T cell-related prognostic risk model and tumor immune environment modulation in lung adenocarcinoma based on single-cell and bulk RNA sequencing

Zhang, Jingyuan; Liu, Xinkui; Huang, Zhihong; Wu, Chao; Zhang, Fanqin; Han, Aiqing; Stalin, Antony; Lu, Shan; Guo, Siyu; Huang, Jiaqi; Liu, Pengyun; Shi, Rui; Zhai, Yiyan; Chen, Meilin; Zhou, Wei; Bai, Meirong; Wu, Jiarui

doi:10.1016/j.compbiomed.2022.106460

Cited by 14 publications

(11 citation statements)

References 65 publications

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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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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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“…The prognostic markers and the indications for prognosis in these studies are summarized in Table 2 . 36 , 44 , 45 , 47 – 49 , 55 – 89 DEGs and their combined prognostic model were the most common types of prognostic markers in these studies. For example, one study identified nine cancer-specific DEGs (CBFA2T3, CR2, SEL1L3, TM6SF1, TSPAN32, ITGA6, MAPK11, RASA3, and TLR6) and established a prognostic risk model in combination with clinical factors.…”

Section: The Prognostic Time Markers For Luadmentioning

confidence: 91%

Unraveling the tumor immune microenvironment of lung adenocarcinoma using single-cell RNA sequencing

Song,

Gong,

Wang

et al. 2024

Ther Adv Med Oncol

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Tumor immune microenvironment (TIME) and its indications for lung cancer patient prognosis and therapeutic response have become new hotspots in cancer research in recent years. Tumor cells, immune cells, various regulatory factors, and their interactions in the TIME have been suggested to commonly influence lung cancer development and therapeutic outcome. The heterogeneity of TIME is composed of dynamic immune-related components, including various cancer cells, immune cells, cytokine/chemokine environments, cytotoxic activity, or immunosuppressive factors. The specific composition of cell subtypes may facilitate or hamper the response to immunotherapy and influence patient prognosis. Various markers have been found to stratify the patient prognosis or predict the therapeutic outcome. In this article, we systematically reviewed the recent advancement of TIME studies in lung adenocarcinoma (LUAD) using single-cell RNA sequencing (scRNA-seq) techniques, with specific focuses on the roles of TIME in LUAD development, TIME heterogeneity, indications of TIME in patient prognosis and therapeutic response during immunotherapy and drug resistance. The main findings in TIME heterogeneity and relevant markers or models for prognosis stratification and response prediction have been summarized. We hope that this review provides an overview of TIME status in LUAD and an inspiration for future development of strategies and biomarkers in LUAD treatment.

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“…1). Additionally, the probability of cell-cell communication was calculated to examine the cell-cell communication network [9]. The deduction of this network was extended by utilizing particular pathways and interactions between ligands and receptors.…”

Section: Scrna-seq and Cell Typing Of Normal And Colon Adenocarcinoma...mentioning

confidence: 99%

Establishing and validating a prognostic model for colorectal cancer patients by integrating data from single-cell RNA sequencing and bulk RNA sequencing

Huang,

You,

Huang

et al. 2024

Preprint

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Background Colorectal adenocarcinoma (COAD) is a major global cause of mortality. While conventional RNA sequencing (RNA-seq) has been used to study its prognostic indicators, it lacks precision in identifying cellular alterations. This study aimed to develop a predictive framework for COAD by integrating scRNA-seq with conventional RNA-seq. Methods This study acquired primary RNA sequencing data from The Cancer Genome Atlas (TCGA) database and single-cell RNA sequencing data on colorectal adenocarcinoma (COAD) from the Gene Expression Omnibus (GEO) database. The t-SNE method reduced dimensionality and identified clusters. Additionally, Weighted Gene Correlation Network Analysis (WGCNA) identified crucial modules and genes with differential expression (DEGs). Cox regression analysis was utilized to construct the prognostic model and explore mutation profiles and immune statuses across different risk groups. Results Integration of scRNA-seq data from four samples revealed 15 distinct clusters covering 8 cell types. Differential analysis identified important cell types, including B cells (Naïve and Plasma cells), Endothelial cells, Epithelial cells, Monocytes, Natural Killer (NK) cells, Smooth muscle cells, and T cells (CD8+). Subsequently, a prognostic model was built using 28 genes showing differential expression, with four DEGs displaying a significant correlation with higher risk scores, poorer survival outcomes, and increased APC mutation rates. Various prognostic and immune characteristics were observed within this context. Conclusion Integrating 10x scRNA-seq and bulk RNA-seq data, we established a prognostic framework for colorectal adenocarcinoma (COAD) in this study. Additionally, we identified two distinct groups, each displaying different prognoses and immune features.

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T cell-related prognostic risk model and tumor immune environment modulation in lung adenocarcinoma based on single-cell and bulk RNA sequencing

Cited by 14 publications

References 65 publications

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

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

Unraveling the tumor immune microenvironment of lung adenocarcinoma using single-cell RNA sequencing

Establishing and validating a prognostic model for colorectal cancer patients by integrating data from single-cell RNA sequencing and bulk RNA sequencing

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