Role of signaling pathways in the interaction between microbial, inflammation and cancer

This review highlights Receptor Expressed in Lymphoid Tissues (RELT), a Tumor Necrosis Factor Superfamily member, and its two paralogs, RELL1 and RELL2. Collectively, these three proteins are referred to as RELTfms and have gained much interest in recent years due to their association with cancer and other human diseases. A thorough knowledge of their physiological functions, including the ligand for RELT, is lacking, yet emerging evidence implicates RELTfms in a variety of processes including cytokine signaling and pathways that either promote cell death or survival. T cells from mice lacking RELT exhibit increased responses against tumors and increased inflammatory cytokine production, and multiple lines of evidence indicate that RELT may promote an immunosuppressive environment for tumors. The relationship of individual RELTfms in different cancers is not universal however, as evidence indicates that individual RELTfms may be risk factors in certain cancers yet appear to be protective in other cancers. RELTfms are important for a variety of additional processes related to human health including microbial pathogenesis, inflammation, behavior, reproduction, and development. All three proteins have been strongly conserved in all vertebrates, and this review aims to provide a clearer understanding of the current knowledge regarding these interesting proteins.

Source：Holistic Integrative Oncology

Prompt diagnostics and appropriate cancer therapy necessitate the use of gene expression databases. The integration of analytical methods can enhance detection precision by capturing intricate patterns and subtle connections in the data. This study proposes a diagnostic-integrated approach combining Empirical Bayes Harmonization (EBS), Jensen–Shannon Divergence (JSD), deep learning, and contour mathematics for cancer detection using gene expression data. EBS preprocesses the gene expression data, while JSD measures the distributional differences between cancerous and non-cancerous samples, providing invaluable insights into gene expression patterns. Deep learning (DL) models are employed for automatic deep feature extraction and to discern complex patterns from the data. Contour mathematics is applied to visualize decision boundaries and regions in the high-dimensional feature space. JSD imparts significant information to the deep learning model, directing it to concentrate on pertinent features associated with cancerous samples. Contour visualization elucidates the model’s decision-making process, bolstering interpretability. The amalgamation of JSD, deep learning, and contour mathematics in gene expression dataset analysis diagnostics presents a promising pathway for precise cancer detection. This method taps into the prowess of deep learning for feature extraction while employing JSD to pinpoint distributional differences and contour mathematics for visual elucidation. The outcomes underscore its potential as a formidable instrument for cancer detection, furnishing crucial insights for timely diagnostics and tailor-made treatment strategies.