Ena-DATASET-DKFZ-06-01-2017-08:08:34:376-15 - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001003131 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Genome wide DNA methylation profiling of normal healthy patients and tumor and non-tumor samples of patients with colorectal cancer (CRC). The Illumina Infinium MethylationEPIC BeadChip was used to obtain DNA methylation across ~850,000 CpG sites. Samples include 78 normal sample from CRC patients, 76 tumor samples from CRC patients, and 68 samples from non-CRC patients
2022-03-23 | GSE199057 | GEO
Project description:This study investigates gene expression profiles in metastatic melanoma lymph node samples using the Nanostring nCounter platform. Samples were obtained from patients who were treatment-naïve, resistant, or responsive to immune checkpoint inhibitor (ICI) therapy. We applied both the PanCancer Immune Profiling Panel and a custom tissue-resident memory (TRM) T cell panel to assess immune cell composition and activation states. The dataset reveals transcriptional signatures associated with ICI response, including enrichment of TRM-associated genes and interferon-stimulated pathways in responders, and immunosuppressive features in resistant tumors. These data provide insights into the role of tumour-resident T cells and the tumour immune microenvironment in shaping response to immunotherapy.
2026-06-08 | GSE294441 | GEO
Project description:Colorectal cancer (CRC) is the third most common cancer worldwide and the second leading cause of cancer-related mortality. CRC can be classified into DNA mismatch repair proficient (MMRp) and deficient (MMRd) subtypes, with only ~50% of MMRd tumours responding to immunotherapy. Tumour architecture is spatially heterogeneous, ranging from the necrotic core to the invasive front, accompanied by diverse stromal and immune responses that influence tumour progression and treatment outcomes. To explore the spatial organisation of the tumour-immune microenvironment, we profiled the expression of ~1,000 genes in 846,469 cells from 23 tumour and normal tissue samples using the CosMx Spatial Molecular Imager (SMI). Using a custom bioinformatic pipeline, we performed quality control, cell type annotation, identified 9 spatial niches and provide an interactive web application to explore this data. Spatial niches included normal colonic crypts, tumour masses, neutrophil-rich regions, and lymphoid aggregates. Differential gene expression analysis revealed niche-specific changes to chemokine signalling, T cell infiltration and myeloid cell polarisation could either support or inhibit tumours depending on the niche. To expand the analysis, we integrated public single-cell RNA-Seq datasets, identifying increased tumour stemness in samples with higher neutrophil content that appeared to be driven by inflammation-mediated stromal reprogramming. Additionally, samples enriched in lymphoid aggregates, as defined by a spatial lymphoid aggregate signature, displayed heightened interferon signalling and an increased abundance of proliferating B cells. Finally, we identify gene expression patterns that distinguish tumour cells from normal epithelial cells within tumours. Our analysis reveals that many of the most pronounced changes in tumour gene expression originate from alterations in the tumour microenvironment, while also revealing tumour-intrinsic gene expression. By studying the spatial niches of CRC, we reveal niche-specific dynamics that drive tumours through innate stromal signalling and suggest both tumour-specific and stromally-mediated potential therapeutic targets.
2025-12-09 | GSE303070 | GEO
Project description:Gene expression analysis was performed through RNA-Sequencing on samples prepared from a panel of four CRC cell lines (HT29, HCT116, LS174T, LS513) grown in either 2D or 3D cell culture conditions. Our gene expression data reveal that more than 2000 gene expression profiles are affected by the growth conditions for each cell lines even if samples prepared from each cell line still group together. Our data also demonstrate that hierarchical clustering mostly relies on the MSS/MSI phenotypes of the CRC cells.
2021-11-17 | GSE185055 | GEO
Project description:Colorectal cancer (CRC) is one of the most prevalent cancers, with over one million new cases per year. Overall, prognosis of CRC largely depends on the disease stage and metastatic status. As precision oncology for patients with CRC continues to improve, this study aimed to integrate genomic, transcriptomic, and proteomic analyses to identify significant differences in expression during CRC progression using a unique set of paired patient samples while considering tumour heterogeneity.We analysed fresh-frozen tissue samples prepared under strict cryogenic conditions of matched healthy colon mucosa, colorectal carcinoma, and liver metastasis from the same patients. Somatic mutations of known cancer-related genes were analysed using Illumina's TruSeq Amplicon Cancer Panel; the transcriptome was assessed comprehensively using Clariom D microarrays. The global proteome was evaluated by liquid chromatography-coupled mass spectrometry (LC‒MS/MS) and validated by two-dimensional difference in-gel electrophoresis. Subsequent unsupervised principal component clustering, statistical comparisons, and gene set enrichment analyses were calculated based on differential expression results.Although panomics revealed low RNA and protein expression of CA1, CLCA1, MATN2, AHCYL2, and FCGBP in malignant tissues compared to healthy colon mucosa, no differentially expressed RNA or protein targets were detected between tumour and metastatic tissues. Subsequent intra-patient comparisons revealed highly specific expression differences (e.g., SRSF3, OLFM4, and CEACAM5) associated with patient-specific transcriptomes and proteomes.Our research results highlight the importance of inter- and intra-tumour heterogeneity as well as individual, patient-paired evaluations for clinical studies. In addition to changes among groups reflecting CRC progression, we identified significant expression differences between normal colon mucosa, primary tumour, and liver metastasis samples from individuals, which might accelerate implementation of precision oncology in the future.
2023-03-11 | PXD036434 | Pride
Project description:Aberrant DNA methylation, one of the major epigenetic alterations in cancer, has been reported to accumulate in a subset of colorectal cancer (CRC), so-called CpG island methylator phenotype (CIMP), which was known to correlate with microsatellite instability (MSI)-high CRC. To select new methylation markers genome-widely and epigenotype CRC by DNA methylation comprehensively, we performed methylated DNA immunoprecipitation-on-chip analysis using MSI-high CRC cell line HCT116 and microsatellite-stable SW480, and re-expression array analysis by 5-aza-2-deoxycytidine/Trichostatin A. Methylation levels of 44 new markers selected and 16 previously reported markers were analyzed quantitatively in 149 clinical CRC samples using MALDI-TOF mass spectrometry. By unsupervised two-way hierarchical clustering, CRC was clustered into high-, intermediate-, and low-methylation epigenotypes. Methylation markers were clustered into two groups: Group-1 showing methylation in high-methylation epigenotype and including all the 11 CIMP-related markers except NEUROG1, and Group-2 showing methylation in high- and intermediate-methylation epigenotypes. Marker panel deciding methylation epigenotypes with the highest accuracy was developed: 1st-Panel consisting of Group-1 genes (CACNA1G, LOX, SLC30A10) to extract high-methylation epigenotype, and 2nd-Panel consisting of Group-2 genes (ELMO1, FBN2, THBD, HAND1) and SLC30A10 again to divide the remains into intermediate- and low-methylation epigenotypes. High-methylation epigenotype correlated significantly with BRAF mutation, MSI, proximal tumor location, and mucinous component, in concordance with reported CIMP. Intermediate- and low-methylation epigenotypes significantly correlated with KRAS-mutation(+) and KRAS-mutation(-), respectively. KRAS-mutation(+) intermediate-methylation epigenotype showed worse prognosis than KRAS-mutation(-) low-methylation epigenotype (p=0.030). These three epigenotypes with different genetic characteristics suggested different molecular CRC genesis, and the markers might be useful to predict prognosis.
2010-01-05 | GSE14526 | GEO
Project description:Purpose: the goals of this study was to find the differential genes of spleen in chickens infected with Escherichia coli Methods: The spleen samples from control group and the spleen samples from infection group were generated by deep sequencing using Illumina system with 9 biological repetitions and 2 technical repetitions, respectively. Results: Using an optimized data analysis workflow, we mapped about 68-91 million sequence reads per sample to the chicken spleens from the infection and control group by Hisat2. Approximately a quarter of the transcripts showed differential expression between the infection and control group, with a 5x SD fold change and p value <0.05.
2023-01-11 | GSE182967 | GEO
Project description:INTRODUCTION. Consensus molecular subtypes (CMSs) can guide precision treatment of colorectal cancer (CRC). Currently available dedicated assays can identify CMS1 and CMS4 cases very well while a dedicated test to distinguish CMS2 and 3 is lacking. This study aimed to identify a panel of methylation markers to distinguish between CMS2 and 3 in patients with CRC. METHODS. Fresh-frozen tumor tissue of 239 patients with stage I-III CRC who underwent surgery between 2007 and 2014 was included. CMS classification was performed on RNA-seq data using the single-sample prediction parameter from the “CMSclassifier” package. Methylation profiles were gathered/obtained using the Infinium HumanMethylation450 BeadChip for 146 samples (124 CMS2 and 22 CMS3) . We performed adaptive group-regularised logistic ridge regression with post-hoc group-weighted elastic net marker selection to build prediction models for classification of CMS2 and CMS3 based on 15, 10 or 5 methylation markers. Data from the Cancer Genome Atlas project was used for independent validation of our obtained models. RESULTS. Overall methylation profiles differed between CMS2 and CMS3 tumors. Group-regularisation of the methylation probes was done based on their location either relative to a CpG island or relative to a gene present in the CMS classifier. This resulted in two different prediction models and subsequently different marker panels. For both panels, even when using only 5 markers, the sensitivity, specificity, and accuracy were >90%. Independent validation of the fixed models in TCGA data showed comparable performances. CONCLUSION. Our highly sensitive and specific methylation marker panel can be used to distinguish CMS2 and 3. This enables future development of a qPCR DNA methylation assay in patients with CRC to provide a specific and non-invasive classification tool.
2021-12-08 | GSE164811 | GEO
Project description:RNA extracted from tumor samples of 86 patients affected by MPM was analyzed on the nCounter system using a custom panel called MPM_C9073 (Nanostring Technologies).
2022-08-03 | GSE183088 | GEO
Project description:Background: Liver metastasis is the major cause of death following a diagnosis of colorectal cancer (CRC) and is a major health burden. Most molecular studies of CRC have profiled primary tumor samples and not the metastasis samples. In this study, we compared the copy number profiles of matched primary and liver metastatic CRC to better understand how the genomic structure of primary CRC differs from the metastasis. This has important implications for whether it is justified to base therapeutic approaches solely on data from the primary tumour. Methods: Paired primary and metastatic tumours from 16 patients and their adjacent normal tissue samples were analyzed using Single-Nucleotide-Polymorphism (SNP) arrays to determine copy number alterations. Nine patients had a synchronous liver metastasis at the time of CRC diagnosis and 7 patients developed a liver metastasis metachronously. Genome-wide chromosomal copy number alterations were assessed, with particular attention to 189 genes known to be somatically altered in CRC and 25 genes that are clinically actionable in CRC. These data were analyzed with respect to the timing of primary and metastatic tissue resection and with exposure to chemotherapy. Results: The genomic divergence with the whole genome duplication correction applied the average percent copy number discordance across all pairs of samples was 22.02%. The pairs of tumour samples collected prior to treatment revealed a significantly higher copy number differences compared to previously treated liver metastasis samples (P=0.024). However, loss of heterozygosity (LOH) acquired in metastasis was significantly higher in previously treated liver metastasis samples compared to treatment naïve liver metastasis samples (P= 0.0064) and which included where KRAS mutation was present in the primary cancers but was not detectable in the metastatic sample following chemotherapy. With regard to 25 genes that are clinically actionable in CRC, amplification of the genes ERBB2, FGFR1, CDK8 or PIK3CA was observed in the metastatic tissue of 4 patients but not in the matched primary CRC. In these cases, knowledge of these metastatic specific alterations could have informed therapeutic decision making and may have improved patient outcome. Conclusion: Intra-patient genomic discrepancies observed between primary and metastatic tissue
2018-02-14 | GSE100787 | GEO