SLP76 integrates into the B-cell receptor signaling cascade in chronic lymphocytic leukemia cells and is associated with an aggressive disease course

Dezorella, Nili; Katz, Ben‐Zion; Shapiro, Mika; Polliack, Aaron; Perry, Chava; Herishanu, Yair

doi:10.3324/haematol.2015.139154

Cited by 17 publications

(22 citation statements)

References 53 publications

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“…Krysiak et al found that the B cell receptor signaling pathway affects the biology, prognosis, and treatment of follicular lymphoma [ 40 ]. Another study revealed that B cell receptor signaling affects the prognosis of patients with chronic lymphocytic leukemia [ 41 ]. Based on these results, the hub genes are speculated to play an important role in the CESC microenvironment.…”

Section: Discussionmentioning

confidence: 99%

Identification of a tumor microenvironment-related gene signature to improve the prediction of cervical cancer prognosis

et al. 2021

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Background Previous studies have found that the microenvironment of cervical cancer (CESC) affects the progression and treatment of this disease. Thus, we constructed a multigene model to assess the survival of patients with cervical cancer. Methods We scored 307 CESC samples from The Cancer Genome Atlas (TCGA) and divided them into high and low matrix and immune scores using the ESTIMATE algorithm for differential gene analysis. Cervical cancer patients were randomly divided into a training group, testing group and combined group. The multigene signature prognostic model was constructed by Cox analyses. Multivariate Cox analysis was applied to evaluate the significance of the multigene signature for cervical cancer prognosis. Prognosis was assessed by Kaplan–Meier curves comparing the different groups, and the accuracy of the prognostic model was analyzed by receiver operating characteristic-area under the curve (ROC-AUC) analysis and calibration curve. The Tumor Immune Estimation Resource (TIMER) database was used to analyze the relationship between the multigene signature and immune cell infiltration. Results We obtained 420 differentially expressed genes in the tumor microenvironment from 307 patients with cervical cancer. A three-gene signature (SLAMF1, CD27, SELL) model related to the tumor microenvironment was constructed to assess patient survival. Kaplan–Meier analysis showed that patients with high risk scores had a poor prognosis. The ROC-AUC value indicated that the model was an accurate predictor of cervical cancer prognosis. Multivariate cox analysis showed the three-gene signature to be an independent risk factor for the prognosis of cervical cancer. A nomogram combining the three-gene signature and clinical features was constructed, and calibration plots showed that the nomogram resulted in an accurate prognosis for patients. The three-gene signature was associated with T stage, M stage and degree of immune infiltration in patients with cervical cancer. Conclusions This research suggests that the developed three-gene signature may be applied as a biomarker to predict the prognosis of and personalized therapy for CESC.

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Section: Discussionmentioning

confidence: 99%

Identification of a tumor microenvironment-related gene signature to improve the prediction of cervical cancer prognosis

et al. 2021

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“…HCLS1 is expressed mainly in hematopoietic cells (74), which regulates the migration of leukemic cells and may be a promising target for the treatment of chronic lymphocytic leukemia (75,76). LCP2 acts as a substrate to activate the T cell antigen receptor signaling pathway and is expressed in chronic lymphocytic leukemia cells (77). SELL is expressed in inflammatory cells including T cells and B cells and serves as a biomarker to predict the metastatic risk of high-grade urothelial carcinoma (78).…”

Section: Discussionmentioning

confidence: 99%

Transcriptome-Based Network Analysis Unveils Eight Immune-Related Genes as Molecular Signatures in the Immunomodulatory Subtype of Triple-Negative Breast Cancer

Zhang

Wang

et al. 2020

Front. Oncol.

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Objective: Triple-negative breast cancer (TNBC) is a high heterogeneity cancer. The identification of genomic aberrations that drive each of the TNBC subtypes may predict the prognosis of patients with TNBC and provide novel therapeutic strategies in clinical practice. This study focuses on the transcriptome-based gene expression of TNBC and aims to generate comprehensive gene co-expression networks correlated with the immune-related subtype of TNBC. Methods: The transcriptome profiles of 107 female patients with TNBC were analyzed. Weighted gene co-expression network analysis (WGCNA) was applied to construct related networks and to sort hub-genes associated with the survival of TNBC patients. The data of the transcriptional expression, genomic alteration, survival status, and tumor immune microenvironment, which associated with hub-genes, were extracted, retrieved, and analyzed from Oncomine, UALCAN, TCGA, starBase, Kaplan-Meier Plotter, cBioPortal, and TIMER databases. Results: Immune-related hub-genes, including BIRC3, BTN3A1, CSF2RB, GIMAP7, GZMB, HCLS1, LCP2, and SELL, were found to be associated with clinical features of TNBC evaluated by WGCNA. These hub-genes belonged to the immunomodulatory subtype of TNBC and were upregulated in the TNBC cells. The protein expression of eight immune-related hub-genes was further confirmed to be upregulated in TNBC/CD8+ tissues detected by immunohistochemical staining. Survival analysis revealed that overexpression of eight immune-related hub-genes was in favor of the survival of patients with TNBC. Moreover, a positive correlation between eight immune-related hub-genes and immune cell infiltration was observed in TNBC patients. Furthermore, checkpoint inhibitor genes such as PD-L1, PD-1, and CTLA4 were more enrichment in the immunomodulatory subtype of TNBC and the expression of PD-L1, PD-1, and CTLA4 was positively correlated with eight immune-related hub-genes in the breast cancer dataset of TCGA. Zhang et al. Identifying Gene Signature in TNBC Conclusions: Eight immune-related hub-genes were identified to be molecular signatures in the immunomodulatory subtype of TNBC, which may provide therapeutic targets for the treatment of patients with breast cancer.

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“…It is possible that Lck plays a role in the assembly of the BLNK/Btk/Grb2 signalosome, which is required for the activation of PLCγ2 (indeed Lck regulates PLCγ2 in acute lymphoblastic leukemia, see below) or that Lck regulates PLCγ2 activation via alternative aberrant pathways. In this regard, it was shown that some CLL cells express SLP-76, a T-cell specific molecule required for the activation of PLCγ1, and that Lck is involved in the phosphorylation of SLP-76 in CLL cells [86]. Suppression of SLP-76 results in the inhibition of PLCγ2 phosphorylation and NFκB activation.…”

Section: Expression and Function Of Lck In Leukemic Cellsmentioning

confidence: 99%

Beyond TCR Signaling: Emerging Functions of Lck in Cancer and Immunotherapy

Bommhardt

Schraven

Simeoni

2019

IJMS

115

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In recent years, the lymphocyte-specific protein tyrosine kinase (Lck) has emerged as one of the key molecules regulating T-cell functions. Studies using Lck knock-out mice or Lck-deficient T-cell lines have shown that Lck regulates the initiation of TCR signaling, T-cell development, and T-cell homeostasis. Because of the crucial role of Lck in T-cell responses, strategies have been employed to redirect Lck activity to improve the efficacy of chimeric antigen receptors (CARs) and to potentiate T-cell responses in cancer immunotherapy. In addition to the well-studied role of Lck in T cells, evidence has been accumulated suggesting that Lck is also expressed in the brain and in tumor cells, where it actively takes part in signaling processes regulating cellular functions like proliferation, survival and memory. Therefore, Lck has emerged as a novel druggable target molecule for the treatment of cancer and neuronal diseases. In this review, we will focus on these new functions of Lck.

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SLP76 integrates into the B-cell receptor signaling cascade in chronic lymphocytic leukemia cells and is associated with an aggressive disease course

Cited by 17 publications

References 53 publications

Identification of a tumor microenvironment-related gene signature to improve the prediction of cervical cancer prognosis

Identification of a tumor microenvironment-related gene signature to improve the prediction of cervical cancer prognosis

Transcriptome-Based Network Analysis Unveils Eight Immune-Related Genes as Molecular Signatures in the Immunomodulatory Subtype of Triple-Negative Breast Cancer

Beyond TCR Signaling: Emerging Functions of Lck in Cancer and Immunotherapy

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