Exposure to bisphenol A affects transcriptome-wide N6-methyladenine methylation in ovarian granulosa cells.

Bisphenol A (BPA) is an endocrine disruptor of potential reproductive toxicities. Increasingly research elucidated that BPA exposure to the environment would change the epigenetic modifications of transcriptome, but the mechanism by which BPA affects m6A methylation in interfering with female reproductive health remains uncertain. Therefore, this study preliminarily proposed and tested the hypothesis that BPA exposure alters the m6A modification level in transcripts in female ovarian granulosa cells. After BPA was exposed to granulosa cells for 24 h, RNA methylation related regulatory genes (such as METTL3, METTL14, ALKBH5, FTO) and the global m6A levels showed significant differences. Next, we applied MERIP-seq analysis to obtain information on the genome-wide m6A modification changes and identified 1595 differentially methylated mRNA transcripts, and 50 differentially methylated lncRNA transcripts. Further joint analysis of gene common expression showed that 33 genes were hypermethylated and up-regulated, 71 were hypermethylated and down-regulated, 49 were hypomethylated and up-regulated, and 20 were hypomethylated and down-regulated. Enriched Gene Ontology (GO) and biological pathway analysis revealed that these unique genes were mainly enriched in lipid metabolism, cell proliferation, and apoptosis related pathways. Six of these genes (mRNAs IMPA1, MCOLN1, DCTN3, BRCA2, and lncRNAs MALAT1, XIST) were validated using RT-qPCR and IGV software. Through comprehensive analysis of epitranscriptome and protein-protein interaction (PPI) data, lncRNAs MALAT1 and XIST are expected to serve as new markers for BPA interfering with the female reproductive system. In brief, these data show a novel and necessary connection between the damage of BPA exposure on female ovarian granulosa cells and RNA methylation modification.

Therapeutic potential of vasculogenic mimicry in urological tumors.

Angiogenesis is an essential process in the growth and metastasis of cancer cells, which can be hampered by an anti-angiogenesis mechanism, thereby delaying the progression of tumors. However, the benefit of this treatment modality could be restricted, as most patients tend to develop acquired resistance during treatment. Vasculogenic mimicry (VM) is regarded as a critical alternative mechanism of tumor angiogenesis, where studies have demonstrated that patients with tumors supplemented with VM generally have a shorter survival period and a poorer prognosis. Inhibiting VM may be an effective therapeutic strategy to prevent cancer progression, which could prove helpful in impeding the limitations of lone use of anti-angiogenic therapy when performed concurrently with other anti-tumor therapies. This review summarizes the mechanism of VM signaling pathways in urological tumors, i.e., prostate cancer, clear cell renal cell carcinoma, and bladder cancer. Furthermore, it also summarizes the potential of VM as a therapeutic strategy for urological tumors.

Multi-omics Deep-learning Prediction of Homologous Recombination Deficiency-like Phenotype Improved Risk Stratification and Guided Therapeutic Decisions in Gynecological Cancers.

Homologous recombination deficiency (HRD) is a well-recognized important biomarker in determining the clinical benefits of platinum-based chemotherapy and PARP inhibitor therapy for patients diagnosed with gynecologic cancers. Accurate prediction of HRD phenotype remains challenging. Here, we proposed a novel Multi-Omics integrative Deep-learning framework named MODeepHRD for detecting HRD-positive phenotype. MODeepHRD utilizes a convolutional attention autoencoder that effectively leverages omics-specific and cross-omics complementary knowledge learning. We trained MODeepHRD on 351 ovarian cancer (OV) patients using transcriptomic, DNA methylation and mutation data, and validated it in 2133 OV samples of 22 datasets. The predicted HRD-positive tumors were significantly associated with improved survival (HR = 0.68; 95% CI, 0.60-0.77; log-rank p < 0.001 for meta-cohort; HR = 0.5; 95% CI, 0.29-0.86; log-rank p = 0.01 for ICGC-OV cohort) and higher response to platinum-based chemotherapy compared to predicted HRD-negative tumors. The translational potential of MODeepHRDs was further validated in multicenter breast and endometrial cancer cohorts. Furthermore, MODeepHRD outperforms conventional machine-learning methods and other similar task approaches. In conclusion, our study demonstrates the promising value of deep learning as a solution for HRD testing in the clinical setting. MODeepHRD holds potential clinical applicability in guiding patient risk stratification and therapeutic decisions, providing valuable insights for precision oncology and personalized treatment strategies.

Upregulation of SLC12A3 and SLC12A9 Mediated by the HCP5/miR-140-5p Axis Confers Aggressiveness and Unfavorable Prognosis in Uveal Melanoma.

Perturbation of solute carriers (SLCs) has been implicated in metabolic disorders and cancer, highlighting the potential for drug discovery and therapeutic opportunities. However, there is relatively little exploration of the clinical relevance and potential molecular mechanisms underlying the role of the SLC12 family in uveal melanoma (UVM). Here, we performed an integrative multiomics analysis of the SLC12 family in multicenter UVM datasets and found that high expression of SLC12A3 and SLC12A9 was associated with unfavorable prognosis. Moreover, SLC12A3 and SLC12A9 were highly expressed in UVM in vivo. We experimentally characterized the roles of these proteins in tumorigenesis in vitro and explored their association with the prognosis of UVM. Lastly, we identified the HCP5-miR-140-5p axis as a potential noncoding RNA pathway upstream of SLC12A3 and SLC12A9, which was associated with immunomodulation and may represent a novel predictor for clinical prognosis and responsiveness to checkpoint blockade immunotherapy. These findings may facilitate a better understanding of the SLCome and guide future rationalized development of SLC-targeted therapy and drug discovery for UVM.

Integrated immunogenomic analysis of single-cell and bulk tissue transcriptome profiling unravels a macrophage activation paradigm associated with immunologically and clinically distinct behaviors in ovarian cancer.

INTRODUCTION: Increasing evidence demonstrates that the activation states and diverse spectrum of macrophage subtypes display dynamic heterogeneity in the tumor microenvironment, which plays a critical role in a variety of cancer types. OBJECTIVES: To investigate the heterogeneity and the homeostasis of different macrophage subtypes, as well as their effect on biological and clinical manifestations of ovarian cancer (OV). METHOD: Integrated immunogenomic analysis of single-cell and bulk tissuetranscriptome profiling was performed to systematically investigate the association between macrophage activation and prognostic and therapeutic efficacy. Consensus clustering analysis was used to define novel macrophage subtypes. An artificial neural network was used to simulate the dynamic activation of macrophages. RESULTS: The pan-cohort results suggested that high relative infiltration abundance of M0 and M1 macrophages was associated with improved outcome and therapeutic efficacy. However, it was the opposite for M2 macrophages. Unsupervised consensus clustering analysis revealed two OV subgroups characterized by a balance between M0, M1 and M2 macrophages with distinct clinical and immunological behaviors. Finally, a macrophage polarization-derived artificial neural network model was proposed to serve as a robust prognostic factor and predictive biomarker for therapeutic efficacy, which was validated in different independent patient cohorts. CONCLUSION: The present study provides a new understanding of macrophage heterogeneity and its association with OV prognosis and underlines the future clinical potential of a macrophage activation model for tumor prevention and treatment.

A blood-based miRNA signature for early non-invasive diagnosis of preeclampsia.

BACKGROUND: Preeclampsia (PE) is a multisystemic maternal syndrome with substantial maternal and fetal morbidity and mortality. Currently, there is no clinically viable non-invasive biomarker assay for early detection, thus limiting the effective prevention and therapeutic strategies for PE. METHODS: We conducted a discovery-training-validation three-phase retrospective and prospective study with cross-platform and multicenter cohorts. The initial biomarkers were discovered and verified in tissue specimens by small RNA sequencing and qRT-PCR. A miRNA signature (miR2PE-score) was developed using Firth's bias-reduced logistic regression analysis and subsequently validated in two independent multinational retrospective cohorts and two prospective plasma cohorts. RESULTS: We initially identified five PE-associated differentially expressed miRNAs from miRNA sequencing data and subsequently validated two miRNAs (miR-196b-5p and miR-584-5p) as robust biomarkers by association analysis with clinical characteristics and qRT-PCR in tissue specimens in the discovery phase. Using Firth's bias-reduced logistic regression analysis, we developed the miR2PE-score for the early detection of PE. The miR2PE-score showed a high diagnostic performance with an area under the receiver operating characteristic curve (AUROC) of 0.920, 0.848, 0.864, and 0.812 in training, internal, and two external validation cross-platform and multicenter cohorts, respectively. Finally, we demonstrated the non-invasive diagnostic performance of the miR2PE-score in two prospective plasma cohorts with AUROC of 0.933 and 0.787. Furthermore, the miR2PE-score revealed superior performance in non-invasive diagnosis compared with previously published miRNA biomarkers. CONCLUSIONS: We developed and validated a novel and robust blood-based miRNA signature, which may serve as a promising clinically applicable non-invasive tool for the early detection of PE.

Ovarian transcriptome and metabolic responses of RNAi-mediated farnesyl pyrophosphate synthase knockdown in Neocaridina denticulata sinensis.

Methyl farnesoate (MF) is considered the equivalent of JH in crustaceans and plays an essential role in many crucial physiological processes. It is believed that farnesyl pyrophosphate synthase (FPPS) plays an essential role in the biosynthesis of mevalonate, which is a branch of the JH/MF pathway. The full-length cDNA of FPPS (NdFPPS) from Neocaridina denticulata sinensis was isolated and characterized, and the deduced amino acid of NdFPPS contained a polyprenyl_synt domain. In addition to its ubiquitous tissue expression, NdFPPS was significantly expressed in the ovary. In vivo gene silencing with dsRNA interference was performed to further investigate the function of NdFPPS. An ovarian transcriptomic analysis of dsNdFPPS experimental and control groups was used to compare, annotate, and classify differentially expressed genes (DEGs). A total of 9230 DEGs were identified in the experimental and control groups based on FPKM values, of which 5082 were up-regulated genes and 4148 were down-regulated genes. 761 GO terms and 102 KEGG pathways were enriched for the DEGs. Our results suggest that NdFPPS might play an important role in ovarian development, however, further functional study is needed to elucidate physiological role of NdFPPS in reproduction.

iUMRG: multi-layered network-guided propagation modeling for the inference of susceptibility genes and potential drugs against uveal melanoma.

Uveal melanoma (UM) is the most common primary malignant intraocular tumor. The use of precision medicine for UM to enable personalized diagnosis, prognosis, and treatment require the development of computer-aided strategies and predictive tools that can identify novel high-confidence susceptibility genes (HSGs) and potential therapeutic drugs. In the present study, a computational framework via propagation modeling on integrated multi-layered molecular networks (abbreviated as iUMRG) was proposed for the systematic inference of HSGs in UM. Under the leave-one-out cross-validation experiments, the iUMRG achieved superior predictive performance and yielded a higher area under the receiver operating characteristic curve value (0.8825) for experimentally verified SGs. In addition, using the experimentally verified SGs as seeds, genome-wide screening was performed to detect candidate HSGs using the iUMRG. Multi-perspective validation analysis indicated that most of the top 50 candidate HSGs were indeed markedly associated with UM carcinogenesis, progression, and outcome. Finally, drug repositioning experiments performed on the HSGs revealed 17 potential targets and 10 potential drugs, of which six have been approved for UM treatment. In conclusion, the proposed iUMRG is an effective supplementary tool in UM precision medicine, which may assist the development of new medical therapies and discover new SGs.

Immuno-genomic characterisation of high-grade serous ovarian cancer reveals immune evasion mechanisms and identifies an immunological subtype with a favourable prognosis and improved therapeutic efficacy.

BACKGROUND: Immunotherapy has revolutionised the field of cancer therapy and immunology, but has demonstrated limited therapeutic efficacy in high-grade serous ovarian cancer (HGSOC). METHODS: Multi-omics data of 495 TCGA HGSOC tumours and RNA-seq data of 1708 HGSOC tumours were analyzed. Multivariate Cox regression analysis and meta-analyses were used to identify prognostic genes. The immune microenvironment was characterised using the ssGSEA methods for 28 immune cell types. Immunohistochemistry staining of tumour tissues of 14 patients was used to validate the key findings further. RESULTS: A total of 1142 genes were identified as favourable prognostic genes, which are prevailing in immune-related pathways and the infiltration of most immune subpopulations was observed to be associated with a favourable prognosis suggesting that tumour immunogenicity was the most prominent factor associated with improved clinical outcomes and response to chemotherapy of HGSOC. We identified multiple genomic and transcriptomic determinants of immunogenicity, including the copy loss of chromosome 4q and deficiencies of the homologous recombination pathway. Finally, an immunological subtype characterised by increased infiltration of activated CD8 T cells and decreased Tregs was associated with favourable prognosis and improved therapeutic efficacy. CONCLUSIONS: Our study characterised the immunogenomic landscape and refined the immunological classifications of HGSOC. This may improve the selection of patients with HGSOC who are suitable candidates for immunotherapy.

Cell-free DNA 5-hydroxymethylcytosine profiles of long non-coding RNA genes enable early detection and progression monitoring of human cancers.

BACKGROUND: 5-Hydroxymethylcytosine (5hmC) is a significant DNA epigenetic modification. However, the 5hmC modification alterations in genomic regions encoding long non-coding RNA (lncRNA) and their clinical significance remain poorly characterized. RESULTS: A three-phase discovery-modeling-validation study was conducted to explore the potential of the plasma-derived 5hmC modification level in genomic regions encoding lncRNAs as a superior alternative biomarker for cancer diagnosis and surveillance. Genome-wide 5hmC profiles in the plasma circulating cell-free DNA of 1632 cancer and 1379 non-cancerous control samples from different cancer types and multiple centers were repurposed and characterized. A large number of altered 5hmC modifications were distributed at genomic regions encoding lncRNAs in cancerous compared with healthy subjects. Furthermore, most 5hmC-modified lncRNA genes were cancer-specific, with only a relatively small number of 5hmC-modified lncRNA genes shared by various cancer types. A 5hmC-LncRNA diagnostic score (5hLD-score) comprising 39 tissue-shared 5hmC-modified lncRNA gene markers was developed using elastic net regularization. The 5hLD-score was able to accurately distinguish tumors from healthy controls with an area under the curve (AUC) of 0.963 [95% confidence interval (CI) 0.940-0.985] and 0.912 (95% CI 0.837-0.987) in the training and internal validation cohorts, respectively. Results from three independent validations confirmed the robustness and stability of the 5hLD-score with an AUC of 0.851 (95% CI 0.786-0.916) in Zhang's non-small cell lung cancer cohort, AUC of 0.887 (95% CI 0.852-0.922) in Tian's esophageal cancer cohort, and AUC of 0.768 (95% CI 0.746-0.790) in Cai's hepatocellular carcinoma cohort. In addition, a significant association was identified between the 5hLD-score and the progression from hepatitis to liver cancer. Finally, lncRNA genes modified by tissue-specific 5hmC alteration were again found to be capable of identifying the origin and location of tumors. CONCLUSION: The present study will contribute to the ongoing effort to understand the transcriptional programs of lncRNA genes, as well as facilitate the development of novel invasive genomic tools for early cancer detection and surveillance.

The differential diagnosis of pneumonia in two patients infected by atypical pathogens.

The current study presents two patients who lived in a rural family with close contact and suffered from rapidly progressive pneumonia. Chest computed tomography images and lymphocytopenia indicated the possibility of COVID-19 infection, but antibody and nucleic acid tests excluded this possibility. Negative results were obtained from corresponding tests for pneumococcal, adenovirus, fungal and legionella infection. Metagenomics analysis and subsequent antibody tests confirmed mycoplasma pneumonia. After treating with moxifloxacin, both patients recovered well and left the hospital. In terms of complicated infectious disease, consideration of atypical pathogens and medical and epidemiological history were important for differential diagnosis of COVID-19; metagenomics analysis was useful to provide direct references for diagnosis.

Pan-cancer characterization of lncRNA modifiers of immune microenvironment reveals clinically distinct de novo tumor subtypes.

The emerging field of long noncoding RNA (lncRNA)-immunity has provided a new perspective on cancer immunity and immunotherapies. The lncRNA modifiers of infiltrating immune cells in the tumor immune microenvironment (TIME) and their impact on tumor behavior and disease prognosis remain largely uncharacterized. In the present study, a systems immunology framework integrating the noncoding transcriptome and immunogenomics profiles of 9549 tumor samples across 30 solid cancer types was used, and 36 lncRNAs were identified as modifier candidates underlying immune cell infiltration in the TIME at the pan-cancer level. These TIME lncRNA modifiers (TIL-lncRNAs) were able to subclassify various tumors into three de novo pan-cancer subtypes characterized by distinct immunological features, biological behaviors, and disease prognoses. Finally, a TIL-lncRNA-derived immune state index (TISI) that was reflective of immunological and oncogenic states but also predictive of patients' prognosis was proposed. Furthermore, the TISI provided additional prognostic value for existing tumor immunological and molecular subtypes. By applying the TISI to tumors from different clinical immunotherapy cohorts, the TISI was found to be significantly negatively correlated with immune-checkpoint genes and to have the ability to predict the effectiveness of immunotherapy. In conclusion, the present study provided comprehensive resources and insights for future functional and mechanistic studies on lncRNA-mediated cancer immunity and highlighted the potential of the clinical application of lncRNA-based immunotherapeutic strategies in precision immunotherapy.

Computational recognition of lncRNA signature of tumor-infiltrating B lymphocytes with potential implications in prognosis and immunotherapy of bladder cancer.

Long noncoding RNAs (lncRNAs) have been associated with cancer immunity regulation and the tumor microenvironment (TME). However, functions of lncRNAs of tumor-infiltrating B lymphocytes (TIL-Bs) and their clinical significance have not yet been fully elucidated. In the present study, a machine learning-based computational framework is presented for the identification of lncRNA signature of TIL-Bs (named 'TILBlncSig') through integrative analysis of immune, lncRNA and clinical profiles. The TILBlncSig comprising eight lncRNAs (TNRC6C-AS1, WASIR2, GUSBP11, OGFRP1, AC090515.2, PART1, MAFG-DT and LINC01184) was identified from the list of 141 B-cell-specific lncRNAs. The TILBlncSig was capable of distinguishing worse compared with improved survival outcomes across different independent patient datasets and was also independent of other clinical covariates. Functional characterization of TILBlncSig revealed it to be an indicator of infiltration of mononuclear immune cells (i.e. natural killer cells, B-cells and mast cells), and it was associated with hallmarks of cancer, as well as immunosuppressive phenotype. Furthermore, the TILBlncSig revealed predictive value for the survival outcome and immunotherapy response of patients with anti-programmed death-1 (PD-1) therapy and added significant predictive power to current immune checkpoint gene markers. The present study has highlighted the value of the TILBlncSig as an indicator of immune cell infiltration in the TME from a noncoding RNA perspective and strengthened the potential application of lncRNAs as predictive biomarkers of immunotherapy response, which warrants further investigation.

Mechanistically derived patient-level framework for precision medicine identifies a personalized immune prognostic signature in high-grade serous ovarian cancer.

An accurate prognosis assessment for cancer patients could aid in guiding clinical decision-making. Reliance on traditional clinical features alone in a complex clinical environment is challenging and unsatisfactory in the era of precision medicine; thus, reliable prognostic biomarkers are urgently required to improve a patient staging system. In this study, we proposed a patient-level computational framework from mechanistic and translational perspectives to establish a personalized prognostic signature (named PLPPS) in high-grade serous ovarian carcinoma (HGSOC). The PLPPS composed of 68 immune genes achieved accurate prognostic risk stratification for 1190 patients in the meta-training cohort and was rigorously validated in multiple cross-platform independent cohorts comprising 792 HGSOC patients. Furthermore, the PLPPS was shown to be the better prognostic factor compared with clinical parameters in the univariate analysis and retained a significant independent association with prognosis after adjusting for clinical parameters in the multivariate analysis. In benchmark comparisons, the performance of PLPPS (hazard ratio (HR), 1.371; concordance index (C-index), 0.604 and area under the curve (AUC), 0.637) is comparable to or better than other published gene signatures (HR, 0.972 to 1.340; C-index, 0.495 to 0.592 and AUC, 0.48-0.624). With further validation in prospective clinical trials, we hope that the PLPPS might become a promising genomic tool to guide personalized management and decision-making of HGSOC in clinical practice.

Computational principles and practice for decoding immune contexture in the tumor microenvironment.

Tumor-infiltrating immune cells (TIICs) have been recognized as crucial components of the tumor microenvironment (TME) and induced both beneficial and adverse consequences for tumorigenesis as well as outcome and therapy (particularly immunotherapy). Computer-aided investigation of immune cell components in the TME has become a promising avenue to better understand the interplay between the immune system and tumors. In this study, we presented an overview of data sources, computational methods and software tools, as well as their application in inferring the composition of tumor-infiltrating immune cells in the TME. In parallel, we explored the future perspectives and challenges that may be faced with more accurate quantitative infiltration of immune cells in the future. Together, our study provides a little guide for scientists in the field of clinical and experimental immunology to look for dedicated resources and more competent tools for accelerating the unraveling of tumor-immune interactions with the implication in precision immunotherapy.

Machine learning-based integrative analysis of methylome and transcriptome identifies novel prognostic DNA methylation signature in uveal melanoma.

Uveal melanoma (UVM) is the most common primary intraocular human malignancy with a high mortality rate. Aberrant DNA methylation has rapidly emerged as a diagnostic and prognostic signature in many cancers. However, such DNA methylation signature available in UVM remains limited. In this study, we performed a genome-wide integrative analysis of methylome and transcriptome and identified 40 methylation-driven prognostic genes (MDPGs) associated with the tumorigenesis and progression of UVM. Then, we proposed a machine-learning-based discovery and validation strategy to identify a DNA methylation-driven signature (10MeSig) composing of 10 MDPGs (AZGP1, BAI1, CCDC74A, FUT3, PLCD1, S100A4, SCN8A, SEMA3B, SLC25A38 and SLC44A3), which stratified 80 patients of the discovery cohort into two risk subtypes with significantly different overall survival (HR = 29, 95% CI: 6.7-126, P < 0.001). The 10MeSig was validated subsequently in an independent cohort with 57 patients and yielded a similar prognostic value (HR = 2.1, 95% CI: 1.2-3.7, P = 0.006). Multivariable Cox regression analysis showed that the 10MeSig is an independent predictive factor for the survival of patients with UVM. With a prospective validation study, this 10MeSig will improve clinical decisions and provide new insights into the pathogenesis of UVM.

Identification of tumor immune infiltration-associated lncRNAs for improving prognosis and immunotherapy response of patients with non-small cell lung cancer.

BACKGROUND: Increasing evidence has demonstrated the functional relevance of long non-coding RNAs (lncRNAs) to immunity regulation and the tumor microenvironment in non-small cell lung cancer (NSCLC). However, tumor immune infiltration-associated lncRNAs and their value in improving clinical outcomes and immunotherapy remain largely unexplored. METHODS: We developed a computational approach to identify an lncRNA signature (TILSig) as an indicator of immune cell infiltration in patients with NSCLC through integrative analysis for lncRNA, immune and clinical profiles of 115 immune cell lines, 187 NSCLC cell lines and 1533 patients with NSCLC. Then the influence of the TILSig on the prognosis and immunotherapy in NSCLC was comprehensively investigated. RESULTS: Computational immune and lncRNA profiling analysis identified an lncRNA signature (TILSig) consisting of seven lncRNAs associated with tumor immune infiltration. The TILSig significantly stratified patients into the immune-cold group and immune-hot group in both training and validation cohorts. These immune-hot patients exhibit significantly improved survival outcome and greater immune cell infiltration compared with immune-cold patients. Multivariate analysis revealed that the TILSig is an independent predictive factor after adjusting for other clinical factors. Further analysis accounting for TILSig and immune checkpoint gene revealed that the TILSig has a discriminatory power in patients with similar expression levels of immune checkpoint genes and significantly prolonged survival was observed for patients with low TILSig and low immune checkpoint gene expression implying a better response to immune checkpoint inhibitor (ICI) immunotherapy. CONCLUSIONS: Our finding demonstrated the importance and value of lncRNAs in evaluating the immune infiltrate of the tumor and highlighted the potential of lncRNA coupled with specific immune checkpoint factors as predictive biomarkers of ICI response to enable a more precise selection of patients.

Ubiquitin-specific peptidase 39 regulates the process of proliferation and migration of human ovarian cancer via p53/p21 pathway and EMT.

Ovarian cancer is one of the most lethal gynecological cancers; owning to its late detection and chemoresistance, understanding the pathogenesis of this malignant tumor is much critical. Previous studies have reported that ubiquitin-specific peptidase 39 (USP39) is generally overexpressed in a variety of cancers, including hepatocellular carcinoma, gastric cancer and so forth. Furthermore, USP39 is proved to be associated with the proliferation of malignant tumors. However, the function and mechanism of USP39 in ovarian cancer have not been elucidated. In the present study, we observed that USP39 was frequently overexpressed in human ovarian cancer and was highly correlated with TNM stage. Suppression of USP39 markedly inhibited the growth and migration of ovarian cancer cell lines HO-8910 and SKOV3 and induced cell cycle G2/M arrest. Moreover, knockdown of USP39 inhibited ovarian tumor growth in a xenograft model. In addition, our findings indicated that cell cycle arrest induced by USP39 knockdown might be involved in p53/p21 signaling pathway. Furthermore, we found that the depletion of USP39 inhibited the migration of ovarian cancer cells via blocking epithelial-mesenchymal transition. Taken together, these results suggest that USP39 may play vital roles in the genesis and progression and may serve as a potential biomarker for diagnosis and therapeutic target of ovarian cancer.

Involvement of Wnt/ß-catenin signaling in the mesenchymal stem cells promote metastatic growth and chemoresistance of cholangiocarcinoma.

Mesenchymal stem cells (MSCs) are multi-potent progenitor cells with ability to differentiate into multiple lineages, including bone, cartilage, fat, and muscles. Recent research indicates that MSCs can be efficiently recruited to tumor sites, modulating tumor growth and metastasis. However, the underlying molecular mechanisms are not fully understood. Here, we first demonstrated that human umbilical cord-derived mesenchymal stem cells (hUC-MSCs), when mixed with human cholangiocarcinoma cell lines QBC939 in a xenograft tumor model, significantly increased the cancer cells proliferation and metastatic potency. MSCs and their conditioned media (MSC-CM) could improve the drug resistance of tumor when the compound K (CK) as an anti-cancer drug, a major intestinal bacterial metabolite of panaxoside, was administered to xenograft tumor mice. Furthermore, MSCs greatly increased the colony formation and invasion of cholangiocarcinoma cells QBC939 and Mz-ChA-1. Immunochemistry studies of cholangiocarcinoma tissue chips and transplantation tumor from nude mice showed that the expression of ß-catenin was important for cholangiocarcinoma development. We further demonstrated that MSCs and MSCs-CM could promote proliferation and migration of cholangiocarcinoma cells through targeting the Wnt/ß-catenin signaling pathway. hUC-MSCs or MSCs-CM stimulated Wnt activity by promoting the nuclear translocation of ß-catenin, and up-regulated Wnt target genes MMPs family, cyclin D1 and c-Myc. Together, our studies highlight a critical role for MSCs on cancer metastasis and indicate MSCs promote metastatic growth and chemoresistance of cholangiocarcinoma cells via activation of Wnt/ß-catenin signaling.