Dataset Information

Identification of Prognostic and Chemopredictive microRNAs for Non-Small-Cell Lung Cancer by Integrating SEER-Medicare Data.

ABSTRACT: This study developed a novel methodology to correlate genome-scale microRNA (miRNA) expression profiles in a lung squamous cell carcinoma (LUSC) cohort (n = 57) with Surveillance, Epidemiology, and End Results (SEER)-Medicare LUSC patients (n = 33,897) as a function of composite tumor progression indicators of T, N, and M cancer stage and tumor grade. The selected prognostic and chemopredictive miRNAs were extensively validated with miRNA expression profiles of non-small-cell lung cancer (NSCLC) patient samples collected from US hospitals (n = 156) and public consortia including NCI-60, The Cancer Genome Atlas (TCGA; n = 1016), and Cancer Cell Line Encyclopedia (CCLE; n = 117). Hsa-miR-142-3p was associated with good prognosis and chemosensitivity in all the studied datasets. Hsa-miRNA-142-3p target genes (NUP205, RAN, CSE1L, SNRPD1, RPS11, SF3B1, COPA, ARCN1, and SNRNP200) had a significant impact on proliferation in 100% of the tested NSCLC cell lines in CRISPR-Cas9 (n = 78) and RNA interference (RNAi) screening (n = 92). Hsa-miR-142-3p-mediated pathways and functional networks in NSCLC short-term survivors were elucidated. Overall, the approach integrating SEER-Medicare data with comprehensive external validation can identify miRNAs with consistent expression patterns in tumor progression, with potential implications for prognosis and prediction of chemoresponse in large NSCLC patient populations.

SUBMITTER: Ye Q

PROVIDER: S-EPMC8306800 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of Prognostic and Chemopredictive microRNAs for Non-Small-Cell Lung Cancer by Integrating SEER-Medicare Data.

Ye Qing Q Putila Joseph J Raese Rebecca R Dong Chunlin C Qian Yong Y Dowlati Afshin A Guo Nancy Lan NL

International journal of molecular sciences 20210717 14

This study developed a novel methodology to correlate genome-scale microRNA (miRNA) expression profiles in a lung squamous cell carcinoma (LUSC) cohort (<i>n</i> = 57) with Surveillance, Epidemiology, and End Results (SEER)-Medicare LUSC patients (<i>n</i> = 33,897) as a function of composite tumor progression indicators of T, N, and M cancer stage and tumor grade. The selected prognostic and chemopredictive miRNAs were extensively validated with miRNA expression profiles of non-small-cell lung ...[more]

PMID: 34299277

Similar Datasets

Project description:BackgroundAs biomarkers, microRNAs (miRNAs) are closely associated with the occurrence, progression, and prognosis of non-small cell lung cancer (NSCLC). However, the prognostic predictive value of miRNAs in NSCLC has rarely been explored. In this study, the value in prognosis prediction of NSCLC was mined based on data mining models using clinical data and plasma miRNAs biomarkers.MethodsA total of 69 patients were included in this prospective cohort study. After informed consent, they filled out questionnaires and had their peripheral blood collected. The expressions of plasma miRNAs were examined by quantitative polymerase chain reaction (qPCR). The Whitney U test was used to analyze non-normally distributed data. Kaplan-Meier was used to plot the survival curve, the log-rank test was used to compare with the overall survival curve, and the Cox proportional hazards model was used to screen the factors related to the prognosis of lung cancer. Data mining techniques were utilized to predict the prognostic status of patients.ResultsWe identified that smoking (HR = 2.406, 95% CI = 1.256-4.611), clinical stage III + IV (HR = 5.389, 95% CI = 2.290-12.684), the high expression group of miR-20a (HR = 4.420, 95% CI = 1.760-11.100), the high expression group of miR-197 (HR = 3.828, 95% CI = 1.778-8.245), the low expression group of miR-145 ( HR = 0.286, 95% CI = 0.116-0.709), and the low expression group of miR-30a (HR = 0.307, 95% CI = 0.133-0.706) was associated with worse prognosis. Among the five data mining models, the decision trees (DT) C5.0 model performs the best, with accuracy and Area Under Curve (AUC) of 93.75% and 0.929 (0.685, 0.997), respectively.ConclusionThe results showed that the high expression level of miR-20a and miR-197, the low expression level of miR-145 and miR-30a were strongly associated with poorer prognosis in NSCLC patients, and the DT C5.0 model may serve as a novel, accurate, method for predicting prognosis of NSCLC.

Project description:PurposeDisparities in access to a multidisciplinary cancer consultation (MDCc) persist, and the role of physician relationships remains understudied. This study examined the extent to which multilevel factors, including patient characteristics and patient-sharing network measures reflecting the structure of physician relationships, are associated with an MDCc and receipt of stereotactic body radiation therapy versus surgery among patients with early-stage non-small cell lung cancer (NSCLC).Methods and materialsIn this cross-sectional study, we analyzed Surveillance, Epidemiology, and End Results (SEER)-Medicare data for patients diagnosed with stage I-IIA NSCLC from 2016 to 2017. We assembled patient-sharing networks and identified cancer specialists who were locally unique for their specialty, herein referred to as linchpins. The proportion of linchpin cancer specialists for each hospital referral region (HRR) was calculated as a network-based measure of specialist scarcity. We used multilevel multinomial logistic regression to estimate associations between study variables and receipt of an MDCc and multilevel logistic regression to examine the relationship between patient receipt of an MDCc and initial treatment.ResultsOur study included 6120 patients with stage I-IIA NSCLC, of whom 751 (12.3%) received an MDCc, 1729 (28.3%) consulted only a radiation oncologist, 2010 (32.8%) consulted only a surgeon, and 1630 (26.6%) consulted neither specialist within 2 months of diagnosis. Compared with patients residing in an HRR with a low proportion of linchpin surgeons, those residing in an HRR with a high proportion of linchpin surgeons had a 2.99 (95% CI, 1.87-4.78) greater relative risk of consulting only a radiation oncologist versus receiving an MDCc and a 2.70 (95% CI, 1.68-4.35) greater relative risk of consulting neither specialist versus receiving an MDCc. Patients who received an MDCc were 5.32 times (95% CI, 4.27-6.63) more likely to receive stereotactic body radiation therapy versus surgery.ConclusionsPhysician networks are associated with receipt of an MDCc and treatment, underscoring the potential for leveraging patient-sharing network analysis to improve access to lung cancer care.

Project description:BackgroundTo explore the prognostic factors of survival in non-small cell lung cancer (NSCLC) patients using the competing risk analysis.MethodsThis was a retrospective cohort study. NSCLC patients with complete data were selected from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. Outcomes were censored, cancer-specific mortality in NSCLC, and other-cause mortality. Gray's test was used in univariable analysis, and a multivariable Fine-Gray competing risk model with backward elimination was used to explore the prognostic factors of survival. The screened variables were incorporated into a random survival forest (RSF) model for the prediction of 1-, 3-, and 5-year survival in patients with NSCLC. Receiver operator characteristic (ROC) curves, calibration curves, the value of area under the curve (AUC), and decision curve analysis (DCA) were used to evaluate the performance.ResultsTotally 1,251 eligible patients were included, 678 (54.20%) patients were cancer-specific mortality, and 128 (10.23%) patients were other-cause mortality. The median follow-up time was 26 months. Age, primary site, N stage, M stage, surgery type, tumor size, and lymph nodes (LNs) count were included in the multivariable Fine-Gray model for further analysis (P<0.05). The six most important features (surgery type, tumor size, M stage, LNs count, N stage, and primary site) were included in the competing risk analysis using the RSF model. The value of AUC for predicting 1-, 3-, and 5-year survival in the testing set were 0.796, 0.804, and 0.792, respectively. Calibration curves were well-fitted. DCA curves showed that the RSF model had similar or greater clinical net benefits in survival compared with the 8th edition the American Joint Committee on Cancer (AJCC) staging. The good performance of the RSF model under different surgery types, T, N, and M stages.ConclusionsWe conducted a competing risk analysis using the RSF model for predicting the 1-, 3-, and 5-year survival of NSCLC. We generated a web calculator (https://github.com/YingChen19/Prognostic-factors-of-long-term-survival-of-non-small-cell-lung-cancer), which could provide a convenient assessment and could help improve the prognosis and survival of NSCLC.

Dataset Information

Identification of Prognostic and Chemopredictive microRNAs for Non-Small-Cell Lung Cancer by Integrating SEER-Medicare Data.

Publications

Identification of Prognostic and Chemopredictive microRNAs for Non-Small-Cell Lung Cancer by Integrating SEER-Medicare Data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets