Caspase-1 and Gasdermin D Afford the Optimal Targets with Distinct Switching Strategies in NLRP1b Inflammasome-Induced Cell Death

Emerging role of necroptosis, pyroptosis, and ferroptosis in breast cancer: New dawn for overcoming therapy resistance

Breast cancer (BC) is one of the primary causes of death in women worldwide. The challenges associated with adverse outcomes have increased significantly, and the identification of novel therapeutic targets has become increasingly urgent. Regulated cell death (RCD) refers to a type of cell death that can be regulated by several different biomacromolecules, which is distinctive from accidental cell death (ACD). In recent years, apoptosis, a representative RCD pathway, has gained significance as a target for BC medications. However, tumor cells exhibit avoidance of apoptosis and result in treatment resistance, which emphasizes further studies devoted to alternative cell death processes, namely necroptosis, pyroptosis, and ferroptosis. Here, in this review, we focus on summarizing the crucial signaling pathways of these RCD in BC. We further discuss the molecular mechanism and potentiality in clinical application of several prospective drugs, nanoparticles, and other small compounds targeting different RCD subroutines of BC. We also discuss the benefits of modulating RCD processes on drug resistance and the advantages of combining RCD modulators with conventional treatments in BC. This review will deepen our understanding of the relationship between RCD and BC, and shed new light on future directions to attack cancer vulnerabilities with RCD modulators for therapeutic purposes.

Identifying vital nodes for yeast network by dynamic network entropy

BACKGROUND

The progress of the cell cycle of yeast involves the regulatory relationships between genes and the interactions proteins. However, it is still obscure which type of protein plays a decisive role in regulation and how to identify the vital nodes in the regulatory network. To elucidate the sensitive node or gene in the progression of yeast, here, we select 8 crucial regulatory factors from the yeast cell cycle to decipher a specific network and propose a simple mixed K2 algorithm to identify effectively the sensitive nodes and genes in the evolution of yeast.

RESULTS

Considering the multivariate of cell cycle data, we first utilize the K2 algorithm limited to the stationary interval for the time series segmentation to measure the scores for refining the specific network. After that, we employ the network entropy to effectively screen the obtained specific network, and simulate the gene expression data by a normal distribution approximation and the screened specific network by the partial least squares method. We can conclude that the robustness of the specific network screened by network entropy is better than that of the specific network with the determined relationship by comparing the obtained specific network with the determined relationship. Finally, we can determine that the node CDH1 has the highest score in the specific network through a sensitivity score calculated by network entropy implying the gene CDH1 is the most sensitive regulatory factor.

CONCLUSIONS

It is clearly of great potential value to reconstruct and visualize gene regulatory networks according to gene databases for life activities. Here, we present an available algorithm to achieve the network reconstruction by measuring the network entropy and identifying the vital nodes in the specific nodes. The results indicate that inhibiting or enhancing the expression of CDH1 can maximize the inhibition or enhancement of the yeast cell cycle. Although our algorithm is simple, it is also the first step in deciphering the profound mystery of gene regulation.

Mechanistic elucidation of ferroptosis and ferritinophagy: implications for advancing our understanding of arthritis

In recent years, the emerging phenomenon of ferroptosis has garnered significant attention as a distinctive mode of programmed cell death. Distinguished by its reliance on iron and dependence on reactive oxygen species (ROS), ferroptosis has emerged as a subject of extensive investigation. Mechanistically, this intricate process involves perturbations in iron homeostasis, dampening of system Xc-activity, morphological dynamics within mitochondria, and the onset of lipid peroxidation. Additionally, the concomitant phenomenon of ferritinophagy, the autophagic degradation of ferritin, assumes a pivotal role by facilitating the liberation of iron ions from ferritin, thereby advancing the progression of ferroptosis. This discussion thoroughly examines the detailed cell structures and basic processes behind ferroptosis and ferritinophagy. Moreover, it scrutinizes the intricate web of regulators that orchestrate these processes and examines their intricate interplay within the context of joint disorders. Against the backdrop of an annual increase in cases of osteoarthritis, rheumatoid arthritis, and gout, these narrative sheds light on the intriguing crossroads of pathophysiology by dissecting the intricate interrelationships between joint diseases, ferroptosis, and ferritinophagy. The newfound insights contribute fresh perspectives and promising therapeutic avenues, potentially revolutionizing the landscape of joint disease management.

In silico analysis unveiling potential biomarkers in gallbladder carcinogenesis

Gallbladder cancer (GBC) is a rare but very aggressive most common digestive tract cancer with a high mortality rate due to delayed diagnosis at the advanced stage. Moreover, GBC progression shows asymptomatic characteristics making it impossible to detect at an early stage. In these circumstances, conventional therapy like surgery, chemotherapy, and radiotherapy becomes refractive. However, few studies reported some molecular markers like KRAS (Kirsten Rat Sarcoma) mutation, upregulation of HER2/neu, EGFR (Epidermal Growth Factor Receptor), and microRNAs in GBC. However, the absence of some specific early diagnostic and prognostic markers is the biggest hurdle for the therapy of GBC to date. The present study has been designed to identify some specific molecular markers for precise diagnosis, and prognosis, for successful treatment of the GBC. By In Silico a network-centric analysis of two microarray datasets; (GSE202479) and (GSE13222) from the Gene Expression Omnibus (GEO) database, shows 50 differentially expressed genes (DEGs) associated with GBC. Further network analysis revealed that 12 genes are highly interconnected based on the highest MCODE (Molecular Complex Detection) value, among all three genes; TRIP13 (Thyroid Receptor Interacting Protein), NEK2 (Never in Mitosis gene-A related Kinase 2), and TPX2 (Targeting Protein for Xklp2) having highest network interaction with transcription factors and miRNA suggesting critically associated with GBC. Further survival analysis data corroborate the association of these genes; TRIP13, NEK2, and TPX2 with GBC. Thus, TRIP13, NEK2, and TPX2 genes are significantly correlated with a greater risk of mortality, transforming them from mere biomarkers of the GBC for early detections and may emerge as prognostic markers for treatment.

Cell death shapes cancer immunity: spotlighting PANoptosis

PANoptosis represents a novel type of programmed cell death (PCD) with distinctive features that incorporate elements of pyroptosis, apoptosis, and necroptosis. PANoptosis is governed by a newly discovered cytoplasmic multimeric protein complex known as the PANoptosome. Unlike each of these PCD types individually, PANoptosis is still in the early stages of research and warrants further exploration of its specific regulatory mechanisms and primary targets. In this review, we provide a brief overview of the conceptual framework and molecular components of PANoptosis. In addition, we highlight recent advances in the understanding of the molecular mechanisms and therapeutic applications of PANoptosis. By elucidating the complex crosstalk between pyroptosis, apoptosis and necroptosis and summarizing the functional consequences of PANoptosis with a special focus on the tumor immune microenvironment, this review aims to provide a theoretical basis for the potential application of PANoptosis in cancer therapy.

SEnSCA: Identifying possible ligand-receptor interactions and its application in cell-cell communication inference

Multicellular organisms have dense affinity with the coordination of cellular activities, which severely depend on communication across diverse cell types. Cell-cell communication (CCC) is often mediated via ligand-receptor interactions (LRIs). Existing CCC inference methods are limited to known LRIs. To address this problem, we developed a comprehensive CCC analysis tool SEnSCA by integrating single cell RNA sequencing and proteome data. SEnSCA mainly contains potential LRI acquisition and CCC strength evaluation. For acquiring potential LRIs, it first extracts LRI features and reduces the feature dimension, subsequently constructs negative LRI samples through K-means clustering, finally acquires potential LRIs based on Stacking ensemble comprising support vector machine, 1D-convolutional neural networks and multi-head attention mechanism. During CCC strength evaluation, SEnSCA conducts LRI filtering and then infers CCC by combining the three-point estimation approach and single cell RNA sequencing data. SEnSCA computed better precision, recall, accuracy, F1 score, AUC and AUPR under most of conditions when predicting possible LRIs. To better illustrate the inferred CCC network, SEnSCA provided three visualization options: heatmap, bubble diagram and network diagram. Its application on human melanoma tissue demonstrated its reliability in CCC detection. In summary, SEnSCA offers a useful CCC inference tool and is freely available at https://github.com/plhhnu/SEnSCA.

Altered Metabolism and Inflammation Driven by Post-translational Modifications in Intervertebral Disc Degeneration

Intervertebral disc degeneration (IVDD) is a prevalent cause of low back pain and a leading contributor to disability. IVDD progression involves pathological shifts marked by low-grade inflammation, extracellular matrix remodeling, and metabolic disruptions characterized by heightened glycolytic pathways, mitochondrial dysfunction, and cellular senescence. Extensive posttranslational modifications of proteins within nucleus pulposus cells and chondrocytes play crucial roles in reshaping the intervertebral disc phenotype and orchestrating metabolism and inflammation in diverse contexts. This review focuses on the pivotal roles of phosphorylation, ubiquitination, acetylation, glycosylation, methylation, and lactylation in IVDD pathogenesis. It integrates the latest insights into various posttranslational modification-mediated metabolic and inflammatory signaling networks, laying the groundwork for targeted proteomics and metabolomics for IVDD treatment. The discussion also highlights unexplored territories, emphasizing the need for future research, particularly in understanding the role of lactylation in intervertebral disc health, an area currently shrouded in mystery.

Prognostic model revealing pyroptosis-related signatures in oral squamous cell carcinoma based on bioinformatics analysis

One of the most common oral carcinomas is oral squamous cell carcinoma (OSCC), bringing a heavy burden to global health. Although progresses have been made in the intervention of OSCC, 5 years survival of patients suffering from OSCC is poor like before regarding to the high invasiveness of OSCC, which causes metastasis and recurrence of the tumor. The relationship between pyroptosis and OSCC remains to be further investigated as pyroptosis in carcinomas has gained much attention. Herein, the key pyroptosis-related genes were identified according to The Cancer Genome Atlas (TCGA) dataset. Additionally, a prognostic model was constructed based upon three key genes (CTLA4, CD5, and IL12RB2) through least absolute shrinkage and selection operator (LASSO) analyses, as well as univariate and multivariate COX regression in OSCC. It was discovered that the high expression of these three genes was associated with the low-risk group. We also identified LAIR2 as a hub gene, whose expression negatively correlated with the risk score and the different immune cell infiltration. Finally, we proved that these three genes were independent prognostic factors linked to overall survival (OS), and reliable consequences could be predicted by this model. Our study revealed the relationship between pyroptosis and OSCC, providing insights into new treatment targets for preventing and treating OSCC.

SeFilter-DIA: Squeeze-and-Excitation Network for Filtering High-Confidence Peptides of Data-Independent Acquisition Proteomics

Mass spectrometry is crucial in proteomics analysis, particularly using Data Independent Acquisition (DIA) for reliable and reproducible mass spectrometry data acquisition, enabling broad mass-to-charge ratio coverage and high throughput. DIA-NN, a prominent deep learning software in DIA proteome analysis, generates peptide results but may include low-confidence peptides. Conventionally, biologists have to manually screen peptide fragment ion chromatogram peaks (XIC) for identifying high-confidence peptides, a time-consuming and subjective process prone to variability. In this study, we introduce SeFilter-DIA, a deep learning algorithm, aiming at automating the identification of high-confidence peptides. Leveraging compressed excitation neural network and residual network models, SeFilter-DIA extracts XIC features and effectively discerns between high and low-confidence peptides. Evaluation of the benchmark datasets demonstrates SeFilter-DIA achieving 99.6% AUC on the test set and 97% for other performance indicators. Furthermore, SeFilter-DIA is applicable for screening peptides with phosphorylation modifications. These results demonstrate the potential of SeFilter-DIA to replace manual screening, providing an efficient and objective approach for high-confidence peptide identification while mitigating associated limitations.

HormoNet: a deep learning approach for hormone-drug interaction prediction

Several experimental evidences have shown that the human endogenous hormones can interact with drugs in many ways and affect drug efficacy. The hormone drug interactions (HDI) are essential for drug treatment and precision medicine; therefore, it is essential to understand the hormone-drug associations. Here, we present HormoNet to predict the HDI pairs and their risk level by integrating features derived from hormone and drug target proteins. To the best of our knowledge, this is one of the first attempts to employ deep learning approach for prediction of HDI prediction. Amino acid composition and pseudo amino acid composition were applied to represent target information using 30 physicochemical and conformational properties of the proteins. To handle the imbalance problem in the data, we applied synthetic minority over-sampling technique technique. Additionally, we constructed novel datasets for HDI prediction and the risk level of their interaction. HormoNet achieved high performance on our constructed hormone-drug benchmark datasets. The results provide insights into the understanding of the relationship between hormone and a drug, and indicate the potential benefit of reducing risk levels of interactions in designing more effective therapies for patients in drug treatments. Our benchmark datasets and the source codes for HormoNet are available in: https://github.com/EmamiNeda/HormoNet .

Bioinformatics analysis for the identification of Sprouty-related EVH1 domain-containing protein 3 expression and its clinical significance in thyroid carcinoma

The poorly differentiated thyroid carcinoma (THCA) subtype is associated with an aggressive disease course, a less favorable overall prognosis, and an increased risk of distant organ metastasis. In this study, our objective was to explore the potential utility of the Sprouty-related EVH1 domain-containing protein 3 (SPRED3) as a biomarker for early diagnosis and prognosis in THCA patients. The differentially expressed prognostic-related genes associated with THCA were identified by querying The Cancer Genome Atlas (TCGA) database. The difference in the expression of the SPRED3 gene between thyroid carcinoma (THCA) tissues and normal tissues was analyzed using data from The Cancer Genome Atlas (TCGA) and further validated through immunohistochemistry. Univariate and multivariate Cox regression models were used, along with clinical information from THCA patients, to analyze the prognostic value of the SPRED3 gene in THCA patients. Functional enrichment analysis was subsequently performed to elucidate the molecular mechanisms underlying the regulatory effects of the SPRED3 gene on thyroid carcinoma. Additionally, we calculated the percentage of infiltrating immune cells in THCA patients and evaluated their correlation with SPRED3 gene expression. Compared with those in noncancerous thyroid tissue, the gene and protein expression levels of SPRED3 were found to be elevated in thyroid carcinoma tissues. Furthermore, the expression of SPRED3 in thyroid carcinoma exhibited significant correlations with tumor location, histological grade, pathological stage, and tumor node metastasis classification (TNM) stage. Univariate and multivariate Cox proportional hazards (Cox) regression analyses demonstrated that SPRED3 could serve as an independent prognostic factor for predicting the overall survival of THCA patients. The results of functional enrichment analysis suggested the potential involvement of SPRED3 in the regulation of extracellular matrix organization, epidermal development, signaling receptor activator activity, skin development, receptor ligand activity, glycosaminoglycan binding, neuroactive ligand‒receptor interaction, the IL-17 signaling pathway, and the PI3K-Akt signaling pathway. Additionally, there were significant correlations between the expression level of the SPRED3 gene and the infiltration of various immune cells (eosinophils, central memory T cells, neutrophils, macrophages, and NK cells) within the thyroid tumor microenvironment. SPRED3 can be used as a prognostic biomarker in patients with THCA could potentially be therapeutic target for THCA.

Exploring the therapeutic mechanisms and prognostic targets of Biochanin A in glioblastoma via integrated computational analysis and in vitro experiments

Glioblastoma (GBM) is the most aggressive brain tumor and is characterized by a poor prognosis and high recurrence and mortality rates. Biochanin A (BCA) exhibits promising clinical anti-tumor effects. In this study, we aimed to explore the pharmacological mechanisms by which BCA acts against GBM. Network pharmacology was employed to identify overlapping target genes between BCA and GBM. Differentially expressed genes from the Gene Expression Profiling Interactive Analysis 2 (GEPIA2) database were visualized using VolcaNose. Interactions among these overlapping genes were analyzed using the Search Tool for the Retrieval of Interacting Genes/Proteins database. Protein-protein interaction networks were constructed using Cytoscape 3.8.1. The Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology enrichment analyses were conducted using the Database for Annotation, Visualization, and Integrated Discovery. Survival analyses for these genes were performed using the GEPIA2 database. The Chinese Glioma Genome Atlas database was used to study the correlations between key prognostic genes. Molecular docking was confirmed using the DockThor database and visualized with PyMol software. Cell viability was assessed via the CCK-8 assay, apoptosis and the cell cycle stages were examined using flow cytometry, and protein expression was detected using western blotting. In all, 63 genes were initially identified as potential targets for BCA in treating GBM. Enrichment analysis suggested that the pharmacological mechanisms of BCA primarily involved cell cycle inhibition, induction of cell apoptosis, and immune regulation. Based on these findings, AKT1, EGFR, CASP3, and MMP9 were preliminarily predicted as key prognostic target genes for BCA in GBM treatment. Furthermore, molecular docking analysis suggested stable binding of BCA to the target protein. In vitro experiments revealed the efficacy of BCA in inhibiting GBM, with an IC50 value of 98.37 ± 2.21 μM. BCA inhibited cell proliferation, induced cell apoptosis, and arrested the cell cycle of GBM cells. Furthermore, the anti-tumor effects of BCA on U251 cells were linked to the regulation of the target protein. We utilized integrated bioinformatics analyses to predict targets and confirmed through experiments that BCA possesses remarkable anti-tumor activities. We present a novel approach for multi-target treatment of GBM using BCA.

Identification and analysis of key circRNAs in the mouse embryonic ovary provides insight into primordial follicle development

BACKGROUND

CircRNAs are a class of noncoding RNAs with tissue- and development-specific expression characteristics. In many mammals, primordial follicle development begins in the embryonic stage. However, the study of circRNAs in primordial follicle development in mice has not been reported.

RESULTS

In this study, ovaries were collected from mouse foetuses at 15.5 days post coitus (dpc) and 17.5 dpc, which are two key stages of primordial follicle development. A total of 4785 circRNAs were obtained by using RNA-seq. Of these, 83 differentially expressed circRNAs were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that these differential circRNAs were mainly involved in the regulation of reproductive development. Through qRT-PCR, back-splice sequence detection and enzyme digestion protection experiments, we found that circ-009346, circ-014674, circ-017054 and circ-008296 were indeed circular. Furthermore, circ-009346, circ-014674 and circ-017054 were identified as three key circRNAs by analysing their expression in the ovaries of mice at different developmental stages. The circRNA-miRNA-mRNA interaction network was constructed and validated for target miRNA and mRNA using qRT-PCR. The interacting genes circ-009346, circ-014674, and circ-017054 were subjected to KEGG enrichment analysis. We found that circ-014674 may participate in the assembly and reserve of primordial follicles through oestrogen and the Janus kinase (JAK) signal transducer and activator of transcription (STAT) signalling pathway (JAK-SATA). Circ-009346 and circ-017054 may have similar functions and are involved in the activation and growth of primordial follicles through the mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase (PI3K) signalling pathways.

CONCLUSIONS

Based on our findings, three circRNAs associated with primordial follicle development were identified, and their potential mechanisms of regulating primordial follicle development were revealed. These findings will help us better understand the molecular mechanism of circRNAs in primordial follicles and provide important references and targets for the development of primordial follicles.

PYCARD gene polymorphisms and susceptibility to periodontal and coronary heart diseases

Numerous studies have established a link between gene variants within the inflammasome complex and the incidence of periodontitis and cardiovascular illness across various ethnic groups. This study investigated the association between PYCARD gene polymorphism and susceptibility to periodontal disease and coronary heart disease (CHD) and their correlation with clinical periodontal indices. A total of 120 participants were enrolled, categorized into four groups: 30 healthy controls (C), 30 patients with generalized periodontitis (P), 30 patients with atherosclerotic CHD but clinically healthy periodontium (AS-C), and 30 patients with both atherosclerotic CHD and generalized periodontitis (AS-P). We recorded demographic data, collected blood samples, and measured periodontal indices, including plaque index, clinical attachment loss, bleeding on probing, and pocket depth. The genomic variant of the PYCARD gene was analyzed using a conventional polymerase reaction. A significant prevalence of T and G allele mutations and a higher distribution of CT and TT genotypes in PYCARD C/T (rs8056505) and the AG genotype in PYCARD A/G (rs372507365) were observed in groups P, AS-P, and AS-C. These single nucleotide polymorphisms (SNPs) were also positively correlated with the severity of clinical periodontitis indices. Our findings suggest that the increased frequency of T and G alleles and the distribution of CT, TT, and AG genotypes in PYCARD SNPs are significantly associated with an elevated risk for periodontal disease and CHD. These SNPs may participate in the pathogenesis of these conditions. The study reinforces the potential role of these genetic markers as risk factors for both diseases in the Iraqi population.

Construction of an abnormal glycosylation risk model and its application in predicting the prognosis of patients with head and neck cancer

Head and neck squamous cell carcinoma (HNSCC) is the most common malignant tumor of the head and neck, and the incidence rate is increasing year by year. Protein post-translational modification, recognized as a pivotal and extensive form of protein modification, has been established to possess a profound association with tumor occurrence and progression. This study employed bioinformatics analysis utilizing transcriptome sequencing data, patient survival data, and clinical data from HNSCC to establish predictive markers of genes associated with glycosylation as prognostic risk markers. The R procedure WGCNA was employed to construct a gene co-expression network using the gene expression profile and clinical characteristics of HNSCC samples. Multiple Cox Proportional Hazards Regression Model (Cox regression) and LASSO analysis were conducted to identify the key genes exhibiting the strongest association with prognosis. A risk score, known as the glycosylation-related genes risk score (GLRS), was subsequently formulated utilizing the aforementioned core genes. This scoring system facilitated the classification of samples into high-risk and low-risk categories, thereby enabling the prediction of patient prognosis. The association between GLRS and clinical variables was examined through both univariate and multivariate Cox regression analysis. The validation of six core genes was accomplished using quantitative real-time polymerase chain reaction (qRT-PCR). The findings demonstrated noteworthy variations in risk scores among subgroups, thereby affirming the efficacy of GLRS in prognosticating patient outcomes. Furthermore, a correlation has been observed between the risk-scoring model and immune infiltration. Moreover, significant disparities exist in the expression levels of diverse immune checkpoints, epithelial-mesenchymal transition genes, and angiogenic factors between the high and low-risk groups.

Development and validation of a novel lysosome-related LncRNA signature for predicting prognosis and the immune landscape features in colon cancer

Lysosomes are essential components for managing tumor microenvironment and regulating tumor growth. Moreover, recent studies have also demonstrated that long non-coding RNAs could be used as a clinical biomarker for diagnosis and treatment of colorectal cancer. However, the influence of lysosome-related lncRNA (LRLs) on the progression of colon cancer is still unclear. This study aimed to identify a prognostic LRL signature in colon cancer and elucidated potential biological function. Herein, 10 differential expressed lysosome-related genes were obtained by the TCGA database and ultimately 4 prognostic LRLs for conducting a risk model were identified by the co-expression, univariate cox, least absolute shrinkage and selection operator analyses. Kaplan-Meier analysis, principal-component analysis, functional enrichment annotation, and nomogram were used to verify the risk model. Besides, the association between the prognostic model and immune infiltration, chemotherapeutic drugs sensitivity were also discussed in this study. This risk model based on the LRLs may be promising for potential clinical prognosis and immunotherapeutic responses related indicator in colon cancer patients.

Dynamic network link prediction with node representation learning from graph convolutional networks

Dynamic network link prediction is extensively applicable in various scenarios, and it has progressively emerged as a focal point in data mining research. The comprehensive and accurate extraction of node information, as well as a deeper understanding of the temporal evolution pattern, are particularly crucial in the investigation of link prediction in dynamic networks. To address this issue, this paper introduces a node representation learning framework based on Graph Convolutional Networks (GCN), referred to as GCN_MA. This framework effectively combines GCN, Recurrent Neural Networks (RNN), and multi-head attention to achieve comprehensive and accurate representations of node embedding vectors. It aggregates network structural features and node features through GCN and incorporates an RNN with multi-head attention mechanisms to capture the temporal evolution patterns of dynamic networks from both global and local perspectives. Additionally, a node representation algorithm based on the node aggregation effect (NRNAE) is proposed, which synthesizes information including node aggregation and temporal evolution to comprehensively represent the structural characteristics of the network. The effectiveness of the proposed method for link prediction is validated through experiments conducted on six distinct datasets. The experimental outcomes demonstrate that the proposed approach yields satisfactory results in comparison to state-of-the-art baseline methods.

Identification and validation of prognostic signature genes of bladder cancer by integrating methylation and transcriptomic analysis

Being a frequent malignant tumor of the genitourinary system, Bladder Urothelial Carcinoma (BLCA) has a poor prognosis. This study focused on identifying and validating prognostic biomarkers utilizing methylation, transcriptomics, and clinical data from The Cancer Genome Atlas Bladder Urothelial Carcinoma (TCGA BLCA) cohort. The impact of altered differentially methylated hallmark pathway genes was subjected to clustering analysis to observe changes in the transcriptional landscape on BLCA patients and identify two subtypes of patients from the TCGA BLCA population where Subtype 2 was associated with the worst prognosis with a p-value of 0.00032. Differential expression and enrichment analysis showed that subtype 2 was enriched in immune-responsive and cancer-progressive pathways, whereas subtype 1 was enriched in biosynthetic pathways. Following, regression and network analyses revealed Epidermal Growth Factor Receptor (EGFR), Fos-related antigen 1 (FOSL1), Nuclear Factor Erythroid 2 (NFE2), ADP-ribosylation factor-like protein 4D (ARL4D), SH3 domain containing ring finger 2 (SH3RF2), and Cadherin 3 (CDH3) genes to be the most significant prognostic gene markers. These genes were used to construct a risk model that separated the BLCA patients into high and low-risk groups. The risk model was also validated in an external dataset by performing survival analysis between high and low-risk groups with a p-value < 0.001 and the result showed the high group was significantly associated with poor prognosis compared to the low group. Single-cell analyses revealed the elevated level of these genes in the tumor microenvironment and associated with immune response. High-grade patients also tend to have a high expression of these genes compared to low-grade patients. In conclusion, this research developed a six-gene signature that is pertinent to the prediction of overall survival (OS) and might contribute to the advancement of precision medicine in the management of bladder cancer.

A novel and innovative cancer classification framework through a consecutive utilization of hybrid feature selection

Cancer prediction in the early stage is a topic of major interest in medicine since it allows accurate and efficient actions for successful medical treatments of cancer. Mostly cancer datasets contain various gene expression levels as features with less samples, so firstly there is a need to eliminate similar features to permit faster convergence rate of classification algorithms. These features (genes) enable us to identify cancer disease, choose the best prescription to prevent cancer and discover deviations amid different techniques. To resolve this problem, we proposed a hybrid novel technique CSSMO-based gene selection for cancer classification. First, we made alteration of the fitness of spider monkey optimization (SMO) with cuckoo search algorithm (CSA) algorithm viz., CSSMO for feature selection, which helps to combine the benefit of both metaheuristic algorithms to discover a subset of genes which helps to predict a cancer disease in early stage. Further, to enhance the accuracy of the CSSMO algorithm, we choose a cleaning process, minimum redundancy maximum relevance (mRMR) to lessen the gene expression of cancer datasets. Next, these subsets of genes are classified using deep learning (DL) to identify different groups or classes related to a particular cancer disease. Eight different benchmark microarray gene expression datasets of cancer have been utilized to analyze the performance of the proposed approach with different evaluation matrix such as recall, precision, F1-score, and confusion matrix. The proposed gene selection method with DL achieves much better classification accuracy than other existing DL and machine learning classification models with all large gene expression dataset of cancer.

Gamma-glutamyl transferase and calculus of kidney incidence: a Mendelian randomization study

Elevated Gamma-glutamyl transferase (GGT) levels are often suggestive of cholelithiasis, and previous studies have indicated that GGT is highly expressed in the urinary system. Therefore, we hypothesized that there may be an association between GGT levels and calculus of kidney (CK) incidence. To investigate this potential causal relationship, we employed Mendelian randomization (MR) analysis. Additionally, we analyzed the levels of other liver enzymes, including alanine transaminase (ALT) and alkaline phosphatase (ALP). The relationship between GGT levels and CK incidence was analyzed using two-sample Mendelian randomization. Summary Genome-Wide Association Studies data were utilized for this analysis. 33 single nucleotide polymorphisms known to be associated with GGT levels were employed as instrumental variables. We employed several MR methods including IVW (inverse variance weighting), MR-Egger, weighted median, weighted mode, and MR-PRESSO (Mendelian Randomization Pleiotropy RESidual Sum and Outlier). Furthermore, we conducted tests for horizontal multivariate validity, heterogeneity, and performed leave-one-out analysis to ensure the stability of the results. Overall, several MR methods yielded statistically significant results with a p-value < 0.05. The results from the IVW analysis yielded an odds ratio (OR) of 1.0062 with a 95% confidence interval (CI) of 1.0016-1.0109 (p = 0.0077). Additional MR methods provided supplementary results: MR-Egger (OR 1.0167, 95% CI 1.0070-1.0266, p = 0.0040); weighted median (OR 1.0058, 95% CI 1.0002-1.0115, p = 0.0423); and weighted mode (OR 1.0083, 95% CI 1.0020-1.0146, p- = 0.0188). Sensitivity analyses did not reveal heterogeneity or outliers. Although potential horizontal pleiotropy emerged, we speculate that this could be attributed to inadequate test efficacy. However, subsequent use of MR-PRESSO did not provide evidence of pleiotropy. Our analysis suggests a positive association between elevated GGT levels and CK incidence, indicating an increased risk of CK development. However, no causal relationship was observed between levels of ALP or ALT and CK incidence.

SUnet: A multi-organ segmentation network based on multiple attention

Organ segmentation in abdominal or thoracic computed tomography (CT) images plays a crucial role in medical diagnosis as it enables doctors to locate and evaluate organ abnormalities quickly, thereby guiding surgical planning, and aiding treatment decision-making. This paper proposes a novel and efficient medical image segmentation method called SUnet for multi-organ segmentation in the abdomen and thorax. SUnet is a fully attention-based neural network. Firstly, an efficient spatial reduction attention (ESRA) module is introduced not only to extract image features better, but also to reduce overall model parameters, and to alleviate overfitting. Secondly, SUnet's multiple attention-based feature fusion module enables effective cross-scale feature integration. Additionally, an enhanced attention gate (EAG) module is considered by using grouped convolution and residual connections, providing richer semantic features. We evaluate the performance of the proposed model on synapse multiple organ segmentation dataset and automated cardiac diagnostic challenge dataset. SUnet achieves an average Dice of 84.29% and 92.25% on these two datasets, respectively, outperforming other models of similar complexity and size, and achieving state-of-the-art results.

Exploring ceRNA networks for key biomarkers in breast cancer subtypes and immune regulation

Breast cancer is a major global health concern, and recent researches have highlighted the critical roles of non-coding RNAs in both cancer and the immune system. The competing endogenous RNA hypothesis suggests that various types of RNA, including coding and non-coding RNAs, compete for microRNA targets, acting as molecular sponges. This study introduces the Pre_CLM_BCS pipeline to investigate the potential of long non-coding RNAs and circular RNAs as biomarkers in breast cancer subtypes. The pipeline identifies specific modules within each subtype that contain at least one long non-coding RNA or circular RNA exhibiting significantly distinct expression patterns when compared to other subtypes. The results reveal potential biomarker genes for each subtype, such as circ_001845, circ_001124, circ_003925, circ_000736, and circ_003996 for the basal-like subtype, circ_00306 and circ_00128 for the luminal B subtype, circ_000709 and NPHS1 for the normal-like subtype, CAMKV and circ_001855 for the luminal A subtype, and circ_00128 and circ_00173 for the HER2+ subtype. Additionally, certain long non-coding RNAs and circular RNAs, including RGS5-AS1, C6orf223, HHLA3-AS1, circ_000349, circ_003996, circ_003925, circ_002665, circ_001855, and DLEU1, are identified as potential regulators of T cell mechanisms, underscoring their importance in understanding breast cancer progression in various subtypes. This pipeline provides valuable insights into cancer and immune-related processes in breast cancer subtypes.

CoDock-Ligand: combined template-based docking and CNN-based scoring in ligand binding prediction

For ligand binding prediction, it is crucial for molecular docking programs to integrate template-based modeling with a precise scoring function. Here, we proposed the CoDock-Ligand docking method that combines template-based modeling and the GNINA scoring function, a Convolutional Neural Network-based scoring function, for the ligand binding prediction in CASP15. Among the 21 targets, we obtained successful predictions in top 5 submissions for 14 targets and partially successful predictions for 4 targets. In particular, for the most complicated target, H1114, which contains 56 metal cofactors and small molecules, our docking method successfully predicted the binding of most ligands. Analysis of the failed systems showed that the predicted receptor protein presented conformational changes in the backbone and side chains of the binding site residues, which may cause large structural deviations in the ligand binding prediction. In summary, our hybrid docking scheme was efficiently adapted to the ligand binding prediction challenges in CASP15.

Comparative analysis of four nutritional scores predicting the incidence of MACE in older adults with acute coronary syndromes after PCI

To determine the most appropriate nutritional assessment tool for predicting the occurrence of major adverse cardiovascular events (MACE) within 1 year in elderly ACS patients undergoing PCI from four nutritional assessment tools including PNI, GNRI, CONUT, and BMI. Consecutive cases diagnosed with acute coronary syndrome (ACS) and underwent percutaneous coronary intervention (PCI) in the Department of Cardiovascular Medicine of the Air force characteristic medical center from 1 January 2020 to 1 April 2022 were retrospectively collected. The basic clinical characteristics and relevant test and examination indexes were collected uniformly, and the cases were divided into the MACE group (174 cases) and the non-MACE group (372 cases) according to whether a major adverse cardiovascular event (MACE) had occurred within 1 year. Predictive models were constructed to assess the nutritional status of patients with the Prognostic Nutritional Index (PNI), Geriatric Nutritional Risk Index (GNRI), Controlling nutritional status (CONUT) scores, and Body Mass Index (BMI), respectively, and to analyze their relationship with prognosis. The incremental value of the four nutritional assessment tools in predicting risk was compared using the Integrated Discriminant Improvement (IDI) and the net reclassification improvement (NRI). The predictive effect of each model on the occurrence of major adverse cardiovascular events (MACE) within 1 year in elderly ACS patients undergoing PCI was assessed using area under the ROC curve (AUC), calibration curves, decision analysis curves, and clinical impact curves; comparative analyses were performed. Among the four nutritional assessment tools, the area under the curve (AUC) was significantly higher for the PNI (AUC: 0.798, 95%CI 0.755-0.840 P < 0.001) and GNRI (AUC: 0.760, 95%CI 0.715-0.804 P < 0.001) than for the CONUT (AUC: 0.719,95%CI 0.673-0.765 P < 0.001) and BMI (AUC: 0.576, 95%CI 0.522-0.630 P < 0.001). The positive predictive value (PPV) of PNI: 67.67% was better than GNRI, CONUT, and BMI, and the negative predictive value (NPV): of 83.90% was better than CONUT and BMI and similar to the NPV of GNRI. The PNI, GNRI, and CONUT were compared with BMI, respectively. The PNI had the most significant improvement in the Integrated Discriminant Improvement Index (IDI) (IDI: 0.1732, P < 0.001); the PNI also had the most significant improvement in the Net Reclassification Index (NRI) (NRI: 0.8185, P < 0.001). In addition, of the four nutritional assessment tools used in this study, the PNI was more appropriate for predicting the occurrence of major adverse cardiovascular events (MACE) within 1 year in elderly ACS patients undergoing PCI.

Exploration of the molecular mechanism of intercellular communication in paediatric neuroblastoma by single-cell sequencing

Neuroblastoma (NB) is an embryonic tumour that originates in the sympathetic nervous system and occurs most often in infants and children under 2 years of age. Moreover, it is the most common extracranial solid tumour in children. Increasing studies suggest that intercellular communication within the tumour microenvironment is closely related to tumour development. This study aimed to construct a prognosis-related intercellular communication-associated genes model by single-cell sequencing and transcriptome sequencing to predict the prognosis of patients with NB for precise management. Single-cell data from patients with NB were downloaded from the gene expression omnibus database for comprehensive analysis. Furthermore, prognosis-related genes were screened in the TARGET database based on epithelial cell marker genes through a combination of Cox regression and Lasso regression analyses, using GSE62564 and GSE85047 for external validation. The patients' risk scores were calculated, followed by immune infiltration analysis, drug sensitivity analysis, and enrichment analysis of risk scores, which were conducted for the prognostic model. I used the Lasso regression feature selection algorithm to screen characteristic genes in NB and developed a 21-gene prognostic model. The risk scores were highly correlated with multiple immune cells and common anti-tumour drugs. Furthermore, the risk score was identified as an independent prognostic factor for NB. In this study, I constructed and validated a prognostic signature based on epithelial marker genes, which may provide useful information on the development and prognosis of NB.

Construction of an anoikis-associated lncRNA-miRNA-mRNA network reveals the prognostic role of β-elemene in non-small cell lung cancer

β-Elemene is the main active ingredient in Curcumae Rhizoma that exerts antitumour effects. Anoikis affects tumour development through various biological pathways in non-small cell lung cancer (NSCLC), but the regulation between β-elemene and anoikis remains to be explored. First, we explored the molecular expression patterns of anoikis-associated genes (AAGs) using consensus clustering and characterized the impact of AAGs on patient prognosis, clinical characteristics, and genomic instability. In addition, we revealed that AAG regulatory genes have rich interactions with β-elemene targets, and established a lncRNA-miRNA-mRNA network to explain the effect of β-elemene on anoikis. Finally, to reveal the prognostic effect of their correlation, the prognostic scoring model and clinical nomogram of β-elemene and anoikis were successfully established by least absolute shrinkage and selection operator (LASSO) and random forest algorithms. This prognostic scoring model containing noncoding RNA (ncRNA) can indicate the immunotherapy and mutational landscape, providing a novel theoretical basis and direction for the study of the antitumour mechanism of β-elemene in NSCLC patients.

Prognostic value and immune landscapes of immunogenic cell death-associated lncRNAs in lung adenocarcinoma

Immunogenic cell death (ICD) has been demonstrated to activate T cells to kill tumor cells, which is closely related to tumor development, and long noncoding RNAs (lncRNAs) are also involved. However, it is not known whether ICD-related lncRNAs are associated with the development of lung adenocarcinoma (LUAD). We downloaded ICD-related genes from GeneCards and the transcriptome statistics of LUAD patients from The Cancer Genome Atlas (TCGA) and subsequently developed and verified a predictive model. A successful model was used together with other clinical features to construct a nomogram for predicting patient survival. To further study the mechanism of tumor action and to guide therapy, we performed enrichment analysis, tumor microenvironment analysis, somatic mutation analysis, drug sensitivity analysis and real-time quantitative polymerase chain reaction (RT-qPCR) analysis. Nine ICD-related lncRNAs with significant prognostic relevance were selected for model construction. Survival analysis demonstrated that overall survival was substantially shorter in the high-risk group than in the low-risk group (P < 0.001). This model was predictive of prognosis across all clinical subgroups. Cox regression analysis further supported the independent prediction ability of the model. Ultimately, a nomogram depending on stage and risk score was created and showed a better predictive performance than the nomogram without the risk score. Through enrichment analysis, the enriched pathways in the high-risk group were found to be primarily associated with metabolism and DNA replication. Tumor microenvironment analysis suggested that the immune cell concentration was lower in the high-risk group. Somatic mutation analysis revealed that the high-risk group contained more tumor mutations (P = 0.00018). Tumor immune dysfunction and exclusion scores exhibited greater sensitivity to immunotherapy in the high-risk group (P < 0.001). Drug sensitivity analysis suggested that the predictive model can also be applied to the choice of chemotherapy drugs. RT-qPCR analysis also validated the accuracy of the constructed model based on nine ICD-related lncRNAs. The prognostic model constructed based on the nine ICD-related lncRNAs showed good application value in assessing prognosis and guiding clinical therapy.

Expression signature of ten small nuclear RNAs serves as novel biomarker for prognosis prediction of acute myeloid leukemia

This study aimed to screen for small nuclear RNAs (snRNAs) associated with the prognosis of acute myeloid leukemia (AML) by using The Cancer Genome Atlas (TCGA) whole-transcriptome sequencing dataset. A total of 130 AML patients from TCGA cohort with complete prognostic information and transcriptome data were enrolled in the current study. Comprehensive survival and functional enrichment analyses were performed to explore the prognostic value and potential biological functions of prognostic snRNAs in AML patients. In the current study, we screened 72 snRNAs that were notably associated with the clinical outcome of AML and developed an expression signature consist of ten snRNAs, that can be accurately applied to assess the overall survival of AML patients. Functional mechanism analysis revealed that this expression signature may be strongly linked to some classical tumor-associated pathways, such as Notch and Wnt pathways, as well as being closely related to B and T cell receptor pathways. Furthermore, we screened six compounds (chicago sky blue 6 B, 5230742, clorsulon, nefopam, nicardipine, and streptomycin) that may serve as targeted therapeutic drugs for AML using connectivity maps. Tumor immunoassays indicated significant differences in the immune microenvironment of the bone marrow tissue between high-risk and low-risk AML patients. Immune infiltration analysis also revealed significant differences in the abundance of multiple immune cells in the bone marrow of the two groups of AML patients groups. In conclusion, our results revealed a novel prognostic expression signature of AML consisting of ten snRNAs, and we conducted a preliminary exploration of its potential biological functions and tumor immunity.

LncRNA RARA-AS1 could serve as a novel prognostic biomarker in pan-cancer and promote proliferation and migration in glioblastoma

Long non-coding RNAs (lncRNAs) have emerged as crucial regulators of cancer progression and are potential biomarkers for diagnosis and treatment. This study investigates the role of RARA Antisense RNA 1 (RARA-AS1) in cancer and its implications for diagnosis and treatment. Various bioinformatics tools were conducted to analyze the expression patterns, immune-related functions, methylation, and gene expression correlations of RARA-AS1, mainly including the comparisons of different subgroups and correlation analyses between RARA-AS1 expression and other factors. Furthermore, we used short hairpin RNA to perform knockdown experiments, investigating the effects of RARA-AS1 on cell proliferation, invasion, and migration in glioblastoma. Our results revealed that RARA-AS1 has distinct expression patterns in different cancers and exhibits notable correlation with prognosis. Additionally, RARA-AS1 is highly correlated with certain immune checkpoints and mismatch repair genes, indicating its potential role in immune infiltration and related immunotherapy. Further analysis identified potential effective drugs for RARA-AS1 and demonstrated its potential RNA binding protein (RBP) mechanism in glioblastoma. Besides, a series of functional experiments indicated inhibiting RARA-AS1 could decrease cell proliferation, invasion, and migration of glioblastoma cell lines. Finally, RARA-AS1 could act as an independent prognostic factor for glioblastoma patients and may serve as a promising therapeutic target. All in all, Our study provides a comprehensive understanding of the functions and implications of RARA-AS1 in pan-cancer, highlighting it as a promising biomarker for survival. It is also an independent risk factor affecting prognosis in glioblastoma and an important factor affecting proliferation and migration in glioblastoma, setting the stage for further mechanistic investigations.

A hybrid attention and dilated convolution framework for entity and relation extraction and mining

Mining entity and relation from unstructured text is important for knowledge graph construction and expansion. Recent approaches have achieved promising performance while still suffering from inherent limitations, such as the computation efficiency and redundancy of relation prediction. In this paper, we propose a novel hybrid attention and dilated convolution network (HADNet), an end-to-end solution for entity and relation extraction and mining. HADNet designs a novel encoder architecture integrated with an attention mechanism, dilated convolutions, and gated unit to further improve computation efficiency, which achieves an effective global receptive field while considering local context. For the decoder, we decompose the task into three phases, relation prediction, entity recognition and relation determination. We evaluate our proposed model using two public real-world datasets that the experimental results demonstrate the effectiveness of the proposed model.

scAAGA: Single cell data analysis framework using asymmetric autoencoder with gene attention

In recent years, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating cellular heterogeneity and structure. However, analyzing scRNA-seq data remains challenging, especially in the context of COVID-19 research. Single-cell clustering is a key step in analyzing scRNA-seq data, and deep learning methods have shown great potential in this area. In this work, we propose a novel scRNA-seq analysis framework called scAAGA. Specifically, we utilize an asymmetric autoencoder with a gene attention module to learn important gene features adaptively from scRNA-seq data, with the aim of improving the clustering effect. We apply scAAGA to COVID-19 peripheral blood mononuclear cell (PBMC) scRNA-seq data and compare its performance with state-of-the-art methods. Our results consistently demonstrate that scAAGA outperforms existing methods in terms of adjusted rand index (ARI), normalized mutual information (NMI), and adjusted mutual information (AMI) scores, achieving improvements ranging from 2.8% to 27.8% in NMI scores. Additionally, we discuss a data augmentation technology to expand the datasets and improve the accuracy of scAAGA. Overall, scAAGA presents a robust tool for scRNA-seq data analysis, enhancing the accuracy and reliability of clustering results in COVID-19 research.

Bacterial lipopolysaccharide related genes signature as potential biomarker for prognosis and immune treatment in gastric cancer

The composition of microbial microenvironment is an important factor affecting the development of tumor diseases. However, due to the limitations of current technological levels, we are still unable to fully study and elucidate the depth and breadth of the impact of microorganisms on tumors, especially whether microorganisms have an impact on cancer. Therefore, the purpose of this study is to conduct in-depth research on the role and mechanism of prostate microbiome in gastric cancer (GC) based on the related genes of bacterial lipopolysaccharide (LPS) by using bioinformatics methods. Through comparison in the Toxin Genomics Database (CTD), we can find and screen out the bacterial LPS related genes. In the study, Venn plots and lasso analysis were used to obtain differentially expressed LPS related hub genes (LRHG). Afterwards, in order to establish a prognostic risk score model and column chart in LRHG features, we used univariate and multivariate Cox regression analysis for modeling and composition. In addition, we also conducted in-depth research on the clinical role of immunotherapy with TMB, MSI, KRAS mutants, and TIDE scores. We screened 9 LRHGs in the database. We constructed a prognostic risk score and column chart based on LRHG, indicating that low risk scores have a protective effect on patients. We particularly found that low risk scores are beneficial for immunotherapy through TIDE score evaluation. Based on LPS related hub genes, we established a LRHG signature, which can help predict immunotherapy and prognosis for GC patients. Bacterial lipopolysaccharide related genes can also be biomarkers to predict progression free survival in GC patients.

Multidimensional computational study to understand non-coding RNA interactions in breast cancer metastasis

Metastasis is a major breast cancer hallmark due to which tumor cells tend to relocate to regional or distant organs from their organ of origin. This study is aimed to decipher the interaction among 113 differentially expressed genes, interacting non-coding RNAs and drugs (614 miRNAs, 220 lncRNAs and 3241 interacting drugs) associated with metastasis in breast cancer. For an extensive understanding of genetic interactions in the diseased state, a backbone gene co-expression network was constructed. Further, the mRNA-miRNA-lncRNA-drug interaction network was constructed to identify the top hub RNAs, significant cliques and topological parameters associated with differentially expressed genes. Then, the mRNAs from the top two subnetworks constructed are considered for transcription factor (TF) analysis. 39 interacting miRNAs and 1641 corresponding TFs for the eight mRNAs from the subnetworks are also utilized to construct an mRNA-miRNA-TF interaction network. TF analysis revealed two TFs (EST1 and SP1) from the cliques to be significant. TCGA expression analysis of miRNAs and lncRNAs as well as subclass-based and promoter methylation-based expression, oncoprint and survival analysis of the mRNAs are also done. Finally, functional enrichment of mRNAs is also performed. Significant cliques identified in the study can be utilized for identification of newer therapeutic interventions for breast cancer. This work will also help to gain a deeper insight into the complicated molecular intricacies to reveal the potential biomarkers involved with breast cancer progression in future.

Development of a nomogram model for the early prediction of sepsis-associated acute kidney injury in critically ill patients

Sepsis-associated acute kidney injury is a common complication of sepsis, but it is difficult to predict sepsis-associated acute kidney injury. In this retrospective observational study, adult septic patients were recruited from the MIMIC-III database as the training cohort (n = 4764) and from Xiangya Hospital (n = 1568) and Zhang's database as validation cohorts. We identified eleven predictors with seven independent risk predictors of sepsis-associated acute kidney injury [fluid input_day1 ≥ 3390 ml (HR hazard ratio 1.42), fluid input_day2 ≥ 2734 ml (HR 1.64), platelet_min_day5 ≤ 224.2 × 109/l (HR 0.86), length of ICU stay ≥ 2.5 days (HR 1.24), length of hospital stay ≥ 5.8 days (HR 1.18), Bun_max_day1 ≥ 20 mmol/l (HR 1.20), and mechanical ventilation time ≥ 96 h (HR 1.11)] by multivariate Cox regression analysis, and the eleven predictors were entered into the nomogram. The nomogram model showed a discriminative ability for estimating sepsis-associated acute kidney injury. These results indicated that clinical parameters such as excess input fluid on the first and second days after admission and longer mechanical ventilation time could increase the risk of developing sepsis-associated acute kidney injury. With our study, we built a real-time prediction model for potentially forecasting acute kidney injury in septic patients that can help clinicians make decisions as early as possible to avoid sepsis-associated acute kidney injury.

Comprehensive analysis of copper-metabolism-related genes about prognosis and immune microenvironment in osteosarcoma

Despite being significant in various diseases, including cancers, the impact of copper metabolism on osteosarcoma (OS) remains largely unexplored. This study aimed to use bioinformatics analyses to identify a reliable copper metabolism signature that could improve OS patient prognosis prediction, immune landscape understanding, and drug sensitivity. Through nonnegative matrix factorization (NMF) clustering, we revealed distinct prognosis-associated clusters of OS patients based on copper metabolism-related genes (CMRGs), showing differential gene expression linked to immune processes. The risk model, comprising 13 prognostic CMRGs, was established using least absolute shrinkage and selection operator (LASSO) Cox regression, closely associated with the OS microenvironment's immune situation and drug sensitivity. Furthermore, we developed an integrated nomogram, combining the risk score and clinical traits to quantitatively predict OS patient prognosis. The calibration plot, timeROC, and timeROC analyses demonstrated its predictable accuracy and clinical usefulness. Finally, we identified three independent prognostic signatures for OS patients: COX11, AP1B1, and ABCB6. This study confirmed the involvement of CMRGs in OS patient prognosis, immune processes, and drug sensitivity, suggesting their potential as promising prognostic signatures and therapeutic targets for OS.

Identification of a visualized web-based nomogram for overall survival prediction in patients with limited stage small cell lung cancer

Small-cell lung cancer (SCLC) is an aggressive lung cancer subtype with an extremely poor prognosis. The 5-year survival rate for limited-stage (LS)-SCLC cancer is 10-13%, while the rate for extensive-stage SCLC cancer is only 1-2%. Given the crucial role of the tumor stage in the disease course, a well-constructed prognostic model is warranted for patients with LS-SCLC. The LS-SCLC patients' clinical data extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2018 were reviewed. A multivariable Cox regression approach was utilized to identify and integrate significant prognostic factors. Bootstrap resampling was used to validate the model internally. The Area Under Curve (AUC) and calibration curve evaluated the model's performance. A total of 5463 LS-SCLC patients' clinical data was collected from the database. Eight clinical parameters were identified as significant prognostic factors for LS-SCLC patients' OS. The predictive model achieved satisfactory discrimination capacity, with 1-, 2-, and 3-year AUC values of 0.91, 0.88, and 0.87 in the training cohort; and 0.87, 0.87, and 0.85 in the validation cohort. The calibration curve showed a good agreement with actual observations in survival rate probability. Further, substantial differences between survival curves of the different risk groups stratified by prognostic scores were observed. The nomogram was then deployed into a website server for ease of access. This study developed a nomogram and a web-based predictor for predicting the overall survival of patients with LS-SCLC, which may help physicians make personalized clinical decisions and treatment strategies.

Identifying SARS-CoV-2 infected cells with scVDN

Introduction

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for understanding cellular heterogeneity and identifying cell types in virus-related research. However, direct identification of SARS-CoV-2-infected cells at the single-cell level remains challenging, hindering the understanding of viral pathogenesis and the development of effective treatments.

Methods

In this study, we propose a deep learning framework, the single-cell virus detection network (scVDN), to predict the infection status of single cells. The scVDN is trained on scRNA-seq data from multiple nasal swab samples obtained from several contributors with varying cell types. To objectively evaluate scVDN's performance, we establish a model evaluation framework suitable for real experimental data.

Results and Discussion

Our results demonstrate that scVDN outperforms four state-of-the-art machine learning models in identifying SARS-CoV-2-infected cells, even with extremely imbalanced labels in real data. Specifically, scVDN achieves a perfect AUC score of 1 in four cell types. Our findings have important implications for advancing virus research and improving public health by enabling the identification of virus-infected cells at the single-cell level, which is critical for diagnosing and treating viral infections. The scVDN framework can be applied to other single-cell virus-related studies, and we make all source code and datasets publicly available on GitHub at https://github.com/studentiz/scvdn.

Development and validation of an interpretable radiomic nomogram for severe radiation proctitis prediction in postoperative cervical cancer patients

Background

Radiation proctitis is a common complication after radiotherapy for cervical cancer. Unlike simple radiation damage to other organs, radiation proctitis is a complex disease closely related to the microbiota. However, analysis of the gut microbiota is time-consuming and expensive. This study aims to mine rectal information using radiomics and incorporate it into a nomogram model for cheap and fast prediction of severe radiation proctitis prediction in postoperative cervical cancer patients.

Methods

The severity of the patient's radiation proctitis was graded according to the RTOG/EORTC criteria. The toxicity grade of radiation proctitis over or equal to grade 2 was set as the model's target. A total of 178 patients with cervical cancer were divided into a training set (n = 124) and a validation set (n = 54). Multivariate logistic regression was used to build the radiomic and non-raidomic models.

Results

The radiomics model [AUC=0.6855(0.5174-0.8535)] showed better performance and more net benefit in the validation set than the non-radiomic model [AUC=0.6641(0.4904-0.8378)]. In particular, we applied SHapley Additive exPlanation (SHAP) method for the first time to a radiomics-based logistic regression model to further interpret the radiomic features from case-based and feature-based perspectives. The integrated radiomic model enables the first accurate quantitative assessment of the probability of radiation proctitis in postoperative cervical cancer patients, addressing the limitations of the current qualitative assessment of the plan through dose-volume parameters only.

Conclusion

We successfully developed and validated an integrated radiomic model containing rectal information. SHAP analysis of the model suggests that radiomic features have a supporting role in the quantitative assessment of the probability of radiation proctitis in postoperative cervical cancer patients.

The spring-like effect of microRNA-31 in balancing inflammatory and regenerative responses in colitis

Inflammatory bowel diseases (IBDs) are chronic inflammatory disorders caused by the disruption of immune tolerance to the gut microbiota. MicroRNA-31 (MIR31) has been proven to be up-regulated in intestinal tissues from patients with IBDs and colitis-associated neoplasias. While the functional role of MIR31 in colitis and related diseases remain elusive. Combining mathematical modeling and experimental analysis, we systematically explored the regulatory mechanism of MIR31 in inflammatory and epithelial regeneration responses in colitis. Level of MIR31 presents an "adaptation" behavior in dextran sulfate sodium (DSS)-induced colitis, and the similar behavior is also observed for the key cytokines of p65 and STAT3. Simulation analysis predicts MIR31 suppresses the activation of p65 and STAT3 but accelerates the recovery of epithelia in colitis, which are validated by our experimental observations. Further analysis reveals that the number of proliferative epithelial cells, which characterizes the inflammatory process and the recovery of epithelia in colitis, is mainly determined by the inhibition of MIR31 on IL17RA. MIR31 promotes epithelial regeneration in low levels of DSS-induced colitis but inhibits inflammation with high DSS levels, which is dominated by the competition for MIR31 to either inhibit inflammation or promote epithelial regeneration by binding to different targets. The binding probability determines the functional transformation of MIR31, but the functional strength is determined by MIR31 levels. Thus, the role of MIR31 in the inflammatory response can be described as the "spring-like effect," where DSS, MIR31 action strength, and proliferative epithelial cell number are regarded as external force, intrinsic spring force, and spring length, respectively. Overall, our study uncovers the vital roles of MIR31 in balancing inflammation and the recovery of epithelia in colitis, providing potential clues for the development of therapeutic targets in drug design.

Mathematical Models of Death Signaling Networks

This review provides an overview of the progress made by computational and systems biologists in characterizing different cell death regulatory mechanisms that constitute the cell death network. We define the cell death network as a comprehensive decision-making mechanism that controls multiple death execution molecular circuits. This network involves multiple feedback and feed-forward loops and crosstalk among different cell death-regulating pathways. While substantial progress has been made in characterizing individual cell death execution pathways, the cell death decision network is poorly defined and understood. Certainly, understanding the dynamic behavior of such complex regulatory mechanisms can be only achieved by applying mathematical modeling and system-oriented approaches. Here, we provide an overview of mathematical models that have been developed to characterize different cell death mechanisms and intend to identify future research directions in this field.