A Prognosis Marker Dynein Cytoplasmic 1 Heavy Chain 1 Correlates with EMT and Immune Signature in Liver Hepatocellular Carcinoma by Bioinformatics and Experimental Analysis

Transcriptomic screening of novel targets of sericin in human hepatocellular carcinoma cells

Sericin, a natural protein derived from Bombyx mori, is known to ameliorate liver tissue damage; however, its molecular mechanism remains unclear. Herein, we aimed to identify the possible novel targets of sericin in hepatocytes and related cellular pathways. RNA sequencing analysis indicated that a low dose of sericin resulted in 18 differentially expressed genes (DEGs) being upregulated and 68 DEGs being downregulated, while 61 DEGs were upregulated and 265 DEGs were downregulated in response to a high dose of sericin (FDR ≤ 0.05, fold change > 1.50). Functional analysis revealed that a low dose of sericin regulated pathways associated with the complement and coagulation cascade, metallothionine, and histone demethylate (HDMs), whereas a high dose of sericin was associated with pathways involved in lipid metabolism, mitogen-activated protein kinase (MAPK) signaling and autophagy. The gene network analysis highlighted twelve genes, A2M, SERPINA5, MT2A, MT1G, MT1E, ARID5B, POU2F1, APOB, TRAF6, HSPA8, FGFR1, and OGT, as novel targets of sericin. Network analysis of transcription factor activity revealed that sericin affects NFE2L2, TFAP2C, STAT1, GATA3, CREB1 and CEBPA. Additionally, the protective effects of sericin depended on the counterregulation of APOB, POU2F1, OGT, TRAF6, and HSPA5. These findings suggest that sericin exerts hepatoprotective effects through diverse pathways at different doses, providing novel potential targets for the treatment of liver diseases.

Screening for MicroRNA combination with engineered exosomes as a new tool against osteosarcoma in elderly patients

The most common primary malignant bone sarcoma is Osteogenic sarcoma (OS) which has a bimodal age distribution. Unfortunately, the treatment of OS was less effective for elderly patients than for younger ones. The study aimed to explore a new microRNA (miRNA) which can bind to combining engineered exosomes for treatment of older OS patients. Based on GSE65071 and miRNet 2.0, two up-regulated miRNAs (miR-328, miR-107) and seven down-regulated miRNAs (miR-133b, miR-206, miR-1-3p, miR-133a, miR-449a, miR-181daysay, miR-134) were selected. Next, we used FunRich software to predict the up-stream transcription factors (TFs) of differentially expressed miRNAs (DE-miRNAs). By comparing target genes predicted from DE-miRNAs with differentially expressed genes, we identified 12 down-regulated and 310 up-regulated mRNAs. For KEGG analysis, the most enriched KEGG pathway was Cell cycle, Spliceosome, and Protein digestion and absorption. By using protein-protein interactions network, topological analysis algorithm and GEPIA database, miR-449a /CCNB1 axis was identified. Experiments in vitro were conducted to confirm the results too. MiRNA-449a is down-regulated in osteosarcoma and suppresses cell proliferation by targeting CCNB1. Our findings not only reveal a novel mechanism of miR-449a /CCNB1 in OS but also had laid the groundwork for further investigation and analysis in the field of exosome engineering.

Mechanisms of Xiaochaihu Decoction on Treating Hepatic Fibrosis Explored by Network Pharmacology

Purpose. To explore the material basis and pharmacological mechanism of Xiaochaihu Decoction (XCHD), the classic Traditional Chinese Medicine (TCM) formula in inhibiting hepatic fibrosis (HF). Methods. The main components in XCHD were screened from the TCMSP database, ETCM database, and literature, and their potential targets were detected and predicted using the Swiss Target Prediction platform. The HF-related targets were retrieved and screened through GeneCard database and OMIM database, combined with GEO gene chips. The XCHD targets and HF targets were mapped to search common targets. The protein-protein interaction (PPI) network was acquired via the STRING11.0 database and analyzed visually using Cytoscape 3.8.0 software. The potential mechanisms of the common targets identified through GO and KEGG pathway enrichment analysis were analyzed by using Metascape database. The results were visualized through OmicShare Tools. The “XCHD compound-HF target” network was visually constructed by Cytoscape 3.8.0 software. AutoDockVina1.1.2 and PyMoL software were used to verify the molecular docking of XCHD main active compounds and HF key targets. Results. A total of 164 potential active compounds from XCHD were screened to act on 95 HF-related targets. Bioinformatics analysis revealed that quercetin, β-sitosterol, and kaempferol may be candidate agents, which acted on multiple targets like PTGS2, HSP90AA1, and PTGS1 and regulate multiple key biological pathways like IL-17 signaling pathway, TNF signaling pathway and PI3K-Akt signaling pathway to relieve HF. Moreover, molecular docking suggested that quercetin and PTGS2 could statically bind and interact with each other through amino acid residues val-349, LEU-352, PHE-381, etc. Conclusion. This work provides a systems perspective to study the relationship between Chinese medicines and diseases. The therapeutic efficacy of XCHD on HF was the sum of multitarget and multi-approach effects from the bioactive ingredients. This study could be one of the cornerstones for further research.

Integrated Bioinformatics and Experimental Analysis Identified TRIM28 a Potential Prognostic Biomarker and Correlated with Immune Infiltrates in Liver Hepatocellular Carcinoma

Background

Since the 1970s, liver hepatocellular carcinoma (LIHC) has experienced a constant rise in incidence and mortality rates, making the identification of LIHC biomarkers very important. Tripartite Motif-Containing 28 (TRIM28) is a protein-coding gene which encodes the tripartite motif-containing proteins (TRIMs) family and is associated with specific chromatin regions. TRIM28 expression and its prognostic value and impact on the immune system in LIHC patients are being investigated for the first time.

Methods

The TRIM28 expression data from TCGA database was used to analyze TRIM28 expression, clinicopathological information, gene enrichment, and immune infiltration and conduct additional bioinformatics analysis. R language was used for statistical analysis. TIMER, CIBERSORT, and ssGSEA were used to assess immune responses of TRIM28 in LIHC. Next, the results were validated using GEPIA, ROC analysis, and immunohistochemical staining pictures from the THPA. GSE14520, GSE63898, and GSE87630 datasets were analyzed using ROC analysis to further evaluate TRIM28's diagnostic value. To ultimately determine TRIM28 expression, we performed qRT-PCR (quantitative real-time polymerase chain reaction).

Results

High TRIM28 expression level was associated with T classification, pathologic stage, histologic grade, and serum AFP levels. In patients with LIHC, TRIM28 was an independent risk factor for a poor prognosis. The pathways ligand-receptor interaction, which is critical in LIHC patients, were closely associated with TRIM28 expression, and the function of DC could be suppressed by overexpression of TRIM28. As a final step, our results were validated by GEO data and qRT-PCR.

Conclusions

TRIM28 will shed new light on LIHC mechanisms. As an effective diagnostic and intervention tool, this gene will be able to diagnose and treat LIHC at an early stage.

Identification of hub genes for early detection of bone metastasis in breast cancer

BACKGROUND

Globally, among all women, the most frequently detected and diagnosed and the most lethal type of cancer is breast cancer (BC). In particular, bone is one of the most frequent distant metastases 24in breast cancer patients and bone metastasis arises in approximately 80% of advanced patients. Thus, we need to identify and validate early detection markers that can differentiate metastasis from non-metastasis breast cancers.

METHODS

GSE55715, GSE103357, and GSE146661 gene expression profiling data were downloaded from the GEO database. There was 14 breast cancer with bone metastasis samples and 8 breast cancer tissue samples. GEO2R was used to screen for differentially expressed genes (DEGs). The volcano plots, Venn diagrams, and annular heatmap were generated by using the ggplot2 package. By using the cluster Profiler R package, KEGG and GO enrichment analyses of DEGs were conducted. Through PPI network construction using the STRING database, key hub genes were identified by cytoHubba. Finally, K-M survival and ROC curves were generated to validate hub gene expression.

RESULTS

By GO enrichment analysis, 143 DEGs were enriched in the following GO terms: extracellular structure organization, extracellular matrix organization, leukocyte migration class II protein complex, collagen tridermic protein complex, extracellular matrix structural constituent, growth factor binding, and platelet-derived growth factor binding. In the KEGG pathway enrichment analysis, DEGs were enriched in Staphylococcus aureus infection, Complement and coagulation cascades, and Asthma. By PPI network analysis, we selected the top 10 genes, including SLCO2B1, STAB1, SERPING1, HLA-DOA, AIF1, GIMAP4, C1orf162, HLA-DMB, ADAP2, and HAVCR2. By using TCGA and THPA databases, we validated 2 genes, SERPING1 and GIMAP4, that were related to the early detection of bone metastasis in BC.

CONCLUSIONS

2 abnormally expressed hub genes could play a pivotal role in the breast cancer with bone metastasis by affecting bone homeostasis imbalance in the bone microenvironment.