In recent years, miRNAs have been shown to play an important role in many diseases, most notably cancers. Traditional experiments for cancer diagnostics are time-consuming and expensive, thus more attention is being diverted towards discovering computational methods for predicting miRNA–disease association and using them in cancer diagnostics. miRNA numerical sequence information and genes targeted by miRNA were used for the construction of descriptors for machine-learning models. Next, we generated a table of miRNA descriptors using all of the miRNAs in a specific cancer dataset for disease classification. To show the effectiveness of the system, we constructed miRNA descriptor systems for pancreatic cancer, lung cancer, and breast cancer. With the Random Forest classifier, we obtained classification accuracies of 86.9%, 86.3%, and 85.1% for the above-mentioned cancers, respectively. Next, different disease datasets were tested on each model, including new miRNA sets for each cancer type from other studies. The models were able to classify the corresponding cancer miRNAs with >90% accuracy and other disease and cancer datasets with <60% accuracy. With this information, we constructed a hard-voting scheme using the three cancer classification models that is able to perform cancer diagnostics. The results suggest that our method is effective in miRNA–disease association prediction and performing cancer diagnostics. Full article

DNA methylation is essential for regulating tissue-specific gene expression, genomic imprinting, X chromosome inactivation and retroviral element silencing. The transformation from normal to cancer cells is accompanied by changes in DNA methylation resulting in the activation of oncogenes and inactivation of tumor suppressor genes. This process is regulated by methylation and contributes to the support and development of tumors. Epigenetic modifications account for the development of resistance in cancer cells treated with anticancer drugs. Dysregulated signaling pathways involved in tumor drug resistance include the Wnt canonical and non-canonical pathways and the PI3K/PTEN/AKT/mTOR pathway. This review considers the mechanisms and specific methylated biomarkers that participate in such resistances and how resistance to individual treatments for breast, ovarian, uterine and cervix tumors are introduced. Full article

Regulatory T cells (Tregs) are a vital cell subset that induces immune tolerance in the tumor microenvironment by secreting suppressive cytokines and inhibiting innate immune cells. Transforming growth factor-beta (TGF-β) plays an important role in this process. However, the effect of TGF-β blockade on intratumoral Tregs and its specific biological role remains unclear. Quantitative and functional changes in Tregs were evaluated after TGF-β blockade with gradient doses of monoclonal antibody 1D11 in a murine pancreatic ductal adenocarcinoma model. The number of tumor infiltrating Tregs decreased significantly (high dose, low dose and control, 38.6 ± 8.1, 38.6 ± 1.8, 74.6 ± 4.9 /40× field, p = 0.024) after 1D11 administration, while CD8⁺ T cells in the tumor microenvironment significantly increased in the low dose group but reversed in the high dose group (3.1 ± 1.4, 12.3 ± 2.1, 5.4 ± 0.5 /40× field, p = 0.016). The frequency of CD4⁺, CD8⁺ or Treg cells in the peripheral blood and spleen showed no significant change. The typical cytokines TGF-β and inerleukin-10 secreted by Tregs as well as interferon-γ produced by cytotoxic T cells in tumor tissues did not change compared with the controls. TGF-β blockade with a monoclonal antibody can reduce Tregs in the tumor niche, however, its therapeutic efficacy in PDAC patients remains limited. Further investigation of combination therapies is required. Full article