GAL3ST2 from mammary gland epithelial cells affects differentiation of 3T3-L1 preadipocytes

Functional impact of multi-omic interactions in lung cancer

Lung tumors are a leading cause of cancer-related death worldwide. Lung cancers are highly heterogeneous on their phenotypes, both at the cellular and molecular levels. Efforts to better understand the biological origins and outcomes of lung cancer in terms of this enormous variability often require of high-throughput experimental techniques paired with advanced data analytics. Anticipated advancements in multi-omic methodologies hold potential to reveal a broader molecular perspective of these tumors. This study introduces a theoretical and computational framework for generating network models depicting regulatory constraints on biological functions in a semi-automated way. The approach successfully identifies enriched functions in analyzed omics data, focusing on Adenocarcinoma (LUAD) and Squamous cell carcinoma (LUSC, a type of NSCLC) in the lung. Valuable information about novel regulatory characteristics, supported by robust biological reasoning, is illustrated, for instance by considering the role of genes, miRNAs and CpG sites associated with NSCLC, both novel and previously reported. Utilizing multi-omic regulatory networks, we constructed robust models elucidating omics data interconnectedness, enabling systematic generation of mechanistic hypotheses. These findings offer insights into complex regulatory mechanisms underlying these cancer types, paving the way for further exploring their molecular complexity.

3'-sulfated LewisA/C: An oncofetal epitope associated with metaplastic and oncogenic plasticity of the gastrointestinal foregut

Metaplasia, dysplasia, and cancer arise from normal epithelia via a plastic cellular transformation, typically in the setting of chronic inflammation. Such transformations are the focus of numerous studies that strive to identify the changes in RNA/Protein expression that drive such plasticity along with the contributions from the mesenchyme and immune cells. However, despite being widely utilized clinically as biomarkers for such transitions, the role of glycosylation epitopes is understudied in this context. Here, we explore 3'-Sulfo-Lewis A/C, a clinically validated biomarker for high-risk metaplasia and cancer throughout the gastrointestinal foregut: esophagus, stomach, and pancreas. We discuss the clinical correlation of sulfomucin expression with metaplastic and oncogenic transformation, as well as its synthesis, intracellular and extracellular receptors and suggest potential roles for 3'-Sulfo-Lewis A/C in contributing to and maintaining these malignant cellular transformations.

3'-Sulfo-TF Antigen Determined by GAL3ST2/ST3GAL1 Is Essential for Antitumor Activity of Fungal Galectin AAL/AAGL

Many lectins have been reported to have antitumor activities; identifying the glycan ligands in tumor cells of lectins is crucial for lectin clinical application. An edible mushroom galectin, Agrocybe aegerita lectin (AAL/AAGL), that has a high antitumor activity has been reported. In this paper, based on the glycan array data, it is showed that the Thomsen-Friedenreich antigen (TF antigen)-related O-glycans were found to be highly correlated with the antitumor activity of AAL/AAGL. Further glycosyltransferase quantification suggested that the ratio between GAL3ST2 and ST3GAL1 (GAL3ST2/ST3GAL1), which determined the 3'-sulfo-TF expression level, was highly correlated with the antitumor activity of AAL/AAGL. Overexpressing the enzyme of GAL3ST2 in HL60 and HeLa cell lines could increase the growth inhibition ratio of AAL/AAGL from 22.7 to 43.9% and 27.8 to 39.1%, respectively. However, ST3GAL1 in Jurkat cells could decrease the growth inhibition ratio from 44.7 to 35.6%. All the data suggested that the 3'-sulfo-TF antigen is one of the main glycan ligands that AAL/AAGL recognizes in tumor cells. AAL/AAGL may potentially serve as a reagent for cancer diagnosis and a targeted therapy for the 3'-sulfo-TF antigen.

An optimal prognostic model based on gene expression for clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of RCC; however, prognostic prediction tools for ccRCC are scant. Developing mRNA or long non-coding RNA (lncRNA)-based risk assessment tools may improve the prognosis in patients with ccRCC. RNA-sequencing and prognostic data from patients with ccRCC were downloaded from The Cancer Genome Atlas and the European Bioinformatics Institute Array database at the National Center for Biotechnology Information. Differentially expressed (DE) RNAs (DERs) and prognostic DERs were screened between less favorable and favorable prognoses using the limma package in R 3.4.1, and analyzed using univariate and multivariate Cox regression analyses, respectively. Risk score models were constructed using optimal combinations of DEmRNAs and DElncRNAs identified using the Least Absolute Shrinkage And Selection Operator Cox regression model of the penalized package. Associations between risk score models and overall survival time were evaluated. Independent prognostic clinical factors were screened using univariate and multivariate Cox regression analyses, and nomogram models were constructed. Gene Ontology biological processes and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted using the clusterProfiler package in R3.4.1. A total of 451 DERs were identified, including 404 mRNAs and 47 lncRNAs, between less favorable and favorable prognoses, and 269 DERs, including 233 mRNAs and 36 lncRNAs, were identified as independent prognostic factors. Optimal combinations including 10 DEmRNAs or 10 DElncRNAs were screened using four risk score models based on the status or expression levels of the 10 DEmRNAs or 10 DElncRNAs. The model based on the expression levels of the 10 DEmRNAs had the highest prognostic power. These prognostic DEmRNAs may be involved in biological processes associated with the inflammatory response, complement and coagulation cascades and neuroactive ligand-receptor interaction pathways. The present validated risk assessment tool based on the expression levels of these 10 DEmRNAs may help to identify patients with ccRCC at a high risk of mortality. These 10 DEmRNAs in optimal combinations may serve as prognostic biomarkers and help to elucidate the pathogenesis of ccRCC.

Microenvironmental influence on microtumour infiltration patterns: 3D-mathematical modelling supported by in vitro studies

Mathematical modelling approaches have become increasingly abundant in cancer research. Tumour infiltration extent and its spatial organization depend both on the tumour type and stage and on the bio-physicochemical characteristics of the microenvironment. This sets a complex scenario that often requires a multidisciplinary and individually adjusted approach. The ultimate goal of this work is to present an experimental/numerical combined method for the development of a three-dimensional mathematical model with the ability to reproduce the growth and infiltration patterns of a given avascular microtumour in response to different microenvironmental conditions. The model is based on a diffusion-convection reaction equation that considers logistic proliferation, volumetric growth, a rim of proliferative cells at the tumour surface, and invasion with diffusive and convective components. The parameter values of the model were fitted to experimental results while radial velocity and diffusion coefficients were made spatially variable in a case-specific way through the introduction of a shape function and a diffusion-limited-aggregation (DLA)-derived fractal matrix, respectively, according to the infiltration pattern observed. The in vitro model consists of multicellular tumour spheroids (MTSs) of an epithelial mammary tumour cell line (LM3) immersed in a collagen I gel matrix with a standard culture medium ("naive" matrix) or a conditioned medium from adipocytes or preadipocytes ("conditioned" matrix). It was experimentally determined that both adipocyte and preadipocyte conditioned media had the ability to change the MTS infiltration pattern from collective and laminar to an individual and atomized one. Numerical simulations were able to adequately reproduce qualitatively and quantitatively both kinds of infiltration patterns, which were determined by area quantification, analysis of fractal dimensions and lacunarity, and Bland-Altman analysis. These results suggest that the combined approach presented here could be established as a new framework with interesting potential applications at both the basic and clinical levels in the oncology area.