Identification of ATP1B1, a key copy number driver gene in diffuse large B-cell lymphoma and potential target for drugs

Zhang, Shuo; Wang, Hongmin; Liu, Aichun

doi:10.21037/atm-22-4709

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…S2 A. Previous studies have reported that CNV levels are positively correlated with the malignancy of T-cell [ 52 ] and B-cell [ 53 ] lymphomas. So InferCNVs analysis was performed across B cell subtypes.…”

Section: Resultsmentioning

confidence: 99%

Single-cell and spatial transcriptomics reveal a high glycolysis B cell and tumor-associated macrophages cluster correlated with poor prognosis and exhausted immune microenvironment in diffuse large B-cell lymphoma

Dai,

Fan,

Xie

et al. 2024

Biomark Res

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Background Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous malignancy characterized by varied responses to treatment and prognoses. Understanding the metabolic characteristics driving DLBCL progression is crucial for developing personalized therapies. Methods This study utilized multiple omics technologies including single-cell transcriptomics (n = 5), bulk transcriptomics (n = 966), spatial transcriptomics (n = 10), immunohistochemistry (n = 34), multiple immunofluorescence (n = 20) and to elucidate the metabolic features of highly malignant DLBCL cells and tumor-associated macrophages (TAMs), along with their associated tumor microenvironment. Metabolic pathway analysis facilitated by scMetabolism, and integrated analysis via hdWGCNA, identified glycolysis genes correlating with malignancy, and the prognostic value of glycolysis genes (STMN1, ENO1, PKM, and CDK1) and TAMs were verified. Results High-glycolysis malignant DLBCL tissues exhibited an immunosuppressive microenvironment characterized by abundant IFN_TAMs (CD68+CXCL10+PD-L1+) and diminished CD8+ T cell infiltration. Glycolysis genes were positively correlated with malignancy degree. IFN_TAMs exhibited high glycolysis activity and closely communicating with high-malignancy DLBCL cells identified within datasets. The glycolysis score, evaluated by seven genes, emerged as an independent prognostic factor (HR = 1.796, 95% CI: 1.077–2.995, p = 0.025 and HR = 2.631, 95% CI: 1.207–5.735, p = 0.015) along with IFN_TAMs were positively correlated with poor survival (p < 0.05) in DLBCL. Immunohistochemical validation of glycolysis markers (STMN1, ENO1, PKM, and CDK1) and multiple immunofluorescence validation of IFN_TAMs underscored their prognostic value (p < 0.05) in DLBCL. Conclusions This study underscores the significance of glycolysis in tumor progression and modulation of the immune microenvironment. The identified glycolysis genes and IFN_TAMs represent potential prognostic markers and therapeutic targets in DLBCL.

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Section: Resultsmentioning

confidence: 99%

Single-cell and spatial transcriptomics reveal a high glycolysis B cell and tumor-associated macrophages cluster correlated with poor prognosis and exhausted immune microenvironment in diffuse large B-cell lymphoma

Dai,

Fan,

Xie

et al. 2024

Biomark Res

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NLRRC: A Novel Clustering Method of Jointing Non-Negative LRR and Random Walk Graph Regularized NMF for Single-Cell Type Identification

Wang

Yuan

et al. 2023

IEEE J. Biomed. Health Inform.

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Integrating Multi-Omics Using Bayesian Ridge Regression with Iterative Similarity Bagging

Almutiri,

Alomar,

Alganmi

2024

Applied Sciences

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Cancer research has increasingly utilized multi-omics analysis in recent decades to obtain biomolecular information from multiple layers, thereby gaining a better understanding of complex biological systems. However, the curse of dimensionality is one of the most significant challenges when handling omics or biological data. Additionally, integrating multi-omics by transforming different omics types into a new representation can reduce a model’s interpretability, as the extracted features may lose the biological context. This paper proposes Iterative Similarity Bagging (ISB), assisted by Bayesian Ridge Regression (BRR). BRR serves as a domain-oriented supervised feature selection method, choosing essential features by calculating the coefficients for each feature. Despite this, the BRR output datasets contain many features, leading to complexity and high dimensionality. To address this, ISB was introduced to dynamically reduce dimensionality and complexity without losing the biological integrity of the omics data, which often occurs with transformation-based integration approaches. The evaluation measures employed were Root Mean Square Error (RMSE), the Pearson Correlation Coefficient (PCC), and the coefficient of determination (R2). The results demonstrate that the proposed method outperforms some current models in terms of regression performance, achieving an RMSE of 0.12, a PCC of 0.879, and an R2 of 0.77 for the CCLE. For the GDSC, it achieved an RMSE of 0.029, a PCC of 0.90, and an R2 of 0.80.

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Identification of ATP1B1, a key copy number driver gene in diffuse large B-cell lymphoma and potential target for drugs

Cited by 3 publications

References 32 publications

Single-cell and spatial transcriptomics reveal a high glycolysis B cell and tumor-associated macrophages cluster correlated with poor prognosis and exhausted immune microenvironment in diffuse large B-cell lymphoma

Single-cell and spatial transcriptomics reveal a high glycolysis B cell and tumor-associated macrophages cluster correlated with poor prognosis and exhausted immune microenvironment in diffuse large B-cell lymphoma

NLRRC: A Novel Clustering Method of Jointing Non-Negative LRR and Random Walk Graph Regularized NMF for Single-Cell Type Identification

Integrating Multi-Omics Using Bayesian Ridge Regression with Iterative Similarity Bagging

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