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pmid:33028275
Prognosis prediction model based on competing endogenous RNAs for recurrence of colon adenocarcinoma.
Li Peng Jin · Tao Liu · Fan Qi Meng · Jian Dong Tai
BMC cancer · 2020 doi:10.1186/s12885-020-07163-y
Paper
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Experiment
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Data
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Analysis
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Complete file
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Our study aimed to establish effective prognosis prediction model based on competing endogenous RNAs (ceRNAs) for recurrence of COAD.\nMETHODS: COAD expression profilings downloaded from The Cancer Genome Atlas (TCGA) were used as training dataset, and expression profilings of GSE29623 retrieved from Gene Expression Omnibus (GEO) were set as validation dataset. Differentially expressed RNAs (DERs) between non-recurrent and recurrent specimens in training dataset were screened, and optimum prognostic signature DERs were revealed to establish prognostic score (PS) model. Kaplan-Meier survival analysis was conducted for PS model, and GEO dataset was used for validation. Prognosis prediction efficiencies were evaluated by area under curve (AUC) and C-index. Meanwhile, ceRNA regulatory network was constructed by using signature mRNAs, lncRNAs and miRNAs.\nRESULTS: We identified 562 DERs including 42 lncRNAs, 36 miRNAs, and 484 mRNAs. PS prediction model, consisting of 17 optimum prognostic signature DERs, showed that high risk group had significantly poorer prognosis (5-year AUC = 0.951, C-index = 0.788), which also validated in GSE29623. Prognosis prediction model incorporating multi-RNAs with pathologic distant metastasis (M) and pathologic primary tumor (T) (5-year AUC = 0.969, C-index = 0.812) had better efficiency than clinical prognosis prediction model (5-year AUC = 0.712, C-index = 0.680). In the constructed ceRNA regulatory network, lncRNA NCBP2-AS1 could interact with hsa-miR-34c and hsa-miR-363, and lncRNA LINC00115 could interact with hsa-miR-363 and hsa-miR-4709. SIX4, GRAP, NKAIN4, MMAA, and ERVMER34-1 are regulated by hsa-miR-4709.\nCONCLUSION: Prognosis prediction model incorporating multi-RNAs with pathologic M and pathologic T may have great value in COAD prognosis prediction.", "claims": [{"id": "claim-method", "statement": "Afterwards, the differentially expressed RNAs between non-recurrent and recurrent specimens in training dataset were screened by Limma (Linear Models for Microarray Data) package (Version 3.34.7; https://bioconductor.org/packages/release/bioc/html/limma.html) [19] of R3.4.1.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33028275", "locator": "sections[4] \"Identification of differentially expressed RNAs\" paragraph 2", "quote": "Afterwards, the differentially expressed RNAs between non-recurrent and recurrent specimens in training dataset were screened by Limma (Linear Models for Microarray Data) package (Version 3.34.7; https://bioconductor.org/packages/release/bioc/html/limma.html) [19] of R3.4.1."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data were collected and analyzed by the Authors, and are not ready to share their data because the data have not been published.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33028275", "locator": "sections[18] \"Availability of data and materials\" paragraph 2", "quote": "The raw data were collected and analyzed by the Authors, and are not ready to share their data because the data have not been published."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:33028275"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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