The Hippo pathway plays essential roles in organ size control and cancer prevention via restricting its downstream effector, Yes‐associated protein (YAP). Previous studies have revealed an oncogenic ...function of YAP in reprogramming glucose metabolism, while the underlying mechanism remains to be fully clarified. Accumulating evidence suggests long noncoding RNAs (lncRNAs) as attractive therapeutic targets, given their roles in modulating various cancer‐related signaling pathways. In this study, we report that lncRNA breast cancer anti‐estrogen resistance 4 (BCAR4) is required for YAP‐dependent glycolysis. Mechanistically, YAP promotes the expression of BCAR4, which subsequently coordinates the Hedgehog signaling to enhance the transcription of glycolysis activators HK2 and PFKFB3. Therapeutic delivery of locked nucleic acids (LNAs) targeting BCAR4 attenuated YAP‐dependent glycolysis and tumor growth. The expression levels of BCAR4 and YAP are positively correlated in tissue samples from breast cancer patients, where high expression of both BCAR4 and YAP is associated with poor patient survival outcome. Taken together, our study not only reveals the mechanism by which YAP reprograms glucose metabolism, but also highlights the therapeutic potential of targeting YAP‐BCAR4‐glycolysis axis for breast cancer treatment.
Synopsis
Yes‐associated protein promotes cancer formation by reprogramming glucose metabolism. A long noncoding RNA BCAR4 is a key downstream effector of YAP, in regulation of glycolysis and tumorigenesis via GLI2‐mediated expression of key glycolytic enzymes.
BCAR4 is a direct transcriptional target of YAP.
BCAR4 promotes glycolysis by increasing the expression of HK2 and PFKFB3.
GLI2 activation is required for the expression of glycolytic enzymes downstream of BCAR4
High YAP and BCAR4 expression levels positively correlate in breast cancer patient samples and are linked to poor clinical outcomes.
Inhibition of BCAR4 via Locked Nucleic Acids (LNAs) attenuated YAP‐dependent glycolysis and tumor growth.
Yes‐associated protein activation triggers transcription of long noncoding RNA BCAR4, leading to GLI2‐mediated expression of key glycolytic enzymes.
The national “Internet +” policies and the emergence of internet hospitals have created a new direction for the management of pain outside of the hospital. Nevertheless, there are no consolidated ...studies conducted by pain physicians on the current state of internet hospital–based online medical services used by patients with pain outside of a hospital setting. In this retrospective study, we aimed to examine the status of the use of internet hospitals by patients who experience pain. Moreover, we identified the factors that influenced patients' decisions to make an online visit through the internet hospital. Detailed information was collected online and offline from outpatients with pain at the information technology center of West China Hospital of Sichuan University from February 2020 to April 2022. Binary logistic regression analysis was conducted to identify the determinants that influenced patients' decisions to make an online visit to the internet hospital. Over a 2-year period, 85,266 pain-related clinic visits were recorded. Ultimately, 39,260 patients were enrolled for the analysis, with 12.9% (5088/39,260) having online visits. Both online and offline clinics had a greater number of visits by women than men. The average age of patients attending the online clinic was 46.85 (SD 16.56) years, whereas the average age of patients attending the offline clinic was 51.48 (SD 16.12) years. The majority of online clinic visitors (3059/5088, 60.1%) were employed, and one of the most common occupations was farming (721/5088, 14.2%). In addition, 51.8% (2635/5088) of patients who participated in the online clinics lived outside the hospital vicinity. Young (odds ratio OR 1.35, 95% CI 1.01-1.81; P=.045) and middle-aged (OR 1.98, 95% CI 1.81-2.16; P<.001) patients, employed patients (OR 1.11, 95% CI 1.04-1.18; P=.002), nonlocal patients (OR 1.57, 95% CI 1.48-1.67; P<.001), and the ordinary staff (OR 1.19, 95%CI 1.01-1.39; P=.03) were more likely to have the intention to choose online visits through the internet hospitals. Internet hospitals are flourishing as a more efficient and promising method of pain management and follow-up for patients with pain outside the hospital. People with pain who are young, working, and not in the vicinity of hospitals are more likely to visit internet hospitals.
No study so far investigates the change of PEEP setting in prone positioning for > 24 h. We conducted a preliminary study to examine the trend of optimal PEEP and the resulting physiological ...parameters in the course of prone positioning up to 42 h. The investigated parameters included Crs, mechanical power and the ratio of partial pressure of oxygen in arterial blood (PaO2) to the fraction of inspiratory oxygen concentration (FiO2). In our study, a substantial deterioration in mechanical power post-30-h proning was noted in one patient, with a concurrent drop in PaO2/FiO2 at TP36 (Fig. 1 bottom purple lines). Future investigations should explore the link between personalized proning durations, ventilator adjustments and patient outcomes such as ventilator-free days and mortality rates, aiming to provide valuable insights for clinical practice.
The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep ...learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission. We develop this model using a cohort of 1590 patients from 575 medical centers, with internal validation performance of concordance index 0.894 We further validate the model on three separate cohorts from Wuhan, Hubei and Guangdong provinces consisting of 1393 patients with concordance indexes of 0.890, 0.852 and 0.967 respectively. This model is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness, ensuring that patients at greatest risk of severe illness receive appropriate care as early as possible and allow for effective allocation of health resources.
Long noncoding RNAs (lncRNAs) are emerging as a new class of important regulators of signal transduction in tissue homeostasis and cancer development. Liquid-liquid phase separation (LLPS) occurs in ...a wide range of biological processes, while its role in signal transduction remains largely undeciphered. In this study, we uncovered a lipid-associated lncRNA, small nucleolar RNA host gene 9 (SNHG9) as a tumor-promoting lncRNA driving liquid droplet formation of Large Tumor Suppressor Kinase 1 (LATS1) and inhibiting the Hippo pathway. Mechanistically, SNHG9 and its associated phosphatidic acids (PA) interact with the C-terminal domain of LATS1, promoting LATS1 phase separation and inhibiting LATS1-mediated YAP phosphorylation. Loss of SNHG9 suppresses xenograft breast tumor growth. Clinically, expression of SNHG9 positively correlates with YAP activity and breast cancer progression. Taken together, our results uncover a novel regulatory role of a tumor-promoting lncRNA (i.e., SNHG9) in signal transduction and cancer development by facilitating the LLPS of a signaling kinase (i.e., LATS1).
Background
The ongoing coronavirus disease 2019 (COVID-2019) pandemic has swept all over the world, posing a great pressure on critical care resources due to large number of patients needing critical ...care. Statements from front-line experts in the field of intensive care are urgently needed.
Methods
Sixteen front-line experts in China fighting against the COVID-19 epidemic in Wuhan were organized to develop an expert statement after 5 rounds of expert seminars and discussions to provide trustworthy recommendation on the management of critically ill COVID-19 patients. Each expert was assigned tasks within their field of expertise to provide draft statements and rationale. Parts of the expert statement are based on epidemiological and clinical evidence, without available scientific evidences.
Results
A comprehensive document with 46 statements are presented, including protection of medical personnel, etiological treatment, diagnosis and treatment of tissue and organ functional impairment, psychological interventions, immunity therapy, nutritional support, and transportation of critically ill COVID-19 patients. Among them, 5 recommendations were strong (Grade 1), 21 were weak (Grade 2), and 20 were experts’ opinions. A strong agreement from voting participants was obtained for all recommendations.
Conclusion
There are still no targeted therapies for COVID-19 patients. Dynamic monitoring and supportive treatment for the restoration of tissue vascularization and organ function are particularly important.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent for coronavirus 2019 (COVID-19) pneumonia. Little is known about the kinetics, tissue distribution, ...cross-reactivity, and neutralization antibody response in patients with COVID-19. Two groups of patients with RT-PCR-confirmed COVID-19 were enrolled in this study: 12 severely ill patients in intensive care units who needed mechanical ventilation and 11 mildly ill patients in isolation wards. Serial clinical samples were collected for laboratory detection. Results showed that most of the severely ill patients had viral shedding in a variety of tissues for 20-40 days after onset of disease (8/12, 66.7%), while the majority of mildly ill patients had viral shedding restricted to the respiratory tract and had no detectable virus RNA 10 days after onset (9/11, 81.8%). Mildly ill patients showed significantly lower IgM response compared with that of the severe group. IgG responses were detected in most patients in both the severe and mild groups at 9 days after onset, and remained at a high level throughout the study. Antibodies cross-reactive to SARS-CoV and SARS-CoV-2 were detected in patients with COVID-19 but not in patients with MERS. High levels of neutralizing antibodies were induced after about 10 days after onset in both severely and mildly ill patients which were higher in the severe group. SARS-CoV-2 pseudotype neutralization test and focus reduction neutralization test with authentic virus showed consistent results. Sera from patients with COVID-19 inhibited SARS-CoV-2 entry. Sera from convalescent patients with SARS or Middle East respiratory syndrome (MERS) did not. Anti-SARS-CoV-2 S and N IgG levels exhibited a moderate correlation with neutralization titers in patients' plasma. This study improves our understanding of immune response in humans after SARS-CoV-2 infection.
Purpose
An ongoing outbreak of coronavirus disease 2019 (COVID-19) emerged in Wuhan since December 2019 and spread globally. However, information about critically ill patients with COVID-19 is still ...limited. We aimed to describe the clinical characteristics and outcomes of critically ill patients with COVID-19 and figure out the risk factors of mortality.
Methods
We extracted data retrospectively regarding 733 critically ill adult patients with laboratory-confirmed COVID-19 from 19 hospitals in China through January 1 to February 29, 2020. Demographic data, symptoms, laboratory values, comorbidities, treatments, and clinical outcomes were collected. The primary outcome was 28-day mortality. Data were compared between survivors and non-survivors.
Results
Of the 733 patients included in the study, the median (IQR) age was 65 (56–73) years and 256 (34.9%) were female. Among these patients, the median (IQR) APACHE II score was 10 (7 to 14) and 28-day mortality was 53.8%. Respiratory failure was the most common organ failure (597 81.5%), followed by shock (20%), thrombocytopenia (18.8%), central nervous system (8.6%) and renal dysfunction (8%). Multivariate Cox regression analysis showed that older age, malignancies, high APACHE II score, high
d
-dimer level, low PaO
2
/FiO
2
level, high creatinine level, high hscTnI level and low albumin level were independent risk factors of 28-day mortality in critically ill patients with COVID-19.
Conclusion
In this case series of critically ill patients with COVID-19 who were admitted into the ICU, more than half patients died at day 28. The higher percentage of organ failure in these patients indicated a significant demand for critical care resources.
Abstract
Background
A significant proportion of septic patients with acute lung injury (ALI) are recognized late due to the absence of an efficient diagnostic test, leading to the postponed ...treatments and consequently higher mortality. Identifying diagnostic biomarkers may improve screening to identify septic patients at high risk of ALI earlier and provide the potential effective therapeutic drugs. Machine learning represents a powerful approach for making sense of complex gene expression data to find robust ALI diagnostic biomarkers.
Methods
The datasets were obtained from GEO and ArrayExpress databases. Following quality control and normalization, the datasets (GSE66890, GSE10474 and GSE32707) were merged as the training set, and four machine learning feature selection methods (Elastic net, SVM, random forest and XGBoost) were applied to construct the diagnostic model. The other datasets were considered as the validation sets. To further evaluate the performance and predictive value of diagnostic model, nomogram, Decision Curve Analysis (DCA) and Clinical Impact Curve (CIC) were constructed. Finally, the potential small molecular compounds interacting with selected features were explored from the CTD database.
Results
The results of GSEA showed that immune response and metabolism might play an important role in the pathogenesis of sepsis-induced ALI. Then, 52 genes were identified as putative biomarkers by consensus feature selection from all four methods. Among them, 5 genes (ARHGDIB, ALDH1A1, TACR3, TREM1 and PI3) were selected by all methods and used to predict ALI diagnosis with high accuracy. The external datasets (E-MTAB-5273 and E-MTAB-5274) demonstrated that the diagnostic model had great accuracy with AUC value of 0.725 and 0.833, respectively. In addition, the nomogram, DCA and CIC showed that the diagnostic model had great performance and predictive value. Finally, the small molecular compounds (Curcumin, Tretinoin, Acetaminophen, Estradiol and Dexamethasone) were screened as the potential therapeutic agents for sepsis-induced ALI.
Conclusion
This consensus of multiple machine learning algorithms identified 5 genes that were able to distinguish ALI from septic patients. The diagnostic model could identify septic patients at high risk of ALI, and provide potential therapeutic targets for sepsis-induced ALI.
PDF
