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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Project description:Recent studies have shown that high cell cycle activity negatively correlates with antitumor immunity in certain cancer types. However, a similar correlation has not been proven in liver cancer.We downloaded transcriptomic profiles of TCGA-LIHC (the cancer genome atlas-liver hepatocellular carcinoma) and assessed the cell cycle distribution of samples using single sample gene set enrichment analysis (ssGSEA), termed the cell cycle score (CCS). We obtained cell cycle-related differentially expressed prognostic genes and identified CENPA, CDC20 and CTSV using LASSO regression. We studied the effect of CTSV on clinical features and immune alterations in liver cancer based on TCGA-LIHC data. In vitro and in vivo experiments were performed to validate the role of CTSV in liver cancer using liver cancer cell lines and tissues.We found that the CCS closely correlated with the clinical features and prognosis of patients in TCGA-LIHC. Analysis of differentially expressed genes (DEGs), univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression identified cathepsin V (CTSV) with prognostic significance in LIHC. Importantly, single-gene survival analysis of CTSV using microarray and sequencing data indicated that high levels of CTSV expression correlated with an unfavorable prognosis in various cancers. Gene set enrichment analysis (GSEA) revealed that high CTSV expression closely correlated with decreased expression of metabolic genes and increased expression of cell cycle genes. Furthermore, difference and correlation analyses of the relationship between CTSV expression and immune infiltrates, determined using CIBERSORT and TIMER algorithms, revealed that CTSV expression correlated with macrophages and CD4+ T cells. In vitro and in vivo experiments revealed that knockdown of CTSV inhibited liver cancer cells proliferation. Immunohistochemical staining showed that high CTSV expression correlated with macrophage infiltration in liver cancer tissues, predicted a poor prognosis, and may serve as a biomarker for HCC therapy.In conclusion, CTSV is a novel cell cycle prognostic gene that can affect HCC cells proliferation, and a potential biomarker for HCC therapy.
2023-07-18 | GSE233817 | GEO
Project description:Lung cancer is the leading cause of cancer deaths in the United States. Patients with early stage lung cancer have the best prognosis with surgical removal of the tumor, but the disease is often asymptomatic until advanced disease develops, and there are no effective blood-based screening methods for early detection of lung cancer in at-risk populations. We have explored the lipid profiles of blood plasma exosomes using ultra high-resolution Fourier transform mass spectrometry (UHR-FTMS) for early detection of the prevalent non-small cell lung cancers (NSCLC). Exosomes are nanovehicles released by various cells and tumor tissues to elicit important biofunctions such as immune modulation and tumor development. Plasma exosomal lipid profiles were acquired from 39 normal and 91 NSCLC subjects (44 early stage and 47 late stage). We have applied two multivariate statistical methods, Random Forest (RF) and Least Absolute Shrinkage and Selection Operator (LASSO) to classify the data. For the RF method, the Gini importance of the assigned lipids was calculated to select 16 lipids with top importance. Using the LASSO method, 7 features were selected based on a grouped LASSO penalty. The Area Under the Receiver Operating Characteristic curve for early and late stage cancer versus normal subjects using the selected lipid features was 0.85 and 0.88 for RF and 0.79 and 0.77 for LASSO, respectively. These results show the value of RF and LASSO for metabolomics data-based biomarker development, which provide robust an independent classifiers with sparse data sets. Application of LASSO and Random Forests identifies lipid features that successfully distinguish early stage lung cancer patient from healthy individuals.
2019-10-17 | ST001269 | MetabolomicsWorkbench
Project description:This multicenter retrospectively observational cohort study was conducted on participants with histologically confirmed gastric and colorectal cancer who underwent radical surgery in 11 medical centers in China from August 1, 2015, to June 31, 2018. Baseline clinicopathologic data and nutritional status assessments including Nutrition Risk Screening 2002 (NRS 2002) score and Patient-generated Subjective Global Assessment (PG-SGA) rating were collected. Variables will be screened using the least absolute shrinkage and selection operator (LASSO) regression model and Cox regression analysis. Internal and external validations will be performed via the receiver operating curve (ROC), the area under the curve (AUC), the concordance index (C-index), calibration plots, decision curve analysis (DCA), and Five folds cross-validation by 200 times.
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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. 94 samples
2013-12-01 | E-GEOD-50890 | biostudies-arrayexpress
Project description:Serum Reponse of CF1 primary MEFs , E13
| PRJNA510577 | ENA
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.
2023-04-18 | MTBLS3505 | MetaboLights
Project description:An integrated transcriptome-wide gene expression analysis, including differential gene expression analysis and weighted gene co-expression network analysis (WGCNA), was carried out based on transcriptomics data from a series of nine matched, histologically confirmed PCa and benign samples using the Affymetrix Clariom D Human array. The analysis identified a set of potential gene markers highly associated with tumor status (malignant vs. benign). We then used these genes to establish a minimal progression-free survival (PFS)-associated gene signature (GS) (PCBP1, PABPN1, PTPRF, DANCR, and MYC) using the least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox regression analyses from The Cancer Genome Atlas prostate adenocarcinoma (TCGA-PRAD) dataset. Our signature was able to predict PFS over 1, 3, and 5 years in the TCGA-PRAD dataset, with an area under the curve (AUC) of 0.64–0.78, and our signature remained as a prognostic factor independent of age, Gleason score, and pathological T and N stages. A nomogram combining the signature and Gleason score demonstrated improved predictive capability for PFS (AUC: 0.71–0.85) and was superior to the Cambridge Prognostic Group (CPG) model alone and some conventionally used clinicopathological factors in predicting PFS. In conclusion, we have identified and validated a novel five-gene signature and established a nomogram that effectively predicted PFS in patients with PCa. These findings may improve current prognostic tools for PFS and contribute to clinical decision-making in PCa treatment.
2023-10-31 | GSE246282 | GEO
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.
2018-09-25 | GSE118683 | GEO
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:The diagnosis of primary lung adenocarcinomas with intestinal or mucinous differentiation (PAIM) remains challenging due to the overlapping histomorphological, immunohistochemical and genetic characteristics with lung metastatic colorectal cancer (lmCRC). This study aimed to explore the protein biomarkers that could distinguish between PAIM and lmCRC. To uncover differences between the two diseases, we used tandem mass tagging (TMT)-based shotgun proteomics to characterize proteomes of formalin-fixed paraffin-embedded (FFPE) tumor samples of PAIM (n = 22) and lmCRC (n = 17). Then three machine learning algorithms, namely support vector machine (SVM), random forest and the Least Absolute Shrinkage and Selection Operator (LASSO), were utilized to select protein features with diagnostic significance. These candidate proteins were further validated in an independent cohort (PAIM, n = 11; lmCRC, n = 19) by immunochemistry (IHC) to confirm their diagnostic performance. In total, 105 proteins out of 7871 proteins were significantly dysregulated between PAIM and lmCRC samples and well-separated two groups by Uniform Manifold Approximation and Projection (UMAP). The upregulated proteins in PAIM were involved in actin cytoskeleton organization, platelet degranulation, and regulation of leukocyte chemotaxis, while downregulated ones were involved in mitochondrial transmembrane transport, vasculature development, and stem cell proliferation. A set of 10 candidate proteins (high-level expression in lmCRC: CDH17, ATP1B3, GLB1, OXNAD1, LYST, FABP1; high-level expression in PAIM: NARR, MLPH, S100A14, CK7) was ultimately selected to distinguish PAIM from lmCRC by machine learning algorithms. We further confirmed using IHC that the five protein biomarkers including CDH17, CK7, MLPH, FABP1 and NARR were effective biomarkers for distinguishing PAIM from lmCRC. Our study depicts PAIM-specific proteomic characteristics and demonstrates the potential utility of new protein biomarkers for the differential diagnosis of PAIM and lmCRC. These findings may contribute to improving the diagnostic accuracy and guide appropriate treatments for these patients.
2024-06-23 | PXD050416 | Pride