Establishment and bioinformatics evaluation of the ethanol combined with palmitic acid-induced mouse hepatocyte AFLD model (the Hu-Qiu Model).

Chronic alcoholic liver disease has brought great harm to human health. Alcoholic fatty liver disease is the first stage in the progression of all chronic alcoholic liver diseases. At present, there is no cell model that fully matches the etiology (high-fat diet + alcohol) of human alcoholic fatty liver disease. We used 100 mM ethanol +6.25 µM PA to establish the ethanol combined with PA-induced mouse hepatocyte AFLD model (EP-AFLD hepatocyte model) and performed the RNA-seq transcriptome sequencing. Through bioinformatics analysis and comparison, we discovered that the EP-AFLD hepatocyte model was more suitable for studying the pathological mechanism of AFLD than the mouse AFLD hepatocyte model induced by ethanol alone. And through bioinformatics analysis, we further discovered that 77 genes from the differential expression gene set of EP-AFLD hepatocyte model were engaged in the pathological process of mouse AFLD and 40 genes were involved in the pathogenesis of both mouse AFLD and human AFLD. In this study, a novel mouse hepatocyte AFLD model was successfully established by combining ethanol and PA, which can be used to study the molecular mechanism of the pathogenesis of AFLD in mice or humans. This study will provide a brand-new in vitro experimental platform for the in-depth study of AFLD pathogenesis and the screening of AFLD therapeutic drugs.

Epiregulin increases stemness-associated genes expression and promotes chemoresistance of non-small cell lung cancer via ERK signaling.

BACKGROUND: Chemoresistance often causes the failure of treatment and death of patients with advanced non-small-cell lung cancer. However, there is still no resistance genes signature and available enriched signaling derived from a comprehensive RNA-Seq data analysis of lung cancer patients that could act as a therapeutic target to re-sensitize the acquired resistant cancer cells to chemo-drugs. Hence, in this study, we aimed to identify the resistance signature for clinical lung cancer patients and explore the regulatory mechanism. METHOD: Analysis of RNA-Seq data from clinical lung cancer patients was conducted in R studio to identify the resistance signature. The resistance signature was validated by survival time of lung cancer patients and qPCR in chemo-resistant cells. Cytokine application, small-interfering RNA and pharmacological inhibition approaches were applied to characterize the function and molecular mechanism of EREG and downstream signaling in chemoresistance regulation via stemness. RESULTS: The RTK and vitamin D signaling were enriched among resistance genes, where 6 genes were validated as resistance signature and associated with poor survival in patients. EREG/ERK signaling was activated by chemo-drugs in NSCLC cells. EREG protein promoted the NSCLC resistance to chemo-drugs by increasing stemness genes expression. Additionally, inhibition of EREG/ErbB had downregulated ERK signaling, resulting in decreased expression of stemness-associated genes and subsequently re-sensitized the resistant NSCLC cells and spheres to chemo-drugs. CONCLUSIONS: These findings revealed 6 resistance genes signature and proved that EREG/ErbB regulated the stemness to maintain chemoresistance of NSCLC via ERK signaling. Therefore, targeting EREG/ErbB might significantly and effectively resolve the chemoresistance issue.

Apigenin Inhibits the Growth of Hepatocellular Carcinoma Cells by Affecting the Expression of microRNA Transcriptome.

BACKGROUND: Apigenin, as a natural flavonoid, has low intrinsic toxicity and has potential pharmacological effects against hepatocellular carcinoma (HCC). However, the molecular mechanisms involving microRNAs (miRNAs) and their target genes regulated by apigenin in the treatment of HCC have not been addressed. OBJECTIVE: In this study, the molecular mechanisms of apigenin involved in the prevention and treatment of HCC were explored in vivo and in vitro using miRNA transcriptomic sequencing to determine the basis for the clinical applications of apigenin in the treatment of HCC. METHODS: The effects of apigenin on the proliferation, cell cycle progression, apoptosis, and invasion of human hepatoma cell line Huh7 and Hep3B were studied in vitro, and the effects on the tumorigenicity of Huh7 cells were assessed in vivo. Then, a differential expression analysis of miRNAs regulated by apigenin in Huh7 cells was performed using next-generation RNA sequencing and further validated by qRT-PCR. The potential genes targeted by the differentially expressed miRNAs were identified using a curated miRTarBase miRNA database and their molecular functions were predicted using Gene Ontology and KEGG signaling pathway analysis. RESULTS: Compared with the control treatment group, apigenin significantly inhibited Huh7 cell proliferation, cell cycle, colony formation, and cell invasion in a concentration-dependent manner. Moreover, apigenin reduced tumor growth, promoted tumor cell necrosis, reduced the expression of Ki67, and increased the expression of Bax and Bcl-2 in the xenograft tumors of Huh7 cells. Bioinformatics analysis of the miRNA transcriptome showed that hsa-miR-24, hsa-miR-6769b-3p, hsa-miR-6836-3p, hsa-miR-199a-3p, hsa-miR-663a, hsa-miR-4739, hsa-miR-6892-3p, hsa-miR-7107-5p, hsa-miR-1273g-3p, hsa-miR-1343, and hsa-miR-6089 were the most significantly up-regulated miRNAs, and their key gene targets were MAPK1, PIK3CD, HRAS, CCND1, CDKN1A, E2F2, etc. The core regulatory pathways of the up-regulated miRNAs were associated with the hepatocellular carcinoma pathway. The down-regulated miRNAs were hsa-miR-181a-5p and hsa-miR-148a-3p, and the key target genes were MAPK1, HRAS, STAT3, FOS, BCL2, SMAD2, PPP3CA, IFNG, MET, and VAV2, with the core regulatory pathways identified as proteoglycans in cancer pathway. CONCLUSION: Apigenin can inhibit the growth of HCC cells, which may be mediated by up-regulation or down-regulation of miRNA molecules and their related target genes.

Association of SMAD4 loss with drug resistance in clinical cancer patients: A systematic meta-analysis.

BACKGROUND: Drug resistance frequently led to the failure of chemotherapy for malignant cancers, hence causing cancer relapse. Thus, understanding mechanism of drug resistance in cancer is vital to improve the treatment efficacy. Here, we aim to evaluate the association between SMAD4 expression and the drug resistance in cancers by performing a meta-analysis. METHOD: Relevant studies detecting SMAD4 expression in cancer patients treated with chemo-drugs up till December 2020 were systematically searched in four common scientific databases using selected keywords. The pooled hazard ratio (HR) was the ratio of hazard rate between SMAD4neg population vs SMAD4pos population. The HRs and risk ratios (RRs) with 95% confidence intervals (CIs) were used to explore the association between SMAD4 expression losses with drug resistance in cancers. RESULT: After an initial screening according to the inclusion and exclusion criteria, eleven studies were included in the meta-analysis. There were a total of 2092 patients from all the included studies in this analysis. Results obtained indicated that loss of SMAD4 expression was significantly correlated with drug resistance with pooled HRs (95% CI) of 1.23 (1.01-1.45), metastasis with pooled RRs (95% CI) of 1.10 (0.97-1.25) and recurrence with pooled RRs (95% CI) of 1.32 (1.06-1.64). In the subgroup analysis, cancer type, drug type, sample size and antibody brand did not affect the significance of association between loss of SMAD4 expression and drug resistance. In addition, there was no evidence of publication bias as suggested by Begg's test. CONCLUSION: Findings from our meta-analysis demonstrated that loss of SMAD4 expression was correlated with drug resistance, metastasis and recurrence. Therefore, SMAD4 expression could be potentially used as a molecular marker for cancer resistance.

Analyzing the potential therapeutic mechanism of Huashi Baidu Decoction on severe COVID-19 through integrating network pharmacological methods.

BACKGROUND AND AIM: Huashi Baidu Decoction (HSBD) is a novel complex prescription which has positive effects on severe COVID-19. This study was aimed to discover key Chinese materia medica, main active compounds, hub therapeutic target proteins and core signal pathways in the potential therapeutic mechanism of HSBD on severe COVID-19 through integrating network pharmacological methods. EXPERIMENTAL PROCEDURE: TCMSP, TCMID and STITCH databases were used to screen out active compounds and target proteins of HSBD. GeneCards database was used to screen out disease genes of severe COVID-19. The potential therapeutic targets of HSBD on severe COVID-19 were used to construct protein-protein interaction network through STRING database and the hub target proteins were discovered. Next, GO and KEGG enrichment analysis were carried out to discover core signal pathways. Finally, the network diagram of "Chinese materia medica-active compounds-therapeutic target proteins" was built, then key Chinese materia medica and main active compounds were selected. RESULTS AND CONCLUSION: HSBD might treat severe COVID-19 through 45 potential target genes, among them, there were 13 hub target genes: RELA, TNF, IL6, IL1B, MAPK14, TP53, CXCL8, MAPK3, MAPK1, IL4, MAPK8, CASP8, STAT1. Meanswhile, GO_BiologicalProcess and KEGG signaling pathways analysis results showed that the core signal pathways were inflammation and immune regulation pathways. Finally, 4 key Chinese materia medica and 11 main active compounds were discovered in the HSBD. In conclusion, the therapeutic mechanism of HSBD on severe COVID-19 might involve its pharmacological effects of anti-inflammation and immune regulation via acting on 45 disease-related proteins of severe COVID-19. TAXONOMY CLASSIFICATION BY EVISE: Viral Pneumonia, COVID-19, Acute Respiratory Distress Syndrome, Septic Shock, Chinese Herbal Medicine.

The Study of Yin-Chen-Hao-Tang Preventing and Treating Alcoholic Fatty Liver Disease through PPAR Signaling Pathway Based on Network Pharmacology and RNA-Seq Transcriptomics.

BACKGROUND: Alcoholic fatty liver disease (AFLD) is the first stage of the alcoholic liver disease course. Yin-Chen-Hao-Tang (YCHT) has a good clinical effect on the treatment of AFLD, but its molecular mechanism has not been elucidated. In this study, we tried to explore the molecular mechanism of YCHT in improving hepatocyte steatosis in AFLD mice through network pharmacology and RNA sequencing (RNA-Seq) transcriptomics. METHODS: Network pharmacological methods were used to analyze the potential therapeutic signaling pathways and targets of YCHT on AFLD. Then, the AFLD mice model was induced and YCHT was administered concurrently. Liver injury was measured by serum alanine aminotransferase (ALT) activity and liver tissue H&E staining, and liver steatosis was determined by serum triglyceride (TG) level and liver tissue Oil Red staining. The molecular mechanism of YCHT on prevention and treatment of mice AFLD was investigated according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the differential expression genes data obtained by liver tissue RNA-Seq. Finally, ethanol-induced AFLD AML12 hepatocyte model was established, YCHT with or without PPARα agonist pemafibrate or PPARγ inhibitor GW9662 was administered, Nile Red fluorescent staining was used to evaluate steatosis levels in AML12 hepatocytes, and qRT-PCR was used to detect PPARα and PPARγ gene expression. RESULTS: The results of network pharmacology analysis showed that YCHT may exert its pharmacological effect on AFLD through 312 potential targets which are involved in many signaling pathways including the PPAR signaling pathway. AFLD mice experiments results showed that YCHT markedly decreased mice serum ALT activity and serum TG levels. YCHT also significantly improved alcohol-induced hepatic injury and steatosis in mice livers. Furthermore, KEGG pathway enrichment results of RNA-Seq showed that the PPAR signaling pathway should be the most relevant pathway of YCHT in the prevention and treatment of AFLD. AFLD hepatocyte model experiment results showed that YCHT could remarkably reduce hepatocyte steatosis through reducing PPARγ expression and increasing PPARα expression. CONCLUSIONS: Our study discovered that PPARγ and PPARα are the key targets and the PPAR signaling pathway is the main signaling pathway for YCHT to prevent and treat AFLD.