Serum Reponse of CF1 primary MEFs , E13
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE124024 | GEO | 2019/05/10
REPOSITORIES: GEO
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2013-12-01 | E-GEOD-50890 | biostudies-arrayexpress
Project description:In this study, we aimed to identify potential metabolic biomarkers that can improve the diagnostic accuracy of sepsis. Sixty-six patients including 30 septic and 36 nonsepsis patients from an intensive care unit were recruited. The global plasma and urine metabolomic profiles were determined by ultraperformance liquid chromatography coupled with a quadrupole time-of-flight mass spectrometry-based methodology. The risk factors, including both traditional physiological indicators and metabolic biomarkers, were investigated by binary logistic regression analysis and used to build a least absolute shrinkage and selection operator (Lasso) regression model to evaluate the ability of diagnosis. Fifty-five metabolites in plasma and 11 metabolites in urine were identified through orthogonal projections to latent structures discriminant analysis (OPLS-DA). Among them, ten (PE (20:4(5Z, 8Z, 11Z, 14Z)/P-18:0), harderoporphyrinogen, chloropanaxydiol, (<i>Z</i>)-2-octenal, <i>N</i><sup>1</sup>,<i>N</i><sup>8</sup>-diacetylspermidine, 1-nitroheptane, venoterpine, α-CEHC, LysoPE (20:0/0:0), corticrocin) metabolites were identified as risk factors. The Lasso regression model incorporating these ten metabolic biomarkers and five traditional physiological indicators displayed better differentiation than the traditional model, represented by the elevated area under receiver operating characteristic curve (AUROC) from 96.80 to 100.0%. Furthermore, patients with septic shock presented a significantly lower level of PE-Cer (d16:1(4E)/19:0). This study suggests that metabolomic profiling could be an effective tool for sepsis diagnosis.
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Project description:Glioma growth is often accompanied by a hypoxic microenvironment favorable for the induction and maintenance of the glioma stem cell (GSC) phenotype. Due to the paucity of cell models of Isocitrate Dehydrogenase 1 mutant (IDH1mut) GSCs, biology under hypoxic conditions has not been sufficiently studied as compared to IDH1 wildtype (IDH1wt) GSCs. We therefore grew well-characterized IDH1mut (n = 4) and IDH1wt (n = 4) GSC lines under normoxic (20%) and hypoxic (1.5%) culture conditions and harvested mRNA after 72 h. Transcriptome analyses were performed and hypoxia regulated genes were further analyzed using the expression and clinical data of the lower grade glioma cohort of The Cancer Genome Atlas (LGG TCGA) in a confirmatory approach and to test for possible survival associations. Results show that global expression changes were more pronounced in IDH1wt than in IDH1mut GSCs. However, when focusing on known hypoxia-regulated gene sets, enrichment analyses showed a comparable regulation in both IDH1mut and IDH1wt GSCs. Of 272 significantly up-regulated genes under hypoxic conditions in IDH1mut GSCs a hypoxia-related survival score (HRS-score) of five genes (LYVE1, FAM162A, WNT6, OTP, PLOD1) was identified by the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm which was able to predict survival independent of age, 1p19q co-deletion status and WHO grade (II vs. III) in the LGG TCGA cohort and in the Rembrandt dataset. Altogether, we were able to identify and validate a novel hypoxia-related survival score in IDH1mut GSCs consisting of five hypoxia-regulated genes which was significantly associated with patient survival independent of known prognostic confounders.
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Project description:Serum Reponse of CF1 primary MEFs , E13
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Project description:Purpose: The current stratification system for acute promyelocytic leukemia (APL) is based on the white blood cell (WBC) and the platelet counts (i.e., Sanz score) over the past two decades. However, the borderlines among different risk groups are sometimes ambiguous, and for some patients, early death and relapse remained challenges. Besides, with the evolving of the treatment strategy from ATRA-chemotherapy to ATRA-ATO-based synergistic targeted therapy, the precise risk stratification with molecular markers is needed. Patients and Methods: This study performed a systematic analysis of APL genomics and transcriptomics to identify genetic abnormalities in 348 patients mainly from the APL2012 trial (NCT01987297) to illustrate the potential molecular background of Sanz score and further optimize it. The least absolute shrinkage and selection operator (LASSO) algorithm was used to analyze the gene expression in 323 cases to establish a scoring system (i.e., APL9 score). Results: Through combining NRAS mutations, APL9 score with WBC, 321 cases can be stratified into two groups with significantly different outcomes. The estimated 5-year overall (P = 0.00031), event-free (P < 0.0001), and disease-free (P = 0.001) survival rates in the revised standard-risk group (95.6%, 93.8%, and 98.1%, respectively) were significantly better than those in the revised high-risk group (82.9%, 77.4%, and 88.4%, respectively), which could be validated using The Cancer Genome Atlas dataset. Conclusions: We have proposed a two-category system improving prognosis in APL patients. Molecular markers identified in this study may also provide genomic insights into the disease mechanism for improved therapy.
2021-04-14 | GSE172057 | GEO
Project description:Carcinomas of unknown primary origin constitute 3-5% of all newly diagnosed metastatic cancers, of which the primary source is difficult to classify with current histological methods. Effective cancer treatment depends on early and accurate identification of the tumor, which is why patients with metastases of unknown origin have poor prognosis and short survival. Because microRNA expression is highly tissue specific, the microRNA profile of a metastasis may be used to identify its origin. As a first step to realize this goal, we evaluated the potential of microRNA profiling for identification of the primary tumor of known metastases. 208 formalin-fixed paraffin-embedded samples representing 15 different histologies were profiled on an LNA-enhanced microarray platform, which allows for highly sensitive and specific detection of microRNA. Based on these data, we developed and cross-validated a novel classification algorithm, LASSO (Least Absolute Shrinkage and Selection Operator), which had an overall accuracy of 85%. When the classifier was applied on an independent test set of 48 metastases, the primary site was correctly identified in 42 cases (88% accuracy). Our findings suggest that microRNA expression profiling on paraffin tissue can efficiently predict the primary origin of a tumor, and may provide pathologists with a molecular diagnostic tool that can improve their capability to correctly identify the origin of hitherto unidentifiable metastatic tumors, and eventually, enable tailored therapy.
2013-12-01 | GSE50890 | GEO