Peripheral T-cell lymphoma: Molecular profiling recognizes subclasses and identifies prognostic markers
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PROVIDER: PRJNA757742 | ENA |
REPOSITORIES: ENA
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Project description:Peripheral T-cell lymphoma: Molecular profiling recognizes subclasses and identifies prognostic markers
| PRJNA757742 | ENA
Project description:Peripheral T-cell lymphoma (PTCL) is a clinically aggressive disease, with a poor response to therapy and a low overall survival rate of around 30% after 5 years. We have analyzed a series of 105 cases with a diagnosis of PTCL using a customized NanoString platform that includes 208 genes associated with T-cell differentiation, oncogenes and tumor suppressor genes, deregulated pathways and stromal cell subpopulations. A comparative analysis of the various histological types of PTCL (angioimmunoblastic T-cell lymphoma, AITL; PTCL-with T follicular helper phenotype, PTCL-TFH; PTCL-not otherwise signified, PTCL-NOS) showed that specific sets of genes were associated with each of the diagnoses. These included TFH markers, cytotoxic markers and genes whose expression was a surrogate for specific cellular subpopulations, including follicular dendritic cells, mast cells and genes belonging to precise survival (NF-κB) and other pathways. Furthermore, the mutational profile was analyzed using a custom panel that targeted 62 genes in 76 cases distributed in AITL, PTCL-TFH and PTCL-NOS. The main differences between the three nodal PTCL classes involved the RHOAG17V mutations (p<0.0001), which were approximately twice as frequent in AITL (34.09%) as in PTCL-TFH (16.66%) cases, but were not detected in PTCL-NOS. A multivariate analysis identified gene sets that allowed the series of cases to be stratified into different risk groups. This study supports and validates the current division of PTCL into these three categories, identifies sets of markers that can be used for a more precise diagnosis, and recognizes the expression of B-cell genes as an IPI-independent prognostic factor for AITL.
2021-08-26 | GSE182769 | GEO
Project description:Peripheral T-cell lymphoma (PTCL) is a clinically aggressive disease, with a poor response to therapy and a low overall survival rate of approximately 30% after 5 years. We have analyzed a series of 105 cases with a diagnosis of PTCL using a customized NanoString platform (NanoString Technologies, Seattle, WA) that includes 208 genes associated with T-cell differentiation, oncogenes and tumor suppressor genes, deregulated pathways, and stromal cell subpopulations. A comparative analysis of the various histological types of PTCL (angioimmunoblastic T-cell lymphoma [AITL]; PTCL with T follicular helper [TFH] phenotype; PTCL not otherwise specified [NOS]) showed that specific sets of genes were associated with each of the diagnoses. These included TFH markers, cytotoxic markers, and genes whose expression was a surrogate for specific cellular subpopulations, including follicular dendritic cells, mast cells, and genes belonging to precise survival (NF-κB) and other pathways. Furthermore, the mutational profile was analyzed using a custom panel that targeted 62 genes in 76 cases distributed in AITL, PTCL-TFH, and PTCL-NOS. The main differences among the 3 nodal PTCL classes involved the RHOAG17V mutations (P < .0001), which were approximately twice as frequent in AITL (34.09%) as in PTCL-TFH (16.66%) cases but were not detected in PTCL-NOS. A multivariate analysis identified gene sets that allowed the series of cases to be stratified into different risk groups. This study supports and validates the current division of PTCL into these 3 categories, identifies sets of markers that can be used for a more precise diagnosis, and recognizes the expression of B-cell genes as an IPI-independent prognostic factor for AITL.
| S-EPMC8714715 | biostudies-literature
Project description:Novel prognostic subclasses of high-grade astrocytoma are identified and discovered to resemble stages in neurogenesis. One tumor class displaying neuronal lineage markers shows longer survival, while two tumor classes enriched for neural stem cell markers display equally short survival. Poor prognosis subclasses exhibit either markers of proliferation or of angiogenesis and mesenchyme. Analysis of gene expression data is described in Phillips et al., Cancer Cell, 2006. Keywords: Disease state comparison
2006-03-15 | GSE4271 | GEO
Project description:Peripheral T-cell lymphoma: Molecular profiling recognizes subclasses and identifies prognostic markers
| PRJNA752277 | ENA
Project description:Novel prognostic subclasses of high-grade astrocytoma are identified and discovered to resemble stages in neurogenesis. One tumor class displaying neuronal lineage markers shows longer survival, while two tumor classes enriched for neural stem cell markers display equally short survival. Poor prognosis subclasses exhibit either markers of proliferation or of angiogenesis and mesenchyme. Analysis of gene expression data is described in Phillips et al., Cancer Cell, 2006. Experiment Overall Design: 77 primary high-grade astrocytomas and 23 matched recurrences were profiled to identify changes in gene expression that relate to both survival and disease progression. Samples include WHO grade III and IV astrocytomas with a wide range of survival times.
2007-10-02 | E-GEOD-4271 | biostudies-arrayexpress
Project description:proteomics identified potential prognostic markers in early ovarian
2020-11-09 | PXD022436 |
Project description:An assessment of prognostic immunity markers in breast cancer
| PRJNA454288 | ENA
Project description:This SuperSeries is composed of the following subset Series: GSE22216: microRNA expression profiling of early primary breast cancer to identify prognostic markers and associated pathways GSE22219: Gene expression profiling of early primary breast cancer to identify prognostic markers and associated pathways Refer to individual Series. Supplementary file: Shows correspondence between mRNA and miRNA samples.
2011-07-08 | E-GEOD-22220 | biostudies-arrayexpress
Project description:Ovarian cancer is one of the leading causes of death among patients with gynecological malignancies worldwide. To identify prognostic markers for ovarian cancer, we performed RNA-sequencing and analyzed the transcriptome data from 51 patients who received conventional therapies for high-grade serous ovarian carcinoma (HGSC). Patients with early-stage (I or II) HGSC exhibited higher immune gene expression than patients with advanced stage (III or IV) HGSC. To predict the prognosis of HGSC patients, we created machine learning-based models and identified RPL23 and USP19 as candidate prognostic markers. Specifically, patients with higher RPL23 mRNA levels had a worse prognosis and patients with higher USP19 mRNA levels had a better prognosis. This model was then used to analyze HGSC patient data hosted on The Cancer Genome Atlas; this exercise validated the prognostic abilities of these two genes with respect to patient survival. Taken together, the transcriptome profiles of RPL23 and USP19 determined using a machine-learning model could serve as prognostic markers in HGSC patients receiving conventional therapy.
2021-08-09 | GSE165808 | GEO