The Influence of Adipocyte Secretome on Selected Metabolic Fingerprints of Breast Cancer Cell Lines Representing the Four Major Breast Cancer Subtypes

Exploring variations in glycolytic and gluconeogenic enzymes and isoforms across breast cancer cell lines and tissues

It is well established that tumour cells undergo metabolic changes to acquire biological advantage over normal cells with activation of the glycolytic pathway, a process termed "Warburg effect". Enzyme isoforms are alternative enzymatic forms with the same function but with different biochemical or epigenetic features. Moreover, isoforms may have varying impacts on different metabolic pathways. We challenge ourselves to analyse the glycolytic and gluconeogenic enzymes and isoforms in breast cancer, a complex and heterogeneous pathology, associated with high incidence and mortality rates especially among women. We analysed epithelial and tumour cell lines by RT-PCR and compared values to a publicly available database for the expression profile of normal and tumour tissues (Gepia) of enzymes and enzymatic isoforms from glycolytic and gluconeogenic pathways. Additionally, GeneMANIA was used to evaluate interactions, pathways, and attributes of each glycolytic/gluconeogenic steps. The findings reveal that the enzymes and enzymatic isoforms expressed in cell culture were somewhat different from those in breast tissue. We propose that the tumor microenvironment plays a crucial role in the expression of glycolytic and gluconeogenic enzymes and isoforms in tumour cells. Nonetheless, they not only participate in glycolytic and gluconeogenic enzymatic activities but may also influence other pathways, such as the Pentose-Phosphate-Pathway, TCA cycle, as well as other carbohydrate, lipid, and amino acid metabolism.

FEN1 Inhibition as a Potential Novel Targeted Therapy against Breast Cancer and the Prognostic Relevance of FEN1

To improve breast cancer treatment and to enable new strategies for therapeutic resistance, therapeutic targets are constantly being studied. Potential targets are proteins of DNA repair and replication and genomic integrity, such as Flap Endonuclease 1 (FEN1). This study investigated the effects of FEN1 inhibitor FEN1-IN-4 in combination with ionizing radiation on cell death, clonogenic survival, the cell cycle, senescence, doubling time, DNA double-strand breaks and micronuclei in breast cancer cells, breast cells and healthy skin fibroblasts. Furthermore, the variation in the baseline FEN1 level and its influence on treatment prognosis was investigated. The cell lines show specific response patterns in the aspects studied and have heterogeneous baseline FEN1 levels. FEN1-IN-4 has cytotoxic, cytostatic and radiosensitizing effects, expressed through increasing cell death by apoptosis and necrosis, G2M share, senescence, double-strand breaks and a reduced survival fraction. Nevertheless, some cells are less affected by the cytotoxicity and fibroblasts show a rather limited response. In vivo, high FEN1 mRNA expression worsens the prognosis of breast cancer patients. Due to the increased expression in breast cancer tissue, FEN1 could represent a new tumor and prognosis marker and FEN1-IN-4 may serve as a new potent agent in personalized medicine and targeted breast cancer therapy.

BIR: Biomedical Information Retrieval System for Cancer Treatment in Electronic Health Record Using Transformers

The rapid growth of electronic health records (EHRs) has led to unprecedented biomedical data. Clinician access to the latest patient information can improve the quality of healthcare. However, clinicians have difficulty finding information quickly and easily due to the sheer data mining volume. Biomedical information retrieval (BIR) systems can help clinicians find the information required by automatically searching EHRs and returning relevant results. However, traditional BIR systems cannot understand the complex relationships between EHR entities. Transformers are a new type of neural network that is very effective for natural language processing (NLP) tasks. As a result, transformers are well suited for tasks such as machine translation and text summarization. In this paper, we propose a new BIR system for EHRs that uses transformers for predicting cancer treatment from EHR. Our system can understand the complex relationships between the different entities in an EHR, which allows it to return more relevant results to clinicians. We evaluated our system on a dataset of EHRs and found that it outperformed state-of-the-art BIR systems on various tasks, including medical question answering and information extraction. Our results show that Transformers are a promising approach for BIR in EHRs, reaching an accuracy and an F1-score of 86.46%, and 0.8157, respectively. We believe that our system can help clinicians find the information they need more quickly and easily, leading to improved patient care.