SH3KBP1 Promotes Glioblastoma Tumorigenesis by Activating EGFR Signaling

Song, Hai; Wang, Yanpei; Shi, Chengmin; Lu, Jian-Xiang; Tian, Ye; Wang, Xiangpeng

doi:10.3389/fonc.2020.583984

Cited by 7 publications

(5 citation statements)

References 40 publications

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“…For example, recently, the overexpression of SKIP (ranked 10th in patient TCGA-BH-A1FD) was included in the pathogenesis and diagnosis of breast cancer, which could possibly serve as a future therapeutic target for breast cancer [ 64 ]. Besides, SH3KBP1 (ranked 10th in patient TCGA-BH-A1FD) was reported to serve as a new regulator of carcinogenic EGFR (Epidermal Growth Factor Receptor), and it could also serve as a potential therapy target for GBM (Glioblastoma multiforme, a kind of cancer) patients with EGFR activation [ 65 ].…”

Section: Resultsmentioning

confidence: 99%

Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation

Huang,

Chen,

Sun

et al. 2024

BMC Bioinformatics

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Background Driver genes play a vital role in the development of cancer. Identifying driver genes is critical for diagnosing and understanding cancer. However, challenges remain in identifying personalized driver genes due to tumor heterogeneity of cancer. Although many computational methods have been developed to solve this problem, few efforts have been undertaken to explore gene-patient associations to identify personalized driver genes. Results Here we propose a method called LPDriver to identify personalized cancer driver genes by employing linear neighborhood propagation model on individual genetic data. LPDriver builds personalized gene network based on the genetic data of individual patients, extracts the gene-patient associations from the bipartite graph of the personalized gene network and utilizes a linear neighborhood propagation model to mine gene-patient associations to detect personalized driver genes. The experimental results demonstrate that as compared to the existing methods, our method shows competitive performance and can predict cancer driver genes in a more accurate way. Furthermore, these results also show that besides revealing novel driver genes that have been reported to be related with cancer, LPDriver is also able to identify personalized cancer driver genes for individual patients by their network characteristics even if the mutation data of genes are hidden. Conclusions LPDriver can provide an effective approach to predict personalized cancer driver genes, which could promote the diagnosis and treatment of cancer. The source code and data are freely available at https://github.com/hyr0771/LPDriver.

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Section: Resultsmentioning

confidence: 99%

Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation

Huang,

Chen,

Sun

et al. 2024

BMC Bioinformatics

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“…The differences of seven ASEs related to prognosis were KIAA0895L (AA), INTS2 (AD), SH3KBP1 (AP), BEST3 (AT), IL1R1 (ES), TPM2 (ME), and C10orf32 (RI). Among them, SH3KBP1 is highly expressed in glioblastoma stem cells, resulting in poor survival of glioma patients, affecting tumor cell proliferation, migration, and germline stem cells (GSCs) self-renewal ability ( 27 ). In addition, TPM2 is associated with the prognosis of patients with breast, colorectal, and prostate cancer, and is involved in tumor progression and chemotherapy resistance ( 28 - 30 ).…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis of the prognostic role of alternative splicing data in lung adenocarcinoma

Zhao,

He,

Liu

et al. 2024

J Thorac Dis

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Background As a post-transcriptional regulatory mechanism, alternative splicing (AS) is engaged in a variety of pathophysiological processes, and it has been widely reported in connection with the occurrence, progression, metastasis, and drug resistance of cancer. However, the research on AS in lung adenocarcinoma (LUAD) is very limited. In addition, the prognostic effect of AS event (ASE) on LUAD and its related mechanism are not clear. This study aimed to explore the role and potential prognostic value of ASE in LUAD. Methods Relevant data and ASE datasets of the sample were acquired from The Cancer Genome Atlas (TCGA) and TCGASpliceSeq databases. We constructed a new prognostic criterion based on ASEs. Then, Cox regression and least absolute shrinkage and selection operator (LASSO) regression analysis were used to construct the model. Based on this model, the risk score of each ASE was calculated, and the reliability of this model was evaluated by Kaplan-Meier survival and receiver operating characteristic (ROC) curve analyses. Finally, these results were verified on different network platforms. Results We identified seven types of ASEs related to survival. The prognostic risk model for ASEs was established. The Kaplan-Meier curve showed that compared to the low-risk group, the overall survival (OS) rate of LUAD patients in the high-risk group was lower. ROC curve analysis showed that the prognostic risk model of LUAD patients was well predicted, and the area under the curve (AUC) also confirmed this. Conclusions This study screened the ASE related to the prognosis of LUAD patients, and provided a theoretical basis for further study of the correlation between ASE and the prognosis of LUAD patients. It has provided new ideas for developing new biomarkers and therapeutic targets for LUAD patients.

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“…The upregulation of CDK1 can promote the growth and the proliferation of melanoma tumor cells [ 34 ]. SH3KBP1 was considered promoting glioblastoma tumorigenesis by activating EGFR signaling [ 35 ]. Deregulated expression of SYN1 may maintain a cancer stem-like phenotype that contributes to the development of gliomas [ 36 ].…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Liquid-Liquid Phase Separation-Related Gene Signature as Prognostic Biomarker for Low-Grade Gliomas

et al. 2022

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Aim. To explore whether the liquid-liquid phase separation- (LLPS-) related genes were potential prognostic markers that could contribute to the further classification of low-grade gliomas (LGGs). Methods. The LLPS-related genes were subjected to functional enrichment analysis. The univariable, least absolute shrinkage and selection operator, and multivariable stepwise Cox regression analyses were performed to develop an LLPS-related gene signature (GS) in the discovery data set. The biological characteristics of the high-risk LGG were explored using gene set enrichment analysis. Two independent external data sets were used to validate the LLPS-related GS. Results. LLPS-related genes are involved in multiple important cancer-related biological processes and pathways in LGG. Nine LLPS-related genes were identified to construct the LLPS-related GS, which was significantly associated with the prognosis of LGG patients. The LLPS-related GS could successfully divide patients with LGG into high- and low-risk groups, and the high-risk group showed a poorer prognosis than the low-risk group. Furthermore, the LLPS-related GS was independent of IDH and 1p19q status. Several cancer-related pathways may be more active in high-risk LGGs, such as IL6 JAK STAT3 signaling pathway. The LLPS-related GS was successfully validated with two independent external data sets. Conclusion. We developed and validated a novel LLPS-related GS for risk stratification of LGG. Our findings may provide more precise management for LGGs and a useful reference for LLPS mechanism to link LGG studies.

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SH3KBP1 Promotes Glioblastoma Tumorigenesis by Activating EGFR Signaling

Cited by 7 publications

References 40 publications

Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation

Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation

Bioinformatics analysis of the prognostic role of alternative splicing data in lung adenocarcinoma

Development and Validation of a Liquid-Liquid Phase Separation-Related Gene Signature as Prognostic Biomarker for Low-Grade Gliomas

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