Independent Datasets Research Articles

Optimization of SVR and CatBoost models using metaheuristic algorithms to assess landslide susceptibility

In this study, a landslide susceptibility assessment is performed by combining two machine learning regression algorithms (MLRA), such as support vector regression (SVR) and categorical boosting (CatBoost), with two population-based optimization algorithms, such as grey wolf optimizer (GWO) and particle swarm optimization (PSO), to evaluate the potential of a relatively new algorithm and the impact that optimization algorithms can have on the performance of regression models. The Kerala state in India has been chosen as the test site due to the large number of recorded incidents in the recent past. The study started with 18 potential predisposing factors, which were reduced to 14 after a multi-approach feature selection technique. Six susceptibility models were implemented and compared using the machine learning algorithms alone and combining each of them with the two optimization algorithms: SVR, CatBoost, SVR-PSO, CatBoost-PSO, SVR-GWO, and CatBoost-GWO. The resulting maps were validated with an independent dataset. The performance rankings, based on the area under the receiver operating characteristic curve (AUC) metric, are as follows: CatBoost-GWO (AUC = 0.910) had the highest performance, followed by CatBoost-PSO (AUC = 0.909), CatBoost (AUC = 0.899), SVR-GWO (AUC = 0.868), SVR-PSO (AUC = 0.858), and SVR (AUC = 0.840). Other validation statistics corroborated these outcomes, and the Friedman and Wilcoxon-signed rank tests verified the statistical significance of the models. Our case study showed that CatBoost outperformed SVR both in case the models were optimized or not; the introduction of optimization algorithms significantly improves the results of machine learning models, with GWO being slightly more effective than PSO. However, optimization cannot drastically alter the results of the model, highlighting the importance of setting up of a rigorous susceptibility model since the early steps of any research.

MiRNA in Machine-Learning-Based Diagnostics of Oral Cancer.

MicroRNAs (miRNAs) are crucial regulators of gene expression, playing significant roles in various cellular processes, including cancer pathogenesis. Traditional cancer diagnostic methods, such as biopsies and histopathological analyses, while effective, are invasive, costly, and require specialized skills. With the rising global incidence of cancer, there is a pressing need for more accessible and less invasive diagnostic alternatives. This research investigates the potential of machine-learning (ML) models based on miRNA attributes as non-invasive diagnostic tools for oral cancer. Methods and Tools: We utilized a comprehensive methodological framework involving the generation of miRNA attributes, including sequence characteristics, target gene associations, and cancer-specific signaling pathways. The miRNAs were classified using various ML algorithms, with the BayesNet classifier demonstrating superior performance, achieving an accuracy of 95% and an area under receiver operating characteristic curve (AUC) of 0.98 during cross-validation. The model's effectiveness was further validated using independent datasets, confirming its potential clinical utility. Our findings highlight the promise of miRNA-based ML models in enhancing early cancer detection, reducing healthcare burdens, and potentially saving lives. This study paves the way for future research into miRNA biomarkers, offering a scalable and adaptable diagnostic approach for various cancers.

Near-surface particulate backscattering observations with bio-optical Lagrangian drifters

Abstract Particulate matter concentration is an essential ocean variable (EOV) useful for understanding biogeochemical processes, improving ocean productivity estimates, and constraining coupled physical and biogeochemical numerical models. While direct observations of this parameter are difficult to obtain, the optical backscattering coefficient of marine particles (bbp) can be used as a reliable proxy. However, most in-situ multi-spectral bbp measurements are ship-borne or obtained from moored systems, thus with a limited spatial and temporal coverage. Utilizing Surface Velocity Programme (SVP) drifting buoys with self-contained backscatter sensors designed for extended deployment periods offers a promising solution for collecting data in challenging marine environments. In this study, we presented for the first time the integration of a commercially available, high-frequency and multispectral optical backscatter sensor into an SVP drifter, the so-called BO-SVP drifter designed to collect bbp measurements near the ocean surface. bbp measurements obtained by the BO-SVP drifter were reliable over a wide range of environmental conditions, showing a good agreement with independent datasets (relative bias < 10%; relative standard deviation < 36%). The BO-SVP drifter captures the satellite sub- and pixel scale variability by combining the Lagrangian approach and a high frequency sampling. This configuration enables the acquisition of measurements across numerous pixels in a single day, enhancing validation activities for ongoing and future satellite products and the quantification of associated uncertainties. The measurements acquired by these platforms can offer valuable insights into particle distribution at fine spatial scales, daily variability, and its relationship with water masses type and ocean dynamics.

Explaining Environmental Distribution of Aedes albopictus using Machine Learning

Abstract. The Aedes albopictus mosquito, known for its role in transmitting diseases such as dengue fever, Zika virus, and chikungunya, poses a significant public health threat globally. Understanding its distribution patterns is crucial for effective disease surveillance and control. This study employs machine learning techniques, specifically MaxEnt modeling, to elucidate the relationship between environmental factors and the distribution of Aedes albopictus. Using presence-only data and a suite of environmental variables, we trained MaxEnt models to predict the potential distribution of Aedes albopictus across a geographical region. The models were validated using independent datasets and evaluated for their predictive accuracy and robustness. Our results reveal significant associations between Aedes albopictus presence and environmental factors such as temperature related variables. Furthermore, we employed spatial analysis techniques to identify areas at high risk of Aedes albopictus presence, aiding in targeted vector control strategies and disease prevention efforts. MaxEnt models demonstrated high predictive performance, effectively capturing the complex relationships between environmental variables and mosquito distribution in Nepal, India and Myanmar, along with Spain and Italy. By integrating machine learning algorithms with environmental data, this study provides valuable insights into the ecological drivers of Aedes albopictus distribution, enhancing our ability to mitigate the risk of mosquito-borne diseases in affected regions.

Identification of three subtypes of ovarian cancer and construction of prognostic models based on immune-related genes

BackgroundImmunotherapy has revolutionized the treatment of ovarian cancer (OC), but different immune microenvironments often constrain the efficacy of immunotherapeutic interventions. Therefore, there is an imperative to delineate novel immune subtypes for development of efficacious immunotherapeutic strategies.MethodsThe immune subtypes of OC were identified by consensus cluster analysis. The differences in clinical features, genetic mutations, mRNA stemness (mRNAsi) and immune microenvironments were analyzed among subtypes. Subsequently, prognostic risk models were constructed based on differentially expressed genes (DEGs) of the immune subtypes using weighted correlation network analysis.ResultsOC patients were classified into three immune subtypes with distinct survival rates and clinical features. Different subtypes exhibited varying tumor mutation burdens, homologous recombination deficiencies, and mRNAsi levels. Significant differences were observed among immune subtypes in terms of immune checkpoint expression and immunogenic cell death. Prognostic risk models were validated as independent prognostic factors demonstrated great predictive performance for survival of OC patients.ConclusionIn this study, three distinct immune subtypes were identified based on gene sets related to vaccine response, with the C2 subtype exhibiting significantly worse prognosis. While no statistically significant differences in tumor mutation burden (TMB) were observed across the three subtypes, the homologous recombination deficiency (HRD) score and mRNA stemness index (mRNAsi) were notably elevated in the C2 group compared to the others. Immune infiltration analysis indicated that the C2 subtype may have an increased presence of regulatory T (Treg) cells, potentially contributing to a more favorable response to combination therapies involving PARP inhibitors and immunotherapy. These findings offer a precision medicine approach for tailoring immunotherapy in ovarian cancer patients. Moreover, the C3 subtype demonstrated significantly lower expression levels of immune checkpoint genes, a pattern validated by independent datasets, and associated with a better prognosis. Further investigation revealed that the immune-related gene FCRL5 correlates with ovarian cancer prognosis, with in vitro experiments showing that it influences the proliferation and migration of the ovarian cancer cell line SKOV3.

IDLB-Pred: identification of disordered lipid binding residues in protein sequences using convolutional neural network

Proteins, nucleic acids, and lipids all interact with intrinsically disordered protein areas. Lipid-binding regions are involved in a variety of biological processes as well as a number of human illnesses. The expanding body of experimental evidence for these interactions and the dearth of techniques to anticipate them from the protein sequence serve as driving forces. Although large-scale laboratory techniques are considered to be essential for equipment for studying binding residues, they are time consuming and costly, making it challenging for researchers to predict lipid binding residues. As a result, computational techniques are being looked at as a different strategy to overcome this difficulty. To predict disordered lipid-binding residues (DLBRs), we proposed iDLB-Pred predictor utilizing benchmark dataset to compute feature through extraction techniques to identify relevant patterns and information. Various classification techniques, including deep learning methods such as Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Multilayer Perceptrons (MLPs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs), were employed for model training. The proposed model, iDLB-Pred, was rigorously validated using metrics such as accuracy, sensitivity, specificity, and Matthew’s correlation coefficient. The results demonstrate the predictor’s exceptional performance, achieving accuracy rates of 81% on an independent dataset and 86% in 10-fold cross-validation.

MetaCGRP is a high-precision meta-model for large-scale identification of CGRP inhibitors using multi-view information

Migraine is considered one of the debilitating primary headache conditions with an estimated worldwide occurrence of approximately 14–15%, contributing highly to factors responsible for global disability. Calcitonin gene-related peptide (CGRP) is a neuropeptide that plays a crucial role in the pathophysiology of migraines and thus, its inhibition can help relieve migraine symptoms. However, conventional process of CGRP drug development has been laborious and time-consuming with incurred costs exceeding one billion dollars. On the other hand, machine learning (ML)-based approaches that are capable of accurately identifying CGRP inhibitors could greatly facilitate in expediting the discovery of novel CGRP drugs. Therefore, this study proposes a novel and high-accuracy meta-model, namely MetaCGRP, that can precisely identify CGRP inhibitors. To the best of our knowledge, MetaCGRP is the first SMILES-based approach that has been developed to identify CGRP inhibitors without the use of 3D structural information. In brief, we initially employed different molecular representation methods coupled with popular ML algorithms to construct a pool of baseline models. Then, all baseline models were optimized and used to generate multi-view features. Finally, we employed the feature selection method to optimize the multi-view features and determine the best feature subset to enable the construction of the meta-model. Both cross-validation and independent tests indicated that MetaCGRP clearly outperforms several conventional ML classifiers, with accuracies of 0.898 and 0.799 on the training and independent test datasets, respectively. In addition, MetaCGRP in conjunction with molecular docking was utilized to identify five potential natural product candidates from Thai herbal pharmacopoeia and analyze their binding affinity and interactions to CGRP. To facilitate community-wide efforts in expediting the discovery of novel CGRP inhibitors, a user-friendly web server for MetaCGRP is freely available at https://pmlabqsar.pythonanywhere.com/MetaCGRP.

Comprehensive multi-omics integration uncovers mitochondrial gene signatures for prognosis and personalized therapy in lung adenocarcinoma

The therapeutic efficacy of lung adenocarcinoma (LUAD), the most prevalent histological subtype of primary lung cancer, remains inadequate, with accurate prognostic assessment posing significant challenges. This study sought to elucidate the prognostic significance of mitochondrial-related genes in LUAD through an integrative multi-omics approach, aimed at developing personalized therapeutic strategies. Utilizing transcriptomic and single-cell RNA sequencing (scRNA-seq) data, alongside clinical information from publicly available databases, we first applied dimensionality reduction and clustering techniques to the LUAD single-cell dataset, focusing on the subclassification of fibroblasts, epithelial cells, and T cells. Mitochondrial-related prognostic genes were subsequently identified using TCGA-LUAD data, and LUAD cases were stratified into distinct molecular subtypes through consensus clustering, allowing for the exploration of gene expression profiles and clinical feature distributions across subtypes. By leveraging an ensemble of machine learning algorithms, we developed an Artificial Intelligence-Derived Prognostic Signature (AIDPS) model based on mitochondrial-related genes and validated its prognostic accuracy across multiple independent datasets. The AIDPS model demonstrated robust predictive power for LUAD patient outcomes, revealing significant differences in responses to immunotherapy and chemotherapy, as well as survival outcomes between risk groups. Furthermore, we conducted comprehensive analyses of tumor mutation burden (TMB), immune microenvironment characteristics, and genome-wide association study (GWAS) data, providing additional insights into the mechanistic roles of mitochondrial-related genes in LUAD pathogenesis. This study not only offers a novel approach to improving prognostic assessments in LUAD but also establishes a strong foundation for the development of personalized therapeutic interventions.

Assessment of the Effects of Biochar on the Physicochemical Properties of Saline–Alkali Soil Based on Meta-Analysis

Enhancing global agricultural sustainability critically requires improving the physicochemical properties of saline–alkali soil. Biochar has gained increasing attention as a strategy due to its unique properties. However, its effect on the physicochemical properties of saline–alkali soil varies significantly. This study uses psychometric meta-analysis across 137 studies to synthesize the findings from 1447 relatively independent data sets. This study investigates the effects of biochar with different characteristics on the top 20 cm of various saline–alkali soils. In addition, aggregated boosted tree (ABT) analysis was used to identify the key factors of biochar influencing the physicochemical properties of saline soils. The results showed that biochar application has a positive effect on improving soil properties by reducing the sodium adsorption ratio (SAR) and the exchangeable sodium percentage (ESP) by 30.31% and 28.88%, respectively, with a notable 48.97% enhancement in cation exchange capacity (CEC). A significant inverse relationship was found between soil salinity (SC) and ESP, while other factors were synergistic. Biochar application to mildly saline soil (<0.2%) and moderately saline soil (0.2–0.4%) demonstrated greater improvement in soil bulk density (SBD), total porosity (TP), and soil moisture content (SMC) compared to highly saline soil (>0.4%). However, the reduction in SC in highly saline soil was 4.9 times greater than in moderately saline soils. The enhancement of soil physical properties positively correlated with higher biochar application rates, largely driven by soil movements associated with the migration of soil moisture. Biochar produced at 401–500 °C was generally the most effective in improving the physicochemical properties of various saline–alkali soils. In water surplus regions, for mildly saline soil with pH < 8.5, mixed biochar (pH 6–8) at 41–80 t ha−1 was the most effective in soil improvement. Moreover, in water deficit areas with soil at pH ≥ 8.5, biochar with pH ≤ 6 applied at rates of >80 t ha−1 showed the greatest benefits. Agricultural residue biochar showed superior efficiency in ameliorating highly alkaline (pH ≥ 8.5) soil. In contrast, the use of mixed types of biochar was the most effective in the amelioration of other soil types.

Association of hyperactivated transposon expression with exacerbated immune activation in systemic lupus erythematosus

BackgroundSystemic Lupus Erythematosus (SLE) is a complex autoimmune disorder, and transposable elements (TEs) have been hypothesized to play a significant role in its development. However, limited research has explored this connection. Our study aimed to examine the relationship between TE expression and SLE pathogenesis.MethodsWe analyzed whole blood RNA-seq datasets from 198 SLE patients and 84 healthy controls. The REdiscoverTE pipeline was employed to quantify TE and other gene expressions, identifying differentially expressed TEs. A TE score was calculated to measure overall TE expression for each sample. Gene ontology and gene set enrichment analyses were conducted to explore the functional implications of TE upregulation. Independent datasets were utilized to replicate the results and investigate cell type-specific TE expression.ResultsOur analysis identified two distinct patient groups: one with high TE expression and another with TE expression comparable to controls. Patients with high TE expression exhibited upregulation of pathways involving nucleic acid sensors, and TE expression was strongly correlated with interferon (IFN) signatures. Furthermore, these patients displayed deregulated cell composition, including increased neutrophils and decreased regulatory T cells. Neutrophils were suggested as the primary source of TE expression, contributing to IFN production.ConclusionsOur findings suggest that TE expression may serve as a crucial mediator in maintaining the activation of interferon pathways, acting as an endogenous source of nucleic acid stimulators in SLE patients.

Minimum data sampling requirements for accurate detection of terrain-induced gait alterations change with mobile sensor position

Human gait is a key biomarker for health, independence and quality of life. Advances in wearable inertial sensor technologies have paved the way for out-of-the-lab human gait analysis, which is important for the assessment of mobility and balance in natural environments and has applications in multiple fields from healthcare to urban planning. Automatic recognition of the environment where walking takes place is a prerequisite for successful characterisation of terrain-induced gait alterations. A key question which remains unexplored in the field is how minimum data requirements for high terrain classification accuracy change depending on the sensor placement on the body. To address this question, we evaluate the changes in performance of five canonical machine learning classifiers by varying several data sampling parameters including sampling rate, segment length, and sensor configuration. Our analysis on two independent datasets clearly demonstrate that a single inertial measurement unit is sufficient to recognise terrain-induced gait alterations, accuracy and minimum data requirements vary with the device position on the body, and choosing correct data sampling parameters for each position can improve classification accuracy up to 40% or reduce data size by 16 times. Our findings highlight the need for adaptive data collection and processing algorithms for resource-efficient computing on mobile devices.

HLA-DR4Pred2: An improved method for predicting HLA-DRB1*04:01 binders

HLA-DRB1*04:01 is associated with numerous diseases, including sclerosis, arthritis, diabetes, and COVID-19, emphasizing the need to scan for binders in the antigens to develop immunotherapies and vaccines. Current prediction methods are often limited by their reliance on the small datasets. This study presents HLA-DR4Pred2, developed on a large dataset containing 12,676 binders and an equal number of non-binders. It’s an improved version of HLA-DR4Pred, which was trained on a small dataset, containing 576 binders and an equal number of non-binders. All models were trained, optimized, and tested on 80 % of the data using five-fold cross-validation and evaluated on the remaining 20 %. A range of machine learning techniques was employed, achieving maximum AUROC of 0.90 and 0.87, using composition and binary profile features, respectively. The performance of the composition-based model increased to 0.93, when combined with BLAST search. Additionally, models developed on the realistic dataset containing 12,676 binders and 86,300 non-binders, achieved a maximum AUROC of 0.99. Our proposed method outperformed existing methods when we compared the performance of our best model to that of existing methods on the independent dataset. Finally, we developed a standalone tool and a webserver for HLADR4Pred2, enabling the prediction, design, and virtual scanning of HLA-DRB1*04:01 binding peptides, and we also released a Python package available on the Python Package Index (https://webs.iiitd.edu.in/raghava/hladr4pred2/; https://github.com/raghavagps/hladr4pred2; https://pypi.org/project/hladr4pred2/).

Nocturnal Awakening Associated with Greater Social Disconnectedness in Older Adults: The Compensatory Role of Cognition

ABSTRACT Objectives This study aimed to explore the association between sleep and social disconnectedness by examining the role of global cognitive function in diverse samples of older adults. Methods Study 1 compared differences in social disconnectedness between older adults with clinical sleep disorders and healthy sleepers. Studies 2 and 3 examined the relationship between objective sleep and social disconnectedness in community-dwelling older adults from two independent datasets. In the three studies, we analyzed the moderating effect of global cognitive function in the association between objective sleep and social disconnectedness. Results Study 1 showed that older adults with clinical sleep disorders had greater social disconnectedness, among whom those with better cognition showed less influence of sleep disorder on social disconnectedness. Studies 2 and 3 showed that nocturnal awakening was robustly associated with social disconnectedness in community-dwelling older adults. Global cognitive function moderated this association, counteracting the negative effect of nocturnal awakening on social function. Conclusions These findings suggest a relationship between objective sleep, particularly nocturnal awakening, and social disconnectedness, and the compensatory role of global cognitive function. Clinical Implications Geriatric caregivers are encouraged to consider cognitive interventions to mitigate sleep-related, specifically excessive nocturnal awakening-related, social disconnectedness in older adults.

Impact of senescence cell signature in patients with non-small cell carcinoma and melanoma receiving PD-L1/PD-1 inhibitors

Tumor cell senescence plays a crucial role in tumor immunity. We investigated whether the senescent cell signature (SCS) could predict prognosis in non-small cell carcinoma (NSCLC) and melanoma datasets treated with PD-L1/PD-1 inhibitors. Patients with high SCS expression exhibited elevated levels of interferon-gamma and T cell-inflamed signatures in three lung adenocarcinomas (LUAD), two squamous cell carcinoma (LUSC) and three melanoma datasets. The high SCS group was associated with PD-L1-related pathways such as IL6/JAK/STAT3 and TNF-alpha signaling via NF-kB in LUAD, LUSC, and melanoma datasets. A positive correlation was observed between several immune checkpoint markers and the SCS, indicating an immunosuppressive state in LUAD, LUSC and melanoma datasets. In patients treated with PD-1/PD-L1 inhibitors, a higher SCS was associated with a better prognosis, and a positive correlation between SCS and PD-L1 was observed in six independent NSCLC and three independent melanoma datasets. We used the LASSO Cox regression model to build a risk model focusing on the SCS genes that particularly predict prognosis. We confirmed that the model accurately predicts prognosis. However, the senescent immunohistochemical markers p16 and p21 could predict the response to PD-1/PD-L1 inhibitors in patients with LUSC and melanoma but not in patients with LUAD. SCS could serve as a valuable biomarker to complement PD-L1 expression in patients receiving PD-L1/PD-1 inhibitors.

Construction and multiple validations of a robust ferroptosis-related prognostic model in bladder cancer: A comprehensive study.

Ferroptosis is iron-dependent programmed cell death that inhibits tumor growth, particularly in traditional treatment-resistant tumors. Prognostic models constructed from ferroptosis-related genes are lacking; prognostic biomarkers remain insufficient. We acquired gene expression data and corresponding clinical information for bladder cancer (BC) samples from public databases. Ferroptosis-related genes from the ferroptosis database were screened for clinical predictive value. We validated gene expression differences between tumors and normal tissues through polymerase chain reaction and western blotting. Gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses were conducted to explore signaling pathways affecting the overall survival of patients with BC. CIBERSORT was used to quantify the infiltration of 22 immune cell types. We identified 6 genes (EGFR, FADS1, ISCU, PGRMC1, PTPN6, and TRIM26) to construct the prognostic risk model. The high-risk group had a poorer overall survival than the low-risk group. Receiver operating characteristic curves demonstrated excellent predictive accuracy. The validation cohort and 3 independent datasets confirmed the models' general applicability and stability. BC tissues had elevated FADS1, PTPN6, and TRIM26 mRNA and protein levels and decreased ISCU levels. Enrichment analysis indicated that neurosecretory activity might be the main pathway affecting the overall survival. High- and low-risk groups had significantly different immune cell infiltration. Specific ferroptosis-related gene expression was associated with immune cell infiltration levels. The risk score was significantly correlated with patients' clinical characteristics. A novel, widely applicable risk model with independent predictive value for the prognosis of patients with BC was established; candidate molecules for future BC research were identified.

P10.09.A BLOOD-BASED CLASSIFICATION OF IDH-STATUS OF NON-ENHANCING GLIOMAS: A MACHINE LEARNING APPROACH

Abstract BACKGROUND Non-invasive determination of IDH mutational status in patients with glioma could offer significant therapeutic opportunities. While IDH wildtype tumors (e.g., GBM) typically show enhancement on MRI, IDH mutant tumors often lack this enhancement. However, relying solely on anatomic radiology may lead to misclassification, and currently, tissue acquisition is the primary method for assessing IDH status in gliomas. This limitation hampers the development of neoadjuvant or intraoperative therapeutic strategies based on IDH status; thus, more minimally invasive methods to determine IDH status are needed. In this study, we assessed immune cell proportions, which vary by glioma subtype, from pre-surgery peripheral blood samples as an alternative method of classifying the IDH status in gliomas without enhancement. MATERIAL AND METHODS First, we employed a highly accurate large language model (GPT-4-Turbo-128k) with over 99% accuracy to read radiology notes and exclude patients with enhancing gliomas. Then, we identified twelve immune cell subtypes from whole blood using deconvolution algorithms based on DNA methylation data. These immune cell subtypes, along with patient age, were integrated into a machine learning model (random forest) to predict IDH status (wildtype vs. mutant), leveraging conditional Generative Adversarial Networks to generate synthetic data and mitigate bias in the datasets. Two independent datasets were included for training and validating the model. RESULTS The random forest model had an area under the ROC curve (AUC) of 0.90 and accurately identified IDH status in 81% of a training set of 287 gliomas (65 wildtype and 222 mutant tumors at varied time points after diagnosis). In an independent validation data set of 99 gliomas (6 wildtype and 93 mutant) from pre-surgery blood samples, the AUC was 0.93, and accuracy predicted IDH status in 92% of cases with a sensitivity of 93% and specificity of 83%. CONCLUSION We could accurately predict IDH status in pre-surgical peripheral blood samples from patients with non-enhancing gliomas. This minimally invasive approach is a promising step toward reducing risk and exploring opportunities for earlier therapies based on IDH status.

MMG4: Recognition of G4-Forming Sequences Based on Markov Model.

G-quadruplexes (G4s) are special nucleic acid structures with various important biological functions. Existing tools and technologies for G4-forming sequences recognition are limited to time-consuming and costly methods such as circular dichroism and nuclear magnetic resonance. Developing a fast and accurate model for G4-forming sequences recognition has far-reaching significance. In this study, MMG4, a novel model to recognize G4-forming sequences based on Markov model (MM), was developed and the phenomenon of high recognition accuracy in the central region of the sequence and low accuracy in the two end regions was discovered. It was further found that the differences in base transfer probabilities, ratio distribution, and G4-motif structural content in different regions may be the causes of this phenomenon. The study also explored the impact of sequence length on recognition accuracy and found the optimal recognition interval to be [910-1049], with the highest recognition accuracy reaching 85.95%. By extracting sequence features, the study constructed three types of machine learning models: random forest (RF), support vector machine, and back-propagation neural network. It was found that recognition performance of MM was significantly better than that of the other three machine learning models, proving that the recognition method based on MM can effectively capture the correlation information between adjacent nucleotides of G4. By combining MM with the three machine learning models, the predictive performance of MMG4 improved. Among them, the RF model combined with MM has the best performance, achieving an area under the receiver operating characteristic curve value of 0.93 and an area under the precision-recall curve value of 0.9. Finally, the study validated the model robustness and generalization ability through independent testing dataset.

Fatty acid metabolism prognostic signature predicts tumor immune microenvironment and immunotherapy, and identifies tumorigenic role of MOGAT2 in lung adenocarcinoma

BackgroundAberrant fatty acid metabolism (FAM) plays a critical role in the tumorigenesis of human malignancies. However, studies on its impact in lung adenocarcinoma (LUAD) are limited.MethodsWe developed a prognostic signature comprising 10 FAM-related genes (GPR115, SOAT2, CDH17, MOGAT2, COL11A1, TCN1, LGR5, SLC34A2, RHOV, and DKK1) using data from LUAD patients in The Cancer Genome Atlas (TCGA). This signature was validated using six independent LUAD datasets from the Gene Expression Omnibus (GEO). Patients were classified into high- and low-risk groups, and overall survival (OS) was compared by Kaplan-Meier analysis. The signature’s independence as a prognostic indicator was assessed after adjusting for clinicopathological features. Receiver operating characteristic (ROC) analysis validated the signature. Tumor immune microenvironment (TIME) was analyzed using ESTIMATE and multiple deconvolution algorithms. Functional assays, including CCK8, cell cycle, apoptosis, transwell, and wound healing assays, were performed on MOGAT2-silenced H1299 cells using CRISPR/Cas9 technology.ResultsLow-risk group patients exhibited decreased OS. The signature was an independent prognostic indicator and demonstrated strong risk-stratification utility for disease relapse/progression. ROC analysis confirmed the signature’s validity across validation sets. TIME analysis revealed higher infiltration of CD8+ T cells, natural killers, and B cells, and lower tumor purity, stemness index, and tumor mutation burden (TMB) in low-risk patients. These patients also showed elevated T cell receptor richness and diversity, along with reduced immune cell senescence. High-risk patients exhibited enrichment in pathways related to resistance to immune checkpoint blockades, such as DNA repair, hypoxia, epithelial-mesenchymal transition, and the G2M checkpoint. LUAD patients receiving anti-PD-1 treatment had lower risk scores among responders compared to non-responders. MOGAT2 was expressed at higher levels in low-risk LUAD patients. Functional assays revealed that MOGAT2 knockdown in H1299 cells promoted proliferation and migration, induced G2 cell cycle arrest, and decreased apoptosis.ConclusionsThis FAM-related gene signature provides a valuable tool for prognostic stratification and monitoring of TIME and immunotherapy responses in LUAD. MOGAT2 is identified as a potential anti-tumor regulator, offering new insights into its role in LUAD pathogenesis.

Large Scale Comparative Analysis of Canine and Human Osteosarcomas Uncovers Conserved Clinically Relevant Tumor Microenvironment Subtypes.

Osteosarcoma is an aggressive bone cancer lacking robust biomarkers for personalized treatment. Despite its scarcity in humans, it is relatively common in adult pet dogs. This study aimed to analyze clinically annotated bulk tumor transcriptomic datasets of canine and human osteosarcoma patients to identify potentially conserved patterns of disease progression. Bulk transcriptomic data from 245 pet dogs with treatment-naïve appendicular osteosarcoma were analyzed using deconvolution to characterize the tumor microenvironment (TME). The TME of both primary and metastatic tumors derived from the same dog was compared, and its impact on canine survival was assessed. A machine learning model was developed to classify the TME based on its inferred composition using canine tumor data. This model was applied to 8 independent human osteosarcoma datasets to assess its generalizability and prognostic value. This study found three distinct TME subtypes of canine osteosarcoma based on cell type composition of bulk tumor samples: Immune Enriched (IE), Immune Enriched Dense Extra-Cellular Matrix-like (IE-ECM), and Immune Desert (ID). These three TME-based subtypes of canine osteosarcomas were conserved in humans and could predict progression-free survival outcomes of human patients, independent of conventional prognostic factors such as percent tumor necrosis post standard of care chemotherapy treatment and disease stage at diagnosis. These findings demonstrate the potential of leveraging data from naturally occurring cancers in canines to model the complexity of the human osteosarcoma TME, offering a promising avenue for the discovery of novel biomarkers and developing more effective precision oncology treatments.

RANS Simulation of Minimum Ignition Energy of Stoichiometric and Leaner CH4/Air Mixtures at Higher Pressures in Quiescent Conditions

Minimum ignition energy (MIE) has been extensively studied via experiments and simulations. However, our literature review reveals little quantitative consistency, with results varying from 0.324 to 1.349 mJ for ϕ = 1.0 and from 0.22 to 0.944 mJ for ϕ = 0.9. Therefore, there is a need to resolve these discrepancies. This RANS study aims to partially address this knowledge gap. Additionally, it presents other flame evolution parameters essential for robust combustion design. Using the reactingFOAM solver, we predict the threshold energy required to ignite the fuel mixture. For this, the single step using the Arrhenius law is selected to model ignition in the flame kernel of stochiometric and lean CH4/air mixtures, allowing it to develop into a self-sustained flame. The ignition power density, an energy quantity normalised with volume, is incrementally varied, keeping the kernel critical radius rs constant at 0.5 mm in the quiescent mixture of two equivalence ratios ϕ 0.9 and 1.0, for varied operating pressures of 1, 5, and 10 bar at the constant initial temperature of 300 K. The minimum ignition energy is validated with twelve independent 1-bar datasets both numerically and experimentally. The effect of pressure on MIEs, which diminish as pressure rises, is significant. At ϕ = 1.0 (and 0.9), the flame temperature reached 481.24 K (457.803 K) at 1 bar, 443.176 K (427.356 K) at 5 bar, and 385.56 K (382.688 K) at 10 bar. The minimum ignition energy was validated using twelve independent 1-bar datasets from both numerical simulations and experiments. The results show strong agreement with many experimental findings. Finally, a mathematical formulation of MIE is devised; a function of pressure and equivalence ratio shows a slightly curved relationship.