Identification of clinical and molecular features for recurrent serous borderline ovarian tumor
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE190526 | GEO | 2024/12/09
REPOSITORIES: GEO
Similar Datasets
Project description:The prognosis of hepatocellular carcinoma (HCC) after surgery is poor due to its high recurrence rate. Oligonucleotide microarrays were used to analyze the gene expression of HCC patients with differing recurrent-free survival times following resection to see if gene expression can predict recurrence of HCC. Keywords: Disease states analysis
2009-04-24 | GSE11260 | GEO
Project description:Background: Local recurrence is the major manifestation of treatment failure in patients with operable laryngeal carcinoma. Established clinicopathological factors cannot sufficiently predict patients that are likely to recur after treatment. Additional tools are therefore required to accurately identify patients at high risk for recurrence. Methods: Using Affymetrix U133A Genechips, we profiled fresh-frozen tumor tissues from 59 patients with operable laryngeal cancer. All patients were treated locally with surgery, with or without radiation therapy. We performed Cox regression proportional hazards modeling to identify multigene predictors of recurrence. The end-point of our analysis was disease-free survival (DFS). Gene models were directly validated in a separate, similarly treated cohort of 50 patients using Affymetrix chips. In an attempt to further validate our results, we profiled 12 selected genes of our model in formalin-fixed tumor tissues from an independent cohort of 75 patients, using quantitative real time-polymerase chain reaction (qRT-PCR). Results: We focused on genes univariately associated with DFS (p<0.05) in the training set. Among several gene models comprising different numbers of genes, a 30-gene model demonstrated optimal performance (log-rank, p<0.001). We directly applied these gene models to the validation set, after adjusting for non-biological experimental variability, and observed similar results. Specifically, median DFS, as predicted by the 30-gene model, was 34 and 80 months for high- and low-risk patients, respectively (p=0.01). Hazard Ratio (HR) for recurrence for the high-risk group was 3.87 (95% CI 1.28-11.73, p=0.017). Furthermore, unsupervised hierarchical clustering of the 75 patients, based on the qRT-PCR 12-gene profile, yielded two groups, which differed significantly in DFS (log-rank, p=0.027). HR= for recurrence was 2.26, (95% CI 1.08-4.76, p=0.031). Conclusion: We have established and validated gene models that can successfully stratify patients with laryngeal cancer, based on their risk for recurrence. Thus, patients with unfavorable prognosis, when accurately identified, could be ideal candidates for the application of more aggressive treatment modalities. Training set comprises 59 samples and validation set 50 samples
2013-10-30 | E-GEOD-27020 | biostudies-arrayexpress
Project description:Background: Changes in DNA methylation patterns are a pivotal mechanism of carcinogenesis. In some tumors, aberrant methylation precedes genetic changes, while gene expression may be more frequently modified due to methylation alterations than by mutations. Methods: Herein, 128 serous ovarian tumors were analyzed, including borderline ovarian tumors (BOTS) with (BOT.V600E) and without (BOT) the BRAF V600E mutation, low-grade (lg), and high-grade (hg) ovarian cancers (OvCa). The methylome of the samples was profiled with Infinium MethylationEPIC microarrays. Results: The biggest number of differentially methylated (DM) CpGs and regions (DMRs) was found between lgOvCa and hgOvCa. By contrast, the BOT.V600E tumors had the lowest number of DM CpGs and DMRs compared to all other groups and, in relation to BOT, their genome was strongly downmethylated. Remarkably, the ten most significant DMRs, discriminating BOT from lgOvCa, encompassed the MHC region on chromosome 6. We also identified hundreds of DMRs, being of potential use as predictive biomarkers in BOTS and hgOvCa. DMRs with the best discriminative capabilities overlapped the following genes: BAIAP3, IL34, WNT10A, NEU1, SLC44A4, and HMOX1, TCN2, PES1, RP1-56J10.8, ABR, NCAM1, RP11-629G13.1, AC006372.4, NPTXR in BOTS and hgOvCa, respectively. Conclusions: The global genome-wide hypomethylation positively correlates with the increasing aggressiveness of ovarian tumors. We also assume that the immune system may play a pivotal role in the transition from BOTS to lgOvCa. Given that the BOT.V600E tumors had the lowest number of DM CpGs and DMRs compared to all other groups, when methylome is considered, such tumors might be placed in-between BOT and OvCa.
2024-10-18 | GSE267068 | GEO
Project description:We aimed to investigate the microbial community composition in patients with intracerebral hemorrhage (ICH) and its effect on prognosis. The relationship between changes in bacterial flora and the prognosis of spontaneous cerebral hemorrhage was studied in two cohort studies. Fecal samples from healthy volunteers and patients with intracerebral hemorrhage were subjected to 16S rRNA sequencing at three time points: T1 (within 24 hours of admission), T2 (3 days post-surgery), and T3 (7 days post-surgery) using Illumina high-throughput sequencing technology.
2024-08-22 | E-MTAB-14351 | biostudies-arrayexpress
Project description:Anatomical staging is a critical, although imperfect, instrument to assess gastric cancer prognosis and define indication for surgery and adjuvant therapy. Despite recent advances, treatment results, as a whole, remain less than satisfactory. Thus, biomarkers are sorely needed to improve risk categorization and define new molecular targets for therapy. We used microarrays to identify genes differentially expressed according to prognosis of the patients. A cohort of 15 patients was prospectively identified with a histological diagnosis of gastric adenocarcinoma, submitted to surgery with curative intent. Two prognostic patient groups were defined, according to overall survival (short x long), with a 24 month cut-off. These groups were matched by known prognostic factors: TNM staging, histological subtype (intestinal/diffuse) and gender.
2012-09-01 | E-GEOD-38749 | biostudies-arrayexpress
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
Project description:Background: Local recurrence is the major manifestation of treatment failure in patients with operable laryngeal carcinoma. Established clinicopathological factors cannot sufficiently predict patients that are likely to recur after treatment. Additional tools are therefore required to accurately identify patients at high risk for recurrence. Methods: Using Affymetrix U133A Genechips, we profiled fresh-frozen tumor tissues from 59 patients with operable laryngeal cancer. All patients were treated locally with surgery, with or without radiation therapy. We performed Cox regression proportional hazards modeling to identify multigene predictors of recurrence. The end-point of our analysis was disease-free survival (DFS). Gene models were directly validated in a separate, similarly treated cohort of 50 patients using Affymetrix chips. In an attempt to further validate our results, we profiled 12 selected genes of our model in formalin-fixed tumor tissues from an independent cohort of 75 patients, using quantitative real time-polymerase chain reaction (qRT-PCR). Results: We focused on genes univariately associated with DFS (p<0.05) in the training set. Among several gene models comprising different numbers of genes, a 30-gene model demonstrated optimal performance (log-rank, p<0.001). We directly applied these gene models to the validation set, after adjusting for non-biological experimental variability, and observed similar results. Specifically, median DFS, as predicted by the 30-gene model, was 34 and 80 months for high- and low-risk patients, respectively (p=0.01). Hazard Ratio (HR) for recurrence for the high-risk group was 3.87 (95% CI 1.28-11.73, p=0.017). Furthermore, unsupervised hierarchical clustering of the 75 patients, based on the qRT-PCR 12-gene profile, yielded two groups, which differed significantly in DFS (log-rank, p=0.027). HR= for recurrence was 2.26, (95% CI 1.08-4.76, p=0.031). Conclusion: We have established and validated gene models that can successfully stratify patients with laryngeal cancer, based on their risk for recurrence. Thus, patients with unfavorable prognosis, when accurately identified, could be ideal candidates for the application of more aggressive treatment modalities.
2013-10-30 | GSE27020 | GEO
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. 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.
2013-05-01 | E-GEOD-44001 | biostudies-arrayexpress
Project description:We developed a 33-gene signature that is strongly correlated to the time to recurrence in non-small cell lung cancer (NSCLC). The signature was validated retrospectively in 5 cohorts of 972 NSCLC patients and in one prospective study of 111 NSCLC Stage IA patients. In all cohorts, and all stages of the disease, the signature identified a rare, aggressive tumor type that had a high proportion of recurrence after surgery and a median survival of 35 months (95% C.I.: 19-58). This tumor type forms a separate cluster in an analysis of the expression of the 33 genes in patient tumors. The signature is associated with cellular processes required by rapidly growing and spreading tumors: cell migration and invasion, vascularization, and response to hypoxia. The signature also identifies patients with good prognosis (median survival 114 months, (95% C.I.: 85-160), and intermediate prognosis (median survival 61 months (95% C. I.: 50-73). The signature is quite robust and works on tumor samples archived in RNAlater, Tissue-Tek, or formalin-fixed and paraffin embedded. 156 samples -------------------------------- *** Submitter has not provided information such as time to recurrence. Thus, the data is incomplete.
2016-02-11 | E-GEOD-77803 | biostudies-arrayexpress
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