Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA110461 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Despite continual efforts to establish pre-operative prognostic model of gastric cancer by using clinical and pathological parameters, a staging system that reliably separates patients with early and advanced gastric cancer into homogeneous groups with respect to prognosis does not exist. With use of microarray and quantitative RT-PCR technologies, we exploited series of experiments in combination with complementary data analyses on tumor specimens from 161 gastric cancer patients. Various statistical analyses were applied to gene expression data to uncover subgroups of gastric cancer, to identify potential biomarkers associated with prognosis, and to construct molecular predictor of risk from identified prognostic biomarkers.Two subgroups of gastric cancer with strong association with prognosis were uncovered. The robustness of prognostic gene expression signature was validated in independent patient cohort with use of support vector machines prediction model. For easy translation of our finding to clinics, we develop scoring system based on expression of six genes that can predict the likelihood of recurrence after curative resection of tumors. In multivariate analysis, our novel risk score was an independent predictor of recurrence (P=0.004) in cohort of 96 patients, and its robustness was validated in two other independent cohorts. We identified novel prognostic subgroups of gastric cancer that are distinctive in gene expression patterns. Six-gene signature and risk score derived from them has been validated for predicting the likelihood of survival at diagnosis. 65 primary gastric adenocarcinoma, 6 GIST and 19 surrounding normal fresh frozen tissues were used for microarray. All the tissues were obtained after curative resection after pathologic confirm at Yonsei cancer center(Seoul, Korea). Microarray experiment and data analysis were done at Dept. of systems biology, MDACC DNA microarray (Illumina human V3)
2011-07-18 | E-GEOD-13861 | biostudies-arrayexpress
Project description:Despite continual efforts to establish pre-operative prognostic model of gastric cancer by using clinical and pathological parameters, a staging system that reliably separates patients with early and advanced gastric cancer into homogeneous groups with respect to prognosis does not exist. With use of microarray and quantitative RT-PCR technologies, we exploited series of experiments in combination with complementary data analyses on tumor specimens from 161 gastric cancer patients. Various statistical analyses were applied to gene expression data to uncover subgroups of gastric cancer, to identify potential biomarkers associated with prognosis, and to construct molecular predictor of risk from identified prognostic biomarkers.Two subgroups of gastric cancer with strong association with prognosis were uncovered. The robustness of prognostic gene expression signature was validated in independent patient cohort with use of support vector machines prediction model. For easy translation of our finding to clinics, we develop scoring system based on expression of six genes that can predict the likelihood of recurrence after curative resection of tumors. In multivariate analysis, our novel risk score was an independent predictor of recurrence (P=0.004) in cohort of 96 patients, and its robustness was validated in two other independent cohorts. We identified novel prognostic subgroups of gastric cancer that are distinctive in gene expression patterns. Six-gene signature and risk score derived from them has been validated for predicting the likelihood of survival at diagnosis.
2011-07-19 | GSE13861 | GEO
Project description:Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking. In this retrospective analysis, we use our machine learning algorithm NTriPath [Park, Sunho et al. “An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types.” Bioinformatics (2016): 1643-51. doi:10.1093/bioinformatics/btv692] to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identify four molecular subtypes that are prognostic for survival. We then built a support vector machine with linear kernel to generate a risk score that is prognostic for five-year overall survival and validate the risk score using three independent datasets. We also find that the molecular subtypes predict response to adjuvant 5-fluorouracil and platinum therapy after gastrectomy and to immune checkpoint inhibitors in patients with metastatic or recurrent disease. In sum, we show that the 32-gene signature is a promising prognostic and predictive biomarker to guide the clinical care of gastric cancer patients and should be validated in a prospective manner.
2022-01-01 | GSE183136 | GEO
Project description:Gastric cancer is one of the leading causes of cancer mortality worldwide. We compared transcriptomic profiles of gastric cancer with different ferroptosis-related-scores to identify the prognostic significance of ferroptosis-related-score in gastric cancer.
2021-11-29 | GSE184336 | GEO
Project description:Gastric cancer is one of the leading causes of cancer mortality worldwide. We compared transcriptomic profiles of advanced gastric cancer with different tumour-stroma-scores to identify the prognostic significance of tumour-stroma-score in advanced gastric cancer.
2021-11-29 | GSE179252 | GEO
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
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 | GSE208117 | GEO
Project description:Background Bladder cancer represents a heterogeneous disease with distinct clinical challenges. Non-muscle invasive bladder cancer (NMIBC) typically presents as indolent and slow-growing, yet a critical clinical challenge remains: identifying which patients will progress to muscle-invasive disease requiring radical interventions. Early detection of progression propensity is essential, as once muscle invasion occurs, the risk of distant metastasis increases substantially, and treatment shifts from conservative TURBT (Transurethral Resection of Bladder Tumor) to aggressive surgical interventions with significant morbidity. Current risk stratification methods fail to adequately predict this transition in approximately 30% of cases, highlighting the urgent need for more accurate prognostic tools. Objective This retrospective study aimed to develop and validate a transcriptomics-based mRNA score for predicting early NMIBC recurrence, comparing its performance against traditional risk stratification methods. Methods We analyzed mRNA expression profiles from primary retrospective NMIBC tumor specimens (n=25) collected between [2018-2022]. Traditional risk stratification tools, including EORTC scoring, were applied alongside our novel mRNA-based risk score to evaluate predictive accuracy for recurrence. Results The transcriptomics-based mRNA score demonstrated a median prediction accuracy of 90% across 10,000 resampling iterations for predicting early NMIBC recurrence, significantly outperforming traditional EORTC risk scores. Our comprehensive gene set identified 435 differentially expressed genes associated with recurrence. Kaplan-Meier analysis showed significantly different recurrence-free survival between high and low mRNA risk score groups (Bonferroni corrected p-value<0.0001). Conclusions This retrospective analysis confirms that mRNA expression-based risk stratification provides superior predictive accuracy compared to conventional clinicopathologic risk tools. Implementation of this gene signature could potentially reduce over-investigation and improve surveillance cost-effectiveness after TURBT in patients with primary high-risk NMIBC. These findings may transform the clinical management paradigm by enabling more personalized follow-up protocols based on molecular risk assessment.
2025-05-22 | GSE295809 | GEO
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
Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models.
I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.
2024-07-23 | MODEL2407230001 | BioModels