Single-cell RNA sequencing integrated with bulk RNA sequencing analysis reveals diagnostic and prognostic signatures and immunoinfiltration in gastric cancer

Zhai, Yiyan; Zhang, Jingyuan; Huang, Zhihong; Shi, Rui; Guo, Fengying; Zhang, Fanqin; Chen, Meilin; Gao, Yifei; Tao, Xiaoyu; Jin, Zhe; Guo, Siyu; Lin, Yifan; Ye, Peizhi; Wu, Jiarui

doi:10.1016/j.compbiomed.2023.107239

Cited by 6 publications

(3 citation statements)

References 55 publications

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“…Moreover, we studied the relation between prognostic strati cation and immune in ltration, utilizing ssGSEA to assess immune function. Our ndings indicated that low-ANRS groups exhibited heightened activity in anti-cancer immune cycle steps, suggesting more robust anti-tumor immune activity (He, Jiang, Chen and Wang 2018; Zhai et al 2023).…”

Section: Discussionmentioning

confidence: 76%

Identification of anoikis-related tumor microenvironment characteristics and prognostic signature in ovarian cancer at bulk and single-cell levels

Tang,

Zhou,

et al. 2024

Preprint

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Background Ovarian cancer (OC) is one of the deadliest malignancies affecting women globally. Anoikis, a critical process that prevents the establishment of detached cells in non-native sites, is closely associated with cancer cell aggressiveness and poor patient outcomes. Despite its significance, research into the prognostic impact of anoikis-related genes (ARGs) in OC remains scant. Methods Single-cell RNA sequencing (scRNA-seq) was adopted to analyze anoikis activity using 41 ARGs across diverse cell types. The genomic and clinicopathological data was sourced from GSE26712 project (training cohort) and TCGA-OV project (independent validation set), respectively. Cox regression and the least absolute shrinkage and selection operator (LASSO) technique were utilized to develop an anoikis-related risk score (ANRS) for prognosis evaluation. Additionally, the correlation between ANRS and tumor microenvironment (TME) characteristics was explored. Functional experiments were conducted to elucidate the molecular role of the key gene AP1S2 in OC. Results Survival analyses in both cohorts categorized OC patients into two groups based on the median ANRS. The high-ANRS category exhibited evidently worse survival outcomes. Our findings highlighted a strong link between ARGs and TME characteristics, particularly the stromal components, at both bulk and single-cell transcriptomic levels. This underscores the complex interplay between cancer progression and the tumor-promoting stroma. Additionally, AP1S2 knockdown markedly reduced the proliferative and aggressive capabilities of OC cells. Conclusion The ANRS-derived prognostic tool offers substantial promise for advancing our understanding of OC progression and assisting gynecologists in developing effective treatment strategies for women suffering from this malignancy.

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Section: Discussionmentioning

confidence: 76%

Identification of anoikis-related tumor microenvironment characteristics and prognostic signature in ovarian cancer at bulk and single-cell levels

Tang,

Zhou,

et al. 2024

Preprint

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“…scRNA-seq enables transcriptomic analysis of individual cells to characterize the cellular diversity in the TME in detail, thereby improving understanding of the disease [33][34][35] . In this study, our analysis of scRNA-seq data from TNBC revealed that there are multiple immune cells infiltrating in the TME and interacting frequently with each other, and they may impede or promote tumor progression [36] .…”

Section: Discussionmentioning

confidence: 99%

Integrated Analysis of Single-Cell and Bulk RNA-Seq Data reveals that Ferroptosis-Related Genes Mediated the Tumor Microenvironment predicts Prognosis, and guides Drug Selection in Triple-Negative Breast Cancer

Gong,

Gu,

Yang

et al. 2024

Preprint

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BackgroundTNBC is aggressive, lacking methods to predict recurrence and drug sensitivity. Ferroptotic heterogeneity varies in TNBC subtypes. However, the TME mediated by ferroptosis genes is unclear. Our study aims to integrate single-cell and bulk RNA-seq data to reveal the ferroptosis-mediated TME in TNBC, predicting prognosis and guiding treatment.MethodsThe single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data of TNBC were sourced from the Gene Expression Omnibus (GEO) database. Using these data, a machine learning algorithm was employed to integrate and analyze the characteristics of the TME mediated by ferroptosis-related genes in TNBC. Prediction models for TNBC survival prognosis and drug treatment response were established and then validated in an independent set.ResultsAt the individual cell level, T cells were categorized into three distinct subpopulations, and local macrophages into two subpopulations. The infiltration degree of these different cell subpopulations was closely associated with prognosis and treatment outcomes. Based on this, the risk score model we developed effectively predicted recurrence-free survival in TNBC patients, with independently validated pooled predicted 3-, 4-, and 5-year Area Under the Curves(AUCs) of 0.65, 0.67, and 0.71, respectively. Additionally, we found that patients in the high-risk group may be more responsive to 27 drugs.ConclusionsWe have uncovered the tumor immune cell clusters in TNBC mediated by ferroptosis. A risk score model was constructed to identify high-risk TNBC patients, which can assist physicians in disease monitoring and precision therapy. The genes identified hold significant potential as therapeutic targets for TNBC patients.FundingThis project is funded by the National Natural Science Foundation of China (81974268, 82304151), the Talent Incentive Program of Cancer Hospital Chinese, Academy of Medical Sciences (801032247), the Cancer Hospital of Chinese Academy of Medical Sciences-Shenzhen Hospital Cooperation Fund (CFA202202023), and the open project of Beijing Key Laboratory of Tumor Invasion and Metastasis Mechanism, Capital Medical University(2023ZLKF03).

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“… 13 , 14 On the contrary, single‐cell sequencing provides high‐resolution information on cell types and states, revealing the distribution and function of immune cells in the gastric cancer microenvironment. 15 , 16 By combining these two methods, we can gain a more comprehensive understanding of the immune system status in gastric cancer patients and explore the molecular mechanisms underlying immune evasion.…”

Section: Introductionmentioning

confidence: 99%

Exploring the immune escape mechanisms in gastric cancer patients based on the deep AI algorithms and single‐cell sequencing analysis

Chen,

Liu,

Wang

et al. 2024

J Cellular Molecular Medi

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Gastric cancer is a prevalent and deadly malignancy, and the response to immunotherapy varies among patients. This study aimed to develop a prognostic model for gastric cancer patients and investigate immune escape mechanisms using deep machine learning and single‐cell sequencing analysis. Data from public databases were analysed, and a prediction model was constructed using 101 algorithms. The high‐AIDPS group, characterized by increased AIDPS expression, exhibited worse survival, genomic variations and immune cell infiltration. These patients also showed immunotherapy tolerance. Treatment strategies targeting the high‐AIDPS group identified three potential drugs. Additionally, distinct cluster groups and upregulated AIDPS‐associated genes were observed in gastric adenocarcinoma cell lines. Inhibition of GHRL expression suppressed cancer cell activity, inhibited M2 polarization in macrophages and reduced invasiveness. Overall, AIDPS plays a critical role in gastric cancer prognosis, genomic variations, immune cell infiltration and immunotherapy response, and targeting GHRL expression holds promise for personalized treatment. These findings contribute to improved clinical management in gastric cancer.

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Single-cell RNA sequencing integrated with bulk RNA sequencing analysis reveals diagnostic and prognostic signatures and immunoinfiltration in gastric cancer

Cited by 6 publications

References 55 publications

Identification of anoikis-related tumor microenvironment characteristics and prognostic signature in ovarian cancer at bulk and single-cell levels

Identification of anoikis-related tumor microenvironment characteristics and prognostic signature in ovarian cancer at bulk and single-cell levels

Integrated Analysis of Single-Cell and Bulk RNA-Seq Data reveals that Ferroptosis-Related Genes Mediated the Tumor Microenvironment predicts Prognosis, and guides Drug Selection in Triple-Negative Breast Cancer

Exploring the immune escape mechanisms in gastric cancer patients based on the deep AI algorithms and single‐cell sequencing analysis

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