Multiple brain regions are involved in reaction to acute restraint stress in CYLD-knockout mice.

The lysine 63 deubiquitinase cylindromatosis (CYLD) is expressed at high levels in the brain and is considered to be involved in anxious and depressive behavior, cognitive inflexibility, and autism disorders. Previous research was limited in some brain regions, including the hippocampus, striatum, and amygdala. To better understand whether CYLD plays a role in adaptation to stress and which brain regions are involved, we analyzed the behavior of CYLD-knockout mice in the elevated plus maze (EPM) and light-dark box test (LDT) after acute restraint stress (ARS) and mapped their c-Fos immunoreactivity in brain sections. Here we report that CYLD deficiency leads to an unexpected reaction to ARS in mice, and is accompanied by significant neuronal activation of brain regions including the medial prefrontal cortex (mPFC), dorsal striatum (DS), nucleus accumbens (NAc), and basal lateral amygdala (BLA), but not ventral hippocampus (vHPC). Our findings show that CYLD participates in ARS-induced anxious behavior and that this involves multiple brain regions.

Is red cell distribution width a prognostic factor in patients with breast cancer? A meta-analysis.

Purpose: The current study aimed to investigate whether red blood cell distribution width (RDW) can predict the prognosis of patients with breast cancer (BC). Methods: We searched four databases, including PubMed, Embase, Cochrane Library databases, and CNKI, from inception to Jun 13, 2022. The primary outcome was overall survival (OS), and the secondary outcome was disease-free survival (DFS). A subgroup analysis was conducted based on different treatments. This meta-analysis was performed with RevMan 5.3 (The Cochrane Collaboration, London, United Kingdom). Results: A total of seven studies including 4,884 BC patients were identified. The high RDW group had a larger tumor size (OR = 2.12, 95% CI = 1.67 to 2.68, P < 0.01), higher proportions of advanced stage tumors (OR = 1.77, 95% CI = 1.38 to 2.27, P < 0.01), more lymph node metastases (OR = 2.00, 95% CI = 1.58 to 2.51, P < 0.01) and lower HER-2 expression (OR = 0.76, 95% CI = 0.61 to 0.95, P = 0.02). For prognosis, after pooling all the data, we found that the high RDW group was associated with worse OS (HR = 2.12, 95% CI = 1.47 to 3.08, P < 0.01) and DFS (HR = 1.77, 95% CI = 1.32 to 2.37, P < 0.01). The subgroup analysis found that RDW had prognostic significance but only for surgery-only patients (HR = 2.41, 95% CI = 1.67 to 3.49, P < 0.01). Conclusion: High RDW was associated with worse OS and DFS. Therefore, RDW was a simple predictive factor for the prognosis of BC patients.

Noninvasive prediction of axillary lymph node status in breast cancer using promoter profiling of circulating cell-free DNA.

BACKGROUND: Lymph node metastasis (LNM) is one of the most important factors affecting the prognosis of breast cancer. The accurate evaluation of lymph node status is useful to predict the outcomes of patients and guide the choice of cancer treatment. However, there is still lack of a low-cost non-invasive method to assess the status of axillary lymph node (ALN). Gene expression signature has been used to assess lymph node metastasis status of breast cancer. In addition, nucleosome footprint of cell-free DNA (cfDNA) carries gene expression information of its original tissues, so it may be used to evaluate the axillary lymph node status in breast cancer. METHODS: In this study, we found that the cfDNA nucleosome footprints between the ALN-positive patients and ALN-negative patients showed different patterns by implementing whole-genome sequencing (WGS) to detect 15 ALN-positive and 15 ALN-negative patients. In order to further evaluate its potential for assessing ALN status, we developed a classifier with multiple machine learning models by using 330 WGS data of cfDNA from 162 ALN-positive and 168 ALN-negative samples to distinguish these two types of patients. RESULTS: We found that the promoter profiling between the ALN-positive patients and ALN-negative patients showed distinct patterns. In addition, we observed 1071 genes with differential promoter coverage and their functions were closely related to tumorigenesis. We found that the predictive classifier based on promoter profiling with a support vector machine model, named PPCNM, produced the largest area under the curve of 0.897 (95% confidence interval 0.86-0.93). CONCLUSIONS: These results indicate that promoter profiling can be used to distinguish ALN-positive patients from ALN-negative patients, which may be helpful to guide the choice of cancer treatment.

Mucocele: a rare complication following stapled haemorrhoidopexy.

BACKGROUND: Stapled haemorrhoidopexy (SH) has resulted in a unique collection of procedural complications with postoperative mucocele a particularly rare example. This study is designed to comprehensively describe the characteristics of rectal mucocele and discuss its pathogenesis following SH surgery. METHODS: A database of patients presenting with a rectal mucocele following an SH procedure was established and studied retrospectively. RESULTS: Seven patients (5 males; median age 32 years, range 20-75 years) were identified. All patients complained of variable anal discomfort with 5/7 presenting with inconstant anal pain, 2 with de novo evacuatory difficulty. These cases appeared at a median time of 6 months (range 2-84 months) after SH surgery. CONCLUSION: Rectal Mucocele develops when mucosal fragments become embedded and isolated under the mucosa. It is a preventable complication of SH surgery by ensuring correct purse string placement prior to stapled haemorrhoid excision.

A full-view management method based on artificial neural networks for energy and material-savings in wastewater treatment plants.

Carbon neutrality has been received extensive attention in the field of wastewater treatment. The optimal management of wastewater treatment plants (WWTPs) has great significance and urgency since the serious energy and materials waste. In this study, a full-view management method based on artificial neural networks (ANNs) for energy and material savings in WWTPs was established. More than 5 years of historical operating data from two typical plants (size 40,000 t/d and 10,000 t/d) located in Chongqing, China, were obtained, and public data in the service area of each plant were systematically collected from open channels. These abundant historical and public data were used to train two ANNs (GRA-CNN-LSTM model and PCA-BPNN model) to predict the inlets/outlets wastewater quality and quantity. The overall average prediction accuracy of inlets/outlets wastewater indicators are greater than 92.60% and 93.76%, respectively. By combining the two models, more appropriate process operation strategies can be obtained 2 weeks in advance, with more than 11.20% and 16.91% reduction of energy and material costs, respectively. This proposed method can provide full-view decision support for the optimal management of WWTPs and is also expected to support carbon emission control and carbon neutrality in the field of wastewater treatment.

Plasma-Derived Extracellular Vesicles Circular RNAs Serve as Biomarkers for Breast Cancer Diagnosis.

Breast cancer is the second cause of cancer-associated death among women and seriously endangers women's health. Therefore, early identification of breast cancer would be beneficial to women's health. At present, circular RNA (circRNA) not only exists in the extracellular vesicles (EVs) in plasma, but also presents distinct patterns under different physiological and pathological conditions. Therefore, we assume that circRNA could be used for early diagnosis of breast cancer. Here, we developed classifiers for breast cancer diagnosis that relied on 259 samples, including 144 breast cancer patients and 115 controls. In the discovery stage, we compared the genome-wide long RNA profiles of EVs in patients with breast cancer (n=14) and benign breast (n=6). To further verify its potential in early diagnosis of breast cancer, we prospectively collected plasma samples from 259 individuals before treatment, including 144 breast cancer patients and 115 controls. Finally, we developed and verified the predictive classifies based on their circRNA expression profiles of plasma EVs by using multiple machine learning models. By comparing their circRNA profiles, we found 439 circRNAs with significantly different levels between cancer patients and controls. Considering the cost and practicability of the test, we selected 20 candidate circRNAs with elevated levels and detected their levels by quantitative real-time polymerase chain reaction. In the training cohort, we found that BCExoC, a nine-circRNA combined classifier with SVM model, achieved the largest AUC of 0.83 [95% CI 0.77-0.88]. In the validation cohort, the predictive efficacy of the classifier achieved 0.80 [0.71-0.89]. Our work reveals the application prospect of circRNAs in plasma EVs as non-invasive liquid biopsies in the diagnosis and management of breast cancer.

A Deep-Learning Pipeline for TSS Coverage Imputation From Shallow Cell-Free DNA Sequencing.

Cell-free DNA (cfDNA) serves as a footprint of the nucleosome occupancy status of transcription start sites (TSSs), and has been subject to wide development for use in noninvasive health monitoring and disease detection. However, the requirement for high sequencing depth limits its clinical use. Here, we introduce a deep-learning pipeline designed for TSS coverage profiles generated from shallow cfDNA sequencing called the Autoencoder of cfDNA TSS (AECT) coverage profile. AECT outperformed existing single-cell sequencing imputation algorithms in terms of improvements to TSS coverage accuracy and the capture of latent biological features that distinguish sex or tumor status. We built classifiers for the detection of breast and rectal cancer using AECT-imputed shallow sequencing data, and their performance was close to that achieved by high-depth sequencing, suggesting that AECT could provide a broadly applicable noninvasive screening approach with high accuracy and at a moderate cost.

A plasma-derived extracellular vesicle mRNA classifier for the detection of breast cancer.

BACKGROUND: According to the global cancer burden data released in 2020, breast cancer (BC) has become the most common cancer in the world. Similar to those of other cancers, the present methods used in clinic for diagnosing early BC are invasive, inaccurate, and insensitive. Hence, new non-invasive methods capable of early diagnosis are needed. METHODS: We applied next-generation sequencing and analyzed the messenger RNA (mRNA) profiles of plasma extracellular vesicles (EVs) derived from 14 BC patients and 6 patients with benign breast lesions. We used 3 regression models, namely support vector machine (SVM), linear discriminate analysis (LDA), and logistic regression (LR), to develop classifiers for use in making predictive BC diagnoses; and used 259 plasma samples, including those obtained from 144 patients with BC, 72 patients with benign breast lesions, and 43 healthy women, which were divided into training groups and validation groups to verify their performances as classifiers by quantitative reverse transcription polymerase chain reaction (RT-qPCR). The area under the curve (AUC) and accuracy, sensitivity, and specificity of the classifiers were cross-validated with the leave-1-out cross-validation (LOOCV) method. RESULTS: Among all combinations assessed with the 3 different regression models, an 8-mRNA combination, named EXOBmRNA, exhibited high performance [accuracy =71.9% and AUC =0.718, 95% confidence interval (CI): 0.652 to 0.784] in the training cohort after LOOCV was performed, showing the largest AUC in the SVM model. The mRNAs in EXOBmRNA were HLA-DRB1, HAVCR1, ENPEP, TIMP1, CD36, MARCKS, DAB2, and CXCL14. In the validation cohort, the AUC of EXOBmRNA was 0.737 (95% CI: 0.636 to 0.837). In addition, gene function and pathway analyses revealed that different levels of gene expression were associated with cancer. CONCLUSIONS: We developed a high-performing predictive classifiers including 8 mRNAs from plasma extracellular vesicles for diagnosing breast cancer.

Noninvasive discrimination of benign and malignant breast lesions using genome-wide nucleosome profiles of plasma cell-free DNA.

BACKGROUND: Breast malignancy is the most frequently diagnosed malignancy in women worldwide, and the diagnosis relies on invasive examinations. However, most clinical breast changes in women are benign, and invasive diagnostic approaches cause unnecessary suffering for the patients. Thus, a novel noninvasive approach for discriminating malignant breast lesions from benign lesions is needed. METHODS: We performed cell-free DNA (cfDNA) sequencing on plasma samples from 173 malignant breast lesion patients, 158 benign breast lesion patients, and 102 healthy women. We then analyzed the cfDNA-based nucleosome profiles, which reflect the various tissues of origin and transcription factor activities. Moreover, by using machine learning classifiers along with the cfDNA sequencing data, we built classifiers for discriminating benign from malignant breast lesions. Receiver operating characteristic curve analyses were used to evaluate the performance of the classifiers. RESULTS: cfDNA-based nucleosome profiles reflected the various tissues of origin and transcription factor activities in benign and malignant breast lesions. The cfDNA-based transcription factor activities and breast malignancy-specific transcription factor-binding site accessibility profiles could accurately distinguish benign and malignant breast lesions, with area under the curve values of 0.777 and 0.824, respectively. CONCLUSIONS: Our proof-of-principle study established a methodology for noninvasively discriminating benign from malignant breast lesions.

Association between the nucleosome footprint of plasma DNA and neoadjuvant chemotherapy response for breast cancer.

Gene expression signatures have been used to predict the outcome of chemotherapy for breast cancer. The nucleosome footprint of cell-free DNA (cfDNA) carries gene expression information of the original tissues and thus may be used to predict the response to chemotherapy. Here we carried out the nucleosome positioning on cfDNA from 85 breast cancer patients and 85 healthy individuals and two cancer cell lines T-47D and MDA-MB-231 using low-coverage whole-genome sequencing (LCWGS) method. The patients showed distinct nucleosome footprints at Transcription Start Sites (TSSs) compared with normal donors. In order to identify the footprints of cfDNA corresponding with the responses to neoadjuvant chemotherapy in patients, we mapped on nucleosome positions on cfDNA of patients with different responses: responders (pretreatment, n = 28; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 12) and nonresponders (pretreatment, n = 10; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 10). The coverage depth near TSSs in plasma cfDNA differed significantly between responders and nonresponders at pretreatment, and also after neoadjuvant chemotherapy treatment cycles. We identified 232 TSSs with differential footprints at pretreatment and 321 after treatment and found enrichment in Gene Ontology terms such as cell growth inhibition, tumor suppressor, necrotic cell death, acute inflammatory response, T cell receptor signaling pathway, and positive regulation of vascular endothelial growth factor production. These results suggest that cfDNA nucleosome footprints may be used to predict the efficacy of neoadjuvant chemotherapy for breast cancer patients and thus may provide help in decision making for individual patients.

A Genotype Signature for Predicting Pathologic Complete Response in Locally Advanced Rectal Cancer.

PURPOSE: To construct and validate a predicting genotype signature for pathologic complete response (pCR) in locally advanced rectal cancer (PGS-LARC) after neoadjuvant chemoradiation. METHODS AND MATERIALS: Whole exome sequencing was performed in 15 LARC tissues. Mutation sites were selected according to the whole exome sequencing data and literature. Target sequencing was performed in a training cohort (n = 202) to build the PGS-LARC model using regression analysis, and internal (n = 76) and external validation cohorts (n = 69) were used for validating the results. Predictive performance of the PGS-LARC model was compared with clinical factors and between subgroups. The PGS-LARC model comprised 15 genes. RESULTS: The area under the curve (AUC) of the PGS model in the training, internal, and external validation cohorts was 0.776 (0.697-0.849), 0.760 (0.644-0.867), and 0.812 (0.690-0.915), respectively, and demonstrated higher AUC, accuracy, sensitivity, and specificity than cT stage, cN stage, carcinoembryonic antigen level, and CA19-9 level for pCR prediction. The predictive performance of the model was superior to clinical factors in all subgroups. For patients with clinical complete response (cCR), the positive prediction value was 94.7%. CONCLUSIONS: The PGS-LARC is a reliable predictive tool for pCR in patients with LARC and might be helpful to enable nonoperative management strategy in those patients who refuse surgery. It has the potential to guide treatment decisions for patients with different probability of tumor regression after neoadjuvant therapy, especially when combining cCR criteria and PGS-LARC.

Noninvasive inferring expressed genes and in vivo monitoring of the physiology and pathology of pregnancy using cell-free DNA.

BACKGROUND: Noninvasive monitoring of fetal development and the early detection of pregnancy-associated complications is challenging, largely because of the lack of information about the molecular spectrum during pregnancy. Recently, cell-free DNA in plasma was found to reflect the global nucleosome footprint and status of gene expression and showed potential for noninvasive health monitoring during pregnancy. OBJECTIVE: We aimed to test the relationships between plasma cell-free DNA profiles and pregnancy biology and evaluate the use of a cell-free DNA profile as a noninvasive method for physiological and pathologic status monitoring during pregnancy. STUDY DESIGN: We used genome cell-free DNA sequencing data generated from noninvasive prenatal testing in a total of 2937 pregnant women. For each physiological and pathologic condition, features of the cell-free DNA profile were identified using the discovery cohort, and support vector machine classifiers were built and evaluated using independent training and validation cohorts. RESULTS: We established nucleosome occupancy profiles at transcription start sites in different gestational trimesters, demonstrated the relationships between gene expression and cell-free DNA coverage at transcription start sites, and showed that the cell-free DNA profiles at transcription start sites represented the biological processes of pregnancy. In addition, using cell-free DNA data, nucleosome profiles of transcription factor binding sites were identified to reflect the transcription factor footprint, which may help to reveal the molecular mechanisms underlying pregnancy. Finally, by using machine-learning models on low-coverage noninvasive prenatal testing data, we evaluated the use of cell-free DNA nucleosome profiles for distinguishing gestational trimesters, fetal sex, and fetal trisomy 21 and highlighted its potential utility for predicting physiological and pathologic fetal conditions by using low-coverage noninvasive prenatal testing data. CONCLUSION: Our analyses profiled nucleosome footprints and regulatory networks during pregnancy and established a noninvasive proof-of-principle methodology for health monitoring during pregnancy.

Molecular epidemiology of Brucella abortus strains from cattle in Inner Mongolia, China.

Although the prevalence of brucellosis in Inner Mongolia Autonomous Region currently remains high, data available on the epidemiological of circulating Brucella abortus strains were limited. A total of 75 isolates obtained from cattle, sheep, and humans were analysed using both the classical method and multiple locus variable-number tandem repeat analysis (MLVA). There are at least three B. abortus biovars (1, 3 and 6) in this region, and B. abortus biovar 3 is the predominant one. Ten known MLVA-11 genotypes were identified, of which five genotypes (72, 75, 78, 82 and 210) were shared among strains from this study and others previously collected in two to seven different nations, suggesting that this population has multiple geographic origins. An MLVA-16 assay sorted the 75 B. abortus strains into two groups (I and II), 5 clusters (A-E) and 44 genotypes (GT1-44), with 26 unique genotypes represented by single isolates, indicating that these B. abortus brucellosis cases were not directly epidemiologically related. The remaining 18 shared genotypes (among a total of 47 isolates) were represented by two to eight isolates, suggesting that there were epidemiologically related pathogens from each shared genotype among the cases. Importantly, the cluster B1 branch including 22 cluster isolates with identical or similar genotypes confirmed the occurrence of a concentrated outbreak epidemic in the eastern region during 1988-1995. This work will contribute to better understanding of B. abortus brucellosis epidemiology in Inner Mongolia.

RBPTD: a database of cancer-related RNA-binding proteins in humans.

RNA-binding proteins (RBPs) play important roles in regulating the expression of genes involved in human physiological and pathological processes, especially in cancers. Many RBPs have been found to be dysregulated in cancers; however, there was no tool to incorporate high-throughput data from different dimensions to systematically identify cancer-related RBPs and to explore their causes of abnormality and their potential functions. Therefore, we developed a database named RBPTD to identify cancer-related RBPs in humans and systematically explore their functions and abnormalities by integrating different types of data, including gene expression profiles, prognosis data and DNA copy number variation (CNV), among 28 cancers. We found a total of 454 significantly differentially expressed RBPs, 1970 RBPs with significant prognostic value, and 53 dysregulated RBPs correlated with CNV abnormality. Functions of 26 cancer-related RBPs were explored by analysing high-throughput RNA sequencing data obtained by crosslinking immunoprecipitation, and the remaining RBP functions were predicted by calculating their correlation coefficient with other genes. Finally, we developed the RBPTD for users to explore functions and abnormalities of cancer-related RBPs to improve our understanding of their roles in tumorigenesis. Database URL: http: //www.rbptd.com.

Anammox and partial denitrification coupling: a review.

As a new wastewater biological nitrogen removal process, anammox and partial denitrification coupling not only plays a significant role in the nitrogen cycle, but also holds high engineering application value. Because anammox and some denitrifying bacteria are coupled under harsh living conditions, certain operating conditions and mechanisms of the coupling process are not clear; thus, it is more difficult to control the process, which is why the process has not been widely applied. This paper analyzes the research focusing on the coupling process in recent years, including anammox and partial denitrification coupling process inhibitors such as nitrogen (NH4 +, NO2 -), organics (toxic and non-toxic organics), and salts. The mechanism of substrate removal in anammox and partial denitrification coupling nitrogen removal is described in detail. Due to the differences in process methods, experimental conditions, and sludge choices between the rapid start-up and stable operation stages of the reactor, there are significant differences in substrate inhibition. Multiple process parameters (such as pH, temperature, dissolved oxygen, redox potential, carbon-to-nitrogen ratio, and sludge) can be adjusted to improve the coupling of anammox and partial denitrification to modify nitrogen removal performance.

A hMTR4-PDIA3P1-miR-125/124-TRAF6 Regulatory Axis and Its Function in NF kappa B Signaling and Chemoresistance.

BACKGROUND AND AIMS: DNA damage-induced NF-κB activation is a major obstacle to effective antitumour chemotherapy. Long noncoding RNAs (lncRNAs) that regulate chemoresistance of cancer cells remain largely unknown. This study aimed to characterize the lncRNAs that may affect chemotherapy sensitivity. APPROACH AND RESULTS: We found that lncRNA PDIA3P1 (protein disulfide isomerase family A member 3 pseudogene 1) was up-regulated in multiple cancer types and following treatment with DNA-damaging chemotherapeutic agents, like doxorubicin (Dox). Higher PDIA3P1 level was associated with poorer recurrence-free survival of human hepatocellular carcinoma (HCC). Both gain-of-function and loss-of-function studies revealed that PDIA3P1 protected cancer cells from Dox-induced apoptosis and allowed tumor xenografts to grow faster and to be more resistant to Dox treatment. Mechanistically, miR-125a/b and miR-124 suppressed the expression of tumor necrosis factor receptor-associated factor 6 (TRAF6), but PDIA3P1 bound to miR-125a/b/miR-124 and relieved their repression on TRAF6, leading to activation of the nuclear factor kappa B (NF-κB) pathway. Consistently, the effect of PDIA3P1 inhibition in promoting Dox-triggered apoptosis was antagonized by silencing the inhibitor of κBα (IκBα) or overexpressing TRAF6. Administration of BAY 11-7085, an NF-κB inhibitor attenuated PDIA3P1-induced resistance to Dox treatment in mouse xenografts. Moreover, up-regulation of PDIA3P1 was significantly correlated with elevation of TRAF6, phosphorylated p65, or NF-κB downstream anti-apoptosis genes in human HCC tissues. These data indicate that enhanced PDIA3P1 expression may confer chemoresistance by acting as a microRNA sponge to increase TRAF6 expression and augment NF-κB signaling. Subsequent investigations into the mechanisms of PDIA3P1 up-regulation revealed that human homologue of mRNA transport mutant 4 (hMTR4), which promotes RNA degradation, could bind to PDIA3P1, and this interaction was disrupted by Dox treatment. Overexpression of hMTR4 attenuated Dox-induced elevation of PDIA3P1, whereas silencing hMTR4 increased PDIA3P1 level, suggesting that Dox may up-regulate PDIA3P1 by abrogating the hMTR4-mediated PDIA3P1 degradation. CONCLUSION: There exists a hMTR4-PDIA3P1-miR-125/124-TRAF6 regulatory axis that regulates NF-κB signaling and chemoresistance, which may be exploited for anticancer therapy.

ERK1/2-HNF4α axis is involved in epigallocatechin-3-gallate inhibition of HBV replication.

Epigallocatechin gallate (EGCG), a major polyphenol in green tea, exhibits diverse biological activities. Previous studies show that EGCG could effectively suppress HBV gene expression and replication, but the role of EGCG in HBV replication and its underlying mechanisms, especially the signaling pathways involved, remain unclear. In this study we investigated the mechanisms underlying EGCG inhibition on HBV replication with a focus on the signaling pathways. We showed that EGCG (12.5-50 µM) dose-dependently inhibited HBV gene expression and replication in HepG2.2.15 cells. Similar results were observed in HBV mice receiving EGCG (25 mg· kg-1· d-1, ip) for 5 days. In HepG2.2.15 cells, we showed that EGCG (12.5-50 µM) significantly activate ERK1/2 MAPK signaling, slightly activate p38 MAPK and JAK2/STAT3 signaling, while had no significant effect on the activation of JNK MAPK, PI3K/AKT/mTOR and NF-κB signaling. By using specific inhibitors of these signaling pathways, we demonstrated that ERK1/2 signaling pathway, but not other signaling pathways, was involved in EGCG-mediated inhibition of HBV transcription and replication. Furthermore, we showed that EGCG treatment dose-dependently decreased the expression of hepatocyte nuclear factor 4α (HNF4α) both at the mRNA and protein levels, which could be reversed by pretreatment with the ERK1/2 inhibitor PD98059 (20 µM). Moreover, we revealed that EGCG treatment dose-dependently inhibited the activity of HBV core promoter and the following HBV replication. In summary, our results demonstrate that EGCG inhibits HBV gene expression and replication, which involves ERK1/2-mediated downregulation of HNF4α.These data reveal a novel mechanism for EGCG to inhibit HBV gene expression and replication.

Translated Long Non-Coding Ribonucleic Acid ZFAS1 Promotes Cancer Cell Migration by Elevating Reactive Oxygen Species Production in Hepatocellular Carcinoma.

Micropeptides (≤100 amino acids) are essential regulators of physiological and pathological processes, which can be encoded by small open reading frames (smORFs) derived from long non-coding RNAs (lncRNAs). Recently, lncRNA-encoded micropeptides have been shown to have essential roles in tumorigenesis. Since translated smORF identification remains technically challenging, little is known of their pathological functions in cancer. Therefore, we created classifiers to identify translated smORFs derived from lncRNAs based on ribosome-protected fragment sequencing and machine learning methods. In total, 537 putative translated smORFs were identified and the coding potential of five smORFs was experimentally validated via green fluorescent protein-tagged protein generation and mass spectrometry. After analyzing 11 lncRNA expression profiles of seven cancer types, we identified one validated translated lncRNA, ZFAS1, which was significantly up-regulated in hepatocellular carcinoma (HCC). Functional studies revealed that ZFAS1 can promote cancer cell migration by elevating intracellular reactive oxygen species production by inhibiting nicotinamide adenine dinucleotide dehydrogenase expression, indicating that translated ZFAS1 may be an essential oncogene in the progression of HCC. In this study, we systematically identified translated smORFs derived from lncRNAs and explored their potential pathological functions in cancer to improve our comprehensive understanding of the building blocks of living systems.

SELER: a database of super-enhancer-associated lncRNA- directed transcriptional regulation in human cancers.

Super-enhancers (SEs) are enriched with a cluster of mediator binding sites, which are major contributors to cell-type-specific gene expression. Currently, a large quantity of long non-coding RNAs has been found to be transcribed from or to interact with SEs, which constitute super-enhancer associated long non-coding RNAs (SE-lncRNAs). These SE-lncRNAs play essential roles in transcriptional regulation through controlling SEs activity to regulate a broad range of physiological and pathological processes, especially tumorigenesis. However, the pathological functions of SE-lncRNAs in tumorigenesis are still obscure. In this paper, we characterized 5056 SE-lncRNAs and their associated genes by analysing 102 SE data sets. Then, we analysed their expression profiles and prognostic information derived from 19 cancer types to identify cancer-related SE-lncRNAs and to explore their potential functions. In total, 436 significantly differentially expressed SE-lncRNAs and 2035 SE-lncRNAs with high prognostic values were identified. Additionally, 3935 significant correlations between SE-lncRNAs and their regulatory genes were further validated by calculating their correlation coefficients in each cancer type. Finally, the SELER database incorporating the aforementioned data was provided for users to explore their physiological and pathological functions to comprehensively understand the blocks of living systems.