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Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Genetic profiling to predict recurrence of early cervical cancer

ABSTRACT: We compared the prediction powers for disease recurrence between gene set prognostic model and clinical prognostic model developed in a single large population to see whether genetic quantitative approach will have significant prognostic role in early cervical cancer patients who underwent radical hysterectomy with or without adjuvant therapies. Gene set model to predict disease free survival of early cervical cancer was developed using DASL assay dataset from the cohort of early cervical cancer patients who were treated with radical surgery with or without adjuvant therapies at the Samsung Medical Center of Sungkyunkwan University School of Medicine in Seoul, Korea, between January 2002 and September 2008. Clinical prediction model was also developed in the same cohort and the ability of predicting recurrence from each model was compared. Adequate DASL assay profiles were obtained in 300 patients and we selected 12 genes for the gene set model. When the proportions of patients were categorized as having a low or high risk by the prognostic scores using these genes from LOOCV procedure, the Kaplan-Meier curve showed significant different recurrence rate between two groups. Clinical model was developed using FIGO stage as well as post-surgical pathological findings.

ORGANISM(S): Homo sapiens

SUBMITTER: Yuyoung Lee

PROVIDER: E-GEOD-44001 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Genetic profiling to predict recurrence of early cervical cancer

Project description:We compared the prediction powers for disease recurrence between gene set prognostic model and clinical prognostic model developed in a single large population to see whether genetic quantitative approach will have significant prognostic role in early cervical cancer patients who underwent radical hysterectomy with or without adjuvant therapies. Gene set model to predict disease free survival of early cervical cancer was developed using DASL assay dataset from the cohort of early cervical cancer patients who were treated with radical surgery with or without adjuvant therapies at the Samsung Medical Center of Sungkyunkwan University School of Medicine in Seoul, Korea, between January 2002 and September 2008. Clinical prediction model was also developed in the same cohort and the ability of predicting recurrence from each model was compared.

2013-05-01 | GSE44001 | GEO

Radical prostatectomy specimens with and without TMPRSS2:ERG fusion from 139 patients with prostate in a Toronto cohort

Project description:Prostate tumors with the gene fusion TMPRSS2:ERG have been reported to have a significantly higher risk of recurrence compared with tumors lacking the fusion. Tumors from 139 patients who underwent radical prostatectomy were analyzed for the expression of 502 cancer-related genes to identify genes differentially regulated in TMPRSS2:ERG fusion tumors as well as identify biomarkers of biochemical recurrence. 139 prostate fresh-frozen tumors from radical prostatectomy surgery where profiled on the Illumina Human Cancer DASL Panel. 69 tumors were positive for the gene fusion TMPRSS2:ERG while 70 where not. 33 of the 139 patients experienced biochemical recurrence. Data was analyzed for differential genes in TMPRSS2:ERG fusion positive tumors as well as clinical and molecular biomarkers of recurrence.

2009-10-22 | E-GEOD-18655 | biostudies-arrayexpress

A novel prognostic risk model for cervical cancer based on immune checkpoint HLA-G-driven differentially expressed genes [SiHa]

Project description:The aim of this study was to construct and validate a prognostic risk model to predict the overall survival (OS) of patients with cervical cancer, providing a reference for individualized clinical treatment that may lead to better clinical outcomes. HLA-G-driven DEG signature consisting of the eight most important prognostic genes CD46, LGALS9, PGM1, SPRY4, CACNB3, PLIN2, MSMO1, and DAGLB was identified as a key predictor of cervical cancer. To summarize, we developed and validated a novel prognostic risk model for cervical cancer based on HLA-G-driven DEGs, and the prognostic signature showed great ability in predicting the overall survival of patients with cervical cancer.

2022-07-19 | GSE208118 | GEO

A novel prognostic risk model for cervical cancer based on immune checkpoint HLA-G-driven differentially expressed genes [HeLa]

2022-07-19 | GSE208117 | GEO

Identification and validation of gene expression models that predict clinical outcome in patients with early stage laryngeal cancer

Project description:Background Despite improvement in diagnostic and therapeutic techniques, a significant percentage of patients with early stage laryngeal cancer still recur after treatment. Gene expression models prognostic of recurrence risk could suggest which patients with early stage laryngeal cancer would be more appropriate for testing adjuvant strategies. Patients and Methods Expression profiling using whole genome DASL arrays was performed on 56 formalin-fixed paraffin-embedded tumor samples of patients with early stage laryngeal cancer, treated with surgery or radiation therapy. We split the samples into a training set and a validation set. Using the supervised principal components survival analysis in the first cohort, we identified multiple gene expression profiles that predict the risk of recurrence. These profiles were then validated in the second independent cohort. Results Gene models comprising different number of genes (40-100) identified a subgroup of patients who were at high risk of recurrence. Of these, the best prognostic model distinguished between a high- and a low-risk group (median DFS: 92 and 123 months, log rank p<0.005, permutation p<0.05), Hazard Ratio (HR): 8.51 (95% CI, 1.01 to 71.77; p<0.05). These models performed similarly in the independent cohort of our study (median DFS: 38 vs 161 months, log rank p=0.018), HR=5.19 (95% CI, 1.14 to 23.57; p<0.05). Conclusions We have identified gene expression prognostic models which can refine the estimation of a patient’s risk of recurrence. These findings, if further validated, should aid in patient stratification for testing adjuvant treatment strategies. 56 patients with early stage laryngeal cancer were included in this study.

2011-11-26 | E-GEOD-25727 | biostudies-arrayexpress

[miRNA Training Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy

Project description:To identify biomarkers predictive of biochemical recurrence, we isolated the RNA from 70 formalin-fixed paraffin-embedded (FFPE) radical prostatectomy specimens with known long term outcome to perform DASL expression profiling with a custom-designed panel of 522 prostate cancer relevant genes that we designed. We identified a panel of ten protein-coding genes and two miRNA genes that could be used to separate patients with and without biochemical recurrence (p < 0.001), as well as for the subset of 42 Gleason score 7 patients (p < 0.001). We performed an independent validation analysis on 40 samples and found that the biomarker panel was also significant at prediction of recurrence for all cases (p = 0.013) and for a subset of 19 Gleason score 7 cases (p = 0.010), both of which were adjusted for relevant clinical information including T-stage, PSA and Gleason score. Importantly, these biomarkers could significantly predict clinical recurrence for Gleason 7 patients. These biomarkers may increase the accuracy of prognostication following radical prostatectomy using formalin-fixed specimens. Total RNA prepared from FFPE cores from prostatectomy samples of 70 patients were used for the training phase (29 with biochemical recurrence and 41 controls). All samples were analyzed by both custom Prostate DASL of 522 genes and by Illumina miRNA microarray. Subsequently in the validation phase, samples from 40 patients were used on the same platforms (13 with biochemical recurrence and 27 controls). For the training set, 45 cases were from Sunnybrook Health Science Center (Toronto, ON), and 25 patients from Emory University. For the validation set, all samples were from Emory University. Relevant clinical metadata included are PSA, T-stage, and Gleason Score.

2011-06-01 | E-GEOD-26245 | biostudies-arrayexpress

[miRNA Validation Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy

2011-06-01 | E-GEOD-26247 | biostudies-arrayexpress

[Gene Expression Validation Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy

2011-06-01 | E-GEOD-26242 | biostudies-arrayexpress

Discovery and Validation of a 15-Gene Prognostic Signature for Clear Cell Renal Cell Carcinoma

Project description:We report a kidney cancer tissue-based prognostic biomarker encompassing 15 genes (15G score) to classify patients into low versus high risk for recurrence after curative nephrectomy. The 15G score was independently associated with disease free survival adjusting for clinicopathologic variables as well as existing clinical risk calculators or nomograms. By improving risk stratification of patients with ccRCC, the 15G score has the capacity to facilitate selection of biopsy confirmed small renal cancers (T1a) for treatment versus surveillance; inform intensity and duration of surveillance after curative nephrectomy; and to potentially facilitate patient selection for adjuvant systemic therapy. We retrospectively identified 110 patients who underwent radical nephrectomy for ccRCC (discovery cohort). Patients who recurred were matched based on grade/stage to patients without recurrence. Capture whole transcriptome sequencing was performed on RNA isolated from archival tissue using the Illumina platform. We developed a gene-expression signature to predict recurrence/disease-free survival (DFS) using a 15-fold lasso and elastic-net regularized linear Cox model. We derived the 31-gene cell cycle progression (mxCCP) score using RNAseq data for each patient. Kaplan-Meier (KM) curves and multivariable Cox proportional hazard testing were used to validate the independent prognostic impact of the gene-expression signature on DFS, disease specific survival (DSS) and overall survival (OS) in two validation datasets (combined n=761).

2024-04-01 | GSE254566 | GEO

[Gene Expression Training Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy

2011-06-01 | E-GEOD-26022 | biostudies-arrayexpress

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