Dataset Information

Top-ranked expressed gene transcripts of human protein-coding genes investigated with GTEx dataset.

ABSTRACT: With considerable accumulation of RNA-Seq transcriptome data, we have extended our understanding about protein-coding gene transcript compositions. However, alternatively compounded patterns of human protein-coding gene transcripts would complicate gene expression data processing and interpretation. It is essential to exhaustively interrogate complex mRNA isoforms of protein-coding genes with an unified data resource. In order to investigate representative mRNA transcript isoforms to be utilized as transcriptome analysis references, we utilized GTEx data to establish a top-ranked transcript isoform expression data resource for human protein-coding genes. Distinctive tissue specific expression profiles and modulations could be observed for individual top-ranked transcripts of protein-coding genes. Protein-coding transcripts or genes do occupy much higher expression fraction in transcriptome data. In addition, top-ranked transcripts are the dominantly expressed ones in various normal tissues. Intriguingly, some of the top-ranked transcripts are noncoding splicing isoforms, which imply diverse gene regulation mechanisms. Comprehensive investigation on the tissue expression patterns of top-ranked transcript isoforms is crucial. Thus, we established a web tool to examine top-ranked transcript isoforms in various human normal tissue types, which provides concise transcript information and easy-to-use graphical user interfaces. Investigation of top-ranked transcript isoforms would contribute understanding on the functional significance of distinctive alternatively spliced transcript isoforms.

SUBMITTER: Tung KF

PROVIDER: S-EPMC7530651 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Top-ranked expressed gene transcripts of human protein-coding genes investigated with GTEx dataset.

Tung Kuo-Feng KF Pan Chao-Yu CY Chen Chao-Hsin CH Lin Wen-Chang WC

Scientific reports 20201001 1

With considerable accumulation of RNA-Seq transcriptome data, we have extended our understanding about protein-coding gene transcript compositions. However, alternatively compounded patterns of human protein-coding gene transcripts would complicate gene expression data processing and interpretation. It is essential to exhaustively interrogate complex mRNA isoforms of protein-coding genes with an unified data resource. In order to investigate representative mRNA transcript isoforms to be utilized ...[more]

PMID: 33004865

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Project description:Importance:Hospital networks formed around top-ranked cancer hospitals represent an opportunity to optimize complex cancer care in the community. Objective:To compare the short- and long-term survival after complex cancer treatment at top-ranked cancer hospitals and the affiliates of top-ranked hospitals. Design, Setting, and Participants:This cohort study was conducted using data from the unabridged version of the National Cancer Database. Included patients were individuals 18 years or older who underwent surgical treatment for esophageal, gastric, lung, pancreatic, colorectal, or bladder cancer diagnosed between January 1, 2012, and December 31, 2016. Patient outcomes after complex surgical procedures for cancer at top-ranked cancer hospitals (as ranked in top 50 by US News and World Report) were compared with outcomes at affiliates of top-ranked cancer hospitals (affiliation listed in American Hospitals Association survey and confirmed by search of internet presence). Data were analyzed from July through December 2019. Exposures:Undergoing complex cancer treatment at a top-ranked cancer hospital or an affiliated hospital. Main Outcomes and Measures:The association of affiliate status with short-term survival (ie, 90-day mortality) was compared using logistic regression, and the association of affiliate status with long-term survival was compared using time-to-event models, adjusting for patient demographic, payer, clinical, and treatment factors. Results:Among 119?834 patients who underwent surgical treatment for cancer, 79?981 patients (66.7%) were treated at top-ranked cancer hospitals (median [interquartile range] age, 66 [58-74] years; 40?910 [54.9%] men) and 39?853 patients (33.3%) were treated at affiliate hospitals (median [interquartile range] age, 69 [60-77] years; 19?004 [50.0%] men). In a pooled analysis of all cancer types, adjusted perioperative mortality within 90 days of surgical treatment was higher at affiliate hospitals compared with top-ranked hospitals (odds ratio, 1.67 [95% CI, 1.49-1.89]; P?<?.001). Adjusted long-term survival following cancer treatment at affiliate hospitals was only 77% that of top-ranked hospitals (time ratio, 0.77 [95% CI, 0.72-0.83]; P?<?.001). The survival advantage was not fully explained by differences in annual surgical volume, with both long- and short-term survival remaining superior at top-ranked hospitals even after models were adjusted for volume. Conclusions and Relevance:These findings suggest that short- and long-term survival after complex cancer treatment were superior at top-ranked hospitals compared with affiliates of top-ranked hospitals. Further study of cancer care within top-ranked cancer networks could reveal collaborative opportunities to improve survival across a broad contingent of the US population.

Project description:ImportanceLeading cancer hospitals have increasingly shared their brands with other hospitals through growing networks of affiliations. However, the brand of top-ranked cancer hospitals may evoke distinct reputations for safety and quality that do not extend to all hospitals within these networks.ObjectiveTo assess perioperative mortality of Medicare beneficiaries after complex cancer surgery across hospitals participating in networks with top-ranked cancer hospitals.Design, setting, and participantsA cross-sectional study was performed of the Centers for Medicare & Medicaid Services 100% Medicare Provider and Analysis Review file from January 1, 2013, to December 31, 2016, for top-ranked cancer hospitals (as assessed by U.S. News and World Report) and affiliated hospitals that share their brand. Participants were 29 228 Medicare beneficiaries older than 65 years who underwent complex cancer surgery (lobectomy, esophagectomy, gastrectomy, colectomy, and pancreaticoduodenectomy [Whipple procedure]) between January 1, 2013, and October 1, 2016.ExposuresUndergoing complex cancer surgery at a top-ranked cancer hospital vs an affiliated hospital.Main outcomes and measuresRisk-adjusted 90-day mortality estimated using hierarchical logistic regression and comparison of the relative safety of hospitals within each cancer network estimated using standardized mortality ratios.ResultsA total of 17 300 patients (59.2%; 8612 women and 8688 men; mean [SD] age, 74.7 [6.2] years) underwent complex cancer surgery at 59 top-ranked hospitals and 11 928 patients (40.8%; 6287 women and 5641 men; mean [SD] age, 76.2 [6.9] years) underwent complex cancer surgery at 343 affiliated hospitals. Overall, surgery performed at affiliated hospitals was associated with higher 90-day mortality (odds ratio, 1.40; 95% CI, 1.23-1.59; P < .001), with odds ratios that ranged from 1.32 (95% CI, 1.12-1.56; P = .001) for colectomy to 2.04 (95% CI, 1.41-2.95; P < .001) for gastrectomy. When the relative safety of each top-ranked cancer hospital was compared with its collective affiliates, the top-ranked hospital was safer than the affiliates in 41 of 49 studied networks (83.7%; 95% CI, 73.1%-93.3%).Conclusions and relevanceThe likelihood of surviving complex cancer surgery appears to be greater at top-ranked cancer hospitals compared with the affiliated hospitals that share their brand. Further investigation of performance across trusted cancer networks could enhance informed decision making for complex cancer care.

Dataset Information

Top-ranked expressed gene transcripts of human protein-coding genes investigated with GTEx dataset.

Publications

Top-ranked expressed gene transcripts of human protein-coding genes investigated with GTEx dataset.

Similar Datasets

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