Zakład Genetyki Bakterii, Instytut Mikrobiologii, Wydział Biologii, Uniwersytet Warszawski, Warszawa.
Published: March 2013
AI Article Synopsis
Article Abstract
Gene therapy represents a potential new strategy for cancer treatment. In order to deliver a transgene into target tumor cells, a vector system is required. To date, most of the cancer therapies are based on the use of different viral vectors. However, bacteria such as Salmonella, Clostridium or non-pathogenic Bifidobacterium can selectively accumulate in tumors in vivo what renders them useful for cancer gene therapy vectors. Although the mechanism of DNA transfer from bacteria to mammalian cells is not completely understood their potential to deliver therapeutic genes into tumor cells have been demonstrated in vitro and in vivo. The review presents recent achievements in bacteria-mediated cancer gene therapy.
Adipocytes represent a significant proportion of breast tissue, comprising between 3.7 and 37% of stromal tissue. They play a pivotal role in metabolic regulation, energy supply, metabolic regulation, support effects, and cytokine release within the breast.
METTL3, an m6A methyltransferase, is integral to the regulation of messenger RNA (mRNA) biogenesis, degradation, and translation through the N6-methyladenosine (m6A) modification. Alterations in m6A homeostasis have been implicated in the development, progression, invasion, and metastasis of certain cancers. The present research aims to examine the consequences of METTL3 knockdown using short hairpin RNA (shRNA) on the proliferation and invasive capabilities of human colorectal and melanoma cancer cell lines.
Polycystic ovary syndrome (PCOS), a major cause of female infertility, affects 4%-20% of reproductive-age women. Metabolic and hormonal alterations are key features of PCOS, potentially raising the risk of endometrial (EC) and ovarian (OVCA) cancers. This systematic review aims to summarise the proposed molecular mechanisms involved in the association between PCOS and EC or OVCA.
It is now understood that brain metastases do not occur randomly but have distinct spatial patterns depending on the origin of the cancer. According to the "seed and soil" hypothesis, the final colonization of metastatic cells is the result of their adaptation to the altered environment. To investigate the most favorable microenvironment for brain metastasis, we analyzed neuroimaging data from 177 patients with breast cancer brain metastasis and 548 patients with lung cancer brain metastasis to create a replicable probabilistic map of metastatic locations.
This paper develops a method for cancer classification from microRNA data using a convolutional neural network (CNN)-based model optimized by genetic algorithm. The convolutional neural network has performed well in various recognition and perception tasks. This paper contributes to the cancer classification using a union of two CNNs.
