cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28652463.v1
title: Motor imagery EEG microstates are influenced by alpha power

description: Electroencephalogram (EEG) microstates are pivotal in understanding brain dynamics, reflecting transitions between global states. These parameters undergo selective inhibition within cortical areas, modulated by alpha oscillations. This study investigates how alpha band power influences microstate parameters across various task conditions, including resting state, actual motor execution, and imagined motor tasks. By comparing these three conditions, we aim to elucidate the distinct effects of alpha power on microstate dynamics, as each condition represents a unique pattern of brain activity. Motor imagery (MI) induces event-related desynchronization/synchronization, modulating Mu (alpha) and Beta rhythms in sensorimotor areas. However, the relationship between MI-EEG microstates and alpha power remains unclear. Our results show that alpha power was highest in resting state, followed by imagined motion, and lowest during actual motion. As alpha power increased, microstate A parameters in resting state (occurrence, coverage) decreased, while those in actual motion increased. Additionally, microstate B parameters rose with alpha power in resting state but decreased during imagined motion. Notably, alpha power correlated more strongly with microstate parameters in task states than in resting state. In addition, alpha, theta, and beta powers during task performance were negatively correlated with the duration of microstates A, B, and C, while being positively correlated with the occurrence of microstates A, B, C, and D. These findings suggest that alpha power influences microstate parameters differently depending on the brain, underscoring the significance of inter-band interactions in shaping microstate dynamics.
DOI: 10.6084/m9.figshare.28122388
title: Identification of ferroptosis-related LncRNAs as potential targets for improving immunotherapy in glioblastoma

description: The effect of ferroptosis-related long non-coding RNAs (lncRNAs) in predicting immunotherapy response to glioblastoma (GBM) remains obscure. This study established a 11-lncRNAs prognostic signature. Differential gene expression analysis, univariate and multivariate Cox regression analyses and the least absolute shrinkage and selection operator (LASSO) regression algorithm were used to identify prognostic ferroptosis-related genes and establish a nomogram model of risk score. Kaplan-Meier survival plots and receiver operating characteristic (ROC) curve analysis were used to evaluate the prognostic accuracy of the model in the TCGA-GBM cohort. To verify the expression of these signatures, we analyzed the expression levels of three lncRNAs (AGAP2-AS1, OSMR-AS1, UNC5B-AS1) in LN229 and U87 cells. The ROC analysis showed that the area under curve (AUC) of this signature is 0.814, suggesting that it has a promising performance on GBM prognostic prediction. Kaplan-Meier analysis showed that the survival rate of GBM patients in high-risk group was significantly lower than low-risk group, and the performance of this signature on GBM prognostic prediction was superior to conventional clinicopathological factors. Further qRT-PCR experiment also confirmed our prediction of lncRNA signatures. These ferroptosis-related lncRNAs might be therapeutic targets for glioblastoma, and targeting these lncRNAs can also improve the efficacy of immunotherapy, especially immune checkpoint inhibitors. Mechanistically, these findings might attribute to N6-methyladenosine (m6A) mRNA modification on lncRNAs.
DOI: 10.6084/m9.figshare.28122388.v1
title: Identification of ferroptosis-related LncRNAs as potential targets for improving immunotherapy in glioblastoma

description: The effect of ferroptosis-related long non-coding RNAs (lncRNAs) in predicting immunotherapy response to glioblastoma (GBM) remains obscure. This study established a 11-lncRNAs prognostic signature. Differential gene expression analysis, univariate and multivariate Cox regression analyses and the least absolute shrinkage and selection operator (LASSO) regression algorithm were used to identify prognostic ferroptosis-related genes and establish a nomogram model of risk score. Kaplan-Meier survival plots and receiver operating characteristic (ROC) curve analysis were used to evaluate the prognostic accuracy of the model in the TCGA-GBM cohort. To verify the expression of these signatures, we analyzed the expression levels of three lncRNAs (AGAP2-AS1, OSMR-AS1, UNC5B-AS1) in LN229 and U87 cells. The ROC analysis showed that the area under curve (AUC) of this signature is 0.814, suggesting that it has a promising performance on GBM prognostic prediction. Kaplan-Meier analysis showed that the survival rate of GBM patients in high-risk group was significantly lower than low-risk group, and the performance of this signature on GBM prognostic prediction was superior to conventional clinicopathological factors. Further qRT-PCR experiment also confirmed our prediction of lncRNA signatures. These ferroptosis-related lncRNAs might be therapeutic targets for glioblastoma, and targeting these lncRNAs can also improve the efficacy of immunotherapy, especially immune checkpoint inhibitors. Mechanistically, these findings might attribute to N6-methyladenosine (m6A) mRNA modification on lncRNAs.
DOI: 10.6084/m9.figshare.26311869
title: Prognostic signature based on S100 calcium-binding protein family members for lung adenocarcinoma and its clinical significance

description: The S100 family proteins (S100s) participate in multiple stages of tumorigenesis and are considered to have potential value as biomarkers for detecting and predicting various cancers. But the role of S100s in lung adenocarcinoma (LUAD) prognosis is elusive. Transcriptional data of LUAD patients were retrieved from TCGA, and relevant literature was extensively reviewed to collect S100 genes. Differential gene expression analysis was performed on the LUAD data, followed by intersection analysis between the differentially expressed genes (DEGs) and S100 genes. Unsupervised consensus clustering analysis identified two clusters. Significant variations in overall survival between the two clusters were shown by Kaplan-Meier analysis. DEGs between the two clusters were analyzed using Lasso regression and univariate/multivariate Cox regression analysis, leading to construction of an 11-gene prognostic signature. The signature exhibited stable and accurate predictive capability in TCGA and GEO datasets. Subsequently, we observed distinct immune cell infiltration, immunotherapy response, and tumor mutation characteristics in high and low-risk groups. Finally, small molecular compounds targeting prognostic genes were screened using CellMiner database, and molecular docking confirmed the binding of AMG-176, Estramustine, and TAK-632 with prognostic genes. In conclusion, we generated a prognostic signature with robust and reliable predictive ability, which may provide guidance for prognosis and treatment of LUAD.
DOI: 10.6084/m9.figshare.26311869.v1
title: Prognostic signature based on S100 calcium-binding protein family members for lung adenocarcinoma and its clinical significance

description: The S100 family proteins (S100s) participate in multiple stages of tumorigenesis and are considered to have potential value as biomarkers for detecting and predicting various cancers. But the role of S100s in lung adenocarcinoma (LUAD) prognosis is elusive. Transcriptional data of LUAD patients were retrieved from TCGA, and relevant literature was extensively reviewed to collect S100 genes. Differential gene expression analysis was performed on the LUAD data, followed by intersection analysis between the differentially expressed genes (DEGs) and S100 genes. Unsupervised consensus clustering analysis identified two clusters. Significant variations in overall survival between the two clusters were shown by Kaplan-Meier analysis. DEGs between the two clusters were analyzed using Lasso regression and univariate/multivariate Cox regression analysis, leading to construction of an 11-gene prognostic signature. The signature exhibited stable and accurate predictive capability in TCGA and GEO datasets. Subsequently, we observed distinct immune cell infiltration, immunotherapy response, and tumor mutation characteristics in high and low-risk groups. Finally, small molecular compounds targeting prognostic genes were screened using CellMiner database, and molecular docking confirmed the binding of AMG-176, Estramustine, and TAK-632 with prognostic genes. In conclusion, we generated a prognostic signature with robust and reliable predictive ability, which may provide guidance for prognosis and treatment of LUAD.
DOI: 10.6084/m9.figshare.28028588
title: Analysis of nitrogen metabolism-related gene expression in hepatocellular carcinoma to establish relevant indicators for prediction of prognosis and guidance of immunotherapy

description: Background: The prognosis of cancers is strongly connected with nitrogen metabolism (NM), which plays a critical role in the microenvironment and growth of tumors. It is unsubstantiated, however, how important NM-related genes are for the prognosis of hepatocellular carcinoma (HCC). Methods: Using publicly available data, we examined potential mechanisms of NM-related genes in HCC, created a predictive model, and assessed immune infiltration and medication sensitivity. Results: A prognostic model, which included 12 NM genes (COLQ, GNE, ISCU, MSRA, SARS2, SPHK1, CBS, GOT2, CHST1, EXTL2, GCLM, YARS1), was constructed based on regression analysis. The robustness of the model was validated using multiple methods. The high-risk (HR) and low-risk (LR) groups had varying degrees of immune infiltration, according to an immunology-related study. Of these, B cells and Type_II_IFN_Response were greatly infiltrated in the LR group, whereas aCDs, Macrophages, and Treg were heavily infiltrated in the HR group (<i>p</i> &lt; 0.05). Because of higher immunophenoscore, the low-risk group could benefit from immunotherapy more. Drug sensitivity predictions indicated that people with high CBS expression and low GOT2 and ISCU expression may benefit more from treatment with SCH-772984, Pimasertib, Cobimetinib (isomer1), TAK-733, LY-3214996, ARRY-162, Cladribine, Fludarabine, and Hydroxyurea. Conclusion: This work created a 12-gene signature based on NM, preliminary investigated immune infiltration in two risk categories, and discovered some possible anti-tumor medications. To sum up, our study findings offer fresh perspectives on the roles played by NM-associated genes in HCC development, prognosis, immunological response, and medication screening.
DOI: 10.6084/m9.figshare.28028588.v1
title: Analysis of nitrogen metabolism-related gene expression in hepatocellular carcinoma to establish relevant indicators for prediction of prognosis and guidance of immunotherapy

description: Background: The prognosis of cancers is strongly connected with nitrogen metabolism (NM), which plays a critical role in the microenvironment and growth of tumors. It is unsubstantiated, however, how important NM-related genes are for the prognosis of hepatocellular carcinoma (HCC). Methods: Using publicly available data, we examined potential mechanisms of NM-related genes in HCC, created a predictive model, and assessed immune infiltration and medication sensitivity. Results: A prognostic model, which included 12 NM genes (COLQ, GNE, ISCU, MSRA, SARS2, SPHK1, CBS, GOT2, CHST1, EXTL2, GCLM, YARS1), was constructed based on regression analysis. The robustness of the model was validated using multiple methods. The high-risk (HR) and low-risk (LR) groups had varying degrees of immune infiltration, according to an immunology-related study. Of these, B cells and Type_II_IFN_Response were greatly infiltrated in the LR group, whereas aCDs, Macrophages, and Treg were heavily infiltrated in the HR group (<i>p</i> &lt; 0.05). Because of higher immunophenoscore, the low-risk group could benefit from immunotherapy more. Drug sensitivity predictions indicated that people with high CBS expression and low GOT2 and ISCU expression may benefit more from treatment with SCH-772984, Pimasertib, Cobimetinib (isomer1), TAK-733, LY-3214996, ARRY-162, Cladribine, Fludarabine, and Hydroxyurea. Conclusion: This work created a 12-gene signature based on NM, preliminary investigated immune infiltration in two risk categories, and discovered some possible anti-tumor medications. To sum up, our study findings offer fresh perspectives on the roles played by NM-associated genes in HCC development, prognosis, immunological response, and medication screening.
DOI: 10.6084/m9.figshare.28496877
title: Prognostic model for cervical cancer based on apoptosis-related genes

description: This study attempts to develop a novel apoptosis-related predictive model for cervical cancer. Differentially expressed apoptosis-related genes were identified using TCGA, GEO, and MSigDB databases. A 13-gene prognostic model was constructed using multiple regression analyses. The low-risk group exhibited low tumor purity and high ESTIMATE and immune scores. Most of the immune checkpoints in the low-risk group were expressed at higher levels than those in the high-risk group. The low-risk group also had relatively more infiltrating immune cells. An independent prognostic model pertaining to cell apoptosis has been built by this work, which performs well in prediction.
DOI: 10.6084/m9.figshare.28496877.v1
title: Prognostic model for cervical cancer based on apoptosis-related genes

description: This study attempts to develop a novel apoptosis-related predictive model for cervical cancer. Differentially expressed apoptosis-related genes were identified using TCGA, GEO, and MSigDB databases. A 13-gene prognostic model was constructed using multiple regression analyses. The low-risk group exhibited low tumor purity and high ESTIMATE and immune scores. Most of the immune checkpoints in the low-risk group were expressed at higher levels than those in the high-risk group. The low-risk group also had relatively more infiltrating immune cells. An independent prognostic model pertaining to cell apoptosis has been built by this work, which performs well in prediction.
DOI: 10.6084/m9.figshare.29039807
title: The changes of upper airway flow in adolescents with maxillary cleft before and after bone grafting and orthodontic treatment: a CFD simulation study

description: This study used CFD to analyze upper airway airflow in a 14-year-old girl with a unilateral incomplete maxillary cleft across three stages: before surgery, after bone grafting, and after orthodontic treatment. Results revealed that the maxillary cleft affected the airway volume. Post-surgery and orthodontics, airway cross-sectional areas changed and turbulence in nasopharynx/oropharynx reduced; One year post-orthodontics, nasal airflow velocity normalized (5.032 m/s vs. healthy adolescents’ 5.024 m/s); the shear stress distribution improved, and total pressure drop from the nostrils to the pharynx was 20.64 Pa, close to healthy adolescents’ 20.69 Pa. These findings indicate that respiratory function normalized after treatment.
DOI: 10.6084/m9.figshare.29039807.v1
title: The changes of upper airway flow in adolescents with maxillary cleft before and after bone grafting and orthodontic treatment: a CFD simulation study

description: This study used CFD to analyze upper airway airflow in a 14-year-old girl with a unilateral incomplete maxillary cleft across three stages: before surgery, after bone grafting, and after orthodontic treatment. Results revealed that the maxillary cleft affected the airway volume. Post-surgery and orthodontics, airway cross-sectional areas changed and turbulence in nasopharynx/oropharynx reduced; One year post-orthodontics, nasal airflow velocity normalized (5.032 m/s vs. healthy adolescents’ 5.024 m/s); the shear stress distribution improved, and total pressure drop from the nostrils to the pharynx was 20.64 Pa, close to healthy adolescents’ 20.69 Pa. These findings indicate that respiratory function normalized after treatment.
DOI: 10.6084/m9.figshare.28248652
title: Identification of circadian rhythm-related biomarkers and development of diagnostic models for Crohn’s disease using machine learning algorithms

description: The global rise in Crohn’s Disease (CD) incidence has intensified diagnostic challenges. This study identified circadian rhythm-related biomarkers for CD using datasets from the GEO database. Differentially expressed genes underwent Weighted Gene Co-Expression Network Analysis, with 49 hub genes intersected from GeneCards data. Diagnostic models were constructed using machine learning algorithms, and biologic therapy efficacy was predicted with advanced regression techniques. Single-cell sequencing showed high gene expression in stem cells, immune, and endothelial cells, with validation confirming significant differences between CD patients and controls. These findings suggest circadian rhythm-related genes as promising diagnostic biomarkers for CD.
DOI: 10.6084/m9.figshare.28248652.v1
title: Identification of circadian rhythm-related biomarkers and development of diagnostic models for Crohn’s disease using machine learning algorithms

description: The global rise in Crohn’s Disease (CD) incidence has intensified diagnostic challenges. This study identified circadian rhythm-related biomarkers for CD using datasets from the GEO database. Differentially expressed genes underwent Weighted Gene Co-Expression Network Analysis, with 49 hub genes intersected from GeneCards data. Diagnostic models were constructed using machine learning algorithms, and biologic therapy efficacy was predicted with advanced regression techniques. Single-cell sequencing showed high gene expression in stem cells, immune, and endothelial cells, with validation confirming significant differences between CD patients and controls. These findings suggest circadian rhythm-related genes as promising diagnostic biomarkers for CD.
DOI: 10.6084/m9.figshare.25377001
title: CFD simulation of cannabidiol delivery through microneedle patches

description: This study investigates the efficiency and influence of microneedle parameters, specifically Needle Point Angle (a) and Needle Height (h), on the diffusion of Cannabidiol (CBD) across varying skin depths. Utilizing the Latin Hypercube Sampling method, twelve distinct cases were analyzed. Observations reveal a consistent high concentration of CBD delivered <i>via</i> the microneedle patch, with a notable decrease in concentration as the depth increases, displaying a non-linear trend. Multivariate polynomial regression offers a quantitative relationship between the variables, with the third-order bivariate fitting providing the most accurate representation. Compared to other CBD delivery mechanisms, microneedle patches present enhanced CBD concentrations, circumventing challenges faced by other methods such as dosage inaccuracy, systemic absorption issues, and CBD degradation. The results highlight the potential of microneedle patches as a promising avenue for optimized transdermal drug delivery.
DOI: 10.6084/m9.figshare.25377001.v1
title: CFD simulation of cannabidiol delivery through microneedle patches

description: This study investigates the efficiency and influence of microneedle parameters, specifically Needle Point Angle (a) and Needle Height (h), on the diffusion of Cannabidiol (CBD) across varying skin depths. Utilizing the Latin Hypercube Sampling method, twelve distinct cases were analyzed. Observations reveal a consistent high concentration of CBD delivered <i>via</i> the microneedle patch, with a notable decrease in concentration as the depth increases, displaying a non-linear trend. Multivariate polynomial regression offers a quantitative relationship between the variables, with the third-order bivariate fitting providing the most accurate representation. Compared to other CBD delivery mechanisms, microneedle patches present enhanced CBD concentrations, circumventing challenges faced by other methods such as dosage inaccuracy, systemic absorption issues, and CBD degradation. The results highlight the potential of microneedle patches as a promising avenue for optimized transdermal drug delivery.
DOI: 10.6084/m9.figshare.29040347
title: Identification of immune-related biomarkers and immune infiltrations of intracranial aneurysm with subarachnoid hemorrhage by machine-learning strategies

description: <b>Background:</b> Subarachnoid hemorrhage (SAH) risk increases with intracranial aneurysms (IA), but their relationship remains unclear. <b>Methods:</b> We explored SAH-IA links using machine learning and bioinformatics, identifying 66 IA-related SAH genes. KEGG analysis highlighted pathways like NF-κB, TNF, and COVID-19. <b>Results:</b> Two immune-related genes (ZNF281, LRRN3) were identified, and a ceRNA network was constructed. Ten potential SAH-IA drugs were screened via CMAP. <b>Conclusion:</b> ZNF281 and LRRN3 may regulate immune pathways (T cells, NK cells, macrophages), influencing IA-related SAH development, and could serve as therapeutic targets.
DOI: 10.6084/m9.figshare.29040347.v1
title: Identification of immune-related biomarkers and immune infiltrations of intracranial aneurysm with subarachnoid hemorrhage by machine-learning strategies

description: <b>Background:</b> Subarachnoid hemorrhage (SAH) risk increases with intracranial aneurysms (IA), but their relationship remains unclear. <b>Methods:</b> We explored SAH-IA links using machine learning and bioinformatics, identifying 66 IA-related SAH genes. KEGG analysis highlighted pathways like NF-κB, TNF, and COVID-19. <b>Results:</b> Two immune-related genes (ZNF281, LRRN3) were identified, and a ceRNA network was constructed. Ten potential SAH-IA drugs were screened via CMAP. <b>Conclusion:</b> ZNF281 and LRRN3 may regulate immune pathways (T cells, NK cells, macrophages), influencing IA-related SAH development, and could serve as therapeutic targets.
DOI: 10.6084/m9.figshare.25895984
title: Potential molecular mechanisms of Huangqin Tang for liver cancer treatment by network pharmacology and molecular dynamics simulations

description: This study aims to investigate the mechanism of Huangqin Tang in treating liver cancer. Active ingredients and corresponding targets of Huangqin Tang were obtained from the Traditional Chinese Medicine Systems Pharmacology Database. Differentially expressed genes in liver cancer were identified from mRNA expression data. A protein–protein interaction (PPI) network was constructed using differentially expressed genes and Huangqin Tang targets. Random walk with restart (RWR) analysis was performed on the PPI network. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were conducted. A drug-active ingredient–gene interaction network was established, and molecular docking and molecular dynamics simulations were performed. Finally, the stability of binding between CDK1 and oroxylin was tested according to cellular thermal shift assay (CETSA). 160 active ingredients, 239 targets, and 1093 differentially expressed genes were identified. RWR analysis identified 10 potential targets for liver cancer. Enrichment analysis revealed protein kinase regulator activity and Steroid hormone biosynthesis as significant pathways. Molecular docking suggested a stable complex between oroxylin A and CDK1. CETSA demonstrated that the combination of oroxylin A and CDK1 increased the stability of CDK1, and the combination efficiency was high. Huangqin Tang may treat liver cancer by targeting CDK1 with oroxylin A. Protein kinase regulator activity and Steroid hormone biosynthesis pathways may play a role in liver cancer treatment with Huangqin Tang. This study provides insight into the mechanistic basis of Huangqin Tang for liver cancer treatment.