Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels T cell-related prognostic risk model and tumor immune microenvironment modulation in triple-negative breast cancer

Guo, Siyu; Liu, Xinkui; Zhang, Jingyuan; Huang, Zhihong; Ye, Peizhi; Shi, Jian; Stalin, Antony; Wu, Chao; Lu, Shiqi; Zhang, Fanqin; Gao, Yiwen; Zhou, Jin; Tao, Xiaoyu; Huang, Jiaqi; Zhai, Yiyan; Shi, Rui; Guo, Fengying; Zhou, Weiping; Wu, Jiarui

doi:10.1016/j.compbiomed.2023.107066

Cited by 13 publications

(6 citation statements)

References 62 publications

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“…Furthermore, Siyu Guo et al. ( 192 ) employed scRNA-seq data to examine T-cell heterogeneity in TNBC tumor microenvironment, and created and validated the risk models related to T-cell marker genes and prognosis, which are useful in predicting TNBC treatment response and prognosis.…”

Section: Application Of Single-cell Sequencing In Cancer Tme Researchmentioning

confidence: 99%

Application of single-cell sequencing to the research of tumor microenvironment

Chen,

Zhou,

et al. 2023

Front. Immunol.

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Single-cell sequencing is a technique for detecting and analyzing genomes, transcriptomes, and epigenomes at the single-cell level, which can detect cellular heterogeneity lost in conventional sequencing hybrid samples, and it has revolutionized our understanding of the genetic heterogeneity and complexity of tumor progression. Moreover, the tumor microenvironment (TME) plays a crucial role in the formation, development and response to treatment of tumors. The application of single-cell sequencing has ushered in a new age for the TME analysis, revealing not only the blueprint of the pan-cancer immune microenvironment, but also the heterogeneity and differentiation routes of immune cells, as well as predicting tumor prognosis. Thus, the combination of single-cell sequencing and the TME analysis provides a unique opportunity to unravel the molecular mechanisms underlying tumor development and progression. In this review, we summarize the recent advances in single-cell sequencing and the TME analysis, highlighting their potential applications in cancer research and clinical translation.

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Section: Application Of Single-cell Sequencing In Cancer Tme Researchmentioning

confidence: 99%

Application of single-cell sequencing to the research of tumor microenvironment

Chen,

Zhou,

et al. 2023

Front. Immunol.

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“…In the context of immunotherapy, the innovative value of several methods, including the T‐cell‐related prognostic index and the multimetric analysis of biomarkers for immunotherapy, such as CAMOIP, for assessing the prognosis of patients treated with ICIs has been identified as an additional direction for the development of new prognostic models made possible by immunometabolomics 221,222 . In addition, scRNA‐seq has proven valuable for mining T‐cell marker genes of lung adenocarcinoma, 223 lung squamous cell carcinoma 224 and triple‐negative breast cancer 225 and identifying the associated enrichment terms and pathways to construct independent predictive models of disease risk and immunotherapy responses. The comprehensive coverage of differentially expressed genes, the robust analysis of gene set enrichment pathways, and the sensitive and specific detection of tumour infiltration‐related genes in T cells make single‐cell‐based sequencing data a valuable resource for constructing precise and sensitive infiltrating T‐cell metabolic prognostic risk models 226 …”

Section: Discussionmentioning

confidence: 99%

Unlocking the potential of T‐cell metabolism reprogramming: Advancing single‐cell approaches for precision immunotherapy in tumour immunity

Huang,

Li,

Zhang

et al. 2024

Clinical & Translational Med

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As single‐cell RNA sequencing enables the detailed clustering of T‐cell subpopulations and facilitates the analysis of T‐cell metabolic states and metabolite dynamics, it has gained prominence as the preferred tool for understanding heterogeneous cellular metabolism. Furthermore, the synergistic or inhibitory effects of various metabolic pathways within T cells in the tumour microenvironment are coordinated, and increased activity of specific metabolic pathways generally corresponds to increased functional activity, leading to diverse T‐cell behaviours related to the effects of tumour immune cells, which shows the potential of tumour‐specific T cells to induce persistent immune responses. A holistic understanding of how metabolic heterogeneity governs the immune function of specific T‐cell subsets is key to obtaining field‐level insights into immunometabolism. Therefore, exploring the mechanisms underlying the interplay between T‐cell metabolism and immune functions will pave the way for precise immunotherapy approaches in the future, which will empower us to explore new methods for combating tumours with enhanced efficacy.

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“…Currently, scRNA-seq is a powerful tool for characterizing the basic properties of cells in solid tumors. Meanwhile, the cell subtypes in TME and cellular communication have been identified [ 63 , 64 ]. In this research, we used the GSE125449 scRNA-seq dataset to assess the heterogeneity of HCC.…”

Section: Discussionmentioning

confidence: 99%

Identification of fatty acids synthesis and metabolism-related gene signature and prediction of prognostic model in hepatocellular carcinoma

Zhengdong,

Xiaoying,

Shuhui

et al. 2024

Cancer Cell Int

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Background Fatty acids synthesis and metabolism (FASM)-driven lipid mobilization is essential for energy production during nutrient shortages. However, the molecular characteristics, physiological function and clinical prognosis value of FASM-associated gene signatures in hepatocellular carcinoma (HCC) remain elusive. Methods The Gene Expression Omnibus database (GEO), the Cancer Genome Atlas (TCGA), and International Cancer Genome Consortium (ICGC) database were utilized to acquire transcriptome data and clinical information of HCC patients. The ConsensusClusterPlus was employed for unsupervised clustering. Subsequently, immune cell infiltration, stemness index and therapeutic response among distinct clusters were decoded. The tumor immune dysfunction and exclusion (TIDE) algorithm was utilized to anticipate the response of patients towards immunotherapy, and the genomics of drug sensitivity in cancer (GDSC) tool was employed to predict their response to antineoplastic medications. Least absolute shrinkage and selection operator (LASSO) regression analysis and protein–protein interaction (PPI) network were employed to construct prognostic model and identity hub gene. Single cell RNA sequencing (scRNA-seq) and CellChat were used to analyze cellular interactions. The hub gene of FASM effect on promoting tumor progression was confirmed through a series of functional experiments. Results Twenty-six FASM-related genes showed differential expression in HCC. Based on these FASM-related differential genes, two molecular subtypes were established, including Cluster1 and Cluster2 subtype. Compared with cluster2, Cluster1 subtype exhibited a worse prognosis, higher risk, higher immunosuppressive cells infiltrations, higher immune escape, higher cancer stemness and enhanced treatment-resistant. PPI network identified Acetyl-CoA carboxylase1 (ACACA) as central gene of FASM and predicted a poor prognosis. A strong interaction between cancer stem cells (CSCs) with high expression of ACACA and macrophages through CD74 molecule (CD74) and integrin subunit beta 1 (ITGB1) signaling was identified. Finally, increased ACACA expression was observed in HCC cells and patients, whereas depleted ACACA inhibited the stemness straits and drug resistance of HCC cells. Conclusions This study provides a resource for understanding FASM heterogeneity in HCC. Evaluating the FASM patterns can help predict the prognosis and provide new insights into treatment response in HCC patients.

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Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels T cell-related prognostic risk model and tumor immune microenvironment modulation in triple-negative breast cancer

Cited by 13 publications

References 62 publications

Application of single-cell sequencing to the research of tumor microenvironment

Application of single-cell sequencing to the research of tumor microenvironment

Unlocking the potential of T‐cell metabolism reprogramming: Advancing single‐cell approaches for precision immunotherapy in tumour immunity

Identification of fatty acids synthesis and metabolism-related gene signature and prediction of prognostic model in hepatocellular carcinoma

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