Integrative insights and clinical applications of single-cell sequencing in cancer immunotherapy

Li, Huanyun; Dang, Qin; Weng, Siyuan; Duo, Mengjie; Lv, Jinxiang; Han, Xinwei

doi:10.1007/s00018-022-04608-4

Cited by 14 publications

(11 citation statements)

References 184 publications

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“…1 Therefore, future research on clonal evolution and heterogeneity in AML should emphasize comprehensive molecular characterization of patient samples and utilization of mouse leukemia models. 1 Well-characterized mouse models not only deepen our understanding of the underlying molecular pathogenesis of AML but also serve as 45 This limitation is overcome by the sc-seq technology, which enables the sequencing of individual cell genomes or transcriptomes to obtain genomic, transcriptomic, or multi-omics information, offering a comprehensive understanding of cell population disparities as well as cellular evolutionary relationships. 46 The field of sc-seq has witnessed significant advancements, allowing for high-resolution characterization of individual cells.…”

Section: Unraveling the Heterogeneity Of Aml: Insights From Molecular...mentioning

confidence: 99%

“…As a result, it surpasses previous sequencing technologies in uncovering the complexity of the tumor microenvironment (TME), heterogeneity, cell expression, function, composition, interaction, cell lineage tracking, and mechanisms of drug resistance. 45,49 It has revolutionized our understanding of cells, providing insights into transcriptional, genomic, epigenomic, and metabolic features of individual cells, which allows for unbiased profiling of cells within neoplastic lesions. 50 Furthermore, it provides molecular insights into variations in different aspects.…”

Section: Unraveling the Heterogeneity Of Aml: Insights From Molecular...mentioning

confidence: 99%

“…Traditional sequencing methods analyze entire tissues, representing the expression of individual cells by their average values, which conceals the characteristics of each cell and tissue heterogeneity 45 . This limitation is overcome by the sc‐seq technology, which enables the sequencing of individual cell genomes or transcriptomes to obtain genomic, transcriptomic, or multi‐omics information, offering a comprehensive understanding of cell population disparities as well as cellular evolutionary relationships 46 .…”

Section: Single‐cell Sequencingmentioning

confidence: 99%

“…The sc‐seq technology encompasses various dimensions of cellular characterization at the single‐cell level, including genomics, transcriptomics, epigenomics, proteomics, and multi‐omics. As a result, it surpasses previous sequencing technologies in uncovering the complexity of the tumor microenvironment (TME), heterogeneity, cell expression, function, composition, interaction, cell lineage tracking, and mechanisms of drug resistance 45,49 . It has revolutionized our understanding of cells, providing insights into transcriptional, genomic, epigenomic, and metabolic features of individual cells, which allows for unbiased profiling of cells within neoplastic lesions 50 .…”

Section: Single‐cell Sequencingmentioning

confidence: 99%

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Big data and single‐cell sequencing in acute myeloid leukemia research

Zou,

Zhang,

2023

MedComm – Oncology

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The advancement of diverse technologies has led to a substantial increase in valuable biomedical data, particularly in the field of acute myeloid leukemia (AML). Effective utilization of this wealth of data is crucial for attaining a comprehensive and in‐depth understanding of AML, thereby facilitating optimal diagnosis, treatment, and prognosis. Among the various approaches to data acquisition, single‐cell sequencing has emerged as an impressive tool. The developments of single‐cell sequencing methods have empowered researchers to analyze the genome, transcriptome, proteome, and epigenome data at the single‐cell level. It also offers a means to uncover fine information, providing unique prognostic insights and aiding in the identification of therapeutic targets. Furthermore, it enhances our understanding of AML heterogeneity, clonal evolution, and resistance mechanisms, ultimately leading to the development of better treatment strategies. In this review, we present an overview of AML as well as single‐cell sequencing technologies, then explore their potential contributions to AML research in different aspects, and provide some information about resources and data processing.

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Section: Unraveling the Heterogeneity Of Aml: Insights From Molecular...mentioning

confidence: 99%

Section: Unraveling the Heterogeneity Of Aml: Insights From Molecular...mentioning

confidence: 99%

Section: Single‐cell Sequencingmentioning

confidence: 99%

Section: Single‐cell Sequencingmentioning

confidence: 99%

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Big data and single‐cell sequencing in acute myeloid leukemia research

Zou,

Zhang,

2023

MedComm – Oncology

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“…Recently, scRNA sequencing importantly contributed to improving the resolution of identifying the unique characteristics of each cell ( 13 ). scRNA sequencing can effectively analyze tumor immune infiltration ( 14 ). Therefore, we used scRNA analyses in this study to evaluate the genetic abundance in HCC immune cells.…”

Section: Introductionmentioning

confidence: 99%

Combined bulk RNA and single-cell RNA analyses reveal TXNL4A as a new biomarker for hepatocellular carcinoma

Zhu

Zhou

et al. 2023

Front. Oncol.

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IntroductionHepatocellular carcinoma (HCC) has a high mortality rate worldwide. The dysregulation of RNA splicing is a major event leading to the occurrence, progression, and drug resistance of cancer. Therefore, it is important to identify new biomarkers of HCC from the RNA splicing pathway.MethodsWe performed the differential expression and prognostic analyses of RNA splicing-related genes (RRGs) using The Cancer Genome Atlas-liver hepatocellular carcinoma (LIHC). The International Cancer Genome Consortium (ICGC)-LIHC dataset was used to construct and validate prognostic models, and the PubMed database was used to explore genes in the models to identify new markers. The screened genes were subjected to genomic analyses, including differential, prognostic, enrichment, and immunocorrelation analyses. Single-cell RNA (scRNA) data were used to further validate the immunogenetic relationship.ResultsOf 215 RRGs, we identified 75 differentially expressed prognosis-related genes, and a prognostic model incorporating thioredoxin like 4A (TXNL4A) was identified using least absolute shrinkage and selection operator regression analysis. ICGC-LIHC was used as a validation dataset to confirm the validity of the model. PubMed failed to retrieve HCC-related studies on TXNL4A. TXNL4A was highly expressed in most tumors and was associated with HCC survival. Chi-squared analyses indicated that TXNL4A expression positively correlated positively with the clinical features of HCC. Multivariate analyses revealed that high TXNL4A expression was an independent risk factor for HCC. Immunocorrelation and scRNA data analyses indicated that TXNL4A was correlated with CD8 T cell infiltration in HCC.ConclusionTherefore, we identified a prognostic and immune-related marker for HCC from the RNA splicing pathway.

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