Discovery of cancer prognostic markers based on comparison of gene expression in colorectal cancer samples.
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ABSTRACT:
PROVIDER: EGAS00001005068 | EGA |
REPOSITORIES: EGA
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Project description:There are no widely-accepted prognostic markers currently available to predict outcomes in patients with triple-negative breast cancer (TNBC), and no targeted therapies with confirmed benefit. We have used MALDI mass spectrometry imaging (MSI) of tryptic peptides to compare regions of cancer and benign tissue in 10 formalin-fixed, paraffin-embedded sections of TNBC tumors. Proteins were identified by reference to a peptide library constructed by LC-MALDI-MS/MS analyses of the same tissues. The prognostic significance of proteins that distinguished between cancer and benign regions was estimated by Kaplan-Meier analysis of their gene expression from public databases. Among peptides that distinguished between cancer and benign tissue in at least 3 tissues with a ROC area under the curve >0.7, 14 represented proteins identified from the reference library, including proteins not previously associated with breast cancer. Initial network analysis using the STRING database showed no obvious functional relationships except among collagen subunits COL1A1, COL1A2, and COL63A, but manual curation, including the addition of EGFR to the analysis, revealed a unique network connecting 10 of the 14 proteins. Kaplan-Meier survival analysis to examine the relationship between tumor expression of genes encoding the 14 proteins, and recurrence-free survival (RFS) in patients with basal-like TNBC showed that, compared to low expression, high expression of nine of the genes was associated with significantly worse RFS, most with hazard ratios >2. In contrast, in estrogen receptor-positive tumors, high expression of these genes showed only low, or no, association with worse RFS. These proteins are proposed as putative markers of RFS in TNBC, and some may also be considered as possible targets for future therapies.
2019-05-06 | PXD013397 | Pride
Project description:Ovarian carcinoma has the highest mortality rate among gynecological malignancies. In this project, we investigated the hypothesis that molecular markers are able to predict outcome of ovarian cancer independently of classical clinical predictors, and that these molecular markers can be validated using independent data sets. We applied a semi-supervised method for prediction of patient survival. Microarrays from a cohort of 80 ovarian carcinomas (TOC cohort) were used for the development of a predictive model, which was then evaluated in an entirely independent cohort of 118 carcinomas (Duke cohort). A 300 gene ovarian prognostic index (OPI) was generated and validated in a leave-one-out approach in the TOC cohort (Kaplan-Meier analysis, p=0.0087). In a second validation step the prognostic power of the OPI was confirmed in an independent data set (Duke cohort, p=0.0063). In multivariate analysis, the OPI was independent of the postoperative residual tumour, the main clinico-pathological prognostic parameter with an adjusted hazard ratio of 6.4 (TOC cohort, CI 1.8 – 23.5, p=0.0049) and 1.9 (Duke cohort, CI 1.2 – 3.0, p=0.0068). We constructed a combined score of molecular data (OPI) and clinical parameters (residual tumour), which was able to define patient groups with highly significant differences in survival. The integrated analysis of gene expression data as well as residual tumour can be used for optimised assessment of prognosis. As traditional treatment options are limited, this analysis may be able to optimise clinical management and to identify those patients that would be candidates for new therapeutic strategies. Keywords: disease state analysis RNA from 80 frozen ovarian cancer samples was analysed with oligonucleotide microarrays
2009-02-09 | E-GEOD-14764 | biostudies-arrayexpress
Project description:Quantitative (iTRAQ) proteomics of matched normal and tumour breast tissues from Cypriot patients with different stages of the disease.
2014-04-08 | PRD000748 | Pride
Project description:Ovarian carcinoma has the highest mortality rate among gynecological malignancies. In this project, we investigated the hypothesis that molecular markers are able to predict outcome of ovarian cancer independently of classical clinical predictors, and that these molecular markers can be validated using independent data sets. We applied a semi-supervised method for prediction of patient survival. Microarrays from a cohort of 80 ovarian carcinomas (TOC cohort) were used for the development of a predictive model, which was then evaluated in an entirely independent cohort of 118 carcinomas (Duke cohort). A 300 gene ovarian prognostic index (OPI) was generated and validated in a leave-one-out approach in the TOC cohort (Kaplan-Meier analysis, p=0.0087). In a second validation step the prognostic power of the OPI was confirmed in an independent data set (Duke cohort, p=0.0063). In multivariate analysis, the OPI was independent of the postoperative residual tumour, the main clinico-pathological prognostic parameter with an adjusted hazard ratio of 6.4 (TOC cohort, CI 1.8 – 23.5, p=0.0049) and 1.9 (Duke cohort, CI 1.2 – 3.0, p=0.0068). We constructed a combined score of molecular data (OPI) and clinical parameters (residual tumour), which was able to define patient groups with highly significant differences in survival. The integrated analysis of gene expression data as well as residual tumour can be used for optimised assessment of prognosis. As traditional treatment options are limited, this analysis may be able to optimise clinical management and to identify those patients that would be candidates for new therapeutic strategies. Keywords: disease state analysis
2009-02-09 | GSE14764 | GEO
Project description:The project was aimed at identifying diGlycine (diGly) conjugated peptides obtained from sixty PDACs' patients' derived xenografs (PDXs) then to identify potential diGLy profiles that could be used as prognosis markers of the aggressiveness of the tumour and overall survival of patients as well as theranostic makers able to predict tumour sensitivity to the different chemotherapeutic drugs commonly used to treat this form of cancer.
2023-07-20 | PXD030011 | Pride
Project description:Gene expression profiling of sarcoma cell lines of increasing metastatic potential: SW684 (low grade), HT1080 (high grade), GCT (metastatic). Differential gene expression may elucidate biomarkers of tumour progression and metastasis. Common reference design. Reference cell line: Pooled Normal fibroblast cell line MRC5, labelled with Alexa Fluor 555 (Cy3) in every experiment. Samples: Sarcoma cell lines labelled with Alexa Fluor 647 (Cy5). At least 3 replicates for each tumour grade.
2010-05-22 | E-GEOD-8172 | biostudies-arrayexpress
Project description:Cervical cancer (CC) is a complex disease. Many factors contribute to the tumourigenesis and progression of CC neoplasms. Here, we analysed transcriptomic differences and simulated tumour progression to explore the pathogenesis of CC. RNA-seq was performed to analyse the transcriptomic differences among normal tissue (NC), paracarcinoma tissue (TP), and primary tumour tissue (TT). Pseudo-time analysis was performed to simulate tumour progression. RT-qPCR and immunohistochemistry were used to analyse the expression of ISG15. Cell proliferation, wound healing, and Transwell assays were used to verify the effect of ISG15 on HeLa cells. The RT-qPCR and immunohistochemistry results indicated that ISG15 had significantly higher expression in TT. We observed an increasing trend of ISG15 expression from NC to TP to TT, which suggests that elevated expression of ISG15 was closely associated with malignant evolution in CC tissues. HeLa cell experiments showed that ISG15-siRNA inhibited cell proliferation and invasion. Our study verifies that ISG15 is upregulated in and positively associated with the development of CC. ISG15 may act as an oncogene in the tumourigenesis of CC.
2022-01-02 | GSE192804 | GEO
Project description:AimTo develop a prognostic gene set that can predict patient overall survival status based on the whole genome expression analysis.MethodsUsing Illumina HumanWG-6 BeadChip followed by semi-supervised analysis, we analyzed the expression of 47,296 transcripts in two batches of gastric cancer patients who underwent surgical resection. Thirty-nine samples in the first batch were used as the training set to discover candidate markers correlated to overall survival, and thirty-three samples in the second batch were used for validation.ResultsA panel of ten genes were identified as prognostic marker in the first batch samples and classified patients into a low- and a high-risk group with significantly different survival times (P = 0.000047). This prognostic marker was then verified in an independent validation sample batch (P = 0.0009). By comparing with the traditional Tumor-node-metastasis (TNM) staging system, this ten-gene prognostic marker showed consistent prognosis results. It was the only independent prognostic value by multivariate Cox regression analysis (P = 0.007). Interestingly, six of these ten genes are ribosomal proteins, suggesting a possible association between the deregulation of ribosome related gene expression and the poor prognosis.ConclusionA ten-gene marker correlated with overall prognosis, including 6 ribosomal proteins, was identified and verified, which may complement the predictive value of TNM staging system.
| S-EPMC3072635 | biostudies-literature
Project description:We performed single-cell RNA sequencing (scRNA-seq) and deep analysis of T cell receptor (TCR) clonotypes (scTCR-seq) in cancer patients across several indications, assessing the profiles of TCRs in the various populations of T cells in tumours, normal adjacent tissue (NAT), and peripheral blood. In a subset of patients, we find clear evidence of clonotypic expansion of T effector-like cells not only within the tumour but also in NAT.
2019-12-09 | GSE139555 | GEO
Project description:Gene expression profiling of sarcoma cell lines of increasing metastatic potential: SW684 (low grade), HT1080 (high grade), GCT (metastatic). Differential gene expression may elucidate biomarkers of tumour progression and metastasis. Keywords: disease state analysis
2008-03-31 | GSE8172 | GEO