Single-cell RNA sequencing integrated with bulk RNA sequencing analysis identifies a tumor immune microenvironment-related lncRNA signature in lung adenocarcinoma.

BACKGROUND: Recently, long non-coding RNAs (lncRNAs) have been demonstrated as essential roles in tumor immune microenvironments (TIME). Nevertheless, researches on the clinical significance of TIME-related lncRNAs are limited in lung adenocarcinoma (LUAD). METHODS: Single-cell RNA sequencing and bulk RNA sequencing data are integrated to identify TIME-related lncRNAs. A total of 1368 LUAD patients are enrolled from 6 independent datasets. An integrative machine learning framework is introduced to develop a TIME-related lncRNA signature (TRLS). RESULTS: This study identified TIME-related lncRNAs from integrated analysis of single­cell and bulk RNA sequencing data. According to these lncRNAs, a TIME-related lncRNA signature was developed and validated from an integrative procedure in six independent cohorts. TRLS exhibited a robust and reliable performance in predicting overall survival. Superior prediction performance barged TRLS to the forefront from comparison with general clinical features, molecular characters, and published signatures. Moreover, patients with low TRLS displayed abundant immune cell infiltration and active lipid metabolism, while patients with high TRLS harbored significant genomic alterations, high PD-L1 expression, and elevated DNA damage repair (DDR) relevance. Notably, subclass mapping analysis of nine immunotherapeutic cohorts demonstrated that patients with high TRLS were more sensitive to immunotherapy. CONCLUSIONS: This study developed a promising tool based on TIME-related lncRNAs, which might contribute to tailored treatment and prognosis management of LUAD patients.

Integrated Exploration of Epigenetic Dysregulation Reveals a Stemness/EMT Subtype and MMP12 Linked to the Progression and Prognosis in Hepatocellular Carcinoma.

Epigenetic dysregulation drives aberrant transcriptional programs playing a critical role in hepatocellular carcinoma (HCC), which may provide novel insights into the heterogeneity of HCC. This study performed an integrated exploration on the epigenetic dysregulation of miRNA and methylation. We discovered and validated three patterns endowed with gene-related transcriptional traits and clinical outcomes. Specially, a stemness/epithelial-mesenchymal transition (EMT) subtype was featured by immune exhaustion and the worst prognosis. Besides, MMP12, a characteristic gene, was highly expressed in the stemness/EMT subtype, which was verified as a pivotal regulator linked to the unfavorable prognosis and further proven to promote tumor proliferation, invasion, and metastasis in vitro experiments. Proteomic analysis by mass spectrometry sequencing also indicated that the overexpression of MMP12 was significantly associated with cell proliferation and adhesion. Taken together, this study unveils innovative insights into epigenetic dysregulation and identifies a stemness/EMT subtype-specific gene, MMP12, correlated with the progression and prognosis of HCC.

Cellular Ligand-Receptor Perturbations Unravel MEIS2 as a Key Factor for the Aggressive Progression and Prognosis in Stage II/III Colorectal Cancer.

Approximately 10-15% of stage II and 25-30% of stage III colorectal cancer (CRC) patients experience recurrence within 5 years after surgery, and existing taxonomies are insufficient to meet the needs of clinical precision treatment. Thus, robust biomarkers and precise management were urgently required to stratify stage II and III CRC and identify potential patients who will benefit from postoperative adjuvant therapy. Alongside, interactions of ligand-receptor pairs point to an emerging direction in tumor signaling with far-reaching implications for CRC, while their impact on tumor subtyping has not been elucidated. Herein, based on multiple large-sample multicenter cohorts and perturbations of the ligand-receptor interaction network, four well-characterized ligand-receptor-driven subtypes (LRDS) were established and further validated. These molecular taxonomies perform with unique heterogeneity in terms of molecular characteristics, immune and mutational landscapes, and clinical features. Specifically, MEIS2, a key LRDS4 factor, performs significant associations with proliferation, invasion, migration, and dismal prognosis of stage II/III CRC, revealing promising directions for prognostic assessment and individualized treatment of CRC patients. Overall, our study sheds novel insights into the implications of intercellular communication on stage II/III CRC from a ligand-receptor interactome perspective and revealed MEIS2 as a key factor in the aggressive progression and prognosis for stage II/III CRC.

Crosstalk of cell death pathways unveils an autophagy-related gene AOC3 as a critical prognostic marker in colorectal cancer.

The intricate crosstalk of various cell death forms was recently implicated in cancers, laying a foundation for exploring the association between cell death and cancers. Recent evidence has demonstrated that biological networks outperform snapshot gene expression profiles at discovering promising biomarkers or heterogenous molecular subtypes across different cancer types. In order to investigate the behavioral patterns of cell death-related interaction perturbation in colorectal cancer (CRC), this study constructed the interaction-perturbation network with 11 cell death pathways and delineated four cell death network (CDN) derived heterogeneous subtypes (CDN1-4) with distinct molecular characteristics and clinical outcomes. Specifically, we identified a subtype (CDN4) endowed with high autophagy activity and the worst prognosis. Furthermore, AOC3 was identified as a potential autophagy-related biomarker, which demonstrated exceptional predictive performance for CDN4 and significant prognostic value. Overall, this study sheds light on the complex interplay of various cell death forms and reveals an autophagy-related gene AOC3 as a critical prognostic marker in CRC.

Monitoring of Sweat Ions and Physiological Parameters via a Reconfigurable Modular System.

In recent years, wearable sensors have revolutionized health monitoring by enabling continuous, real-time tracking of human health and performance. These noninvasive devices are usually designed to monitor human physical state and biochemical markers. However, enhancing their functionalities often demands intricate customization by designers and additional expenses for users. Here, we present a strategy using assembled modular circuits to customize health monitoring wearables. The modular circuits can be effortlessly reconfigured to meet various specific requirements, facilitating the incorporation of diverse functions at a lower cost. To validate this approach, modular circuits were employed to develop four distinct systems for in vitro evaluations. These systems enabled the detection of sweat biomarkers and physical signals under various scenarios, including sedentary state, exercise, and daily activities with or without incorporating iontophoresis to induce sweat. Four key sweat markers (K+, Ca2+, Na+, and pH) and three essential physical indicators (heart rate, blood oxygen levels, and skin temperature) are selected as the detection targets. Commercial methods were also used to evaluate the potential for effective health monitoring with our technique. This reconfigurable modular wearable (ReModuWear) system promises to provide more easy-to-use and comprehensive health assessments. Additionally, it may contribute to environmental sustainability by reusing modules.

Establishment and validation of a bad outcomes prediction model based on EEG and clinical parameters in prolonged disorder of consciousness.

Objective: This study aimed to explore the electroencephalogram (EEG) indicators and clinical factors that may lead to poor prognosis in patients with prolonged disorder of consciousness (pDOC), and establish and verify a clinical predictive model based on these factors. Methods: This study included 134 patients suffering from prolonged disorder of consciousness enrolled in our department of neurosurgery. We collected the data of sex, age, etiology, coma recovery scales (CRS-R) score, complications, blood routine, liver function, coagulation and other laboratory tests, resting EEG data and follow-up after discharge. These patients were divided into two groups: training set (n = 107) and verification set (n = 27). These patients were divided into a training set of 107 and a validation set of 27 for this study. Univariate and multivariate regression analysis were used to determine the factors affecting the poor prognosis of pDOC and to establish nomogram model. We use the receiver operating characteristic (ROC) and calibration curves to quantitatively test the effectiveness of the training set and the verification set. In order to further verify the clinical practical value of the model, we use decision curve analysis (DCA) to evaluate the model. Result: The results from univariate and multivariate logistic regression analyses suggested that an increased frequency of occurrence microstate A, reduced CRS-R scores at the time of admission, the presence of episodes associated with paroxysmal sympathetic hyperactivity (PSH), and decreased fibrinogen levels all function as independent prognostic factors. These factors were used to construct the nomogram. The training and verification sets had areas under the curve of 0.854 and 0.920, respectively. Calibration curves and DCA demonstrated good model performance and significant clinical benefits in both sets. Conclusion: This study is based on the use of clinically available and low-cost clinical indicators combined with EEG to construct a highly applicable and accurate model for predicting the adverse prognosis of patients with prolonged disorder of consciousness. It provides an objective and reliable tool for clinicians to evaluate the prognosis of prolonged disorder of consciousness, and helps clinicians to provide personalized clinical care and decision-making for patients with prolonged disorder of consciousness and their families.

OLFM4 modulates intestinal inflammation by promoting IL-22+ILC3 in the gut.

Group 3 innate lymphoid cells (ILC3s) play key roles in intestinal inflammation. Olfactomedin 4 (OLFM4) is highly expressed in the colon and has a potential role in dextran sodium sulfate-induced colitis. However, the detailed mechanisms underlying the effects of OLFM4 on ILC3-mediated colitis remain unclear. In this study, we identify OLFM4 as a positive regulator of IL-22+ILC3. OLFM4 expression in colonic ILC3s increases substantially during intestinal inflammation in humans and mice. Compared to littermate controls, OLFM4-deficient (OLFM4-/-) mice are more susceptible to bacterial infection and display greater resistance to anti-CD40 induced innate colitis, together with impaired IL-22 production by ILC3, and ILC3s from OLFM4-/-mice are defective in pathogen resistance. Besides, mice with OLFM4 deficiency in the RORγt compartment exhibit the same trend as in OLFM4-/-mice, including colonic inflammation and IL-22 production. Mechanistically, the decrease in IL-22+ILC3 caused by OLFM4 deficiency involves the apoptosis signal-regulating kinase 1 (ASK1)- p38 MAPK signaling-dependent downregulation of RAR-related orphan receptor gamma (RORγt) protein. The OLFM4-metadherin (MTDH) complex upregulates p38/RORγt signaling, which is necessary for IL-22+ILC3 activation. The findings indicate that OLFM4 is a novel regulator of IL-22+ILC3 and essential for modulating intestinal inflammation and tissue homeostasis.

Machine learning-driven diagnostic signature provides new insights in clinical management of hypertrophic cardiomyopathy.

AIMS: In an era of evolving diagnostic possibilities, existing diagnostic systems are not fully sufficient to promptly recognize patients with early-stage hypertrophic cardiomyopathy (HCM) without symptomatic and instrumental features. Considering the sudden death of HCM, developing a novel diagnostic model to clarify the patients with early-stage HCM and the immunological characteristics can avoid misdiagnosis and attenuate disease progression. METHODS AND RESULTS: Three hundred eighty-five samples from four independent cohorts were systematically retrieved. The weighted gene co-expression network analysis, differential expression analysis (|log2(foldchange)| > 0.5 and adjusted P < 0.05), and protein-protein interaction network were sequentially performed to identify HCM-related hub genes. With a machine learning algorithm, the least absolute shrinkage and selection operator regression algorithm, a stable diagnostic model was developed. The immune-cell infiltration and biological functions of HCM were also explored to characterize its underlying pathogenic mechanisms and the immune signature. Two key modules were screened based on weighted gene co-expression network analysis. Pathogenic mechanisms relevant to extracellular matrix and immune pathways have been discovered. Twenty-seven co-regulated genes were recognized as HCM-related hub genes. Based on the least absolute shrinkage and selection operator algorithm, a stable HCM diagnostic model was constructed, which was further validated in the remaining three cohorts (n = 385). Considering the tight association between HCM and immune-related functions, we assessed the infiltrating abundance of various immune cells and stromal cells based on the xCell algorithm, and certain immune cells were significantly different between high-risk and low-risk groups. CONCLUSIONS: Our study revealed a number of hub genes and novel pathways to provide potential targets for the treatment of HCM. A stable model was developed, providing an efficient tool for the diagnosis of HCM.

Assessment of isthmus filling using two obturation techniques performed by students with different levels of clinical experience.

Background/purpose: Root canal filling is a necessary skill for dental students and an important aspect of endodontic education. This study aimed to evaluate the effect of students' clinical experiences on isthmus filling using different techniques and sealers. Materials and methods: One hundred eight three-dimensional-printed resin replicas of isthmus were divided into six groups and either continuous wave of condensation (CWC) or single-cone obturation (SC) was performed. One of three sealers (AH Plus Jet®, GuttaFlow2, iRoot SP) was used together with a size-fitted gutta-percha master cone. All the obturations were completed by students with three different levels of clinical experience including senior postgraduate students (SPS), junior postgraduate students (JPS), and undergraduate students (US). The percentages of filled areas (PFA) at 2, 4, 6, and 8 mm from the apex were analyzed using a light microscope. Data were analyzed using the Mann-Whitney U test or Kruskal-Wallis 1-way ANOVA with Dunn's tests (α = 0.05). Results: The CWC group exhibited a higher PFA than the SC group (P < 0.05). The PFA was higher in the SPS group than in the JPS group or the US group with CWC (P < 0.05). The three clinical experience groups showed similar PFAs with SC (P > 0.05); however, when using SC with iRoot SP, the PFA was higher than with either of the other two sealers (P < 0.05). Conclusion: CWC was found to be technique-sensitive and required clinical training. With SC, clinical experience did not improve the quality of isthmus filling without additional training. CWC was superior to SC for type IV isthmuses. When using SC, better filling quality was obtained with a bioceramic sealer.

Corrigendum: Pyruvate kinase M2 promotes prostate cancer metastasis through regulating ERK1/2-COX-2 signaling.

[This corrects the article DOI: 10.3389/fonc.2020.544288.].

Diabetes mellitus as a risk factor for severe dengue fever and West Nile fever: A meta-analysis.

BACKGROUND: Dengue fever (DF) and West Nile fever (WNF) have become endemic worldwide in the last two decades. Studies suggest that individuals with diabetes mellitus (DM) are at a higher risk of developing severe complications from these diseases. Identifying the factors associated with a severe clinical presentation is crucial, as prompt treatment is essential to prevent complications and fatalities. This article aims to summarize and assess the published evidence regarding the link between DM and the risk of severe clinical manifestations in cases of DF and WNF. METHODOLOGY/PRINCIPAL FINDINGS: A systematic search was conducted using the PubMed and Web of Science databases. 27 studies (19 on DF, 8 on WNF) involving 342,873 laboratory-confirmed patients were included in the analysis. The analysis showed that a diagnosis of DM was associated with an increased risk for severe clinical presentations of both DF (OR 3.39; 95% CI: 2.46, 4.68) and WNF (OR 2.89; 95% CI: 1.89, 4.41). DM also significantly increased the risk of death from both diseases (DF: OR 1.95; 95% CI: 1.09, 3.52; WNF: OR 1.74; 95% CI: 1.40, 2.17). CONCLUSIONS/SIGNIFICANCE: This study provides strong evidence supporting the association between DM and an increased risk of severe clinical manifestations in cases of DF and WNF. Diabetic individuals in DF or WNF endemic areas should be closely monitored when presenting with febrile symptoms due to their higher susceptibility to severe disease. Early detection and appropriate management strategies are crucial in reducing the morbidity and mortality rates associated with DF and WNF in diabetic patients. Tailored care and targeted public health interventions are needed to address this at-risk population. Further research is required to understand the underlying mechanisms and develop effective preventive and therapeutic approaches.