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.

Convolutional neural networks combined with classification algorithms for the diagnosis of periodontitis.

OBJECTIVES: We aim to develop a deep learning model based on a convolutional neural network (CNN) combined with a classification algorithm (CA) to assist dentists in quickly and accurately diagnosing the stage of periodontitis. MATERIALS AND METHODS: Periapical radiographs (PERs) and clinical data were collected. The CNNs including Alexnet, VGG16, and ResNet18 were trained on PER to establish the PER-CNN models for no periodontal bone loss (PBL) and PBL. The CAs including random forest (RF), support vector machine (SVM), naive Bayes (NB), logistic regression (LR), and k-nearest neighbor (KNN) were added to the PER-CNN model for control, stage I, stage II and stage III/IV periodontitis. Heat map was produced using a gradient-weighted class activation mapping method to visualize the regions of interest of the PER-Alexnet model. Clustering analysis was performed based on the ten PER-CNN scores and the clinical characteristics. RESULTS: The accuracy of the PER-Alexnet and PER-VGG16 models with the higher performance was 0.872 and 0.853, respectively. The accuracy of the PER-Alexnet + RF model with the highest performance for control, stage I, stage II and stage III/IV was 0.968, 0.960, 0.835 and 0.842, respectively. Heat map showed that the regions of interest predicted by the model were periodontitis bone lesions. We found that age and smoking were significantly related to periodontitis based on the PER-Alexnet scores. CONCLUSION: The PER-Alexnet + RF model has reached high performance for whole-case periodontal diagnosis. The CNN models combined with CA can assist dentists in quickly and accurately diagnosing the stage of periodontitis.

TRIM65 knockout inhibits the development of HCC by polarization tumor-associated macrophages towards M1 phenotype via JAK1/STAT1 signaling pathway.

BACKGROUND & AIMS: Tumor-associated macrophages (TAMs) are main components of immune cells in tumor microenvironment (TME), and play a crucial role in tumor progression. Tripartite motif-containing protein 65 (TRIM65) has been associated with tumor progression. However, whether TRIM65 regulate the interaction of tumor cell and TAMs in HCC and the underlying mechanisms remain unknown. In this study, we investigated the role of TRIM65 in TME of HCC and explored its underlying mechanisms. METHODS: The relation of TRIM65 expression level with tumor grades, TNM stages, and worse prognosis of HCC patients was evaluated by bioinformatics analysis, as well as immune infiltration level of macrophages. TRIM65 shRNA was transfected into HepG2 cells, and TRIM65 overexpression plasmid was transfected into Huh7 cells, and the effect of TRIM65 on cell growth was examined by EdU assay. The mouse subcutaneous Hep1-6 tumor-bearing model with WT and TRIM65-/- mice was established to study the role of TRIM65 in HCC. Immunohistochemistry staining, Immunofluorescence staining, qRT-PCR and western blot were performed to evaluate the effect of TRIM65 on TAM infiltration, TAM polarization and JAK1/STAT1 signaling pathway. RESULTS: Bioinformatics analysis revealed that TRIM65 was upregulated in 16 types of cancer especially in HCC, and high level of TRIM65 was strongly correlated with higher tumor grades, TNM stages, and worse prognosis of patients with HCC as well as immune infiltration level of macrophages (M0, M1, and M2). Moreover, we observed that TRIM65 shRNA-mediated TRIM65 knockdown significantly inhibited the HepG2 cells growth while TRIM65 overexpression highly increased the Huh7 cells growth in vitro. TRIM65 knockout significantly inhibited the tumor growth as well as macrophages polarization towards M2 but promoted macrophages polarization towards M1 in vivo. Mechanistically, the results demonstrate that TRIM65 knockout promoted macrophage M1 polarization in conditioned medium-stimulated peritoneal macrophages and in tumor tissues by activating JAK1/STAT1 signaling pathway. CONCLUSIONS: Taken together, our study suggests that tumor cells utilize TRIM65-JAK1/STAT1 axis to inhibit macrophage M1 polarization and promote tumor growth, reveals the role of TRIM65 in TAM-targeting tumor immunotherapy.

Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer.

BACKGROUND: Chemotherapy resistance is the main cause of ovarian cancer progression and even death. However, there are no clear indicators for predicting the risk of drug resistance in patients. Intra-tumor heterogeneity (ITH) is one of the characteristics of malignant tumors, which is associated with the treatment and prognosis of tumors. Accordingly, our study aims to investigate the correlation between the image features of intra-tumor heterogeneity and drug resistance of ovarian cancer based on artificial intelligence. METHODS: We obtained hematoxylin and eosin staining frozen histopathological images of ovarian cancer and paracarcinoma tissues from the Cancer Genome Atlas. We extracted quantitative image features of whole-slide images based on the automatic image nuclear segmentation processing technology. After that, we used bioinformatics analysis to find the relationship between image features of intra-tumor heterogeneity and drug resistance. RESULTS: Our results show that our automatic image processing process based on computer artificial intelligence can extract image features effectively, and the key image features extracted are closely related to ITH. Among them, the Perimeter.sd image feature with the most prominent ITH feature can accurately predict the risk of platinum-based chemotherapy drug resistance in ovarian cancer patients. CONCLUSION: Automatic image processing and feature extraction based on artificial intelligence have excellent results. Perimeter.sd can be used as a useful image feature indicator for evaluating ITH. ITH is associated with drug resistance of ovarian cancer, so ITH characteristics can be used as an effective indicator to evaluate drug resistance in patients with ovarian cancer.

Osteopontin Promotes Macrophage M1 Polarization by Activation of the JAK1/STAT1/HMGB1 Signaling Pathway in Nonalcoholic Fatty Liver Disease.

Background and Aims: Osteopontin (OPN) is reported to be associated with the pathogenesis of nonalcoholic fatty liver disease (NAFLD). However, the function of OPN in NAFLD is still inconclusive. Therefore, our aim in this study was to evaluate the role of OPN in NAFLD and clarify the involved mechanisms. Methods: We analyzed the expression change of OPN in NAFLD by bioinformatic analysis, qRT-PCR, western blotting and immunofluorescence staining. To clarify the role of OPN in NAFLD, the effect of OPN from HepG2 cells on macrophage polarization and the involved mechanisms were examined by FACS and western blotting. Results: OPN was significantly upregulated in NAFLD patients compared with normal volunteers by microarray data, and the high expression of OPN was related with disease stage and progression. OPN level was also significantly increased in liver tissue samples of NAFLD from human and mouse, and in HepG2 cells treated with oleic acid (OA). Furthermore, the supernatants of OPN-treated HepG2 cells promoted the macrophage M1 polarization. Mechanistically, OPN activated the janus kinase 1(JAK1)/signal transducers and activators of transcription 1 (STAT1) signaling pathway in HepG2 cells, and consequently HepG2 cells secreted more high-mobility group box 1 (HMGB1), thereby promoting macrophage M1 polarization. Conclusions: OPN promoted macrophage M1 polarization by increasing JAK1/STAT1-induced HMGB1 secretion in hepatocytes.

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.

Assessing electrocardiogram changes after ischemic stroke with artificial intelligence.

OBJECTIVE: Ischemic stroke (IS) with subsequent cerebrocardiac syndrome (CCS) has a poor prognosis. We aimed to investigate electrocardiogram (ECG) changes after IS with artificial intelligence (AI). METHODS: We collected ECGs from a healthy population and patients with IS, and then analyzed participant demographics and ECG parameters to identify abnormal features in post-IS ECGs. Next, we trained the convolutional neural network (CNN), random forest (RF) and support vector machine (SVM) models to automatically detect the changes in the ECGs; Additionally, We compared the CNN scores of good prognosis (mRS ≤ 2) and poor prognosis (mRS > 2) to assess the prognostic value of CNN model. Finally, we used gradient class activation map (Grad-CAM) to localize the key abnormalities. RESULTS: Among the 3506 ECGs of the IS patients, 2764 ECGs (78.84%) led to an abnormal diagnosis. Then we divided ECGs in the primary cohort into three groups, normal ECGs (N-Ns), abnormal ECGs after the first ischemic stroke (A-ISs), and normal ECGs after the first ischemic stroke (N-ISs). Basic demographic and ECG parameter analyses showed that heart rate, QT interval, and P-R interval were significantly different between 673 N-ISs and 3546 N-Ns (p < 0.05). The CNN has the best performance among the three models in distinguishing A-ISs and N-Ns (AUC: 0.88, 95%CI = 0.86-0.90). The prediction scores of the A-ISs and N-ISs obtained from the all three models are statistically different from the N-Ns (p < 0.001). Futhermore, the CNN scores of the two groups (mRS > 2 and mRS ≤ 2) were significantly different (p < 0.05). Finally, Grad-CAM revealed that the V4 lead may harbor the highest probability of abnormality. CONCLUSION: Our study showed that a high proportion of post-IS ECGs harbored abnormal changes. Our CNN model can systematically assess anomalies in and prognosticate post-IS ECGs.

Resting-state EEG-based convolutional neural network for the diagnosis of depression and its severity.

Purpose: The study aimed to assess the value of the resting-state electroencephalogram (EEG)-based convolutional neural network (CNN) method for the diagnosis of depression and its severity in order to better serve depressed patients and at-risk populations. Methods: In this study, we used the resting state EEG-based CNN to identify depression and evaluated its severity. The EEG data were collected from depressed patients and healthy people using the Nihon Kohden EEG-1200 system. Analytical processing of resting-state EEG data was performed using Python and MATLAB software applications. The questionnaire included the Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), Symptom Check-List-90 (SCL-90), and the Eysenck Personality Questionnaire (EPQ). Results: A total of 82 subjects were included in this study, with 41 in the depression group and 41 in the healthy control group. The area under the curve (AUC) of the resting-state EEG-based CNN in depression diagnosis was 0.74 (95%CI: 0.70-0.77) with an accuracy of 66.40%. In the depression group, the SDS, SAS, SCL-90 subscales, and N scores were significantly higher in the major depression group than those in the non-major depression group (p < 0.05). The AUC of the model in depression severity was 0.70 (95%CI: 0.65-0.75) with an accuracy of 66.93%. Correlation analysis revealed that major depression AI scores were significantly correlated with SAS scores (r = 0.508, p = 0.003) and SDS scores (r = 0.765, p < 0.001). Conclusion: Our model can accurately identify the depression-specific EEG signal in terms of depression diagnosis and severity identification. It would eventually provide new strategies for early diagnosis of depression and its severity.

Prognostic signature for hepatocellular carcinoma based on 4 pyroptosis-related genes.

BACKGROUND: Hepatocellular carcinoma (HCC) is a cancer with a poor prognosis. Many recent studies have suggested that pyroptosis is important in tumour progression. However, the role of pyroptosis-related genes (PRGs) in HCC remains unclear. MATERIALS AND METHODS: We identified differentially expressed PRGs in tumours versus normal tissues. Through univariate, LASSO, and multivariate Cox regression analyses, a prognostic PRG signature was established. The signature effectiveness was evaluated by time-dependent receiver operating characteristic (t-ROC) curve and Kaplan-Meier (KM) survival analysis. The signature was validated in the ICGC (LIRI-JP) cohort. In addition, single-sample gene enrichment analysis (ssGSEA) showed the infiltration of major immune cell types and the activity of common immune pathways in different subgroups. RESULTS: Twenty-nine pyroptosis-related DEGs from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset were detected, and four genes (CTSV, CXCL8, MKI67 and PRF1) among them were selected to construct a prognostic signature. Then, the patients were divided into high- and low-risk groups. The pyroptosis-related signature was significantly associated with overall survival (OS). In addition, the patients in the high-risk group had lower levels of immune infiltration. CONCLUSION: The prognostic signature for HCC based on 4 pyroptosis-related genes has reliable prognostic and predictive value for HCC patients.

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.

Remediation of ABCG5-Linked Macrothrombocytopenia With Ezetimibe Therapy.

To investigate refractory hypercholesterolemia, a female patient and relatives were subjected to whole-genome sequencing. The proband was found to have compound heterozygous substitutions p. Arg446Gln and c.1118+3G>T in ABCG5, one of two genes causing sitosterolemia. When tracing these variants in the full pedigree, all maternally related heterozygotes for the intronic ABCG5 variant exhibited large platelets (over 30 fl), which segregated in an autosomal dominant manner, consistent with macrothrombocytopenia, or large platelet syndrome which may be associated with a bleeding tendency. In vitro cell-line and in vivo rat model experiments supported a pathogenic role for the variant and the macrothrombocytopenia was recapitulated in heterozygous rats and human cell lines exhibiting that single variant. Ezetimibe treatment successfully ameliorated all the symptoms of the proband with sitosterolemia and resolved the macrothrombocytopenia of the treated heterozygote relatives. Subsequently, in follow up these observations, platelet size, and size distribution were measured in 1,180 individuals; 30 were found to be clinically abnormal, three of which carried a single known pathogenic ABCG5 variant (p.Arg446Ter) and two individuals carried novel ABCG5 variants of uncertain significance. In this study, we discovered that identification of large platelets and therefore a possible macrothrombocytopenia diagnosis could easily be inadvertently missed in clinical practice due to variable instrument settings. These findings suggest that ABCG5 heterozygosity may cause macrothrombocytopenia, that Ezetimibe treatment may resolve macrothrombocytopenia in such individuals, and that increased attention to platelet size on complete blood counts can aid in the identification of candidates for ABCG5 genetic testing who might benefit from Ezetimibe treatment.

QSOX2 Is an E2F1 Target Gene and a Novel Serum Biomarker for Monitoring Tumor Growth and Predicting Survival in Advanced NSCLC.

BACKGROUND: Quiescin Q6 sulfhydryl oxidase 2 (QSOX2), an enzyme that can be directly secreted into the extracellular space, is known to be associated with oxidative protein folding. However, whether QSOX2 is abnormally expressed in non-small cell lung cancer (NSCLC) and its role in tumor growth remains unclear. METHODS: Real-time quantitative PCR (qPCR), immunohistochemistry (IHC), bioinformatics analyses were applied to analyze the expression pattern and prognostic significance of QSOX2 in NSCLC. Xenografts model, enzyme-linked immunosorbent assays (ELISA), western blot analysis (WB), and IHC were preformed to examine in vivo tumor suppression and intracellular and extracellular expression of QSOX2. Flow cytometry, WB and qPCR analyses were used to elucidate the role of QSOX2 in cell cycle regulation. Chromatin immunoprecipitation assay (ChIP) assay and Dual-Luciferase reporter assay were employed to investigate transcriptional regulation of QSOX2 by E2F Transcription Factor 1 (E2F1). RESULTS: Quiescin sulfhydryl oxidase 2 was significantly overexpressed in NSCLC and associated with poor survival in advanced-stage patients. The intracellular and extracellular expression of QSOX2 by tumor cells markedly decreased after anti-cancer therapy in vitro, in vivo and in the clinic. Moreover, QSOX2 silencing in NSCLC cell lines resulted in inhibition of cancer cell proliferation, induction of apoptosis, and decreased expression of cell division-related genes (CENPF and NUSAP1) and Wnt pathway activators (PRRX2 and Nuc-ß-catenin). Mechanistically, QSOX2 was expressed periodically during cell cycle and directly regulated by E2F1. CONCLUSIONS: Our findings demonstrate that QSOX2 is directly regulated by E2F1 in the cell cycle, which is essential for the proliferation of NSCLC cells. Furthermore, QSOX2 is a prognostic indicator for NSCLC and may be developed into a biomarker for monitoring tumor burden and therapeutic progress.

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 multi-effective and long-acting immunotherapy through one single hydrogel based injection.

A dual-effective (photothermal and immune) therapy employing gold nanorods (AuNRs) with a drug (two macrophage migration inhibitory factor (MIF) inhibitors) sustained release hydrogel was designed in this paper. The subsequent cellular and animal studies demonstrated that the proposed therapy can not only inhibit the proliferation, migration, and recurrence of cancer cells, but also improve the immune function (increase the infiltration of CD8+ killer T cells in tumors) without the traditional multiple injections of expensive immune drugs.

Clinical and genomic landscape of hepatocellular carcinoma subtypes with various proportions of nonleukocyte stromal cells.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common malignancies and inflicts high mortality worldwide. The effect of tumor microenvironment components on HCC oncogenesis remains unknown. In particular, the nonleukocyte portion of the stromal fraction (SF) is poorly understood. METHODS: We comprehensively evaluated the proportional cell counts and gene expression data from The Cancer Genome Atlas (TCGA) to examine the contributions of cell components to the tumor microenvironment. Single-cell sequencing data from the Gene Expression Omnibus (GEO) were also analyzed to verify the association between the nonleukocyte SF and genes. We classified HCC using a hierarchical clustering method based on diversity of nonleukocyte SF-related gene expression among different components, and we used an appropriate GEO dataset to verify the clusters with a support vector machine (SVM) model. The prognosis of subtypes and their relationship with tumor microenvironmental cell proportions, clinicopathogenesis factors, and other indicators were evaluated. RESULTS: Based on linear regression, 711 genes related to nonleukocyte SF were selected from the TCGA dataset. We classified HCC into three subtypes using genes related to the nonleukocyte SF. Additionally, the GEO single-cell sequencing data confirmed the relationship between genes and the nonleukocyte SF. The tumor microenvironment of Type 2 contained the most significant mutually reinforcing interaction between the nonleukocyte SF and tumor cells. Meanwhile, Type 2 patients had the poorest prognosis and the most severe tumor-node-metastasis (TNM) stages, histological grades, etc. The analysis based on the GEO dataset verified the classification results with an SVM model. Type 2 was associated with worse clinicopathological characteristics, including tumor grading and staging, than the other types. In addition, the pathway analysis revealed that signals related to the SF and cell proliferation were significantly enhanced in Type 2 compared to the other group, which consisted of Types 1 and 3. CONCLUSION: The nonleukocyte SF in the tumor microenvironment contributed greatly to HCC oncogenesis. We can use these HCC classification criteria to stratify patients into subtypes for personalized treatment.

Exploration of the effects of the CYCLOPS gene RBM17 in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most lethal and malignant tumours worldwide. New therapeutic targets for HCC are urgently needed. CYCLOPS (copy number alterations yielding cancer liabilities owing to partial loss) genes have been noted to be associated with cancer-targeted therapies. Therefore, we intended to explore the effects of the CYCLOPS gene RBM17 on HCC oncogenesis to determine if it could be further used for targeted therapy. METHODS: We collected data on 12 types of cancer from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) queries for comparison with adjacent non-tumour tissues. RBM17 expression levels, clinicopathological factors and survival times were analysed. RNAseq data were downloaded from the Encyclopaedia of DNA Elements database for molecular mechanism exploration. Two representative HCC cell models were built to observe the proliferation capacity of HCC cells when RBM17 expression was inhibited by shRBM17. Cell cycle progression and apoptosis were also examined to investigate the pathogenesis of RBM17. RESULTS: Based on 6,136 clinical samples, RBM17 was markedly overexpressed in most cancers, especially HCC. Moreover, data from 442 patients revealed that high RBM17 expression levels were related to a worse prognosis. Overexpression of RBM17 was related to the iCluster1 molecular subgroup, TNM stage, and histologic grade. Pathway analysis of RNAseq data suggested that RBM17 was involved in mitosis. Further investigation revealed that the proliferation rates of HepG2 (P = 0.003) and SMMC-7721 (P = 0.030) cells were significantly reduced when RBM17 was knocked down. In addition, RBM17 knockdown also arrested the progression of the cell cycle, causing cells to halt at the G2/M phase. Increased apoptosis rates were also found in vitro. CONCLUSION: These results suggest that RBM17 is a potential therapeutic target for HCC treatment.

Comprehensive Analysis of a Long Noncoding RNA-Associated Competing Endogenous RNA Network in Wilms Tumor.

Long noncoding RNA (lncRNA) plays crucial roles in various biological processes of different cancers, especially acting as a competing endogenous RNA (ceRNA). However, the role of lncRNA-mediated ceRNA in Wilms tumor (WT), which is the most common malignant kidney cancer in children, remains unknown. In present study, RNA sequence profiles and clinical data of 125 patients with WT consisting of 119 tumor and 6 normal tissues from Therapeutically Applicable Research To Generate Effective Treatments database were analyzed. A total of 1833 lncRNAs, 156 microRNAs (miRNAs), and 3443 messenger RNAs (mRNAs) were identified as differentially expressed (DE) using "DESeq2" package. The lncRNA-miRNA-mRNA ceRNA regulatory network involving 748 DElncRNAs, 33 DEmiRNAs, and 189 DEmRNAs was constructed based on miRcode, Targetscan, miRTarBase, and miRDB database. Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that DEmRNAs were mainly enriched in cell proliferation-related processes and tumor-related pathways, respectively, and 13 hub genes were identified by a protein-protein interaction network. Survival analysis detected 48 lncRNAs, 7 miRNAs, and 16 mRNAs to have significant impact on the overall survival of patients with WT. Additionally, we found that 6 DElncRNAs with potential prognostic value were correlated with tumor stage (DENND5B-AS1) and histologic classification (TMPO-AS1, RP3-523K23.2, RP11-598F7.3, LAMP5-AS1, and AC013275.2) of patients with WT. Our research provides a great insight into understanding the molecular mechanism underlying occurrence and progression of WT, as well as the potential to develop targeted therapies and prognostic biomarkers.

Identification of human peripheral blood monocyte gene markers for early screening of solid tumors.

As cancer mortality is high in most regions of the world, early screening of cancer has become increasingly important. Minimally invasive screening programs that use peripheral blood mononuclear cells (PBMCs) are a new and reliable strategy that can achieve early detection of tumors by identifying marker genes. From 797 datasets, four (GSE12771, GSE24536, GSE27562, and GSE42834) including 428 samples, 236 solid tumor cases, and 192 healthy controls were chosen according to the inclusion criteria. A total of 285 genes from among 440 reported genes were selected by meta-analysis. Among them, 4 of the top significantly differentially expressed genes (ANXA1, IFI44, IFI44L, and OAS1) were identified as marker genes of PBMCs. Pathway enrichment analysis identified, two significant pathways, the 'primary immunodeficiency' pathway and the 'cytokine-cytokine receptor interaction' pathway. Protein- protein interaction (PPI) network analysis revealed the top 27 hubs with a degree centrality greater than 23 to be hub genes. We also identified 3 modules in Molecular Complex Detection (MCODE) analysis: Cluster 1 (related to ANXA1), Cluster 2 (related to IFI44 and IFI44L) and Cluster 3 (related to OAS1). Among the 4 marker genes, IFI44, IFI44L, and OAS1 are potential diagnostic biomarkers, even though their results were not as remarkable as those for ANXA1 in our study. ANXA1 is involved in the immunosuppressive mechanism in tumor-bearing hosts and may be used in a new strategy involving the use of the host's own immunity to achieve tumor suppression.

Tautomerase Activity-Lacking of the Macrophage Migration Inhibitory Factor Alleviates the Inflammation and Insulin Tolerance in High Fat Diet-Induced Obese Mice.

Macrophage migration inhibitory factor (MIF) has multiple intrinsic enzymatic activities of the dopachrome/phenylpyruvate tautomerase and thiol protein oxidoreductase, and plays an important role in the development of obesity as a pro-inflammatory cytokine. However, which enzymatic activity of MIF is responsible for regulating in obesity are still unknown. In the present study, we investigated the roles of the tautomerase of MIF in high fat diet (HFD)-induced obesity using MIF tautomerase activity-lacking (MIFP1G/P1G) mice. Our results showed that the serum MIF and the expression of MIF in adipose tissue were increased in HFD-treated mice compared with normal diet fed mice. The bodyweights were significantly reduced in MIFP1G/P1G mice compared with WT mice fed with HFD. The sizes of adipocytes were smaller in MIFP1G/P1G mice compared with WT mice fed with HFD using haematoxylin and eosin (H&E) staining. In addition, the MIFP1G/P1G mice reduced the macrophage infiltration, seen as the decreases of the expression of inflammatory factors such as F4/80, IL-1ß, TNFα, MCP1, and IL-6. The glucose tolerance tests (GTT) and insulin tolerance tests (ITT) assays showed that the glucose tolerance and insulin resistance were markedly improved, and the expressions of IRS and PPARγ were upregulated in adipose tissue from MIFP1G/P1G mice fed with HFD. Furthermore, we observed that the expressions of Bax, a pro-apoptotic protein, and the cleaved caspase 3-positive cells in white tissues were decreased and the ratio of Bcl2/Bax was increased in MIFP1G/P1G mice compared with WT mice. Taken together, our results demonstrated that the tautomerase activity-lacking of MIF significantly alleviated the HFD-induced obesity and adipose tissue inflammation, and improved insulin resistance in MIFP1G/P1G mice.

A Pooling Genome-Wide Association Study Identifies Susceptibility Loci and Signaling Pathways of Immune Thrombocytopenia in Chinese Han Population.

Immune thrombocytopenia (ITP) is an acquired bleeding disease due to immune-mediated destruction of antilogous platelets and ineffective thrombopoiesis. Although the etiology of ITP remains unknown, genetic variants are thought to predispose individuals to the disease. Several candidate gene analyses have identified several loci that increased ITP susceptibility, but no systematic genetic analysis on a genome-wide scope. To extend the genetic evidence and to identify novel candidates of ITP, we performed a pooling genome-wide association study (GWAS) by IlluminaHumanOmniZhongHua-8 combining pathway analysis in 200 ITP cases and 200 controls from Chinese Han population (CHP). The results revealed that 4 novel loci (rs117503120, rs5998634, rs4483616, and rs16866133) were strongly associated with ITP (P < 1.0 × 10-7). Expect for rs4483616, other three loci were validated by the TaqMan probe genotyping assay (P < 0.05) in another cohort including 250 ITP cases and 250 controls. And rs5998634 T allele was more sensitive to glucocorticoids for ITP patients (χ 2 = 7.30, P < 0.05). Moreover, we identified three overrepresented signaling pathways including the neuroactive ligand-receptor interaction, pathways in cancer, and the JAK-STAT pathway, which involved in the etiology of ITP. In conclusion, our results revealed four novel loci and three pathways related to ITP and provided new clues to explore the pathogenesis of ITP.