Integrated analysis of scRNA-seq and bulk RNA-seq identifies FBXO2 as a candidate biomarker associated with chemoresistance in HGSOC.

Background: High-grade serous ovarian carcinoma (HGSOC) is the most prevalent and aggressive histological subtype of epithelial ovarian cancer. Around 80% of individuals will experience a recurrence within five years because of resistance to chemotherapy, despite initially responding well to platinum-based treatment. Biomarkers associated with chemoresistance are desperately needed in clinical practice. Methods: We jointly analyzed the transcriptomic profiles of single-cell and bulk datasets of HGSOC to identify cell types associated with chemoresistance. Copy number variation (CNV) inference was performed to identify malignant cells. We subsequently analyzed the expression of candidate biomarkers and their relationship with patients' prognosis. The enrichment analysis and potential biological function of candidate biomarkers were explored. Then, we validated the candidate biomarker using in vitro experiments. Results: We identified 8871 malignant epithelial cells in a single-cell RNA sequencing dataset, of which 861 cells were associated with chemoresistance. Among these malignant epithelial cells, FBXO2 (F-box protein 2) is highly expressed in cells related to chemoresistance. Moreover, FBXO2 expression was found to be higher in epithelial cells from chemoresistance samples compared to those from chemosensitivity samples in a separate single-cell RNA sequencing dataset. Patients exhibiting elevated levels of FBXO2 experienced poorer outcomes in terms of both overall survival (OS) and progression-free survival (PFS). FBXO2 could impact chemoresistance by influencing the PI3K-Akt signaling pathway, focal adhesion, and ECM-receptor interactions and regulating tumorigenesis. The 50% maximum inhibitory concentration (IC50) of cisplatin decreased in A2780 and SKOV3 ovarian carcinoma cell lines with silenced FBXO2 during an in vitro experiment. Conclusions: We determined that FBXO2 is a potential biomarker linked to chemoresistance in HGSOC by combining single-cell RNA-seq and bulk RNA-seq dataset. Our results suggest that FBXO2 could serve as a valuable prognostic marker and potential target for drug development in HGSOC.

A Five-LLPS Gene Risk Score Prognostic Signature Predicts Survival in Hepatocellular Carcinoma.

Background: Primary liver cancer, dominated by hepatocellular carcinoma (HCC), is one of the most common cancer types and the third leading cause of cancer death in 2020. Previous studies have shown that liquid-liquid phase separation (LLPS) plays an important role in the occurrence and development of cancer including HCC, but its influence on the patient prognosis is still unknown. It is necessary to explore the effect of LLPS genes on prognosis to accurately forecast the prognosis of HCC patients and identify relevant targeted therapeutic sites. Methods: Using The Cancer Genome Atlas dataset and PhaSepDB dataset, we identified LLPS genes linked to the overall survival (OS) of HCC patients. We applied Least Absolute Shrinkage and Selection Operator (LASSO) Cox penalized regression analysis to choose the best genes for the risk score prognostic signature. We then analysed the validation dataset and evaluated the effectiveness of the risk score prognostic signature. Finally, we performed quantitative real-time PCR experiments to validate the genes in the prognostic signature. Results: We identified 43 differentially expressed LLPS genes that were associated with the OS of HCC patients. Five of these genes (BMX, FYN, KPNA2, PFKFB4, and SPP1) were selected to generate a prognostic risk score signature. Patients in the low-risk group were associated with better OS than those in the high-risk group in both the training dataset and the validation dataset. We found that BMX and FYN had lower expression levels in HCC tumour tissues, whereas KPNA2, PFKFB4, and SPP1 had higher expression levels in HCC tumour tissues. The validation demonstrated that the five-LLPS gene risk score signature has the capability of predicting the OS of HCC patients. Conclusion: Our study constructed a five-LLPS gene risk score signature that can be applied as an effective and convenient prognostic tool. These five genes might serve as potential targets for therapy and the treatment of HCC.

ARL13B promotes angiogenesis and glioma growth by activating VEGFA-VEGFR2 signaling.

BACKGROUND: Tumor angiogenesis is essential for solid tumor progression, invasion and metastasis. The aim of this study was to identify potential signaling pathways involved in tumor angiogenesis. METHODS: Genetically engineered mouse models were used to investigate the effects of endothelial ARL13B(ADP-ribosylation factor-like GTPase 13B) over-expression and deficiency on retinal and cerebral vasculature. An intracranially transplanted glioma model and a subcutaneously implanted melanoma model were employed to examine the effects of ARL13B on tumor growth and angiogenesis. Immunohistochemistry was used to measure ARL13B in glioma tissues, and scRNA-seq was used to analyze glioma and endothelial ARL13B expression. GST-fusion protein-protein interaction and co-immunoprecipitation assays were used to determine the ARL13B-VEGFR2 interaction. Immunobloting, qPCR, dual-luciferase reporter assay and functional experiments were performed to evaluate the effects of ARL13B on VEGFR2 activation. RESULTS: Endothelial ARL13B regulated vascular development of both the retina and brain in mice. Also, ARL13B in endothelial cells regulated the growth of intracranially transplanted glioma cells and subcutaneously implanted melanoma cells by controlling tumor angiogenesis. Interestingly, this effect was attributed to ARL13B interaction with VEGFR2, through which ARL13B regulated the membrane and ciliary localization of VEGFR2 and consequently activated its downstream signaling in endothelial cells. Consistent with its oncogenic role, ARL13B was highly expressed in human gliomas, which was well correlated with the poor prognosis of glioma patients. Remarkably, ARL13B, transcriptionally regulated by ZEB1, enhanced the expression of VEGFA by activating Hedgehog signaling in glioma cells. CONCLUSIONS: ARL13B promotes angiogenesis and tumor growth by activating VEGFA-VEGFR2 signaling. Thus, targeting ARL13B might serve as a potential approach for developing an anti-glioma or anti-melanoma therapy.

Integrated analysis of single-cell RNA-seq dataset and bulk RNA-seq dataset constructs a prognostic model for predicting survival in human glioblastoma.

BACKGROUND: Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. For patients with GBM, the median overall survival (OS) is 14.6 months and the 5-year survival rate is 7.2%. It is imperative to develop a reliable model to predict the survival probability in new GBM patients. To date, most prognostic models for predicting survival in GBM were constructed based on bulk RNA-seq dataset, which failed to accurately reflect the difference between tumor cores and peripheral regions, and thus show low predictive capability. An effective prognostic model is desperately needed in clinical practice. METHODS: We studied single-cell RNA-seq dataset and The Cancer Genome Atlas-glioblastoma multiforme (TCGA-GBM) dataset to identify differentially expressed genes (DEGs) that impact the OS of GBM patients. We then applied the least absolute shrinkage and selection operator (LASSO) Cox penalized regression analysis to determine the optimal genes to be included in our risk score prognostic model. Then, we used another dataset to test the accuracy of our risk score prognostic model. RESULTS: We identified 2128 DEGs from the single-cell RNA-seq dataset and 6461 DEGs from the bulk RNA-seq dataset. In addition, 896 DEGs associated with the OS of GBM patients were obtained. Five of these genes (LITAF, MTHFD2, NRXN3, OSMR, and RUFY2) were selected to generate a risk score prognostic model. Using training and validation datasets, we found that patients in the low-risk group showed better OS than those in the high-risk group. We validated our risk score model with the training and validating datasets and demonstrated that it can effectively predict the OS of GBM patients. CONCLUSION: We constructed a novel prognostic model to predict survival in GBM patients by integrating a scRNA-seq dataset and a bulk RNA-seq dataset. Our findings may advance the development of new therapeutic targets and improve clinical outcomes for GBM patients.

Positive feedback of SuFu negating protein 1 on Hedgehog signaling promotes colorectal tumor growth.

Hedgehog (Hh) signaling plays a critical role in embryogenesis and tissue homeostasis, and its deregulation has been associated with tumor growth. The tumor suppressor SuFu inhibits Hh signaling by preventing the nuclear translocation of Gli and suppressing cell proliferation. Regulation of SuFu activity and stability is key to controlling Hh signaling. Here, we unveil SuFu Negating Protein 1 (SNEP1) as a novel Hh target, that enhances the ubiquitination and proteasomal degradation of SuFu and thus promotes Hh signaling. We further show that the E3 ubiquitin ligase LNX1 plays a critical role in the SNEP1-mediated degradation of SuFu. Accordingly, SNEP1 promotes colorectal cancer (CRC) cell proliferation and tumor growth. High levels of SNEP1 are detected in CRC tissues and are well correlated with poor prognosis in CRC patients. Moreover, SNEP1 overexpression reduces sensitivity to anti-Hh inhibitor in CRC cells. Altogether, our findings demonstrate that SNEP1 acts as a novel feedback regulator of Hh signaling by destabilizing SuFu and promoting tumor growth and anti-Hh resistance.

A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) remains the most frequent liver cancer, accounting for approximately 90% of primary liver cancers worldwide. The recurrence-free survival (RFS) of HCC patients is a critical factor in devising a personal treatment plan. Thus, it is necessary to accurately forecast the prognosis of HCC patients in clinical practice. METHODS: Using The Cancer Genome Atlas (TCGA) dataset, we identified genes associated with RFS. A robust likelihood-based survival modeling approach was used to select the best genes for the prognostic model. Then, the GSE76427 dataset was used to evaluate the prognostic model's effectiveness. RESULTS: We identified 1331 differentially expressed genes associated with RFS. Seven of these genes were selected to generate the prognostic model. The validation in both the TCGA cohort and GEO cohort demonstrated that the 7-gene prognostic model can predict the RFS of HCC patients. Meanwhile, the results of the multivariate Cox regression analysis showed that the 7-gene risk score model could function as an independent prognostic factor. In addition, according to the time-dependent ROC curve, the 7-gene risk score model performed better in predicting the RFS of the training set and the external validation dataset than the classical TNM staging and BCLC. Furthermore, these seven genes were found to be related to the occurrence and development of liver cancer by exploring three other databases. CONCLUSION: Our study identified a seven-gene signature for HCC RFS prediction that can be used as a novel and convenient prognostic tool. These seven genes might be potential target genes for metabolic therapy and the treatment of HCC.

Construction and validation of a 6-gene nomogram discriminating lung metastasis risk of breast cancer patients.

Breast cancer is the most common malignant disease in women. Metastasis is the foremost cause of death. Breast tumor cells have a proclivity to metastasize to specific organs. The lung is one of the most common sites of breast cancer metastasis. Therefore, we aimed to build a useful and convenient prediction tool based on several genes that may affect lung metastasis-free survival (LMFS). We preliminarily identified 319 genes associated with lung metastasis in the training set GSE5327 (n = 58). Enrichment analysis of GO functions and KEGG pathways was conducted based on these genes. The best genes for modeling were selected using a robust likelihood-based survival modeling approach: GOLGB1, TMEM158, CXCL8, MCM5, HIF1AN, and TSPAN31. A prognostic nomogram for predicting lung metastasis in breast cancer was developed based on these six genes. The effectiveness of the nomogram was evaluated in the training set GSE5327 and the validation set GSE2603. Both the internal validation and the external validation manifested the effectiveness of our 6-gene prognostic nomogram in predicting the lung metastasis risk of breast cancer patients. On the other hand, in the validation set GSE2603, we found that neither the six genes in the nomogram nor the risk predicted by the nomogram were associated with bone metastasis of breast cancer, preliminarily suggesting that these genes and nomogram were specifically associated with lung metastasis of breast cancer. What's more, five genes in the nomogram were significantly differentially expressed between breast cancer and normal breast tissues in the TIMER database. In conclusion, we constructed a new and convenient prediction model based on 6 genes that showed practical value in predicting the lung metastasis risk for clinical breast cancer patients. In addition, some of these genes could be treated as potential metastasis biomarkers for antimetastatic therapy in breast cancer. The evolution of this nomogram will provide a good reference for the prediction of tumor metastasis to other specific organs.

The critical role of dysregulated Hh-FOXM1-TPX2 signaling in human hepatocellular carcinoma cell proliferation.

BACKGROUND: Aberrant activation of the Hedgehog (Hh) signaling pathway is frequently observed in hepatocellular carcinoma (HCC), nevertheless, the precise molecular mechanism remains unclear. Forkhead box M1 (FOXM1), a target of the Hh pathway, is a key oncofetal transcription factor and a master cell cycle regulator. Targeting protein for Xenopus kinesin-like protein 2 (TPX2) is an oncogene critical for mitosis. However, how these molecular events affect HCC progression remains unclear. METHODS: Realtime PCR, immunohistochemistry, western blotting, and analyses of datasets TCGA and Gene Expression Omnibus (GEO) were conducted to assess the expression of TPX2 and FOXM1 at the mRNA and protein levels in HCC samples or HCC cells. Expression and knockdown of TPX2 and FOXM1 were performed to assess their role in regulating HCC cell proliferation in vitro and in vivo. Dual luciferase report assay and chromosome immunoprecipitation (ChIP) were investigated to seek the FOXM1 binding sites in the promoter of TPX2. RESULTS: Specific antagonists (cyclopamine and GANT61) of the Hh pathway down-regulated TPX2, whereas activation of Hh signaling stimulated TPX2 expression. Furthermore, TPX2 over-expression accelerated HCC cell proliferation when upstream events of Hh signaling were inhibited, and TPX2 knockdown significantly alleviated Sonic Hh ligand (Shh)-induced HCC cell proliferation. Reporter assays and ChIP showed that FOXM1 bound to the TPX2 promoter, confirming that TPX2 is a direct downstream target of FOXM1. Xenograft model further verified the cell function and expression regulation of TPX2 and FOXM1 in vivo. Furthermore, FOXM1 regulated TPX2 activity to drive HCC proliferation. Immunohistochemical (IHC) analysis indicated that FOXM1 and TPX2 were highly-expressed in HCC samples and cohort study revealed that FOXM1 and TPX2 may act as negative predictors for the prognosis of patients with HCC. CONCLUSIONS: TPX2 acts as a novel downstream target and effector of the Hh pathway, and Hh signaling contributes to HCC proliferation via regulating the FOXM1-TPX2 cascade, suggesting that this signaling axis may be a novel therapeutic target for HCC.

FOXM1 promotes hepatocellular carcinoma progression by regulating KIF4A expression.

BACKGROUND: Forkhead box M1 (FOXM1) is a proliferation-associated transcription factor of the forkhead box proteins superfamily, which includes four isoforms FOXM1a, b, c, and d. FOXM1 has been implicated in hepatocellular carcinoma (HCC) progression, but the underlying molecular mechanism remains elusive. In this study, we aim to clarify the molecular basis for FOXM1-mediated HCC progression. METHODS: Bioinformatic analysis was used to explore the differentially expressed genes predicting HCC proliferation. The expression of FOXM1 and kinesin family member (KIF)4A was confirmed by western blotting and immunohistochemistry in HCC tissues. Kaplan-Meier survival analysis was conducted to analyze the clinical impact of FOXM1 and KIF4A on HCC. The effect of FOXM1 on the regulation of KIF4A expression was studied in cell biology experiments. The interaction between KIF4A and FOXM1 was analyzed by chromatin immunoprecipitation and luciferase experiments. A series of experiments was performed to explore the functions of FOXM1/KIF4A in HCC progression, such as cell proliferation, cell growth, cell viability, and cell cycle. A xenograft mouse model was used to explore the regulatory effect of FOXM1-KIF4A axis on HCC tumor growth. RESULTS: FOXM1 and KIF4A were overexpressed in human primary HCC tissues compared to that in matched adjacent normal liver tissue and are significant risk factors for HCC recurrence and shorter survival. We found that KIF4A was dominantly regulated by FOXM1c among the four isoforms, and further identified KIF4A as a direct downstream target of FOXM1c. Inhibiting FOXM1 decreased KIF4A expression in HCC cells, whereas its overexpression had the opposite effect. FOXM1-induced HCC cell proliferation was dependent on elevated KIF4A expression as KIF4A knockdown abolished FOXM1-induced proliferation of HCC cells both in vitro and in vivo. CONCLUSION: The FOXM1-KIF4A axis mediates human HCC progression and is a potential therapeutic target for HCC treatment.

A 5-gene prognostic nomogram predicting survival probability of glioblastoma patients.

BACKGROUND: Glioblastoma (GBM) remains the most biologically aggressive subtype of gliomas with an average survival of 10 to 12 months. Considering that the overall survival (OS) of each GBM patient is a key factor in the treatment of individuals, it is meaningful to predict the survival probability for GBM patients newly diagnosed in clinical practice. MATERIAL AND METHODS: Using the TCGA dataset and two independent GEO datasets, we identified genes that are associated with the OS and differentially expressed between GBM tissues and the adjacent normal tissues. A robust likelihood-based survival modeling approach was applied to select the best genes for modeling. After the prognostic nomogram was generated, an independent dataset on different platform was used to evaluate its effectiveness. RESULTS: We identified 168 differentially expressed genes associated with the OS. Five of these genes were selected to generate a gene prognostic nomogram. The external validation demonstrated that 5-gene prognostic nomogram has the capability of predicting the OS of GBM patients. CONCLUSION: We developed a novel and convenient prognostic tool based on five genes that exhibited clinical value in predicting the survival probability for newly diagnosed GBM patients, and all of these five genes could represent potential target genes for the treatment of GBM. The development of this model will provide a good reference for cancer researchers.

Analysis of risk factors and changing trends the infection rate of intestinal schistosomiasis caused by S. japonicum from 2005 to 2014 in Lushan city.

Intestinal schistosomiasis caused by S. japonicum has long been a threat to the health of residents within endemic areas, especially along the mid-tier of the Yangtze River basin as well as the Dongting and Poyang lakes. Therefore, we collected monitoring data from 2005 to 2014 in Lushan City, Jiujiang City, Jiangxi Province, which is located downstream of Poyang Lake. We conducted a logistic regression analysis in 2005 and in 2008 and then conducted a time series analysis from 2005 to 2014 in Lushan city. The results of the logistic regression analysis showed that after integrated measures were implemented in Lushan city in 2004, the infection rate of intestinal schistosomiasis decreased sharply in different populations, but fishermen had a greater risk of contracting intestinal schistosomiasis in both 2005 and 2008. From the time series analysis, we found that the infection rate decreased sharply from 2005 to 2009 and then increased slowly from 2009 to 2011 before finally becoming relatively stable and the predicated infection rates in HES, SM2, and SM3 are -1.14%, 0.35%, 0.29%, respectively, compared with 0.41% of schistosomiasis infection in 2014, showing a downward trend. Our study indicated that the integrated measures initiated in 2004 in Lushan city had a positive effect on controlling intestinal schistosomiasis, but we should still emphasize special treatment of particular populations, such as fishermen, and should consider environmental changes, such as changes in the water level of Poyang Lake, in the future.

Meta-analysis of mRNA expression profiles to identify differentially expressed genes in lung adenocarcinoma tissue from smokers and non-smokers.

Compared to other types of lung cancer, lung adenocarcinoma patients with a history of smoking have a poor prognosis during the treatment of lung cancer. How lung adenocarcinoma-related genes are differentially expressed between smoker and non-smoker patients has yet to be fully elucidated. We performed a meta-analysis of four publicly available microarray datasets related to lung adenocarcinoma tissue in patients with a history of smoking using R statistical software. The top 50 differentially expressed genes (DEGs) in smoking vs. non­smoking patients are shown using heat maps. Additionally, we conducted KEGG and GO analyses. In addition, we performed a PPI network analysis for 8 genes that were selected during a previous analysis. We identified a total of 2,932 DEGs (1,806 upregulated, 1,126 downregulated) and five genes (CDC45, CDC20, ANAPC7, CDC6, ESPL1) that may link lung adenocarcinoma to smoking history. Our study may provide new insights into the complex mechanisms of lung adenocarcinoma in smoking patients, and our novel gene expression signatures will be useful for future clinical studies.

Arl13b Promotes Gastric Tumorigenesis by Regulating Smo Trafficking and Activation of the Hedgehog Signaling Pathway.

Inhibitors of the Hedgehog (Hh) pathway transducer Smoothened (Smo) have been approved for cancer treatment, but Smo mutations often lead to tumor resistance and it remains unclear how Smo is regulated. In this study, we identified the small GTPase Arl13b as a novel partner and regulator of Smo. Arl13b regulated Smo stability, trafficking, and localization, which are each crucial for Hh signaling. In gastric cancer cells, Arl13b stimulated proliferation, migration, and invasion in vitro and in vivo In clinical specimens of gastric cancer, Arl13b expression correlated strongly with tumor size and depth of invasion; patients with high levels of Arl13b had a poor prognosis. Our results show how Arl13b participates in Hh pathway activation in gastric cancer. Cancer Res; 77(15); 4000-13. ©2017 AACR.

Inhibition of the Hedgehog signaling pathway suppresses cell proliferation by regulating the Gli2/miR-124/AURKA axis in human glioma cells.

Multiple lines of evidence indicate that aberrant activation of Hedgehog (Hh) signaling plays an important role in tumorigenesis in human glioma. However, the underlying molecular mechanism and crucial downstream targets of glioma-associated oncogene (Gli), a primary transcriptional regulator of Hh signaling, are not fully understood. Here, we report the identification of miR-124 as a novel downstream target of the transcriptional factor Gli2, which is important for proliferation and tumor growth in human glioma cells. Blockade of Hh signaling leads to a remarkable increase in miR-124 expression in glioma cells, whereas overexpression of Gli2 suppresses miR-124 expression by increasing the direct binding of Gli2 to the upstream region of the transcriptional start site for miR-124. Furthermore, we found that miR-124 potentially interacts with the 3'-UTR region of AURKA. Overexpression of miR-124 significantly decreased the expression of AURKA in glioma cells. In contrast, the loss of miR-124 led to the increased expression of AURKA mRNA and protein. In addition, cell proliferation and colony formation ability were significantly decreased following Gli2 knockdown in human glioma cells, while transfection with a miR-124 inhibitor rescued the proliferative ability of cells. These results demonstrate that miR-124 is an important downstream target gene of Hh signaling, and the Gli2/miR-124/AURKA axis is essential for the proliferation and growth of human glioma cells.

Aberrant activation of hedgehog signaling promotes cell proliferation via the transcriptional activation of forkhead Box M1 in colorectal cancer cells.

BACKGROUND: Recent evidence suggests that the aberrant activation of Hedgehog (Hh) signaling by Gli transcription factors is characteristic of a variety of aggressive human carcinomas, including colorectal cancer (CRC). Forkhead box M1 (FoxM1) controls the expression of a number of cell cycle regulatory proteins, and FoxM1 expression is elevated in a broad range of human malignancies, which suggests that it plays a crucial role in tumorigenesis. However, the mechanisms underlying FoxM1 expression are not fully understood. Here, we aim to further investigate the molecular mechanism by which Gli1 regulates FoxM1 in CRC. METHODS: Western blotting and immunohistochemistry (IHC) were used to evaluate FoxM1 and Gli1 protein expression, respectively, in CRC tissues and matched adjacent normal mucosa. BrdU (5-bromo-2'-deoxyuridine) and clone formation assays were used to clarify the influence of FoxM1 on CRC cell growth and proliferation. Chromatin immunoprecipitation (ChIP) and luciferase experiments were performed to explore the potential mechanisms by which Gli1 regulates FoxM1. Additionally, the protein and mRNA expression levels of Gli1 and FoxM1 in six CRC cell lines were measured using Western blotting and real-time PCR. Finally, the effect of Hh signaling on the expression of FoxM1 was studied in cell biology experiments, and the effects of Hh signaling activation and FoxM1 inhibition on the distribution of CRC cells among cell cycle phases was assessed by flow cytometry. RESULTS: Gli1 and FoxM1 were abnormally elevated in human CRC tissues compared with matched adjacent normal mucosa samples, and FoxM1 is a downstream target gene of the transcription factor Gli1 in CRC and promoted CRC cell growth and proliferation. Moreover, the aberrant activation of Hh signaling promoted CRC cell proliferation by directly binding to the promoter of FoxM1 and transactivating the activity of FoxM1 in CRC cells. CONCLUSION: The dysregulation of the Hh-Gli1-FoxM1 axis is essential for the proliferation and growth of human CRC cells and offers a potent target for therapeutic intervention in CRC.

Nek2A/SuFu feedback loop regulates Gli-mediated Hedgehog signaling pathway.

Suppressor of Fused (SuFu), one of the most conserved components of the Hedgehog (Hh) signaling, binds Gli transcription factors and impedes activation of target gene expression in mammalian cells. Despite the central importance of SuFu in the Hh pathway, little is known about SuFu regulation. In a previous study, we identified NIMA-related expressed kinase 2A (Nek2A) as a SuFu-interacting protein. Here, we show that Nek2A stabilizes SuFu through impairing ubiquitin/proteasome degradation of SuFu. In addition, Nek2A negatively regulates target genes of Hh signaling as well as Gli2 transcriptional activity. In turn, inhibition of Hh signaling by GANT61 diminishes mRNA and protein levels of Nek2A, and Hh agonist promotes transcription of NEK2A gene. Chromatin immunoprecipitation assays revealed that Gli1 and Gli2 directly bind to the promoter regions of NEK2A gene and induced its transcription. Thus, we uncovered one of the mechanisms by which Nek2A acts as a modulator of the Hh signaling pathway in the context of a novel negative-feedback loop, which may offer new insights into Gli-mediated Hh signaling regulation in development and human diseases.

Nek2A phosphorylates and stabilizes SuFu: A new strategy of Gli2/Hedgehog signaling regulatory mechanism.

Suppressor of Fused (SuFu) plays a conservative role in the regulation of the Gli transcription factors within the Hedgehog (Hh) signaling pathway. Despite the central importance of SuFu in the Hh pathway, little is known about its regulation. Here, we performed a GAL4-based yeast two-hybrid screen using human SuFu as bait, and identified NIMA-related expressed kinase 2A (Nek2A) as a new SuFu-interacting protein, which was also confirmed by glutathione-S-transferase pull-down and co-immunoprecipitation assays. Intriguingly, Nek2A is found to stabilize SuFu at least partly depending on its kinase activity, thereby triggering phosphorylation of the SuFu protein. Moreover, the phosphorylated SuFu inhibits the nuclear localization and transcriptional activity of Gli2/Hh signaling. These findings reveal a new mechanism of mammalian SuFu regulation, and offers novel insights into Hh signaling regulation in development and human disease.

Aberrantly activated Gli2-KIF20A axis is crucial for growth of hepatocellular carcinoma and predicts poor prognosis.

UNLABELLED: Glioma-associated oncogene 2 (Gli2), a primary transcriptional regulator of Hedgehog (Hh) signaling, is essential for hepatocellular carcinoma (HCC) growth and survival. However, the underlying molecular mechanism and crucial downstream targets of Gli2 in human HCC are not fully understood. Here, we report the identification of kinesin family member 20A (KIF20A) as a novel downstream target of Gli2, which is important for HCC proliferation and tumor growth. Inhibition of Hh signaling leads to a remarkable decrease of KIF20A expression in HCC cells, whereas overexpression of Gli2 elevates KIF20A expression by activating Forkhead Box M1 (FoxM1)-MMB complex-mediated transcription of this kinesin gene. Gli2-induced HCC cell growth requires enhanced expression of KIF20A, and knockdown of Gli2 or KIF20A represses the proliferation of HCC cells in vitro and in vivo. Correlated with these results, analyses of clinical HCC samples show that Gli2, FoxM1 and KIF20A are highly elevated in primary HCC samples and represent significant risk factors for HCC recurrence and survival. CONCLUSION: KIF20A is an important downstream target gene of Hh signaling. And, the Gli2-KIF20A axis is essential for the proliferation and growth of human HCC cells. Our study also suggests Gli2-KIF20A axis as a potential target for future therapeutic intervention and as an independent prognostic biomarker for HCC.

Aldehyde dehydrogenase 1A1 stabilizes transcription factor Gli2 and enhances the activity of Hedgehog signaling in hepatocellular cancer.

The Gli transcription factors are primary transcriptional regulators that mediate the activation of Hedgehog (Hh) signaling. Recent studies have revealed that Gli proteins are also regulated transcriptionally and post-translationally through noncanonical mechanisms, independent of Hh signaling. However, the precise mechanisms involved in the regulation of Gli proteins remain unclear. Using a differential mass-spectrometry approach, we found that aldehyde dehydrogenase 1A1 (ALDH1A1) is associated with transcription factor Gli2. Overexpression of ALDH1A1 increased Gli2 protein levels; in contrast, ALDH1A1 depletion facilitated Gli2 degradation. In addition, Gli2 mRNA expression was not affected by ectopic expression of ALDH1A1, indicating the role of ALDH1A1 in the stabilization of Gli2. Further investigation showed that ALDH1A1 prolonged the stability of Gli2 protein in a catalytic-independent manner. Finally, we showed that overexpression of ALDH1A1 activated the Hh signaling pathway and promoted cell growth, migration and invasion in hepatocellular cancer cells. Together, these results illustrate regulatory roles of ALDH1A1 in the activation of the Hh signaling pathway and highlight a novel mechanism for the aberrant activation of the Hh signaling pathway in hepatocellular cancer cells.

The Inhibition of Heat Shock Protein 90 Facilitates the Degradation of Poly-Alanine Expanded Poly (A) Binding Protein Nuclear 1 via the Carboxyl Terminus of Heat Shock Protein 70-Interacting Protein.

BACKGROUND: Since the identification of poly-alanine expanded poly(A) binding protein nuclear 1 (PABPN1) as the genetic cause of oculopharyngeal muscular dystrophy (OPMD), considerable progress has been made in our understanding of the pathogenesis of the disease. However, the molecular mechanisms that regulate the onset and progression of the disease remain unclear. RESULTS: In this study, we show that PABPN1 interacts with and is stabilized by heat shock protein 90 (HSP90). Treatment with the HSP90 inhibitor 17-AAG disrupted the interaction of mutant PABPN1 with HSP90 and reduced the formation of intranuclear inclusions (INIs). Furthermore, mutant PABPN1 was preferentially degraded in the presence of 17-AAG compared with wild-type PABPN1 in vitro and in vivo. The effect of 17-AAG was mediated through an increase in the interaction of PABPN1 with the carboxyl terminus of heat shock protein 70-interacting protein (CHIP). The overexpression of CHIP suppressed the aggregation of mutant PABPN1 in transfected cells. CONCLUSIONS: Our results demonstrate that the HSP90 molecular chaperone system plays a crucial role in the selective elimination of abnormal PABPN1 proteins and also suggest a potential therapeutic application of the HSP90 inhibitor 17-AAG for the treatment of OPMD.