Dataset Information

A 3-MicroRNA Signature Identified From Serum Predicts Clinical Outcome of the Locally Advanced Gastric Cancer.

ABSTRACT: Background: Current staging systems are inadequate for evaluating the prognosis of patients with locally advanced gastric cancer (LAGC, stages II-III). Therefore, we developed a serum microRNA (miRNA) signature to facilitate individualized management of these patients. Methods: Using microarray analysis, we analyzed 12 serum specimens based on different prognoses (good survival group, n = 7; poor survival group, n = 5). We identified and confirmed differential expression of these miRNAs using quantitative reverse transcription PCR (qRT-PCR) of serum from 51 patients with LAGC. A three miRNA-based classifier was established as a training set by Cox proportional hazard regression and risk-score analysis. We validated the prognostic accuracy of this model in an internal validation cohort (Sun Yat-Sen University Cancer Center, SYSUCC validation cohort, n = 50) and an external independent cohort (Beijing Cancer Hospital, BJCH cohort, n = 67). Results: Three miRNAs were found to be associated with survival of LAGC (P < 0.001 for miR-132, P = 0.011 for miR-548a-3p, and P < 0.001 for miR-1826). A three-miRNA signature was developed for the training set, and a significant difference was found between the survival of low- and high-risk score patients (P < 0.01). The combination of the miRNA signature and tumor-node-metastasis (TNM) stage exhibited superior discrimination. Consistent results were obtained by further validation of the internal set and the BJCH set, which confirmed the predictive value of the model. Conclusions: We built an easy-to-use prognostic signature using three serum miRNAs as markers. Our miRNA signature may improve postoperative risk stratification and serve as a complement to the TNM staging system.

SUBMITTER: Chen S

PROVIDER: S-EPMC7323914 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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A 3-MicroRNA Signature Identified From Serum Predicts Clinical Outcome of the Locally Advanced Gastric Cancer.

Chen Shangxiang S Lao Jiawen J Geng Qirong Q Zhang Ji J Wu Aiwen A Xu Dazhi D

Frontiers in oncology 20200619

Background: Current staging systems are inadequate for evaluating the prognosis of patients with locally advanced gastric cancer (LAGC, stages II-III). Therefore, we developed a serum microRNA (miRNA) signature to facilitate individualized management of these patients. Methods: Using microarray analysis, we analyzed 12 serum specimens based on different prognoses (good survival group, n = 7; poor survival group, n = 5). We identified and confirmed differential express ...[more]

PMID: 32656071

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Project description:PURPOSE:To assess the utility of circulating serum microRNAs (c-miRNAs) to predict response to high-dose radiation therapy for locally advanced non-small cell lung cancer (NSCLC). METHODS AND MATERIALS:Data from 80 patients treated from 2004 to 2013 with definitive standard- or high-dose radiation therapy for stages II-III NSCLC as part of 4 prospective institutional clinical trials were evaluated. Pretreatment serum levels of 62 miRNAs were measured by quantitative reverse transcription-polymerase chain reaction array. We combined miRNA data and clinical factors to generate a dose-response score (DRS) for predicting overall survival (OS) after high-dose versus standard-dose radiation therapy. Elastic net Cox regression was used for variable selection and parameter estimation. Model assessment and tuning parameter selection were performed through full cross-validation. The DRS was also correlated with local progression, distant metastasis, and grade 3 or higher cardiac toxicity using Cox regression, and grade 2 or higher esophageal and pulmonary toxicity using logistic regression. RESULTS:Eleven predictive miRNAs were combined with clinical factors to generate a DRS for each patient. In patients with low DRS, high-dose radiation therapy was associated with significantly improved OS compared to treatment with standard-dose radiation therapy (hazard ratio 0.22). In these patients, high-dose radiation also conferred lower risk of distant metastasis and local progression, although the latter association was not statistically significant. Patients with high DRS exhibited similar rates of OS regardless of dose (hazard ratio 0.78). The DRS did not correlate with treatment-related toxicity. CONCLUSIONS:Using c-miRNA signature and clinical factors, we developed a DRS that identified a subset of patients with locally advanced NSCLC who derive an OS benefit from high-dose radiation therapy. This DRS may guide dose escalation in a patient-specific manner.

Project description:BackgroundDepression and long non-coding RNA (lncRNA) have been reported to be associated with tumor progression and prognosis in gastric cancer (GC). This study aims to build a GC risk classification and prognosis model based on depression-related lncRNAs (DRLs).MethodsTo develop a risk model, we performed univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses using RNA sequencing data of GC from The Cancer Genome Atlas (TCGA) and depression-related genes (DRGs) from previous studies. Kaplan-Meier analysis, receiver operating characteristic (ROC) curve analysis, nomogram construction, pathway enrichment analysis, assessment of immunological features, and drug sensitivity testing were conducted using a series of bioinformatics methods.ResultsSeven DRLs were identified to build a prognostic model, whose robustness was verified in an internal cohort. Subsequent prognostic analyses found that high risk scores were associated with worse overall survival (OS). Univariate and multivariate analyses revealed that the risk score could be used as an independent prognostic factor. Furthermore, the ROC curve indicated that the risk score had higher diagnostic efficiency than traditional clinicopathological features. The calibration curve confirmed the accuracy and reliability of the nomogram. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that there were differences in digestive system and nervous system-related pathways between the high- and low-risk groups. Results of tumor mutational burden (TMB) and tumor immune dysfunction and exclusion (TIDE) analyses indicated that patients in the low-risk group had a better response rate to immunotherapy. Finally, the results of drug sensitivity analysis showed that risk score could influence sensitivity to EHT 1864 in GC.ConclusionWe have successfully developed and verified a 7-DRL risk model which can assess the prognosis and immunological features and guide individualized therapy of GC patients.

Project description:BackgroundOne of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasets having 295, 286, and 118 samples, respectively.Methods and findingsOur results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER- patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome.ConclusionsThe 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds prognostic value for both ER-positive and ER-negative breast cancer. The signature was selected using a novel biological approach and hence holds promise to represent the key biological processes of breast cancer.

Dataset Information

A 3-MicroRNA Signature Identified From Serum Predicts Clinical Outcome of the Locally Advanced Gastric Cancer.

Publications

A 3-MicroRNA Signature Identified From Serum Predicts Clinical Outcome of the Locally Advanced Gastric Cancer.

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