Identification of 5 Hub Genes Related to the Early Diagnosis, Tumour Stage, and Poor Outcomes of Hepatitis B Virus-Related Hepatocellular Carcinoma by Bioinformatics Analysis

Targeting Cyclin-Dependent Kinase 1 Induces Apoptosis and Cell Cycle Arrest of Activated Hepatic Stellate Cells

Liver fibrosis is the integral process of chronic liver diseases caused by multiple etiologies and characterized by excessive deposition of extracellular matrix (ECM). During liver fibrosis, hepatic stellate cells (HSCs) transform into a highly proliferative, activated state, producing various cytokines, chemokines, and ECM. However, the precise mechanisms that license HSCs into the highly proliferative state remain unclear. Cyclin-dependent kinase 1 (CDK1) is a requisite event for the transition of the G1/S and G2/M phases in eukaryotic cells. In this study, it is demonstrated that CDK1 and its activating partners, Cyclin A2 and Cyclin B1, are upregulated in both liver fibrosis/cirrhosis patient specimens and the murine hepatic fibrosis models, especially in activated HSCs. In vitro, CDK1 is upregulated in spontaneously activated HSCs, and inhibiting CDK1 with specific small-molecule inhibitors (CGP74514A, RO-3306, or Purvalanol A) orshort hairpin RNAs (shRNAs) resulted in HSC apoptosis and cell cycle arrest by regulating Survivin expression. Above all, it is illustrated that increased CDK1 expression licenses the HSCs into a highly proliferative state and can serve as a potential therapeutic target in liver fibrosis.

Identification of metabolic biomarkers associated with nonalcoholic fatty liver disease

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) is the most common liver disease. Metabolism-related genes significantly influence the onset and progression of the disease. Hence, it is necessary to screen metabolism-related biomarkers for the diagnosis and treatment of NAFLD patients.

METHODS

GSE48452, GSE63067, and GSE89632 datasets including nonalcoholic steatohepatitis (NASH) and healthy controls (HC) analyzed in this study were retrieved from the Gene Expression Omnibus (GEO) database. First, differentially expressed genes (DEGs) between NASH and HC samples were obtained. Next, metabolism-related DEGs (MR-DEGs) were identified by overlapping DEGs and metabolism-related genes (MRG). Further, a protein-protein interaction (PPI) network was developed to show the interaction among MR-DEGs. Subsequently, the "Least absolute shrinkage and selection operator regression" and "Random Forest" algorithms were used to screen metabolism-related genes (MRGs) in patients with NAFLD. Next, immune cell infiltration and gene set enrichment analyses (GSEA) were performed on these metabolism-related genes. Finally, the expression of metabolism-related gene was determined at the transcription level.

RESULTS

First, 129 DEGs related to NAFLD development were identified among patients with nonalcoholic steatohepatitis (NASH) and healthy control. Next, 18 MR-DEGs were identified using the Venn diagram. Subsequently, four genes, including AMDHD1, FMO1, LPL, and P4HA1, were identified using machine learning algorithms. Moreover, a regulatory network consisting of four genes, 25 microRNAs (miRNAs), and 41 transcription factors (TFs) was constructed. Finally, a significant increase in FMO1 and LPL expression levels and a decrease in AMDHD1 and P4HA1 expression levels were observed in patients in the NASH group compared to the HC group.

CONCLUSION

Metabolism-related genes associated with NAFLD were identified, containing AMDHD1, FMO1, LPL, and P4HA1, which provide insights into diagnosing and treating patients with NAFLD.

Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning

Hepatocellular carcinoma (HCC) is the most common lethal malignancy of the liver worldwide. Thus, it is important to dig the key genes for uncovering the molecular mechanisms and to improve diagnostic and therapeutic options for HCC. This study aimed to encompass a set of statistical and machine learning computational approaches for identifying the key candidate genes for HCC. Three microarray datasets were used in this work, which were downloaded from the Gene Expression Omnibus Database. At first, normalization and differentially expressed genes (DEGs) identification were performed using limma for each dataset. Then, support vector machine (SVM) was implemented to determine the differentially expressed discriminative genes (DEDGs) from DEGs of each dataset and select overlapping DEDGs genes among identified three sets of DEDGs. Enrichment analysis was performed on common DEDGs using DAVID. A protein-protein interaction (PPI) network was constructed using STRING and the central hub genes were identified depending on the degree, maximum neighborhood component (MNC), maximal clique centrality (MCC), centralities of closeness, and betweenness criteria using CytoHubba. Simultaneously, significant modules were selected using MCODE scores and identified their associated genes from the PPI networks. Moreover, metadata were created by listing all hub genes from previous studies and identified significant meta-hub genes whose occurrence frequency was greater than 3 among previous studies. Finally, six key candidate genes (TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C) were determined by intersecting shared genes among central hub genes, hub module genes, and significant meta-hub genes. Two independent test datasets (GSE76427 and TCGA-LIHC) were utilized to validate these key candidate genes using the area under the curve. Moreover, the prognostic potential of these six key candidate genes was also evaluated on the TCGA-LIHC cohort using survival analysis.

Cell cycle kinases (AUKA, CDK1, PLK1) are prognostic biomarkers and correlated with tumor-infiltrating leukocytes in HBV related HCC

Hepatocellular carcinoma (HCC) is one of the high incidence cancers and third leading cause of cancer-related mortality. HBV is the top most risk factor accounting for 50-80% of the HCC cases. Kinases: Aurora kinase A (AURKA), cyclin-dependent kinase (CDK1) and Polo-like kinase 1 (PLK1), the key regulators of cell mitosis are overexpressed in varieties of cancers including HCC. However, the exact role of these genes in prognosis of HCC is not fully unveiled. In addition, there is no such an accurate prognostic biomarker for HBV-related HCC. To address this issue, we performed a multidimensional analysis of AURKA, CDK1 and PLK1 with a series of publicly available databases in multiple cancers and with experimental validation in HBV-related HCC tissues. Overexpression of AURKA, CDK1 and PLK1 was found in multiple cancers including HCC. Elevated expression of these genes could result from lowered DNA methylation and genomic alterations. Transcriptional overexpression was significantly correlated with poor prognosis of HCC patients. The expression levels were also significantly positively associated with tumor grades and stages. Furthermore, the expression levels of these genes had a strong correlation with infiltration of immune cells. Our analysis shows that AURKA, CDK1 and PLK1 are correlated with immune infiltration and are the prognostic biomarkers for HBV-induced HCC.Communicated by Ramaswamy H. Sarma.

Bibliometric study of neuroinflammation in autism spectrum disorder

BACKGROUND

Neuroinflammation is closely associated with the occurrence and development of autism spectrum disorder (ASD). This study aims to describe the global development history and current status of neuroinflammation in ASD from 2004 to 2021 and reveal the research hotspots and frontiers to provide a reference for scholars in related fields to carry out further research.

METHODS

Journal articles on ASD and neuroinflammation-related research were obtained from the Web of Science Core Collection (WOSCC) database from its inception to 2021. Literature was analyzed visually by VOSviewer, CiteSpace, and R language, including publication analysis, author, institution, national/regional cooperative network analysis, and keyword analysis. We screened the most accumulatively cited 10 experimental papers in the field and the most cited 10 experimental papers in the last 2 years (2020 and 2021) for combing.

RESULTS

A total of 620 publications were included in this study, and the number of publications has increased in recent years. The United States (256, 41.29%) was the country with the largest number of publications. King Saud University (40, 6.45%) was the most published institution; Laila Al-Ayadhi Yousef was the most published researcher; the Brain Behavior and Immunity was the main journal for the study of neuroinflammation in autism, having published 22 related articles. Keyword co-occurrence analysis showed that short chain fatty acid, mast cells, and glial cells have been the focus of recent attention. Burst keywords show that gut microbiota and immune system are the future research trends.

CONCLUSION

This bibliometric study describes the basic framework for the development in the field of neuroinflammation and ASD through an exploration of key indicators (countries, institutions, journals, authors, and keywords). We found that the key role of neuroinflammation in the development of ASD is attracting more and more researchers' attention. Future studies can investigate the changes in cytokines and glial cells and their related pathways in ASD neuroinflammation. Immunotherapy to inhibit neuroinflammation may be intensively studied as a direction for ASD treatment or intervention.

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.

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.

N7-Methylguanosine Genes Related Prognostic Biomarker in Hepatocellular Carcinoma

Background: About 90% of liver cancer-related deaths are caused by hepatocellular carcinoma (HCC). N7-methylguanosine (m7G) modification is associated with the biological process and regulation of various diseases. To the best of our knowledge, its role in the pathogenesis and prognosis of HCC has not been thoroughly investigated.

Aim: To identify N7-methylguanosine (m7G) related prognostic biomarkers in HCC. Furthermore, we also studied the association of m7G–related prognostic gene signature with immune infiltration in HCC.

Methods: The TCGA datasets were used as a training and GEO dataset “GSE76427” for validation of the results. Statistical analyses were performed using the R statistical software version 4.1.2.

Results: Functional enrichment analysis identified some pathogenesis related to HCC. We identified 3 m7G-related genes (CDK1, ANO1, and PDGFRA) as prognostic biomarkers for HCC. A risk score was calculated from these 3 prognostic m7G-related genes which showed the high-risk group had a significantly poorer prognosis than the low-risk group in both training and validation datasets. The 3- and 5-years overall survival was predicted better with the risk score than the ideal model in the entire cohort in the predictive nomogram. Furthermore, immune checkpoint genes like CTLA4, HAVCR2, LAG3, and TIGT were expressed significantly higher in the high-risk group and the chemotherapy sensitivity analysis showed that the high-risk groups were responsive to sorafenib treatment.

Conclusion: These 3 m7G genes related signature model can be used as prognostic biomarkers in HCC and a guide for immunotherapy and chemotherapy response. Future clinical study on this biomarker model is required to verify its clinical implications.

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

Background

Liver hepatocellular carcinoma (LIHC) has had a continuous increase in incidence and mortality rates over the last 40 years. Dynein Cytoplasmic 1 Heavy Chain 1 (DYNC1H1) is a protein coding gene which encodes the cytoplasmic dynein heavy chain family. This is the first investigation into the expression of DYNC1H1 and its mechanisms of action in LIHC patients.

Methods

Based on the DYNC1H1 expression data from the TCGA database, we performed the DYNC1H1 expression, clinicopathological data, gene enrichment, and immune infiltration analysis. TIMER and CIBERSORT were used to assess immune responses of DYNC1H1 in LIHC. GEPIA, K-M survival analysis, and immunohistochemical staining pictures from the THPA were used to validate the results. In order to evaluate the diagnostic value of DYNC1H1, GEO datasets were analyzed by using ROC analysis. And quantitative real-time polymerase chain reaction was also carried out to evaluate the expression of DYNC1H1.

Results

DYNC1H1 expression levels were associated with T classification, pathologic stage, histologic grade, and serum AFP levels. DYNC1H1 is an independent factor for a poor prognosis in patients with LIHC. Further study showed that high expression of DYNC1H1 was enriched in epithelial–mesenchymal transition (EMT) and the TGF β signaling pathway by GSEA analysis enrichment, indicating that DYNC1H1 might play a key role in the progression of CRC through EMT and immune response, which also had been validated by the experimental assays.

Conclusions

DYNC1H1 will provide a novel and important perspective for the mechanisms of LIHC by regulating EMT. This gene will be able to act as an efficacious tool for the early diagnosis and effective intervention of LIHC.

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.

CD137 Regulates Bone Loss via the p53 Wnt/β-Catenin Signaling Pathways in Aged Mice

Senile osteoporosis is a chronic skeletal disease, leading to increased bone brittleness and risk of fragile fractures. With the acceleration of population aging, osteoporosis has gradually become one of the most serious and prevalent problems worldwide. Bone formation is highly dependent on the proper osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs) in the bone marrow microenvironment, which is generated by the functional relationship among different cell types, including osteoblasts, adipogenic cells, and bone marrow stromal cells in the bone marrow. It is still not clear how osteoporosis is caused by its molecular mechanism. With aging, bone marrow is able to restrain osteogenesis. Discovering the underlying signals that oppose BMSC osteogenic differentiation from the bone marrow microenvironment and identifying the unusual changes in BMSCs with aging is important to elucidate possible mechanisms of senile osteoporosis. We used 3 gene expression profiles (GSE35956, GSE35957, and GSE35959) associated with osteoporosis. And a protein-protein interaction (PPI) network was also built to identify the promising gene CD137. After that, we performed in vivo experiments to verify its function and mechanism. In this experiment, we found that significant bone loss was observed in aged (18-month-old) mice compared with young (6-month-old) mice. The adipose tissue in bone marrow cavity from aged mice reached above 10 times more than young mice. Combining bioinformatics analysis and vivo experiments, we inferred that CD137 might be involved in the p53 and canonical Wnt/β-catenin signaling pathways and thereby influenced bone mass through regulation of marrow adipogenesis. Importantly, osteoporosis can be rescued by blocking CD137 signaling in vivo. Our research will contribute to our understanding not only of the pathogenesis of age-related bone loss but also to the identification of new targets for treating senile osteoporosis.