miR-769-3p inhibits cellular proliferation of KSHV-infected SH-SY5Y cells through targeting mTOR.

The infection by Kaposi's sarcoma-associated herpesvirus (KSHV) is one of the most common causes of death in AIDS patients. Our studies have found that KSHV can infect SH-SY5Y cells (named SK-RG) in vivo and mTOR was up-regulated, which results in remarkable enhancement of cell proliferation, migration. But the regulatory role of mTOR in KSHV infected neurons has not yet been fully elucidated. Here, we find that miR-769-3p is decreased in SK-RG cells, which can exert anti-KSHV effect through negatively regulating the expression of mTOR. The knockdown of mTOR or overexpress of miR-769-3p decreased the proliferation, migration ability and cell cycle related protein of SK-RG cells, and the expression of KSHV related genes. In contrast, activating mTOR function by 3BDO treatment weakened the cellular behaviors of miR-769-3p overexpressing cells. Meanwhile, overexpressed miR-769-3p and rapamycin showed a shared inhibition trend in the effects on cell proliferation and motility. Our data indicated that miR-769-3p can inhibit cell proliferation and migration by down regulating mTOR in KSHV infected SH-SY5Y cells, and can be a candidate molecule for anti-KSHV therapy.

Develop prediction model to help forecast advanced prostate cancer patients' prognosis after surgery using neural network.

Background: The effect of surgery on advanced prostate cancer (PC) is unclear and predictive model for postoperative survival is lacking yet. Methods: We investigate the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database, to collect clinical features of advanced PC patients. According to clinical experience, age, race, grade, pathology, T, N, M, stage, size, regional nodes positive, regional nodes examined, surgery, radiotherapy, chemotherapy, history of malignancy, clinical Gleason score (composed of needle core biopsy or transurethral resection of the prostate specimens), pathological Gleason score (composed of prostatectomy specimens) and prostate-specific antigen (PSA) are the potential predictive variables. All samples are divided into train cohort (70% of total, for model training) and test cohort (30% of total, for model validation) by random sampling. We then develop neural network to predict advanced PC patients' overall. Area under receiver operating characteristic curve (AUC) is used to evaluate model's performance. Results: 6380 patients, diagnosed with advanced (stage III-IV) prostate cancer and receiving surgery, have been included. The model using all collected clinical features as predictors and based on neural network algorithm performs best, which scores 0.7058 AUC (95% CIs, 0.7021-0.7068) in train cohort and 0.6925 AUC (95% CIs, 0.6906-0.6956) in test cohort. We then package it into a Windows 64-bit software. Conclusion: Patients with advanced prostate cancer may benefit from surgery. In order to forecast their overall survival, we first build a clinical features-based prognostic model. This model is accuracy and may offer some reference on clinical decision making.

Eukaryotic translation initiation factor 4A1 in the pathogenesis and treatment of cancers.

Abnormal translate regulation is an important phenomenon in cancer initiation and progression. Eukaryotic translation initiation factor 4A1 (eIF4A1) protein is an ATP-dependent Ribonucleic Acid (RNA) helicase, which is essential for translation and has bidirectional RNA unwinders function. In this review, we discuss the levels of expression, regulatory mechanisms and protein functions of eIF4A1 in different human tumors. eIF4A1 is often involved as a target of microRNAs or long non-coding RNAs during the epithelial-mesenchymal transition, associating with the proliferation and metastasis of tumor cells. eIF4A1 protein exhibits the promising biomarker for rapid diagnosis of pre-cancer lesions, histological phenotypes, clinical staging diagnosis and outcome prediction, which provides a novel strategy for precise medical care and target therapy for patients with tumors at the same time, relevant small molecule inhibitors have also been applied in clinical practice, providing reliable theoretical support and clinical basis for the development of this gene target.

A deep learning-based model (DeepMPM) to help predict survival in patients with malignant pleural mesothelioma.

Background: Malignant pleural mesothelioma (MPM) is a rare disease with limited treatment and poor prognosis, and a precise and reliable means to predicting MPM remains lacking for clinical use. Methods: In the population-based cohort study, we collected clinical characteristics from the Surveillance, Epidemiology, and End Results (SEER) database. According to the time of diagnosis, the SEER data were divided into 2 cohorts: the training cohort (from 2010 to 2016) and the test cohort (from 2017 to 2019). The training cohort was used to train a deep learning-based predictive model derived from DeepSurv theory, which was validated by both the training and the test cohorts. All clinical characteristics were included and analyzed using Cox proportional risk regression or Kaplan-Meier curve to determine the risk factors and protective factors of MPM. Results: The survival model included 3,130 cases (2,208 in the training cohort and 922 in the test cohort). As for model's performance, the area under the receiver operating characteristics curve (AUC) was 0.7037 [95% confidence interval (CI): 0.7030-0.7045] in the training cohort and 0.7076 (95% CI: 0.7067-0.7086) in the test cohort. Older age; male sex, sarcomatoid mesothelioma; and T4, N2, and M1 stage tended to be the risk factors for survival. Meanwhile, epithelioid mesothelioma, surgery, radiotherapy, and chemotherapy tended to be the protective factors. The median overall survival (OS) of patients who underwent surgery combined with radiotherapy was the longest, followed by those who underwent a combination of surgery, radiotherapy, and chemotherapy. Conclusions: Our deep learning-based model precisely could predict the survival of patients with MPM; moreover, multimode combination therapy might provide more meaningful survival benefits.

Human umbilical cord mesenchymal stem cells alleviated TNBS-induced colitis in mice by restoring the balance of intestinal microbes and immunoregulation.

AIMS: Human umbilical cord mesenchymal stem cells (HUMSCs) have been documented to be effective for several immune disorders including inflammatory bowel diseases (IBD). However, it remains unclear how HUMSCs function in regulating immune responses and intestinal flora in the trinitrobenzene sulfonic acid (TNBS)-induced IBD model. MATERIALS AND METHODS: We assessed the regulatory effects of HUMSCs on the gut microbiota, T lymphocyte subpopulations and related immune cytokines in the TNBS-induced IBD model. The mice were divided into the normal, TNBS, and HUMSC-treated groups. The effect of HUMSCs was evaluated by Hematoxylin and Eosin (H&E) staining, fluorescence-activated cell sorting (FACS), and enzyme-linked immunosorbent assay (ELISA) analyses. Metagenomics Illumina sequencing was conducted for fecal samples. KEY FINDINGS: We demonstrated that the disease symptoms and pathological changes in the colon tissues of TNBS-induced colitis mice were dramatically ameliorated by HUMSCs, which improved the gut microbiota and rebalanced the immune system, increasing the abundance of healthy bacteria (such as Lactobacillus murinus and Lactobacillus johnsonii), the Firmicutes/Bacteroidetes ratio, and the proportion of Tregs; the Th1/Th17 ratio was decreased. Consistently, the expression levels of IFN-γ and IL-17 were significantly decreased, and transforming growth factor-ß1 (TGF-ß1) levels were significantly increased in the plasma of colitis mice HUMSC injection. SIGNIFICANCE: Our experiment revealed that HUMSCs mitigate acute colitis by regulating the rebalance of Th1/Th17/Treg cells and related cytokines and remodeling the gut microbiota, providing potential future therapeutic targets in IBD.

Neural network-based prognostic predictive tool for gastric cardiac cancer: the worldwide retrospective study.

BACKGROUNDS: The incidence of gastric cardiac cancer (GCC) has obviously increased recently with poor prognosis. It's necessary to compare GCC prognosis with other gastric sites carcinoma and set up an effective prognostic model based on a neural network to predict the survival of GCC patients. METHODS: In the population-based cohort study, we first enrolled the clinical features from the Surveillance, Epidemiology and End Results (SEER) data (n = 31,397) as well as the public Chinese data from different hospitals (n = 1049). Then according to the diagnostic time, the SEER data were then divided into two cohorts, the train cohort (patients were diagnosed as GCC in 2010-2014, n = 4414) and the test cohort (diagnosed in 2015, n = 957). Age, sex, pathology, tumor, node, and metastasis (TNM) stage, tumor size, surgery or not, radiotherapy or not, chemotherapy or not and history of malignancy were chosen as the predictive clinical features. The train cohort was utilized to conduct the neural network-based prognostic predictive model which validated by itself and the test cohort. Area under the receiver operating characteristics curve (AUC) was used to evaluate model performance. RESULTS: The prognosis of GCC patients in SEER database was worse than that of non GCC (NGCC) patients, while it was not worse in the Chinese data. The total of 5371 patients were used to conduct the model, following inclusion and exclusion criteria. Neural network-based prognostic predictive model had a satisfactory performance for GCC overall survival (OS) prediction, which owned 0.7431 AUC in the train cohort (95% confidence intervals, CI, 0.7423-0.7439) and 0.7419 in the test cohort (95% CI, 0.7411-0.7428). CONCLUSIONS: GCC patients indeed have different survival time compared with non GCC patients. And the neural network-based prognostic predictive tool developed in this study is a novel and promising software for the clinical outcome analysis of GCC patients.

Interpretable deep learning survival predictive tool for small cell lung cancer.

Background: Small cell lung cancer (SCLC) is an aggressive and almost universally lethal neoplasm. There is no accurate predictive method for its prognosis. Artificial intelligence deep learning may bring new hope. Methods: By searching the Surveillance, Epidemiology, and End Results database (SEER), 21,093 patients' clinical data were eventually included. Data were then divided into two groups (train dataset/test dataset). The train dataset (diagnosed in 2010-2014, N = 17,296) was utilized to conduct a deep learning survival model, validated by itself and the test dataset (diagnosed in 2015, N = 3,797) in parallel. According to clinical experience, age, sex, tumor site, T, N, M stage (7th American Joint Committee on Cancer TNM stage), tumor size, surgery, chemotherapy, radiotherapy, and history of malignancy were chosen as predictive clinical features. The C-index was the main indicator to evaluate model performance. Results: The predictive model had a 0.7181 C-index (95% confidence intervals, CIs, 0.7174-0.7187) in the train dataset and a 0.7208 C-index (95% CIs, 0.7202-0.7215) in the test dataset. These indicated that it had a reliable predictive value on OS for SCLC, so it was then packaged as a Windows software which is free for doctors, researchers, and patients to use. Conclusion: The interpretable deep learning survival predictive tool for small cell lung cancer developed by this study had a reliable predictive value on their overall survival. More biomarkers may help improve the prognostic predictive performance of small cell lung cancer.

Deep learning survival model for colorectal cancer patients (DeepCRC) with Asian clinical data compared with different theories.

Introduction: Colorectal cancer (CRC) is the third most common cancer. Precise prediction of CRC patients' overall survival (OS) probability could offer advice on its treatment. Neural network (NN) is the first-class algorithm, but a consensus on which NN survival models are better has not been established yet. A predictive model on CRC using Asian data is also lacking. Methods: We conducted 8 NN survival models of CRC (n = 416) with different theories and compared them using Asian data. Results: DeepSurv performed best with a C-index value of 0.8300 in the training cohort and 0.7681 in the test cohort. Conclusions: The deep learning survival model for CRC patients (DeepCRC) could predict CRC's OS accurately.

Serum metabolite signatures in normal individuals and patients with colorectal adenoma or colorectal cancer using UPLC-MS/MS method.

Colorectal cancer (CRC) is one of the main causes of cancer-related deaths worldwide. Sporadic CRC develops from normal mucosa via adenoma to adenocarcinoma, which provides a long screening window for clinical detection. However, early diagnosis of sporadic colorectal adenoma (CRA) and CRC using serum metabolic screening remains unclear. The purpose of this study was to identify some promising signatures for distinguishing the different pathological metabolites of colorectal mucosal malignant transformation. A total of 238 endogenous metabolites were elected. We found that CRA and CRC patients had 72 and 73 different metabolites compared with healthy controls, respectively. There were 20 different metabolites between CRA and CRC patients. The potential metabolites of tumor growth (including patients with CRA and CRC) were found, such as A-d-glucose, D-mannose, N-acetyl-D-glucosamine, L-cystine, Sarcosine, TXB 2, 12-Hete, and chenodeoxycholic acid. Compared with CRA, 3,4,5-trimethoxybenzoic acid was significantly higher in CRC patients. There results prompt us to use the potential serum signatures to screen CRC as the novel strategy. Serum metabolite screening is useful for early detection of mucosal intestinal malignancy. We will further investigate the roles of these promising biomarkers during intestinal tumorigenesis in future. SIGNIFICANCE: CRC is one of the main causes of cancer-related deaths worldwide. Sporadic CRC develops from normal mucosa via adenomas to adenocarcinoma, which provides a long screening window for about 5-10 years. We adopt the metabolic analysis of extensive targeted metabolic technology. The main purpose of the metabolic group analysis is to detect and screen the different metabolites, thereby performing related functional prediction and analysis of the differential metabolites. In our study, 30 samples are selected, divided into 3 groups for metabolic analysis, and 238 metabolites are elected. In 238 metabolites, we find that CRA patients have 72 different metabolites compared with health control. Compared with health control, CRC have 73 different metabolites. Compared with CRA and CRC patients, there are 20 different metabolites. The annotation results of the significantly different metabolites are classified according to the KEGG pathway type. The potential metabolites of tumor growth stage (including patients with CRA and CRC) are found, such as A-d-glucose, D-mannose, N-acetyl-D-glucosamine, L-cystine, sarcosine, TXB 2, 12-Hete and chenodeoxycholic acid. Compared with CRA patients, CRC patients had significantly higher 3,4,5-trimethoxybenzoic acid level. It is prompted to use serum different metabolites to screen CRC to provide new possibilities.

Assessing the efficacy of immunotherapy in lung squamous carcinoma using artificial intelligence neural network.

Background: At present, immunotherapy is a very promising treatment method for lung cancer patients, while the factors affecting response are still controversial. It is crucial to predict the efficacy of lung squamous carcinoma patients who received immunotherapy. Methods: In our retrospective study, we enrolled lung squamous carcinoma patients who received immunotherapy at Beijing Chest Hospital from January 2017 to November 2021. All patients were grouped into two cohorts randomly, the training cohort (80% of the total) and the test cohort (20% of the total). The training cohort was used to build neural network models to assess the efficacy and outcome of immunotherapy in lung squamous carcinoma based on clinical information. The main outcome was the disease control rate (DCR), and then the secondary outcomes were objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). Results: A total of 289 patients were included in this study. The DCR model had area under the receiver operating characteristic curve (AUC) value of 0.9526 (95%CI, 0.9088-0.9879) in internal validation and 0.9491 (95%CI, 0.8704-1.0000) in external validation. The ORR model had AUC of 0.8030 (95%CI, 0.7437-0.8545) in internal validation and 0.7040 (95%CI, 0.5457-0.8379) in external validation. The PFS model had AUC of 0.8531 (95%CI, 0.8024-0.8975) in internal validation and 0.7602 (95%CI, 0.6236-0.8733) in external validation. The OS model had AUC of 0.8006 (95%CI, 0.7995-0.8017) in internal validation and 0.7382 (95%CI, 0.7366-0.7398) in external validation. Conclusions: The neural network models show benefits in the efficacy evaluation of immunotherapy to lung squamous carcinoma patients, especially the DCR and ORR models. In our retrospective study, we found that neoadjuvant and adjuvant immunotherapy may bring greater efficacy benefits to patients.

SPAG5 Activates PI3K/AKT Pathway and Promotes the Tumor Progression and Chemo-Resistance in Gastric Cancer.

The sperm-associated antigen 5 (SPAG5) is an important protein in mitosis and cell cycle checkpoint regulation, with more attention as a novel oncogene in various cancers. High level of SPAG5 expression has been detected in our clinical gastric cancer (GC) samples and The Cancer Genome Atlas GC data. However, the bio-function and potential mechanism of SPAG5 in GC remain unclear. In this study, we investigated the role of SPAG5 in GC development and the correlation between SPAG5 and 5-fluorouracil (5-FU) treatment. SPAG5 expression was increased in GC samples compared with that in normal tissues (80.8% vs. 22.0%), which was apparently associated with a worse outcome. Biological experiments showed that knockdown of SPAG5 induced apoptosis and suppressed proliferation in cells and animal models. Downregulation of SPAG5 enhanced the sensitivity of 5-FU in GC cells. Gene microarray chip identified 856 upregulated and 787 downregulated genes in SPAG5 silencing cells. Furthermore, 12 significant genes, including CDKN1A, CDKN1B, EIF4E, MAPK1, and HSP90B1, belonged to the PI3K/AKT signaling pathway using ingenuity pathway analysis. Meanwhile, real-time PCR and Western blotting results showed that knockdown of SPAG5 inhibited PI3K/AKT signaling pathway. Collectively, SPAG5 promotes the growth of GC cells by regulating PI3K/AKT signaling pathway, which could be the promising target gene in GC therapy.

Iron(II)-activated phase transformation of Cd-bearing ferrihydrite: Implications for cadmium mobility and fate under anaerobic conditions.

The factors and mechanisms affecting the fate of the associated Cd during the Fe(II)-activated Cd-bearing ferrihydrite transformation remain poorly understood. Herein we have conducted a series of batch reactions containing ferrihydrite with diverse pH values and initial Fe(II) and Cd concentrations coupled with chemical analyses and spectroscopic examination on the transformation products to probe the mechanisms of the Cd partitioning and the processes of Fe(II)-activated Cd-bearing ferrihydrite transformation under anaerobic conditions. Chemical analyses, Fourier transform infrared spectroscopy (FTIR), and powder X-ray diffraction (PXRD) results show that the initial Fe(II) and Cd concentrations as well as pH values all have significant effects on the rates and pathways of ferrihydrite transformation. Increasing Cd loading enhances the inhibition of the Fe(II)-activated ferrihydrite transformation rates. High Cd loading alters the Fe(II)-activated ferrihydrite transformation pathways by hindering the recrystallization of both ferrihydrite to more stable iron minerals and the newly formed lepidocrocite to goethite. Chemical analyses show that the release of Cd to solutions during ferrihydrite transformation is accompanied by a reduction in the 0.4 M HCl extractable Cd fraction and that a significant amount of the released Cd is transformed to a 0.4 M HCl unextractable form. Moreover, enhanced Cd release during the Fe(II)-activated ferrihydrite transformation is observed by reducing the pH value or increasing the initial Cd concentration. Results from synchrotron X-ray absorption spectroscopy (XAS) confirm that the majority of the 0.4 M HCl unextractable Cd form is associated with structural incorporation into the recrystallized iron (hydr)oxides via isomorphous substitution for Fe(III). These findings not only provide molecular-level understanding on the behavior of Cd under natural anoxic environments, but also are useful in predicting the geochemical cycling of Cd and developing long-term Cd contaminant management strategies.

CpG Site-Specific Methylation-Modulated Divergent Expression of PRSS3 Transcript Variants Facilitates Nongenetic Intratumor Heterogeneity in Human Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is one of the most lethal human tumors with extensive intratumor heterogeneity (ITH). Serine protease 3 (PRSS3) is an indispensable member of the trypsin family and has been implicated in the pathogenesis of several malignancies, including HCC. However, the paradoxical effects of PRSS3 on carcinogenesis due to an unclear molecular basis impede the utilization of its biomarker potential. We hereby explored the contribution of PRSS3 transcripts to tumor functional heterogeneity by systematically dissecting the expression of four known splice variants of PRSS3 (PRSS3-SVs, V1~V4) and their functional relevance to HCC. Methods: The expression and DNA methylation of PRSS3 transcripts and their associated clinical relevance in HCC were analyzed using several publicly available datasets and validated using qPCR-based assays. Functional experiments were performed in gain- and loss-of-function cell models, in which PRSS3 transcript constructs were separately transfected after deleting PRSS3 expression by CRISPR/Cas9 editing. Results: PRSS3 was aberrantly differentially expressed toward bipolarity from very low (PRSS3Low ) to very high (PRSS3High ) expression across HCC cell lines and tissues. This was attributable to the disruption of PRSS3-SVs, in which PRSS3-V2 and/or PRSS3-V1 were dominant transcripts leading to PRSS3 expression, whereas PRSS3-V3 and -V4 were rarely or minimally expressed. The expression of PRSS3-V2 or -V1 was inversely associated with site-specific CpG methylation at the PRSS3 promoter region that distinguished HCC cells and tissues phenotypically between hypermethylated low-expression (mPRSS3-SVLow ) and hypomethylated high-expression (umPRSS3-SVHigh ) groups. PRSS3-SVs displayed distinct functions from oncogenic PRSS3-V2 to tumor-suppressive PRSS3-V1, -V3 or PRSS3-V4 in HCC cells. Clinically, aberrant expression of PRSS3-SVs was translated into divergent relevance in patients with HCC, in which significant epigenetic downregulation of PRSS3-V2 was seen in early HCC and was associated with favorable patient outcome. Conclusions: These results provide the first evidence for the transcriptional and functional characterization of PRSS3 transcripts in HCC. Aberrant expression of divergent PRSS3-SVs disrupted by site-specific CpG methylation may integrate the effects of oncogenic PRSS3-V2 and tumor-suppressive PRSS3-V1, resulting in the molecular diversity and functional plasticity of PRSS3 in HCC. Dysregulated expression of PRSS3-V2 by site-specific CpG methylation may have potential diagnostic value for patients with early HCC.

The fate of co-existent cadmium and arsenic during Fe(II)-induced transformation of As(V)/Cd(II)-bearing ferrihydrite.

Ubiquitous co-existence of arsenic (As) and cadmium (Cd) in smelting operations and mine drainage presents a major challenge to the environment. Fe(II)-induced ferrihydrite transformation into secondary, more crystalline minerals often controls the geochemical behavior of associated contaminants including arsenate (As(V)) and Cd(II) in natural and contaminated environments. However, the fate of co-existent As(V) and Cd(II) and the underlying mechanism during this transformation process remain unclear. In this contribution, ferrihydrite containing co-precipitated Cd(II) and As(V) with Fe(II) under diverse pH conditions has been investigated. Results from powder X-ray diffraction (PXRD), Fourier transform infrared spectroscopy (FTIR), and Raman spectra show that the co-existence of As(V) and Cd(II) significantly retards the transformation rates of As(V)/Cd(II)-bearing ferrihydrite to more stable iron oxides and reduces that from the newly formed lepidocrocite to goethite. Compared to Cd(II), the co-existent As(V) has stronger influence on the compositions of the transformation products. Chemical analysis shows that phosphate-unextractable As(V) and 0.4 M HCl unextractable Cd(II) both increase as the reaction proceeds during the recrystallization of As(V)/Cd(II)-bearing ferrihydrite, indicating that both As(V) and Cd(II) partially transform to a more stable phase. The co-existent Cd(II) has negligible effects on the As(V) redistribution, but the co-existent As(V) at high loadings has a significant modification in the distribution of Cd(II) during the transformation, which reduces the liberation of Cd(II) into solution, thus decreasing the mobility of Cd(II). These findings have important implications for understanding the mobility and fate of the co-existent As(V) and Cd(II) under natural anoxic environments, remediating the co-existent contaminants, and predicting the long-term behavior of As(V) and Cd(II) in natural and contaminated environments.

miR-34a-5p inhibits the malignant progression of KSHV-infected SH-SY5Y cells by targeting c-fos.

Background: We aimed to investigate the effects of miR-34a-5p on c-fos regulation mediating the malignant behaviors of SH-SY5Y cells infected with Kaposi's sarcoma-associated herpesvirus (KSHV). Methods: The KSHV-infected (SK-RG) and uninfected SH-SY5Y parent cells were compared for differentially expressed miRNAs using transcriptome sequencing. Then miR-34a-5p was upregulated in SK-RG cells by the miRNA mimics transfection. Cell proliferation ability was determined by MTT and plate clone assays. The cell cycle was assessed by flow cytometry analysis, and CDK4, CDK6, cyclin D1 levels were determined by Western blot analysis. The migration behavior was detected by wound healing and transwell assays. The protein levels of MMP2 and MMP9 were measured by Western blot analysis. The regulation of c-fos by miR-34a-5p was detected by the dual-luciferase reporter gene assay. Rescue assays were carried out by upregulating c-fos in miR-34a-5p-overexpressing SK-RG cells. KSHV DNA copy numbers and relative virus gene expressions were detected. Xenograft tumor experiments and immunohistochemistry assays were further used to detect the effects of miR-34a-5p. Results: miR-34a-5p was lower in SK-RG cells. Restoration of miR-34a-5p decreased cell proliferation and migration, leading to a G1 cell cycle arrest and down-regulation of CDK4/6, cyclin D1, MMP2, MMP9. KSHV copy number and expression of virus gene including latency-associated nuclear antigen (LANA), replication and transcription activator (RTA), open reading frame (K8.1), and KSHV G protein-coupled receptor (v-GPCR) were also reduced. Furthermore, c-fos is the target of miR-34a-5p, while enhanced c-fos weakened cellular behaviors of miR-34a-5p-overexpressing cells. Xenograft experiments and immunohistochemistry assays showed that miR-34a-5p inhibited tumor growth and virus gene expression. Conclusion: Upregulated miR-34a-5p in KSHV-infected SH-SY5Y cells suppressed cell proliferation and migration through down-regulating c-fos. miR-34a-5p was a candidate molecular drug for KSHV-infected neuronal cells.

Tumor suppressor ATP4B serve as a promising biomarker for worsening of gastric atrophy and poor differentiation.

BACKGROUND: Hydrogen/potassium ATPase ß (ATP4B) is a proton pump acting an essential role in gastric acid secretion. This study aimed to investigate the diagnostic performance of ATP4B and its biological role in tumor progression in gastric cancer. METHODS: The correlations between ATP4B expression level and clinicopathologic parameters, as well as the relevance of ATP4B expression with overall survival were assessed. The functional roles of ATP4B in gastric cancer were verified by gain- and loss-of-function cell models and tumor xenograft models. The possible downstream effects of ATP4B were analyzed by iTRAQ-based quantitative proteomics analysis. RESULTS: A dramatic decrease in ATP4B was associated with malignant transformation in gastric mucosa lesions and correlated with poor differentiation. Restoration of ATP4B expression in gastric cancer cells significantly suppressed cell proliferation, cell viability, migration, invasion, tumorigenicity and induced apoptosis, whereas ATP4B silencing exerted the opposite effects. Mechanistically, we found a quality control on mitochondrial metabolism and functions in ATP4B-overexpression GC cells. CONCLUSIONS: Our data suggest that decreasing ATP4B is an indicator for gastric mucosa malignant transformation and GC aggressive phenotype and it plays an inhibitory role in gastric cancer as a tumor suppressor via regulating mitochondrial metabolism and apoptosis pathway.

Glucose-coated Berberine Nanodrug for Glioma Therapy through Mitochondrial Pathway.

INTRODUCTION: Glioma is the primary malignant brain tumor with poor prognosis. Berberine (BBR) was the potential drug for anti-tumor in glioma cells. Based on its limitation of poor aqueous solubility and instability, little information of BBR nanoparticles is reported in glioma. METHODS: Different solutions including 5% glucose, 1*PBS, ddH2O, 0.9% NaCl, cell culture medium were selected, and only 5% glucose and ddH2O exhibited BBR-related nanoparticles. After heating for a longer time or adding a higher concentration of glucose solution, BBR nanoparticles were detected by TEM analysis. The uptake of BBR-Glu or BBR-Water nanoparticles were detected by immunofluorescence analysis for BBR autofluorescence. Cell viability was measured by MTT assay and Western blotting analysis. Apoptosis was performed with flow cytometric analysis and was detected by cleaved caspase-3 immuno-fluorescent staining. Cell cycle was used by flow cytometric analysis. Cytoskeleton was observed by confocal analysis using the neuron specific Class III ß-tubulin and ß-tubulin antibodies. Mitochondrial-related proteins were detected by Western blotting analyses and mito-tracker staining in live cells. Mitochondrion structures were observed by TEM analysis. ROS generation and ATP production were detected by related commercial kits. The tracking of BBR-Glu or BBR-Water nanoparticles into blood-brain barrier was observed in primary tumor-bearing models. The fluorescence of BBR was detected by confocal analyses in brains and gliomas. RESULTS: BBR-Glu nanoparticles became more homogenized and smaller with dose- and time-dependent manners. BBR-Glu nanoparticles were easily absorbed in glioma cells. The IC50 of BBR-Glu in U87 and U251 was far lower than that of BBR-Water. BBR-Glu performed better cytotoxicity, with higher G2/M phase arrest, decreased cell viability by targeting mitochondrion. In primary U87 glioma-bearing mice, BBR-Glu exhibited better imaging in brains and gliomas, indicating that more BBR moved across the blood-brain tumor barrier. DISCUSSION: BBR-Glu nanoparticles have better solubility and stability, providing a promising strategy in glioma precision treatment.

Dysregulation of MiR-30a-3p/Gastrin Enhances Tumor Growth and Invasion throughSTAT3/MMP11 Pathway in Gastric Cancer.

BACKGROUND: Gastrin (GAST) is a well-known hormone regulating gastric biofunctions in the secretion of acid and maintaining its structural integrity. Furthermore, the dysregulation of GAST is also involved in the development of various forms of cancer. However, there are some limitations for illustrating the cellular regulation of GAST and its regulatory mechanisms in gastric malignant transformation and the potential epigenetic regulators systematically. METHODS: We explored the role of GAST expression in gastric cancer (GC) and normal tissues with the clinical features and investigated the potential relationship between GAST and STAT3/MMP11 pathway by gain or loss of function analyses. Besides, based on our microRNA/mRNA expression profiles, miR-30a-3p was the potential epigenetic regulator and additional experiments were performed to identify the hypothesis. RESULTS: Elevated GAST expression was frequently detected in GC and was associated with worse outcomes (p<0.001). And we firstly demonstrated that GAST was negatively regulated by miR-30a-3p. Moreover, GAST induced GC cell proliferation, migration and invasion mediating STAT3/MMP11 pathway in this study. CONCLUSION: MiR-30a-3p was the promising suppressor gene through negatively regulating the expression of GAST, and dysregulation of GAST was a prognostic signature associated cell proliferation and metastasis through STAT3/MMP11 pathway in GC.

Serum metabolite profiling of familial adenomatous polyposis using ultra performance liquid chromatography and tandem mass spectrometry.

Familial adenomatous polyposis (FAP) is an autosomal dominantly inherited intestinal polyposis syndrome accounting for about 1% of colorectal cancers (CRC). Despite increasing researches on the molecular pathogenesis of CRC, we are still unclear about metabolic pathways and alterations probably involved in the development of CRC. To obtain new insights into the mechanisms underlying APC mutation and to elucidate the mechanisms of CRC development, we performed to identify the potential metabolites in FAP based on metabolomic strategy. Serum metabolites from FAP patients (n = 30) and healthy individuals (n = 34) were detected and qualified using Ultra Performance Liquid Chromatography and Tandem Mass Spectrometry (UPLC- MS/MS). 118 metabolites were identified with statistical tests of orthogonal partial least-squares-discriminant analysis (OPLS-DA), with the conditions of variable importance in projection (VIP) >1, p < 0.05 using the Mann-Whitney U test, and fold change (FC) ≥2 or ≤0.5. OPLS-DA model was useful for distinguishing FAP patients from healthy controls. Unique metabolic signatures were pooled in FAP patients covering tricarboxylic acid (TCA) cycle, amino acids metabolism, vitamin D, fatty acids metabolism, and bile acids (BAs) metabolism. Our results demonstrated that metabolites alterations in FAP can be helpful for further analysis of metabonomics induced by APC mutation, and these alterations might be involved in the progress of intestinal carcinogenesis.