Search

Hemodynamic parameters derived from pulse wave analysis have been shown to predict long-term outcomes in patients with heart failure (HF). Here we aimed to develop a deep-learning based algorithm that incorporates pressure waveforms for the identification and risk stratification of patients with HF. The first study, with a case–control study design to address data imbalance issue, included 431 subjects with HF exhibiting typical symptoms and 1545 control participants with no history of HF (non-HF). Carotid pressure waveforms were obtained from all the participants using applanation tonometry. The HF score, representing the probability of HF, was derived from a one-dimensional deep neural network (DNN) model trained with characteristics of the normalized carotid pressure waveform. In the second study of HF patients, we constructed a Cox regression model with 83 candidate clinical variables along with the HF score to predict the risk of all-cause mortality along with rehospitalization. To identify subjects using the HF score, the sensitivity, specificity, accuracy, F1 score, and area under receiver operating characteristic curve were 0.867, 0.851, 0.874, 0.878, and 0.93, respectively, from the hold-out cross-validation of the DNN, which was better than other machine learning models, including logistic regression, support vector machine, and random forest. With a median follow-up of 5.8 years, the multivariable Cox model using the HF score and other clinical variables outperformed the other HF risk prediction models with concordance index of 0.71, in which only the HF score and five clinical variables were independent significant predictors (p < 0.05), including age, history of percutaneous coronary intervention, concentration of sodium in the emergency room, N-terminal pro-brain natriuretic peptide, and hemoglobin. Our study demonstrated the diagnostic and prognostic utility of arterial waveforms in subjects with HF using a DNN model. Pulse wave contains valuable information that can benefit the clinical care of patients with HF. [ABSTRACT FROM AUTHOR]

Little is known about genes that induce stem cells differentiation into astrocytes. We previously described that heat shock protein 27 (HSP27) downregulation is directly related to neural differentiation under chemical induction in placenta-derived multipotent stem cells (PDMCs). Using this neural differentiation cell model, we cross-compared transcriptomic and proteomic data and selected 26 candidate genes with the same expression trends in both omics analyses. Those genes were further compared with a transcriptomic database derived from Alzheimer's disease (AD). Eighteen out of 26 candidates showed opposite expression trends between our data and the AD database. The mRNA and protein expression levels of those candidates showed downregulation of HSP27, S100 calcium-binding protein A16 (S100A16) and two other genes in our neural differentiation cell model. Silencing these four genes with various combinations showed that co-silencing HSP27 and S100A16 has stronger effects than other combinations for astrocyte differentiation. The induced astrocyte showed typical astrocytic star-shape and developed with ramified, stringy and filamentous processes as well as differentiated endfoot structures. Also, some of them connected with each other and formed continuous network. Immunofluorescence quantification of various neural markers indicated that HSP27 and S100A16 downregulation mainly drive PDMCs differentiation into astrocytes. Immunofluorescence and confocal microscopic images showed the classical star-like shape morphology and co-expression of crucial astrocyte markers in induced astrocytes, while electrophysiology and Ca2+ influx examination further confirmed their functional characteristics. In conclusion, co-silencing of S100A16 and HSP27 without chemical induction leads to PDMCs differentiation into functional astrocytes. [ABSTRACT FROM AUTHOR]

Introduction: The aim of the study was to perform digital RNA counting to validate a gene expression signature for operable breast cancers initially treated with curative intention, and the risk of recurrence, distant metastasis, and mortality was predicted. Methods: Candidate genes were initially discovered from the coherent genomic and transcriptional alternations from microarrays, and the extended concurrent genes were used to build a risk stratification model from archived formalin-fixed paraffin-embedded (FFPE) tissues with the NanoString nCounter. Results: The extended concurrent genes signature was prognostic in 144 Taiwanese breast cancers (5-year relapse-free survival: 89.8 and 69.4% for low- and high-risk group, log-rank test: P = 0.004). Cross-platform comparability was evidenced from significant and positive correlations for most genes as well as equal covariance matrix across 64 patients assayed for both microarray and digital RNA counting. Discussion: Archived FFPE samples could be successfully assayed by the NanoString nCounter. The purposed signature was prognostic stratifying breast cancer patients into groups with distinct survival patterns, and clinical applicability of the residual risk model was proved. [ABSTRACT FROM AUTHOR]

Several prognostic signatures have been identified for breast cancer. However, these signatures vary extensively in their gene compositions, and the poor concordance of the risk groups defined by the prognostic signatures hinders their clinical applicability. Breast cancer risk prediction was refined with a novel approach to finding concordant genes from leading edge analysis of prognostic signatures. Each signature was split into two gene sets, which contained either up-regulated or down-regulated genes, and leading edge analysis was performed within each array study for all up-/down-regulated gene sets of the same signature from all training datasets. Consensus of leading edge subsets among all training microarrays was used to synthesize a predictive model, which was then tested in independent studies by partial least squares regression. Only a small portion of six prognostic signatures (Amsterdam, Rotterdam, Genomic Grade Index, Recurrence Score, and Hu306 and PAM50 of intrinsic subtypes) was significantly enriched in the leading edge analysis in five training datasets ( n = 2,380), and that the concordant leading edge subsets (43 genes) could identify the core signature genes that account for the enrichment signals providing prognostic power across all assayed samples. The proposed concordant leading edge algorithm was able to discriminate high-risk from low-risk patients in terms of relapse-free or distant metastasis-free survival in all training samples (hazard ratios: 1.84-2.20) and in three out of four independent studies (hazard ratios: 3.91-8.31). In some studies, the concordant leading edge subset remained a significant prognostic factor independent of clinical ER, HER2, and lymph node status. The present study provides a statistical framework for identifying core consensus across microarray studies with leading edge analysis, and a breast cancer risk predictive model was established. [ABSTRACT FROM AUTHOR]

Background: An elevated plasma level of C-reactive protein (CRP) is a risk for, and prognostic factor of, colorectal cancer (CRC). In other reports of CRP concerning cardiovascular disease, CRP level correlated with its gene polymorphisms. We hypothesized that CRP polymorphisms associate risk and prognosis of CRC. Methods: This study enrolled 421 patients with CRC and 218 healthy control subjects. After preliminary studies, we selected four single nucleotide polymorphisms (SNPs) in the CRP gene: +2147A > G (rs1205), +942G > C (rs1800947), −717A > G (rs2794521), and −757T > C (rs3093059). At first, analyzing distributions of four SNPs between CRC case and non-CRC control groups was performed. Subsequently, the impacts of these SNPs with other prognostic factors of disease-free interval (DFI) and cancer-specific survival (CSS) were analyzed using uni- and multivariate Cox regression analyses. Results: The case and control groups differed in the frequency of −757T > C ( P = 0.002). The CRC case group had a higher percentage of the TT genotype (odds, 1.75). Regarding prognoses, multivariate analyses revealed that four factors, including stage (I, II, III), gross tumor type (polypoid, ulcerative, infiltrative), location (right, left, rectum), and −757T > C SNP (odds, 1.29; P = 0.048), correlated with DFI; two factors, including stage and +2147A > G SNP (odds, 0.71; P = 0.03), correlated with CSS. Conclusions: The −757T > C SNP is a risk for and prognostic factor of DFI; the +2147A > G SNP is a prognostic factor of CSS. CRP polymorphisms associate the risk and survival of CRC. [ABSTRACT FROM AUTHOR]

Microcalcification is one of the most common radiological and pathological features of breast ductal carcinoma in situ (DCIS), and to a lesser extent, invasive ductal carcinoma. We evaluated messenger RNA (mRNA) transcriptional profiles associated with ectopic mammary mineralization. A total of 109 breast cancers were assayed with oligonucleotide microarrays. The associations of mRNA abundance with microcalcifications and relevant clinical features were evaluated. Microcalcifications were present in 86 (79%) patients by pathological examination, and 81 (94%) were with coexistent DCIS, while only 13 (57%) of 23 patients without microcalcification, the invasive diseases were accompanied with DCIS (χ2-test, P < 0.001). There were 69 genes with differential mRNA abundance between breast cancers with and without microcalcifications, and 11 were associated with high-grade (comedo) type DCIS. Enriched Gene Ontology categories included glycosaminoglycan and aminoglycan metabolic processes and protein ubiquitination, indicating an active secretory process. The intersection (18 genes) of microcalcificaion-associated and DCIS-associated genes provided the best predictive accuracy of 82% with Bayesian compound covariate predictor. Ten genes were further selected for prognostic index score construction, and five-year relapse free survival was 91% for low-risk and 83% for high-risk group (log-rank test, P = 0.10). Our study suggested that microcalcification is not only the earliest detectable radiological sign for mammography screening but the phenomenon itself may reflect the underling events during mammary carcinogenesis. Future studies to evaluate the prognostic significance of microcalcifications are warranted. [ABSTRACT FROM AUTHOR]