Network pharmacological analysis of active components of Xiaoliu decoction in the treatment of glioblastoma multiforme.

Background: Glioblastoma multiforme (GBM) is the most aggressive primary nervous system brain tumor. There is still a lack of effective methods to control its progression and recurrence in clinical treatment. It is clinically found that Xiaoliu Decoction (XLD) has the effect of treating brain tumors and preventing tumor recurrence. However, its mechanism is still unclear. Methods: Search the Traditional Chinese Medicine System Pharmacology Database (TCSMP) for efficient substances for the treatment of XLD in the treatment of GBM, and target the targeted genes of the effective ingredients to construct a network. At the same time, download GBM-related gene expression data from the TCGA and GTEX databases, screen differential expression bases, and establish a drug target disease network. Through bioinformatics analysis, the target genes and shared genes of the selected Chinese medicines are analyzed. Finally, molecular docking was performed to further clarify the possibility of XLD in multiple GBMs. Results: We screened 894 differentially expressed genes in GBM, 230 XLD active ingredients and 169 predicted targets of its active compounds, of which 19 target genes are related to the differential expression of GBM. Bioinformatics analysis shows that these targets are closely related to cell proliferation, cell cycle regulation, and DNA synthesis. Finally, through molecular docking, it was further confirmed that Tanshinone IIA, the active ingredient of XLD, was tightly bound to key proteins. Conclusion: To sum up, the results of this study suggest that the mechanism of XLD in the treatment of GBM involves multiple targets and signal pathways related to tumorigenesis and development. This study not only provides a new theoretical basis for the treatment of glioblastoma multiforme with traditional Chinese medicine, but also provides a new idea for the research and development of targeted drugs for the treatment of glioblastoma multiforme.

Bioinformatics analysis combined with experiments to verify potential autophagy genes in wound healing.

Background: The skin is the most exposed tissue and has multiple functions. Wound healing is a major medical problem due to trauma and pathophysiological alterations suffered by patients. The aim of the present study was to search for potential autophagy genes associated with wound healing. Methods: The GSE168760 dataset was obtained from the Gene Expression Omnibus (GEO) database, and sequencing results were obtained for 14 patient traumas at different time periods. Differentially expressed gene (DEG) analysis, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. Immune cell and correlation analysis were performed for autophagy genes and DEGs. Peripheral blood was collected from patients at different time periods and Western blot (WB) assay was performed to verify autophagy genes. Results: A total of 226 DEGs were screened on days 0, 7, and 14, of which 162 genes were upregulated and 64 genes were downregulated. Of these, eukaryotic translation initiation factor 2-alpha kinase 2 (EIF2AK2) and retinoblastoma 1 (RB1) were autophagy-associated genes. The DEGs were mainly involved in response to virus, cellular response to type I interferon Epstein-Barr virus infection, human papillomavirus infection, ribosome, hepatitis B and RIG-I-like. EIF2AK2 and RB1 showed positive correlation with some of the immune cells, and WB showed that EIF2AK2 and RB1 proteins were significantly increased with wound healing. Conclusions: The comprehensive analysis of GEO data in the present study provides a new theoretical basis for the molecular pathogenesis of trauma healing and potential autophagy-related therapeutic targets.

Constructing a competing endogenous RNA network for osteoarthritis.

Background: Osteoarthritis (OA) is one of the most common diseases in elderly people; however, the correlation between molecular alterations and the occurrence and progression of OA are still not well understood. We conducted this study to investigate the molecular changes in OA via the competing endogenous ribonucleic acid (ceRNA) network. Methods: We downloaded the messenger RNA (mRNA) data set, GSE48556, the microRNA (miRNA) data set, GSE105027, and the long non-coding (lncRNA) data set, GSE126963 from the Gene Expression Omnibus (GEO) database, and examined the differentially expressed genes (DEGs) in these data sets. Further, we constructed a ceRNA network of the differentially expressed miRNAs, mRNAs, and lncRNAs. To determine the biological functions of the ceRNA network, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. Finally, we conducted an immune cell infiltration analysisusing single-sample gene set enrichment analysis to examine the abundance of immune cells in healthy and OA patients, and compared the infiltration of 28 immune cells between the healthy and OA samples. We also analyzed the relationship between the abundance of immune cells and mRNA expression levels in the ceRNA network. Results: Ultimately hsa-mir-425-3p, dual specificity phosphatase 1, and 24 lncRNAs were identified in the ceRNA network. The functional enrichment analyses showed that these lncRNAs, miRNAs, and mRNAs are involved in various significant biological process, such as the regulation of leukocyte migration, Mitogen-Activated Protein (MAP) kinase tyrosine/serine/threonine phosphatase activity, the interleukin-17 signaling pathway, the tumor necrosis factor signaling pathway, and osteoclast differentiation, and can also have a strong effect on immune cell infiltration. Conclusions: The dual-specificity phosphatase 1-specific ceRNA network can be used as a diagnostic tool to assess the progression of OA patients.

IL-1B can serve as a healing process and is a critical regulator of diabetic foot ulcer.

Background: Diabetic foot ulcer (DFU) is the main cause of disability in diabetic patients. However, the molecular changes underlying the occurrence and progression of DFU remain unclear. We conducted this study to examine gene alterations in different DFU patients. Methods: GSE143735 and GSE134431 transcriptome data sets were acquired from the Gene Expression Omnibus database, and differential expression analyses of the genes in these data sets were performed. A functional enrichment analysis of the differentially expressed genes (DEGs) was performed using clusterProfiler package in R. To examine the correlations between DEGs and significant immune-related genes, we identified the intersecting ulcer-related DEGs, healing-related DEGs, and immune-related DEGs. Finally, we further investigate the relationship between the selected genes with immune cell regulation via a single-sample gene set enrichment analysis, and the infiltration of 28 immune cells in common diabetes samples, unhealed DFU samples, and healed samples DFU were compared. Results: We found 238 upregulated genes and 207 downregulated genes in the diabetic foot (DF) patients with ulcers compared to the DF patients without ulcers, and 74 upregulated genes and 28 downregulated genes in the healed samples compared to the unhealed samples. To examine the main biological functions, we conducted a functional enrichment analysis. The results showed that the biological functions of functional enrichment analysis included neutrophil degranulation, leukocyte chemotaxis, myeloid leukocyte migration, phagosome, cytokine-cytokine receptor interaction, and the chemokine signaling pathway. Interleukin (IL)-1B was more highly expressed in patients with ulcers and healed DFU patients than those without ulcers and unhealed DFU patients. Finally, the immune cell abundance difference results showed that activated cluster of differentiation (CD)8 T cells, central memory CD8 T cells, T follicular helper cells, myeloid-derived suppressor cells, natural killer T cells and monocytes were more highly infiltrated in normal diabetes patients and healed DFU patients than unhealed DFU patients. However, no difference was found between DF patients with and without ulcers. Conclusions: IL-1B is an inflammation gene that can be used to assess and regulate DFU progression.

Key immune-related gene ITGB2 as a prognostic signature for acute myeloid leukemia.

BACKGROUND: The tumor microenvironment (TME) has an essential role in tumorigenesis, progression, and therapeutic response in many cancers. Currently, the role of TME in acute myeloid leukemia (AML) is unclear. This study investigated the correlation between immune-related genes and prognosis in AML patients. METHODS: Transcriptome RNA-Seq data for 151 AML samples were downloaded from TCGA database (https://portal.gdc.cancer.gov/), and the immune related genes (irgs) were selected from Immport database. Bioinformatics screening was used to identify irgs for AML, and genes with a critical role in the prognosis of AML were selected for further analysis. To confirm the prognostic role of irgs in AML, we undertook protein-protein interaction (PPI) network analysis of the top 30 interacting genes. We then investigated associations between immune cell infiltration and prognosis in AML patients. Immunohistochemistry was used to validate protein expression levels between AML and normal bone marrow samples. Analysis of the drug sensitivity of the selected gene was then performed. RESULTS: The integrin lymphocyte function-associated antigen 1 (CD11A/CD18; ITGAL/ITGB2) was identified as the key immune-related gene that significantly influenced prognosis in AML patients. Overexpression of ITGB2 indicated poor prognosis in AML patients (P=0.007). Risk modeling indicated that a high-risk score led to poor outcomes (P=3.076e-08) in AML patients. The risk model showed accuracy for predicting prognosis in AML patients, with area under curve (AUC) at 1 year, 0.816; AUC at 3 years, 0.82; and AUC at 5 years, 0.875. In addition, we found that ITGB2 had a powerful influence on immune cell infiltration into AML TME. The results of immunohistochemistry showed that AML patients had significantly higher ITGB2 protein expression than normal samples. The AML patients were divided into 2 groups based on ITGB2 risk scores. Drug sensitivity test results indicated that the high-risk group was sensitive to cytarabine, axitinib, bosutinib, and docetaxel, but resistant to cisplatin and bortezomib. CONCLUSIONS: In the present study, we found that ITGB2 may be able to serve as a biomarker for assessing prognosis and drug sensitivity in AML patients.

Classification and immune invasion analysis of breast cancer based on m6A genes.

BACKGROUND: Breast cancer (BRCA) shows genetic, epigenetic, and phenotypic diversity. Methylation of N6-methyladenosine (m6A) affects the occurrence, development, and therapeutic efficacy of BRCA. However, the characteristics and prognostic value of m6A in BRCA remain unclear. We aimed to classify and construct a scoring system for the m6A regulatory gene in BRCA, and to explore its potential mechanisms. METHODS: In this study, we selected 23 m6A regulatory genes and analyzed their genetic variation in BRCA, including copy number variation (CNV) data, expression differences, mutations, gene types, and correlations between genes. Survival curves were drawn by the Kaplan-Meier method, and a log-rank P<0.05 was considered statistically significant. The partitioning around medoids (PAM) algorithm was used for molecular subtype analysis of m6A, single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to quantify the relative infiltration levels of various immune cell subgroups, and a scoring system was built based on principal component analysis (PCA). RESULTS: In BRCA, m6A regulatory gene mutation frequency is not high, while that of CNV mutation is high, which is related to gene expression and closely related to prognosis. In this study, we identified 3 different m6A subtypes, which are closely related to the level of immune cell infiltration. We further constructed an m6A score system, in which lower scores were correlated with low tumor mutation burden (TMB), later clinical staging, programmed cell death 1 ligand 1 (PD-L1) expression, and triple-negative breast cancer (TNBC). CONCLUSIONS: This study highlights the diversity and complexity of the role of m6A in BRCA. The classification of BRCA based on the m6A regulatory gene can help us understand the characteristics of BRCA and help develop individualized immunotherapy regimens.

N6-methyladenosine (m6A) regulatory gene divides hepatocellular carcinoma into three subtypes.

BACKGROUND: The N6-methyladenosine (m6A) plays an important role in epigenetic modification and tumor progression, but the modulations of m6A in hepatocellular carcinoma (HCC) have not been determined while the relationship between m6A regulation and immune cell infiltration remains unclear. METHODS: This study investigated the modification patterns of m6A by analyzing HCC samples from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) dataset, and performed molecular typing based on the characteristics of immune cell infiltration. The m6Ascore was also constructed to quantify m6A modifications and predict the immunotherapy response and prognosis of HCC patients. RESULTS: Of the 364 samples, 31 (8.52%) were genetically altered in the m6A regulatory gene, with the highest frequency of mutations in HNRNPC, ZC3H13, and LRPPRC. Three distinct molecular subtypes of m6A were identified in 590 HCC samples, which were associated with different immune cell infiltrates: immunodepletion type, immune activation type, and immune immunity type. According to the construction of the m6Ascore system in the m6A genotype, HCC patients could be divided into high and low groups. The m6A modified pattern, characterized by immune immunity and immune failure, showed a lower score and a better prognosis. However, the immune-activated type of m6A had a higher score and a poorer prognosis. Further analysis showed that the m6Ascore was correlated with tumor mutation burden (TMB), and the higher the TMB, the worse the prognosis. m6Ascore was also correlated with the expression of cytotoxic T-lymphocyte-associated protein 4 (CTAL-4), and the higher the score, the higher the expression of HCC in patients. CONCLUSIONS: HCC has a unique m6A modification pattern, and 3 different m6A subtypes help to classify HCC, provide knowledge of drug regimens for immunotherapy, and can be used to predict treatment response and prognosis.

Screening potential prognostic biomarkers for portal vein emboli in patients with hepatocellular carcinoma.

BACKGROUND: The formation of portal vein tumor thrombus (PVTT) is closely related to the prognosis of patients with hepatocellular carcinoma (HCC). However, the mechanisms by which PVTTs form and the biomarkers involved are still little understood. METHODS: The Genome Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were used to obtain transcriptome data from normal tissue, HCC tissue, primary tumors (PTs) of HCC, and paired PVTT tissue. Differentially expressed genes (DEGs) in PTs and PVTTs were analyzed. The differentially expressed immune genes were further investigated in terms of their prognostic significance, immune infiltration, function. Finally, we explored the relationship between risk scores and drug sensitivity based on the R package. RESULTS: In the two datasets, there were 458 DEGs identified in the PT and PVTT tissues, of which, 58 were immune-related genes. The differentially expressed immune genes may promote the progression of PVTT by participating in the regulation of non-cellular components such as the extracellular matrix, inflammatory factors, and chemokines. Furthermore, the immune genes KDR, AKT3, FCGR2B, KIAA1429, and TPT1 were correlated with the prognosis of HCC in patients with PVTT. Using this data, a model was constructed to predict the prognosis of patients, thus allowing for the identification of high- and low-risk patients. CONCLUSIONS: This study demonstrated that immune-related genes may be involved in the regulation of the extracellular matrix and acellular components, and subsequently, in the formation of PVTT. These five genes KDR, AKT3, FCGR2B, KIAA1429, and TPT1 may be potential prognostic biomarkers and treatment targets for HCC patients with PVTT.

Screening a novel signature and predicting the immune landscape of metastatic osteosarcoma in children via immune-related lncRNAs.

BACKGROUND: The immune microenvironment plays an essential role in osteosarcoma (OSs); however, differences in immune-related long non-coding ribonucleic acids (irlncRNAs) in children with localized OSs and metastatic OSs have not yet been investigated. METHODS: The clinical data and the transcriptome of OSs were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database, and the immune-related genes were derived from the imported dataset. The correlations between immune-related genes and lncRNAs were examined. Next, the differential expressions of the irlncRNA pairs (IRLPs) in localized OSs and distant metastatic OSs were analyzed, and a prognostic model was constructed based on the significant differentially expressed IRLPs. We also analyzed the association between the IRLPs' signature risk score and the infiltration of the immune cells. Finally, we investigated the correlation between risk score and drug resistance. RESULTS: Thirty upregulated and 22 downregulated lncRNAs were identified in the localized and metastatic OSs samples. Univariate and multivariate cox regression analyses were undertaken to select 6 lncRNA pairs to establish the prognostic signature, the model was valuable in predicting OSs prognosis. Further, the expression of the finally selected irlncRNAs indicated that VPS9D1-AS1 (P=0.031), AP003086.2 (P=0.041), AL031847.1 (P=0.008), AL020997.3 (P=0.020), AC011444.1 (P=0.025), and AC006449.2 (P=0.003) were significantly upregulated in metastasis patients, but USP27X-AS1 (P=0.046), AL008721.2 (P=0.005), AC002091.1 (P=0.033), and AL118558.4 (P=0.049) were significantly overexpressed in localized patients. The overexpression of AC002091.1 (P=0.038) and AL118558.4 (P=0.004) resulted in better overall survival, but the upregulation of AC011444.1 (P=0.045), AL031847.1 (P=0.020), VPS9D1-AS1 (P=0.039), and AC006449.2 (0.006) led to a poor outcome. Differences in immune cell infiltration indicated that metastatic patients and localized have significant difference of 4 (CD4) T cells (P=0.006), monocytes (P=0.029), activated mast cells (P=0.018), and neutrophils (P=0.026), and a high abundance of activated dendritic cells (P=0.010) and activated mast cells (P=0.049) resulted in poor prognosis. Patients in the high-risk-score group were resistant to axitinib, but sensitive to dasatinib, bortezomib, and cisplatin. CONCLUSIONS: In the present study, IRLPs were used to construct a novel and practical model for predicting the prognosis of localized and metastatic OSs in children.

Immune-related long non-coding RNAs can serve as prognostic biomarkers for clear cell renal cell carcinoma.

BACKGROUND: The immune microenvironment is a critical regulator of clear cell renal cell carcinoma (ccRCC) progression. However, the underlying mechanisms the regulatory role of immune-related long non-coding RNAs (irlncRNAs) in the ccRCC tumor microenvironment (TME) are still obscure. Herein, we investigated prognostics role of irlncRNAs for ccRCC. METHODS: The raw data of patients with ccRCC were downloaded from The Cancer Genome Atlas (TCGA) database, and immune-related genes were obtained from the ImmPort database. First, we investigated the correlation between the immune-related genes and irlncRNAs. Then, we identified the differentially expressed irlncRNA pairs (ILRPs) between normal and cancer tissue samples, and prognostic model was constructed with the differentially expressed ILRPs. We further explored whether the signature risk scores of ILRPs had a considerable impact on immune cell infiltration. Finally, we performed a drug sensitivity analysis based on risk score. RESULTS: There were 13 upregulated and 40 downregulated irlncRNAs between the ccRCC and normal tissue samples. We further selected the irlncRNAs that significantly affect the prognosis of patients with ccRCC via univariate Cox, lasso regression, and multivariate regression analyses. Twelve ILRPs were used to construct a prognostic signature. The model showed the ILRPs model could be used to assess the prognosis of ccRCC patients. Study of the influence of risk score and clinical characteristics on the prognosis of patients with ccRCC showed risk score to be an independent factor affecting the outcome of ccRCC. We further performed the difference analysis of immune cell abundance between ccRCC and normal tissue samples. The results showed that patients with higher abundance of M0 macrophages, plasma cells, follicular helper T cells, and regulatory T cells (Tregs) had a poor outcome. Finally, we performed a drug sensitivity analysis based on risk score. The results showed that high-risk score patients are sensitive to orafenib, sunitinib, temsirolimus, cisplatin, and gemcitabine. CONCLUSIONS: Our study has developed a novel and reasonable ILPRs model for prognostic prediction, which does not require transcriptional levels to be detected.

Focal pyroptosis-related genes AIM2 and ZBP1 are prognostic markers for triple-negative breast cancer with brain metastases.

BACKGROUND: Extensive research has shown the role of pyroptosis in the occurrence, progression, and prognosis of breast cancer. The study sought to screen important pyroptosis-related genes and their role in the prognosis of triple-negative breast cancer (TNBC) patients with brain metastasis (BM). METHODS: The Gene Expression Omnibus database was used to obtain transcriptome data from primary TNBC and from TNBC BM patients. Differentially expressed genes (DEGs) between the primary tumors and BMs were analyzed, and the expression, prognostic significance, immune infiltration, function, and drug sensitivity of the pyroptosis genes in the DEGs were analyzed. RESULTS: In both data sets, 456 genes differed between primary TNBC and TNBC BM. Absent in melanoma 2 (AIM2) and Z-deoxyribonucleic acid-binding protein 1 (ZBP1) were found to be important pyroptosis genes in DEGs, and significant differences in their expression in primary lesions and BMs were observed. Patients with a high expression of AIM2 had a worse prognosis than low expression, while patients with a high expression of ZBP1 had a better prognosis than low expression. AIM2 and ZBP1 were positively correlated with the infiltration of most immune cells; however, AIM2 was negatively correlated with the infiltration of neural cell adhesion molecule 1 (CD56) bright natural killer cells and central memory cluster of differentiation 8 (CD8) T cells. Increased expression of ZBP1 is negatively correlated with high infiltration levels of central memory CD8 T cells and memory B cells. CONCLUSIONS: Our findings suggest that AIM2 and ZBP1 increase immune cell infiltration and may be potential targets for predicting and treating TNBC BM.

ZBP1 is a significant pyroptosis regulator for systemic lupus erythematosus.

BACKGROUND: Systemic lupus erythematosus (SLE) is a common autoimmune disease that affects all organs. Recently, several studies have shown that pyroptosis playsa significant process in the occurrence and progression of SLE. However, no study has investigated the association between pyroptosis genes and SLE. We conducted this study to examine this association. METHODS: The GSE11090, GSE20864, and GSE112087 gene microarrays of normal and SLE patient samples were downloaded from the Gene Expression Omnibus database. A differentially expressed gene (DEG) analysis was performed using the LIMMA package in R software. Log2 fold change |logFC| >0.5 and a false discovery rate (FDR) <0.05 setting for DEGs' screening value. We also performed an enrichment function analysis of the DEGs. To explore the role of pyroptosis genes in SLE, we selected pyroptosis genes that intersected with the DEGs for further analysis, we also examined the expression levels of the selected genes, their association with immune cell infiltration, and conducted western blotting and polymerase chain reaction analyses to confirm the selected genes expression levels in the SLE and normal samples. RESULTS: A total of 3,398 identical genes were obtained from 3 datasets for the differential analysis. 84 upregulated genes and 52 downregulated genes were identified in SLE. The enrichment function analysis revealed that DEGs act as key regulators of nicotinamide adenine dinucleotide (NADH) dehydrogenase activity, phospholipid scramblase activity, double-stranded ribonucleic acid (RNA) binding, and the interferon signaling pathway. We identified the SLE-related pyroptosis gene, Z-DNA binding protein 1 (ZBP1), by intersecting the DEGs of SLE and 40 pyroptosis genes. The differential analysis indicated that ZBP1 was more highly expressed in SLE patients compared to normal samples (P<0.001). Additionally, the expression of ZBP1 was higher in females than males (P=0.008). The SLE samples had different immune cell infiltration than the normal samples, and ZBP1 was significantly correlated with immune cell infiltration in the SLE samples. Finally, the validation experiments results showed that ZBP1 expression levels were significantly more highly expressed in female and SLE patients, than male and normal patients. CONCLUSIONS: ZBP1 may indicate that females have a high incidence rate of SLE, and it plays a significant role in the occurrence and progression of SLE.