To determine the possible role of the epigenetic mechanisms in carcinogenesis of the hepatocellular carcinoma, we methylation-profiled the promoter CpG islands of twenty four genes both in HCC tumors and the neighboring non-cancerous tissues of twenty eight patients using the methylation-specific PCR (MSP) method in conjunction with the DNA sequencing. In comparison with the normal liver tissues from the healthy donors, it was found that while remained unmethylated the ABL, CAV, EPO, GATA3, LKB1, NEP, NFL, NIS and p27KIP1 genes, varying extents of the HCC specific hypermethylation were found associated with the ABO, AR, CSPG2, cyclin a1, DBCCR1, GALR2, IRF7, MGMT, MT1A, MYOD1, OCT6, p57 KIP2 , p73, WT1 genes, and demethylation with the MAGEA1 gene, respectively. Judged by whether the hypermethylated occurred in HCC more frequently than in their neighboring normal tissues, the hypermethylation status of the AR, DBCCR1, IRF7, OCT6, and p73 genes was considered as the event specific to the late stage, while that the rest that lacked such a distinguished contrast, as the event specific to the early stage of HCC carcinogenesis. Among all the clinical pathological parameters tested for the association with, the hypermethylation of the cyclin a1 gene was more prevalent in the non-cirrhosis group (P=0.021) while the hypermethylated p16INK4a gene was more common in the cirrhosis group (P=0.017). The concordant methylation behaviors of nineteen genes, including the four previously studied and their association with cirrhosis has been evaluated by the best subgroup selection method. The data presented in this report would enable us to shape our understanding of the mechanisms for the HCC specific loss of the epigenetic stability of the genome, as well as the strategy of developing the novel robust methylation based diagnostic and prognostic tools.

Background: Early-stage diagnosis and treatment can improve survival rates of liver cancer patients. Dynamic contrast-enhanced MRI provides the most comprehensive information for differential diagnosis of liver tumors. However, MRI diagnosis is affected by subjective experience, so deep learning may supply a new diagnostic strategy. We used convolutional neural networks (CNNs) to develop a deep learning system (DLS) to classify liver tumors based on enhanced MR images, unenhanced MR images, and clinical data including text and laboratory test results. Methods: Using data from 1,210 patients with liver tumors (N = 31,608 images), we trained CNNs to get seven-way classifiers, binary classifiers, and three-way malignancyclassifiers (Model A-Model G). Models were validated in an external independent extended cohort of 201 patients (N = 6,816 images). The area under receiver operating characteristic (ROC) curve (AUC) were compared across different models. We also compared the sensitivity and specificity of models with the performance of three experienced radiologists. Results: Deep learning achieves a performance on par with three experienced radiologists on classifying liver tumors in seven categories. Using only unenhanced images, CNN performs well in distinguishing malignant from benign liver tumors (AUC, 0.946; 95% CI 0.914-0.979 vs. 0.951; 0.919-0.982, P = 0.664). New CNN combining unenhanced images with clinical data greatly improved the performance of classifying malignancies as hepatocellular carcinoma (AUC, 0.985; 95% CI 0.960-1.000), metastatic tumors (0.998; 0.989-1.000), and other primary malignancies (0.963; 0.896-1.000), and the agreement with pathology was 91.9%.These models mined diagnostic information in unenhanced images and clinical data by deepneural-network, which were different to previous methods that utilized enhanced images. The sensitivity and specificity of almost every category in these models reached the same high level compared to three experienced radiologists. Zhen et al. Deep Learning for Liver Tumor Diagnosis Conclusion: Trained with data in various acquisition conditions, DLS that integrated these models could be used as an accurate and time-saving assisted-diagnostic strategy for liver tumors in clinical settings, even in the absence of contrast agents. DLS therefore has the potential to avoid contrast-related side effects and reduce economic costs associated with current standard MRI inspection practices for liver tumor patients.

Intra-tumor injection of the genetically engineered adenovirus H101 exhibits potential anti-tumor activity to refractory malignant tumors in combination with chemotherapy. Low toxicity and good tolerance of patients to H101were observed.

ObjectiveThis study aimed to investigate the effect and possible underlying mechanisms of high-fat diet-induced obesity on spermatogenesis in male rats.MethodsA total of 45 male rats were randomly divided into control (n = 15, normal diet) and obesity groups (n = 30, high-fat diet) and were fed for 16 weeks. Body weight and organ indexes were determined after sacrifice. Indicators of reproductive function, including sperm count, sperm motility, apoptosis of spermatogenic cells, and oxidative stress levels, were measured. Serum metabolic parameters and reproductive hormones were also assayed.ResultsCompared with the control group, epididymal sperm motility in the obese rats was significantly decreased (P < 0.01). Morphological analysis of the obesity group showed vacuolar changes in seminiferous tubules, spermatogenic cell dysfunction, and increased apoptosis of spermatogenic cells in testicular tissue (P < 0.05). The calculated free testosterone (cFT) concentration in serum was decreased (P < 0.05), whereas the serum sex hormone-binding globulin (SHBG) level was significantly increased (P < 0.01). The superoxide dismutase (SOD) concentration decreased and the malondialdehyde (MDA) concentration increased in testis tissues; however, neither changes were statistically significant (P > 0.05).ResultsNutritional obesity can damage spermatogenesis in male rats due to long-term effects on spermatogenesis.

BackgroundAstrocytoma is a common aggressive intracranial tumor and presents a formidable challenge in the clinic. Association of altered DNA methylation patterns of the promoter CpG islands with the expression profile of cancer-related genes, has been found in many human tumors. Therefore, DNA methylation status as such may serve as an epigenetic biomarker for both diagnosis and prognosis of human tumors, including astrocytoma.MethodsWe used the methylation specific PCR in conjunction with sequencing verification to establish the methylation profile of the promoter CpG island of thirty four genes in astrocytoma tissues from fifty three patients (The WHO grading:. I: 14, II: 15, III: 12 and IV: 12 cases, respectively). In addition, compatible tissues (normal tissues distant from lesion) from three non-astrocytoma patients were included as the control.ResultsSeventeen genes (ABL, APC, APAF1, BRCA1, CSPG2, DAPK1, hMLH1, LKB1, PTEN, p14ARF, p15INK4b, p27KIP1, p57KIP2, RASSF1C, RB1, SURVIVIN, and VHL) displayed a uniformly unmethylated pattern in all the astrocytoma and non-astrocytoma tissues examined. However, the MAGEA1 gene that was inactivated and hypermethylated in non-astrocytoma tissues, was partially demethylated in 24.5% of the astrocytoma tissues (co-existence of the hypermethylated and demethylated alleles). Of the astrocytoma associated hypermethylated genes, the methylation pattern of the CDH13, cyclin a1, DBCCR1, EPO, MYOD1, and p16INK4a genes changed in no more than 5.66% (3/53) of astrocytoma tissues compared to non-astrocytoma controls, while the RASSF1A, p73, AR, MGMT, CDH1, OCT6,, MT1A, WT1, and IRF7 genes were more frequently hypermethylated in 69.8%, 47.2%, 41.5%, 35.8%, 32%, 30.2%, 30.2%, 30.2% and 26.4% of astrocytoma tissues, respectively. Demethylation mediated inducible expression of the CDH13, MAGEA1, MGMT, p73 and RASSF1A genes was established in an astrocytoma cell line (U251), demonstrating that expression of these genes is likely regulated by DNA methylation. AR gene hypermethylation was found exclusively in female patients (22/27, 81%, 0/26, 0%, P < 0.001), while the IRF7 gene hypermethylation preferentially occurred in the male counterparts (11/26, 42.3% to 3/27, 11%, P < 0.05). Applying the mathematic method "the Discovery of Association Rules", we have identified groups consisting of up to three genes that more likely display the altered methylation patterns in concert in astrocytoma.ConclusionsOf the thirty four genes examined, sixteen genes exhibited astrocytoma associated changes in the methylation profile. In addition to the possible pathological significance, the established concordant methylation profiles of the subsets consisting of two to three target genes may provide useful clues to the development of the useful prognostic as well as diagnostic assays for astrocytoma.

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Background The scientific understanding of long non-coding RNAs (lncRNAs) has improved in recent decades. Nevertheless, there has been little research into the role that lncRNAs play in clear cell renal cell carcinoma (ccRCC). More lncRNAs are assumed to influence the progression of ccRCC via their own molecular mechanisms. Methods This study investigated the prognostic significance of differentially expressed lncRNAs by mining high-throughput lncRNA-sequencing data from The Cancer Genome Atlas (TCGA) containing 13,198 lncRNAs from 539 patients. Differentially expressed lncRNAs were assessed using the R packages edgeR and DESeq. The prognostic significance of lncRNAs was measured using univariate Cox proportional hazards regression. ccRCC patients were then categorized into high- and low-score cohorts based on the cumulative distribution curve inflection point the of risk score, which was generated by the multivariate Cox regression model. Samples from the TCGA dataset were divided into training and validation subsets to verify the prognostic risk model. Bioinformatics methods, gene set enrichment analysis, and protein–protein interaction networks, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analyses were subsequently used. Results It was found that the risk score based on 6 novel lncRNAs (CTA-384D8.35, CTD-2263F21.1, LINC01510, RP11-352G9.1, RP11-395B7.2, RP11-426C22.4) exhibited superior prognostic value for ccRCC. Moreover, we categorized the cases into two groups (high-risk and low-risk), and also examined related pathways and genetic differences between them. Kaplan–Meier curves indicated that the median survival time of patients in the high-risk group was 73.5 months, much shorter than that of the low-risk group (112.6 months; P < 0.05). Furthermore, the risk score predicted the 5-year survival of all 539 ccRCC patients (AUC at 5 years, 0.683; concordance index [C-index], 0.853; 95% CI 0.817–0.889). The training set and validation set also showed similar performance (AUC at 5 years, 0.649 and 0.681, respectively; C-index, 0.822 and 0.891; 95% CI 0.774–0.870 and 0.844–0.938). Conclusions The results of this study can be applied to analyzing various prognostic factors, leading to new possibilities for clinical diagnosis and prognosis of ccRCC.

To develop a routine and effectual procedure of detecting bladder cancer (BlCa), an optimized combination of epigenetic biomarkers that work synergistically with high sensitivity and specificity is necessary. In this study, methylation levels of seven biomarkers (EOMES, GDF15, NID2, PCDH17, POU4F2, TCF21, and ZNF154) in 148 individuals—which including 58 urothelial cell carcinoma (UCC) patients, 20 infected urinary calculi (IUC) patients, 20 kidney cancer (KC) patients,20 prostate cancer (PC) patients, and 30 healthy volunteers (HV)—were quantified by qMSP using the urine sediment DNA. Receiver operating characteristic (ROC) curves were generated for each biomarker. The combining predictors of possible combinations were calculated through logistic regression model. Subsequently, ROC curves of the three best performing combinations were constructed. Then, we validated the three best performing combinations and POU4F2 in another 72 UCC, 21 IUC, 26 KC and 22 PC, and 23 HV urine samples. The combination of POU4F2/PCDH17 has yielded the highest sensitivity and specificity of 90.00% and 93.96% in all the 312 individuals, showing the capability of detecting BlCa effectively among pathologically varied sample groups.