Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA349480 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:A miRs expression profiling of a total of 746 miRs (664 human + 82 viral) in 72 Head and Neck Squamous Cell Carcinoma (HNSCC) primary tumors to classify patients at high risk to develop recurrences
2017-10-02 | GSE89000 | 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:Background: In rheumatoid arthritis (RA), only a subset of patients develop an irreversible bone destruction. Our aim was to develop a microRNA (miR)-based osteoclast-related signature to predict erosiveness in RA. Results: We identified 5 miRs from PBMC-derived osteoclasts differentially expressed in RNASeq also in qPCR assessment. Conclusion: We found 5 miRNAs differentially refulated in erosive rheumtoid arhtritis compare to no erosve patient. This study could improve the current approach to identify RA subjects at risk of erosions, by adding biomarker signatures based on the profile of miRs in the osteoclasts.
2023-09-01 | GSE209982 | 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. Keywords: Functional genomics, metastatic colon cancer, mouse model, human colon cancer
2009-11-14 | GSE17537 | 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. Keywords: Functional genomics, metastatic colon cancer, mouse model, human colon cancer
2009-11-14 | GSE17536 | GEO
Project description:Non-muscle invasive bladder cancer (NMIBC) has a recurrence rate of more than 50% after transurethral resection (TUR) and Calmette-Guérin (BCG) treatment. This study aimed to develop a high-frequency recurrence index (HfRI) to predict multiple recurrences in NMIBC patients and guide personalized treatment strategies. In this study, we analyzed transcriptome data (Discovery cohort) of 45 high-risk NMIBC patients who received intravesical BCG treatment, including patients with >=2 recurrences, to identify genes significantly associated with high-frequency recurrence and construct a signature. In addition, we validated the predictive performance using transcriptome data of 94 patients (Validation cohort).
2026-02-07 | GSE294705 | GEO
Project description:Non-muscle invasive bladder cancer (NMIBC) has a recurrence rate of more than 50% after transurethral resection (TUR) and Calmette-Guérin (BCG) treatment. This study aimed to develop a high-frequency recurrence index (HfRI) to predict multiple recurrences in NMIBC patients and guide personalized treatment strategies. In this study, we analyzed transcriptome data (Discovery cohort) of 45 high-risk NMIBC patients who received intravesical BCG treatment, including patients with >=2 recurrences, to identify genes significantly associated with high-frequency recurrence and construct a signature. In addition, we validated the predictive performance using transcriptome data of 94 patients (Discovery cohort).
2026-02-07 | GSE294703 | GEO
Project description:Recently it was shown that gene expression signatures generated from DNA microarray analyses have promise as biomarkers of clinical outcome and that the molecular characteristics of tumors could be elucidated. Through this study, we have determined a high-risk signature for recurrence as a prognostic biomarker using formalin-fixed HNSCC tumors and tested the results to an independent data set obtained from fresh frozen tumors as a comparison. Also, we have shown the genes that are involved in epithelial to mesenchymal transition and nuclear factor-ĸB signaling deregulation are the most prominent molecular characteristics of the high-risk tumors. Forty samples including 34 formalin-fixed tissues and 6 matched frozen tissues from 29 HNSCC patients were analyzed for gene expression. The formalin-fixed tumors were classified based on their gene expression by intrinsic analysis and the intrinsic gene list was tested on the classification of previously published 60 frozen HNSCC tumors which highly correlated. Based on the molecular classification, a 75-gene list that is predictive of high-risk for recurrence was determined by training on the formalin-fixed tumor set and tested on the independent frozen tumor set. The difference in recurrence-free survival (RFS) between the high-risk vs. low-risk groups in the training and test sets were statistically significant (Log-rank test, p=0.002 and p=0.03, respectively). Also, the gene expression data was interrogated using Gene Set Enrichment Analysis to determine functional significance. The most significant sets of genes that are enriched in the high-risk tumors were genes involving epithelial to mesenchymal transition (EMT), NF-ĸB activation and cell adhesion. In conclusion, global gene expression analysis is feasible using formalin-fixed tissue, and the data from different sample preparations and array platforms can be reliably combined for analyses. The 75-gene list can be utilized as a prognostic biomarker of recurrence and the molecular characteristics of EMT and NF-ĸB activation can be targeted as the novel therapy in the identified high-risk patients. Experiment Overall Design: 34 HNSCC samples from FFPE tissue and 6 HNSCC samples from fresh frozen tissue. Each independtly hybridized using Affymetrix X3P chips.
2008-06-12 | E-GEOD-2837 | biostudies-arrayexpress
Project description:Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous group of malignancies arising from different sites and the 8th most common type of cancer in the world. While the advent of new techniques and multimodality therapy have improved prognosis of locally HNSCC, the appearance of distant metastases (DM) remains a challenge both for prognosis and treatment. To date, the genomic landscape of dissemination of HNSCC has to be further studied, in order to predict whether a patient will develop distant metastases. Therefore, knowing that the risk of disease spread to DM is directly correlated to initial staging, it suggested the hypothesis that the development of DM could be linked to the biologic characteristics of the primary tumor. The aim of our work is to compare gene expression profile of primary tumors of patients who develop and not develop DM. Additionally, we studied the expression profile of matched distant metastasis.
2020-05-21 | GSE136037 | GEO