Large datasets of hundreds to thousands of individuals measuring RNA-seq in observational studies are becoming available. Many popular software packages for analysis of RNA-seq data were constructed to study differences in expression signatures in an experimental design with well-defined conditions (exposures). In contrast, observational studies may have varying levels of confounding transcript-exposure associations; further, exposure measures may vary from discrete (exposed, yes/no) to continuous (levels of exposure), with non-normal distributions of exposure. We compare popular software for gene expression-DESeq2, edgeR and limma-as well as linear regression-based analyses for studying the association of continuous exposures with RNA-seq. We developed a computation pipeline that includes transformation, filtering and generation of empirical null distribution of association P-values, and we apply the pipeline to compute empirical P-values with multiple testing correction. We employ a resampling approach that allows for assessment of false positive detection across methods, power comparison and the computation of quantile empirical P-values. The results suggest that linear regression methods are substantially faster with better control of false detections than other methods, even with the resampling method to compute empirical P-values. We provide the proposed pipeline with fast algorithms in an R package Olivia, and implemented it to study the associations of measures of sleep disordered breathing with RNA-seq in peripheral blood mononuclear cells in participants from the Multi-Ethnic Study of Atherosclerosis. Multiparametric magnetic resonance imaging (mpMRI) of prostate with targeted biopsy has enhanced detection of high-grade prostatic adenocarcinoma (HG PCa). However, utility of amount of HG PCa (Gleason pattern 4/5) in mpMRI-targeted biopsies versus standard 12-core biopsies in predicting adverse outcomes on radical prostatectomy (RP) is unknown. To examine the utility of amount of HG PCa in mpMRI-targeted biopsies versus standard 12-core biopsies in predicting adverse RP outcomes. We performed a retrospective review of prostate biopsies, which had corresponding RP, 1 or more mpMRI-targeted biopsy, and grade group 2 disease or higher. For the 169 cases identified, total millimeters of carcinoma and HG PCa, and longest length HG PCa in a single core were recorded for 12-core biopsies and each set of mpMRI-targeted biopsies. For RP specimens, Gleason grade, extraprostatic extension, seminal vesicle involvement, and lymph node metastasis were recorded. The main outcome studied was prostate-confined disease mpMRI-targeted biopsies provides additional value over 12-core biopsies alone in predicting nonorgan confined prostate cancer at RP. Linear millimeters of HG PCa in mpMRI-targeted biopsies is a significant parameter associated with higher pathologic stage and could be of value in risk models. "Diffuse midline glioma, H3 K27M-mutant" (DMG) mainly arises within the pontine, thalamic, and spinal cord regions. Because of the rarity of spinal cord gliomas, the general knowledge surrounding DMGs is mainly based on pontine and thalamic gliomas, whereas tumor location tends to influence the clinicopathological features and prognosis. To determine the clinicopathological characteristics and molecular profiles of DMGs located in the spinal cord. The clinical and molecular pathologic features and prognosis were comprehensively analyzed in a series of 44 patients with spinal cord DMGs. The median age was 36 yr, and 88.7% of patients (39/44) were adults (≥18 yr). Histopathologically, malignant grades included grade II (16 cases), grade III (20 cases), and grade IV (8 cases). Compared with patients with histological grade IV, patients with lower histological grade (grade II/III) were older (37vs 24 yr, P=.020) and were associated with longer overall survival (24.1vs 8.6 mo, P=.007). All 30 tested tumors were isocitrate dehydrogenase (IDH) wild type, and 96% of cases (22/23) presented with unmethylated O6-methylguanine-DNA methyltransferase. Univariate and multivariate analyses showed that histological grade and presurgery McCormick Scale scores were independent prognostic factors for overall survival, whereas extensive surgical resection and chemoradiotherapy were not significantly associated with improved survival. The most frequent anatomic locations were the cervical enlargement (C4-T1, n=16) and conus medullaris (T12-L1, n=13), which exhibited distinctive clinical characteristics and molecular features. The findings provide guidelines for the evidence-based practice of the specialized management of spinal cord DMGs.The findings provide guidelines for the evidence-based practice of the specialized management of spinal cord DMGs.Although the prognosis of lower-grade glioma (LGG) patients is better than others, outcomes are highly heterogeneous. Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status can identify patient subsets with different prognosis. However, in the era of precision medicine, there is still a lack of biomarkers that can accurately predict the individual prognosis of each patient. this website In this study, we found that most DNA damage response (DDR) genes were aberrantly expressed in LGG patients and were associated with their prognosis. Consequently, we developed an artificial neural network (ANN) model based on DDR genes to predict outcomes of LGG glioma patients. Then, we validated the predictive ability in an independent external dataset and found that the concordance indexes and area under time-dependent receiver operating characteristic curves of the predict index (PI) calculated based on the model were superior to those of the mutation markers. Subgroup analyses demonstrated that the model could accurately identify patients with the same mutation status but different prognosis. Moreover, the model can also identify patients with favorable prognostic mutation status but poor prognosis or vice versa. Finally, we also found that the PI was associated with the mutation status and with the altered immune microenvironment. These results demonstrated that the ANN model can accurately predict outcomes of LGG patients and will contribute to individualized therapies. In addition, a web-based application program for the model was developed.