cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.31111244.v1
title: Delineation of hub genes related to ferroptosis and radiosensitivity in breast cancer with three identified subtypes

description: This study aimed to explore the roles of radiotherapy-sensitive and ferroptosis genes in breast cancer (BRCA). Genes differentially expressed pre- and post-radiotherapy from the GSE59733 dataset were intersected with ferroptosis-related genes. Through a protein-protein interaction network, 10 hub genes were identified. BRCA patients were categorized into three clusters, with cluster 1 and cluster 2 showing the most significant survival difference. Cluster 1 demonstrated higher immune infiltration levels but poorer response to immune therapy compared to cluster 2. Moreover, cluster 1 and cluster 2 exhibited sensitivity to different drugs. These 10 hub genes can effectively classify patients and suggest potential drugs.
DOI: 10.6084/m9.figshare.26189865
title: Establishment of a prognostic model for pancreatic cancer based on vesicle-mediated transport protein-related genes

description: This study attempted to build a prognostic riskscore model for pancreatic cancer (PC) patients based on vesicle-mediated transport protein-related genes (VMTGs). We initially conducted differential expression analysis and Cox regression analysis, followed by the construction of a riskscore model to classify PC patients into high-risk (HR) and low-risk (LR) groups. The GEO GSE62452 dataset further validated the model. Kaplan-Meier survival analysis was employed to analyze the survival rate of the HR group and LR group. Cox analysis confirmed the independent prognostic ability of the riskscore model. Additionally, we evaluated immune status in both HR and LR groups, utilizing data from the GDSC database to predict drug response among PC patients. We identified six PC-specific genes from 724 VMTGs. Survival analysis revealed that the survival rate of the HR group was lower than that of the LR group (P&lt;0.05). Cox analysis confirmed that the prognostic riskscore model could independently predict the survival status of PC patients (P&lt;0.001). Immunological analysis revealed that the ESTIMATE score, immune score, and stroma score of the HR group were considerably lower than those of the LR group, and the tumor purity score of the HR group was higher. The IC50 values of Gemcitabine, Irinotecan, Oxaliplatin, and Paclitaxel in the LR group were considerably lower than those in the HR group (P&lt;0.001). In summary, the VMTG-based prognostic riskscore model could stratify PC risk and effectively predict the survival of PC patients.
DOI: 10.6084/m9.figshare.26189865.v1
title: Establishment of a prognostic model for pancreatic cancer based on vesicle-mediated transport protein-related genes

description: This study attempted to build a prognostic riskscore model for pancreatic cancer (PC) patients based on vesicle-mediated transport protein-related genes (VMTGs). We initially conducted differential expression analysis and Cox regression analysis, followed by the construction of a riskscore model to classify PC patients into high-risk (HR) and low-risk (LR) groups. The GEO GSE62452 dataset further validated the model. Kaplan-Meier survival analysis was employed to analyze the survival rate of the HR group and LR group. Cox analysis confirmed the independent prognostic ability of the riskscore model. Additionally, we evaluated immune status in both HR and LR groups, utilizing data from the GDSC database to predict drug response among PC patients. We identified six PC-specific genes from 724 VMTGs. Survival analysis revealed that the survival rate of the HR group was lower than that of the LR group (P&lt;0.05). Cox analysis confirmed that the prognostic riskscore model could independently predict the survival status of PC patients (P&lt;0.001). Immunological analysis revealed that the ESTIMATE score, immune score, and stroma score of the HR group were considerably lower than those of the LR group, and the tumor purity score of the HR group was higher. The IC50 values of Gemcitabine, Irinotecan, Oxaliplatin, and Paclitaxel in the LR group were considerably lower than those in the HR group (P&lt;0.001). In summary, the VMTG-based prognostic riskscore model could stratify PC risk and effectively predict the survival of PC patients.
DOI: 10.6084/m9.figshare.27602242
title: The role of m5C RNA methylation regulators in the diagnosis and immune microenvironment of osteoarthritis

description: The 5-methylcytosine (m5C) is a common post-transcriptional RNA methylation modification and is involved in the pathological process of many diseases. However, little is known about the role of m5C in osteoarthritis (OA). OA gene data and the corresponding information were downloaded from the Gene Expression Omnibus database. Based on 36 m5C regulators, we constructed the landscape and diagnostic model for OA. Later, two m5C modification patterns were identified, and functional analyses were performed to evaluate whether these patterns were related to endoplasmic reticulum (ER) stress and mitochondrial autophagy. We further comprehensively analyzed the immune cell infiltration characteristics in different modification patterns in OA. We also established the post-transcriptional regulatory networks and drug-gene networks. Our findings suggested that m5C regulators were differentially expressed between OA and normal samples and could serve as novel biomarkers for the diagnosis of OA. Besides, m5C regulators may be involved in regulating ER stress, mitochondrial autophagy, and immune infiltration in OA. The m5C modification can influence the sensitivity to drugs and the potential post-transcriptional regulatory mechanisms might provide promising targets.
DOI: 10.6084/m9.figshare.27602242.v1
title: The role of m5C RNA methylation regulators in the diagnosis and immune microenvironment of osteoarthritis

description: The 5-methylcytosine (m5C) is a common post-transcriptional RNA methylation modification and is involved in the pathological process of many diseases. However, little is known about the role of m5C in osteoarthritis (OA). OA gene data and the corresponding information were downloaded from the Gene Expression Omnibus database. Based on 36 m5C regulators, we constructed the landscape and diagnostic model for OA. Later, two m5C modification patterns were identified, and functional analyses were performed to evaluate whether these patterns were related to endoplasmic reticulum (ER) stress and mitochondrial autophagy. We further comprehensively analyzed the immune cell infiltration characteristics in different modification patterns in OA. We also established the post-transcriptional regulatory networks and drug-gene networks. Our findings suggested that m5C regulators were differentially expressed between OA and normal samples and could serve as novel biomarkers for the diagnosis of OA. Besides, m5C regulators may be involved in regulating ER stress, mitochondrial autophagy, and immune infiltration in OA. The m5C modification can influence the sensitivity to drugs and the potential post-transcriptional regulatory mechanisms might provide promising targets.
DOI: 10.6084/m9.figshare.28287189
title: The construction of a breast cancer prognostic model by combining genes related to hypoxia and endoplasmic reticulum stress

description: Breast cancer (BC) is a malignant tumor that occurs in breast tissue. This project aims to predict the prognosis of BC patients using genes related to hypoxia and endoplasmic reticulum stress (ERS). RNA-seq and clinical data for BC were downloaded from TCGA and GEO databases. Hypoxia and ERS-related genes were collected from the Genecards database. Univariate/multivariate Cox regression and Lasso regression analyses were used to screen genes and construct prognostic models. Patients were divided into high-risk (HR) and low-risk (LR) groups based on risk scores. The CIBERSORT algorithm was used to analyze differences in immune infiltration between the two groups. The mutations of the two groups were analyzed statistically. The CellMiner database was used for drug prediction and the TISCH database for single-cell sequencing analysis. We screened 8 feature genes to construct a prognostic model. Patients in the HR group had a remarkably worse prognosis. TP53 exhibited a higher mutation frequency in the HR group. CIBERSORT analysis uncovered a remarkable increase in the infiltration levels of Macrophages M0 and Tregs in cancer patients and HR patients. Drug sensitivity prediction demonstrated that the expression of IVL was greatly negatively linked with the sensitivity of COLCHICINE. PTGS2 had a remarkably negative correlation with the Vincristine sensitivity. The prognostic model based on 8 hypoxia and ERS-related genes can predict the survival, immune status, and potential drugs of BC patients, bringing a new perspective on individualized treatment.
DOI: 10.6084/m9.figshare.28287189.v1
title: The construction of a breast cancer prognostic model by combining genes related to hypoxia and endoplasmic reticulum stress

description: Breast cancer (BC) is a malignant tumor that occurs in breast tissue. This project aims to predict the prognosis of BC patients using genes related to hypoxia and endoplasmic reticulum stress (ERS). RNA-seq and clinical data for BC were downloaded from TCGA and GEO databases. Hypoxia and ERS-related genes were collected from the Genecards database. Univariate/multivariate Cox regression and Lasso regression analyses were used to screen genes and construct prognostic models. Patients were divided into high-risk (HR) and low-risk (LR) groups based on risk scores. The CIBERSORT algorithm was used to analyze differences in immune infiltration between the two groups. The mutations of the two groups were analyzed statistically. The CellMiner database was used for drug prediction and the TISCH database for single-cell sequencing analysis. We screened 8 feature genes to construct a prognostic model. Patients in the HR group had a remarkably worse prognosis. TP53 exhibited a higher mutation frequency in the HR group. CIBERSORT analysis uncovered a remarkable increase in the infiltration levels of Macrophages M0 and Tregs in cancer patients and HR patients. Drug sensitivity prediction demonstrated that the expression of IVL was greatly negatively linked with the sensitivity of COLCHICINE. PTGS2 had a remarkably negative correlation with the Vincristine sensitivity. The prognostic model based on 8 hypoxia and ERS-related genes can predict the survival, immune status, and potential drugs of BC patients, bringing a new perspective on individualized treatment.
DOI: 10.6084/m9.figshare.28776337.v1
title: Identification of DYRK2 and TRIM32 as keloids programmed cell death-related biomarkers: insights from bioinformatics and machine learning in multiple cohorts

description: This study aims to explore the expression patterns and mechanisms of programmed cell death-related genes in keloids and identify molecular targets for early diagnosis and treatment. We first explored the expression, immune, and biological function profiles of keloids. Using various machine learning methods, two key genes, DYRK2 and TRIM32, were identified, with ROC curves demonstrating their diagnostic potential. Further analyses, including GSEA, immune cell profiling, competing endogenous RNA network, and single-cell analysis, revealed their mechanism of action and regulatory network. Finally, SB-431542 was identified as a potential therapeutic agent for keloids through CMap and molecular docking.
DOI: 10.6084/m9.figshare.28581465
title: Transcriptome-based insights into the role of cancer-associated fibroblasts in lung adenocarcinoma prognosis and therapy

description: Cancer-associated fibroblasts (CAFs) are related to drug resistance and prognosis of tumor patients. This study aimed to investigate the relationship between prognosis and drug treatment response in patients with CAF and lung adenocarcinoma (LUAD). The data pertaining to LUAD patients were obtained from The Cancer Genome Atlas-LUAD and GSE68465 datasets. Four different algorithms were used to quantify CAF infiltration and stromal scores. Weighted gene network co-expression analysis was used to identify CAF-related modules and hub genes. Univariate Cox regression analysis, least absolute shrinkage and selection operator regression analysis, and multivariate Cox regression analysis were used to construct CAF signatures, whose ability to predict prognosis was verified by individual CAF scores. The CAF-related signature of eight genes was constructed, and the CAF score was calculated. The prognosis of LUAD patients with high CAF scores was significantly worse than that of patients with low CAF scores. CAF score was an independent risk factor for LUAD prognosis. Patients with high CAF scores were sensitive to some chemotherapy drugs, and in most cases, they were non-responsive to immunotherapy. Eight-gene CAF signature may predict LUAD patient prognosis and evaluate clinical responses to chemotherapy and immunotherapy, enabling individualized treatment for the patients.
DOI: 10.6084/m9.figshare.28581465.v1
title: Transcriptome-based insights into the role of cancer-associated fibroblasts in lung adenocarcinoma prognosis and therapy

description: Cancer-associated fibroblasts (CAFs) are related to drug resistance and prognosis of tumor patients. This study aimed to investigate the relationship between prognosis and drug treatment response in patients with CAF and lung adenocarcinoma (LUAD). The data pertaining to LUAD patients were obtained from The Cancer Genome Atlas-LUAD and GSE68465 datasets. Four different algorithms were used to quantify CAF infiltration and stromal scores. Weighted gene network co-expression analysis was used to identify CAF-related modules and hub genes. Univariate Cox regression analysis, least absolute shrinkage and selection operator regression analysis, and multivariate Cox regression analysis were used to construct CAF signatures, whose ability to predict prognosis was verified by individual CAF scores. The CAF-related signature of eight genes was constructed, and the CAF score was calculated. The prognosis of LUAD patients with high CAF scores was significantly worse than that of patients with low CAF scores. CAF score was an independent risk factor for LUAD prognosis. Patients with high CAF scores were sensitive to some chemotherapy drugs, and in most cases, they were non-responsive to immunotherapy. Eight-gene CAF signature may predict LUAD patient prognosis and evaluate clinical responses to chemotherapy and immunotherapy, enabling individualized treatment for the patients.
DOI: 10.6084/m9.figshare.28776337
title: Identification of DYRK2 and TRIM32 as keloids programmed cell death-related biomarkers: insights from bioinformatics and machine learning in multiple cohorts

description: This study aims to explore the expression patterns and mechanisms of programmed cell death-related genes in keloids and identify molecular targets for early diagnosis and treatment. We first explored the expression, immune, and biological function profiles of keloids. Using various machine learning methods, two key genes, DYRK2 and TRIM32, were identified, with ROC curves demonstrating their diagnostic potential. Further analyses, including GSEA, immune cell profiling, competing endogenous RNA network, and single-cell analysis, revealed their mechanism of action and regulatory network. Finally, SB-431542 was identified as a potential therapeutic agent for keloids through CMap and molecular docking.
DOI: 10.6084/m9.figshare.29153564
title: Scale mechanism design for data analysis in immersive environments

description: This dataset contains experimental results from research investigating how different scale mechanisms affect user interactions and visualization effectiveness in immersive analytics environments. Specifically, the experiments explore the impacts of interaction methods (adjusted sensitivity, interaction distance) and visualization strategies (visual density, information display levels), differentiated by spatial reference frames—allocentric (object-centered) versus egocentric (user-centered).Participants performed predefined analytical tasks across four distinct dataset scales:<b>Small Allocentric Scale (Small Allo)</b>: Dataset smaller than one-third of user's eye level; allowing simultaneous visualization of the complete dataset.<b>Large Allocentric Scale (Large Allo)</b>: Dataset approximately two-thirds of user's eye level.<b>Small Egocentric Scale (Small Ego)</b>: Dataset approximately 1.5 times user's eye level, requiring internal exploration.<b>Large Egocentric Scale (Large Ego)</b>: Dataset three times larger than user's eye level or more, requiring physical navigation and perceived as independent spatial environments.Performance metrics collected include task accuracy, completion time, subjective cognitive load, and immersion ratings. Data were gathered to empirically validate spatial cognition theories applied to immersive analytics, providing valuable insights for optimizing user experience and interface design in VR and immersive environments.This research contributes empirical evidence supporting a structured scale-based classification method for immersive data exploration interfaces, facilitating intuitive interactions and clear cognitive transitions between spatial frames.
DOI: 10.6084/m9.figshare.29153564.v1
title: Scale mechanism design for data analysis in immersive environments

description: This dataset contains experimental results from research investigating how different scale mechanisms affect user interactions and visualization effectiveness in immersive analytics environments. Specifically, the experiments explore the impacts of interaction methods (adjusted sensitivity, interaction distance) and visualization strategies (visual density, information display levels), differentiated by spatial reference frames—allocentric (object-centered) versus egocentric (user-centered).Participants performed predefined analytical tasks across four distinct dataset scales:<b>Small Allocentric Scale (Small Allo)</b>: Dataset smaller than one-third of user's eye level; allowing simultaneous visualization of the complete dataset.<b>Large Allocentric Scale (Large Allo)</b>: Dataset approximately two-thirds of user's eye level.<b>Small Egocentric Scale (Small Ego)</b>: Dataset approximately 1.5 times user's eye level, requiring internal exploration.<b>Large Egocentric Scale (Large Ego)</b>: Dataset three times larger than user's eye level or more, requiring physical navigation and perceived as independent spatial environments.Performance metrics collected include task accuracy, completion time, subjective cognitive load, and immersion ratings. Data were gathered to empirically validate spatial cognition theories applied to immersive analytics, providing valuable insights for optimizing user experience and interface design in VR and immersive environments.This research contributes empirical evidence supporting a structured scale-based classification method for immersive data exploration interfaces, facilitating intuitive interactions and clear cognitive transitions between spatial frames.
DOI: 10.6084/m9.figshare.26378836
title: Bioinformatics analysis of the role of lysosome-related genes in breast cancer

description: This study aimed to investigate the roles of lysosome-related genes in BC prognosis and immunity. Transcriptome data from TCGA and MSigDB, along with lysosome-related gene sets, underwent NMF cluster analysis, resulting in two subtypes. Using lasso regression and univariate/multivariate Cox regression analysis, an 11-gene signature was successfully identified and verified. High- and low-risk populations were dominated by HR+ sample types. There were differences in pathway enrichment, immune cell infiltration, and immune scores. Sensitive drugs targeting model genes were screened using GDSC and CCLE. This study constructed a reliable prognostic model with lysosome-related genes, providing valuable insights for BC clinical immunotherapy. Lysosome-related genes can be used to predict survival outcomes in BRCA patients.Significant differences were showed in the immune status of patient with different prognoses.Immunotherapy may show better therapeutic results in low-risk patients.The most promising targeted drugs in the low-risk group are mainly Lapatinib, Palbociclib and Ribociclib. Lysosome-related genes can be used to predict survival outcomes in BRCA patients. Significant differences were showed in the immune status of patient with different prognoses. Immunotherapy may show better therapeutic results in low-risk patients. The most promising targeted drugs in the low-risk group are mainly Lapatinib, Palbociclib and Ribociclib.
DOI: 10.6084/m9.figshare.26378836.v1
title: Bioinformatics analysis of the role of lysosome-related genes in breast cancer

description: This study aimed to investigate the roles of lysosome-related genes in BC prognosis and immunity. Transcriptome data from TCGA and MSigDB, along with lysosome-related gene sets, underwent NMF cluster analysis, resulting in two subtypes. Using lasso regression and univariate/multivariate Cox regression analysis, an 11-gene signature was successfully identified and verified. High- and low-risk populations were dominated by HR+ sample types. There were differences in pathway enrichment, immune cell infiltration, and immune scores. Sensitive drugs targeting model genes were screened using GDSC and CCLE. This study constructed a reliable prognostic model with lysosome-related genes, providing valuable insights for BC clinical immunotherapy. Lysosome-related genes can be used to predict survival outcomes in BRCA patients.Significant differences were showed in the immune status of patient with different prognoses.Immunotherapy may show better therapeutic results in low-risk patients.The most promising targeted drugs in the low-risk group are mainly Lapatinib, Palbociclib and Ribociclib. Lysosome-related genes can be used to predict survival outcomes in BRCA patients. Significant differences were showed in the immune status of patient with different prognoses. Immunotherapy may show better therapeutic results in low-risk patients. The most promising targeted drugs in the low-risk group are mainly Lapatinib, Palbociclib and Ribociclib.
DOI: 10.6084/m9.figshare.28652463
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.