tRNA-derived small RNAs in human cancers: roles, mechanisms, and clinical application.

Transfer RNA (tRNA)-derived small RNAs (tsRNAs) are a new type of non-coding RNAs (ncRNAs) produced by the specific cleavage of precursor or mature tRNAs. tsRNAs are involved in various basic biological processes such as epigenetic, transcriptional, post-transcriptional, and translation regulation, thereby affecting the occurrence and development of various human diseases, including cancers. Recent studies have shown that tsRNAs play an important role in tumorigenesis by regulating biological behaviors such as malignant proliferation, invasion and metastasis, angiogenesis, immune response, tumor resistance, and tumor metabolism reprogramming. These may be new potential targets for tumor treatment. Furthermore, tsRNAs can exist abundantly and stably in various bodily fluids (e.g., blood, serum, and urine) in the form of free or encapsulated extracellular vesicles, thereby affecting intercellular communication in the tumor microenvironment (TME). Meanwhile, their abnormal expression is closely related to the clinicopathological features of tumor patients, such as tumor staging, lymph node metastasis, and poor prognosis of tumor patients; thus, tsRNAs can be served as a novel type of liquid biopsy biomarker. This review summarizes the discovery, production, and expression of tsRNAs and analyzes their molecular mechanisms in tumor development and potential applications in tumor therapy, which may provide new strategies for early diagnosis and targeted therapy of tumors.

Immunogenomic features of radiologically distinctive nodules in multiple primary lung cancer.

OBJECTIVES: To provide molecular and immunological attributes mechanistic insights for the management of radiologically distinctive multiple primary lung cancer (MPLC). METHODS: The Bulk RNA-seq data of MPLC were obtained from our center. The Bulk RNA-seq data and CT images of patients with single primary lung cancer (SPLC) were obtained from GSE103584. Immune infiltration algorithms were performed to investigate the disparities in the immunological microenvironment between the two groups. Single-cell gene analysis was used to explore immune cells composition and communication relationships between cells in MPLC. RESULTS: In MPLC, 11 pure ground-glass opacity nodules (pGGN) and 10 mixed GGN (mGGN) were identified, while in SPLC, the numbers were 18 pGGN and 22 mGGN, respectively. In MPLC, compared to pGGN, mGGN demonstrated a significantly elevated infiltration of CD8+ T cells. Single-cell gene analysis demonstrated that CD8+ T cells play a central role in the signaling among immune cells in MPLC. The transcription factors including MAFG, RUNX3, and TBX21 may play pivotal roles in regulation of CD8+ T cells. Notably, compared to SPLC nodules for both mGGN and pGGN, MPLC nodules demonstrated a significantly elevated degree of tumor-infiltrating immune cells, with this difference being particularly pronounced in mGGN. There was a positive correlation between the proportion of immune cells and consolidation/tumor ratio (CTR). CONCLUSIONS: Our findings provided a comprehensive description about the difference in the immune microenvironment between pGGN and mGGN in early-stage MPLC, as well as between MPLC and SPLC for both mGGN and pGGN. The findings may provide evidence for the design of immunotherapeutic strategies for MPLC.

Joint inference for competing risks data using multiple endpoints.

Competing risks data are commonly encountered in randomized clinical trials and observational studies. This paper considers the situation where the ending statuses of competing events have different clinical interpretations and/or are of simultaneous interest. In clinical trials, often more than one competing event has meaningful clinical interpretations even though the trial effects of different events could be different or even opposite to each other. In this paper, we develop estimation procedures and inferential properties for the joint use of multiple cumulative incidence functions (CIFs). Additionally, by incorporating longitudinal marker information, we develop estimation and inference procedures for weighted CIFs and related metrics. The proposed methods are applied to a COVID-19 in-patient treatment clinical trial, where the outcomes of COVID-19 hospitalization are either death or discharge from the hospital, two competing events with completely different clinical implications.

Simultaneous hypothesis testing for multiple competing risks in comparative clinical trials.

Competing risks data are commonly encountered in randomized clinical trials or observational studies. Ignoring competing risks in survival analysis leads to biased risk estimates and improper conclusions. Often, one of the competing events is of primary interest and the rest competing events are handled as nuisances. These approaches can be inadequate when multiple competing events have important clinical interpretations and thus of equal interest. For example, in COVID-19 in-patient treatment trials, the outcomes of COVID-19 related hospitalization are either death or discharge from hospital, which have completely different clinical implications and are of equal interest, especially during the pandemic. In this paper we develop nonparametric estimation and simultaneous inferential methods for multiple cumulative incidence functions (CIFs) and corresponding restricted mean times. Based on Monte Carlo simulations and a data analysis of COVID-19 in-patient treatment clinical trial, we demonstrate that the proposed method provides global insights of the treatment effects across multiple endpoints.

Longitudinal genome-wide association studies of milk production traits in Holstein cattle using whole-genome sequence data imputed from medium-density chip data.

Longitudinal traits, such as milk production traits in dairy cattle, are featured by having phenotypic values at multiple time points, which change dynamically over time. In this study, we first imputed SNP chip (50-100K) data to whole-genome sequence (WGS) data in a Chinese Holstein population consisting of 6,470 cows. The imputation accuracies were 0.88 to 0.97 on average after quality control. We then performed longitudinal GWAS in this population based on a random regression test-day model using the imputed WGS data. The longitudinal GWAS revealed 16, 39, and 75 quantitative trait locus regions associated with milk yield, fat percentage, and protein percentage, respectively. We estimated the 95% confidence intervals (CI) for these quantitative trait locus regions using the logP drop method and identified 581 genes involved in these CI. Further, we focused on the CI that covered or overlapped with only 1 gene or the CI that contained an extremely significant top SNP. Twenty-eight candidate genes were identified in these CI. Most of them have been reported in the literature to be associated with milk production traits, such as DGAT1, HSF1, MGST1, GHR, ABCG2, ADCK5, and CSN1S1. Among the unreported novel genes, some also showed good potential as candidate genes, such as CCSER1, CUX2, SNTB1, RGS7, OSR2, and STK3, and are worth being further investigated. Our study provided not only new insights into the candidate genes for milk production traits, but also a general framework for longitudinal GWAS based on random regression test-day model using WGS data.

Antidepressant-like effects of geniposide in chronic unpredictable mild stress-induced mice by regulating the circ_0008405/miR-25-3p/Gata2 and Oip5os1/miR-25-3p/Gata2 networks.

Evidence exists suggesting the anti-depressive activities of geniposide (GP), a major compound in Gardenia jasminoides Ellis. Accordingly, the present study attempts to explore the anti-depressive mechanism of GP in chronic unpredictable mild stress (CUMS)-induced depression-like behaviors of mice. CUMS-induced mice were given GP daily and subjected to behavioral tests to observe the effect of GP on the depression-like behaviors. It was noted that GP administration reduced depression-like behaviors in CUMS mice. Transcriptome sequencing was conducted in three control and three CUMS mice. Differentially expressed circRNAs, lncRNAs and mRNAs were then screened by bioinformatics analyses. Intersection analysis of the transcriptome sequencing results with the bioinformatics analysis results was followed to identify the candidate targets. We found that Gata2 alleviated depression-like behaviors via the metabolism- and synapse-related pathways. Gata2 was a target of miR-25-3p, which had binding sites to circ_0008405 and Oip5os1. circ_0008405 and Oip5os1 competitively bound to miR-25-3p to release the expression of Gata2. GP administration ameliorated depression-like behaviors in CUMS mice through regulation of the circ_0008405/miR-25-3p/Gata2 and Oip5os1/miR-25-3p/Gata2 crosstalk networks. Taken together, GP may exert a potential antidepressant-like effect on CUMS mice, which is ascribed to regulation of the circ_0008405/miR-25-3p/Gata2 and Oip5os1/miR-25-3p/Gata2 crosstalk networks.

Increased suicidal erythrocyte death in patients with hepatitis B-related acute-on-chronic liver failure.

Anemia is a common complication of hepatitis B-related acute-on-chronic liver failure (HB-ACLF). Eryptosis, a suicidal erythrocyte death characterized by phosphatidylserine (PS) externalization and red blood cell-derived microparticle (RMP) generation, decreases erythrocyte lifespan. Herein, we investigated whether enhanced eryptosis is involved in the anemia pathophysiology associated with HB-ACLF. PS exposure, cell volume, cytosolic Ca2+, and reactive oxygen species (ROS) production were determined using flow cytometry. RMPs were extracted using a polyethylene glycol (PEG)-based method. We found that hemoglobin (Hb) and hematocrit (Hct) were significantly lower in patients with HB-ACLF than in healthy controls (HC), patients with chronic hepatitis B (CHB), and patients with cirrhosis. The direct antiglobulin test positive rate was 75.9% in patients with HB-ACLF while its intensity was associated with anemia. The ratio of abnormal erythrocytes was higher in patients with HB-ACLF than in HC, CHB, and cirrhosis. The percentage of PS-exposed erythrocytes was higher in patients with HB-ACLF (2.07 ± 0.11%) compared with HC (0.37 ± 0.05%), CHB (0.38 ± 0.03%), and cirrhosis (0.38 ± 0.04%). The cytosolic Ca2+ and ROS abundance were also higher in patients with HB-ACLF compared with HC, patients with CHB, and patients with cirrhosis, and were inversely correlated with the anemia in patients with HB-ACLF. PS exposure of erythrocytes collected from HC was significantly pronounced following incubation in plasma from patients with HB-ACLF compared with incubation in plasma from HC. The protein concentration and RMPs size significantly increased in patients with HB-ACLF compared with HC. Thus, the anemia in patients with HB-ACLF is associated with increased eryptosis, which is partially triggered by increased cytosolic Ca2+ and oxidative stress.NEW & NOTEWORTHY Acute chronic liver failure (ACLF) is a critical syndrome characterized by multiple organ failures and high short-term mortality. A common complication of HB-ACLF is anemia, however, the mechanism of anemia in HB-ACLF remains to be elucidated. We confirm that the accelerated eryptosis is involved in the pathophysiology of anemia associated with HB-ACLF, which progressively aggravates the clinical outcome. Our study illustrates the mechanism regarding the anemia pathogenesis of HB-ACLF, which may be utilized further toward therapeutic ends.

Obtaining optimal cutoff values for tree classifiers using multiple biomarkers.

In biomedical practices, multiple biomarkers are often combined using a prespecified classification rule with tree structure for diagnostic decisions. The classification structure and cutoff point at each node of a tree are usually chosen on an ad hoc basis, depending on decision makers' experience. There is a lack of analytical approaches that lead to optimal prediction performance, and that guide the choice of optimal cutoff points in a pre-specified classification tree. In this paper, we propose to search for and estimate the optimal decision rule through an approach of rank correlation maximization. The proposed method is flexible, theoretically sound, and computationally feasible when many biomarkers are available for classification or prediction. Using the proposed approach, for a prespecified tree-structured classification rule, we can guide the choice of optimal cutoff points at tree nodes and estimate optimal prediction performance from multiple biomarkers combined.

Association of Lifestyle Activities with Functional Brain Connectivity and Relationship to Cognitive Decline among Older Adults.

This study examines the relationship of engagement in different lifestyle activities to connectivity in large-scale functional brain networks, and whether network connectivity modifies cognitive decline, independent of brain amyloid levels. Participants (N = 153, mean age = 69 years, including N = 126 with amyloid imaging) were cognitively normal when they completed resting-state functional magnetic resonance imaging, a lifestyle activity questionnaire, and cognitive testing. They were followed with annual cognitive tests up to 5 years (mean = 3.3 years). Linear regressions showed positive relationships between cognitive activity engagement and connectivity within the dorsal attention network, and between physical activity levels and connectivity within the default-mode, limbic, and frontoparietal control networks, and global within-network connectivity. Additionally, higher cognitive and physical activity levels were independently associated with higher network modularity, a measure of functional network specialization. These associations were largely independent of APOE4 genotype, amyloid burden, global brain atrophy, vascular risk, and level of cognitive reserve. Moreover, higher connectivity in the dorsal attention, default-mode, and limbic networks, and greater global connectivity and modularity were associated with reduced cognitive decline, independent of APOE4 genotype and amyloid burden. These findings suggest that changes in functional brain connectivity may be one mechanism by which lifestyle activity engagement reduces cognitive decline.

Development of Severe COVID-19 Adaptive Risk Predictor (SCARP), a Calculator to Predict Severe Disease or Death in Hospitalized Patients With COVID-19.

BACKGROUND: Predicting the clinical trajectory of individual patients hospitalized with coronavirus disease 2019 (COVID-19) is challenging but necessary to inform clinical care. The majority of COVID-19 prognostic tools use only data present upon admission and do not incorporate changes occurring after admission. OBJECTIVE: To develop the Severe COVID-19 Adaptive Risk Predictor (SCARP) (https://rsconnect.biostat.jhsph.edu/covid_trajectory/), a novel tool that can provide dynamic risk predictions for progression from moderate disease to severe illness or death in patients with COVID-19 at any time within the first 14 days of their hospitalization. DESIGN: Retrospective observational cohort study. SETTINGS: Five hospitals in Maryland and Washington, D.C. PATIENTS: Patients who were hospitalized between 5 March and 4 December 2020 with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) confirmed by nucleic acid test and symptomatic disease. MEASUREMENTS: A clinical registry for patients hospitalized with COVID-19 was the primary data source; data included demographic characteristics, admission source, comorbid conditions, time-varying vital signs, laboratory measurements, and clinical severity. Random forest for survival, longitudinal, and multivariate (RF-SLAM) data analysis was applied to predict the 1-day and 7-day risks for progression to severe disease or death for any given day during the first 14 days of hospitalization. RESULTS: Among 3163 patients admitted with moderate COVID-19, 228 (7%) became severely ill or died in the next 24 hours; an additional 355 (11%) became severely ill or died in the next 7 days. The area under the receiver-operating characteristic curve (AUC) for 1-day risk predictions for progression to severe disease or death was 0.89 (95% CI, 0.88 to 0.90) and 0.89 (CI, 0.87 to 0.91) during the first and second weeks of hospitalization, respectively. The AUC for 7-day risk predictions for progression to severe disease or death was 0.83 (CI, 0.83 to 0.84) and 0.87 (CI, 0.86 to 0.89) during the first and second weeks of hospitalization, respectively. LIMITATION: The SCARP tool was developed by using data from a single health system. CONCLUSION: Using the predictive power of RF-SLAM and longitudinal data from more than 3000 patients hospitalized with COVID-19, an interactive tool was developed that rapidly and accurately provides the probability of an individual patient's progression to severe illness or death on the basis of readily available clinical information. PRIMARY FUNDING SOURCE: Hopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response.