Inhibition of SIRT7 overcomes sorafenib acquired resistance by suppressing ERK1/2 phosphorylation via the DDX3X-mediated NLRP3 inflammasome in hepatocellular carcinoma.

AIMS: Sirtuin 7 (SIRT7) plays an important role in tumor development, and has been characterized as a potent regulator of cellular stress. However, the effect of SIRT7 on sorafenib acquired resistance remains unclear and a possible anti-tumor mechanism beyond this process in HCC has not been clarified. We examined the therapeutic potential of SIRT7 and determined whether it functions synergistically with sorafenib to overcome chemoresistance. METHODS: Cancer Genome Atlas-liver HCC data and unbiased gene set enrichment analyses were used to identify SIRT7 as a potential effector molecule in sorafenib acquired resistance. Two types of SIRT7 chemical inhibitors were developed to evaluate its therapeutic properties when synergized with sorafenib. Mass spectrometry was performed to discover a direct target of SIRT7, DDX3X, and DDX3X deacetylation levels and protein stability were explored. Moreover, an in vivo xenograft model was used to confirm anti-tumor effect of SIRT7 and DDX3X chemical inhibitors combined with sorafenib. RESULTS: SIRT7 inhibition mediated DDX3X depletion can re-sensitize acquired sorafenib resistance by disrupting NLRP3 inflammasome assembly, finally suppressing hyperactive ERK1/2 signaling in response to NLRP3 inflammasome-mediated IL-1ß inhibition. CONCLUSIONS: SIRT7 is responsible for sorafenib acquired resistance, and its inhibition would be beneficial when combined with sorafenib by suppressing hyperactive pro-cell survival ERK1/2 signaling.

PATHOME-Drug: a subpathway-based polypharmacology drug-repositioning method.

MOTIVATION: Drug repositioning reveals novel indications for existing drugs and in particular, diseases with no available drugs. Diverse computational drug repositioning methods have been proposed by measuring either drug-treated gene expression signatures or the proximity of drug targets and disease proteins found in prior networks. However, these methods do not explain which signaling subparts allow potential drugs to be selected, and do not consider polypharmacology, i.e. multiple targets of a known drug, in specific subparts. RESULTS: Here, to address the limitations, we developed a subpathway-based polypharmacology drug repositioning method, PATHOME-Drug, based on drug-associated transcriptomes. Specifically, this tool locates subparts of signaling cascading related to phenotype changes (e.g. disease status changes), and identifies existing approved drugs such that their multiple targets are enriched in the subparts. We show that our method demonstrated better performance for detecting signaling context and specific drugs/compounds, compared to WebGestalt and clusterProfiler, for both real biological and simulated datasets. We believe that our tool can successfully address the current shortage of targeted therapy agents. AVAILABILITY AND IMPLEMENTATION: The web-service is available at http://statgen.snu.ac.kr/software/pathome. The source codes and data are available at https://github.com/labnams/pathome-drug. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A comprehensive evaluation of regression-based drug responsiveness prediction models, using cell viability inhibitory concentrations (IC50 values).

MOTIVATION: Predicting drug response is critical for precision medicine. Diverse methods have predicted drug responsiveness, as measured by the half-maximal drug inhibitory concentration (IC50), in cultured cells. Although IC50s are continuous, traditional prediction models have dealt mainly with binary classification of responsiveness. However, since there are few regression-based IC50 predictions, comprehensive evaluations of regression-based IC50 prediction models, including machine learning (ML) and deep learning (DL), for diverse data types and dataset sizes, have not been addressed. RESULTS: Here, we constructed 11 input data settings, including multi-omics settings, with varying dataset sizes, then evaluated the performance of regression-based ML and DL models to predict IC50s. DL models considered two convolutional neural network architectures: CDRScan and residual neural network (ResNet). ResNet was introduced in regression-based DL models for predicting drug response for the first time. As a result, DL models performed better than ML models in all the settings. Also, ResNet performed better than or comparable to CDRScan and ML models in all settings. AVAILABILITY AND IMPLEMENTATION: The data underlying this article are available in GitHub at https://github.com/labnams/IC50evaluation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Determining the Factors Predicting the Response to Anti-HER2 Therapy in HER2-Positive Breast Cancer Patients.

PURPOSE: We aimed to identify the differently expressed genes or related pathways associated with good responses to anti-HER2 therapy and to suggest a model for predicting drug response in neoadjuvant systemic therapy with trastuzumab in HER2-positive breast cancer patients. METHODS: This study was retrospectively analyzed from consecutively collected patient data. We recruited 64 women with breast cancer and categorized them into 3 groups: complete response (CR), partial response (PR), and drug resistance (DR). The final number of patients in the study was 20. RNA from 20 core needle biopsy paraffin-embedded tissues and 4 cultured cell lines (SKBR3 and BT474 breast cancer parent cells and cultured resistant cells) was extracted, reverse transcribed, and subjected to GeneChip array analysis. The obtained data were analyzed using Gene Ontology, Kyoto Gene and Genome Encyclopedia, Database for Annotation, Visualization and Integrated Discovery. RESULTS: In total, 6,656 genes differentially expressed between trastuzumab-susceptible and trastuzumab-resistant cell lines were identified. Among these, 3,224 were upregulated and 3,432 were downregulated. Expression changes in 34 genes in several pathways were found to be related to the response to trastuzumab-containing treatment in HER2-type breast cancer, interfering with adhesion to other cells or tissues (focal adhesion) and regulating extracellular matrix interactions and phagosome action. Thus, decreased tumor invasiveness and enhanced drug effects might be the mechanisms explaining the better drug response in the CR group. CONCLUSIONS: This multigene assay-based study provides insights into breast cancer signaling and possible predictions of therapeutic response to targeted therapies such as trastuzumab.

Plasma and urinary extracellular vesicle microRNAs and their related pathways in diabetic kidney disease.

To explore extracellular vesicle microRNAs (EV miRNAs) and their target mRNAs in relation to diabetic kidney disease (DKD), we performed paired plasma and urinary EV small RNA sequencing (n = 18) in patients with type 2 diabetes and DKD (n = 5) and healthy subjects (n = 4) and metabolic network analyses using our own miRNA and public mRNA datasets. We found 13 common differentially expressed EV miRNAs in both fluids and 17 target mRNAs, including RRM2, NT5E, and UGDH. Because succinate dehydrogenase B was suggested to interact with proteins encoded by these three genes, we measured urinary succinate and adenosine in a validation study (n = 194). These two urinary metabolite concentrations were associated with DKD progression. In addition, renal expressions of NT5E and UGDH proteins were increased in db/db mice with DKD compared to control mice. In conclusion, we profiled DKD-related EV miRNAs in plasma and urine samples and found their relevant target pathways.

Improving Geometric Validation Metrics and Ensuring Consistency with Experimental Data through TrioSA: An NMR Refinement Protocol.

Protein model refinement a the crucial step in improving the quality of a predicted protein model. This study presents an NMR refinement protocol called TrioSA (torsion-angle and implicit-solvation-optimized simulated annealing) that improves the accuracy of backbone/side-chain conformations and the overall structural quality of proteins. TrioSA was applied to a subset of 3752 solution NMR protein structures accompanied by experimental NMR data: distance and dihedral angle restraints. We compared the initial NMR structures with the TrioSA-refined structures and found significant improvements in structural quality. In particular, we observed a reduction in both the maximum and number of NOE (nuclear Overhauser effect) violations, indicating better agreement with experimental NMR data. TrioSA improved geometric validation metrics of NMR protein structure, including backbone accuracy and the secondary structure ratio. We evaluated the contribution of each refinement element and found that the torsional angle potential played a significant role in improving the geometric validation metrics. In addition, we investigated protein-ligand docking to determine if TrioSA can improve biological outcomes. TrioSA structures exhibited better binding prediction compared to the initial NMR structures. This study suggests that further development and research in computational refinement methods could improve biomolecular NMR structural determination.

Clinical Significance of Combined Epithelial-Mesenchymal Transition Markers Expression and Role of Rac1 in Hepatocellular Carcinoma.

Epithelial-mesenchymal transition (EMT) has been implicated in cancer progression, invasion, and metastasis. We aimed to evaluate the correlations between clinicopathological characteristics and EMT markers in patients with hepatocellular carcinoma (HCC) who underwent surgical resection and to identify the key regulator in EMT process. Fresh-frozen HCC tissues and adjacent nontumor liver tissues from 30 patients who underwent surgical resection were provided by the Gachon University Gil Medical Center Bio Bank. Human HCC cell lines, Hep3B, SNU449, and Huh7 cells were transfected with Rac1 siRNA and exposed to hypoxic conditions. The combined EMT markers expression (down-expression of E-cadherin and overexpression of p21-activated kinases 1 (PAK1)/Snail) by Western blot in HCC tissues when compared to adjacent nontumor liver tissues was significantly associated with macrovascular invasion (p = 0.021), microvascular invasion (p = 0.001), large tumor size (p = 0.021), and advanced tumor stage (p = 0.015). Patients with combined EMT markers expression showed early recurrence and poor overall survival. In vitro studies showed that Rac1 knockdown decreased the expression of EMT markers including PAK1 and Snail in hypoxia-induced Hep3B cells and suppressed the migration and invasion of hypoxia-induced HCC cells. Rac1 may be a potential therapeutic target for inhibition of EMT process through the inhibition of PAK1 and Snail in HCC.

RHOA protein expression correlates with clinical features in gastric cancer: a systematic review and meta-analysis.

BACKGROUND: Gastric cancer (GC) is one of the most fatal cancers worldwide and is generally only detected after it has progressed to an advanced stage. Since there is a lack of comprehensive data on RHOA protein expression of patients with GC, this study utilized a systematic review and meta-analysis to address the limitation. The objective of this meta-analysis was to link GC clinical features with RHOA protein high- vs. low-expressing patients with GC. METHODS: The PubMed and Web of Science were used for a systematic literature review of GC related to RHOA. The included studies were obtained from two literature databases from past to Aug 31, 2021, by searching keywords. This meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The odds ratios (ORs) and 95% confidential intervals (CIs) for clinical features were estimated according to the high and low protein expression levels of RhoA. The mean effect sizes of ORs were obtained using the random-effects and fixed-effects models of meta-analysis. Heterogeneity of the studies was assesed by using statistics: τ2, I2; and Q values. The symmetry of funnel plots were inspected for publication bias. RESULTS: Finally, 10 studies including 1,389 patients with GC (735 RHOA-positive and 654 RHOA-negative) were eligible for our meta-analysis to estimate associations between the protein expression and clinical features (e.g., Union for International Cancer Control [UICC] stage progression, differentiation, Lauren histological classification, and vascular invasion). In our meta-analysis, RHOA positive expression was determined to have a statistically significant association with UICC stage progression (P = 0.02) and poorly differentiated status (P = 0.02). The association between RHOA positivity and Lauren subtypes was not statistically significant (P = 0.07). CONCLUSIONS: This meta-analysis suggested that RhoA protein expression in patients with GC was associated with clinical features: UICC stage progression and poorly differentiated status. Our findings are inconclusive but indicate that high RHOA protein expressing patients with GC could predict advanced UICC stages. A large prospective cohort study is required for validation in future.

Immune signature as a potential marker for predicting response to immunotherapy in obesity-associated colorectal cancer.

BACKGROUND AND AIM: It remains unclear whether immunotherapy, which is not generally considered for microsatellite stable (MSS) colorectal cancer (CRC), can be used to effectively treat select CRC patients. We investigated the feasibility of obesity-associated MSS CRC patients for immunotherapy based on genomic alterations. METHODS: We evaluated differences in genomic alteration types and immune signatures between obese and non-obese patients with MSS CRC. We performed genomic analyses using 434 CRC patients from The Cancer Genome Atlas (TCGA). Patients with MSS CRC were stratified into subgroups based on their BMI and numbers of nonsynonymous single nucleotide variants (nsSNVs) and frameshift insertions and deletions (fs INDELs) using machine learning. RESULTS: The obese subgroup showed higher incidences of single nucleotide variants (SNV) and insertions and deletions (INDELs) in comparison with healthy weight patients with MSS CRC. The subgroup, who had higher numbers of nsSNVs and fs INDLEs, exhibited increased immune signatures, increased number of SNV-derived neoantigens, and had up-regulated two immune checkpoint genes in comparison with healthy weight patients with MSS CRC, reflecting interactions between the cancer genome and immune system. CONCLUSIONS: This study suggests that immunotherapy may be suitable for some obesity-associated CRC patients.

Regulation of mRNA export through API5 and nuclear FGF2 interaction.

API5 (APoptosis Inhibitor 5) and nuclear FGF2 (Fibroblast Growth Factor 2) are upregulated in various human cancers and are correlated with poor prognosis. Although their physical interaction has been identified, the function related to the resulting complex is unknown. Here, we determined the crystal structure of the API5-FGF2 complex and identified critical residues driving the protein interaction. These findings provided a structural basis for the nuclear localization of the FGF2 isoform lacking a canonical nuclear localization signal and identified a cryptic nuclear localization sequence in FGF2. The interaction between API5 and FGF2 was important for mRNA nuclear export through both the TREX and eIF4E/LRPPRC mRNA export complexes, thus regulating the export of bulk mRNA and specific mRNAs containing eIF4E sensitivity elements, such as c-MYC and cyclin D1. These data show the newly identified molecular function of API5 and nuclear FGF2, and provide a clue to understanding the dynamic regulation of mRNA export.

Plasma Aldo-Keto Reductase Family 1 Member B10 as a Biomarker Performs Well in the Diagnosis of Nonalcoholic Steatohepatitis and Fibrosis.

We found several blood biomarkers through computational secretome analyses, including aldo-keto reductase family 1 member B10 (AKR1B10), which reflected the progression of nonalcoholic fatty liver disease (NAFLD). After confirming that hepatic AKR1B10 reflected the progression of NAFLD in a subgroup with NAFLD, we evaluated the diagnostic accuracy of plasma AKR1B10 and other biomarkers for the diagnosis of nonalcoholic steatohepatitis (NASH) and fibrosis in replication cohort. We enrolled healthy control subjects and patients with biopsy-proven NAFLD (n = 102) and evaluated the performance of various diagnostic markers. Plasma AKR1B10 performed well in the diagnosis of NASH with an area under the receiver operating characteristic (AUROC) curve of 0.834 and a cutoff value of 1078.2 pg/mL, as well as advanced fibrosis (AUROC curve value of 0.914 and cutoff level 1078.2 pg/mL), with further improvement in combination with C3. When we monitored a subgroup of obese patients who underwent bariatric surgery (n = 35), plasma AKR1B10 decreased dramatically, and 40.0% of patients with NASH at baseline showed a decrease in plasma AKR1B10 levels to below the cutoff level after the surgery. In an independent validation study, we proved that plasma AKR1B10 was a specific biomarker of NAFLD progression across varying degrees of renal dysfunction. Despite perfect correlation between plasma and serum levels of AKR1B10 in paired sample analysis, its serum level was 1.4-fold higher than that in plasma. Plasma AKR1B10 alone and in combination with C3 could be a useful noninvasive biomarker for the diagnosis of NASH and hepatic fibrosis.

Performance Comparisons of AlexNet and GoogLeNet in Cell Growth Inhibition IC50 Prediction.

Drug responses in cancer are diverse due to heterogenous genomic profiles. Drug responsiveness prediction is important in clinical response to specific cancer treatments. Recently, multi-class drug responsiveness models based on deep learning (DL) models using molecular fingerprints and mutation statuses have emerged. However, for multi-class models for drug responsiveness prediction, comparisons between convolution neural network (CNN) models (e.g., AlexNet and GoogLeNet) have not been performed. Therefore, in this study, we compared the two CNN models, GoogLeNet and AlexNet, along with the least absolute shrinkage and selection operator (LASSO) model as a baseline model. We constructed the models by taking drug molecular fingerprints of drugs and cell line mutation statuses, as input, to predict high-, intermediate-, and low-class for half-maximal inhibitory concentration (IC50) values of the drugs in the cancer cell lines. Additionally, we compared the models in breast cancer patients as well as in an independent gastric cancer cell line drug responsiveness data. We measured the model performance based on the area under receiver operating characteristic (ROC) curves (AUROC) value. In this study, we compared CNN models for multi-class drug responsiveness prediction. The AlexNet and GoogLeNet showed better performances in comparison to LASSO. Thus, DL models will be useful tools for precision oncology in terms of drug responsiveness prediction.

Rational design of small molecule RHOA inhibitors for gastric cancer.

Previously, we identified Ras homologous A (RHOA) as a major signaling hub in gastric cancer (GC), the third most common cause of cancer death in the world, prompting us to rationally design an efficacious inhibitor of this oncogenic GTPase. Here, based on that previous work, we extend those computational analyses to further pharmacologically optimize anti-RHOA hydrazide derivatives for greater anti-GC potency. Two of these, JK-136 and JK-139, potently inhibited cell viability and migration/invasion of GC cell lines, and mouse xenografts, diversely expressing RHOA. Moreover, JK-136's binding affinity for RHOA was >140-fold greater than Rhosin, a nonclinical RHOA inhibitor. Network analysis of JK-136/-139 vs. Rhosin treatments indicated downregulation of the sphingosine-1-phosphate, as an emerging cancer metabolic pathway in cell migration and motility. We assert that identifying and targeting oncogenic signaling hubs, such as RHOA, represents an emerging strategy for the design, characterization, and translation of new antineoplastics, against gastric and other cancers.

An Endogenous Anti-aging Factor, Sonic Hedgehog, Suppresses Endometrial Stem Cell Aging through SERPINB2.

Endometrial stem cells are located in the basal layer of the endometrium, and they are responsible for the cyclic regeneration of the uterus during the menstrual cycle. Recent studies have revealed that recurrent pregnancy loss is associated with an age-related stem cell deficiency in the endometrium. Therefore, intensive study of endometrial stem cell aging may provide new insights for preventing recurrent pregnancy loss. Sonic hedgehog (SHH) signaling has been identified as a morphogen during the embryonic development processes. In addition to this canonical function, we found that the age-associated decline in regenerative potential in the endometrium may be due to decreased SHH-signaling integrity in local stem cells with aging. Importantly, the current study also showed that SHH activity clearly declines with aging both in vitro and in vivo, and exogenous SHH treatment significantly alleviates various aging-associated declines in multiple endometrial stem cell functions, suggesting that SHH may act as an endogenous anti-aging factor in human endometrial stem cells. Moreover, we found that stem cell senescence may enhance SERPINB2 expression, which in turn mediates the effect of SHH on alleviating senescence-induced endometrial stem cell dysfunctions, suggesting that SERPINB2 is a master regulator of SHH signaling during the aging process.

Differential effects, on oncogenic pathway signalling, by derivatives of the HNF4 α inhibitor BI6015.

BACKGROUND: Gastric cancer (GC) is a highly heterogeneous disease with few "targeted" therapeutic options. Previously, we demonstrated involvement of the transcription factor HNF4α in human GC tumours, and the developmental signal mediator, WNT5A, as a prognostic GC biomarker. One previously developed HNF4α antagonist, BI6015, while not advancing beyond preclinical stages, remains useful for studying GC. METHODS: Here, we characterised the antineoplastic signalling activity of derivatives of BI6015, including transfer of the nitro group from the para position, relative to a methyl group on its benzene ring, to the ortho- and meta positions. We assessed binding efficacy, through surface plasmon resonance and docking studies, while biologic activity was assessed by antimitogenic efficacy against a panel of GC cell lines, and dysregulated transcriptomes, followed by pathway and subpathway analysis. RESULTS: The para derivative of BI6105 was found substantially more growth inhibitory, and effective, in downregulating numerous oncogenic signal pathways, including the embryonic cascade WNT. The ortho and meta derivatives, however, failed to downregulate WNT or other embryonic signalling pathways, unable to suppress GC growth. CONCLUSION: Straightforward strategies, employing bioinformatics analyses, to facilitate the effective design and development of "druggable" transcription factor inhibitors, are useful for targeting specific oncogenic signalling pathways, in GC and other cancers.

A Deep Learning Model for Cell Growth Inhibition IC50 Prediction and Its Application for Gastric Cancer Patients.

Heterogeneity in intratumoral cancers leads to discrepancies in drug responsiveness, due to diverse genomics profiles. Thus, prediction of drug responsiveness is critical in precision medicine. So far, in drug responsiveness prediction, drugs' molecular "fingerprints", along with mutation statuses, have not been considered. Here, we constructed a 1-dimensional convolution neural network model, DeepIC50, to predict three drug responsiveness classes, based on 27,756 features including mutation statuses and various drug molecular fingerprints. As a result, DeepIC50 showed better cell viability IC50 prediction accuracy in pan-cancer cell lines over two independent cancer cell line datasets. Gastric cancer (GC) is not only one of the lethal cancer types in East Asia, but also a heterogeneous cancer type. Currently approved targeted therapies in GC are only trastuzumab and ramucirumab. Responsive GC patients for the drugs are limited, and more drugs should be developed in GC. Due to the importance of GC, we applied DeepIC50 to a real GC patient dataset. Drug responsiveness prediction in the patient dataset by DeepIC50, when compared to the other models, were comparable to responsiveness observed in GC cell lines. DeepIC50 could possibly accurately predict drug responsiveness, to new compounds, in diverse cancer cell lines, in the drug discovery process.

Urinary and Blood MicroRNA-126 and -770 are Potential Noninvasive Biomarker Candidates for Diabetic Nephropathy: a Meta-Analysis.

BACKGROUND/AIMS: Diabetic nephropathy (DN), a major diabetic microvascular complication, has a long and growing list of biomarkers, including microRNA biomarkers, which have not been consistent across preclinical and clinical studies. This meta-analysis aims to identify significant blood- and urine-incident microRNAs as diagnostic/prognostic biomarker candidates for DN. METHODS: PubMed, Web of Science, and Cochrane Library were searched from their earliest records through 12th Dec 2016. Relevant publications for the meta-analysis included (1) human participants; (2) microRNAs in blood and urine; (3) DN studies; and (4) English language. Four reviewers, including two physicians, independently and blindly extracted published data regarding microRNA profiles in blood and/or urine from subjects with diabetic nephropathy. A random-effect model was used to pool the data. Statistical associations between diabetic nephropathy and urinary or blood microRNA expression levels were assessed. RESULTS: Fourteen out of 327 studies (n=2,747 patients) were selected. Blood or urinary microRNA expression data of diabetic nephropathy were pooled for this analysis. The hsa-miR-126 family was significantly (OR: 0.57; 95% CI: 0.44-0.74; p-value < 0.0001) downregulated in blood from patients with diabetic kidney disease, while its urinary level was upregulated (OR: 2931.12; 95% CI: 9.96-862623.21; p-value = 0.0059). The hsa-miR-770 family microRNA were significantly (OR: 10.24; 95% CI: 2.37-44.25; p-value = 0.0018) upregulated in both blood and urine from patients with diabetic nephropathy. CONCLUSIONS: Our meta-analysis suggests that hsa-miR-126 and hsa-miR-770 family microRNA may have important diagnostic and pathogenetic implications for DN, which warrants further systematic clinical studies.

Zingerone Suppresses Tumor Development through Decreasing Cyclin D1 Expression and Inducing Mitotic Arrest.

Cancer cells undergo uncontrolled proliferation resulting from aberrant activity of various cell-cycle proteins. Therefore, despite recent advances in intensive chemotherapy, it is difficult to cure cancer completely. Recently, cell-cycle regulators became attractive targets in cancer therapy. Zingerone, a phenolic compound isolated from ginger, is a nontoxic and inexpensive compound with varied pharmacological activities. In this study, the therapeutic effect of zingerone as an anti-mitotic agent in human neuroblastoma cells was investigated. Following treatment of BE(2)-M17 cells with zingerone, we performed a 3-(4,5-dimethylthiazol-2-yl)-2,5- diphenyltetrazolium bromide (MTT) assay and colony-formation assay to evaluate cellular proliferation, in addition to immunofluorescence cytochemistry and flow cytometry to examine the mitotic cells. The association of gene expression with tumor stage and survival was analyzed. Furthermore, to examine the anti-cancer effect of zingerone, we applied a BALB/c mouse-tumor model using a BALB/c-derived adenocarcinoma cell line. In human neuroblastoma cells, zingerone inhibited cellular viability and survival. Moreover, the number of mitotic cells, particularly those in prometaphase, increased in zingerone-treated neuroblastoma cells. Regarding specific molecular mechanisms, zingerone decreased cyclin D1 expression and induced the cleavage of caspase-3 and poly (ADP-ribose) polymerase 1 (PARP-1). The decrease in cyclin D1 and increase in histone H3 phosphorylated (p)-Ser10 were confirmed by immunohistochemistry in tumor tissues administered with zingerone. These results suggest that zingerone induces mitotic arrest followed by inhibition of growth of neuroblastoma cells. Collectively, zingerone may be a potential therapeutic drug for human cancers, including neuroblastoma.

Novel effects of sphingosylphosphorylcholine on invasion of breast cancer: Involvement of matrix metalloproteinase-3 secretion leading to WNT activation.

Sphingosylphosphorylcholine (SPC) participates in several cellular processes including metastasis. SPC induces keratin reorganization and regulates the viscoelasticity of metastatic cancer cells including PANC-1 cancer cells leading to enhanced migration and invasion. The role of SPC and the relevant mechanism in invasion of breast cell are as yet unknown. SPC dose-dependently induces invasion of breast cancer cells or breast immortalized cells. Reverse transcription polymerase chain reaction and Western blot analyses of MCF10A and ZR-75-1 cells indicated that SPC induces expression and secretion of matrix metalloproteinase-3 (MMP3). From online KMPLOT, relapse free survival is high in patients having low MMP3 expressed basal breast cancer (n=581, p=0.032). UK370106 (MMP3 inhibitor) or gene silencing of MMP3 markedly inhibited the SPC-induced invasion of MCF10A cells. An extracellular signal-regulated kinase (ERK) inhibitor, PD98059, significantly suppressed the secretion and the gelatinolytic activity of MMP3, and invasion in MCF10A cells. Over-expression of ERK1 and ERK2 promoted both the expression and secretion of MMP3. In contrast, gene silencing of ERK1 and ERK2 attenuated the secretion of MMP3 in MCF10A cells. The effects of SPC-induced MMP3 secretion on ß-catenin and TCF/lymphoid enhancer factor (LEF) promoter activity were examined since MMP3 indirectly activates canonical Wnt signaling. SPC induced translocation of ß-catenin to nucleus and increased TCF/LEF promoter activity. These events were suppressed by UK370106 or PD98059. Wnt inhibitor, FH535 inhibited SPC-induced MMP3 secretion and invasion. Taken together, these results suggest that SPC induces MMP3 expression and secretion via ERK leading to Wnt activation.

WNT5A Correlates with Clinicopathological Characteristics in Gastric Cancer: a Meta-Analysis.

BACKGROUND/AIMS: Gastric cancer (GC), the third-leading cause of cancer death in the world, is typically diagnosed only in its advanced stages. WNT signaling has been associated with clinicopathological characteristics in diverse cancer types. But the systematic analysis of WNT5A, a member in the signaling, has not been inspected. Thus, our study used a meta-analysis to statistically associate WNT5A expression with GC clinicopathological characteristics. METHODS: For a systematic literature review of GC in combination with the WNT signaling molecule WNT5A, we searched for PubMed, Cochrane Library, and Web of Science. It led to the five cohorts, in four eligible studies, consisting of 1,034 patients (617 WNT5A-positive and 417 WNT5A-negative patients). These patients were inspected by the library "meta" in R software for our meta-analysis. RESULTS: Our meta-analysis, revealed a statistically significant associations of WNT5A-positivity with lymph node metastasis (p=0.0047), some types of Lauren diffuse subtype GCs (p<0.0001), advanced tumor depth (p<0.0001), and advanced UICC stages (p=0.0461) with no observation of bias or confounding factors. CONCLUSIONS: These results support the feasibility of targeting this embryonic signaling pathway, both for therapy, and as a biomarker to "guide" various individual interventions (i.e., "personalized medicine").