Sixty-nine samples obtained from surgical excision with overall survival.
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE73403 | GEO | 2015/09/25
SECONDARY ACCESSION(S): PRJNA296897
REPOSITORIES: GEO
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2015-09-25 | GSE73402 | GEO
Project description:Objective: Globally, esophageal cancer is among the most deadly cancer forms. Long non-coding RNAs (lncRNA) are frequently found to have important regulatory roles. We aim to assess the lncRNA expression profile of esophageal squamous cell carcinoma (ESCC) and identify prognosis related lncRNAs. Design: LncRNA expression profiles were studied by microarray in paired tumor and normal tissues from 119 ESCC patients, and validated by qRT-PCR. The 119 patients were subsequently divided randomly into training (n=60) and test (n=59) groups. A prognostic signature was developed from the training group using a random forest supervised classification algorithm and a nearest shrunken centroid algorithm, and validated in test group and further in an independent cohort (n=60). The independence of the signature in survival prediction was evaluated by Multivariable Cox regression analysis. Results: LncRNAs showed significantly altered expression in ESCC tissues. From the training group, we identified a three-lncRNA signature (including the lncRNAs ENST00000435885•1, XLOC_013014, and ENST00000547963•1) which classified the patients into two groups with significantly different overall survival (median survival 19•2 months vs. not reached, p<0•0001). The signature was applied to the test group (median survival 21•5 months vs. not reached, p=0•0030) and independent cohort (median survival 25•8 months vs. not reached, p=0•0187) and showed similar prognostic values in both. Multivariable Cox regression analysis showed that the signature was an independent prognostic factor for ESCC patients. Stratified analysis suggested that the signature was prognostic within clinical stages. Conclusions: Our results suggest that the three-lncRNA signature can serve as a novel biomarker for the prognosis of ESCC patients. Application of it allows for more accurate survival prediction. The lncRNA expression profiles of cancer and adjacent normal tissues form 119 ESCC patients were studied by microarray and an lncRNA signature that can perdict the survival of ESCC patients was identified.
2014-03-07 | E-GEOD-53624 | biostudies-arrayexpress
Project description:Objective: Globally, esophageal cancer is among the most deadly cancer forms. Long non-coding RNAs (lncRNA) are frequently found to have important regulatory roles. We aim to assess the lncRNA expression profile of esophageal squamous cell carcinoma (ESCC) and identify prognosis related lncRNAs. Design: LncRNA expression profiles were studied by microarray in paired tumor and normal tissues from 119 ESCC patients, and validated by qRT-PCR. The 119 patients were subsequently divided randomly into training (n=60) and test (n=59) groups. A prognostic signature was developed from the training group using a random forest supervised classification algorithm and a nearest shrunken centroid algorithm, and validated in test group and further in an independent cohort (n=60). The independence of the signature in survival prediction was evaluated by Multivariable Cox regression analysis. Results: LncRNAs showed significantly altered expression in ESCC tissues. From the training group, we identified a three-lncRNA signature (including the lncRNAs ENST00000435885•1, XLOC_013014, and ENST00000547963•1) which classified the patients into two groups with significantly different overall survival (median survival 19•2 months vs. not reached, p<0•0001). The signature was applied to the test group (median survival 21•5 months vs. not reached, p=0•0030) and independent cohort (median survival 25•8 months vs. not reached, p=0•0187) and showed similar prognostic values in both. Multivariable Cox regression analysis showed that the signature was an independent prognostic factor for ESCC patients. Stratified analysis suggested that the signature was prognostic within clinical stages. Conclusions: Our results suggest that the three-lncRNA signature can serve as a novel biomarker for the prognosis of ESCC patients. Application of it allows for more accurate survival prediction. The lncRNA expression profiles of cancer and adjacent normal tissues form 119 ESCC patients were studied by microarray and an lncRNA signature that can perdict the survival of ESCC patients was identified.
2014-03-07 | E-GEOD-53622 | biostudies-arrayexpress
Project description:Claret2009 - Predicting phase III overall survival in colorectal cancer
This model is described in the article:
Model-based prediction of
phase III overall survival in colorectal cancer on the basis of
phase II tumor dynamics.
Claret L, Girard P, Hoff PM, Van
Cutsem E, Zuideveld KP, Jorga K, Fagerberg J, Bruno R.
J. Clin. Oncol. 2009 Sep; 27(25):
4103-4108
Abstract:
PURPOSE: We developed a drug-disease simulation model to
predict antitumor response and overall survival in phase III
studies from longitudinal tumor size data in phase II trials.
METHODS: We developed a longitudinal exposure-response
tumor-growth inhibition (TGI) model of drug effect (and
resistance) using phase II data of capecitabine (n = 34) and
historical phase III data of fluorouracil (FU; n = 252) in
colorectal cancer (CRC); and we developed a parametric survival
model that related change in tumor size and patient
characteristics to survival time using historical phase III
data (n = 245). The models were validated in simulation of
antitumor response and survival in an independent phase III
study (n = 1,000 replicates) of capecitabine versus FU in CRC.
RESULTS: The TGI model provided a good fit of longitudinal
tumor size data. A lognormal distribution best described the
survival time, and baseline tumor size and change in tumor size
from baseline at week 7 were predictors (P < .00001).
Predicted change of tumor size and survival time distributions
in the phase III study for both capecitabine and FU were
consistent with observed values, for example, 431 days (90%
prediction interval, 362 to 514 days) versus 401 days observed
for survival in the capecitabine arm. A modest survival
improvement of 39 days (90% prediction interval, -21 to 110
days) versus 35 days observed was predicted for capecitabine.
CONCLUSION: The modeling framework successfully predicted
survival in a phase III trial on the basis of capecitabine
phase II data in CRC. It is a useful tool to support
end-of-phase II decisions and design of phase III studies.
This model is hosted on
BioModels Database
and identified by:
MODEL1708310001.
To cite BioModels Database, please use:
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To the extent possible under law, all copyright and related or
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the public domain worldwide. Please refer to
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2017-08-31 | MODEL1708310001 | BioModels
Project description:Objective: Globally, esophageal cancer is among the most deadly cancer forms. Long non-coding RNAs (lncRNA) are frequently found to have important regulatory roles. We aim to assess the lncRNA expression profile of esophageal squamous cell carcinoma (ESCC) and identify prognosis related lncRNAs. Design: LncRNA expression profiles were studied by microarray in paired tumor and normal tissues from 119 ESCC patients, and validated by qRT-PCR. The 119 patients were subsequently divided randomly into training (n=60) and test (n=59) groups. A prognostic signature was developed from the training group using a random forest supervised classification algorithm and a nearest shrunken centroid algorithm, and validated in test group and further in an independent cohort (n=60). The independence of the signature in survival prediction was evaluated by Multivariable Cox regression analysis. Results: LncRNAs showed significantly altered expression in ESCC tissues. From the training group, we identified a three-lncRNA signature (including the lncRNAs ENST00000435885•1, XLOC_013014, and ENST00000547963•1) which classified the patients into two groups with significantly different overall survival (median survival 19•2 months vs. not reached, p<0•0001). The signature was applied to the test group (median survival 21•5 months vs. not reached, p=0•0030) and independent cohort (median survival 25•8 months vs. not reached, p=0•0187) and showed similar prognostic values in both. Multivariable Cox regression analysis showed that the signature was an independent prognostic factor for ESCC patients. Stratified analysis suggested that the signature was prognostic within clinical stages. Conclusions: Our results suggest that the three-lncRNA signature can serve as a novel biomarker for the prognosis of ESCC patients. Application of it allows for more accurate survival prediction.
2014-03-07 | GSE53624 | GEO
Project description:Objective: Globally, esophageal cancer is among the most deadly cancer forms. Long non-coding RNAs (lncRNA) are frequently found to have important regulatory roles. We aim to assess the lncRNA expression profile of esophageal squamous cell carcinoma (ESCC) and identify prognosis related lncRNAs. Design: LncRNA expression profiles were studied by microarray in paired tumor and normal tissues from 119 ESCC patients, and validated by qRT-PCR. The 119 patients were subsequently divided randomly into training (n=60) and test (n=59) groups. A prognostic signature was developed from the training group using a random forest supervised classification algorithm and a nearest shrunken centroid algorithm, and validated in test group and further in an independent cohort (n=60). The independence of the signature in survival prediction was evaluated by Multivariable Cox regression analysis. Results: LncRNAs showed significantly altered expression in ESCC tissues. From the training group, we identified a three-lncRNA signature (including the lncRNAs ENST00000435885•1, XLOC_013014, and ENST00000547963•1) which classified the patients into two groups with significantly different overall survival (median survival 19•2 months vs. not reached, p<0•0001). The signature was applied to the test group (median survival 21•5 months vs. not reached, p=0•0030) and independent cohort (median survival 25•8 months vs. not reached, p=0•0187) and showed similar prognostic values in both. Multivariable Cox regression analysis showed that the signature was an independent prognostic factor for ESCC patients. Stratified analysis suggested that the signature was prognostic within clinical stages. Conclusions: Our results suggest that the three-lncRNA signature can serve as a novel biomarker for the prognosis of ESCC patients. Application of it allows for more accurate survival prediction.
2014-03-07 | GSE53622 | GEO
Project description:Surgical samples have long been used as important subjects for cancer research. In accordance with an increase of neoadjuvant therapy, biopsy samples have recently become imperative for cancer transcriptome. On the other hand, both biopsy and surgical samples are available for expression profiling for predicting clinical outcome by adjuvant therapy; however, it is still unclear whether surgical sample expression profiles are useful for the prediction by the use of biopsy samples because little has been done about comparative gene expression profiling between the two kinds of samples. When gene expression profiles were compared between biopsy and surgical samples, artificially induced epithelial-mesenchymal transition (aiEMT) was found in the surgical samples. This study will evoke the fundamental misinterpretation including underestimation of the prognostic evaluation power of markers by overestimation of EMT in past cancer research, and will furnish some advice for the near future as follows: 1) Understanding how long the tissues were under an ischemic condition; 2) Prevalence of biopsy samples for in vivo expression profiling with low biases on basic and clinical research; and 3) Checking cancer cell contents and normal- or necrotic-tissue contamination in biopsy samples for prevalence.
2011-10-20 | GSE33103 | GEO
Project description:Surgical samples have long been used as important subjects for cancer research. In accordance with an increase of neoadjuvant therapy, biopsy samples have recently become imperative for cancer transcriptome. On the other hand, both biopsy and surgical samples are available for expression profiling for predicting clinical outcome by adjuvant therapy; however, it is still unclear whether surgical sample expression profiles are useful for the prediction by the use of biopsy samples because little has been done about comparative gene expression profiling between the two kinds of samples. When gene expression profiles were compared between biopsy and surgical samples, artificially induced epithelial-mesenchymal transition (aiEMT) was found in the surgical samples. This study will evoke the fundamental misinterpretation including underestimation of the prognostic evaluation power of markers by overestimation of EMT in past cancer research, and will furnish some advice for the near future as follows: 1) Understanding how long the tissues were under an ischemic condition; 2) Prevalence of biopsy samples for in vivo expression profiling with low biases on basic and clinical research; and 3) Checking cancer cell contents and normal- or necrotic-tissue contamination in biopsy samples for prevalence. We used microarrays to compare gene expression profiles between 31 biopsy (A, D) and 65 surgical (O) samples derived from the cancerous portion of the esophagus of different esophageal cancer patients.
2011-10-19 | E-GEOD-33103 | biostudies-arrayexpress
Project description:Samples were prospectively collected from patients with histologically normal surgical resection margins. 96 tissue samples (histologically normal margins, oral carcinoma and adjacent normal tissues) from 24 patients comprised the training set. Our study design was guided by the hypothesis that the expression of genes present in oral squamous cell carcinoma (OSCC) but not in healthy oral tissues would be indicative of recurrence in advance of histological alteration. We used meta-analysis of five published microarray data sets (GEO accession GDS2520, Kuriakose et al. 2004; GDS1584, Toruner et al. 2004; GSE6791, Pyeon et al. 2007; GSE9844, Ye et al. 2008; and GSE10121, Sticht et al. 2008), in conjunction with the current training set, to identify genes reliably over-expressed in OSCC. This reduced gene set was used to train a risk model to predict recurrence based on over-expression of a subset of these genes in histologically normal surgical resection margins. Validation of the risk signature was performed using quantitative real-time reverse-transcription PCR in an independent set of 136 samples from an independent cohort of 30 patients. This was a case-only design involving a training set of 23 tumors and 73 margins from 24 patients with squamous cell carcinoma of the tongue.
2011-08-02 | E-GEOD-31056 | biostudies-arrayexpress