cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.2069708.v6
title: Biomarker Benchmark - GSE38958

description: <b><br>[NOTICE: This data set has been deprecated. Please see our new version of the data (and additional data sets) here: https://osf.io/mhk93 ]</b><br><b><br></b>"Idiopathic pulmonary fibrosis (IPF) is a specific form of chronic, progressive fibrosing interstitial disease of unknown cause. It remains impractical to conduct early diagnosis and predict IPF progression just based on gene expression information. Moreover, the relationship between gene expression and quantitative phenotypic value in IPF keeps controversial. To identify biomarkers to predict survival in IPF, we profiled protein-coding gene expression in peripheral blood mononuclear cells (PBMCs). We linked the gene expression level with the quantitative phenotypic variation in IPF, including diffusing capacity of the lung for carbon monoxide (DLCO) and forced vital capacity (FVC) percent predicted. In silico analyses on the expression profiles and quantitative phenotypic data allowed for the generation of a set of IPF molecular signature that predicted survival of IPF effectively."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE38958<br>We have included gene-expression data, the outcome (class) being predicted, and any clinical covariates. When gene-expression data were processed in multiple batches, we have provided batch information. Each data set is organized into a file set, where each contains all pertinent files for an individual dataset. The gene expression files have been normalized using both the SCAN and UPC methods using the SCAN.UPC package in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). We summarized the data at the gene level using the BrainArray resource (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp). We used Ensembl identifiers. The class, clinical, and batch data were hand curated to ensure consistency ("tidy data" formatting). In addition, the data files have been formatted to be imported easily into the ML-Flex machine learning package (http://mlflex.sourceforge.net/).
DOI: 10.6084/m9.figshare.2069707.v5
title: Biomarker Benchmark - Gene expression data from Gene Expression Omnibus - GSE37745

description: " Background: Global gene expression profiling has been widely used in lung cancer research to identify clinically relevant molecular subtypes as well as to predict prognosis and therapy response. So far, the value of these multi-gene signatures in clinical practice is unclear and the biological importance of individual genes is difficult to assess as the published signatures virtually do not overlap.<br><br>Methods: Here we describe a novel single institute cohort, including 196 non-small lung cancer (NSCLC) cases with clinical information and long-term follow-up, which was used as a training set to screen for single genes with prognostic impact. The top 450 gene probe sets identified using a univariate Cox regression model (significance level p&lt;0.01) were tested in a meta-analysis including five publicly available independent lung cancer cohorts (n=860).<br><br>Results: The meta-analysis revealed that 17 probe sets were significantly associated with survival (p&lt;0.0005) with a false discovery rate of 1%. The prognostic impact of one of these genes, the cell adhesion molecule 1 (CADM1), was confirmed by use of immunohistochemistry on a tissue microarray including 355 NSCLC samples. Low CADM1 protein expression was associated with shorter survival (p=0.028), with particular influence in the adenocarcinoma patient subgroup (p=0.002).<br><br>Conclusions: We were able to validate single genes with independent prognostic impact using a novel NSCLC cohort together with a meta-analysis approach. CADM1 was identified as an immunohistochemical marker with a potential application in clinical diagnostics."<br><br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37745<br><br>We have included gene-expression data, the outcome (class) being predicted, and any clinical covariates. When gene-expression data were processed in multiple batches, we have provided batch information. Each data set is organized into a file set, where each contains all pertinent files for an individual dataset. The gene expression files have been normalized using both the SCAN and UPC methods using the SCAN.UPC package in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). We summarized the data at the gene level using the BrainArray resource (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp). We used Ensembl identifiers. The class, clinical, and batch data were hand curated to ensure consistency ("tidy data" formatting). In addition, the data files have been formatted to be imported easily into the ML-Flex machine learning package (http://mlflex.sourceforge.net/).
DOI: 10.6084/m9.figshare.2069704.v3
title: Biomarker Benchmark - Gene expression data from Gene Expression Omnibus - GSE32646

description: "The purpose of the present study was to investigate the association of glutathione S-transferase P1 (GSTP1) expression with resistance to neoadjuvant paclitaxel followed by 5-fluorouracil/epirubicin/cyclophosphamide (P-FEC) in human breast cancers. The relationship of GSTP1 expression and GSTP1 promoter hypermethylation with intrinsic subtypes was also investigated. In this study, primary breast cancer patients (n = 123, stage II-III) treated with neoadjuvant P-FEC were analyzed. Tumor samples were obtained by vacuum-assisted core biopsy before P-FEC. GSTP1 expression was determined using immunohistochemistry, GSTP1 promoter methylation index (MI) using bisulfite methylation assay and intrinsic subtypes using DNA microarray. The pathological complete response (pCR) rate was significantly higher in GSTP1-negative tumors (80.0%) than GSTP1-positive tumors (30.6%) (P = 0.009) among estrogen receptor (ER)-negative tumors but not among ER-positive tumors (P = 0.267). Multivariate analysis showed that GSTP1 was the only predictive factor for pCR (P = 0.013) among ER-negative tumors. Luminal A, luminal B and HER2-enriched tumors showed a significantly lower GSTP1 positivity than basal-like tumors (P = 0.002, P &lt; 0.001 and P = 0.009, respectively), while luminal A, luminal B and HER2-enriched tumors showed a higher GSTP1 MI than basal-like tumors (P = 0.076, P &lt; 0.001 and P &lt; 0.001, respectively). In conclusion, these results suggest the possibility that GSTP1 expression can predict pathological response to P-FEC in ER-negative tumors but not in ER-positive tumors. Additionally, GSTP1 promoter hypermethylation might be implicated more importantly in the pathogenesis of luminal A, luminal B and HER2-enriched tumors than basal-like tumors."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE32646<br>We have included gene-expression data, the outcome (class) being predicted, and any clinical covariates. When gene-expression data were processed in multiple batches, we have provided batch information. Each data set is organized into a file set, where each contains all pertinent files for an individual dataset. The gene expression files have been normalized using both the SCAN and UPC methods using the SCAN.UPC package in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). We summarized the data at the gene level using the BrainArray resource (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp). We used Ensembl identifiers. The class, clinical, and batch data were hand curated to ensure consistency ("tidy data" formatting). In addition, the data files have been formatted to be imported easily into the ML-Flex machine learning package (http://mlflex.sourceforge.net/).<br><br>
DOI: 10.6084/m9.figshare.2069705.v3
title: Biomarker Benchmark - Gene expression data from Gene Expression Omnibus - GSE37147

description: "RNA was isolated from bronchial brushings obtained from current and former smokers with and without COPD. mRNA expression was profiled using Affymetrix Human Gene 1.0 ST Arrays."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37147<br>We have included gene-expression data, the outcome (class) being predicted, and any clinical covariates. When gene-expression data were processed in multiple batches, we have provided batch information. Each data set is organized into a file set, where each contains all pertinent files for an individual dataset. The gene expression files have been normalized using both the SCAN and UPC methods using the SCAN.UPC package in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). We summarized the data at the gene level using the BrainArray resource (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp). We used Ensembl identifiers. The class, clinical, and batch data were hand curated to ensure consistency ("tidy data" formatting). In addition, the data files have been formatted to be imported easily into the ML-Flex machine learning package (http://mlflex.sourceforge.net/).<br><br>
DOI: 10.6084/m9.figshare.2069708.v5
title: Biomarker Benchmark - Gene expression data from Gene Expression Omnibus - GSE38958

description: "Idiopathic pulmonary fibrosis (IPF) is a specific form of chronic, progressive fibrosing interstitial disease of unknown cause. It remains impractical to conduct early diagnosis and predict IPF progression just based on gene expression information. Moreover, the relationship between gene expression and quantitative phenotypic value in IPF keeps controversial. To identify biomarkers to predict survival in IPF, we profiled protein-coding gene expression in peripheral blood mononuclear cells (PBMCs). We linked the gene expression level with the quantitative phenotypic variation in IPF, including diffusing capacity of the lung for carbon monoxide (DLCO) and forced vital capacity (FVC) percent predicted. In silico analyses on the expression profiles and quantitative phenotypic data allowed for the generation of a set of IPF molecular signature that predicted survival of IPF effectively."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE38958<br>We have included gene-expression data, the outcome (class) being predicted, and any clinical covariates. When gene-expression data were processed in multiple batches, we have provided batch information. Each data set is organized into a file set, where each contains all pertinent files for an individual dataset. The gene expression files have been normalized using both the SCAN and UPC methods using the SCAN.UPC package in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). We summarized the data at the gene level using the BrainArray resource (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp). We used Ensembl identifiers. The class, clinical, and batch data were hand curated to ensure consistency ("tidy data" formatting). In addition, the data files have been formatted to be imported easily into the ML-Flex machine learning package (http://mlflex.sourceforge.net/).
DOI: 10.6084/m9.figshare.2069702
title: Biomarker Benchmark - GSE27854

description: <b><br>[NOTICE: This data set has been deprecated. Please see our new version of the data (and additional data sets) here: https://osf.io/mhk93 ]</b><br><b><br></b>"Purpose: The purpose of this study is to identify a novel biomarker related with distant metastases of colorectal cancer (CRC).Experimental Design: We investigated mRNA expression profiles in 115 patients with CRC using an Affymetrix Gene Chip, and copy number profiles in 122 patients with CRC using an Affymetrix DNA Sty array. Genes in common between copy number and expression data were extracted as candidate genes. We analyzed the mRNA expression of candidate gene by quantitative reverse transcription polymerase chain reaction (RT-PCR) in 86 patients as a validation study. Furthermore, we analyzed the protein expression of candidate gene by immunohistochemical study in 269 patients, and investigated the relationship between protein expression and clinicopathologic features.<br>Results: By the combination of copy number analysis and gene expression analysis, We extracted 2 candidate genes related with distant metastases of CRC. Several reports show that NUCKS1, one of candidate genes, is overexpressed in several cancer tissues. But a study about the relationship between NUCKS1 and CRC is none. The mRNA expression of NUCKS1 in cancer tissues was significantly higher than those in normal tissues. Overexpression of NUCKS1 protein was associated with significantly worse relapse-free survival of CRC. Overexpression of NUCKS1 protein was an independent risk factor for recurrence of CRC.<br>Conclusion: The overexpression of NUCKS1 would be a new biomarker predicting recurrence after colorectal surgery."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27854<br>We have included gene-expression data, the outcome (class) being predicted, and any clinical covariates. When gene-expression data were processed in multiple batches, we have provided batch information. Each data set is organized into a file set, where each contains all pertinent files for an individual dataset. The gene expression files have been normalized using both the SCAN and UPC methods using the SCAN.UPC package in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). We summarized the data at the gene level using the BrainArray resource (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp). We used Ensembl identifiers. The class, clinical, and batch data were hand curated to ensure consistency ("tidy data" formatting). In addition, the data files have been formatted to be imported easily into the ML-Flex machine learning package (http://mlflex.sourceforge.net/).
DOI: 10.6084/m9.figshare.2069703
title: Biomarker Benchmark - GSE31048

description: <b><br>[NOTICE: This data set has been deprecated. Please see our new version of the data (and additional data sets) here: https://osf.io/mhk93 ]</b><br><b><br></b>"Wnt pathway is dysregulated in CLL-We characterized Wnt pathway gene expression in normal B and CLL-B cells and identified Wnt targets in normal B and CLL-B cells through this data set.In this dataset, we included normal B cells and CLL-B cells for Wnt pathway gene expression. This leads to the identification of 62 Wnt pathway components which are differnetially expressed between normal and CLl-B cells. We also included normal B cells and CLL-B cells with or without Wnt3a treatment and identified 468 and 676 Wnt regulated genes in normal and CLL B cells, respectively."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE31048<br>We have included gene-expression data, the outcome (class) being predicted, and any clinical covariates. When gene-expression data were processed in multiple batches, we have provided batch information. Each data set is organized into a file set, where each contains all pertinent files for an individual dataset. The gene expression files have been normalized using both the SCAN and UPC methods using the SCAN.UPC package in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). We summarized the data at the gene level using the BrainArray resource (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp). We used Ensembl identifiers. The class, clinical, and batch data were hand curated to ensure consistency ("tidy data" formatting). In addition, the data files have been formatted to be imported easily into the ML-Flex machine learning package (http://mlflex.sourceforge.net/).<br><br>
DOI: 10.6084/m9.figshare.2069704.v5
title: Biomarker Benchmark - GSE32646

description: <b><br>[NOTICE: This data set has been deprecated. Please see our new version of the data (and additional data sets) here: https://osf.io/mhk93 ]</b><br><b><br></b>"The purpose of the present study was to investigate the association of glutathione S-transferase P1 (GSTP1) expression with resistance to neoadjuvant paclitaxel followed by 5-fluorouracil/epirubicin/cyclophosphamide (P-FEC) in human breast cancers. The relationship of GSTP1 expression and GSTP1 promoter hypermethylation with intrinsic subtypes was also investigated. In this study, primary breast cancer patients (n = 123, stage II-III) treated with neoadjuvant P-FEC were analyzed. Tumor samples were obtained by vacuum-assisted core biopsy before P-FEC. GSTP1 expression was determined using immunohistochemistry, GSTP1 promoter methylation index (MI) using bisulfite methylation assay and intrinsic subtypes using DNA microarray. The pathological complete response (pCR) rate was significantly higher in GSTP1-negative tumors (80.0%) than GSTP1-positive tumors (30.6%) (P = 0.009) among estrogen receptor (ER)-negative tumors but not among ER-positive tumors (P = 0.267). Multivariate analysis showed that GSTP1 was the only predictive factor for pCR (P = 0.013) among ER-negative tumors. Luminal A, luminal B and HER2-enriched tumors showed a significantly lower GSTP1 positivity than basal-like tumors (P = 0.002, P &lt; 0.001 and P = 0.009, respectively), while luminal A, luminal B and HER2-enriched tumors showed a higher GSTP1 MI than basal-like tumors (P = 0.076, P &lt; 0.001 and P &lt; 0.001, respectively). In conclusion, these results suggest the possibility that GSTP1 expression can predict pathological response to P-FEC in ER-negative tumors but not in ER-positive tumors. Additionally, GSTP1 promoter hypermethylation might be implicated more importantly in the pathogenesis of luminal A, luminal B and HER2-enriched tumors than basal-like tumors."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE32646<br>We have included gene-expression data, the outcome (class) being predicted, and any clinical covariates. When gene-expression data were processed in multiple batches, we have provided batch information. Each data set is organized into a file set, where each contains all pertinent files for an individual dataset. The gene expression files have been normalized using both the SCAN and UPC methods using the SCAN.UPC package in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). We summarized the data at the gene level using the BrainArray resource (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp). We used Ensembl identifiers. The class, clinical, and batch data were hand curated to ensure consistency ("tidy data" formatting). In addition, the data files have been formatted to be imported easily into the ML-Flex machine learning package (http://mlflex.sourceforge.net/).<br><br>
DOI: 10.6084/m9.figshare.2069705.v5
title: Biomarker Benchmark - GSE37147

description: <b><br>[NOTICE: This data set has been deprecated. Please see our new version of the data (and additional data sets) here: https://osf.io/mhk93 ]</b><br><b><br></b>"RNA was isolated from bronchial brushings obtained from current and former smokers with and without COPD. mRNA expression was profiled using Affymetrix Human Gene 1.0 ST Arrays."<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37147<br>We have included gene-expression data, the outcome (class) being predicted, and any clinical covariates. When gene-expression data were processed in multiple batches, we have provided batch information. Each data set is organized into a file set, where each contains all pertinent files for an individual dataset. The gene expression files have been normalized using both the SCAN and UPC methods using the SCAN.UPC package in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). We summarized the data at the gene level using the BrainArray resource (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp). We used Ensembl identifiers. The class, clinical, and batch data were hand curated to ensure consistency ("tidy data" formatting). In addition, the data files have been formatted to be imported easily into the ML-Flex machine learning package (http://mlflex.sourceforge.net/).<br><br>
DOI: 10.6084/m9.figshare.2069706.v4
title: Biomarker Benchmark - Gene expression data from Gene Expression Omnibus - GSE37199

description: "Background: Inter-patient prostate cancer (PrCa) heterogeneity results in highly variable patient outcomes. Multi-purpose biomarkers to dissect this heterogeneity are urgently required to improve treatment and accelerate drug development in PrCa. Circulating biomarkers are most practical for evaluating this disease. We pursued the analytical validation and clinical qualification of blood mRNA expression arrays.<br>Methods: Whole blood samples were collected into PaxGeneTM tubes from PrCa patients: 31 good prognosis patients selected for active surveillance (AS) and 63 advanced castration resistant PrCa (CRPC) patients. RNA was extracted, amplified, biotinylated, and hybridised to Affymetrix U133plus2 microarrays and analysis of genome-wide expression profiles were analysed using Bayesian Latent Process Decomposition (LPD).<br>Findings: LPD analysis of the mRNA expression data divided the evaluable patients (n=94) into 4 separate groups. LPD1 and LPD2 consisted almost entirely of CRPC patients (14/14; 17/18); LPD3 (15/31) and LDP4 (12/21) comprised both AS and CRPC. Ten patients were unclassifiable by LPD analysis. LPD1 CRPC patients had significantly poorer overall survival (median 10.7 months, CI-95% 4.2-17.2) than CRPC patients in LPD2 to 4 (median 26.5 months, CI-95% 18.1-34.9, p=0.00007). LPD 1 membership remained the strongest prognostic factor in a multivariate analysis (HR 5.0, CI-95% 2.1-11.9, p=0.0002). Gene signatures in the poor prognosis LPD group 1 were associated with increased CD71+ early erythroid cells and decreased B-cell and T-cell immune response. A 9-gene signature, that was validated by RT-PCR studies, classified patients as group LPD 1 with a very low misclassification rate (1.2%).<br>Interpretation: Poor PrCa outcome can be predicted using gene expression signatures from whole blood and merits further evaluation in predictive and pharmacodynamic biomarker studies for novel anticancer drugs including immune therapies"<br>http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37199<br><br>We have included gene-expression data, the outcome (class) being predicted, and any clinical covariates. When gene-expression data were processed in multiple batches, we have provided batch information. Each data set is organized into a file set, where each contains all pertinent files for an individual dataset. The gene expression files have been normalized using both the SCAN and UPC methods using the SCAN.UPC package in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). We summarized the data at the gene level using the BrainArray resource (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp). We used Ensembl identifiers. The class, clinical, and batch data were hand curated to ensure consistency ("tidy data" formatting). In addition, the data files have been formatted to be imported easily into the ML-Flex machine learning package (http://mlflex.sourceforge.net/).<br><br><br>