranéen de Médecine Moléculaire-C3M-Inserm U1065, Bâtiment Universitaire ARCHIMED, 151 Route Saint Antoine de Ginestière, BP 2 3194, CEDEX 3, 06204 Nice, France
| | - Raphaël Bonnet
- Université Côte d’Azur, Inserm C3M, 06200 Nice, France (V.I.); (L.D.); (R.B.); (P.S.R.)
- Team#4: “Fundamental to Translational Research on Dysregulated Hematopoiesis—DysHema”, Centre Méditerranéen de Médecine Moléculaire-C3M-Inserm U1065, Bâtiment Universitaire ARCHIMED, 151 Route Saint Antoine de Ginestière, BP 2 3194, CEDEX 3, 06204 Nice, France
| | - Marie Couralet
- Université de Paris, Inserm, CEA, 92260 Fontenay-aux-Roses, France
- Université Côte d’Azur, CNRS, IPMC, 06560 Valbonne, France; (M.C.); (V.M.); (K.L.)
| | - Virginie Magnone
- Université de Paris, Inserm, CEA, 92260 Fontenay-aux-Roses, France
- Université Côte d’Azur, CNRS, IPMC, 06560 Valbonne, France; (M.C.); (V.M.); (K.L.)
| | - Kevin Lebrigand
- Université de Paris, Inserm, CEA, 92260 Fontenay-aux-Roses, France
- Université Côte d’Azur, CNRS, IPMC, 06560 Valbonne, France; (M.C.); (V.M.); (K.L.)
| | - Pascal Barbry
- Université Côte d’Azur, Inserm C3M, 06200 Nice, France (V.I.); (L.D.); (R.B.); (P.S.R.)
- Team#4: “Fundamental to Translational Research on Dysregulated Hematopoiesis—DysHema”, Centre Méditerranéen de Médecine Moléculaire-C3M-Inserm U1065, Bâtiment Universitaire ARCHIMED, 151 Route Saint Antoine de Ginestière, BP 2 3194, CEDEX 3, 06204 Nice, France
- CHU de Nice, Hôpital de l’Archet, 06000 Nice, France
| | - Pierre S. Rohrlich
- Université Côte d’Azur, Inserm C3M, 06200 Nice, France (V.I.); (L.D.); (R.B.); (P.S.R.)
- Team#4: “Fundamental to Translational Research on Dysregulated Hematopoiesis—DysHema”, Centre Méditerranéen de Médecine Moléculaire-C3M-Inserm U1065, Bâtiment Universitaire ARCHIMED, 151 Route Saint Antoine de Ginestière, BP 2 3194, CEDEX 3, 06204 Nice, France
- CHU de Nice, Hôpital de l’Archet, 06000 Nice, France
| | - Jean-François Peyron
- Université Côte d’Azur, Inserm C3M, 06200 Nice, France (V.I.); (L.D.); (R.B.); (P.S.R.)
- CHU de Nice, Hôpital de l’Archet, 06000 Nice, France
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25
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Ahmad Zawawi SS, Salleh EA, Musa M. Spheroids and organoids derived from colorectal cancer as tools for in vitro drug screening. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2024; 5:409-431. [PMID: 38745769 PMCID: PMC11090692 DOI: 10.37349/etat.2024.00226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 02/02/2024] [Indexed: 05/16/2024] Open
Abstract
Colorectal cancer (CRC) is a heterogeneous disease. Conventional two-dimensional (2D) culture employing cell lines was developed to study the molecular properties of CRC in vitro. Although these cell lines which are isolated from the tumor niche in which cancer develop, the translation to human model such as studying drug response is often hindered by the inability of cell lines to recapture original tumor features and the lack of heterogeneous clinical tumors represented by this 2D model, differed from in vivo condition. These limitations which may be overcome by utilizing three-dimensional (3D) culture consisting of spheroids and organoids. Over the past decade, great advancements have been made in optimizing culture method to establish spheroids and organoids of solid tumors including of CRC for multiple purposes including drug screening and establishing personalized medicine. These structures have been proven to be versatile and robust models to study CRC progression and deciphering its heterogeneity. This review will describe on advances in 3D culture technology and the application as well as the challenges of CRC-derived spheroids and organoids as a mode to screen for anticancer drugs.
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Affiliation(s)
| | - Elyn Amiela Salleh
- Human Genome Centre, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Malaysia
| | - Marahaini Musa
- Human Genome Centre, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Malaysia
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26
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Noh SU, Lim J, Shin SW, Kim Y, Park WY, Batinic-Haberle I, Choi C, Park W. Single-Cell Profiling Reveals Immune-Based Mechanisms Underlying Tumor Radiosensitization by a Novel Mn Porphyrin Clinical Candidate, MnTnBuOE-2-PyP 5+ (BMX-001). Antioxidants (Basel) 2024; 13:477. [PMID: 38671924 PMCID: PMC11047573 DOI: 10.3390/antiox13040477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 04/08/2024] [Accepted: 04/12/2024] [Indexed: 04/28/2024] Open
Abstract
Manganese porphyrins reportedly exhibit synergic effects when combined with irradiation. However, an in-depth understanding of intratumoral heterogeneity and immune pathways, as affected by Mn porphyrins, remains limited. Here, we explored the mechanisms underlying immunomodulation of a clinical candidate, MnTnBuOE-2-PyP5+ (BMX-001, MnBuOE), using single-cell analysis in a murine carcinoma model. Mice bearing 4T1 tumors were divided into four groups: control, MnBuOE, radiotherapy (RT), and combined MnBuOE and radiotherapy (MnBuOE/RT). In epithelial cells, the epithelial-mesenchymal transition, TNF-α signaling via NF-кB, angiogenesis, and hypoxia-related genes were significantly downregulated in the MnBuOE/RT group compared with the RT group. All subtypes of cancer-associated fibroblasts (CAFs) were clearly reduced in MnBuOE and MnBuOE/RT. Inhibitory receptor-ligand interactions, in which epithelial cells and CAFs interacted with CD8+ T cells, were significantly lower in the MnBuOE/RT group than in the RT group. Trajectory analysis showed that dendritic cells maturation-associated markers were increased in MnBuOE/RT. M1 macrophages were significantly increased in the MnBuOE/RT group compared with the RT group, whereas myeloid-derived suppressor cells were decreased. CellChat analysis showed that the number of cell-cell communications was the lowest in the MnBuOE/RT group. Our study is the first to provide evidence for the combined radiotherapy with a novel Mn porphyrin clinical candidate, BMX-001, from the perspective of each cell type within the tumor microenvironment.
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Affiliation(s)
- Sun Up Noh
- Department of Radiation Oncology, Samsung Medical Center, Seoul 06351, Republic of Korea; (S.U.N.); (S.-W.S.); (Y.K.)
- Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Jinyeong Lim
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul 06351, Republic of Korea; (J.L.); (W.-Y.P.)
- Samsung Genome Institute, Samsung Medical Center, Seoul 06351, Republic of Korea
| | - Sung-Won Shin
- Department of Radiation Oncology, Samsung Medical Center, Seoul 06351, Republic of Korea; (S.U.N.); (S.-W.S.); (Y.K.)
| | - Yeeun Kim
- Department of Radiation Oncology, Samsung Medical Center, Seoul 06351, Republic of Korea; (S.U.N.); (S.-W.S.); (Y.K.)
| | - Woong-Yang Park
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul 06351, Republic of Korea; (J.L.); (W.-Y.P.)
- Samsung Genome Institute, Samsung Medical Center, Seoul 06351, Republic of Korea
| | - Ines Batinic-Haberle
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC 27710, USA;
| | - Changhoon Choi
- Department of Radiation Oncology, Samsung Medical Center, Seoul 06351, Republic of Korea; (S.U.N.); (S.-W.S.); (Y.K.)
| | - Won Park
- Department of Radiation Oncology, Samsung Medical Center, Seoul 06351, Republic of Korea; (S.U.N.); (S.-W.S.); (Y.K.)
- Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
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27
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Su Z, He Y, You L, Zhang G, Chen J, Liu Z. Coupled scRNA-seq and Bulk-seq reveal the role of HMMR in hepatocellular carcinoma. Front Immunol 2024; 15:1363834. [PMID: 38633247 PMCID: PMC11021596 DOI: 10.3389/fimmu.2024.1363834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Accepted: 03/20/2024] [Indexed: 04/19/2024] Open
Abstract
Background Hyaluronan-mediated motility receptor (HMMR) is overexpressed in multiple carcinomas and influences the development and treatment of several cancers. However, its role in hepatocellular carcinoma (HCC) remains unclear. Methods The "limma" and "GSVA" packages in R were used to perform differential expression analysis and to assess the activity of signalling pathways, respectively. InferCNV was used to infer copy number variation (CNV) for each hepatocyte and "CellChat" was used to analyse intercellular communication networks. Recursive partitioning analysis (RPA) was used to re-stage HCC patients. The IC50 values of various drugs were evaluated using the "pRRophetic" package. In addition, quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to confirm HMMR expression in an HCC tissue microarray. Flow cytometry (FCM) and cloning, Edu and wound healing assays were used to explore the capacity of HMMR to regulate HCC tumour. Results Multiple cohort studies and qRT-PCR demonstrated that HMMR was overexpressed in HCC tissue compared with normal tissue. In addition, HMMR had excellent diagnostic performance. HMMR knockdown inhibited the proliferation and migration of HCC cells in vitro. Moreover, high HMMR expression was associated with "G2M checkpoint" and "E2F targets" in bulk RNA and scRNA-seq, and FCM confirmed that HMMR could regulate the cell cycle. In addition, HMMR was involved in the regulation of the tumour immune microenvironment via immune cell infiltration and intercellular interactions. Furthermore, HMMR was positively associated with genomic heterogeneity with patients with high HMMR expression potentially benefitting more from immunotherapy. Moreover, HMMR was associated with poor prognosis in patients with HCC and the re-staging by recursive partitioning analysis (RPA) gave a good prognosis prediction value and could guide chemotherapy and targeted therapy. Conclusion The results of the present study show that HMMR could play a role in the diagnosis, prognosis, and treatments of patients with HCC based on bulk RNA-seq and scRAN-seq analyses and is a promising molecular marker for HCC.
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Affiliation(s)
| | | | | | - Guifeng Zhang
- Department of Oncology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Jingbo Chen
- Department of Oncology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Zhenhua Liu
- Department of Oncology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
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28
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Li M. Harnessing atomic force microscopy-based single-cell analysis to advance physical oncology. Microsc Res Tech 2024; 87:631-659. [PMID: 38053519 DOI: 10.1002/jemt.24467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 12/07/2023]
Abstract
Single-cell analysis is an emerging and promising frontier in the field of life sciences, which is expected to facilitate the exploration of fundamental laws of physiological and pathological processes. Single-cell analysis allows experimental access to cell-to-cell heterogeneity to reveal the distinctive behaviors of individual cells, offering novel opportunities to dissect the complexity of severe human diseases such as cancers. Among the single-cell analysis tools, atomic force microscopy (AFM) is a powerful and versatile one which is able to nondestructively image the fine topographies and quantitatively measure multiple mechanical properties of single living cancer cells in their native states under aqueous conditions with unprecedented spatiotemporal resolution. Over the past few decades, AFM has been widely utilized to detect the structural and mechanical behaviors of individual cancer cells during the process of tumor formation, invasion, and metastasis, yielding numerous unique insights into tumor pathogenesis from the biomechanical perspective and contributing much to the field of cancer mechanobiology. Here, the achievements of AFM-based analysis of single cancer cells to advance physical oncology are comprehensively summarized, and challenges and future perspectives are also discussed. RESEARCH HIGHLIGHTS: Achievements of AFM in characterizing the structural and mechanical behaviors of single cancer cells are summarized, and future directions are discussed. AFM is not only capable of visualizing cellular fine structures, but can also measure multiple cellular mechanical properties as well as cell-generated mechanical forces. There is still plenty of room for harnessing AFM-based single-cell analysis to advance physical oncology.
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Affiliation(s)
- Mi Li
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
- University of Chinese Academy of Sciences, Beijing, China
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Liu Q, Lan J, Martínez-Jarquín S, Ge W, Zenobi R. Screening Metabolic Biomarkers in KRAS Mutated Mouse Acinar and Human Pancreatic Cancer Cells via Single-Cell Mass Spectrometry. Anal Chem 2024; 96:4918-4924. [PMID: 38471062 DOI: 10.1021/acs.analchem.3c05741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
Pancreatic cancer is a highly aggressive and rapidly progressing disease, often diagnosed in advanced stages due to the absence of early noticeable symptoms. The KRAS mutation is a hallmark of pancreatic cancer, yet the underlying mechanisms driving pancreatic carcinogenesis remain elusive. Cancer cells display significant metabolic heterogeneity, which is relevant to the pathogenesis of cancer. Population measurements may obscure information about the metabolic heterogeneity among cancer cells. Therefore, it is crucial to analyze metabolites at the single-cell level to gain a more comprehensive understanding of metabolic heterogeneity. In this study, we employed a 3D-printed ionization source for metabolite analysis in both mice and human pancreatic cancer cells at the single-cell level. Using advanced machine learning algorithms and mass spectral feature selection, we successfully identified 23 distinct metabolites that are statistically significantly different in KRAS mutant human pancreatic cancer cells and mouse acinar cells bearing the oncogenic KRAS mutation. These metabolites encompass a variety of chemical classes, including organic nitrogen compounds, organic acids and derivatives, organoheterocyclic compounds, benzenoids, and lipids. These findings shed light on the metabolic remodeling associated with KRAS-driven pancreatic cancer initiation and indicate that the identified metabolites hold promise as potential diagnostic markers for early detection in pancreatic cancer patients.
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Affiliation(s)
- Qinlei Liu
- Key Laboratory of Green Chemistry and Technology of Ministry of Education, College of Chemistry, Sichuan University, Chengdu, Sichuan 610065, China
- Department of Chemistry and Applied Biosciences, ETH Zurich, CH-8093 Zurich, Switzerland
| | - Jiayi Lan
- Department of Chemistry and Applied Biosciences, ETH Zurich, CH-8093 Zurich, Switzerland
| | - Sandra Martínez-Jarquín
- Department of Chemistry and Applied Biosciences, ETH Zurich, CH-8093 Zurich, Switzerland
- Advanced Materials and Healthcare Technologies Division, School of Pharmacym, University of Nottingha, University Park, NG7 2RD Nottingham, United Kingdom
| | - Wenjie Ge
- Cancer Institute, Xuzhou Medical University, 209 Tongshan Road, Xuzhou, Jiangsu 221004, China
- Center of Clinical Oncology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Xuzhou, Jiangsu 221002, China
- Jiangsu Center for the Collaboration and Innovation of Cancer Biotherapy, Xuzhou Medical University, 209 Tongshan Road, Xuzhou, Jiangsu 221004, China
- Department of Biology, ETH Zurich, Otto-Stern-Weg 7, CH-8093 Zurich, Switzerland
| | - Renato Zenobi
- Department of Chemistry and Applied Biosciences, ETH Zurich, CH-8093 Zurich, Switzerland
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Liu ZL, Meng XY, Bao RJ, Shen MY, Sun JJ, Chen WD, Liu F, He Y. Single cell deciphering of progression trajectories of the tumor ecosystem in head and neck cancer. Nat Commun 2024; 15:2595. [PMID: 38519500 PMCID: PMC10959966 DOI: 10.1038/s41467-024-46912-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 03/14/2024] [Indexed: 03/25/2024] Open
Abstract
Head and neck squamous cell carcinoma is the sixth most common cancer worldwide and has high heterogeneity and unsatisfactory outcomes. To better characterize the tumor progression trajectory, we perform single-cell RNA sequencing of normal tissue, precancerous tissue, early-stage, advanced-stage cancer tissue, lymph node, and recurrent tumors tissue samples. We identify the transcriptional development trajectory of malignant epithelial cells and a tumorigenic epithelial subcluster regulated by TFDP1. Furthermore, we find that the infiltration of POSTN+ fibroblasts and SPP1+ macrophages gradually increases with tumor progression; their interaction or interaction with malignant cells also gradually increase to shape the desmoplastic microenvironment and reprogram malignant cells to promote tumor progression. Additionally, we demonstrate that during lymph node metastasis, exhausted CD8+ T cells with high CXCL13 expression strongly interact with tumor cells to acquire more aggressive phenotypes of extranodal expansion. Finally, we delineate the distinct features of malignant epithelial cells in primary and recurrent tumors, providing a theoretical foundation for the precise selection of targeted therapy for tumors at different stages. In summary, the current study offers a comprehensive landscape and deep insight into epithelial and microenvironmental reprogramming throughout initiation, progression, lymph node metastasis and recurrence of head and neck squamous cell carcinoma.
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Affiliation(s)
- Z L Liu
- Department of Oral Maxillofacial & Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology Shanghai, Shanghai, 200011, China
| | - X Y Meng
- Department of Oral Maxillofacial & Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology Shanghai, Shanghai, 200011, China
| | - R J Bao
- Shanghai Institute of Immunology, State Key Laboratory of Oncogenes and Related Genes, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - M Y Shen
- Shanghai Institute of Immunology, State Key Laboratory of Oncogenes and Related Genes, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - J J Sun
- Department of Oral Pathology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology Shanghai, Shanghai, 200011, China
| | - W D Chen
- Novel Bioinformatics Co., Ltd, Shanghai, China
| | - F Liu
- Department of Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
| | - Y He
- Department of Oral Maxillofacial & Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology Shanghai, Shanghai, 200011, China.
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31
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Li JT, Zhang HM, Wang W, Wei DQ. Identification of an immune-related gene signature for predicting prognosis and immunotherapy efficacy in liver cancer via cell-cell communication. World J Gastroenterol 2024; 30:1609-1620. [PMID: 38617448 PMCID: PMC11008408 DOI: 10.3748/wjg.v30.i11.1609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/09/2024] [Accepted: 03/04/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Liver cancer is one of the deadliest malignant tumors worldwide. Immunotherapy has provided hope to patients with advanced liver cancer, but only a small fraction of patients benefit from this treatment due to individual differences. Identifying immune-related gene signatures in liver cancer patients not only aids physicians in cancer diagnosis but also offers personalized treatment strategies, thereby improving patient survival rates. Although several methods have been developed to predict the prognosis and immunotherapeutic efficacy in patients with liver cancer, the impact of cell-cell interactions in the tumor microenvironment has not been adequately considered. AIM To identify immune-related gene signals for predicting liver cancer prognosis and immunotherapy efficacy. METHODS Cell grouping and cell-cell communication analysis were performed on single-cell RNA-sequencing data to identify highly active cell groups in immune-related pathways. Highly active immune cells were identified by intersecting the highly active cell groups with B cells and T cells. The significantly differentially expressed genes between highly active immune cells and other cells were subsequently selected as features, and a least absolute shrinkage and selection operator (LASSO) regression model was constructed to screen for diagnostic-related features. Fourteen genes that were selected more than 5 times in 10 LASSO regression experiments were included in a multivariable Cox regression model. Finally, 3 genes (stathmin 1, cofilin 1, and C-C chemokine ligand 5) significantly associated with survival were identified and used to construct an immune-related gene signature. RESULTS The immune-related gene signature composed of stathmin 1, cofilin 1, and C-C chemokine ligand 5 was identified through cell-cell communication. The effectiveness of the identified gene signature was validated based on experimental results of predictive immunotherapy response, tumor mutation burden analysis, immune cell infiltration analysis, survival analysis, and expression analysis. CONCLUSION The findings suggest that the identified gene signature may contribute to a deeper understanding of the activity patterns of immune cells in the liver tumor microenvironment, providing insights for personalized treatment strategies.
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Affiliation(s)
- Jun-Tao Li
- College of Mathematics and Information Science, Henan Normal University, Xinxiang 453007, Henan Province, China
| | - Hong-Mei Zhang
- College of Mathematics and Information Science, Henan Normal University, Xinxiang 453007, Henan Province, China
| | - Wei Wang
- College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, Henan Province, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
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Ben S, Ma Y, Bai Y, Zhang Q, Zhao Y, Xia J, Yao M. Microglia-endothelial cross-talk regulates diabetes-induced retinal vascular dysfunction through remodeling inflammatory microenvironment. iScience 2024; 27:109145. [PMID: 38414848 PMCID: PMC10897849 DOI: 10.1016/j.isci.2024.109145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/02/2024] [Accepted: 02/01/2024] [Indexed: 02/29/2024] Open
Abstract
Inflammation-mediated crosstalk between neuroglial cells and endothelial cells (ECs) is a fundamental feature of many vascular diseases. Nevertheless, the landscape of inflammatory processes during diabetes-induced microvascular dysfunction remains elusive. Here, we applied single-cell RNA sequencing to elucidate the transcriptional landscape of diabetic retinopathy (DR). The transcriptome characteristics of microglia and ECs revealed two microglial subpopulations and three EC populations. Exploration of intercellular crosstalk between microglia and ECs showed that diabetes-induced interactions mainly participated in the inflammatory response and vessel development, with colony-stimulating factor 1 (CSF1) and CSF1 receptor (CSF1R) playing important roles in early cell differentiation. Clinically, we found that CSF1/CSF1R crosstalk dysregulation was associated with proliferative DR. Mechanistically, ECs secrete CSF1 and activate CSF1R endocytosis and the CSF1R phosphorylation-mediated MAPK signaling pathway, which elicits the differentiation of microglia and triggers the secretion of inflammatory factors, and subsequently foster angiogenesis by remodeling the inflammatory microenvironment through a positive feedback mechanism.
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Affiliation(s)
- Shuai Ben
- Department of Ophthalmology, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200080, China
- National Clinical Research Center for Ophthalmic Diseases, Shanghai 200080, China
- Shanghai Key Laboratory of Fundus Diseases, Shanghai 200080, China
| | - Yan Ma
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing 210000, China
- The Affiliated Eye Hospital, Nanjing Medical University, Nanjing 210000, China
| | - Yun Bai
- College of Information Science, Shanghai Ocean University, Shanghai 201306, China
| | - Qiuyang Zhang
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing 210000, China
- The Affiliated Eye Hospital, Nanjing Medical University, Nanjing 210000, China
| | - Ya Zhao
- Department of Ophthalmology, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200080, China
- National Clinical Research Center for Ophthalmic Diseases, Shanghai 200080, China
- Shanghai Key Laboratory of Fundus Diseases, Shanghai 200080, China
| | - Jiao Xia
- Department of Ophthalmology, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200080, China
- National Clinical Research Center for Ophthalmic Diseases, Shanghai 200080, China
- Shanghai Key Laboratory of Fundus Diseases, Shanghai 200080, China
| | - Mudi Yao
- Department of Ophthalmology, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200080, China
- National Clinical Research Center for Ophthalmic Diseases, Shanghai 200080, China
- Shanghai Key Laboratory of Fundus Diseases, Shanghai 200080, China
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33
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Wan R, Chen Y, Feng X, Luo Z, Peng Z, Qi B, Qin H, Lin J, Chen S, Xu L, Tang J, Zhang T. Exercise potentially prevents colorectal cancer liver metastases by suppressing tumor epithelial cell stemness via RPS4X downregulation. Heliyon 2024; 10:e26604. [PMID: 38439884 PMCID: PMC10909670 DOI: 10.1016/j.heliyon.2024.e26604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 02/15/2024] [Indexed: 03/06/2024] Open
Abstract
Background Colorectal cancer (CRC) is the third most prevalent tumor globally. The liver is the most common site for CRC metastasis, and the involvement of the liver is a common cause of death in patients with late-stage CRC. Consequently, mitigating CRC liver metastasis (CRLM) is key to improving CRC prognosis and increasing survival. Exercise has been shown to be an effective method of improving the prognosis of many tumor types. However, the ability of exercise to inhibit CRLM is yet to be thoroughly investigated. Methods The GSE157600 and GSE97084 datasets were used for analysis. A pan-cancer dataset which was uniformly normalized was downloaded and analyzed from the UCSC database: TCGA, TARGET, GTEx (PANCAN, n = 19,131, G = 60,499). Several advanced bioinformatics analyses were conducted, including single-cell sequencing analysis, correlation algorithm, and prognostic screen. CRC tumor microarray (TMA) as well as cell/animal experiments are used to further validate the results of the analysis. Results The greatest variability was found in epithelial cells from the tumor group. RPS4X was generally upregulated in all types of CRC, while exercise downregulated RPS4X expression. A lowered expression of RPS4X may prolong tumor survival and reduce CRC metastasis. RPS4X and tumor stemness marker-CD44 were highly positively correlated and knockdown of RPS4X expression reduced tumor stemness both in vitro and in vivo. Conclusion RPS4X upregulation may enhance CRC stemness and increase the odds of metastasis. Exercise may reduce CRC metastasis through the regulation of RPS4X.
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Affiliation(s)
- Renwen Wan
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Yisheng Chen
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Xinting Feng
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Zhiwen Luo
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Zhen Peng
- Department of Sports Medicine, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Beijie Qi
- Department of Orthopedics, Shanghai Pudong Hospital, Fudan University Affiliated Pudong Medical Center, Shanghai 201399, China
| | - Haocheng Qin
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jinrong Lin
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Shiyi Chen
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Liangfeng Xu
- Department of Gastroenterology, Sheyang County People's Hospital, Yancheng 224300, Jiangsu, China
| | - Jiayin Tang
- Department of Gastrointestinal Surgery, Renji Hospital, Shanghai 200127, China
| | - Ting Zhang
- Department of Integrative Medicine, Huashan Hospital, Fudan University, Shanghai, China
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34
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Roider T, Baertsch MA, Fitzgerald D, Vöhringer H, Brinkmann BJ, Czernilofsky F, Knoll M, Llaó-Cid L, Mathioudaki A, Faßbender B, Herbon M, Lautwein T, Bruch PM, Liebers N, Schürch CM, Passerini V, Seifert M, Brobeil A, Mechtersheimer G, Müller-Tidow C, Weigert O, Seiffert M, Nolan GP, Huber W, Dietrich S. Multimodal and spatially resolved profiling identifies distinct patterns of T cell infiltration in nodal B cell lymphoma entities. Nat Cell Biol 2024; 26:478-489. [PMID: 38379051 PMCID: PMC10940160 DOI: 10.1038/s41556-024-01358-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 01/15/2024] [Indexed: 02/22/2024]
Abstract
The redirection of T cells has emerged as an attractive therapeutic principle in B cell non-Hodgkin lymphoma (B-NHL). However, a detailed characterization of lymphoma-infiltrating T cells across B-NHL entities is missing. Here we present an in-depth T cell reference map of nodal B-NHL, based on cellular indexing of transcriptomes and epitopes, T cell receptor sequencing, flow cytometry and multiplexed immunofluorescence applied to 101 lymph nodes from patients with diffuse large B cell, mantle cell, follicular or marginal zone lymphoma, and from healthy controls. This multimodal resource revealed quantitative and spatial aberrations of the T cell microenvironment across and within B-NHL entities. Quantitative differences in PD1+ TCF7- cytotoxic T cells, T follicular helper cells or IKZF3+ regulatory T cells were linked to their clonal expansion. The abundance of PD1+ TCF7- cytotoxic T cells was associated with poor survival. Our study portrays lymphoma-infiltrating T cells with unprecedented comprehensiveness and provides a unique resource for the investigation of lymphoma biology and prognosis.
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Affiliation(s)
- Tobias Roider
- Department of Medicine V, Hematology, Oncology and Rheumatology, University of Heidelberg, Heidelberg, Germany
- Molecular Medicine Partnership Unit, Heidelberg, Germany
- European Molecular Biology Laboratory, Heidelberg, Germany
| | - Marc A Baertsch
- Department of Medicine V, Hematology, Oncology and Rheumatology, University of Heidelberg, Heidelberg, Germany
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | - Donnacha Fitzgerald
- Department of Medicine V, Hematology, Oncology and Rheumatology, University of Heidelberg, Heidelberg, Germany
- Molecular Medicine Partnership Unit, Heidelberg, Germany
- European Molecular Biology Laboratory, Heidelberg, Germany
| | - Harald Vöhringer
- Department of Medicine V, Hematology, Oncology and Rheumatology, University of Heidelberg, Heidelberg, Germany
- Molecular Medicine Partnership Unit, Heidelberg, Germany
- European Molecular Biology Laboratory, Heidelberg, Germany
| | - Berit J Brinkmann
- Department of Medicine V, Hematology, Oncology and Rheumatology, University of Heidelberg, Heidelberg, Germany
- Molecular Medicine Partnership Unit, Heidelberg, Germany
- European Molecular Biology Laboratory, Heidelberg, Germany
- Clinical Cooperation Unit Molecular Hematology/Oncology, German Cancer Research Center, Heidelberg, Germany
| | - Felix Czernilofsky
- Department of Medicine V, Hematology, Oncology and Rheumatology, University of Heidelberg, Heidelberg, Germany
- Molecular Medicine Partnership Unit, Heidelberg, Germany
- European Molecular Biology Laboratory, Heidelberg, Germany
| | - Mareike Knoll
- European Molecular Biology Laboratory, Heidelberg, Germany
| | - Laura Llaó-Cid
- Division of Molecular Genetics, German Cancer Research Center, Heidelberg, Germany
- Molecular Pathology of Lymphoid Neoplasms, Fundació de Recerca Clinic Barcelona-Institut d'Investigacions Biomèdiques August Pi i Sunyer (FRCB-IDIBAPS), Barcelona, Spain
| | | | - Bianca Faßbender
- Department of Hematology and Oncology, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Maxime Herbon
- Department of Hematology and Oncology, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Tobias Lautwein
- Genomics and Transcriptomics Laboratory, University of Düsseldorf, Düsseldorf, Germany
| | - Peter-Martin Bruch
- Department of Medicine V, Hematology, Oncology and Rheumatology, University of Heidelberg, Heidelberg, Germany
- Molecular Medicine Partnership Unit, Heidelberg, Germany
- European Molecular Biology Laboratory, Heidelberg, Germany
- Department of Hematology and Oncology, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Nora Liebers
- European Molecular Biology Laboratory, Heidelberg, Germany
- Department of Hematology and Oncology, University Hospital Düsseldorf, Düsseldorf, Germany
- National Center for Tumor Diseases, Heidelberg, Germany
- German Cancer Research Center, Heidelberg, Germany
| | - Christian M Schürch
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Verena Passerini
- Department of Medicine III, Laboratory for Experimental Leukemia and Lymphoma Research, Ludwig-Maximilians-University Hospital, Munich, Germany
| | - Marc Seifert
- Department of Hematology and Oncology, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Alexander Brobeil
- Department of Pathology, University of Heidelberg, Heidelberg, Germany
| | | | - Carsten Müller-Tidow
- Department of Medicine V, Hematology, Oncology and Rheumatology, University of Heidelberg, Heidelberg, Germany
- Molecular Medicine Partnership Unit, Heidelberg, Germany
| | - Oliver Weigert
- German Cancer Research Center, Heidelberg, Germany
- Department of Medicine III, Laboratory for Experimental Leukemia and Lymphoma Research, Ludwig-Maximilians-University Hospital, Munich, Germany
- German Cancer Consortium, Munich, Germany
| | - Martina Seiffert
- Division of Molecular Genetics, German Cancer Research Center, Heidelberg, Germany
| | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Wolfgang Huber
- Molecular Medicine Partnership Unit, Heidelberg, Germany.
- European Molecular Biology Laboratory, Heidelberg, Germany.
| | - Sascha Dietrich
- Department of Medicine V, Hematology, Oncology and Rheumatology, University of Heidelberg, Heidelberg, Germany.
- Molecular Medicine Partnership Unit, Heidelberg, Germany.
- European Molecular Biology Laboratory, Heidelberg, Germany.
- Department of Hematology and Oncology, University Hospital Düsseldorf, Düsseldorf, Germany.
- Center for Integrated Oncology Aachen-Bonn-Cologne-Düsseldorf (CIO ABCD), Aachen Bonn Cologne Düsseldorf, Germany.
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Revsine M, Wang L, Forgues M, Behrens S, Craig AJ, Liu M, Tran B, Kelly M, Budhu A, Monge C, Xie C, Hernandez JM, Greten TF, Wang XW, Ma L. Lineage and ecology define liver tumor evolution in response to treatment. Cell Rep Med 2024; 5:101394. [PMID: 38280378 PMCID: PMC10897542 DOI: 10.1016/j.xcrm.2024.101394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 11/09/2023] [Accepted: 01/03/2024] [Indexed: 01/29/2024]
Abstract
A tumor ecosystem constantly evolves over time in the face of immune predation or therapeutic intervention, resulting in treatment failure and tumor progression. Here, we present a single-cell transcriptome-based strategy to determine the evolution of longitudinal tumor biopsies from liver cancer patients by measuring cellular lineage and ecology. We construct a lineage and ecological score as joint dynamics of tumor cells and their microenvironments. Tumors may be classified into four main states in the lineage-ecological space, which are associated with clinical outcomes. Analysis of longitudinal samples reveals the evolutionary trajectory of tumors in response to treatment. We validate the lineage-ecology-based scoring system in predicting clinical outcomes using bulk transcriptomic data of additional cohorts of 716 liver cancer patients. Our study provides a framework for monitoring tumor evolution in response to therapeutic intervention.
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Affiliation(s)
- Mahler Revsine
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Limin Wang
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Marshonna Forgues
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Shay Behrens
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA; Surgical Oncology Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Amanda J Craig
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Meng Liu
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Bao Tran
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 20701, USA
| | - Michael Kelly
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD 20701, USA
| | - Anuradha Budhu
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA; Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Cecilia Monge
- Thoracic and GI Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Changqing Xie
- Thoracic and GI Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Jonathan M Hernandez
- Surgical Oncology Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Tim F Greten
- Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA; Thoracic and GI Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Xin Wei Wang
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA; Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA.
| | - Lichun Ma
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA; Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA.
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Chen E, Ling AL, Reardon DA, Chiocca EA. Lessons learned from phase 3 trials of immunotherapy for glioblastoma: Time for longitudinal sampling? Neuro Oncol 2024; 26:211-225. [PMID: 37995317 PMCID: PMC10836778 DOI: 10.1093/neuonc/noad211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2023] Open
Abstract
Glioblastoma (GBM)'s median overall survival is almost 21 months. Six phase 3 immunotherapy clinical trials have recently been published, yet 5/6 did not meet approval by regulatory bodies. For the sixth, approval is uncertain. Trial failures result from multiple factors, ranging from intrinsic tumor biology to clinical trial design. Understanding the clinical and basic science of these 6 trials is compelled by other immunotherapies reaching the point of advanced phase 3 clinical trial testing. We need to understand more of the science in human GBMs in early trials: the "window of opportunity" design may not be best to understand complex changes brought about by immunotherapeutic perturbations of the GBM microenvironment. The convergence of increased safety of image-guided biopsies with "multi-omics" of small cell numbers now permits longitudinal sampling of tumor and biofluids to dissect the complex temporal changes in the GBM microenvironment as a function of the immunotherapy.
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Affiliation(s)
- Ethan Chen
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Alexander L Ling
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - David A Reardon
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - E Antonio Chiocca
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA
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37
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Yu T, Xu-Monette ZY, Lagoo A, Shuai W, Wang B, Neff J, Carrillo LF, Carlsen ED, Pina-Oviedo S, Young KH. Flow cytometry quantification of tumor-infiltrating lymphocytes to predict the survival of patients with diffuse large B-cell lymphoma. Front Immunol 2024; 15:1335689. [PMID: 38348048 PMCID: PMC10859492 DOI: 10.3389/fimmu.2024.1335689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 01/08/2024] [Indexed: 02/15/2024] Open
Abstract
Introduction Our previous studies have demonstrated that tumor-infiltrating lymphocytes (TILs), including normal B cells, T cells, and natural killer (NK) cells, in diffuse large B-cell lymphoma (DLBCL) have a significantly favorable impact on the clinical outcomes of patients treated with standard chemoimmunotherapy. In this study, to gain a full overview of the tumor immune microenvironment (TIME), we assembled a flow cytometry cohort of 102 patients diagnosed with DLBCL at the Duke University Medical Center. Methods We collected diagnostic flow cytometry data, including the proportion of T cells, abnormal B cells, normal B cells, plasma cells, NK cells, monocytes, and granulocytes in fresh biopsy tissues at clinical presentation, and analyzed the correlations with patient survival and between different cell populations. Results We found that low T cell percentages in all viable cells and low ratios of T cells to abnormal B cells correlated with significantly poorer survival, whereas higher percentages of normal B cells among total B cells (or high ratios of normal B cells to abnormal B cells) and high percentages of NK cells among all viable cells correlated with significantly better survival in patients with DLBCL. After excluding a small number of patients with low T cell percentages, the normal B cell percentage among all B cells, but not T cell percentage among all cells, continued to show a remarkable prognostic effect. Data showed significant positive correlations between T cells and normal B cells, and between granulocytes and monocytes. Furthermore, we constructed a prognostic model based on clinical and flow cytometry factors, which divided the DLBCL cohort into two equal groups with remarkable differences in patient survival and treatment response. Summary TILs, including normal B cells, T cells, and NK cells, are associated with favorable clinical outcomes in DLBCL, and flow cytometry capable of quantifying the TIME may have additional clinical utility for prognostication.
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Affiliation(s)
- Tiantian Yu
- Hematopathology Division and Department of Pathology, Duke University Medical Center, Durham, NC, United States
| | - Zijun Y. Xu-Monette
- Hematopathology Division and Department of Pathology, Duke University Medical Center, Durham, NC, United States
- Duke University Cancer Institute, Durham, NC, United States
| | - Anand Lagoo
- Hematopathology Division and Department of Pathology, Duke University Medical Center, Durham, NC, United States
| | - Wen Shuai
- Hematopathology Division and Department of Pathology, Duke University Medical Center, Durham, NC, United States
| | - Bangchen Wang
- Department of Pathology, Duke University Medical Center, Durham, NC, United States
| | - Jadee Neff
- Hematopathology Division and Department of Pathology, Duke University Medical Center, Durham, NC, United States
| | - Luis F. Carrillo
- Hematopathology Division and Department of Pathology, Duke University Medical Center, Durham, NC, United States
| | - Eric D. Carlsen
- Hematopathology Division and Department of Pathology, Duke University Medical Center, Durham, NC, United States
- Duke University Cancer Institute, Durham, NC, United States
| | - Sergio Pina-Oviedo
- Hematopathology Division and Department of Pathology, Duke University Medical Center, Durham, NC, United States
| | - Ken H. Young
- Hematopathology Division and Department of Pathology, Duke University Medical Center, Durham, NC, United States
- Duke University Cancer Institute, Durham, NC, United States
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Zahedi R, Ghamsari R, Argha A, Macphillamy C, Beheshti A, Alizadehsani R, Lovell NH, Lotfollahi M, Alinejad-Rokny H. Deep learning in spatially resolved transcriptfomics: a comprehensive technical view. Brief Bioinform 2024; 25:bbae082. [PMID: 38483255 PMCID: PMC10939360 DOI: 10.1093/bib/bbae082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/22/2024] [Accepted: 02/13/2024] [Indexed: 03/17/2024] Open
Abstract
Spatially resolved transcriptomics (SRT) is a pioneering method for simultaneously studying morphological contexts and gene expression at single-cell precision. Data emerging from SRT are multifaceted, presenting researchers with intricate gene expression matrices, precise spatial details and comprehensive histology visuals. Such rich and intricate datasets, unfortunately, render many conventional methods like traditional machine learning and statistical models ineffective. The unique challenges posed by the specialized nature of SRT data have led the scientific community to explore more sophisticated analytical avenues. Recent trends indicate an increasing reliance on deep learning algorithms, especially in areas such as spatial clustering, identification of spatially variable genes and data alignment tasks. In this manuscript, we provide a rigorous critique of these advanced deep learning methodologies, probing into their merits, limitations and avenues for further refinement. Our in-depth analysis underscores that while the recent innovations in deep learning tailored for SRT have been promising, there remains a substantial potential for enhancement. A crucial area that demands attention is the development of models that can incorporate intricate biological nuances, such as phylogeny-aware processing or in-depth analysis of minuscule histology image segments. Furthermore, addressing challenges like the elimination of batch effects, perfecting data normalization techniques and countering the overdispersion and zero inflation patterns seen in gene expression is pivotal. To support the broader scientific community in their SRT endeavors, we have meticulously assembled a comprehensive directory of readily accessible SRT databases, hoping to serve as a foundation for future research initiatives.
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Affiliation(s)
- Roxana Zahedi
- UNSW BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
| | - Reza Ghamsari
- UNSW BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
| | - Ahmadreza Argha
- The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
- Tyree Institute of Health Engineering (IHealthE), UNSW Sydney, 2052, NSW, Australia
| | - Callum Macphillamy
- School of Animal and Veterinary Sciences, University of Adelaide, Roseworthy, 5371, Australia
| | - Amin Beheshti
- School of Computing, Macquarie University, Sydney, 2109, Australia
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, Melbourne, VIC, 3216, Australia
| | - Nigel H Lovell
- The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
- Tyree Institute of Health Engineering (IHealthE), UNSW Sydney, 2052, NSW, Australia
| | - Mohammad Lotfollahi
- Computational Health Center, Helmholtz Munich, Germany
- Wellcome Sanger Institute, Cambridge, UK
| | - Hamid Alinejad-Rokny
- UNSW BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
- Tyree Institute of Health Engineering (IHealthE), UNSW Sydney, 2052, NSW, Australia
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Wang S, An J, Hu X, Zeng T, Li P, Qin J, Shen Y, Chen M, Wen F. Single-cell RNA sequencing reveals immune microenvironment of small cell lung cancer-associated malignant pleural effusion. Thorac Cancer 2024; 15:98-103. [PMID: 38010064 PMCID: PMC10761622 DOI: 10.1111/1759-7714.15145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/16/2023] [Accepted: 10/18/2023] [Indexed: 11/29/2023] Open
Abstract
We used 10 × genomics single-cell transcriptome sequencing technology to reveal the tumor immune microenvironment characteristics of small cell lung cancer (SCLC) in a patient with malignant pleural effusion (MPE). A total of 8008 high-quality cells were finally obtained for subsequent bioinformatic analysis, which were divided into 10 cell clusters further identified as B cells, T cells, myeloid cells, NK cells, and cancer cells. Such SCLC related genes as NOTCH1, MYC, TSC22D1, SOX4, BLNK, YBX3, VIM, CD8A, CD8B, and KLF6 were expressed in different degrees during differentiation of T and B cells. Different ligands and receptors between T, B and tumor cells almost interact through MHC II, IL-16, galectin, and APP signaling pathway.
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Affiliation(s)
- Shuyan Wang
- Department of Respiratory and Critical Care MedicineWest China Hospital of Sichuan UniversityChengduChina
- Division of Pulmonary Diseases, State Key Laboratory of Biotherapy of ChinaWest China Hospital, West China School of Medicine, Sichuan UniversityChengduChina
| | - Jing An
- Department of Respiratory and Critical Care MedicineWest China Hospital of Sichuan UniversityChengduChina
- Division of Pulmonary Diseases, State Key Laboratory of Biotherapy of ChinaWest China Hospital, West China School of Medicine, Sichuan UniversityChengduChina
| | - Xueru Hu
- Department of Respiratory and Critical Care MedicineWest China Hospital of Sichuan UniversityChengduChina
- Division of Pulmonary Diseases, State Key Laboratory of Biotherapy of ChinaWest China Hospital, West China School of Medicine, Sichuan UniversityChengduChina
| | - Tingting Zeng
- Department of Respiratory and Critical Care MedicineWest China Hospital of Sichuan UniversityChengduChina
- Division of Pulmonary Diseases, State Key Laboratory of Biotherapy of ChinaWest China Hospital, West China School of Medicine, Sichuan UniversityChengduChina
| | - Ping Li
- Department of Respiratory and Critical Care MedicineWest China Hospital of Sichuan UniversityChengduChina
- Division of Pulmonary Diseases, State Key Laboratory of Biotherapy of ChinaWest China Hospital, West China School of Medicine, Sichuan UniversityChengduChina
| | - Jiangyue Qin
- Department of Respiratory and Critical Care MedicineWest China Hospital of Sichuan UniversityChengduChina
- Division of Pulmonary Diseases, State Key Laboratory of Biotherapy of ChinaWest China Hospital, West China School of Medicine, Sichuan UniversityChengduChina
| | - Yongchun Shen
- Department of Respiratory and Critical Care MedicineWest China Hospital of Sichuan UniversityChengduChina
- Division of Pulmonary Diseases, State Key Laboratory of Biotherapy of ChinaWest China Hospital, West China School of Medicine, Sichuan UniversityChengduChina
| | - Mei Chen
- School of Medical and Life SciencesChengdu University of Traditional Chinese MedicineChengduChina
- Key Laboratory of Acupuncture for Senile Disease(Chengdu University of TCM), Ministry of EducationChengduChina
- Department of Respiratory and Critical Care MedicineChengdu Fifth People's HospitalChengduChina
| | - Fuqiang Wen
- Department of Respiratory and Critical Care MedicineWest China Hospital of Sichuan UniversityChengduChina
- Division of Pulmonary Diseases, State Key Laboratory of Biotherapy of ChinaWest China Hospital, West China School of Medicine, Sichuan UniversityChengduChina
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Xiu Z, Yang Q, Xie F, Han F, He W, Liao W. Revolutionizing digestive system tumor organoids research: Exploring the potential of tumor organoids. J Tissue Eng 2024; 15:20417314241255470. [PMID: 38808253 PMCID: PMC11131411 DOI: 10.1177/20417314241255470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 05/02/2024] [Indexed: 05/30/2024] Open
Abstract
Digestive system tumors are the leading cause of cancer-related deaths worldwide. Despite ongoing research, our understanding of their mechanisms and treatment remain inadequate. One promising tool for clinical applications is the use of gastrointestinal tract tumor organoids, which serve as an important in vitro model. Tumor organoids exhibit a genotype similar to the patient's tumor and effectively mimic various biological processes, including tissue renewal, stem cell, and ecological niche functions, and tissue response to drugs, mutations, or injury. As such, they are valuable for drug screening, developing novel drugs, assessing patient outcomes, and supporting immunotherapy. In addition, innovative materials and techniques can be used to optimize tumor organoid culture systems. Several applications of digestive system tumor organoids have been described and have shown promising results in related aspects. In this review, we discuss the current progress, limitations, and prospects of this model for digestive system tumors.
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Affiliation(s)
- Zhian Xiu
- Department of Medical Laboratory, Clinical Medical College, Affiliated Hospital of Jiujiang University, Jiujiang, Jiangxi, China
- Jiujiang Clinical Precision Medicine Research Center, Jiujiang, Jiangxi, China
| | - Qian Yang
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Fusheng Xie
- Department of Medical Laboratory, Clinical Medical College, Affiliated Hospital of Jiujiang University, Jiujiang, Jiangxi, China
- Jiujiang Clinical Precision Medicine Research Center, Jiujiang, Jiangxi, China
| | - Feng Han
- Department of Medical Laboratory, Clinical Medical College, Affiliated Hospital of Jiujiang University, Jiujiang, Jiangxi, China
- Jiujiang Clinical Precision Medicine Research Center, Jiujiang, Jiangxi, China
| | - Weiwei He
- Department of Medical Laboratory, Clinical Medical College, Affiliated Hospital of Jiujiang University, Jiujiang, Jiangxi, China
- Jiujiang Clinical Precision Medicine Research Center, Jiujiang, Jiangxi, China
| | - Weifang Liao
- Department of Medical Laboratory, Clinical Medical College, Affiliated Hospital of Jiujiang University, Jiujiang, Jiangxi, China
- Jiujiang Clinical Precision Medicine Research Center, Jiujiang, Jiangxi, China
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Zhang J, Wang H, Tian Y, Li T, Zhang W, Ma L, Chen X, Wei Y. Discovery of a novel lipid metabolism-related gene signature to predict outcomes and the tumor immune microenvironment in gastric cancer by integrated analysis of single-cell and bulk RNA sequencing. Lipids Health Dis 2023; 22:212. [PMID: 38042786 PMCID: PMC10693080 DOI: 10.1186/s12944-023-01977-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 11/21/2023] [Indexed: 12/04/2023] Open
Abstract
Gastric cancer (GC) is a pressing global clinical issue, with few treatment options and a poor prognosis. The onset and spread of stomach cancer are significantly influenced by changes in lipid metabolism-related pathways. This study aimed to discover a predictive signature for GC using lipid metabolism-related genes (LMRGs) and examine its correlation with the tumor immune microenvironment (TIME). Transcriptome data and clinical information from patients with GC were collected from the TCGA and GEO databases. Data from GC samples were analyzed using both bulk RNA-seq and single-cell sequencing of RNA (scRNA-seq). To identify survival-related differentially expressed LMRGs (DE-LMRGs), differential expression and prognosis studies were carried out. We built a predictive signature using LASSO regression and tested it on the TCGA and GSE84437 datasets. In addition, the correlation of the prognostic signature with the TIME was comprehensively analyzed. In this study, we identified 258 DE-LMRGs in GC and further screened seven survival-related DE-LMRGs. The results of scRNA-seq identified 688 differentially expressed genes (DEGs) between the three branches. Two critical genes (GPX3 and NNMT) were identified using the above two gene groups. In addition, a predictive risk score that relies on GPX3 and NNMT was developed. Survival studies in both the TCGA and GEO datasets revealed that patients categorized to be at low danger had a significantly greater prognosis than those identified to be at high danger. Additionally, by employing calibration plots based on TCGA data, the study demonstrated the substantial predictive capacity of a prognostic nomogram, which incorporated a risk score along with various clinical factors. Within the high-risk group, there was a noticeable abundance of active natural killer (NK) cells, quiescent monocytes, macrophages, mast cells, and activated CD4 + T cells. In summary, a two-gene signature and a predictive nomogram have been developed, offering accurate prognostic predictions for general survival in GC patients. These findings have the potential to assist healthcare professionals in making informed medical decisions and providing personalized treatment approaches.
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Affiliation(s)
- Jinze Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Dalian Medical University, Dalian, China
- Department of Scientific Research, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - He Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Dalian Medical University, Dalian, China
| | - Yu Tian
- Department of Epidemiology and Health Statistics, School of Public Health, Dalian Medical University, Dalian, China
- Department of General Surgery, Institute of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China
| | - Tianfeng Li
- Department of General Surgery, Institute of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China
- Affiliated Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Wei Zhang
- Department of General Surgery, Institute of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China
| | - Li Ma
- Department of Epidemiology and Health Statistics, School of Public Health, Dalian Medical University, Dalian, China
| | - Xiangjuan Chen
- Department of Obstetrics, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China.
| | - Yushan Wei
- Department of Scientific Research, First Affiliated Hospital of Dalian Medical University, Dalian, China.
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Cheng S, Li S, Yang P, Wang R, Zhou P, Li J. Dissecting the tumour immune microenvironment in merkel cell carcinoma based on a machine learning framework. ARTIFICIAL CELLS, NANOMEDICINE, AND BIOTECHNOLOGY 2023; 51:397-407. [PMID: 37676035 DOI: 10.1080/21691401.2023.2244998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 07/13/2023] [Accepted: 08/02/2023] [Indexed: 09/08/2023]
Abstract
Merkel cell carcinoma (MCC) is a primary cutaneous neoplasm of neuroendocrine carcinoma of the skin, which is characterized by molecular heterogeneity with diverse tumour microenvironment (TME). However, we are still lack knowledge of the cellular states and ecosystems in MCC. Here, we systematically identified and characterized the landscape of cellular states and ecotypes in MCC based on a machine learning framework. We obtained 30 distinct cellular states from 9 immune cell types and investigated the B cell, CD8 T cell, fibroblast, and monocytes/macrophage cellular states in detail. The functional profiling of cellular states were investigated and found the genes highly expressed in cellular states were significantly enriched in immune- and cancer hallmark-related pathways. In addition, four ecotypes were further identified which were with different patient compositions. Transcriptional regulation analysis revealed the critical transcription factors (i.e. E2F1, E2F3 and E2F7), which play important roles in regulating the TME of MCC. In summary, the findings of this study may provide rich knowledge to understand the intrinsic subtypes of MCCs and the pathways involved in distinct subtype oncogenesis, and will further advance the knowledge in developing a specific therapeutic strategy for these MCC subtypes.
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Affiliation(s)
- Shaowen Cheng
- Department of Wound Repair, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Si Li
- College of Biomedical Information and Engineering, Hainan Medical University, Haikou, China
| | - Ping Yang
- Department of Radiotherapy, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Rong Wang
- Department of Wound Repair, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Ping Zhou
- Department of Radiotherapy, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Jingquan Li
- The First Affiliated Hospital of Hainan Medical University, Haikou, China
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Yi M, Shi J, Tan X, Zhang X, Tao D, Yang Y, Liu Y. Integration and deconvolution methodology deciphering prognosis-related signatures in lung adenocarcinoma. J Cancer Res Clin Oncol 2023; 149:16441-16460. [PMID: 37710052 DOI: 10.1007/s00432-023-05403-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 09/04/2023] [Indexed: 09/16/2023]
Abstract
PURPOSE This study aims to establish a risk prediction model based on prognosis-related genes (PRGs) and clinicopathological factors, and investigate the biological activities of PRGs in lung adenocarcinoma (LUAD). METHODS Risk score signatures were developed by employing multiple algorithms and their amalgamations. A predictive model for overall survival was established through the integration of risk score signatures and several clinicopathological parameters. A comprehensive single-cell atlas, gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to investigate the biological activities of prognosis-related genes in LUAD. RESULTS A risk prediction model was established based on 16 PRGs, exhibiting robust performance in predicting overall survival. The single-cell analysis revealed that epithelial cells were primarily associated with worse survival of LUAD, and PRGs were predominantly enriched in malignant epithelial cells and influenced epithelial cell growth and progression. Furthermore, GSEA and GSVA analysis showed that PRGs were involved in tumor pathways such as epithelial-mesenchymal transition, hypoxia and KRAS_UP, and high GSVA scores are correlated with worse outcome in LUAD patients. CONCLUSIONS The constructed risk prediction model in this study offers clinicians a valuable tool for tailoring treatment strategies of LUAD and provides a comprehensive interpretation on the biological activities of PRGs in LUAD.
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Affiliation(s)
- Ming Yi
- Department of Medical Genetics and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Jiaying Shi
- Department of Medical Genetics and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiaolan Tan
- Department of Medical Genetics and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xinyue Zhang
- Department of Medical Genetics and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Dachang Tao
- Department of Medical Genetics and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yuan Yang
- Department of Medical Genetics and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yunqiang Liu
- Department of Medical Genetics and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China.
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Pan L, Mou T, Huang Y, Hong W, Yu M, Li X. Ursa: A Comprehensive Multiomics Toolbox for High-Throughput Single-Cell Analysis. Mol Biol Evol 2023; 40:msad267. [PMID: 38091963 PMCID: PMC10752348 DOI: 10.1093/molbev/msad267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 09/08/2023] [Accepted: 11/03/2023] [Indexed: 12/28/2023] Open
Abstract
The burgeoning amount of single-cell data has been accompanied by revolutionary changes to computational methods to map, quantify, and analyze the outputs of these cutting-edge technologies. Many are still unable to reap the benefits of these advancements due to the lack of bioinformatics expertise. To address this issue, we present Ursa, an automated single-cell multiomics R package containing 6 automated single-cell omics and spatial transcriptomics workflows. Ursa allows scientists to carry out post-quantification single or multiomics analyses in genomics, transcriptomics, epigenetics, proteomics, and immunomics at the single-cell level. It serves as a 1-stop analytic solution by providing users with outcomes to quality control assessments, multidimensional analyses such as dimension reduction and clustering, and extended analyses such as pseudotime trajectory and gene-set enrichment analyses. Ursa aims bridge the gap between those with bioinformatics expertise and those without by providing an easy-to-use bioinformatics package for scientists in hoping to accelerate their research potential. Ursa is freely available at https://github.com/singlecellomics/ursa.
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Affiliation(s)
- Lu Pan
- Institute of Environmental Medicine, Karolinska Institutet, Solna 171 65, Sweden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna 171 65, Sweden
| | - Tian Mou
- School of Biomedical Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
| | - Yue Huang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna 171 65, Sweden
| | - Weifeng Hong
- Department of Radiation Oncology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Min Yu
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Xuexin Li
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Solna 171 65, Sweden
- Department of General Surgery, The Fourth Affiliated Hospital, China Medical University, Shenyang 110032, China
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Shree A, Pavan MK, Zafar H. scDREAMER for atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier. Nat Commun 2023; 14:7781. [PMID: 38012145 PMCID: PMC10682386 DOI: 10.1038/s41467-023-43590-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 11/14/2023] [Indexed: 11/29/2023] Open
Abstract
Integration of heterogeneous single-cell sequencing datasets generated across multiple tissue locations, time, and conditions is essential for a comprehensive understanding of the cellular states and expression programs underlying complex biological systems. Here, we present scDREAMER ( https://github.com/Zafar-Lab/scDREAMER ), a data-integration framework that employs deep generative models and adversarial training for both unsupervised and supervised (scDREAMER-Sup) integration of multiple batches. Using six real benchmarking datasets, we demonstrate that scDREAMER can overcome critical challenges including skewed cell type distribution among batches, nested batch-effects, large number of batches and conservation of development trajectory across batches. Our experiments also show that scDREAMER and scDREAMER-Sup outperform state-of-the-art unsupervised and supervised integration methods respectively in batch-correction and conservation of biological variation. Using a 1 million cells dataset, we demonstrate that scDREAMER is scalable and can perform atlas-level cross-species (e.g., human and mouse) integration while being faster than other deep-learning-based methods.
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Affiliation(s)
- Ajita Shree
- Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, India
| | - Musale Krushna Pavan
- Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, India
| | - Hamim Zafar
- Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, India.
- Department of Biological Sciences and Bioengineering, Indian Institute of Technology Kanpur, Kanpur, India.
- Mehta Family Centre for Engineering in Medicine, Indian Institute of Technology Kanpur, Kanpur, India.
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Li J, Zhang H, Mu B, Zuo H, Zhou K. Identifying phenotype-associated subpopulations through LP_SGL. Brief Bioinform 2023; 25:bbad424. [PMID: 38008419 PMCID: PMC10753413 DOI: 10.1093/bib/bbad424] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 09/28/2023] [Accepted: 10/31/2023] [Indexed: 11/28/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) enables the resolution of cellular heterogeneity in diseases and facilitates the identification of novel cell types and subtypes. However, the grouping effects caused by cell-cell interactions are often overlooked in the development of tools for identifying subpopulations. We proposed LP_SGL which incorporates cell group structure to identify phenotype-associated subpopulations by integrating scRNA-seq, bulk expression and bulk phenotype data. Cell groups from scRNA-seq data were obtained by the Leiden algorithm, which facilitates the identification of subpopulations and improves model robustness. LP_SGL identified a higher percentage of cancer cells, T cells and tumor-associated cells than Scissor and scAB on lung adenocarcinoma diagnosis, melanoma drug response and liver cancer survival datasets, respectively. Biological analysis on three original datasets and four independent external validation sets demonstrated that the signaling genes of this cell subset can predict cancer, immunotherapy and survival.
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Affiliation(s)
- Juntao Li
- College of Mathematics and Information Science, Henan Normal University, 46 Jianshe East Road, 453007, Xinxiang, China
| | - Hongmei Zhang
- College of Mathematics and Information Science, Henan Normal University, 46 Jianshe East Road, 453007, Xinxiang, China
| | - Bingyu Mu
- College of Arts and Design, Zhengzhou University of Light Industry, No. 5 Dongfeng Road, 450000, Zhengzhou, China
| | - Hongliang Zuo
- College of Mathematics and Information Science, Henan Normal University, 46 Jianshe East Road, 453007, Xinxiang, China
| | - Kanglei Zhou
- School of Computer Science and Engneering, Beihang University, 37 Xueyuan Road, Haidian District, 100191, Beijing, China
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Nance RL, Wang X, Sandey M, Matz BM, Thomas A, Smith BF. Single-Nuclei Multiome (ATAC + Gene Expression) Sequencing of a Primary Canine Osteosarcoma Elucidates Intra-Tumoral Heterogeneity and Characterizes the Tumor Microenvironment. Int J Mol Sci 2023; 24:16365. [PMID: 38003552 PMCID: PMC10671194 DOI: 10.3390/ijms242216365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/09/2023] [Accepted: 11/11/2023] [Indexed: 11/26/2023] Open
Abstract
Osteosarcoma (OSA) is a highly aggressive bone tumor primarily affecting pediatric or adolescent humans and large-breed dogs. Canine OSA shares striking similarities with its human counterpart, making it an invaluable translational model for uncovering the disease's complexities and developing novel therapeutic strategies. Tumor heterogeneity, a hallmark of OSA, poses significant challenges to effective treatment due to the evolution of diverse cell populations that influence tumor growth, metastasis, and resistance to therapies. In this study, we apply single-nuclei multiome sequencing, encompassing ATAC (Assay for Transposase-Accessible Chromatin) and GEX (Gene Expression, or RNA) sequencing, to a treatment-naïve primary canine osteosarcoma. This comprehensive approach reveals the complexity of the tumor microenvironment by simultaneously capturing the transcriptomic and epigenomic profiles within the same nucleus. Furthermore, these results are analyzed in conjunction with bulk RNA sequencing and differential analysis of the same tumor and patient-matched normal bone. By delving into the intricacies of OSA at this unprecedented level of detail, we aim to unravel the underlying mechanisms driving intra-tumoral heterogeneity, opening new avenues for therapeutic interventions in both human and canine patients. This study pioneers an approach that is broadly applicable, while demonstrating significant heterogeneity in the context of a single individual's tumor.
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Affiliation(s)
- Rebecca L. Nance
- Scott-Ritchey Research Center, Auburn University College of Veterinary Medicine, Auburn, AL 36849, USA; (R.L.N.); (X.W.)
- Department of Pathobiology, Auburn University College of Veterinary Medicine, Auburn, AL 36849, USA;
| | - Xu Wang
- Scott-Ritchey Research Center, Auburn University College of Veterinary Medicine, Auburn, AL 36849, USA; (R.L.N.); (X.W.)
- Department of Pathobiology, Auburn University College of Veterinary Medicine, Auburn, AL 36849, USA;
| | - Maninder Sandey
- Department of Pathobiology, Auburn University College of Veterinary Medicine, Auburn, AL 36849, USA;
| | - Brad M. Matz
- Department of Clinical Sciences, Auburn University College of Veterinary Medicine, Auburn, AL 36849, USA;
| | - AriAnna Thomas
- Department of Nursing, Tuskegee University, Tuskegee, AL 36088, USA;
| | - Bruce F. Smith
- Scott-Ritchey Research Center, Auburn University College of Veterinary Medicine, Auburn, AL 36849, USA; (R.L.N.); (X.W.)
- Department of Pathobiology, Auburn University College of Veterinary Medicine, Auburn, AL 36849, USA;
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48
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Arteaga-Arteaga HB, Candamil-Cortés MS, Breaux B, Guillen-Rondon P, Orozco-Arias S, Tabares-Soto R. Machine learning applications on intratumoral heterogeneity in glioblastoma using single-cell RNA sequencing data. Brief Funct Genomics 2023; 22:428-441. [PMID: 37119295 DOI: 10.1093/bfgp/elad002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 01/17/2023] [Indexed: 05/01/2023] Open
Abstract
Artificial intelligence is revolutionizing all fields that affect people's lives and health. One of the most critical applications is in the study of tumors. It is the case of glioblastoma (GBM) that has behaviors that need to be understood to develop effective therapies. Due to advances in single-cell RNA sequencing (scRNA-seq), it is possible to understand the cellular and molecular heterogeneity in the GBM. Given that there are different cell groups in these tumors, there is a need to apply Machine Learning (ML) algorithms. It will allow extracting information to understand how cancer changes and broaden the search for effective treatments. We proposed multiple comparisons of ML algorithms to classify cell groups based on the GBM scRNA-seq data. This broad comparison spectrum can show the scientific-medical community which models can achieve the best performance in this task. In this work are classified the following cell groups: Tumor Core (TC), Tumor Periphery (TP) and Normal Periphery (NP), in binary and multi-class scenarios. This work presents the biomarker candidates found for the models with the best results. The analyses presented here allow us to verify the biomarker candidates to understand the genetic characteristics of GBM, which may be affected by a suitable identification of GBM heterogeneity. This work obtained for the four scenarios covered cross-validation results of $93.03\% \pm 5.37\%$, $97.42\% \pm 3.94\%$, $98.27\% \pm 1.81\%$ and $93.04\% \pm 6.88\%$ for the classification of TP versus TC, TP versus NP, NP versus TP and TC (TPC) and NP versus TP versus TC, respectively.
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Affiliation(s)
| | - Mariana S Candamil-Cortés
- Departamento de Ciencias Computacionales, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia
- Centro de Investigaciones en Medio Ambiente y Desarrollo - CIMAD, Universidad de Manizales, Manizales, Caldas, Colombia
| | - Brian Breaux
- Department of Computer Science, University of Houston Downtown, Houston, Texas, United States of America
| | - Pablo Guillen-Rondon
- Department of Computer Science, University of Houston Downtown, Houston, Texas, United States of America
- Biomedical and Energy Solutions LLC, Houston, Texas, United States of America
| | - Simon Orozco-Arias
- Departamento de Ciencias Computacionales, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia
- Departamento de Sistemas e Informática, Universidad de Caldas, Manizales, Caldas, Colombia
| | - Reinel Tabares-Soto
- Departamento de Electrónica y Automatización, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia
- Departamento de Sistemas e Informática, Universidad de Caldas, Manizales, Caldas, Colombia
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49
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Paas-Oliveros E, Hernández-Lemus E, de Anda-Jáuregui G. Computational single cell oncology: state of the art. Front Genet 2023; 14:1256991. [PMID: 38028624 PMCID: PMC10663273 DOI: 10.3389/fgene.2023.1256991] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023] Open
Abstract
Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.
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Affiliation(s)
- Ernesto Paas-Oliveros
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Guillermo de Anda-Jáuregui
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
- Investigadores por Mexico, Conahcyt, Mexico City, Mexico
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50
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Sajid RS, van Winden LJ, Diamandis P. Towards deciphering glioblastoma intra-tumoral heterogeneity: The importance of integrating multidimensional models. Proteomics 2023; 23:e2200401. [PMID: 37488996 DOI: 10.1002/pmic.202200401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 07/12/2023] [Accepted: 07/13/2023] [Indexed: 07/26/2023]
Abstract
Glioblastoma (GBM) is the most common and severe form of brain cancer among adults. Its aggressiveness is largely attributed to its complex and heterogeneous biology that despite maximal surgery and multimodal chemoradiation treatment, inevitably recurs. Traditional large-scale profiling approaches have contributed substantially to the understanding of patient-to-patient inter-tumoral differences in GBM. However, it is now clear that biological differences within an individual (intra-tumoral heterogeneity) are also a prominent factor in treatment resistance and recurrence of GBM and will likely require integration of data from multiple recently developed omics platforms to fully unravel. Here we dissect the growing geospatial model of GBM, which layers intra-tumoral heterogeneity on a GBM stem cell (GSC) precursor, single cell, and spatial level. We discuss potential unique and inter-dependant aspects of the model including potential discordances between observed genotypes and phenotypes in GBM.
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Affiliation(s)
- Rifat Shahriar Sajid
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - Lennart J van Winden
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - Phedias Diamandis
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Laboratory Medicine Program, University Health Network, Toronto, Canada
- Princess Margaret Cancer Center, University Health Network, Toronto, Canada
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