We examined whether draft 2020 United States Preventive Services Task Force (USPSTF) lung-cancer screening recommendations “partially ameliorate racial disparities in screening eligibility” compared to 2013 guidelines, as claimed. Using data from the 2015 National Health Interview Survey, USPSTF-2020 increased eligibility by similar proportions for minorities (97.1%) and Whites (78.3%). Contrary to the intent of USPSTF-2020, the relative disparity (differences in percentages of model-estimated gainable life-years from National Lung Screening Trial-like screening by eligible Whites vs minorities) actually increased from USPSTF-2013 to USPSTF-2020 (African Americans: 48.3%–33.4%=15.0% to 64.5%–48.5%=16.0%; Asian Americans: 48.3%–35.6%=12.7% to 64.5%–45.2%=19.3%; Hispanic Americans: 48.3%–24.8%=23.5% to 64.5%–37.0%=27.5%). However, augmenting USPSTF-2020 with high-benefit individuals selected by the Life-Years From Screening with Computed Tomography (LYFS-CT) model nearly eliminated disparities for African Americans (76.8%–75.5%=1.2%), and improved screening efficiency for Asian/Hispanic Americans, although disparities were reduced only slightly (Hispanic Americans) or unchanged (Asian Americans). Draft USPSTF-2020 guidelines increased the number of eligible minorities versus USPSTF-2013 but may inadvertently increase racial/ethnic disparities. LYFS-CT could reduce disparities in screening eligibility by identifying ineligible people with high predicted benefit, regardless of race/ethnicity.

Background Chronic lymphocytic leukemia (CLL) is an indolent heme malignancy characterized by the accumulation of CD5+ CD19+ B cells and episodes of relapse. The biological signaling that influence episodes of relapse in CLL are not fully described. Here, we identify gene networks associated with CLL relapse and survival risk. Methods Networks were investigated by using a novel weighted gene network co-expression analysis method and examining overrepresentation of upstream regulators and signaling pathways within co-expressed transcriptome modules across clinically annotated transcriptomes from CLL patients (N = 203). Gene Ontology analysis was used to identify biological functions overrepresented in each module. Differential Expression of modules and individual genes was assessed using an ANOVA (Binet Stage A and B relapsed patients) or T-test (SF3B1 mutations). The clinical relevance of biomarker candidates was evaluated using log-rank Kaplan Meier (survival and relapse interval) and ROC tests. Results Eight distinct modules (M2, M3, M4, M7, M9, M10, M11, M13) were significantly correlated with relapse and differentially expressed between relapsed and non-relapsed Binet Stage A CLL patients. The biological functions of modules positively correlated with relapse were carbohydrate and mRNA metabolism, whereas negatively correlated modules to relapse were protein translation associated. Additionally, M1, M3, M7, and M13 modules negatively correlated with overall survival. CLL biomarkers BTK, BCL2, and TP53 were co-expressed, while unmutated IGHV biomarker ZAP70 and cell survival-associated NOTCH1 were co-expressed in modules positively correlated with relapse and negatively correlated with survival days. Conclusions This study provides novel insights into CLL relapse biology and pathways associated with known and novel biomarkers for relapse and overall survival. The modules associated with relapse and overall survival represented both known and novel pathways associated with CLL pathogenesis and can be a resource for the CLL research community. The hub genes of these modules, e.g., ARHGAP27P2, C1S, CASC2, CLEC3B, CRY1, CXCR5, FUT5, MID1IP1, and URAHP, can be studied further as new therapeutic targets or clinical markers to predict CLL patient outcomes.

Adipocyte development and adipose tissue expansion have many implications for human diseases, including obesity. Obesity is a debilitating disorder and a risk factor for metabolic disorders including insulin resistance and diabetes mellitus, due in part to an overabundance of adipocytes and adipocyte dysfunction. In recent years, obesity has become a global pandemic with approximately one-third of US adults classified as obese. Adipose tissue has recently been identified as a major metabolic organ, classified into white adipose tissue (WAT) and brown adipose tissue (BAT). Other than lifestyle modifications and invasive surgeries, only a very limited number of drugs are available to treat obesity and overweight. P311 has been shown to play a key role in blood pressure regulation, vascular contractility and tissue remodeling. Here we present a role for P311 in adipogenesis using a 3T3-L1 cell culture model. P311 expression is initiated with the induction of adipogenesis and increased during adipogenesis. This increase correlates with an increase in the expression of the key adipogenic transcriptional factors PPARγ2 and C/EBPα. In addition, siRNA-mediated P311 knockdown inhibits adipogenic differentiation in 3T3-L1 cells. Finally, P311 binds to the PPARγ2 promoter, implicating P311 mediates adipogenesis partly through PPARγ activation.

Colorectal cancer (CRC) is driven in part by dysregulated Wnt, Ras-Raf-MAPK, TGF-β, and PI3K-Akt signaling. The progression of CRC is also promoted by molecular alterations and heterogeneous—yet interconnected—gene mutations, chromosomal instability, transcriptomic subtypes, and immune signatures. Genomic alterations of CRC progression lead to changes in RNA expression, which support CRC metastasis. An RNA-based classification system used for CRC, known as consensus molecular subtyping (CMS), has four classes. CMS1 has the lowest survival after relapse of the four CRC CMS phenotypes. Here, we identify gene signatures and associated coding mRNAs that are co-expressed during CMS1 CRC progression. Using RNA-seq data from CRC primary tumor samples, acquired from The Cancer Genome Atlas (TCGA), we identified co-expression gene networks significantly correlated with CMS1 CRC progression. CXCL13, CXCR5, IL10, PIK3R5, PIK3AP1, CCL19, and other co-expressed genes were identified to be positively correlated with CMS1. The co-expressed eigengene networks for CMS1 were significantly and positively correlated with the TNF, WNT, and ERK1 and ERK2 signaling pathways, which together promote cell proliferation and survival. This network was also aligned with biological characteristics of CMS1 CRC, being positively correlated to right-sided tumors, microsatellite instability, chemokine-mediated signaling pathways, and immune responses. CMS1 also differentially expressed genes involved in PI3K-Akt signaling. Our findings reveal CRC gene networks related to oncogenic signaling cascades, cell activation, and positive regulation of immune responses distinguishing CMS1 from other CRC subtypes.

Multiple myeloma (MM) is characterized by the neoplastic proliferation of plasma cells. In 2018, there were approximately 30,770 new cases diagnosed and about 12,770 dead. Characterizing its molecular phenotypes is important for effective treatment of MM and predicting relapse. CXCL13-CXCR5 interactions are involved in malignancy cell homing, adhesion, signal transduction, and calcium flux, all of which promote MM progression. Here, we analyzed RNA-sequence data from matched normal, primary and relapsed MM cases. Patient control mRNA samples matched primary tumor and relapse samples were acquired from the database of Genotypes and Phenotypes to evaluate the mRNA expression patterns of CXCL13, CXCR5 and associated genes in MM. Bioinformatics tools were to integrate published genomic data from MM patients (n=480) and identify genes associated with CXCL13-CXCR5 signaling. DESeq analysis was used to determine differentially expressed genes between normal tissues, primary tumor, and relapse groups. Weighted gene co-expression network analysis identified clusters of genes significantly associated with the molecular phenotypes of MM. Notably, gene expression is driven by NFAT and JUN, known to be activated by the CXCL13-CXCR5 axis, and plasma cell signaling pathways significantly correlated with select MM molecular phenotypes and patient survival. Ingenuity pathway analysis was performed to analyze upstream regulators, gene interaction and canonical pathways. Taken together, our data show CXCR5-CXCL13 signaling networks are significantly expressed and associated with MM pathogenesis and plasma clonality. This study provides a better understanding of the heterogeneous nature of MM and the role of CXCL13-CXCR5 axis in MM.