Identification of crucial genes and prediction of small molecules for multidrug resistance of Hodgkin's lymphomas

Transcriptomics, molecular docking, and cross-resistance profiling of nobiletin in cancer cells and synergistic interaction with doxorubicin upon SOX5 transfection

BACKGROUND

Nobiletin is a polymethoxylated flavone from citrus fruit peels. Among other bioactivities, it acts antioxidative, anti-inflammatory, neuroprotective, and cardiovascular-protective. Nobiletin exerts profound anticancer activity in vitro and in vivo but the underlying mechanisms are not well understood.

PURPOSE

The aim was to unravel the multiple modes of action against cancer cells by bioinformatic and transcriptomic techniques and their verification by molecular pharmacological methods.

METHODS

The in silico methods used were COMPARE analysis of transcriptomic data, signaling pathway analysis, transcription factor binding motif analysis in promoter sequences of target genes, and molecular docking. The in vitro methods used were resazurin assay, isobologram analysis, generation of stably SOX5-tranfected cells, and Western blotting.

RESULTS

Nobiletin was cytotoxic against a wide range of cell lines from different tumor types, including diverse phenotypes to established anticancer drugs (e.g., P-glycoprotein, ABCB5, p53, EGFR). Cross-resistance profiling with 83 standard anticancer drugs revealed a correlation to antihormonal anticancer drugs, which can be explained by the phytoestrogenic features of nobiletin. Transcriptomic analysis showed that the responsiveness of tumor cells was predictable by their specific mRNA expression profile. Nobiletin bound to the transcription factor SOX5 in silico. SOX5 conferred resistance to the control drug doxorubicin but collateral sensitivity to nobiletin in HEK293 cells transfected with a lentiviral GFP-tagged pLOCORF-SOX5 vector. The combination of nobiletin and doxorubicin synergistically killed HEK293-SOX5 cells in isobologram analyses, implying attractive new treatment options.

CONCLUSION

Nobiletin represents an interesting candidate for cancer therapy with broad-spectrum activity and multiple modes of action. The identification of novel targets (i.e., SOX5) may allow its use for targeted tumor therapy in individualized treatment protocols.

Research on the drug resistance mechanism of foodborne pathogens

Foodborne diseases caused by foodborne pathogens are one of the main problems threatening human health and safety. The emergence of multi-drug resistant strains due to the abuse of antibiotics has increased the difficulty of clinical treatment. Research on the drug resistance mechanism of foodborne pathogens has become an effective method to solve multi-drug resistant strains. In this paper, the four main drug resistance mechanisms, including reduced cell membrane permeability, efflux pump mechanism, target site mutation mechanism, and enzymatic hydrolysis, were used to systematically analyze the drug resistance of Salmonella, Listeria monocytogenes, and Escherichia coli. And the new methods were discussed that may be used to solve the drug resistance of foodborne pathogens such as CRISPR and bacteriophages. This review provided a certain theoretical basis for the treatment of foodborne diseases.

Construction of Prognostic Risk Prediction Model of Oral Squamous Cell Carcinoma Based on Nine Survival-Associated Metabolic Genes

The aim was to investigate the independent prognostic factors and construct a prognostic risk prediction model to facilitate the formulation of oral squamous cell carcinoma (OSCC) clinical treatment plan. We constructed a prognostic model using univariate COX, Lasso, and multivariate COX regression analysis and conducted statistical analysis. In this study, 195 randomly obtained sample sets were defined as training set, while 390 samples constituted validation set for testing. A prognostic model was constructed using regression analysis based on nine survival-associated metabolic genes, among which PIP5K1B, NAGK, and HADHB significantly down-regulated, while MINPP1, PYGL, AGPAT4, ENTPD1, CA12, and CA9 significantly up-regulated. Statistical analysis used to evaluate the prognostic model showed a significant different between the high and low risk groups and a poor prognosis in the high risk group (P < 0.05) based on the training set. To further clarify, validation sets showed a significant difference between the high-risk group with a worse prognosis and the low-risk group (P < 0.05). Independent prognostic analysis based on the training set and validation set indicated that the risk score was superior as an independent prognostic factor compared to other clinical characteristics. We conducted Gene Set Enrichment Analysis (GSEA) among high-risk and low-risk patients to identify metabolism-related biological pathways. Finally, nomogram incorporating some clinical characteristics and risk score was constructed to predict 1-, 2-, and 3-year survival rates (C-index = 0.7). The proposed nine metabolic gene prognostic model may contribute to a more accurate and individualized prediction for the prognosis of newly diagnosed OSCC patients, and provide advice for clinical treatment and follow-up observations.

Network pharmacological systems study of Huang-Lian-Tang in the treatment of glioblastoma multiforme

Glioblastoma multiforme (GBM) has a poor prognosis and its recurrence and mortality rates are high. At present, there is no effective clinical method to control its progression and recurrence. Traditional Chinese Medicine has a high status not only in China, but also in the world. Certain drugs are also used in the clinical treatment of tumor diseases. In clinical practice, Huang-Lian-Tang (HLT) has proven efficacy in treating brain diseases and preventing tumor recurrence. However, the mechanisms of action have remained elusive. The present study explored the potential mechanisms of HLT in the treatment of gliomas based on network pharmacology. First, information on the composition of HLT was obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, and the composition and targets of the chemical substances contained in the herbs were analyzed. Subsequently, a pharmacological interaction network for HLT was built. Furthermore, the expressed genes of patients with GBM were obtained from Gene Expression Omnibus database and screened. A protein-protein interaction network was then constructed for both sets of data and they were combined with a topology method for analysis. Finally, the screened genes were subjected to enrichment analysis and pathway analysis. A total of 386 candidate targets and 7 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were screened, which were mainly associated with amino acid metabolism. Gene Ontology enrichment analysis and KEGG signal pathway analysis indicated that these targets are involved in anti-apoptosis, anti-oxidative stress, multicellular biological processes and other physiological and pathological processes related to the occurrence and development of GBM. In conclusion, the present results indicated that the mechanisms of action of HLT against GBM involve multiple targets and signaling pathways that are related to tumorigenesis and progression. The present study not only provided a novel theoretical basis for Traditional Chinese Medicine to treat tumors but also novel ideas for the treatment of GBM.

Identification of key genes associated with the progression of intrahepatic cholangiocarcinoma using weighted gene co-expression network analysis

The present study aimed to identify the key genes that are associated with the progression of intrahepatic cholangiocarcinoma through weighted gene co-expression network analysis (WGCNA). A total of three gene datasets were downloaded from the Gene Expression Omnibus database, including GSE107943, GSE119336 and GSE26566. Differentially expressed genes (DEGs) between intrahepatic cholangiocarcinoma tissues and adjacent liver tissues were identified using GSE107943, while tissue specific genes between bile duct and liver tissues were identified using GSE26566. Following the removal of tissue-specific genes, real DEGs were used to construct the WGCNA to investigate the association between gene modules and clinical traits. Following functional analysis, pathway enrichment analysis and the construction of a protein-protein interaction (PPI) network were performed, hub genes were selected and their diagnostic value was verified in GSE119336 using a receiver operating characteristic curve. Finally, the protein levels of the hub genes were also verified in intrahepatic cholangiocarcinoma tissues. A total of 1,643 real DEGs were identified and used to construct the WGCNA. Additionally, a total of seven co-expressed gene modules were identified following WGCNA, while genes in brown and yellow modules were identified to be associated with multiple clinical traits (the number of clinical traits >3) and used as key modules. A total of 63 core key module genes were subsequently identified, and it was indicated that these genes were most enriched in the nucleus (Gene Ontology term) and the cell cycle pathway (Kyoto Encyclopedia of Genes and Genomes term). Finally, a total of eight genes, including cyclin B1, cell division cycle 20, cell division cycle associated 8, cyclin dependent kinase 1, centrosomal protein 55, kinesin family member 2C, DNA topoisomerase IIα and TPX2 microtubule nucleation factor, exhibited the highest score in PPI analysis and had a high diagnostic value for intrahepatic cholangiocarcinoma. In addition, the protein levels of these genes were also revealed to be increased in most intrahepatic cholangiocarcinoma tissues. These eight genes may be used as novel biomarkers for the diagnosis of intrahepatic cholangiocarcinoma.

Activating miRNA-mRNA network in gemcitabine-resistant pancreatic cancer cell associates with alteration of memory CD4+ T cells

Background

To identify key microRNAs (miRNAs) and their target mRNAs related to gemcitabine-resistant pancreatic cancer (PC) and investigate the association between gemcitabine-resistant-related miRNAs and mRNAs and immune infiltration.

Methods

Expression profiles of miRNAs and mRNAs were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed miRNAs and mRNAs (referred to as "DEmiRNAs" and "DEmRNAs", respectively) were distinguished between gemcitabine-resistant PC cells and its parental cells. The DEmRNAs targeted by the DEmiRNAs were retrieved using miRDB, microT, and Targetscan. Furthermore, GO and KEGG pathway enrichment analysis and GSEA were performed. The Kaplan-Meier plotter was used to analyze the prognosis of key DEmiRNAs and DEmRNAs on PC patients. The relationship between the key DEmRNAs and tumor-infiltrating immune cells in PC was investigated using CIBERSORT method using the LM22 signature as reference. Key infiltrating immune cells were further analyzed for the associations with prognosis of TCGA PAAD patients.

Results

Four DEmiRNAs, including hsa-miR-3178, hsa-miR-485-3p, hsa-miR-574-5p, and hsa-miR-584-5p, were identified to target seven DEmRNAs, including MSI2, TEAD1, GNPDA1, RND3, PRKACB, TRIM68, and YKT6, individually, in gemcitabine-resistant PC cells versus parental cells. Gemcitabine-resistant PC cells were enriched in proteasome-related, immune-related, and memory CD4⁺ T cell-related pathways, indicating a gemcitabine therapeutic effect on PC cells. All four DEmiRNAs and almost all DEmRNAs had an impact on the prognosis of PC patients. All seven DEmRNAs had remarkable effects on CD4⁺ memory T cells, which were affected by the gemcitabine therapeutic effect. Effector memory CD4⁺ T cells rather than central memory CD4⁺ T cells predicted a good prognosis according to the TCGA PAAD dataset.

Conclusions

Gemcitabine resistance can alter the fraction of memory CD4⁺ T cells via hsa-miR-3178, hsa-miR-485-3p, hsa-miR-574-5p and hsa-miR-584-5p targeted MSI2, TEAD1, GNPDA1, RND3, PRKACB, TRIM68, and YKT6 network in PC.