A Multi-task Neural Network for Image Recognition in Magnetically Controlled Capsule Endoscopy.

BACKGROUND AND AIMS: Physicians are required to spend a significant amount of reading time of magnetically controlled capsule endoscopy. However, current deep learning models are limited to completing a single recognition task and cannot replicate the diagnostic process of a physician. This study aims to construct a multi-task model that can simultaneously recognize gastric anatomical sites and gastric lesions. METHODS: A multi-task recognition model named Mul-Recog-Model was established. The capsule endoscopy image data from 886 patients were selected to construct a training set and a test set for training and testing the model. Based on the same test set, the model in this study was compared with the current single-task recognition model with good performance. RESULTS: The sensitivity and specificity of the model for recognizing gastric anatomical sites were 99.8% (95% confidence intervals: 99.7-99.8) and 98.5% (95% confidence intervals: 98.3-98.7), and for gastric lesions were 98.8% (95% confidence intervals: 98.3-99.2) and 99.4% (95% confidence intervals: 99.1-99.7). Moreover, the positive predictive value, negative predictive value, and accuracy of the model were more than 95% in recognizing gastric anatomical sites and gastric lesions. Compared with the current single-task recognition model, our model showed comparable sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (p < 0.01, except for the negative predictive value of ResNet, p > 0.05). The Areas Under Curve of our model were 0.985 and 0.989 in recognizing gastric anatomical sites and gastric lesions. Furthermore, the model had 49.1 M parameters and 38.1G Float calculations. The model took 15.5 ms to recognize an image, which was less than the superposition of multiple single models (p < 0.01). CONCLUSIONS: The Mul-Recog-Model exhibited high sensitivity, specificity, PPV, NPV, and accuracy. The model demonstrated excellent performance in terms of parameters quantity, Float computation, and computing time. The utilization of the model for recognizing gastric images can improve the efficiency of physicians' reports and meet complex diagnostic requirements.

Nrf2 inhibits M1 macrophage polarization to ameliorate renal ischemia-reperfusion injury through antagonizing NF-κB signaling.

Renal ischemia-reperfusion injury (IRI) is a condition that arises from a sudden interruption of the blood flow to the kidney for a period of time followed by restoration of the blood supply. This process contributes to acute kidney injury (AKI), increases morbidity and mortality, and is a major risk factor for chronic kidney disease (CKD). Nuclear factor erythroid-derived 2-like 2 (Nrf2) has been shown to exhibit strong anti-oxidative and anti-inflammatory effects, which are reciprocally regulated by the pro-inflammatory actions of nuclear factor-kappa B (NF-κB) signaling. In this study, we established a model of AKI caused by renal IRI in mice lacking the Nrf2 gene (KO-Nrf2) and mice pre-injected with ML385 (Nrf2 inhibitor). In addition, LPS- or IL-4-induced M1- or M2-type polarized macrophages (RAW264.7), respectively, were also treated with Nrf2 activation and inhibition. The results demonstrated a more pronounced activation of the NF-κB signaling pathway in the Nrf2 inhibition model, accompanied by a more severe inflammatory effect. In cultured macrophages and renal IRI mice, Nrf2 inhibition activated M1 macrophage polarization, thereby increasing the release of proinflammatory cell factors (iNOS and TNF-α) and aggravating renal IRI. Notably, the inhibitory effect of Nrf2 on M1 macrophage polarization was related to the downregulation of the NF-κB signaling pathway activity, resulting in partial relief of renal IRI. Consequently, our findings indicated that Nrf2 inhibits M1 macrophage polarization to ameliorate renal IRI through antagonizing NF-κB signaling. Targeted activation of Nrf2 may be one of the important strategies for renal IRI treatment.

Porous Organic Cage-Based Quasi-Solid-State Electrolyte with Cavity-Induced Anion-Trapping Effect for Long-Life Lithium Metal Batteries.

Porous organic cages (POCs) with permanent porosity and excellent host-guest property hold great potentials in regulating ion transport behavior, yet their feasibility as solid-state electrolytes has never been testified in a practical battery. Herein, we design and fabricate a quasi-solid-state electrolyte (QSSE) based on a POC to enable the stable operation of Li-metal batteries (LMBs). Benefiting from the ordered channels and cavity-induced anion-trapping effect of POC, the resulting POC-based QSSE exhibits a high Li+ transference number of 0.67 and a high ionic conductivity of 1.25 × 10-4 S cm-1 with a low activation energy of 0.17 eV. These allow for homogeneous Li deposition and highly reversible Li plating/stripping for over 2000 h. As a proof of concept, the LMB assembled with POC-based QSSE demonstrates extremely stable cycling performance with 85% capacity retention after 1000 cycles. Therefore, our work demonstrates the practical applicability of POC as SSEs for LMBs and could be extended to other energy-storage systems, such as Na and K batteries.

Integrating network pharmacology, molecular docking and experimental verification to explore the therapeutic effect and potential mechanism of nomilin against triple-negative breast cancer.

BACKGROUND: Nomilin is a limonoid compound known for its multiple biological activities, but its role in triple negative breast cancer (TNBC) remains unclear. This study aims to uncover the potential therapeutic effect of nomilin on TNBC and elucidate the specific mechanism of its action. METHODS: We employed weighted gene co-expression network analysis (WGCNA), differential expression analysis, and the GeneCards database to identify potential targets for TNBC. Simultaneously, we utilized the Swiss Target Prediction, ChEMBL, and STITCH databases to identify potential targets of nomilin. The core targets and mechanisms of nomilin against TNBC were predicted through protein-protein interaction (PPI) network analysis, molecular docking, and enrichment analysis. The results of the network pharmacology were corroborated by conducting experiments. RESULTS: A total of 17,204 TNBC targets were screened, and 301 potential targets of nomilin were identified. Through the PPI network, eight core targets of nomilin against TNBC were pinpointed, namely BCL2, Caspase3, CyclinD1, EGFR, HSP90AA1, KRAS, PARP1, and TNF. Molecular docking, molecular dynamics simulation and proteome microarray revealed that nomilin exhibits strong binding activity to these core proteins. Enrichment analysis results indicated that the anti-TNBC effect of nomilin is associated with PI3K/Akt pathway. In vitro and in vivo experiments have demonstrated that nomilin inhibits TNBC cell proliferation and migration while promoting cell apoptosis through the PI3K/Akt pathway. CONCLUSION: For the first time, the research effectively discovered the objectives and mechanisms of nomilin in combating TNBC using network pharmacology, molecular docking, molecular dynamics simulation, proteome microarray and experimental confirmation, presenting a hopeful approach for treating TNBC.

Development and validation of a prognostic signature of breast cancer based on drug absorption, distribution, metabolism and excretion (ADME)-related genes.

The individual variation of carcinogenesis and drug response is influenced by the absorption, distribution, metabolism, and excretion (ADME) of drugs. The utilization of signatures derived from ADME-related genes holds potential for predicting prognosis and treatment response across diverse cancer types. Further investigation is required to completely understand the role of ADME-associated genes in breast cancer. A signature was constructed through the application of a least absolute shrinkage and selection operator regression model, employing prognostic differentially expressed genes found in both cancer tissue and normal tissue. To assess the robustness of the signature, verification analyses were carried out. RT-qPCR was utilized for the validation of gene expression related to risk. Subsequently, a nomogram was developed to enhance the clinical utility of our prognostic tool. The ADME signature, comprising four genes, was established and exhibited a robust association with the prognoses of individuals diagnosed with breast cancer. The nomogram was created by fusing the clinicopathological characteristics with the ADME signature. The ADME signature demonstrated remarkable superiority when compared to the performance of the other individual predictors. Additionally, the analysis of the immune microenvironment revealed that the ImmuneScores of the low-risk group were elevated. The variation in both the infiltration of immune cells and the expression of immune-related genes in the tissues differed among the two groups. For patients with breast cancer, the utilization of ADME signatures as biomarkers presents a significant reference point for prognosis and individualized treatment strategies.

Identification of a novel histone acetylation-related long non-coding RNA model combined with qRT-PCR experiments for prognosis and therapy in gastric cancer.

Gastric cancer (GC) is considered a global health crisis due to the scarcity of early diagnostic methods. Numerous studies have substantiated the involvement of histone acetylation imbalance in the progression of diverse tumor types. The potential roles of long non-coding RNA (lncRNA) in improving prognostic, predictive as well as therapeutic approaches in cancers have made it a major hotspot in recent years. Nevertheless, existent studies have never concerned the prognostic and clinical value of histone acetylation-related lncRNAs (HARlncs) in GC. Based on the aforementioned rationale, we developed a prognostic model incorporating four HARlncs-AC114730.1, AL445250.1, LINC01778, and AL163953.1-which demonstrated potential as an independent predictor of prognosis. Subsequently, GC patients were stratified into high-risk and low-risk groups. The low-risk group exhibited significantly higher overall survival (OS) compared to the high-risk group. Based on the analyses of the tumor microenvironment (TME) and immune responses, significant differences were observed between the two risk groups in terms of immune cell infiltration, immune checkpoint (ICP) expression, and other TME alterations. Furthermore, the sensitivity of GC patients to some chemotherapeutic drugs and the discrepant biological behaviors of three tumor clusters were studied in this model. In summary, we developed an effective HARlncs model with the objective of offering novel prognostic prediction methods and identifying potential therapeutic targets for GC patients.

Cellular heterogeneity of pluripotent stem cell-derived cardiomyocyte grafts is mechanistically linked to treatable arrhythmias.

Preclinical data have confirmed that human pluripotent stem cell-derived cardiomyocytes (PSC-CMs) can remuscularize the injured or diseased heart, with several clinical trials now in planning or recruitment stages. However, because ventricular arrhythmias represent a complication following engraftment of intramyocardially injected PSC-CMs, it is necessary to provide treatment strategies to control or prevent engraftment arrhythmias (EAs). Here, we show in a porcine model of myocardial infarction and PSC-CM transplantation that EAs are mechanistically linked to cellular heterogeneity in the input PSC-CM and resultant graft. Specifically, we identify atrial and pacemaker-like cardiomyocytes as culprit arrhythmogenic subpopulations. Two unique surface marker signatures, signal regulatory protein α (SIRPA)+CD90-CD200+ and SIRPA+CD90-CD200-, identify arrhythmogenic and non-arrhythmogenic cardiomyocytes, respectively. Our data suggest that modifications to current PSC-CM-production and/or PSC-CM-selection protocols could potentially prevent EAs. We further show that pharmacologic and interventional anti-arrhythmic strategies can control and potentially abolish these arrhythmias.

Glycosylphosphatidylinositol anchor biosynthesis pathway-based biomarker identification with machine learning for prognosis and T cell exhaustion status prediction in breast cancer.

As the primary component of anti-tumor immunity, T cells are prone to exhaustion and dysfunction in the tumor microenvironment (TME). A thorough understanding of T cell exhaustion (TEX) in the TME is crucial for effectively addressing TEX in clinical settings and promoting the efficacy of immune checkpoint blockade therapies. In eukaryotes, numerous cell surface proteins are tethered to the plasma membrane via Glycosylphosphatidylinositol (GPI) anchors, which play a crucial role in facilitating the proper translocation of membrane proteins. However, the available evidence is insufficient to support any additional functional involvement of GPI anchors. Here, we investigate the signature of GPI-anchor biosynthesis in the TME of breast cancer (BC)patients, particularly its correlation with TEX. GPI-anchor biosynthesis should be considered as a prognostic risk factor for BC. Patients with high GPI-anchor biosynthesis showed more severe TEX. And the levels of GPI-anchor biosynthesis in exhausted CD8 T cells was higher than normal CD8 T cells, which was not observed between malignant epithelial cells and normal mammary epithelial cells. In addition, we also found that GPI -anchor biosynthesis related genes can be used to diagnose TEX status and predict prognosis in BC patients, both the TEX diagnostic model and the prognostic model showed good AUC values. Finally, we confirmed our findings in cells and clinical samples. Knockdown of PIGU gene expression significantly reduced the proliferation rate of MDA-MB-231 and MCF-7 cell lines. Immunofluorescence results from clinical samples showed reduced aggregation of CD8 T cells in tissues with high expression of GPAA1 and PIGU.

Intelligent control of self-driving vehicles based on adaptive sampling supervised actor-critic and human driving experience.

Due to the complexity of the driving environment and the dynamics of the behavior of traffic participants, self-driving in dense traffic flow is very challenging. Traditional methods usually rely on predefined rules, which are difficult to adapt to various driving scenarios. Deep reinforcement learning (DRL) shows advantages over rule-based methods in complex self-driving environments, demonstrating the great potential of intelligent decision-making. However, one of the problems of DRL is the inefficiency of exploration; typically, it requires a lot of trial and error to learn the optimal policy, which leads to its slow learning rate and makes it difficult for the agent to learn well-performing decision-making policies in self-driving scenarios. Inspired by the outstanding performance of supervised learning in classification tasks, we propose a self-driving intelligent control method that combines human driving experience and adaptive sampling supervised actor-critic algorithm. Unlike traditional DRL, we modified the learning process of the policy network by combining supervised learning and DRL and adding human driving experience to the learning samples to better guide the self-driving vehicle to learn the optimal policy through human driving experience and real-time human guidance. In addition, in order to make the agent learn more efficiently, we introduced real-time human guidance in its learning process, and an adaptive balanced sampling method was designed for improving the sampling performance. We also designed the reward function in detail for different evaluation indexes such as traffic efficiency, which further guides the agent to learn the self-driving intelligent control policy in a better way. The experimental results show that the method is able to control vehicles in complex traffic environments for self-driving tasks and exhibits better performance than other DRL methods.

Development of a biomarker signature associated with anoikis to predict prognosis and immunotherapy response in melanoma.

Skin cutaneous melanoma (SKCM) is malignant cancer known for its high aggressiveness and unfavorable prognosis, particularly in advanced tumors. Anoikis is a specific pattern of programmed cell death associated with tumor regeneration, migration, and metastasis. Nevertheless, limited research has been conducted to investigate the function of anoikis in SKCM. Anoikis-related genes (ARGs) were extracted from Genecards to identify SKCM subtypes and to explore the immune microenvironment between the different subtypes. Prognostic models of SKCM were developed by LASSO COX regression analysis. Subsequently, the predictive value of risk scores in SKCM and the association with immunotherapy were further explored. Finally, the expression of 6 ARGs involved in the model construction was detected by immunohistochemistry and PCR. This study identified 20 ARGs significantly associated with SKCM prognosis and performed disease subtype analysis of samples based on these genes, different subtypes exhibited significantly different clinical features and tumor immune microenvironment (TIME) landscapes. The risk score prognostic model was generated by further screening and identification of the six ARGs. The model exhibited a high degree of sensitivity and specificity to predict the prognosis of individuals with SKCM. These high- and low-risk populations showed different immune statuses and drug sensitivity. Further immunohistochemical and PCR experiments identified significant differential expression of the six ARGs in tumor and normal samples. Anoikis-based features may serve as novel prognostic biomarkers for SKCM and may provide important new insights for survival prediction and individualized treatment development.

Multi-View Time-Series Hypergraph Neural Network for Action Recognition.

Recently, action recognition has attracted considerable attention in the field of computer vision. In dynamic circumstances and complicated backgrounds, there are some problems, such as object occlusion, insufficient light, and weak correlation of human body joints, resulting in skeleton-based human action recognition accuracy being very low. To address this issue, we propose a Multi-View Time-Series Hypergraph Neural Network (MV-TSHGNN) method. The framework is composed of two main parts: the construction of a multi-view time-series hypergraph structure and the learning process of multi-view time-series hypergraph convolutions. Specifically, given the multi-view video sequence frames, we first extract the joint features of actions from different views. Then, limb components and adjacent joints spatial hypergraphs based on the joints of different views at the same time are constructed respectively, temporal hypergraphs are constructed joints of the same view at continuous times, which are established high-order semantic relationships and cooperatively generate complementary action features. After that, we design a multi-view time-series hypergraph neural network to efficiently learn the features of spatial and temporal hypergraphs, and effectively improve the accuracy of skeleton-based action recognition. To evaluate the effectiveness and efficiency of MV-TSHGNN, we conduct experiments on NTU RGB+D, NTU RGB+D 120 and imitating traffic police gestures datasets. The experimental results indicate that our proposed method model achieves the new state-of-the-art performance.

Humidity-tolerant and highly sensitive gas sensor for hydrogen sulfide based on WO3 nanocubes modified with CeO2.

The influence of ambient humidity on the gas-sensing characteristics of metal oxide semiconductors has been one of the greatest obstacles for gas-sensing applications. In this paper, the pure WO3 and CeO2-modified WO3 nanocubes were prepared by a simple hydrothermal method, and their gas-sensing characteristics in dry and humid atmospheres were investigated. The results show that CeO2/WO3 demonstrated excellent gas-sensing properties toward H2S with high sensitivity and high selectivity at 115 °C. Noteworthy, the humidity independence of the CeO2/WO3 increased compared to the WO3. The response retentions over the whole humidity range of the CeO2/WO3-6 and CeO2/WO3-15 sensors were 70.3, and 76%, respectively, which were much higher than the WO3 sensor (17.9%). The gas-sensing mechanism of CeO2-modified WO3 is discussed based on the gas sensitivity properties. The obtained results provide a promising route to enhance the anti-humidity properties of metal oxide semiconductor gas sensors.

Deep pan-cancer analysis and multi-omics evidence reveal that ALG3 inhibits CD8+ T cell infiltration by suppressing chemokine secretion and is associated with 5-fluorouracil sensitivity.

BACKGROUND: α-1,3-mannosyltransferase (ALG3) holds significance as a key member within the mannosyltransferase family. Nevertheless, the exact function of ALG3 in cancer remains ambiguous. Consequently, the current research aimed to examine the function and potential mechanisms of ALG3 in various types of cancer. METHODS: Deep pan-cancer analyses were conducted to investigate the expression patterns, prognostic value, genetic variations, single-cell omics, immunology, and drug responses associated with ALG3. Subsequently, in vitro experiments were executed to ascertain the biological role of ALG3 in breast cancer. Moreover, the link between ALG3 and CD8+ T cells was verified using immunofluorescence. Lastly, the association between ALG3 and chemokines was assessed using qRT-PCR and ELISA. RESULTS: Deep pan-cancer analysis demonstrated a heightened expression of ALG3 in the majority of tumors based on multi-omics evidence. ALG3 emerges as a diagnostic and prognostic biomarker across diverse cancer types. In addition, ALG3 participates in regulating the tumor immune microenvironment. Elevated levels of ALG3 were closely linked to the infiltration of bone marrow-derived suppressor cells (MDSC) and CD8+ T cells. According to in vitro experiments, ALG3 promotes proliferation and migration of breast cancer cells. Moreover, ALG3 inhibited CD8+ T cell infiltration by suppressing chemokine secretion. Finally, the inhibition of ALG3 enhanced the responsiveness of breast cancer cells to 5-fluorouracil treatment. CONCLUSION: ALG3 shows potential as both a prognostic indicator and immune infiltration biomarker across various types of cancer. Inhibition of ALG3 may represent a promising therapeutic strategy for tumor treatment.

A high-performance room-temperature NH3 gas sensor based on WO3/TiO2 nanocrystals decorated with Pt NPs.

In this work, a high-performance room-temperature ammonia (NH3) gas sensor based on Pt-modified WO3-TiO2 nanocrystals was synthesized via a two-step hydrothermal method. The structural properties were characterized by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, and X-ray photoelectron spectroscopy. The 10 at% Pt@WO3-TiO2 nanocrystals present the highest NH3 sensing performance at room temperature. Compared with the nanocrystals without Pt modification, the sensitivity of the Pt@WO3-TiO2 sensor is tenfold higher, with the lowest concentration threshold reaching the 75 ppb level. The response is approximately 92.28 to 50 ppm, and response and recovery times are 23 s and 8 s, respectively. The improved sensing was attributed to a synergetic mechanism involving the space charge layer effect and Pt metal sensitization, enhancing the electron transfer efficiency, oxygen vacancy and specific surface area. This study is expected to guide the development of high-performance room-temperature ammonia sensors for clinical breath testing.

Causal relationship between atrial fibrillation/warfarin and cutaneous melanoma: a two-sample Mendelian randomization study.

Purpose: In recent years, the relationship between malignant tumors and atrial fibrillation has attracted more and more attention. Atrial fibrillation can also cause a series of adverse events, such as the risk of thromboembolism. Also, Warfarin is often used here. But, the relationship between cutaneous melanoma and atrial fibrillation, and between cutaneous melanoma and warfarin is still unclear. Therefore, we used a two-sample Mendelian randomization to assess the causal relationship between atrial fibrillation/warfarin and cutaneous melanoma (cM). Methods: Firstly, atrial fibrillation (ukb-b-11550; nCase = 3,518, nControl = 459,415) and warfarin (ukb-b-13248; nCase = 4,623, nControl = 458,310) as exposures, with genome-wide association studies (GWAS) data from the United Kingdom Biobank. And cM (ieu-b-4969; nCase = 3,751, nControl = 372,016) as outcome, with GWAS data from the IEU Open GWAS project. Subsequently, single-nucleotide polymorphisms (SNPs) were filtered from GWAS studies using quality control measures. In addition, two-sample Mendelian randomization (MR) analysis was performed to explore the causal relationship between atrial fibrillation or warfarin and cM and used inverse variance weighting (IVW) as the primary analytical method. Finally, relevant heterogeneity and sensitivity analysis were performed to ensure the accuracy of the results. Results: A causal relationship between atrial fibrillation and cutaneous melanoma was observed, and between warfarin and cutaneous melanoma. Conclusion: The atrial fibrillation may play a causal role in the development of cutaneous melanoma, but the mechanism and the causal relationship between warfarin and cutaneous melanoma needs to be further elucidated.

A pan-cancer multi-omics analysis of lactylation genes associated with tumor microenvironment and cancer development.

Background: Lactylation is a significant post-translational modification bridging the gap between cancer epigenetics and metabolic reprogramming. However, the association between lactylation and prognosis, tumor microenvironment (TME), and response to drug therapy in various cancers remains unclear. Methods: First, the expression, prognostic value, and genetic and epigenetic alterations of lactylation genes were systematically explored in a pan-cancer manner. Lactylation scores were derived for each tumor using the single-sample gene set enrichment analysis (ssGSEA) algorithm. The correlation of lactylation scores with clinical features, prognosis, and TME was assessed by integrating multiple computational methods. In addition, GSE135222 data was used to assess the efficacy of lactylation scores in predicting immunotherapy outcomes. The expression of lactylation genes in breast cancers and gliomas were verified by RNA-sequencing. Results: Lactylation genes were significantly upregulated in most cancer types. CREBBP and EP300 exhibited high mutation rates in pan-cancer analysis. The prognostic impact of the lactylation score varied by tumor type, and lactylation score was a protective factor for KIRC, ACC, READ, LGG, and UVM, and a risk factor for CHOL, DLBC, LAML, and OV. In addition, a high lactylation score was associated with cold TME. The infiltration levels of CD8+ T, γδT, natural killer T cell (NKT), and NK cells were lower in tumors with higher lactylation scores. Finally, immunotherapy efficacy was worse in patients with high lactylation scores than other types. Conclusion: Lactylation genes are involved in malignancy formation. Lactylation score serves as a promising biomarker for predicting patient prognosis and immunotherapy efficacy.

Pan-cancer analysis of NUP155 and validation of its role in breast cancer cell proliferation, migration, and apoptosis.

NUP155 is reported to be correlated with tumor development. However, the role of NUP155 in tumor physiology and the tumor immune microenvironment (TIME) has not been previously examined. This study comprehensively investigated the expression, immunological function, and prognostic significance of NUP155 in different cancer types. Bioinformatics analysis revealed that NUP155 was upregulated in 26 types of cancer. Additionally, NUP155 upregulation was strongly correlated with advanced pathological or clinical stages and poor prognosis in several cancers. Furthermore, NUP155 was significantly and positively correlated with DNA methylation, tumor mutational burden, microsatellite instability, and stemness score in most cancers. Additionally, NUP155 was also found to be involved in TIME and closely associated with tumor infiltrating immune cells and immunoregulation-related genes. Functional enrichment analysis revealed a strong correlation between NUP155 and immunomodulatory pathways, especially antigen processing and presentation. The role of NUP155 in breast cancer has not been examined. This study, for the first time, demonstrated that NUP155 was upregulated in breast invasive carcinoma (BRCA) cells and revealed its oncogenic role in BRCA using molecular biology experiments. Thus, our study highlights the potential value of NUP155 as a biomarker in the assessment of prognostic prediction, tumor microenvironment and immunotherapeutic response in pan-cancer.

Identification of CREB5 as a prognostic and immunotherapeutic biomarker in glioma through multi-omics pan-cancer analysis.

BACKGROUND: The functional relevance of cyclic adenosine monophosphate (cAMP)-response element-binding protein 5 (CREB5) in cancers remains elusive, despite its significance as a member of the CREB family. The current research aims to explore the role of CREB5 in multiple cancers. METHODS: Pan-cancer analysis was performed to explore the expression patterns, prognostic value, mutational landscape as well as single-cell omic, immunologic, and drug sensitivity profiles of CREB5. Furthermore, we incorporated five distinct machine learning algorithms and determined that the least absolute shrinkage and selection operator-COX (LASSO-COX) algorithm, which exhibited the highest C index, was the optimal selection. Subsequently, we constructed a prognostic model centered around CREB5-associated genes. To elucidate the biological function of CREB5 in glioma cells, several assays including cell counting kit-8 (CCK-8), wound healing, transwell, flow cytometric were performed. RESULTS: CREB5 was overexpressed in pan-cancer and was linked to unfavorable prognosis, particularly in glioma. Furthermore, genetic alterations were determined in various types of cancer, and modifications in the CREB5 gene were linked to the prognosis. The single-cell omics and enrichment analyses showed that CREB5 was predominantly expressed in malignant glioma cells and was critically involved in the regulation of various oncogenic processes. Elevated levels of CREB5 were strongly linked with the infiltration of cancer-associated fibroblasts and the Th1 subset of CD4+ T cells. The validated CREB5-associated prognostic model reliably predicted the prognosis and drug response of glioma patients. The in vitro experiments showed that CREB5 promoted glioma cell proliferation, invasion, migration, and gap phase 2/mitotic (G2/M) phase arrest and recruited M2 macrophages into glioma cells. CONCLUSION: CREB5 has the potential to act as an oncogene and a biological marker in multiple cancers, particularly glioma.

Pan-cancer Multi-omics Analysis Reveals HMGN1 as a Potential Prognostic and Immune Infiltration-associated Biomarker.

BACKGROUND: The High Mobility Group Nucleosomal Binding Domain 1 Gene (HMGN1) is crucial for epigenetic regulation. However, the specific function of HMGN1 in cancer development is unclear. METHODS: Raw data on HMGN1 expression were procured from Genotype-Tissue Expression (GTEx), the University of Alabama- Birmingham CANcer data analysis Portal (UALCAN), and The Cancer Genome Atlas (TCGA). Thereafter, the pan-cancer analysis was implemented to understand the HMGN1 expression patterns, prognostic value, and immunological features. Furthermore, the Gene Set Enrichment Analysis (GSEA) was executed via R language. In addition, the relationship between HMGN1 and the sensitivity of antitumor drugs was also determined. Finally, real-time PCR (RT-PCR) experiments were carried out. RESULTS: Pan-cancer analysis revealed that HMGN1 was upregulated in several solid tumors and was associated with pathological staging and poor prognosis. In addition, HMGN1 was found to be involved in regulating the tumor microenvironment. The GSEA enrichment analysis indicated that HMGN1 assisted in the regulation of oncogenic processes, especially metabolic and immune pathways. Furthermore, HMGN1 expression was linked to microsatellite instability (MSI) and tumor mutational burden (TMB) across diverse tumor types. RT-PCR assays indicated that HMGN1 was overexpressed in the gastric and breast cancer cell lines and tissues. CONCLUSION: This study highlighted the potential of HMGN1 as a biomarker for pan- - cancer analysis.

Wnt dose escalation during the exit from pluripotency identifies tranilast as a regulator of cardiac mesoderm.

Wnt signaling is a critical determinant of cell lineage development. This study used Wnt dose-dependent induction programs to gain insights into molecular regulation of stem cell differentiation. We performed single-cell RNA sequencing of hiPSCs responding to a dose escalation protocol with Wnt agonist CHIR-99021 during the exit from pluripotency to identify cell types and genetic activity driven by Wnt stimulation. Results of activated gene sets and cell types were used to build a multiple regression model that predicts the efficiency of cardiomyocyte differentiation. Cross-referencing Wnt-associated gene expression profiles to the Connectivity Map database, we identified the small-molecule drug, tranilast. We found that tranilast synergistically activates Wnt signaling to promote cardiac lineage differentiation, which we validate by in vitro analysis of hiPSC differentiation and in vivo analysis of developing quail embryos. Our study provides an integrated workflow that links experimental datasets, prediction models, and small-molecule databases to identify drug-like compounds that control cell differentiation.