Dual gene set enrichment analysis (dualGSEA); an R function that enables more robust biological discovery and pre-clinical model alignment from transcriptomics data.

Gene set enrichment analysis (GSEA) tools can identify biological insights within gene expression-based studies. Although their statistical performance has been compared, the downstream biological implications that arise when choosing between the range of pairwise or single sample forms of GSEA methods remain understudied. We compare the statistical and biological results obtained from various pre-ranking methods/options for pairwise GSEA, followed by a stand-alone comparison of GSEA, single sample GSEA (ssGSEA) and gene set variation analysis (GSVA).

Tumor-associated mesenchymal stromal cells modulate macrophage phagocytosis in stromal-rich colorectal cancer via PD-1 signaling.

CMS4 colorectal cancer (CRC), based on the consensus molecular subtype (CMS), stratifies patients with the poorest disease-free survival rates. It is characterized by a strong mesenchymal stromal cell (MSC) signature, wound healing-like inflammation and therapy resistance. We utilized 2D and 3D , and models to assess the impact of inflammation and stromal cells on immunosuppression in CMS4 CRC.

Educating primary care physicians about eating disorders: Pilot data from a microlearning programme.

Background: Over two-thirds of people present to their primary care physician (or general practitioner; GP) as a first point of contact for mental health concerns. However, eating disorders (EDs) are often not identified in a primary care setting. A significant barrier to early detection and intervention is lack of primary care physician training in EDs; compounded by the significant time commitments required for training by already time-poor general practitioners.

Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer.

Molecular stratification using gene-level transcriptional data has identified subtypes with distinctive genotypic and phenotypic traits, as exemplified by the consensus molecular subtypes (CMS) in colorectal cancer (CRC). Here, rather than gene-level data, we make use of gene ontology and biological activation state information for initial molecular class discovery. In doing so, we defined three pathway-derived subtypes (PDS) in CRC: PDS1 tumors, which are canonical/LGR5 stem-rich, highly proliferative and display good prognosis; PDS2 tumors, which are regenerative/ANXA1 stem-rich, with elevated stromal and immune tumor microenvironmental lineages; and PDS3 tumors, which represent a previously overlooked slow-cycling subset of tumors within CMS2 with reduced stem populations and increased differentiated lineages, particularly enterocytes and enteroendocrine cells, yet display the worst prognosis in locally advanced disease.