Targeting miR-148b-5p Inhibits Immunity Microenvironment and Gastric Cancer Progression

Gastric cancer is the fifth most common cancer and the third most common cause of cancer death globally. Risk factors for the condition include Helicobacter pylori infection, age, high salt intake, and diets low in fruit and vegetables. Gastric cancer is diagnosed histologically after endoscopic biopsy and staged using CT, endoscopic ultrasound, PET, and laparoscopy. It is a molecularly and phenotypically highly heterogeneous disease. The main treatment for early gastric cancer is endoscopic resection. Non-early operable gastric cancer is treated with surgery, which should include D2 lymphadenectomy (including lymph node stations in the perigastric mesentery and along the celiac arterial branches). Perioperative or adjuvant chemotherapy improves survival in patients with stage 1B or higher cancers. Advanced gastric cancer is treated with sequential lines of chemotherapy, starting with a platinum and fluoropyrimidine doublet in the first line; median survival is less than 1 year. Targeted therapies licensed to treat gastric cancer include trastuzumab (HER2-positive patients first line), ramucirumab (anti-angiogenic second line), and nivolumab or pembrolizumab (anti-PD-1 third line).

The immune microenvironment is crucial for epithelial ovarian cancer (EOC) progression and consists of tumor-associated macrophages (TAM) and T lymphocytes, such as regulatory T cells (Treg) and T helper 17 (Th17) cells. In this study, the Treg/Th17 ratio was significantly higher in EOC in situ and in metastatic peritoneal tissues than in benign ovarian tumors and benign peritoneum. The Treg/Th17 ratio was associated with histologic grade and was an independent prognostic factor for overall survival of EOC patients. On the basis of microarray analysis of exosomes derived from TAMs, we identified miRNAs enriched in the exosomes, including miR-29a-3p and miR-21-5p. When the two miRNA mimics were transfected into CD4+ T cells, they directly suppressed STAT3 and regulated Treg/Th17 cells, inducing an imbalance, and they had a synergistic effect on STAT3 inhibition. Taken together, these results indicate that exosomes mediate the interaction between TAMs and T cells, generating an immune-suppressive microenvironment that facilitates EOC progression and metastasis. These findings suggest that targeting these exosomes or their associated miRNAs might pave the way for the development of novel treatments for EOC.

MicroRNAs (miRNAs) are defined as small non-coding RNAs ~22 nt in length. They regulate gene expression at a post-transcriptional level through complementary base pairing with the target mRNA, leading to mRNA degradation and therefore blocking translation. In the last decade, the dysfunction of miRNAs has been related to the development and progression of many diseases. Currently, researchers need a method to identify precisely the miRNA targets, prior to applying experimental approaches that allow a better functional characterization of miRNAs in biological processes and can thus predict their effects. Computational prediction tools provide a rapid method to identify putative miRNA targets. However, since a large number of tools for the prediction of miRNA:mRNA interactions have been developed, all with different algorithms, the biological researcher sometimes does not know which is the best choice for his study and many times does not understand the bioinformatic basis of these tools. This review describes the biological fundamentals of these prediction tools, characterizes the main sequence-based algorithms, and offers some insights into their uses by biologists.