cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20624170
title: CCNE1 is a potential target of Metformin for tumor suppression of ovarian high-grade serous carcinoma

description: High-grade serous ovarian cancer (HGSOC) is the most common and malignant type of ovarian cancer, accounting for 70%–80% of mortality. However, the treatment of HGSOC has improved little in the past few decades. Metformin is the first-line medication for the treatment of type 2 diabetes and has now gained more attention in cancer treatment. In this study, we sought to identify potential hub genes that metformin could target in the treatment of HGSOC. We downloaded GSE69428 and GSE69429 in the Gene Expression Omnibus database and performed the bioinformatics analysis. Subsequently, we analyzed the effect of Metformin in HGSOC through biological experiments. Molecular simulation docking was used to predict the interaction of Metformin and CCNE1. We chose CCNE1 for the study based on bioinformatics analysis, literature studies, and preliminary data. We evaluated that CCNE1 is overexpressed in HGSOC tissues and found that HGSOC cells with high CCNE1 expression increase sensitivity to Metformin treatment in the analysis of cell proliferation and anchorage-independent growth. Metformin could inhibit the expression of CCNE1, which is associated with the anti-proliferative effect of tumor cells. Moreover, Metformin could ameliorate the tumor growth in syngeneic orthotopic transplantation mouse models and xenograft tumorigenesis models. Furthermore, molecular simulation docking showed that Metformin may bind to CCNE1 protein, suggesting that CCNE1 could be a potential target for Metformin. Our data revealed that Metformin has antitumor effects on ovarian cancer and CCNE1 could be a potential target for Metformin.
DOI: 10.6084/m9.figshare.20624170.v1
title: CCNE1 is a potential target of Metformin for tumor suppression of ovarian high-grade serous carcinoma

description: High-grade serous ovarian cancer (HGSOC) is the most common and malignant type of ovarian cancer, accounting for 70%–80% of mortality. However, the treatment of HGSOC has improved little in the past few decades. Metformin is the first-line medication for the treatment of type 2 diabetes and has now gained more attention in cancer treatment. In this study, we sought to identify potential hub genes that metformin could target in the treatment of HGSOC. We downloaded GSE69428 and GSE69429 in the Gene Expression Omnibus database and performed the bioinformatics analysis. Subsequently, we analyzed the effect of Metformin in HGSOC through biological experiments. Molecular simulation docking was used to predict the interaction of Metformin and CCNE1. We chose CCNE1 for the study based on bioinformatics analysis, literature studies, and preliminary data. We evaluated that CCNE1 is overexpressed in HGSOC tissues and found that HGSOC cells with high CCNE1 expression increase sensitivity to Metformin treatment in the analysis of cell proliferation and anchorage-independent growth. Metformin could inhibit the expression of CCNE1, which is associated with the anti-proliferative effect of tumor cells. Moreover, Metformin could ameliorate the tumor growth in syngeneic orthotopic transplantation mouse models and xenograft tumorigenesis models. Furthermore, molecular simulation docking showed that Metformin may bind to CCNE1 protein, suggesting that CCNE1 could be a potential target for Metformin. Our data revealed that Metformin has antitumor effects on ovarian cancer and CCNE1 could be a potential target for Metformin.
DOI: 10.6084/m9.figshare.22324037
title: Impact of <i>NOTCH1</i> polymorphisms on liver cancer in a Chinese Han population

description: NOTCH1, a member of the Notch family, is up-expression in advanced liver cancer (LC) patients and is associated with tumor sizes, tumor stage, metastasis, and invasion. A few studies have discovered the contribution of NOTCH1 variants to LC risk. Our purpose was to assess the relationship of NOTCH1 rs10521, rs2229971, and rs4489420 to LC risk. We enrolled 709 LC patients and 708 healthy controls. Genotyping was determined through the Agena MassARRAY system. Multiple genetic models by logistic regression were useful for odds ratios (ORs) with 95% confidence intervals (CIs). Rs10521-G (p = 0.009, OR = 0.75, 95% CI: 0.61–0.93), rs2229971-A (p = 0.023, OR = 0.81, 95% CI: 0.67–0.97), and rs4489420-A (p = 0.014, OR = 0.38, 95% CI: 0.16–0.85) might be protective factors for LC occurrence in the Chinese Han population, especially rs10521 and rs2229971 (false-positive report probability (FPRP) &lt;0.2 and statistical power &gt;90%). Interestingly, stratified analysis displayed that the contribution of NOTCH1 polymorphisms to LC risk might be associated with gender, age, smoking, and drinking. Our data first determined that NOTCH1 rs10521-G, rs2229971-A, and rs4489420-A might be protective factors for LC susceptibility.
DOI: 10.6084/m9.figshare.22324037.v1
title: Impact of <i>NOTCH1</i> polymorphisms on liver cancer in a Chinese Han population

description: NOTCH1, a member of the Notch family, is up-expression in advanced liver cancer (LC) patients and is associated with tumor sizes, tumor stage, metastasis, and invasion. A few studies have discovered the contribution of NOTCH1 variants to LC risk. Our purpose was to assess the relationship of NOTCH1 rs10521, rs2229971, and rs4489420 to LC risk. We enrolled 709 LC patients and 708 healthy controls. Genotyping was determined through the Agena MassARRAY system. Multiple genetic models by logistic regression were useful for odds ratios (ORs) with 95% confidence intervals (CIs). Rs10521-G (p = 0.009, OR = 0.75, 95% CI: 0.61–0.93), rs2229971-A (p = 0.023, OR = 0.81, 95% CI: 0.67–0.97), and rs4489420-A (p = 0.014, OR = 0.38, 95% CI: 0.16–0.85) might be protective factors for LC occurrence in the Chinese Han population, especially rs10521 and rs2229971 (false-positive report probability (FPRP) &lt;0.2 and statistical power &gt;90%). Interestingly, stratified analysis displayed that the contribution of NOTCH1 polymorphisms to LC risk might be associated with gender, age, smoking, and drinking. Our data first determined that NOTCH1 rs10521-G, rs2229971-A, and rs4489420-A might be protective factors for LC susceptibility.
DOI: 10.6084/m9.figshare.22014607
title: Hsa_circ_0063329 inhibits prostate cancer growth and metastasis by modulating the miR-605-5p/tgif2 axis

description: Circular RNAs play crucial regulatory roles in the progression of various cancers. Nevertheless, the underlying mechanisms of circRNAs in prostate cancer (PCa) proliferation and metastasis remain largely uncertain. Here, we performed circRNA microarray analyses to identify differentially expressed circRNAs in a normal prostate epithelial cell line and PCa cell lines. We found that hsa_circ_0063329 was significantly downregulated in PCa. A series of <i>in vitro</i> and <i>in vivo</i> functional assays showed that overexpression of hsa_circ_0063329 inhibits PCa cell progression, while silencing of hsa_circ_0063329 achieved the opposite effects. Mechanistically, bioinformatics analysis, RNA pulldown, RNA-seq and dual-luciferase reporter assays demonstrated that hsa_circ_0063329 exerts its effect by sponging miR-605-5p to derepress TG-interacting factor 2 (TGIF2) and inactivate the TGF-β pathway. In conclusion, hsa_circ_0063329 inhibits the proliferation and metastasis of PCa <i>via</i> modulation of the miR-605-5p/TGIF2 axis, and targeting hsa_circ_0063329 may provide a promising treatment strategy for aggressive PCa.
DOI: 10.6084/m9.figshare.22014607.v1
title: Hsa_circ_0063329 inhibits prostate cancer growth and metastasis by modulating the miR-605-5p/tgif2 axis

description: Circular RNAs play crucial regulatory roles in the progression of various cancers. Nevertheless, the underlying mechanisms of circRNAs in prostate cancer (PCa) proliferation and metastasis remain largely uncertain. Here, we performed circRNA microarray analyses to identify differentially expressed circRNAs in a normal prostate epithelial cell line and PCa cell lines. We found that hsa_circ_0063329 was significantly downregulated in PCa. A series of <i>in vitro</i> and <i>in vivo</i> functional assays showed that overexpression of hsa_circ_0063329 inhibits PCa cell progression, while silencing of hsa_circ_0063329 achieved the opposite effects. Mechanistically, bioinformatics analysis, RNA pulldown, RNA-seq and dual-luciferase reporter assays demonstrated that hsa_circ_0063329 exerts its effect by sponging miR-605-5p to derepress TG-interacting factor 2 (TGIF2) and inactivate the TGF-β pathway. In conclusion, hsa_circ_0063329 inhibits the proliferation and metastasis of PCa <i>via</i> modulation of the miR-605-5p/TGIF2 axis, and targeting hsa_circ_0063329 may provide a promising treatment strategy for aggressive PCa.
DOI: 10.6084/m9.figshare.22315129
title: Downregulation of lncRNA <i>SNHG1</i> in hypoxia and stem cells is associated with poor disease prognosis in gliomas

description: Gliomas are brain tumors associated with high morbidity, relapse and lethality despite improvement in therapeutic regimes. The hypoxic tumor microenvironment is a key feature associated with such poor outcomes in gliomas. The Hypoxia Inducible Factor (HIF) family of transcription factors are master regulators of cellular proliferation, high metabolic rates and angiogenesis via aberrant expression of downstream genes. Recent studies have implicated long non-coding RNAs (lncRNAs) as potential prognostic and diagnostic biomarkers. In this study, identification of hypoxia regulated lncRNA with a bioinformatic pipeline consisting of a newly developed tool “GenOx” was utilized for the identification of Hypoxia Response Element (HRE) and Hypoxia Ancillary Sequence (HAS) motifs in the promoter regions of lncRNAs. This was coupled with molecular, functional and interactome-based analyses of these hypoxia-relevant lncRNAs in primary tumors and cell-line models. We report on the identification of novel hypoxia regulated lncRNAs <i>SNHG12</i>, <i>CASC7</i> and <i>MF12-AS1</i>. A strong association of RNA splicing mechanisms was observed with enriched lncRNAs. Several lncRNAs have emerged as prognostic biomarkers, of which <i>TP53TG1</i> and <i>SNHG1</i> were identified as highly relevant lncRNAs in glioma progression and validated in hypoxia cultured cells. Significantly, we determined that <i>SNHG1</i> expression in tumor (vs. normal) is different from glioma stem cells, GSC (vs. tumors) and in hypoxia (vs. normoxia), positioning downregulation of <i>SNHG1</i> to be associated with worsened prognosis.
DOI: 10.6084/m9.figshare.22315129.v2
title: Downregulation of lncRNA <i>SNHG1</i> in hypoxia and stem cells is associated with poor disease prognosis in gliomas

description: Gliomas are brain tumors associated with high morbidity, relapse and lethality despite improvement in therapeutic regimes. The hypoxic tumor microenvironment is a key feature associated with such poor outcomes in gliomas. The Hypoxia Inducible Factor (HIF) family of transcription factors are master regulators of cellular proliferation, high metabolic rates and angiogenesis via aberrant expression of downstream genes. Recent studies have implicated long non-coding RNAs (lncRNAs) as potential prognostic and diagnostic biomarkers. In this study, identification of hypoxia regulated lncRNA with a bioinformatic pipeline consisting of a newly developed tool “GenOx” was utilized for the identification of Hypoxia Response Element (HRE) and Hypoxia Ancillary Sequence (HAS) motifs in the promoter regions of lncRNAs. This was coupled with molecular, functional and interactome-based analyses of these hypoxia-relevant lncRNAs in primary tumors and cell-line models. We report on the identification of novel hypoxia regulated lncRNAs <i>SNHG12</i>, <i>CASC7</i> and <i>MF12-AS1</i>. A strong association of RNA splicing mechanisms was observed with enriched lncRNAs. Several lncRNAs have emerged as prognostic biomarkers, of which <i>TP53TG1</i> and <i>SNHG1</i> were identified as highly relevant lncRNAs in glioma progression and validated in hypoxia cultured cells. Significantly, we determined that <i>SNHG1</i> expression in tumor (vs. normal) is different from glioma stem cells, GSC (vs. tumors) and in hypoxia (vs. normoxia), positioning downregulation of <i>SNHG1</i> to be associated with worsened prognosis.
DOI: 10.6084/m9.figshare.20430345
title: TGFβ signaling activation correlates with immune-inflamed tumor microenvironment across human cancers and predicts response to immunotherapy

description: Considering the determining role of TGFβ signaling in the tumor microenvironment (TME) on immune evasion, the inhibition of signaling is expected to enhance the therapeutic efficacy of immunotherapies, especially immune checkpoint blockade (ICB), which is confirmed in preclinical data. However, successive failures in clinical translation occur at the initial stage. To provide a better understanding of TGFβ signaling within the TME and its relation to the individual immunological status, we performed a pan-cancer analysis comparing the activation of TGFβ pathway among different TMEs based on multi-omics data. Compared with non-inflamed tumors, increased TGFβ signaling activity appeared in four non-cancer cell types within TME in inflamed tumors. Significant correlations were revealed between TGFβ signaling and reliable biomarkers for ICB therapy, as well as between TGFβ signaling and HPV status. Our findings contribute to explain the inconsistency between preclinical and clinical research, and are crucial to optimizing upcoming clinical trial design and improving patient stratification for personalized prediction.
DOI: 10.6084/m9.figshare.22315129.v1
title: Downregulation of lncRNA <i>SNHG1</i> in hypoxia and stem cells is associated with poor disease prognosis in gliomas

description: Gliomas are brain tumors associated with high morbidity, relapse and lethality despite improvement in therapeutic regimes. The hypoxic tumor microenvironment is a key feature associated with such poor outcomes in gliomas. The Hypoxia Inducible Factor (HIF) family of transcription factors are master regulators of cellular proliferation, high metabolic rates and angiogenesis via aberrant expression of downstream genes. Recent studies have implicated long non-coding RNAs (lncRNAs) as potential prognostic and diagnostic biomarkers. In this study, identification of hypoxia regulated lncRNA with a bioinformatic pipeline consisting of a newly developed tool “GenOx” was utilized for the identification of Hypoxia Response Element (HRE) and Hypoxia Ancillary Sequence (HAS) motifs in the promoter regions of lncRNAs. This was coupled with molecular, functional and interactome-based analyses of these hypoxia-relevant lncRNAs in primary tumors and cell-line models. We report on the identification of novel hypoxia regulated lncRNAs <i>SNHG12</i>, <i>CASC7</i> and <i>MF12-AS1</i>. A strong association of RNA splicing mechanisms was observed with enriched lncRNAs. Several lncRNAs have emerged as prognostic biomarkers, of which <i>TP53TG1</i> and <i>SNHG1</i> were identified as highly relevant lncRNAs in glioma progression and validated in hypoxia cultured cells. Significantly, we determined that <i>SNHG1</i> expression in tumor (vs. normal) is different from glioma stem cells, GSC (vs. tumors) and in hypoxia (vs. normoxia), positioning downregulation of <i>SNHG1</i> to be associated with worsened prognosis.
DOI: 10.6084/m9.figshare.20430345.v1
title: TGFβ signaling activation correlates with immune-inflamed tumor microenvironment across human cancers and predicts response to immunotherapy

description: Considering the determining role of TGFβ signaling in the tumor microenvironment (TME) on immune evasion, the inhibition of signaling is expected to enhance the therapeutic efficacy of immunotherapies, especially immune checkpoint blockade (ICB), which is confirmed in preclinical data. However, successive failures in clinical translation occur at the initial stage. To provide a better understanding of TGFβ signaling within the TME and its relation to the individual immunological status, we performed a pan-cancer analysis comparing the activation of TGFβ pathway among different TMEs based on multi-omics data. Compared with non-inflamed tumors, increased TGFβ signaling activity appeared in four non-cancer cell types within TME in inflamed tumors. Significant correlations were revealed between TGFβ signaling and reliable biomarkers for ICB therapy, as well as between TGFβ signaling and HPV status. Our findings contribute to explain the inconsistency between preclinical and clinical research, and are crucial to optimizing upcoming clinical trial design and improving patient stratification for personalized prediction.
DOI: 10.6084/m9.figshare.26496134
title: Scalable Methods for Multiple Time Series Comparison in Second Order Dynamics

description: Statistical comparison of multiple time series in their underlying frequency patterns has many real applications. However, existing methods are only applicable to a small number of mutually independent time series, and empirical results for dependent time series are only limited to comparing two time series. We propose scalable methods based on a new algorithm that enables us to compare the spectral density of a large number of time series. The new algorithm helps us efficiently obtain all pairwise feature differences in frequency patterns between M time series, which plays an essential role in our methods. When all M time series are independent of each other, we derive the joint asymptotic distribution of their pairwise feature differences. The asymptotic dependence structure between the feature differences motivates our proposed test for multiple mutually independent time series. We then adapt this test to the case of multiple dependent time series by partially accounting for the underlying dependence structure. Additionally, we introduce a global test to further enhance the approach. To examine the finite sample performance of our proposed methods, we conduct simulation studies. The new approaches demonstrate the ability to compare a large number of time series, whether independent or dependent, while exhibiting competitive power. Finally, we apply our methods to compare multiple mechanical vibrational time series.
DOI: 10.6084/m9.figshare.26496134.v1
title: Scalable Methods for Multiple Time Series Comparison in Second Order Dynamics

description: Statistical comparison of multiple time series in their underlying frequency patterns has many real applications. However, existing methods are only applicable to a small number of mutually independent time series, and empirical results for dependent time series are only limited to comparing two time series. We propose scalable methods based on a new algorithm that enables us to compare the spectral density of a large number of time series. The new algorithm helps us efficiently obtain all pairwise feature differences in frequency patterns between M time series, which plays an essential role in our methods. When all M time series are independent of each other, we derive the joint asymptotic distribution of their pairwise feature differences. The asymptotic dependence structure between the feature differences motivates our proposed test for multiple mutually independent time series. We then adapt this test to the case of multiple dependent time series by partially accounting for the underlying dependence structure. Additionally, we introduce a global test to further enhance the approach. To examine the finite sample performance of our proposed methods, we conduct simulation studies. The new approaches demonstrate the ability to compare a large number of time series, whether independent or dependent, while exhibiting competitive power. Finally, we apply our methods to compare multiple mechanical vibrational time series.
DOI: 10.15468/dl.us6gq7
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((149.018538 -32.804918, 149.018538 -32.807164, 149.020783 -32.807164, 149.020783 -32.804918, 149.018538 -32.804918))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048935-240626123714530/datasets/export for details.
DOI: 10.5281/zenodo.12570785
title: HVSR and TDEM geophysical surveys at Jangany, Madagascar.

description: This dataset is related to the scientific publication Paschetto et al., 2024. Lowcost and portable geophysical solutions for water resources investigation in arid regions. (Submitted to Journal of Applied Geophysics).

It contains raw and processed geophysical TDEM and HVSR data for groundwater exploration in a arid environment, with a geological setting composed of layers of laterite and saprolite.

For more information, see the readme file and the above mentioned publication.
DOI: 10.5281/zenodo.12570786
title: HVSR and TDEM geophysical surveys at Jangany, Madagascar.

description: This dataset is related to the scientific publication Paschetto et al., 2024. Lowcost and portable geophysical solutions for water resources investigation in arid regions. (Submitted to Journal of Applied Geophysics).

It contains raw and processed geophysical TDEM and HVSR data for groundwater exploration in a arid environment, with a geological setting composed of layers of laterite and saprolite.

For more information, see the readme file and the above mentioned publication.
DOI: 10.15468/dl.97ekhk
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Gymnocalycium raineri H.Till)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048937-240626123714530/datasets/export for details.
DOI: 10.15468/dl.z5kzhk
title: Occurrence Download

description: A dataset containing 46715 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chorthippus dorsatus (Zetterstedt, 1821)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 46715 records from 1095 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048936-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.20371/inaf/ds/2019_00001
title: Mercury H-2 Structural Analysis Dataset

description: This dataset contains the Geographic Information System (GIS) shapefiles and data used for the structural analysis of the Victoria quadrangle (H-2) of Mercury. It includes a) the 1:3,000,000-scale H-2 structural mapping; b) the high-resolution mapping of the Victora-Endeavour-Antoniadi array; c) the buffered crater counting analysis of the fault systems.
DOI: 10.15468/dl.8brkqt
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((146.900749 -36.175862, 146.900749 -36.178108, 146.896258 -36.178108, 146.896258 -36.180354, 146.905241 -36.180354, 146.905241 -36.175862, 146.900749 -36.175862))"
 ]
}

 The dataset includes 9 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048938-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.