KIF5A Promotes Bladder Cancer Proliferation <i>In Vitro</i> and <i>In Vivo</i>

Tian, Dan; Wu, Zhouliang; Jiang, Liming; Gao, Jie; Wu, Changli; Hu, Hailong

doi:10.1155/2019/4824902

Cited by 12 publications

(8 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We identified 207 DEGs across the low-risk and high-risk subgroups derived from the DTI-based DLS, and the prognostic values of some DEGs in human cancers has been revealed previously. For instance, high expression of SNAP25 [27] and KIF5A [28] were demonstrated to be associated with worse prognosis in colon cancer and bladder cancer. In addition, PRKCB and CAMK2A were revealed as prognostic oncogenes, and their high expressions predict poor prognosis in GBMs [29] .…”

Section: Discussionmentioning

confidence: 99%

Deep learning features from diffusion tensor imaging improve glioma stratification and identify risk groups with distinct molecular pathway activities

et al. 2021

View full text Add to dashboard Cite

Background To develop and validate a deep learning signature (DLS) from diffusion tensor imaging (DTI) for predicting overall survival in patients with infiltrative gliomas, and to investigate the biological pathways underlying the developed DLS. Methods The DLS was developed based on a deep learning cohort (n = 688). The key pathways underlying the DLS were identified on a radiogenomics cohort with paired DTI and RNA-seq data (n=78), where the prognostic value of the pathway genes was validated in public databases (TCGA, n = 663; CGGA, n = 657). Findings The DLS was associated with survival (log-rank P < 0.001) and was an independent predictor ( P < 0.001). Incorporating the DLS into existing risk system resulted in a deep learning nomogram predicting survival better than either the DLS or the clinicomolecular nomogram alone, with a better calibration and classification accuracy (net reclassification improvement 0.646, P < 0.001). Five kinds of pathways (synaptic transmission, calcium signaling, glutamate secretion, axon guidance, and glioma pathways) were significantly correlated with the DLS. Average expression value of pathway genes showed prognostic significance in our radiogenomics cohort and TCGA/CGGA cohorts (log-rank P < 0.05). Interpretation DTI-derived DLS can improve glioma stratification by identifying risk groups with dysregulated biological pathways that contributed to survival outcomes. Therapies inhibiting neuron-to-brain tumor synaptic communication may be more effective in high-risk glioma defined by DTI-derived DLS. Funding A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

show abstract

Section: Discussionmentioning

confidence: 99%

Deep learning features from diffusion tensor imaging improve glioma stratification and identify risk groups with distinct molecular pathway activities

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Further, KIF5A is a member of the kinesin family, which can modulate the cell cycle, proliferation, and differentiation [ 51 ]. Many studies have demonstrated that high expression of KIF5A is associated with cancer progression and a poor prognosis, such as in bladder, lung, and breast cancers [ 52 , 53 , 54 ]. In addition, exome sequencing for 64 tumor samples from 55 PCa patients demonstrated that the KIF5A mutation was related to aggressive diseases [ 55 ].…”

Section: Discussionmentioning

confidence: 99%

A Novel Four Mitochondrial Respiration-Related Signature for Predicting Biochemical Recurrence of Prostate Cancer

Xia

Liu

Fan

et al. 2023

JCM

View full text Add to dashboard Cite

The biochemical recurrence (BCR) of patients with prostate cancer (PCa) after radical prostatectomy is high, and mitochondrial respiration is reported to be associated with the metabolism in PCa development. This study aimed to establish a mitochondrial respiratory gene-based risk model to predict the BCR of PCa. RNA sequencing data of PCa were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and mitochondrial respiratory-related genes (MRGs) were sourced via GeneCards. The differentially expressed mitochondrial respiratory and BCR-related genes (DE-MR-BCRGs) were acquired through overlapping BCR-related differentially expressed genes (BCR-DEGs) and differentially expressed MRGs (DE-MRGs) between PCa samples and controls. Further, univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analyses were performed to construct a DE-MRGs-based risk model. Then, a nomogram was established by analyzing the independent prognostic factor of five clinical features and risk scores. Moreover, Gene Set Enrichment Analysis (GSEA), tumor microenvironment, and drug susceptibility analyses were employed between high- and low-risk groups of PCa patients with BCR. Finally, qRT-PCR was utilized to validate the expression of prognostic genes. We identified 11 DE-MR-BCRGs by overlapping 132 DE-MRGs and 13 BCR-DEGs and constructed a risk model consisting of 4 genes (APOE, DNAH8, EME2, and KIF5A). Furthermore, we established an accurate nomogram, including a risk score and a Gleason score, for the BCR prediction of PCa patients. The GSEA result suggested the risk model was related to the PPAR signaling pathway, the cholesterol catabolic process, the organic hydroxy compound biosynthetic process, the small molecule catabolic process, and the steroid catabolic process. Simultaneously, we found six immune cell types relevant to the risk model: resting memory CD4+ T cells, monocytes, resting mast cells, activated memory CD4+ T cells, regulatory T cells (Tregs), and macrophages M2. Moreover, the risk model could affect the IC50 of 12 cancer drugs, including Lapatinib, Bicalutamide, and Embelin. Finally, qRT-PCR showed that APOE, EME2, and DNAH8 were highly expressed in PCa, while KIF5A was downregulated in PCa. Collectively, a mitochondrial respiratory gene-based nomogram including four genes and one clinical feature was established for BCR prediction in patients with PCa, which could provide novel strategies for further studies.

show abstract

“…In addition, GPC3 has a low expression in ccRCC tissues than normal kidney tissues, and it can reduce the proliferation of RCC cell lines ( 20 ). Kinesin family member 5A (KIF5A) is a member of the kinesin superfamily which can modulate many cell physiological behavior such as the proliferation of cell cycle ( 21 ). KIF5A mutation causes familial Amyotrophic lateral sclerosis (ALS; OMIM: 05400) ( 22 ).…”

Section: Discussionmentioning

confidence: 99%

“…Tian et al. have proved that KIF5A can regulate the bladder cancer development and progression ( 21 ). Plasminogen activator, urokinase receptor ( PLAUR ) encodes the receptor for urokinase plasminogen activator and is a key molecule in regulating of cell-surface plasminogen activation ( 23 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of a Hypoxia-Related Gene Model for Predicting the Prognosis and Formulating the Treatment Strategies in Kidney Renal Clear Cell Carcinoma

Ning

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

PurposeThe present study aimed to establish a hypoxia related genes model to predict the prognosis of kidney clear cell carcinoma (KIRC) patients using data accessed from The Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) database.MethodsPatients’ data were downloaded from the TCGA and ICGC databases, and hypoxia related genes were accessed from the Molecular Signatures Database. The differentially expressed genes were evaluated and then the differential expressions hypoxia genes were screened. The TCGA cohort was randomly divided into a discovery TCGA cohort and a validation TCGA cohort. The discovery TCGA cohort was used for constructing the hypoxia genes risk model through Lasso regression, univariate and multivariate Cox regression analysis. Receiver operating characteristic (ROC) curves were used to assess the reliability and sensitivity of our model. Then, we established a nomogram to predict the probable one-, three-, and five-year overall survival rates. Lastly, the Tumor Immune Dysfunction and Exclusion (TIDE) score of patients was calculated.ResultsWe established a six hypoxia-related gene prognostic model of KIRC patients in the TCGA database and validated in the ICGC database. The patients with high riskscore present poorer prognosis than those with low riskscore in the three TCGA cohorts and ICGC cohort. ROC curves show our six-gene model with a robust predictive capability in these four cohorts. In addition, we constructed a nomogram for KIRC patients in the TCGA database. Finally, the high risk-group had a high TIDE score than the patients with low riskscore.ConclusionsWe established a six hypoxia-related gene risk model for independent prediction of the prognosis of KIRC patients was established and constructed a robust nomogram. The different riskscores might be a biomarker for immunotherapy strategy.

show abstract

KIF5A Promotes Bladder Cancer Proliferation In Vitro and In Vivo

Cited by 12 publications

References 34 publications

Deep learning features from diffusion tensor imaging improve glioma stratification and identify risk groups with distinct molecular pathway activities

Deep learning features from diffusion tensor imaging improve glioma stratification and identify risk groups with distinct molecular pathway activities

A Novel Four Mitochondrial Respiration-Related Signature for Predicting Biochemical Recurrence of Prostate Cancer

Identification of a Hypoxia-Related Gene Model for Predicting the Prognosis and Formulating the Treatment Strategies in Kidney Renal Clear Cell Carcinoma

Contact Info

Product

Resources

About