Lactylation-driven METTL3-mediated RNA m6A modification promotes immunosuppression of tumor-infiltrating myeloid cells.

Tumor-infiltrating myeloid cells (TIMs) are crucial cell populations involved in tumor immune escape, and their functions are regulated by multiple epigenetic mechanisms. The precise regulation mode of RNA N6-methyladenosine (m6A) modification in controlling TIM function is still poorly understood. Our study revealed that the increased expression of methyltransferase-like 3 (METTL3) in TIMs was correlated with the poor prognosis of colon cancer patients, and myeloid deficiency of METTL3 attenuated tumor growth in mice. METTL3 mediated m6A modification on Jak1 mRNA in TIMs, the m6A-YTHDF1 axis enhanced JAK1 protein translation efficiency and subsequent phosphorylation of STAT3. Lactate accumulated in tumor microenvironment potently induced METTL3 upregulation in TIMs via H3K18 lactylation. Interestingly, we identified two lactylation modification sites in the zinc-finger domain of METTL3, which was essential for METTL3 to capture target RNA. Our results emphasize the importance of lactylation-driven METTL3-mediated RNA m6A modification for promoting the immunosuppressive capacity of TIMs.

Role of magnetic resonance imaging features in diagnosing and localization of disc rupture related to cervical spinal cord injury without radiographic abnormalities.

STUDY DESIGN: A retrospective study. OBJECTIVE: Traumatic cervical spinal cord injury (TSCI) is often associated with disc rupture. It was reported that high signal of disc and anterior longitudinal ligament (ALL) rupture on magnetic resonance imaging (MRI) were the typical signs of ruptured disc. However, for TSCI with no fracture or dislocation, there is still difficult to diagnose disc rupture. The purpose of this study was to investigate the diagnostic efficiency and localization method of different MRI features for cervical disc rupture in patient with TSCI but no any signs of fracture or dislocation. SETTING: Affiliated hospital of University in Nanchang, China. METHODS: Patients who had TSCI and underwent anterior cervical surgery between June 2016 and December 2021 in our hospital were included. All patients received X-ray, CT scan, and MRI examinations before surgery. MRI findings such as prevertebral hematoma, high-signal SCI, high-signal posterior ligamentous complex (PLC), were recorded. The correlation between preoperative MRI features and intraoperative findings was analyzed. Also, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of these MRI features in diagnosing the disc rupture were calculated. RESULTS: A total of 140 consecutive patients, 120 males and 20 females with an average age of 53 years were included in this study. Of these patients, 98 (134 cervical discs) were intraoperatively confirmed with cervical disc rupture, but 59.1% (58 patients) of them had no definite evidence of an injured disc on preoperative MRI (high-signal disc or ALL rupture signal). For these patients, the high-signal PLC on preoperative MRI had the highest diagnostic rate for disc rupture based on intraoperative findings, with a sensitivity of 97%, specificity of 72%, PPV of 84% and NPV of 93%. Combined high-signal SCI with high-signal PLC had higher specificity (97%) and PPV (98%), and a lower FPR (3%) and FNR (9%) for the diagnosis of disc rupture. And combination of three MRI features (prevertebral hematoma, high-signal SCI and PLC) had the highest accuracy in diagnosing traumatic disc rupture. For the localization of the ruptured disc, the level of the high-signal SCI had the highest consistency with the segment of the ruptured disc. CONCLUSION: MRI features, such as prevertebral hematoma, high-signal SCI and PLC, demonstrated high sensitivities for diagnosing cervical disc rupture. High-signal SCI on preoperative MRI could be used to locate the segment of ruptured disc.

LRIG2 regulates cell proliferation, migration and apoptosis of osteosarcoma.

BACKGROUND: Osteosarcoma (OS) is one of the malignant bone tumors with strong aggressiveness and poor prognosis. Leucine-rich repeats and immunoglobulin-like domains2 (LRIG2) is closely associated with the poor prognosis of a variety of tumors, but the role of LRIG2 in osteosarcoma and the underlying molecular mechanism remains unclear. OBJECTIVE: The aim of this study was to determine the function of LRIG2 in OS and the related molecular mechanism on cell proliferation, apoptosis and migration of OS. METHODS: The mRNA and protein expression of LRIG2 in OS tissues and cells was detected by qRT-PCR, western blot (WB) assay and immunohistochemistry (IHC). The cell counting Kit-8 (CCK-8), clone formation, transwell, TdT-mediated dUTP Nick-End Labeling (TUNEL) and WB assay were applied to determine the proliferation, migration and apoptosis abilities of OS cells and its molecular mechanisms. Spontaneous metastasis xenografts were established to confirm the role of LRIG2 in vivo. RESULTS: LRIG2 exhibited high expression in OS tissues and OS cell lines and the expression of which was significantly correlated with Enneking stage of patients, knockdown LRIG2 expression significantly inhibited OS cell proliferation, migration and enhanced apoptosis. Silencing LRIG2 also suppressed the growth of subcutaneous transplanted tumor in nude mice. Further, the mechanism investigation revealed that the protein level of cell proapoptotic proteins (Bax, caspase9 and caspase3) all increased attributed to LRIG2 deficiency, whereas expression of anti-apoptotic protein BCL2 decreased. LRIG2 silencing led to the decrease phosphorylation of AKT signaling, a decrease expression of vimentin and N-cadherin. Additionally, silencing LRIG2 significantly decreased the rate of tumor growth and tumor size. CONCLUSIONS: LRIG2 acts as an oncogene in osteosarcoma, and it might become a novel target in the treatment of human OS.

Upregulation of the APOBEC3 Family Is Associated with a Poor Prognosis and Influences Treatment Response to Raf Inhibitors in Low Grade Glioma.

Apolipoprotein B mRNA-editing enzyme catalytic polypeptide-like 3 (APOBEC3) has been identified as a group of enzymes that catalyze cytosine deamination in single-stranded (ss) DNA to form uracil, causing somatic mutations in some cancers. We analyzed the APOBEC3 family in 33 TCGA cancer types and the results indicated that APOBEC3s are upregulated in multiple cancers and strongly correlate with prognosis, particularly in low grade glioma (LGG). Then we constructed a prognostic model based on family expression in LGG where the APOBEC3 family signature is an accurate predictive model (AUC of 0.85). Gene mutation, copy number variation (CNV), and a differential gene expression (DEG) analysis were performed in different risk groups, and the weighted gene co-expression network analysis (WGCNA) was employed to clarify the role of various members in LGG; CIBERSORT algorithm was deployed to evaluate the landscape of LGG immune infiltration. We found that upregulation of the APOBEC3 family expression can strengthen Ras/MAPK signaling pathway, promote tumor progression, and ultimately reduce the treatment benefits of Raf inhibitors. Moreover, the APOBEC3 family was shown to enhance the immune response mediated by myeloid cells and interferon gamma, as well as PD-L1 and PD-L2 expression, implying that they have immunotherapy potential. Therefore, the APOBEC3 signature enables an efficient assessment of LGG patient survival outcomes and expansion of clinical benefits by selecting appropriate individualized treatment strategies.

A group of long noncoding RNAs identified by data mining can predict the prognosis of lung adenocarcinoma.

Long noncoding RNAs (lncRNA) are reported to be potential cancer biomarkers. This study aims to find new lncRNA biomarker relevant to lung adenocarcinoma. Gene expression profile and clinical data of lung adenocarcinoma and lung squamous cell carcinoma patients were downloaded from the UCSC Xena database. These data were analyzed to identify potential lncRNA prognostic biomarkers, and the candidate lncRNAs were analyzed and verified with association analysis, meta-analysis, survival analysis, gene ontology analysis, gene set enrichment analysis, and other statistical methods. A group of 5 lncRNAs was identified from the 1965 differentially expressed (fold-change >2) genes. Four of these 5 lncRNAs were expressed at a lower level in lung adenocarcinoma tissues and the other one at a higher level (P < .0001). A risk score model was constructed using a linear combination of the expression levels of these lncRNAs. High-risk patients showed poorer overall survival (hazard ratio [HR] = 2.14; 95% confidence interval [CI], 1.67-3.06, P < .0001), disease-free survival (HR = 1.84; 95% CI, 1.26-2.35, P = .0007), and recurrence-free survival (HR = 1.51; 95% CI, 1.02-2.40, P = .04). The 5-fold cross-validation and subsequent meta-analysis further verified that patients in the low-risk group had better survival (95% CI, 0.74-1.79, Z = 4.72, P < .00001). Furthermore, both univariate and multivariate Cox regression analyses revealed that the prognostic value of these 5 lncRNAs was independent of other clinical prognostic factors. Further analysis indicated that these 5 lncRNAs might be associated with tumor metastasis. Taken together, our study suggests new prognostic lncRNA biomarkers for lung adenocarcinoma.

Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data.

Osteoporosis is a major public health concern that significantly increases the risk of fractures. The aim of this study was to develop a Machine Learning based predictive model to screen individuals at high risk of osteoporosis based on chronic disease data, thus facilitating early detection and personalized management. A total of 10,000 complete patient records of primary healthcare data in the German Disease Analyzer database (IMS HEALTH) were included, of which 1293 diagnosed with osteoporosis and 8707 without the condition. The demographic characteristics and chronic disease data, including age, gender, lipid disorder, cancer, COPD, hypertension, heart failure, CHD, diabetes, chronic kidney disease, and stroke were collected from electronic health records. Ten different machine learning algorithms were employed to construct the predictive mode. The performance of the model was further validated and the relative importance of features in the model was analyzed. Out of the ten machine learning algorithms, the Stacker model based on Logistic Regression, AdaBoost Classifier, and Gradient Boosting Classifier demonstrated superior performance. The Stacker model demonstrated excellent performance through ten-fold cross-validation on the training set and ROC curve analysis on the test set. The confusion matrix, lift curve and calibration curves indicated that the Stacker model had optimal clinical utility. Further analysis on feature importance highlighted age, gender, lipid metabolism disorders, cancer, and COPD as the top five influential variables. In this study, a predictive model for osteoporosis based on chronic disease data was developed using machine learning. The model shows great potential in early detection and risk stratification of osteoporosis, ultimately facilitating personalized prevention and management strategies.

Construction and validation of a machine learning model for the diagnosis of juvenile idiopathic arthritis based on fecal microbiota.

Purpose: Human gut microbiota has been shown to be significantly associated with various inflammatory diseases. Therefore, this study aimed to develop an excellent auxiliary tool for the diagnosis of juvenile idiopathic arthritis (JIA) based on fecal microbial biomarkers. Method: The fecal metagenomic sequencing data associated with JIA were extracted from NCBI, and the sequencing data were transformed into the relative abundance of microorganisms by professional data cleaning (KneadData, Trimmomatic and Bowtie2) and comparison software (Kraken2 and Bracken). After that, the fecal microbes with high abundance were extracted for subsequent analysis. The extracted fecal microbes were further screened by least absolute shrinkage and selection operator (LASSO) regression, and the selected fecal microbe biomarkers were used for model training. In this study, we constructed six different machine learning (ML) models, and then selected the best model for constructing a JIA diagnostic tool by comparing the performance of the models based on a combined consideration of area under receiver operating characteristic curve (AUC), accuracy, specificity, F1 score, calibration curves and clinical decision curves. In addition, to further explain the model, Permutation Importance analysis and Shapley Additive Explanations (SHAP) were performed to understand the contribution of each biomarker in the prediction process. Result: A total of 231 individuals were included in this study, including 203 JIA patients and Non-JIA individuals. In the analysis of diversity at the genus level, the alpha diversity represented by Shannon value was not significantly different between the two groups, while the belt diversity was slightly different. After selection by LASSO regression, 10 fecal microbe biomarkers were selected for model training. By comparing six different models, the XGB model showed the best performance, which average AUC, accuracy and F1 score were 0.976, 0.914 and 0.952, respectively, thus being used to construct the final JIA diagnosis model. Conclusion: A JIA diagnosis model based on XGB algorithm was constructed with excellent performance, which may assist physicians in early detection of JIA patients and improve the prognosis of JIA patients.

Gene mutation landscape of a rare patient with acute megakaryoblastic leukemia after treatment of intracranial germ cell tumor.

Introduction: It was first reported that germ cell tumor patients suffer from hematologic malignancies 37 years ago. Since then, the number of relevant reports has increased each year, with most cases being mediastinal germ cell tumor. Theories have been proposed to explain this phenomenon, including a shared origin of progenitor cells, the effects of treatment, and independent development. However, up to now, no widely accepted explanation exists. The case with acute megakaryoblastic leukemia and intracranial germ cell tumor has never been reported before and the association is far less known. Methods: We used whole exome sequencing and gene mutation analysis to study the relationship between intracranial germ cell tumor and acute megakaryoblastic leukemia of our patient. Results: We report a patient who developed acute megakaryoblastic leukemia after treatment for an intracranial germ cell tumor. Through whole exome sequencing and gene mutation analysis, we identified that both tumors shared the same mutation genes and mutation sites, suggesting they originated from the same progenitor cells and differentiated in the later stage. Discussion: Our findings provide the first evidence supporting the theory that acute megakaryoblastic leukemia and intracranial germ cell tumor has the same progenitor cells.

Trend of unplanned reoperation rates after lumbar degenerative surgery from 2011 to 2019: a large patient population study.

BACKGROUND: Unplanned reoperation is commonly performed due to postoperative complications. Previous studies have reported the incidence of unplanned reoperation following lumbar spinal surgery. But few study focused on the trend of reoperation rates, and the reasons of unplanned reoperation were not clear. In this study, we conducted a retrospective study to determine the trend of unplanned reoperation rates after degenerative lumbar spinal surgery from 2011 to 2019, and the reasons and risk factors of unplanned reoperation were also determined. METHODS: Data of patients who were diagnosed with degenerative lumbar spinal disease and underwent posterior lumbar spinal fusion surgery in our institution from January 2011 to December 2019 were reviewed. Those who received unplanned reoperation during the primary admission were identified. The demographics, diagnosis, surgical segments and postoperative complications of these patients were recorded. The rates of unplanned reoperation from 2011 to 2019 were calculated, and the reasons of unplanned reoperation were statistically analysed. RESULTS: A total of 5289 patients were reviewed. Of them, 1.91% (n = 101) received unplanned reoperation during the primary admission. The unplanned reoperation rates of degenerative lumbar spinal surgery firstly increased from 2011 to 2014, with a peak rate in 2014 (2.53%). Then, the rates decreased from 2014 to 2019, with the lowest one in 2019 (1.46%). Patients with lumbar spinal stenosis have a higher rate of unplanned reoperation (2.67%) compared with those diagnosed as lumbar disc herniation (1.50%) and lumbar spondylolisthesis (2.04%) (P < 0.05). The main reasons for unplanned reoperation were wound infection (42.57%), followed by wound hematoma (23.76%). Patients who underwent 2-segment spinal surgery had a higher unplanned reoperation rate (3.79%) than those receiving other segments surgery (P < 0.001). And different spine surgeons had different reoperation rates. CONCLUSIONS: The rates of unplanned reoperation after lumbar degenerative surgery increased at first and then decreased during past 9 years. Wound infection was the major reason for unplanned reoperation. 2-segment surgery and surgeon's surgical skills were related to the reoperation rate.

Persistent high glucose induced EPB41L4A-AS1 inhibits glucose uptake via GCN5 mediating crotonylation and acetylation of histones and non-histones.

BACKGROUND: Persistent hyperglycemia decreases the sensitivity of insulin-sensitive organs to insulin, owing to which cells fail to take up and utilize glucose, which exacerbates the progression of type 2 diabetes mellitus (T2DM). lncRNAs' abnormal expression is reported to be associated with the progression of diabetes and plays a significant role in glucose metabolism. Herein, we study the detailed mechanism underlying the functions of lncRNA EPB41L4A-AS1in T2DM. METHODS: Data from GEO datasets were used to analyze the expression of EPB41L4A-AS1 between insulin resistance or type 2 diabetes patients and the healthy people. Gene expression was evaluated by qRT-PCR and western blotting. Glucose uptake was measured by Glucose Uptake Fluorometric Assay Kit. Glucose tolerance of mice was detected by Intraperitoneal glucose tolerance tests. Cell viability was assessed by CCK-8 assay. The interaction between EPB41L4A-AS1 and GCN5 was explored by RNA immunoprecipitation, RNA pull-down and RNA-FISH combined immunofluorescence. Oxygen consumption rate was tested by Seahorse XF Mito Stress Test. RESULTS: EPB41L4A-AS1 was abnormally increased in the liver of patients with T2DM and upregulated in the muscle cells of patients with insulin resistance and in T2DM cell models. The upregulation was associated with increased TP53 expression and reduced glucose uptake. Mechanistically, through interaction with GCN5, EPB41L4A-AS1 regulated histone H3K27 crotonylation in the GLUT4 promoter region and nonhistone PGC1ß acetylation, which inhibited GLUT4 transcription and suppressed glucose uptake by muscle cells. In contrast, EPB41L4A-AS1 binding to GCN5 enhanced H3K27 and H3K14 acetylation in the TXNIP promoter region, which activated transcription by promoting the recruitment of the transcriptional activator MLXIP. This enhanced GLUT4/2 endocytosis and further suppressed glucose uptake. CONCLUSION: Our study first showed that the EPB41L4A-AS1/GCN5 complex repressed glucose uptake via targeting GLUT4/2 and TXNIP by regulating histone and nonhistone acetylation or crotonylation. Since a weaker glucose uptake ability is one of the major clinical features of T2DM, the inhibition of EPB41L4A-AS1 expression seems to be a potentially effective strategy for drug development in T2DM treatment.

LINC00472 inhibits cell migration by enhancing intercellular adhesion and regulates H3K27ac level via interacting with P300 in renal clear cell carcinoma.

Renal clear cell carcinoma (RCCC) is the most common type of renal cell carcinoma, which is also difficult to diagnose and easy to metastasize. Currently, there is still a lack of effective clinical diagnostic indicators and treatment targets. This study aims to find effective diagnostic markers and therapeutic targets from the perspective of noncoding RNA. In this study, we found that the expression of Long noncoding RNA LINC00472 was significantly decreased in RCCC and showed a downward trend with the progression of cancer stage. Patients with low LINC00472 expression have poor prognosis. Inhibition of LINC00472 significantly increased cell proliferation and migration, while overexpression of LINC00472 obviously inhibited cell proliferation and enhanced intercellular adhesion. Transcriptome sequencing analysis demonstrated that LINC00472 was highly correlated with extracellular matrix and cell metastasis-related pathways, and the consistent results were obtained by The Cancer Genome Atlas (TCGA) data analysis. Additionally, we discovered that the integrin family protein ITGB8 is a potential target gene of LINC00472. Mechanistically, we found that the change of LINC00472 affected the acetylation level of H3K27 site in cells, and we speculate that this effect is likely to be generated through the interaction with acetyltransferase P300. In conclusion, LINC00472 has an important impact on the proliferation and metastasis of renal clear cells, and probably participate in the regulation of histone modification, and it may be used as a potential diagnostic marker of RCCC.

A Whole Exon Screening-Based Score Model Predicts Prognosis and Immune Checkpoint Inhibitor Therapy Effects in Low-Grade Glioma.

Objective: This study aims to identify prognostic factors for low-grade glioma (LGG) via different machine learning methods in the whole genome and to predict patient prognoses based on these factors. We verified the results through in vitro experiments to further screen new potential therapeutic targets. Method: A total of 940 glioma patients from The Cancer Genome Atlas (TCGA) and The Chinese Glioma Genome Atlas (CGGA) were included in this study. Two different feature extraction algorithms - LASSO and Random Forest (RF) - were used to jointly screen genes significantly related to the prognosis of patients. The risk signature was constructed based on these screening genes, and the K-M curve and ROC curve evaluated it. Furthermore, we discussed the differences between the high- and low-risk groups distinguished by the signature in detail, including differential gene expression (DEG), single-nucleotide polymorphism (SNP), copy number variation (CNV), immune infiltration, and immune checkpoint. Finally, we identified the function of a novel molecule, METTL7B, which was highly correlated with PD-L1 expression on tumor cell, as verified by in vitro experiments. Results: We constructed an accurate prediction model based on seven genes (AUC at 1, 3, 5 years= 0.91, 0.85, 0.74). Further analysis showed that extracellular matrix remodeling and cytokine and chemokine release were activated in the high-risk group. The proportion of multiple immune cell infiltration was upregulated, especially macrophages, accompanied by the high expression of most immune checkpoints. According to the in vitro experiment, we preliminarily speculate that METTL7B affects the stability of PD-L1 mRNA by participating in the modification of m6A. Conclusion: The seven gene signatures we constructed can predict the prognosis of patients and identify the potential benefits of immune checkpoint inhibitors (ICI) therapy for LGG. More importantly, METTL7B, one of the risk genes, is a crucial molecule that regulates PD-L1 and could be used as a new potential therapeutic target.

Using Preoperative and Intraoperative Factors to Predict the Risk of Surgical Site Infections After Lumbar Spinal Surgery: A Machine Learning-Based Study.

OBJECTIVE: To develop a model based on machine learning to predict surgical site infection (SSI) risk in patients after lumbar spinal surgery (LSS). METHODS: Patients who developed postoperative SSI after LSS in the First Affiliated Hospital of Nanchang University between December 2010 and December 2019 were retrospectively reviewed. Preoperative and intraoperative variables, including age, diabetes mellitus, hypertension, body mass index, previous spinal surgery history, surgical duration, number of fused segments, blood loss, and surgical procedure were analyzed. Six machine learning algorithms-logistic regression, multilayer perceptron, decision tree, random forest, gradient boosting machine, and extreme gradient boosting-were used to build prediction models. The performance of the models was evaluated using the area under the curve, accuracy, precision, sensitivity, and F1 score. A web predictor was developed based on the best-performing model. RESULTS: The study included 288 patients who underwent LSS, of whom 144 developed SSI and 144 did not develop SSI. The extreme gradient boosting model offers the best predictive performance among these 6 models (area under the curve = 0.923, accuracy = 0.860, precision = 0.900, sensitivity = 0.834, F1 score = 0.864). An extreme gradient boosting model-based web predictor was developed to predict SSI in patients after LSS. CONCLUSIONS: This study developed a machine learning model and a web predictor for predicting SSI in patients after LSS, which may help clinicians screen high-risk patients, provide personalized treatment, and reduce the incidence of SSI after LSS.

Short-segment fixation and transpedicular bone grafting for the treatment of thoracolumbar spine fracture.

Purpose: Thoracolumbar fracture is one of the most common fractures of spine. And short-segment posterior fixation including the fractured vertebra (SSPFI) is usually used for the surgical treatment of it. However, the outcomes of SSPFI for different types of thoracolumbar fractures are not clear, and whether it is necessary to perform transpedicular bone grafting is still controversial. This study was conducted to determine the clinical efficacy of SSPFI for the treatment of different types of single-level thoracolumbar fracture, and make clear what kind of fractures need transpedicular bone grafting during the surgery. Methods: Patients with single-level thoracolumbar fracture undergoing SSPFI surgery between January 2013 and June 2020 were included in this study. The operative duration, intraoperative blood loss, anterior vertebral height ratio (AVHR) and anterior vertebral height compressive ratio (AVHC) of the fractured vertebra, local kyphotic Cobb angle (LKA), vertebral wedge angle (VWA) and correction loss during follow up period were recorded. Outcomes between unilateral and bilateral pedicle screw fixation for fractured vertebra, between SSPFI with and without transpedicular bone grafting (TBG), and among different compressive degrees of fractured vertebrae were compared, respectively. Results: A total of 161 patients were included in this study. All the patients were followed up, and the mean follow-upped duration was 25.2 ± 3.1 months (6-52 months). At the final follow-up, the AVHR was greater, and the LKA and VWA were smaller in patients with bilateral fixation (6-screw fixation) than those with unilateral fixation (5-screw fixation) of AO type A3/A4 fractures (P < 0.001). The correction loss of AVHR, LKA and VWA in fractured vertebra were significantly great when preoperative AVHC was >50% (P < 0.05). For patients with AVHC >50%, the correction loss in patients with TBG were less than those without TBG at the final follow-up (P < 0.05). Conclusions: SSPFI using bilateral fixation was more effective than unilateral fixation in maintaining the fractured vertebral height for AO type A3/A4 fractures. For patients with AVHC >50%, the loss of correction was more obvious and it can be decreased by transpedicular bone grafting.

Downregulation of lncRNA EPB41L4A-AS1 Mediates Activation of MYD88-Dependent NF-κB Pathway in Diabetes-Related Inflammation.

PURPOSE: Long non-coding RNAs (lncRNAs) have been shown to be involved in many human diseases. In this study, we aimed to reveal the role and molecular mechanism of lncRNA EPB41L4A-AS1 in type 2 diabetic mellitus (T2DM)-related inflammation. METHODS: To explore the relationships between the expression of EPB41L4A-AS1 and inflammatory factors in the blood of T2DM patients, we analyzed peripheral blood mononuclear cell (PBMC) expression microarrays of T2DM patients and expression microarrays of PBMC treated with lipopolysaccharide (LPS) from the GEO database. The relationship between EPB41L4A-AS1 and phospho-p65 was explored by Western blotting (WB) and immunofluorescence. The interactions between EPB41L4A-AS1 and myeloid differentiation factor 88 (MYD88) were also verified through quantitative real-time PCR, WB, and chromatin immunoprecipitation. Glycolysis and mitochondrial stress were detected by Seahorse. RESULTS: EPB41L4A-AS1 showed very low expression, which was significantly negatively correlated with levels of inflammatory factors in PBMCs of T2DM patients and PBMCs treated with LPS. These results were verified by cell experiments on PBMC and THP-1 cells. Knockdown of EPB41L4A-AS1 led to the phosphorylation and nuclear translocation of p65 and thus activated the NF-κB signaling pathway; it also reduced the enrichment of H3K9me3 in the MYD88 promoter and increased expression of MYD88. Overall, EPB41L4A-AS1 knockdown promoted the level of glycolysis and ultimately enhanced the inflammatory response. CONCLUSION: EPB41L4A-AS1 knockdown activated the NF-κB signaling pathway through a MYD88-dependent regulatory mechanism, promoted glycolysis, and ultimately enhanced the inflammatory response. These results demonstrate that EPB41L4A-AS1 is closely associated with inflammation in T2DM, and that low expression of EPB41L4A-AS1 may be used as an indicator of chronic inflammation and possible diabetic vascular complications in T2DM patients.

Down-regulation of EPB41L4A-AS1 mediated the brain aging and neurodegenerative diseases via damaging synthesis of NAD+ and ATP.

BACKGROUND: Aging and neurodegenerative diseases are typical metabolic-related processes. As a metabolism-related long non-coding RNA, EPB41L4A-AS has been reported to be potentially involved in the development of brain aging and neurodegenerative diseases. In this study, we sought to reveal the mechanisms of EPB41L4A-AS in aging and neurodegenerative diseases. METHODS: Human hippocampal gene expression profiles downloaded from the Genotype-Tissue Expression database were analyzed to obtain age-stratified differentially expressed genes; a weighted correlation network analysis algorithm was then used to construct a gene co-expression network of these differentially expressed genes to obtain gene clustering modules. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, protein-protein interaction network, and correlation analysis were used to reveal the role of EPB41L4A-AS1. The mechanism was verified using Gene Expression Omnibus dataset GSE5281 and biological experiments (construction of cell lines, Real-time quantitative PCR, Western blot, measurement of ATP and NAD+ levels, nicotinamide riboside treatment, Chromatin Immunoprecipitation) in neurons and glial-derived cells. RESULTS: EPB41L4A-AS1 was downregulated in aging and Alzheimer's disease. EPB41L4A-AS1 related genes were found to be enriched in the electron transport chain and NAD+ synthesis pathway. Furthermore, these genes were highly associated with neurodegenerative diseases and positively correlated with EPB41L4A-AS1. In addition, biological experiments proved that the downregulation of EPB41L4A-AS1 could reduce the expression of these genes via histone H3 lysine 27 acetylation, resulting in decreased NAD+ and ATP levels, while EPB41L4A-AS1 overexpression and nicotinamide riboside treatment could restore the NAD+ and ATP levels. CONCLUSIONS: Downregulation of EPB41L4A-AS1 not only disturbs NAD+ biosynthesis but also affects ATP synthesis. As a result, the high demand for NAD+ and ATP in the brain cannot be met, promoting the development of brain aging and neurodegenerative diseases. However, overexpression of EPB41L4A-AS1 and nicotinamide riboside, a substrate of NAD+ synthesis, can reduce EPB41L4A-AS1 downregulation-mediated decrease of NAD+ and ATP synthesis. Our results provide new perspectives on the mechanisms underlying brain aging and neurodegenerative diseases.

Application of Machine Learning Techniques to Predict Bone Metastasis in Patients with Prostate Cancer.

OBJECTIVE: This study aimed to develop and validate a machine learning model for predicting bone metastases (BM) in prostate cancer (PCa) patients. METHODS: Demographic and clinicopathologic variables of PCa patients in the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2017 were retrospectively analyzed. We used six different machine learning algorithms, including Decision tree (DT), Random forest (RF), Multilayer Perceptron (MLP), Logistic regression (LR), Naive Bayes classifiers (NBC), and eXtreme gradient boosting (XGB), to build prediction models. External validation using data from 644 PCa patients of the First Affiliated Hospital of Nanchang University from 2010 to 2016. The performance of the models was evaluated using the area under receiver operating characteristic curve (AUC), accuracy score, sensitivity (recall rate) and specificity. A web predictor was developed based on the best performance model. RESULTS: A total of 207,137 PCa patients from SEER were included in this study. Of whom, 6725 (3.25%) developed BM. Gleason score, Prostate-specific antigen (PSA) value, T, N stage and age were found to be the risk factors of BM. The XGB model offered the best predictive performance among these 6 models (AUC: 0.962, accuracy: 0.884, sensitivity (recall rate): 0.906, and specificity: 0.879). An XGB model-based web predictor was developed to predict BM in PCa patients. CONCLUSION: This study developed a machine learning model and a web predictor for predicting the risk of BM in PCa patients, which may help physicians make personalized clinical decisions and treatment strategy for patients.

Machine learning for the prediction of bone metastasis in patients with newly diagnosed thyroid cancer.

OBJECTIVES: This study aimed to establish a machine learning prediction model that can be used to predict bone metastasis (BM) in patients with newly diagnosed thyroid cancer (TC). METHODS: Demographic and clinicopathologic variables of TC patients in the Surveillance, Epidemiology, and End Results database from 2010 to 2016 were retrospectively analyzed. On this basis, we developed a random forest (RF) algorithm model based on machine-learning. The area under receiver operating characteristic curve (AUC), accuracy score, recall rate, and specificity are used to evaluate and compare the prediction performance of the RF model and the other model. RESULTS: A total of 17,138 patients were included in the study, with 166 (0.97%) developed bone metastases. Grade, T stage, histology, race, sex, age, and N stage were the important prediction features of BM. The RF model has better predictive performance than the other model (AUC: 0.917, accuracy: 0.904, recall rate: 0.833, and specificity: 0.905). CONCLUSIONS: The RF model constructed in this study could accurately predict bone metastases in TC patients, which may provide clinicians with more personalized clinical decision-making recommendations. Machine learning technology has the potential to improve the development of BM prediction models in TC patients.

LncRNA PPP1R14B-AS1 Promotes Tumor Cell Proliferation and Migration via the Enhancement of Mitochondrial Respiration.

PPP1R14B-AS1 is an antisense long non-coding RNA with unknown functions. Herein, gene differential analyses were performed using the data of patients with liver cancer and lung adenocarcinoma (LUAD) from The Cancer Genome Atlas database. PPP1R14B-AS1 was found to be upregulated and also overexpressed in 10 other types of cancers. In addition, PPP1R14B-AS1 overexpression was associated with poor overall prognosis in eight cancers. Furthermore, PPPAR14B-AS1 upregulation was positively associated with worsening development of liver and LUAD cancers and related to poor disease-free survival. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses suggested that PPP1R14B-AS1 strongly participated in regulating cell aerobic respiration processes, such as mitochondrial electron respiration chain and NADH dehydrogenation processes. Cell cytoplasmic and nuclear RNA purification assessment results revealed that PPP1R14B-AS existed in the cell nucleus and cytoplasm. The knockdown of PPP1R14B-AS1 in HepG2 and A549 cells using PPP1R14B-AS1-specific siRNAs decreased mitochondrial respiration as demonstrated by the reduction in basal respiration and ATP production. Moreover, PPP1R14B-AS1 downregulation did not obviously affect cell glycolysis ability. Finally, PPP1R14B-AS1 inhibition inhibited HepG2 and A549 cell migration and proliferation. In summary, our study found for the first time that PPP1R14B-AS1 could be a potential biomarker for cancer diagnosis and that PPP1R14B-AS1 inhibition could be a potentially effective therapy.

Characterization of aerosol chemical composition and the reconstruction of light extinction coefficients during winter in Wuhan, China.

To evaluate light extinction contributions of aerosol chemical constituents and their impacts on atmospheric visibility, the PM2.5 and its chemical components, light scattering (bsp) and absorption (bap) were continuously measured in Wuhan from January to February 2018. The average of PM2.5 concentration, bsp and bap were 96.5 ±â€¯13.7 µg m-3, 564 ±â€¯124 Mm-1 and 44 ±â€¯8 Mm-1 during polluted days, respectively, which was about 2.0, 2.1 and 1.6 times higher than those of clean days, respectively. Compared with the clean days, the increase of the mass concentrations of SNA (SO42-, NO3-, NH4+) during polluted days was higher than those of organic (OC) and elemental (EC) carbon, indicated the increase of SNA was the main cause of air pollution. The PM2.5 concentration threshold was 66 µg m-3, corresponding to the visibility lower than 10 km. The revised Interagency Monitoring of Protected Visual Environments (IMPROVE) algorithm was used to reconstruct the light extinction coefficient (bext) in Wuhan. The sum of light extinction coefficients of (NH4)2SO4, NH4NO3 and organic matter (OM) accounted for 70.5% and 83.9% of bext during clean and polluted days, respectively. The backward trajectory and potential source contribution function (PSCF) analysis revealed that regional transport accounted for 55.6% of the total airflow, which originated from south, northwest and west of Wuhan. The increases of (NH4)2SO4 and NH4NO3 concentrations, emitted from local vehicle exhaust and coal combustion, and their hygroscopic growth in ambient were the major causes of pollution in Wuhan.