KIF5A Promotes Bladder Cancer Proliferation In Vitro and In Vivo

Retraction Note: CYPA promotes the progression and metastasis of serous ovarian cancer (SOC) in vitro and in vivo

This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1186/s13048-019-0593-2.

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

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.

KIF5A upregulation in hepatocellular carcinoma: A novel prognostic biomarker associated with unique tumor microenvironment status

Liver hepatocellular carcinoma (LIHC) is one of the most common liver malignancies with high mortality and morbidity. Thus, it is crucial to identify potential biomarker that is capable of accurately predicting the prognosis and therapeutic response of LIHC. Kinesin family member 5A (KIF5A) is a microtubule-based motor protein involved in the transport of macromolecules such as organelle proteins in cells. Recent studies have illustrated that the high expression of KIF5A was related to poor prognosis of solid tumors, including bladder cancer, prostate cancer, and breast cancer. However, little is currently known concerning the clinical significance of KIF5A expression in LIHC. Herein, by adopting multi-omics bioinformatics analysis, we comprehensively uncovered the potential function and the predictive value of KIF5A in stratifying clinical features among patients with LIHC, for which a high KIF5A level predicted an unfavorable clinical outcome. Results from KIF5A-related network and enrichment analyses illustrated that KIF5A might involve in microtubule-based process, antigen processing and presentation of exogenous peptide antigen via MHC class II. Furthermore, immune infiltration and immune function analyses revealed upregulated KIF5A could predict a unique tumor microenvironment with more CD8+T cells and a higher level of anti-tumor immune response. Evidence provided by immunohistochemistry staining (IHC) further validated our findings at the protein level. Taken together, KIF5A might serve as a novel prognostic biomarker for predicting immunotherapy response and could be a potential target for anti-cancer strategies for LIHC.

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

Purpose

The 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.

Methods

Patients’ 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.

Results

We 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.

Conclusions

We 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.

Linc00662 plays an oncogenic role in bladder cancer by sponging miR-199a-5p

OBJECTIVE

To investigate the specific roles of linc00662 and miR-199a-5p in bladder cancer (BC).

METHODS

A total of 104 cases of BC tissues and 52 cases of normal para-cancerous tissues were included to detect the expression of linc00662 and miR-199-5p by real-time quantitative PCR. The expression of linc00662 and miR-199a-5p in BC cells T24 was regulated to observe the changes in apoptosis, proliferation, adhesion, invasion, and migration. The nude mice bearing a BC cell transplanted xenograft was constructed, and the expression of linc00662 in rats was regulated. Tumor size and quality were observed within 24 days. The relationship between linc00662 and patients' survival was observed. The targeting relationship between linc00662 and miR-199a-5p was verified by dual luciferase reporter gene assay.

RESULTS

Linc00662 was enhanced and miR-199a-5p was decreased in BC patients. Linc00662 targeted and negatively regulated the expression of miR-199a-5p. Down-regulation of linc00662 could reduce proliferation, migration, invasion, and adhesion activities of BC cells, but enhance the apoptosis. Down-regulation of miR-199a-5p counteracted the cell biological changes caused by linc00662. Down-regulating linc00662 cinduced the expression of miR-199a-5p in BC and suppressed tumor growth.

CONCLUSION

Linc00662 plays an oncogenic role in BC by sponging miR-199a-5p.

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

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.

Determination of hypoxia signature to predict prognosis and the tumor immune microenvironment in melanoma

Melanoma is one of the highly malignant skin tumors, the incidence and death of which continue to increase. The hypoxic microenvironment drives tumor growth, progression, and heterogeneity; it also triggers a cascade of immunosuppressive responses and affects the levels of T cells, macrophages, and natural killer cells. Here, we aim to develop a hypoxia-based gene signature for prognosis evaluation and help evaluate the status of hypoxia and the immune microenvironment in melanoma. Based on the data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, we performed integrated bioinformatics to analyze the hypoxia-related genes. Using Lasso Cox regression, a hypoxia model was constructed. The receiver operating characteristic and the Kaplan-Meier curve were used to evaluate the predictive capacity of the model. With the CIBERSORT algorithm, the abundance of 22 immune cells in the melanoma microenvironment was analyzed. A total of 20 hypoxia-related genes were significantly related to prognosis in the log-rank test. Lasso regression showed that FBP1, SDC3, FOXO3, IGFBP1, S100A4, EGFR, ISG20, CP, PPARGC1A, KIF5A, and DPYSL4 displayed the best features. Based on these genes, a hypoxia model was established, and the area under the curve for the model was 0.734. Furthermore, the hypoxia score was identified as an independent prognostic factor. Besides, the hypoxia score could also predict the immune microenvironment in melanoma. Down-regulated activated CD4 memory T cells, CD8 T cells, and M1-like macrophages, and up-regulated Tregs were observed in patients with a high hypoxia score. The hypoxia-related genes were identified, and the hypoxia score was found to be a prognostic factor for overall survival and a predictor for the immune microenvironment. Our findings provide new ideas for evaluation and require further validation in clinical practice.

Effect of KIF22 on promoting proliferation and migration of gastric cancer cells via MAPK-ERK pathways

Background

Gastric cancer (GC) is one of the most globally prevalent cancers in the world. The pathogenesis of GC has not been fully elucidated, and there still lacks effective targeted therapeutics. The influence of altered kinesin superfamily protein 22 (KIF22) expression in GC progression is still unclearly. The aim of this study was to investigate the KIF22 effects on GC and related mechanisms.

Methods

Gastric carcinoma tissues and matching non-cancerous tissues were collected from patients with GC who have accepted a radical gastrectomy in Lanzhou University Second Hospital from May 2013 to December 2014. The expression of KIF22 was examined in GC of 67 patients and 20 para-carcinoma tissues by immunochemical staining. The relationship between the expression of KIF22 and clinicopathologic characteristics was next investigated in the remaining 52 patients except for 15 patients who did not complete follow-up for 5 years. Cell viability was performed via 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) test and colony formation assay in the MGC-803 and BGC-823 GC cells. Cell scratch and trans-well invasion assay was performed to assess migration ability in the MGC-803 and BGC-823 GC cells. Gene set enrichment analysis (GSEA) pathway enrichment analysis was performed to explore the potential functions. Cell cycle was detected by flow cytometry. In addition, the two GC cell lines were used to elucidate the underlying mechanism of KIF22 in GC in vitro via assessing the effects on mitogen-activated protein kinase and extracellular regulated protein kinases (MAPK/ERK) signal transduction pathway-related expressions by Western blotting assays. The differences were compared by t tests, one-way analysis of variance, and Chi-squared tests.

Results

The study showed that KIF22 was up-regulated in GC, and KIF22 high expression was significantly related to differentiation degree (χ² = 12.842, P = 0.002) and poorly overall survivals. GSEA pathway enrichment analysis showed that KIF22 was correlated with the cell cycle. Silence of KIF22 decreased the ability of the proliferation and migration in gastric cells, induced G1/S phase cell cycle arrest via regulating the MAPK-ERK pathways.

Conclusions

KIF22 protein level was negatively correlated with prognosis. KIF22 knockdown might inhibit proliferation and metastasis of GC cells via the MAPK-ERK signaling pathway.