Gene expression profiling of human colorectal cancer cell lines KM12SM and SW620
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE199223 | GEO | 2022/03/26
REPOSITORIES: GEO
Similar Datasets
Project description:Ovarian cancer is the fifth leading cause of cancer death for women in the U.S. and the seventh most fatal worldwide. Although ovarian cancer is notable for its initial sensitivity to platinum-based therapies, the vast majority of women eventually recurs and succumbs to increasingly platinum-resistant disease. Modern, targeted cancer drugs intervene in cell signaling pathways by compensating for their deregulation, and identifying these key mechanisms and pathways would greatly advance our ability to treat disease. In order to shed light on the molecular diversity of ovarian cancer, we performed comprehensive transcriptional profiling on 129 high grade, late stage serous ovarian cancers. We implemented a novel, re-sampling based version of the ISIS class discovery algorithm (rISIS: robust ISIS) and applied it to the entire set of ovarian cancer transcriptional profiles. rISIS identified a novel stratification of this disease into two groups with significantly different overall survival. Gene set enrichment analysis found strong support for the stratification by extracellular matrix, cell adhesion, and angiogenesis genes. Application of this 'angiogenesis' signature to independent, published ovarian cancer gene expression data confirms its prognostic potential. Additional support for this stratification is provided by micro-RNA expression profiles which exhibit statistically significant expression differences between the groups, and additional mechanistic analyses have allowed development of hypotheses relevant to directed therapeutic intervention for specific subclasses of the disease. In particular, the subgroup stratification we discovered may be relevant for identifying which patients may be best suited for anti-angiogenic therapies that are now being tested in clinical trials.
2011-09-09 | E-MTAB-386 | biostudies-arrayexpress
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:Relapse and metastatic progression is a frequent event in colorectal cancer patients detected at early stages. The risk of recurrence requires the development of new biomarkers to correctly predict biological behavior of early stage II and stage III patients and their response to adjuvant chemotherapy. Here, we combined the proteomic quantification of secreted proteins involved in metastasis with a transcriptional analysis to develop a risk score algorithm based on the expression of six genes (SEC6). The SEC6 signature was predictive of survival and recurrence for stage II and III patients in four different datasets including a total of 1534 patients and was also associated with deficient mismatch repair, CpG-island methylator positive status and BRAF mutation. SEC6 was also predictive of beneficial or detrimental effects from 5-Fluorouracil-containing regimes and the improved response to more aggressive chemotherapies based on FOLFOX and FOLFIRI. In summary, the SEC6 risk-score algorithm may constitute a new tool for decision-making in colorectal cancer management.
2022-12-27 | PXD032899 | Pride
Project description:MACC1 is a decisive driver of metastasis. In this data set the expression changes of shRNA mediated knock down is analyzed. We used microarrays to detail the global programme of gene expression changing upon MACC1 knock down.
2021-12-01 | GSE175995 | GEO
Project description:Using a public reference data set of 82 unique entities, 382 nanopore-sequenced brain tumor samples were classified based on their methylation status through an ad hoc random forest algorithm. As a measure of confidence, score recalibration was performed and platform-specific thresholds were defined.
2022-07-30 | GSE209865 | GEO
Project description:we aimed at developing a urinary biomarker-based algorithm that predicts rapid disease progression in IgAN, thus enabling a personalized risk stratification.
2022-02-15 | PXD020288 | Pride
Project description:These data, combined with other cohorts (GSE6532, GSE12093, and qRT-PCR based cohorts), was used to construct the EP algorithm, which predicts the likelihood of developing of a distant recurrence of early stage breast cancer under endocrine treatment. In addition, EPclin, a combination of the EP score, the nodal status and the tumor size, was constructed.
2011-10-11 | GSE26971 | GEO
Project description:Background and Aims: Staging inadequately predicts metastatic risk in colon cancer patients. We used a gene expression profile derived from invasive murine colon cancer cells that were highly metastatic in an immunocompetent mouse model to identify colon cancer patients at risk for recurrence in a phase I, exploratory biomarker study. Methods: 55 colorectal cancer patients from Vanderbilt Medical Center (VMC) were used as the training dataset and 177 patients from the Moffitt Cancer Center were used as the independent dataset. The metastasis-associated gene expression profile developed from the mouse model was refined using comparative functional genomics in the VMC gene expression profiles to identify a 34-gene classifier associated with high risk of metastasis and death from colon cancer. A recurrence score derived from the biologically based classifier was tested in the Moffitt dataset. Results: A high score was significantly associated with increased risk of metastasis and death from colon cancer across all pathological stages and specifically in stage II and stage III patients. The recurrence score was shown to independently predict risk of cancer recurrence and death in both univariate and multivariate models. For example, among stage III patients, a high score translated to increased relative risk for cancer recurrence (hazard ratio = 4.7 (95% CI=1.566-14.05)). Furthermore, the recurrence score identified stage III patients whose five-year recurrence-free survival was >88% and for whom adjuvant chemotherapy did not provide improved survival. Conclusion: Our biologically based gene expression profile yielded a potentially useful classifier to predict cancer recurrence and death independently of conventional measures in colon cancer patients. Experiment Overall Design: Gene expression array differences between highly invasive mouse colon cancer cells and non-invasive colon cancer cells were used to develop a metastasis gene expression profile. It was refined using gene expression data from 55 patient (VMC) samples and trained using 177 patient (Moffitt) samples.
2009-12-02 | E-GEOD-17537 | biostudies-arrayexpress
Project description:Background and Aims: Staging inadequately predicts metastatic risk in colon cancer patients. We used a gene expression profile derived from invasive murine colon cancer cells that were highly metastatic in an immunocompetent mouse model to identify colon cancer patients at risk for recurrence in a phase I, exploratory biomarker study. Methods: 55 colorectal cancer patients from Vanderbilt Medical Center (VMC) were used as the training dataset and 177 patients from the Moffitt Cancer Center were used as the independent dataset. The metastasis-associated gene expression profile developed from the mouse model was refined using comparative functional genomics in the VMC gene expression profiles to identify a 34-gene classifier associated with high risk of metastasis and death from colon cancer. A recurrence score derived from the biologically based classifier was tested in the Moffitt dataset. Results: A high score was significantly associated with increased risk of metastasis and death from colon cancer across all pathological stages and specifically in stage II and stage III patients. The recurrence score was shown to independently predict risk of cancer recurrence and death in both univariate and multivariate models. For example, among stage III patients, a high score translated to increased relative risk for cancer recurrence (hazard ratio = 4.7 (95% CI=1.566-14.05)). Furthermore, the recurrence score identified stage III patients whose five-year recurrence-free survival was >88% and for whom adjuvant chemotherapy did not provide improved survival. Conclusion: Our biologically based gene expression profile yielded a potentially useful classifier to predict cancer recurrence and death independently of conventional measures in colon cancer patients. Experiment Overall Design: Gene expression array differences between highly invasive mouse colon cancer cells and non-invasive colon cancer cells were used to develop a metastasis gene expression profile. It was refined using gene expression data from 55 patient (VMC) samples and trained using 177 patient (Moffitt) samples.
2009-12-02 | E-GEOD-17536 | biostudies-arrayexpress
Project description:The prognosis of children with metastatic stage 4 neuroblastoma (NB) has remained poor in the past decade. Using microarray analyses of 342 primary tumors, we here developed and validated an easy to use gene expression-based risk score including 18 genes, which can robustly predict the outcome of stage 4 patients. This classifier was a significant predictor of overall survival in two independent validation cohorts (cohort 1 (n=214): P=6.3x10-5; cohort 2 (n=27): P=3.1x10-2). The prognostic value of the risk score was validated by multivariate analysis including the established markers age and MYCN status (P=0.027). In the pooled validation cohorts (n=241), integration of the risk score with the age and/or MYCN status identified subgroups with significantly differing overall survival (ranging from 35% to 100%). Together, the 18-gene risk score classifier can identify patients with stage 4 NB with favorable outcome and may therefore improve risk assessment and treatment stratification of NB patients with disseminated disease. We analyzed expression profiling arrays of 27 Tumor samples from stage 4 neuroblastoma patients.
2016-04-05 | E-GEOD-79910 | biostudies-arrayexpress