A microfluidic cell-migration assay for the prediction of progression-free survival and recurrence time of patients with glioblastoma.
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ABSTRACT:
SUBMITTER: Wong BS
PROVIDER: S-EPMC7855796 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Despite surgical treatment of stage I non-small cell lung cancer (NSCLC), one third of patients will eventually have a recurrence. Robust prognostic markers are required to better manage therapy options. MicroRNAs play important roles in human cancers. The purpose of this study is to identify miRNA expression profiles that would better predict prognosis. Small RNAs extracted from formalin-fixed and paraffin-embedded (FFPE) tumor tissues of 357 stage I NSCLC patients were profiled on the human MicroRNA expression profiling V2 panel (Illumina). The expression differences between cancer subtypes were compared by t tests. The association of miRNA expression profile with recurrence free survival (RFS) was assessed using partial Cox regression models. Two miRNA signatures that are highly predictive of RFS were identified. The first contained 32 miRNAs derived from 357 stage I NSCLC patients independent of cancer subtype; while the second containing 27 miRNAs was adenocarcinoma specific. Both of them were validated using FFPE and/or fresh frozen tissues in independent data set with 170 stage I patients. This has important prognostic or therapeutic implications for the management of patients. The identified miRNAs hold great potential as targets for histology-specific treatment or prevention and treatment of recurrent disease.
2012-03-27 | GSE29135 | GEO
Project description:Background: One of the main fields of lung cancer research is identifying patients who are at high risk of post-resection recurrence. Individual recurrence risk evaluation by accurate but simple and reproducible method is needed for the clinical practice. Results: The log-rank test and further selection by our criteria of assayability generated 87 genes from microarray data with significant level 5%. Of these, by PTQ-PCR, the expression of most significant 18 genes was obtained. Using these gene expression information and clinical parameters, by stepwise variable selection method, the recurrence prediction model, which composed of 6 genes (CALB1, MMP7, SLC1A7, GSTA1, CCL19, IFI44) and pStage and cell differentiation, were developed. Validation into the two independent cohorts showed good results of the proposed model (p=0.0314, 0.0305, respectively). The predicted median recurrence-free survival times for each patient were reflected real ones well. Conclusions: Our method of individualized recurrence risk prediction is accurate, technically simple and reproducible to be used in clinical practice. Therefore, it would be useful in customizing the lung cancer management strategies. Keywords: Recurrence Free Survival Analysis
2007-11-20 | GSE8894 | GEO
Project description:Despite surgical treatment of stage I non-small cell lung cancer (NSCLC), one third of patients will eventually have a recurrence. Robust prognostic markers are required to better manage therapy options. MicroRNAs play important roles in human cancers. The purpose of this study is to identify miRNA expression profiles that would better predict prognosis. Small RNAs extracted from formalin-fixed and paraffin-embedded (FFPE) tumor tissues of 357 stage I NSCLC patients were profiled on the human MicroRNA expression profiling V2 panel (Illumina). The expression differences between cancer subtypes were compared by t tests. The association of miRNA expression profile with recurrence free survival (RFS) was assessed using partial Cox regression models. Two miRNA signatures that are highly predictive of RFS were identified. The first contained 32 miRNAs derived from 357 stage I NSCLC patients independent of cancer subtype; while the second containing 27 miRNAs was adenocarcinoma specific. Both of them were validated using FFPE and/or fresh frozen tissues in independent data set with 170 stage I patients. This has important prognostic or therapeutic implications for the management of patients. The identified miRNAs hold great potential as targets for histology-specific treatment or prevention and treatment of recurrent disease. Small RNAs extracted from formalin-fixed and paraffin-embedded (FFPE) tumor tissues of 357 stage I NSCLC patients were profiled on the human MicroRNA expression profiling V2 panel (Illumina).
2012-03-26 | E-GEOD-29135 | biostudies-arrayexpress
Project description:Accurate prediction of non-small cell lung cancer (NSCLC) prognosis after surgery remains challenging. The Cox proportional hazard (PH) model is widely used, however, there are some limitations associated with it. In this study, we developed novel neural network models called binned time survival analysis (DeepBTS) models using 30 clinico-pathological features of surgically resected NSCLC patients (training cohort, n?=?1,022; external validation cohort, n?=?298). We employed the root-mean-square error (in the supervised learning model, s- DeepBTS) or negative log-likelihood (in the semi-unsupervised learning model, su-DeepBTS) as the loss function. The su-DeepBTS algorithm achieved better performance (C-index?=?0.7306; AUC?=?0.7677) than the other models (Cox PH: C-index?=?0.7048 and AUC?=?0.7390; s-DeepBTS: C-index?=?0.7126 and AUC?=?0.7420). The top 14 features were selected using su-DeepBTS model as a selector and could distinguish the low- and high-risk groups in the training cohort (p?=?1.86?×?10-11) and validation cohort (p?=?1.04?×?10-10). When trained with the optimal feature set for each model, the su-DeepBTS model could predict the prognoses of NSCLC better than the traditional model, especially in stage I patients. Follow-up studies using combined radiological, pathological imaging, and genomic data to enhance the performance of our model are ongoing.
| S-EPMC7005286 | biostudies-literature
Project description:Glioblastoma (GBM) represents 80% of all primary malignant brain tumours in adults. Prognosis is poor, and there is a clear correlation between disease progression and deterioration in functional status. In this pilot study we assess whether we can estimate disease progression and progression free survival (PFS) from routinely collected electronic healthcare data. We identified fifty patients with glioblastoma who had chemo-radiotherapy. For each patient we manually collected a reference data set recording demographics, surgery, radiotherapy, chemotherapy, follow-up and death. We also obtained an electronic routine data set for each patient by combining local data on chemotherapy/radiotherapy and hospital admissions. We calculated overall survival (OS) and PFS using the reference data set, and estimated them using the routine data sets using two different methods, and compared the estimated measures with the reference measures. Overall survival was 68% at 1 year and median OS was 12.8 months. The routine data correctly identified progressive disease in 37 of 40 patients and stable disease in 7 of 10 patients. PFS was 7.4 months and the estimated PFS using routine data was 9.1 and 7.8 months with methods 1 and 2 respectively. There was acceptable agreement between reference and routine data in 49 of 50 patients for OS and 35 of 50 patients for PFS. The event of progression, subsequent treatment and OS are well estimated using our approach, but PFS estimation is less accurate. Our approach could refine our understanding of the disease course and allow us to report PFS, OS and treatment nationally.
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Project description:To identify and evaluate the prognostic ability of progression-free survival-related profile for advanced-stage ovarian cancer, advanced-stage serous ovarian cancer tissues from 110 patients who received primary surgery and a platinum/taxane-based chemotherapy were profiled using oligonucleotide microarrays of more than 40,000 transcripts. We first selected 88 genes by a univariate Cox proportional hazard analysis (p<0.01) and next optimized regression coefficients by ridge regression model using 10-fold cross-validation. The prognostic index was independently associated with PFS time compared to other clinical factors in multivariate analysis [hazard ratio (HR), 3.72; 95% confidence interval (CI), 2.66–5.43; p,0.0001]. In an external dataset, multivariate analysis revealed that this prognostic index was significantly correlated with PFS time (HR, 1.54; 95% CI, 1.20–1.98; p = 0.0008). Furthermore, the correlation between the prognostic index and overall survival time was confirmed in the two independent external datasets (log rank test, p = 0.0010 and 0.0008). In multivariable analysis, our prognostic index was independently associated with progression-free survival times compared to other clinical factors (p<0.001). Furthermore, the prognostic ability of our prognostic index was validated in external, publicly available dataset (n = 87), and was proved in multivariate analysis (p = 0.0032). These results suggest that disease progression or recurrence of advanced-stage serous ovarian cancer can be predicted by gene expression profile. The prognostic ability of our index based on the 88-gene expression profile in ridge regression Cox hazard model was shown to be independent of other clinical factors in predicting cancer prognosis across two distinct datasets. Further study will be necessary to improve predictive accuracy of the prognostic index toward clinical application for evaluation of the risk of recurrence in patients with advanced-stage serous ovarian cancer.
2010-03-22 | GSE17260 | GEO
Project description:Despite surgical treatment of stage I non-small cell lung cancer (NSCLC), one third of patients will eventually have a recurrence. Robust prognostic markers are required to better manage therapy options. MicroRNAs play important roles in human cancers. The purpose of this study is to identify miRNA expression profiles that would better predict prognosis. Small RNAs extracted from formalin-fixed and paraffin-embedded (FFPE) tumor tissues of 357 stage I NSCLC patients were profiled on the human MicroRNA expression profiling V2 panel (Illumina). The expression differences between cancer subtypes were compared by t tests. The association of miRNA expression profile with recurrence free survival (RFS) was assessed using partial Cox regression models. Two miRNA signatures that are highly predictive of RFS were identified. The first contained 32 miRNAs derived from 357 stage I NSCLC patients independent of cancer subtype; while the second containing 27 miRNAs was adenocarcinoma specific. Both of them were validated using FFPE and/or fresh frozen tissues in independent data set with 170 stage I patients. This has important prognostic or therapeutic implications for the management of patients. The identified miRNAs hold great potential as targets for histology-specific treatment or prevention and treatment of recurrent disease. Small RNAs extracted from formalin-fixed and paraffin-embedded (FFPE) tumor tissues of 357 stage I NSCLC patients were profiled on the human MicroRNA expression profiling V2 panel (Illumina).
| S-ECPF-GEOD-29135 | biostudies-other
Project description:ObjectiveGlioblastoma (GBM) is one of the most aggressive brain tumors and often leads to poor outcomes. Studies have indicated that glycan levels are significantly correlated with the pathogenesis and development of cancers. However, whether glycan levels can serve as diagnostic or prognostic biomarkers in GBM remains unclear.MethodsWe obtained glycomic profiles in tissue and serum samples from 55 individuals with GBM using a well-established lectin biochip platform probing with 11 specific lectins.ResultsOur univariate analysis showed that 5 out of the 11 lectin-probed glycans (LPGs) were significantly higher in GBM tissues than in peri-tumoral tissues. After logistic regression analyses, only the Jacalin-probed T-antigen difference between the two groups remained significant (p = 0.037). Moreover, survival-related analyses showed that the level of Jacalin-probed T-antigen was significantly associated with the progression-free survival (p = 0.038) of patients. However, none of the LPG levels were correlated with the overall survival or the chemosensitivity to temozolomide therapy. The correlation coefficient analysis showed a moderate-to-strong correlation in the Jacalin-probed T-antigen levels between GBM tissues and serum samples, indicating its potential usefulness as a non-invasive GBM progression biomarker.InterpretationGlycomics analyses can be helpful in the prediction of GBM recurrences and may provide information useful for GBM glycan-based target therapies or vaccine development.
| S-EPMC11251471 | biostudies-literature
Project description:About 30% stage I non-small cell lung cancer (NSCLC) patients undergoing resection will recur. Robust prognostic markers are required to better manage therapy options. MicroRNAs (miRNAs) are a class of small non-coding RNAs of 19-25 nt and play important roles in gene regulation in human cancers. The purpose of this study is to identify miRNA expression profiles that would better predict prognosis of stage I NSCLC. MiRNAs extracted from 527 stage I NSCLC patients were profiled on the human miRNA expression profiling v2 panel (Illumina). The expression profiles were analyzed for their association with cancer subtypes, lung cancer brain metastasis and recurrence/relapse free survival (RFS). MiRNA expression patterns between lung adenocarcinoma and squamous cell carcinoma differed significantly with 171 miRNAs, including Let-7 family members and miR-205. Ten miRNAs associated with brain metastasis were identified including miR-145*, which inhibit cell invasion and metastasis. Two miRNA signatures that are highly predictive of RFS were identified. The first contained 34 miRNAs derived from 357 stage I NSCLC patients independent of cancer subtype, whereas the second containing 27 miRNAs was adenocarcinoma specific. Both signatures were validated using formalin-fixed paraffin embedded and/or fresh frozen tissues in independent data set with 170 stage I patients. Our findings have important prognostic or therapeutic implications for the management of stage I lung cancer patients. The identified miRNAs hold great potential as targets for histology-specific treatment or prevention and treatment of recurrent disease.
| S-EPMC3334512 | biostudies-literature