Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA113313 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:High-throughput proteomics profiling-derived signature associated with chemotherapy response and survival for stage II/III colorectal cancer
2023-06-19 | PXD043116 | iProX
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:Purpose: A 128-gene signature has been proposed to predict poor outcomes in patients with stage II and III colorectal cancer. In the present study we aimed to validate this previously published 128-gene signature on external and independent data from patients with stage II and III colon cancer.
2011-09-01 | GSE31595 | GEO
Project description:We obtained fibroblast cultures from fresh surgical specimen ressected from patients with primary colorectal carcinoma: normal colonic fibroblasts (NCF=9) from the normal colonic mucosa at least 5-10cm from the surgical margin, carcinoma-associated fibroblasts from the primary tumor (CAF-PT=14) and carcinoma-associated fibroblasts (CAF-LM=11) from fresh surgical specimens of liver metastases. We identified 277 probes, in common between the three types of fibroblasts, whose expression level is sequentially deregulated according to cancer progression (NCF→CAF-PT→CAF-LM; fold change Log2 normalized expression>1.5 in each step). Prediction Analysis of Microarrays was applied to obtain a 25-gene signature that better characterizes each fibroblast class. The signature is able to classify patients carrying primary tumors according to prognosis. This fact was exploited to obtain a 19-gene signature (from the 277 deregulated probes) predicting recurrence with high accuracy in stage II/III colorectal cancer patients. Signature validation has been carried out in two independent datasets and in a meta-cohort of 336 stage II/III patients. Since the 25-gene signature was obtained regardless of gene expression data of tumor specimens or patient’s clinical data, the prognostic power of this signature provides strong evidence of the link between the tumor stroma and cancer progression. Furthermore, the 19-gene signature was able to identify low-risk patients with very high accuracy, especially relevant for those high-risk stage-II patients. We hybridised fibroblast RNA in Affymetrix GeneChip 1.0 st arrays
2013-05-10 | E-GEOD-46824 | biostudies-arrayexpress
Project description:This series is part of a larger series (GSE24549) of colorectal cancer tissue samples analyzed for global gene expression. The expression measures were used to develope a gene signature for prediction of prognosis in stage II and III colorectal cancer.
2012-01-05 | GSE30378 | GEO
Project description:Background & Aims. The current staging system for colorectal cancer (CRC) based on TNM classification allows prediction of potential recurrence. However, it does not necessarily make reliable personalized prediction of prognosis. In this paper we describe combination of clinicopathological data and gene signature of dissected tumor specimen with stage II and III CRC patients would improve the situation.. Methods. A total of 1978 CRC were collected over 5 years, and then 371 stage II and 322 stage III of them with more than 45.9 months records were subjected to clinicopathological feature analyses. Out of this collection, 129 stage II and III CRC cases were selected for analyses of gene expression profiles with resected specimen. The gene signatures were analyzed by repeated random divisions of the samples into training and test sets to extract discriminator genes. After testing the applicability of this discriminator set, it was subjected to validation using a newly obtained set of 69 samples. Results. The pathological factors in solo or in combinations could not make personalized recurrence prediction, except for partial success with stage II patients. The gene signature, on the other hand, was capable of producing a set of discriminator genes, though the accuracy was yet to be improved. We observed that the best result was obtained when discriminators were selected from stage II CRC samples and used for prognosis of stage II CRC. When stage III cases were included in the process of discriminator extraction or in the process to validate samples, the results were poorer. Finally, we examined 31 independent stage II samples with a set of 30 such discriminators and were able to obtain results with 78 % accuracy, 90 % negative predictive value (NPV), and 55% positive predictive value (PPV). Conclusions. Independent clinicopathological variables were not able to predict prognosis of individual patient, unless the factors are combined. On the other hand, gene signatures allowed accurate prediction of prognosis for individuals, especially with stage II CRC, suggesting its potential use for selection of best treatment option for individual patients. The accuracy of discriminator prediction will be further improved when we take the evolution of CRC into consideration.
2009-07-02 | GSE12032 | GEO
Project description:Background & Aims. The current staging system for colorectal cancer (CRC) based on TNM classification allows prediction of potential recurrence. However, it does not necessarily make reliable personalized prediction of prognosis. In this paper we describe combination of clinicopathological data and gene signature of dissected tumor specimen with stage II and III CRC patients would improve the situation.. Methods. A total of 1978 CRC were collected over 5 years, and then 371 stage II and 322 stage III of them with more than 45.9 months records were subjected to clinicopathological feature analyses. Out of this collection, 129 stage II and III CRC cases were selected for analyses of gene expression profiles with resected specimen. The gene signatures were analyzed by repeated random divisions of the samples into training and test sets to extract discriminator genes. After testing the applicability of this discriminator set, it was subjected to validation using a newly obtained set of 69 samples. Results. The pathological factors in solo or in combinations could not make personalized recurrence prediction, except for partial success with stage II patients. The gene signature, on the other hand, was capable of producing a set of discriminator genes, though the accuracy was yet to be improved. We observed that the best result was obtained when discriminators were selected from stage II CRC samples and used for prognosis of stage II CRC. When stage III cases were included in the process of discriminator extraction or in the process to validate samples, the results were poorer. Finally, we examined 31 independent stage II samples with a set of 30 such discriminators and were able to obtain results with 78 % accuracy, 90 % negative predictive value (NPV), and 55% positive predictive value (PPV). Conclusions. Independent clinicopathological variables were not able to predict prognosis of individual patient, unless the factors are combined. On the other hand, gene signatures allowed accurate prediction of prognosis for individuals, especially with stage II CRC, suggesting its potential use for selection of best treatment option for individual patients. The accuracy of discriminator prediction will be further improved when we take the evolution of CRC into consideration. Of 198 samples, 129 represented the discovery phase and 69 represented the validation phase.
2009-07-02 | E-GEOD-12032 | biostudies-arrayexpress
Project description:This series is part of a larger series (GSE24549) of colorectal cancer tissue samples analyzed for global gene expression. The expression measures were used to develope a gene signature for prediction of prognosis in stage II and III colorectal cancer. Gene expression profiling of colorectal cancer tissue samples for prognosis prediction.
2012-01-05 | E-GEOD-30378 | biostudies-arrayexpress
Project description:We obtained fibroblast cultures from fresh surgical specimen ressected from patients with primary colorectal carcinoma: normal colonic fibroblasts (NCF=9) from the normal colonic mucosa at least 5-10cm from the surgical margin, carcinoma-associated fibroblasts from the primary tumor (CAF-PT=14) and carcinoma-associated fibroblasts (CAF-LM=11) from fresh surgical specimens of liver metastases. We identified 277 probes, in common between the three types of fibroblasts, whose expression level is sequentially deregulated according to cancer progression (NCF→CAF-PT→CAF-LM; fold change Log2 normalized expression>1.5 in each step). Prediction Analysis of Microarrays was applied to obtain a 25-gene signature that better characterizes each fibroblast class. The signature is able to classify patients carrying primary tumors according to prognosis. This fact was exploited to obtain a 19-gene signature (from the 277 deregulated probes) predicting recurrence with high accuracy in stage II/III colorectal cancer patients. Signature validation has been carried out in two independent datasets and in a meta-cohort of 336 stage II/III patients. Since the 25-gene signature was obtained regardless of gene expression data of tumor specimens or patient’s clinical data, the prognostic power of this signature provides strong evidence of the link between the tumor stroma and cancer progression. Furthermore, the 19-gene signature was able to identify low-risk patients with very high accuracy, especially relevant for those high-risk stage-II patients.
2013-05-10 | GSE46824 | GEO