Use of Stingless Bee Propolis and Geopropolis against Cancer—A Literature Review of Preclinical Studies

Cancer is one of the major maladies affecting humankind and remains one of the leading causes of death worldwide. The investigation of the biological activities of stingless bee products, especially propolis and geopropolis, has revealed promising therapeutic properties, especially in the research on new antineoplastic agents. This literature review of preclinical trials, involving biological assays of antitumor activity and identification of the chemical composition of propolis and geopropolis of stingless bee species, describes the cytotoxicity in tumor lineages (breast, lung, ovarian, liver, mouth, pharynx, larynx, colon, stomach, colorectal, cervix, kidney, prostate, melanoma, human glioblastoma, canine osteosarcoma, erythroleukemia, human chronic myelocytic leukemia, and human promyelocytic leukemia) of propolis and geopropolis of 33 species of stingless bees. The chemical composition of propolis and geopropolis was identified, indicating that these belong to the chemical classes of phenolic acids, flavonoids, coumarins, benzophenones, anthraquinones, alkaloids, terpenes, steroids, saponins, fatty acids, and carbohydrates and are possibly responsible for the cytotoxicity in tumor cells. Apoptosis was one of the main mechanisms of cytotoxicity of extracts and substances isolated from stingless bee products. Although the results found are encouraging, other preclinical studies and clinical trials are essential for the discovery of new anticancer agents.

Comprehensive DNA methylation-transcriptome profiles association analysis During the Treatment of Acute Myelocytic Leukemia

Background: Epigenetic modifications have recently attracted much attention in the study of the biological mechanisms of acute myelocytic leukemia (AML) for therapy and prognosis. However, studies on DNA methylation changes during AML treatment are limited. Objective: The comprehensive DNA methylation-transcriptome profiles association analysis in this study aimed to establish whole-genome DNA methylation profiles and explore DNA methylation-related genes and their potential functions before and after treatment. And more appropriate biomarkers are expected to be identified for therapy strategies in AML. Method: Illumina 450K and RNA-Seq data were obtained from The Cancer Genome Atlas. We performed comprehensive DNA methylation-transcriptome profiles association analysis, pathway analysis, correlation analysis, and survival analyses. The StarBase database was utilized to predict interactions between lncRNAs, miRNAs and target mRNAs. Results: In total, 1592 distinct CpG sites and 2419 different expression transcripts were identified between pre-treatment and post-treatment AML. The significantly enriched functions of methylated genes were stem cell differentiation, cell population maintenance, and cell development. The expression of UGT3A2, MOG, and VSTM1 was correlated with DNA methylation levels (r2>0.5). Lastly, we identified 4 lncRNAs, 9 miRNAs and 142 mRNAs to construct a lncRNA-miRNA-mRNA ceRNA network. Conclusion: Our results revealed that DNA methylation was altered before and after treatment. Alterations in DNA methylation affected target gene expression and participated in the key biological processes of AML. Therefore, ceRNA networks may provide further insight into the study of favorable therapeutic markers in AML.

Depleting long noncoding RNA HOTAIR attenuates chronic myelocytic leukemia progression by binding to DNA methyltransferase 1 and inhibiting PTEN gene promoter methylation

Long noncoding RNAs (lncRNAs) are known to play a key role in chronic myelocytic leukemia (CML) development, and we aimed to identify the involvement of the lncRNA HOX antisense intergenic RNA (HOTAIR) in CML via binding to DNA methyltransferase 1 (DNMT1) to accelerate methylation of the phosphatase and tensin homolog (PTEN) gene promoter. Bone marrow samples from CML patients and normal bone marrow samples from healthy controls were collected. HOTAIR, DNMT1, DNMT3A, DNMT3B, and PTEN expression was detected. The biological characteristics of CML cells were detected. The relationship among HOTAIR, DNMT1, and PTEN was verified. Tumor volume and weight in mice injected with CML cells were tested. We found that HOTAIR and DNMT1 expression was increased and PTEN expression was decreased in CML. We also investigated whether downregulated HOTAIR or DNMT1 reduced proliferation, colony formation, invasion, and migration and increased the apoptosis rate of CML cells. Moreover, we tested whether low expression of HOTAIR or DNMT1 reduced the volume and weight of tumors in mice with CML. Collectively, the results of this studied showed that depleted HOTAIR demonstrated reduced binding to DNMT1 to suppress CML progression, which may be related to methylation of the PTEN promoter.

The core genome M5C plays an important role in methylation modification and immune infiltration of acute myelocytic leukemia samples

Currently, the pathogenesis of acute myelocytic leukemia(AML) is still unclear. We found the core genome M5C plays a vital role in methylation modification and immune infiltration of AML. At the same time, we created a new M5C score model to define the high and low-risk groups of AML.Our research showed the expression levels of the three molecular subtypes of M5C (C1, C2 and C3);as well as different clinical features ,the results showed significant differences in age\RUNX1-RUNX1T1 fusion, but not in RUNX1 mutation group.We constructed a prognostic risk model based on m5C phenotype from 417 samples in the GSE37642 data set and found 5 differential genes using lasso regression method. And the prognostic KM curve of the 5-gene signature was obtained, from which it can be seen that: all the five genes could significantly reduce the high and low risk of GSE37642 training set samples (P < 0.05).Finally, the robustness of M5C related 5-gene signature for AML prediction was verified by internal and external data using single factor and multifactor COX regression analysis.5-gene signature has strong robustness and can play a stable prediction performance in external validation data sets (GSE12417, TCGA-LAML).