BACKGROUND: Differential expression of chemokines/chemokine receptors in colorectal cancer (CRC) may enable molecular characterization of patients’ tumors for predicting clinical outcome. OBJECTIVE: To evaluate the prognostic ability of these molecules in a CRC cohort and the CRC TCGA-dataset. METHODS: Chemokine (CXCL-12 α , CXCL-12 β , IL-17A, CXCL-8, GM-CSF) and chemokine receptor (CXCR-4, CXCR-7) transcripts were analyzed by RT-qPCR in 76 CRC specimens (normal: 27, tumor: 49; clinical cohort). RNA-Seq data was analyzed from the TCGA-dataset ( n = 375). Transcript levels were correlated with outcome; analyses: univariate, multivariable, Kaplan-Meier. RESULTS: In the clinical cohort, chemokine/chemokine receptor levels were elevated 3-10-fold in CRC specimens ( P ⩽ 0.004) and were higher in patients who developed metastasis ( P = 0.03 – 0.0001). CXCR-4, CXCR-7, CXCL-12 α , CXCL-8, IL-17 and GM-CSF levels predicted metastasis ( P ⩽ 0.0421) and/or overall survival (OS; P ⩽ 0.0373). The CXCR-4 + CXCR-7 + CXCL-12 marker (CXCR-4/7 + CXCL-12 ( α /b) signature) stratified patients into risk for metastasis ( P = 0.0014; OR, 2.72) and OS ( P = 0.0442; OR, 2.7); sensitivity: 86.67%, specificity: 97.06%. In the TCGA-dataset, the CXCR-4/7 + CXCL-12 signature predicted metastasis ( P = 0.011; OR, 2.72) and OS ( P = 0.0006; OR: 4.04). In both datasets, the signature was an independent predictor of clinical outcome. CONCLUSIONS: Results of 451 specimens from both cohorts reveal that the CXCR-4/7 + CXCL-12 signature potentially predicts outcome in CRC patients and may allow earlier intervention.

Purpose: Poor prognosis of patients with muscle-invasive bladder cancer that often metastasizes drives the need for discovery of molecular determinants of bladder cancer progression. Chondroitin sulfate proteoglycans, including CD44, regulate cancer progression; however, the identity of a chondroitinase (Chase) that cleaves chondroitin sulfate from proteoglycans is unknown. HYAL-4 is an understudied gene suspected to encode a Chase, with no known biological function. We evaluated HYAL-4 expression and its role in bladder cancer. Experimental Design: In clinical specimens, HYAL-4 wild-type (Wt) and V1 expression was evaluated by RT-qPCR, IHC, and/or immunoblotting; a novel assay measured Chase activity. Wt and V1 were stably expressed or silenced in normal urothelial and three bladder cancer cell lines. Transfectants were analyzed for stem cell phenotype, invasive signature and tumorigenesis, and metastasis in four xenograft models, including orthotopic bladder. Results: HYAL-4 expression, specifically a novel splice variant (V1), was elevated in bladder tumors; Wt expression was barely detectable. V1 encoded a truncated 349 amino acid protein that was secreted. In bladder cancer tissues, V1 levels associated with metastasis and cancer-specific survival with high efficacy and encoded Chase activity. V1 cleaved chondroitin-6-sulfate from CD44, increasing CD44 secretion. V1 induced stem cell phenotype, motility/invasion, and an invasive signature. CD44 knockdown abrogated these phenotypes. V1-expressing urothelial cells developed angiogenic, muscle-invasive tumors. V1-expressing bladder cancer cells formed tumors at low density and formed metastatic bladder tumors when implanted orthotopically. Conclusions: Our study discovered the first naturally-occurring eukaryotic/human Chase and connected it to disease pathology, specifically cancer. V1-Chase is a driver of malignant bladder cancer and potential predictor of outcome in patients with bladder cancer.

This study assesses differences between users and non-users of unscheduled healthcare for persistent childhood asthma, with regard to select demographic and risk factors. The objectives are to provide important healthcare utilization information and a foundation for future research on self-management effectiveness (SME), informed by a recently developed “holistic framework” for measuring SME in childhood asthma. An 18-month retrospective chart review was conducted on 59 pediatric outpatients with persistent asthma—mild, moderate, or severe, to obtain data on various demographic and risk factors, and healthcare use for each child. The study examined five types of “unscheduled” healthcare use. Users had non-zero encounters (at least one) in any of the five types; non-users had zero encounters (not even one) in all five types. Differences between users and non-users were assessed using contingency table and logistic regression analysis. There were 25 users and 34 non-users of unscheduled healthcare. Each severity category contained users and non-users. The only statistically significant finding was that the mild persistent category had fewer users than severe persistent (p < 0.05). There were no significant differences between users and non-users for any other demographic or risk factor examined. After adjusting for asthma severity, there were no other significant differences between users and non-users of unscheduled healthcare. This is a crucial finding which suggests that something else is driving unscheduled healthcare use in these children, given there were users and non-users in each asthma severity category. These results provide impetus for future research on the role of other aspects of the "holistic framework" in explaining differences in uses of unscheduled healthcare in persistent childhood asthma.

Researchers in genomics are increasingly interested in epigenetic factors such as DNA methylation, because they play an important role in regulating gene expression without changes in the DNA sequence. There have been significant advances in developing statistical methods to detect differentially methylated regions (DMRs) associated with binary disease status. Most of these methods are being developed for detecting differential methylation rates between cases and controls. We consider multiple severity levels of disease, and develop a Bayesian statistical method to detect the region with increasing (or decreasing) methylation rates as the disease severity increases. Patients are classified into more than two groups, based on the disease severity (e.g., stages of cancer), and DMRs are detected by using moving windows along the genome. Within each window, the Bayes factor is calculated to test the hypothesis of monotonic increase in methylation rates corresponding to severity of the disease versus no difference. A mixed-effect model is used to incorporate the correlation of methylation rates of nearby CpG sites in the region. Results from extensive simulation indicate that our proposed method is statistically valid and reasonably powerful. We demonstrate our approach on a bisulfite sequencing dataset from a chronic lymphocytic leukemia (CLL) study.