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Outcomes for patients with acute myeloid leukemia (AML) remain poor due to the inability of current therapeutic regimens to fully eradicate disease-initiating leukemia stem cells (LSC). Previous studies have demonstrated that oxidative phosphorylation (OXPHOS) is an essential process that is targetable in LSC. Sirtuin 3 (SIRT3), a mitochondrial deacetylase with a multi-faceted role in metabolic regulation, has been shown to regulate OXPHOS in cancer models; however, it has not yet been studied in the context of LSC. Thus, we sought to identify if SIRT3 is important for LSC function. Using RNAi and a SIRT3 inhibitor (YC8-02), we demonstrate that SIRT3 is a critical target for the survival of primary human LSC but is not essential for normal human hematopoietic stem and progenitor cell function. In order to elucidate the molecular mechanisms by which SIRT3 is essential in LSC we combined transcriptomic, proteomic, and lipidomic approaches, showing that SIRT3 is important for LSC function through the regulation of fatty acid oxidation (FAO) which is required to support OXPHOS and ATP production in human LSC. Further, we discovered two approaches to further sensitize LSC to SIRT3 inhibition. First, we found that LSC tolerate the toxic effects of fatty acid accumulation induced by SIRT3 inhibition by upregulating cholesterol esterification. Disruption of cholesterol homeostasis sensitizes LSC to YC8-02 and potentiates LSC death. Second, SIRT3 inhibition sensitizes LSC to the BCL-2 inhibitor venetoclax. Together, these findings establish SIRT3 as a regulator of lipid metabolism and potential therapeutic target in primitive AML cells.

Protein-protein interactions (PPIs) play key roles in a broad range of biological processes. The disorder of PPIs often causes various physical and mental diseases, which makes PPIs become the focus of the research on disease mechanism and clinical treatment. Since a large number of PPIs have been identified by in vivo and in vitro experimental techniques, the increasing scale of PPI data with the inherent complexity of interacting mechanisms has encouraged a growing use of computational methods to predict PPIs. Until recently, deep learning plays an increasingly important role in the machine learning field due to its remarkable non-linear transformation ability. In this article, we aim to present readers with a comprehensive introduction of deep learning in PPI prediction, including the diverse learning architectures, benchmarks and extended applications.

Abstract Purpose The aim of this study was to analyze prognostic factors for ovarian metastases (OM) in colorectal cancer (CRC) using data from a Chinese center. In addition, the study aimed at developing a new clinical scoring system for prognosis of OM of CRC patients after surgery. Patients and methods Data of CRC patients with OM were collected from a single Chinese institution (n = 67). Kaplan-Meier analysis was used to evaluate cumulative survival of patients. Factors associated with prognosis of overall survival (OS) were explored using Cox’s proportional hazard regression models. A scoring system to determine effectiveness of prognosis was developed. Results Median OS values for patients with or without surgery were 22 and 7 months, respectively. Size of OM, number of OM, peritoneal metastasis (PM), Peritoneal cancer index (PCI), and completeness of cytoreduction (CC) were associated with OS of patients through univariate analysis. Multivariate analysis using a Cox regression model showed that only CC was an independent predictor for OS. Three variables (the size of OM >15cm, PCI ≥ 10, and carcinoembryonic antigen (CEA) >30 ng/mL) assigned one point each were used to develop a risk score. The resulting score was used for prognosis of OS. Conclusion Surgical treatment of metastatic sites is effective and safe for CRC patients with OM. CC-0 is recommended for improved prognosis. The scoring system developed in this study is effective for prediction of OS of patients after surgery.

Abstract Background The transforming growth factor‐β (TGF‐β) pathway plays a pivotal role in inducing epithelial‐mesenchymal transition (EMT), which is a key step in cancer invasion and metastasis. However, the regulatory mechanism of TGF‐β in inducing EMT in colorectal cancer (CRC) has not been fully elucidated. In previous studies, it was found that S100A8 may regulate EMT. This study aimed to clarify the role of S100A8 in TGF‐β‐induced EMT and explore the underlying mechanism in CRC. Methods S100A8 and upstream transcription factor 2 (USF2) expression was detected by immunohistochemistry in 412 CRC tissues. Kaplan‐Meier survival analysis was performed. In vitro, Western blot, and migration and invasion assays were performed to investigate the effects of S100A8 and USF2 on TGF‐β‐induced EMT. Mouse metastasis models were used to determine in vivo metastasis ability. Luciferase reporter and chromatin immunoprecipitation assay were used to explore the role of USF2 on S100A8 transcription. Results During TGF‐β‐induced EMT in CRC cells, S100A8 and the transcription factor USF2 were upregulated. S100A8 promoted cell migration and invasion and EMT. USF2 transcriptionally regulated S100A8 expression by directly binding to its promoter region. Furthermore, TGF‐β enhanced the USF2/S100A8 signaling axis of CRC cells whereas extracellular S100A8 inhibited the USF2/S100A8 axis of CRC cells. S100A8 expression in tumor cells was associated with poor overall survival in CRC. USF2 expression was positively related to S100A8 expression in tumor cells but negatively related to S100A8‐positive stromal cells. Conclusions TGF‐β was found to promote EMT and metastasis through the USF2/S100A8 axis in CRC while extracellular S100A8 suppressed the USF2/S100A8 axis. USF2 was identified as an important switch on the intracellular and extracellular S100A8 feedback loop.

Objective: This study was conducted in order to construct a competitive endogenous RNA (ceRNA) network to screen RNA that plays an important role in colon cancer and to construct a model to predict the prognosis of patients.Methods: The gene expression data of colon cancer were downloaded from the TCGA database. The difference was analyzed by the R software and the ceRNA network was constructed. The survival-related RNA was screened out by combining with clinical information, and the prognosis model was established by lasso regression. CIBERSORT was used to analyze the infiltration of immune cells in colon cancer, and the differential expression of immune cells related to survival was screened out by combining clinical information. The correlation between RNA and immune cells was analyzed by lasso regression. PCR was used to verify the expression of seven RNAs in colon cancer patients with different prognoses.Results: Two hundred and fifteen lncRNAs, 357 miRNAs, and 2,955 mRNAs were differentially expressed in colon cancer. The constructed ceRNA network contains 18 lncRNAs, 42 miRNAs, and 168 mRNAs, of which 18 RNAs are significantly related to survival. Through lasso analysis, we selected seven optimal RNA construction models. The AUC value of the model was greater than 0.7, and there was a significant difference in the survival rate between the high- and low-risk groups. Two kinds of immune cells related to the prognosis of patients were screened out. The results showed that the expression of seven RNA markers in colon cancer patients with different prognoses was basically consistent with the model analysis.Conclusion: We have established the regulatory network of ceRNA in colon cancer, screened out seven core RNAs and two kinds of immune cells, and constructed a comprehensive prognosis model of colon cancer patients.

Early ventricular fibrillation (VF) prediction is critical for prevention of sudden cardiac death, and can improve patient survival. Generally, electrocardiogram (ECG) signal features are extracted to predict VF, a process which plays an important role in prediction accuracy. Therefore, this study first proposes a novel feature based on topological data analysis (TDA) to improve the accuracy of early ventricular fibrillation prediction. Firstly, the heart activity is regarded as a cardiac dynamical system, which is described by phase space reconstruction. Then the topological structure of the phase space is characterized with persistent homology, and its statistical features are further extracted and defined as TDA features. Finally, 60 subjects (30 VF, 30 healthy) from three public ECG databases are used to validate the prediction performance of the proposed method. Compared to heart rate variability features and box-counting features, TDA features achieve a superior accuracy of 91.7%. Additionally, the three types of features are combined as fusion features, achieving the optimal accuracy of 95.0%. The fusion features are then ranked, and the first seven components are all from the TDA features. It follows that the proposed features provide a significant effect in improving the predictive performance of early VF.

Riboswitch, a part of regulatory mRNA (50-250nt in length), has two main classes: aptamer and expression platform. One of the main challenges raised during the classification of riboswitch is imbalanced data. That is a circumstance in which the records of a sequences of one group are very small compared to the others. Such circumstances lead classifier to ignore minority group and emphasize on majority ones, which results in a skewed classification. We considered sixteen riboswitch families, to be in accord with recent riboswitch classification work, that contain imbalanced sequences. The sequences were split into training and test set using a newly developed pipeline. From 5460 k-mers (k value 1 to 6) produced, 156 features were calculated based on CfsSubsetEval and BestFirst function found in WEKA 3.8. Statistically tested result was significantly difference between balanced and imbalanced sequences (p < 0.05). Besides, each algorithm also showed a significant difference in sensitivity, specificity, accuracy, and macro F-score when used in both groups (p < 0.05). Several k-mers clustered from heat map were discovered to have biological functions and motifs at the different positions like interior loops, terminal loops and helices. They were validated to have a biological function and some are riboswitch motifs. The analysis has discovered the importance of solving the challenges of majority bias analysis and overfitting. Presented results were generalized evaluation of both balanced and imbalanced models, which implies their ability of classifying, to classify novel riboswitches. The Python source code is available at https://github.com/Seasonsling/riboswitch.

Atrial fibrillation (AF) is the most common cardiac arrhythmia. It tends to cause multiple cardiac conditions, such as cerebral artery blockage, stroke, and heart failure. The morbidity and mortality of AF have been progressively increasing over the past few decades, which has raised widespread concern about unobtrusive AF detection in routine life. The up-to-date non-invasive AF detection methods include electrocardiogram (ECG) signals and cardiac dynamics signals, such as the ballistocardiogram (BCG) signal, the seismocardiogram (SCG) signal and the photoplethysmogram (PPG) signal. Cardiac dynamics signals can be collected by cushions, mattresses, fabrics, or even cameras, which is more suitable for long-term monitoring. Therefore, methods for AF detection by cardiac dynamics signals bring about extensive attention for recent research. This paper reviews the current unobtrusive AF detection methods based on the three cardiac dynamics signals, summarized as data acquisition and preprocessing, feature extraction and selection, classification and diagnosis. In addition, the drawbacks and limitations of the existing methods are analyzed, and the challenges in future work are discussed.