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"Wang, Ping"
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HOTAIR contributes to the growth of liver cancer via targeting miR-217
Non-coding RNAs are important in the progression of liver cancer. The present study aimed to investigate the effects of long non-coding RNA HOX transcript antisense RNA (HOTAIR) on the proliferation of liver cancer and the association between HOTAIR and microRNA (miR)-217. It was demonstrated that the expression of HOTAIR was upregulated in liver cancer tissues and 3 liver cancer cell lines (MHCC97H, HepG2 and Hep3B). Inhibition of HOTAIR with HOTAIR small interfering (si) RNA lentiviral vectors significantly suppressed the cell proliferation of HepG2 cells, and downregulated the protein expression levels of two proliferation markers, Ki67 and proliferating cell nuclear antigen (PCNA). Furthermore, inhibition of HOTAIR induced G0/G1 cycle arrest by increasing the expression of p27 and decreasing the expression of cyclin D1. It was then predicted and verified that miR-217 was the target of HOTAIR. Expression of miR-217 was downregulated in liver cancer tissues and the 3 liver cancer cell lines. Further results revealed that inhibition of HOTAIR markedly upregulated the expression of miR-217 in HepG2 cells, and miR-217 inhibitor-induced reduction of miR-217 was significantly suppressed by HOTAIR inhibition. Furthermore, the increased cell proliferation and growth, the upregulated expression of Ki67 and PCNA, and the reduced G0/G1 cycle arrest induced by miR-217 inhibitor were partly rescued by inhibition of HOTAIR. Finally, the in vivo experiment indicated that HOTAIR inhibition suppressed tumorigenesis, including the smaller tumor volume and the reduced levels of Ki67. Overall, HOTAIR contributes to the proliferation and growth of liver cancer via downregulation of miR-217.
Journal Article
Antidepressant-Like Effect and Mechanism of Action of Honokiol on the Mouse Lipopolysaccharide (LPS) Depression Model
There is growing evidence that neuroinflammation is closely linked to depression. Honokiol, a biologically active substance extracted from Magnolia officinalis, which is widely used in traditional Chinese medicine, has been shown to exert significant anti-inflammatory effects and improve depression-like behavior caused by inflammation. However, the specific mechanism of action of this activity is still unclear. In this study, the lipopolysaccharide (LPS) mouse model was used to study the effect of honokiol on depression-like behavior induced by LPS in mice and its potential mechanism. A single administration of LPS (1 mg/kg, intraperitoneal injection) increased the immobility time in the forced swimming test (FST) and tail suspension test (TST), without affecting autonomous activity. Pretreatment with honokiol (10 mg/kg, oral administration) for 11 consecutive days significantly improved the immobility time of depressed mice in the FST and TST experiments. Moreover, honokiol ameliorated LPS-induced NF-κB activation in the hippocampus and significantly reduced the levels of the pro-inflammatory cytokines; tumor necrosis factor α (TNF-α), interleukin 1β (IL-1β), and interferon γ (IFN-γ). In addition, honokiol inhibited LPS-induced indoleamine 2,3-dioxygenase (IDO) activation and quinolinic acid (a toxic product) increase and reduced the level of free calcium in brain tissue, thereby inhibiting calcium overload. In summary, our results indicate that the anti-depressant-like effects of honokiol are mediated by its anti-inflammatory effects. Honokiol may inhibit the LPS-induced neuroinflammatory response through the NF-κB signaling pathway, reducing the levels of related pro-inflammatory cytokines, and furthermore, this may affect tryptophan metabolism and increase neuroprotective metabolites.
Journal Article
Mental Health and Psychosocial Problems of Medical Health Workers during the COVID-19 Epidemic in China
Objective: We explored whether medical health workers had more psychosocial problems than nonmedical health workers during the COVID-19 outbreak. Methods: An online survey was run from February 19 to March 6, 2020; a total of 2,182 Chinese subjects participated. Mental health variables were assessed via the Insomnia Severity Index (ISI), the Symptom Check List-revised (SCL-90-R), and the Patient Health Questionnaire-4 (PHQ-4), which included a 2-item anxiety scale and a 2-item depression scale (PHQ-2). Results: Compared with nonmedical health workers (n = 1,255), medical health workers (n = 927) had a higher prevalence of insomnia (38.4 vs. 30.5%, p < 0.01), anxiety (13.0 vs. 8.5%, p < 0.01), depression (12.2 vs. 9.5%; p< 0.04), somatization (1.6 vs. 0.4%; p < 0.01), and obsessive-compulsive symptoms (5.3 vs. 2.2%; p < 0.01). They also had higher total scores of ISI, GAD-2, PHQ-2, and SCL-90-R obsessive-compulsive symptoms (p ≤ 0.01). Among medical health workers, having organic disease was an independent factor for insomnia, anxiety, depression, somatization, and obsessive-compulsive symptoms (p < 0.05 or 0.01). Living in rural areas, being female, and being at risk of contact with COVID-19 patients were the most common risk factors for insomnia, anxiety, obsessive-compulsive symptoms, and depression (p < 0.01 or 0.05). Among nonmedical health workers, having organic disease was a risk factor for insomnia, depression, and obsessive-compulsive symptoms (p < 0.01 or 0.05). Conclusions: During the COVID-19 outbreak, medical health workers had psychosocial problems and risk factors for developing them. They were in need of attention and recovery programs.
Journal Article
Gut Microbiota-brain Axis
Objective: To systematically review the updated information about the gut microbiota-brain axis.
Data Sources: All articles about gut microbiota-brain axis published up to July 18, 2016, were identified through a literature search on PubMed, ScienceDirect, and Web of Science, with the keywords of \"gut microbiota\", \"gut-brain axis\", and \"neuroscience\".
Study Selection: All relevant articles on gut microbiota and gut-brain axis were included and carefully reviewed, with no limitation of study design.
Results: It is well-recognized that gut microbiota affects the brain's physiological, behavioral, and cognitive functions although its precise mechanism has not yet been fully understood. Gut microbiota-brain axis may include gut microbiota and their metabolic products, enteric nervous system, sympathetic and parasympathetic branches within the autonomic nervous system, neural-immune system, neuroendocrine system, and central nervous system. Moreover, there may be five communication routes between gut microbiota and brain, including the gut-brain's neural network, neuroendocrine-hypothalamic-pituitary-adrenal axis, gut immune system, some neurotransmitters and neural regulators synthesized by gut bacteria, and barrier paths including intestinal mucosal barrier and blood-brain barrier. The microbiome is used to define the composition and functional characteristics of gut microbiota, and metagenomics is an appropriate technique to characterize gut microbiota.
Conclusions: Gut microbiota-brain axis refers to a bidirectional information network between the gut microbiota and the brain, which may provide a new way to protect the brain in the near future.
Journal Article
Integrating genetic and gene expression data in network-based stratification analysis of cancers
Background
Cancers are complex diseases that have heterogeneous genetic drivers and varying clinical outcomes. A critical area of cancer research is organizing patient cohorts into subtypes and associating subtypes with clinical and biological outcomes for more effective prognosis and treatment. Large-scale studies have collected a plethora of omics data across multiple tumor types, providing an extensive dataset for stratifying patient cohorts. Network-based stratification (NBS) approaches have been presented to classify cancer tumors using somatic mutation data. A challenge in cancer stratification is integrating omics data to yield clinically meaningful subtypes. In this study, we investigate a novel approach to the NBS framework by integrating somatic mutation data with RNA sequencing data and investigating the effectiveness of integrated NBS on three cancers: ovarian, bladder, and uterine cancer.
Results
We show that integrated NBS subtypes are more significantly associated with overall survival or histology. Specifically, we observe that integrated NBS subtypes for ovarian and bladder cancer were more significantly associated with patient survival than single-data type NBS subtypes, even when accounting for covariates. In addition, we show that integrated NBS subtypes for bladder and uterine are more significantly associated with tumor histology than single-data type NBS subtypes. Integrated NBS networks also reveal highly influential genes that span across multiple integrated NBS subtypes and subtype-specific genes. Pathway enrichment analysis of integrated NBS subtypes reveal overarching biological differences between subtypes. These genes and pathways are involved in a heterogeneous set of cell functions, including ubiquitin homeostasis, p53 regulation, cytokine and chemokine signaling, and cell proliferation, emphasizing the importance of identifying not only cancer-specific gene drivers but also subtype-specific tumor drivers.
Conclusions
Our study highlights the significance of integrating multi-omics data within the NBS framework to enhance cancer subtyping, specifically its utility in offering profound implications for personalized prognosis and treatment strategies. These insights contribute to the ongoing advancement of computational subtyping methods to uncover more targeted and effective therapeutic treatments while facilitating the discovery of cancer driver genes.
Journal Article