Microarray Analysis in Glioblastomas

A review of traditional Chinese medicine Curcumae Rhizoma for treatment of glioma

Glioma is the most common primary central nervous tumor and its malignant and high recurrence rate are seriously threatening patient's life. The prognosis of glioma patients is still poor with a variety of modern treatments. Traditional Chinese medicine (TCM) is widely used in the adjuvant treatment or alternative medicine of glioma. Curcumae Rhizoma is one of the most commonly used in traditional Chinese medicine prescriptions for its anti-tumor characteristics. There are also many studies that reveals the anti-tumor effect of its active ingredients and some of which have been made into drugs and have been used in clinical practice. This review summarizes the new research progress on Curcumae Rhizoma for the treatment of glioma in recent years.

Cell‑based immunotherapy of glioblastoma multiforme (Review)

Glioblastoma multiforme (GBM) is the most aggressive and lethal primary glial brain tumor. It has an unfavorable prognosis and relatively ineffective treatment protocols, with the median survival of patients being ~15 months. Tumor resistance to treatment is associated with its cancer stem cells (CSCs). At present, there is no medication or technologies that have the ability to completely eradicate CSCs, and immunotherapy (IT) is only able to prolong the patient's life. The present review aimed to investigate systemic solutions for issues associated with immunosuppression, such as ineffective IT and the creation of optimal conditions for CSCs to fulfill their lethal potential. The present review also investigated the main methods involved in local immunosuppression treatment, and highlighted the associated disadvantages. In addition, novel treatment options and targets for the elimination and regulation of CSCs with adaptive and active IT are discussed. Antagonists of TGF-β inhibitors, immune checkpoints and other targeted medication are also summarized. The role of normal hematopoietic stem cells (HSCs) in the mechanisms underlying systemic immune suppression development in cases of GBM is analyzed, and the potential reprogramming of HSCs during their interaction with cancer cells is discussed. Moreover, the present review emphasizes the importance of the aforementioned interactions in the development of immune tolerance and the inactivation of the immune system in neoplastic processes. The possibility of solving the problem of systemic immunosuppression during transplantation of donor HSCs is discussed.

Analysis of Gene Expression Profiles in the Liver of Rats With Intrauterine Growth Retardation

BACKGROUND

Intrauterine growth restriction (IUGR) is highly associated with fetal as well as neonatal morbidity, mortality, and an increased risk metabolic disease development later in life. The mechanism involved in the increased risk has not been established. We compared differentially expressed genes between the liver of appropriate for gestational age (AGA) and IUGR rat models and identified their effects on molecular pathways involved in the metabolic syndrome.

METHODS

We extracted RNA from the liver of IUGR and AGA rats and profiled gene expression by microarray analysis. GO function and KEGG pathway enrichment analyses were conducted using the Search Tool for the Retrieval of Interacting Genes database. Then, the Cytoscape software was used to visualize regulatory interaction networks of IUGR-related genes. The results were further verified via quantitative reverse transcriptase PCR analysis.

RESULTS

In this study, 815 genes were found to be markedly differentially expressed (fold-change >1.5, p < 0.05) between IUGR and AGA, with 347 genes elevated and 468 suppressed in IUGR, relative to AGA. Enrichment and protein-protein interaction network analyses of target genes revealed that core genes including Ppargc1a, Prkaa2, Slc2a1, Rxrg, and Gcgr, and pathways, including the PPAR signaling pathway and FoxO signaling pathway, had a potential association with metabolic syndrome development in IUGR. We also confirmed that at the mRNA level, five genes involved in glycometabolism were differentially expressed between IUGR and AGA.

CONCLUSION

Our findings elucidate on differential gene expression profiles in IUGR and AGA. Moreover, they elucidate on the pathogenesis of IUGR-associated metabolic syndromes. The suggested candidates are potential biomarkers and eventually intended to treat them appropriately.

Current Achievements and Applications of Transcriptomics in Personalized Cancer Medicine

Over the last decades, transcriptome profiling emerged as one of the most powerful approaches in oncology, providing prognostic and predictive utility for cancer management. The development of novel technologies, such as revolutionary next-generation sequencing, enables the identification of cancer biomarkers, gene signatures, and their aberrant expression affecting oncogenesis, as well as the discovery of molecular targets for anticancer therapies. Transcriptomics contribute to a change in the holistic understanding of cancer, from histopathological and organic to molecular classifications, opening a more personalized perspective for tumor diagnostics and therapy. The further advancement on transcriptome profiling may allow standardization and cost reduction of its analysis, which will be the next step for transcriptomics to become a canon of contemporary cancer medicine.

RNA sequencing for research and diagnostics in clinical oncology

Molecular diagnostics is becoming one of the major drivers of personalized oncology. With hundreds of different approved anticancer drugs and regimens of their administration, selecting the proper treatment for a patient is at least nontrivial task. This is especially sound for the cases of recurrent and metastatic cancers where the standard lines of therapy failed. Recent trials demonstrated that mutation assays have a strong limitation in personalized selection of therapeutics, consequently, most of the drugs cannot be ranked and only a small percentage of patients can benefit from the screening. Other approaches are, therefore, needed to address a problem of finding proper targeted therapies. The analysis of RNA expression (transcriptomic) profiles presents a reasonable solution because transcriptomics stands a few steps closer to tumor phenotype than the genome analysis. Several recent studies pioneered using transcriptomics for practical oncology and showed truly encouraging clinical results. The possibility of directly measuring of expression levels of molecular drugs' targets and profiling activation of the relevant molecular pathways enables personalized prioritizing for all types of molecular-targeted therapies. RNA sequencing is the most robust tool for the high throughput quantitative transcriptomics. Its use, potentials, and limitations for the clinical oncology will be reviewed here along with the technical aspects such as optimal types of biosamples, RNA sequencing profile normalization, quality controls and several levels of data analysis.