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pmid:33188157

Development and validation of a novel prognostic signature in gastric adenocarcinoma.

Rui Mao · Zheng Wang · Yuanchuan Zhang · YuanYuan Chen · Qian Liu · Tongtong Zhang · Yanjun Liu

Aging · 2020 doi:10.18632/aging.104161

Paper

complete

Experiment

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Data

partial

Analysis

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pmid:33188157
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2026-11-01T15:41:15Z
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title
Development and validation of a novel prognostic signature in gastric adenocarcinoma.
doi
10.18632/aging.104161
pmid
33188157
authors array · 7 items
[0]
Rui Mao
[1]
Zheng Wang
[2]
Yuanchuan Zhang
[3]
YuanYuan Chen
[4]
Qian Liu
[5]
Tongtong Zhang
[6]
Yanjun Liu
journal
Aging
year
2020
abstract
Competing endogenous RNA networks have attracted increasing attention in gastric adenocarcinoma (GA). The current study aimed to explore ceRNA-based prognostic biomarkers for GA. RNA expression profiles were downloaded from TCGA and GEO databases. A ceRNA network was constructed based on the most relevant modules in the weighted gene coexpression network analysis. Kaplan-Meier (KM) survival analysis revealed prognosis-related RNAs, which were subjected to the multivariate Cox regression analysis. The predictive accuracy and discriminative ability of the signature were determined by KM analyses, receiver operating characteristic curves and area under the curve values. Ultimately, we constructed a ceRNA network consisting of 55 lncRNAs, 17 miRNAs and 73 mRNAs. Survival analyses revealed 3 lncRNAs (LINC01106, FOXD2-AS1, and AC103702.2) and 3 mRNAs (CCDC34, ORC6, and SOX4) as crucial prognostic factors; these factors were then used to construct a survival specific ceRNA network. Patients with high risk scores exhibited significantly worse overall survival than patients with low risk scores, and the AUC for 5-year survival was 0.801. A total of 112 GA specimens and the GSE84437 dataset were used to successfully validate the robustness of our signature by qRT-PCR. In summary, we developed a prognostic signature for GA, that shows better accuracy than the traditional TNM pathological staging system.
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KM survival analysis was performed to evaluate the association between disease prognosis and lncRNAs and mRNAs. We also analyzed the clinical data of 349 GA patients with a nonzero survival time from the TCGA database and drew the survival curves of all lncRNAs and mRNAs in the ceRNA network using the R package “survival”.
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sections[20] "Survival analysis" paragraph 2
quote
KM survival analysis was performed to evaluate the association between disease prognosis and lncRNAs and mRNAs. We also analyzed the clinical data of 349 GA patients with a nonzero survival time from the TCGA database and drew the survival curves of all lncRNAs and mRNAs in the ceRNA network using the R package “survival”.
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Open data sets were downloaded from the TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/gds/) databases, including the lncRNA, mRNA and miRNA expression profiles of GA specimens and the corresponding clinical follow-up data.
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sections[15] "Data acquisition and preprocessing" paragraph 2
quote
Open data sets were downloaded from the TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/gds/) databases, including the lncRNA, mRNA and miRNA expression profiles of GA specimens and the corresponding clinical follow-up data.
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Open data sets were downloaded from the TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/gds/) databases, including the lncRNA, mRNA and miRNA expression profiles of GA specimens and the corresponding clinical follow-up data.
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The clinicopathological data of TCGA and qRT-PCR datasets.
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The results of multivariate Cox analysis.
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KM survival analysis was performed to evaluate the association between disease prognosis and lncRNAs and mRNAs. We also analyzed the clinical data of 349 GA patients with a nonzero survival time from the TCGA database and drew the survival curves of all lncRNAs and mRNAs in the ceRNA network using the R package “survival”.
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KM survival analysis was performed to evaluate the association between disease prognosis and lncRNAs and mRNAs. We also analyzed the clinical data of 349 GA patients with a nonzero survival time from the TCGA database and drew the survival curves of all lncRNAs and mRNAs in the ceRNA network using the R package “survival”.
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33188157", "kind": "science-record", "revision": 8, "content_hash": "sha256:e3957052c01bb7a9e42bad88fe8e801d9b32affb21c27ddc69c319366671b12b", "created_at": "2026-11-01T15:41:15Z", "updated_at": "2026-11-01T22:02:30Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development and validation of a novel prognostic signature in gastric adenocarcinoma.", "doi": "10.18632/aging.104161", "pmid": "33188157", "authors": ["Rui Mao", "Zheng Wang", "Yuanchuan Zhang", "YuanYuan Chen", "Qian Liu", "Tongtong Zhang", "Yanjun Liu"], "journal": "Aging", "year": 2020, "abstract": "Competing endogenous RNA networks have attracted increasing attention in gastric adenocarcinoma (GA). The current study aimed to explore ceRNA-based prognostic biomarkers for GA. RNA expression profiles were downloaded from TCGA and GEO databases. A ceRNA network was constructed based on the most relevant modules in the weighted gene coexpression network analysis. Kaplan-Meier (KM) survival analysis revealed prognosis-related RNAs, which were subjected to the multivariate Cox regression analysis. The predictive accuracy and discriminative ability of the signature were determined by KM analyses, receiver operating characteristic curves and area under the curve values. Ultimately, we constructed a ceRNA network consisting of 55 lncRNAs, 17 miRNAs and 73 mRNAs. Survival analyses revealed 3 lncRNAs (LINC01106, FOXD2-AS1, and AC103702.2) and 3 mRNAs (CCDC34, ORC6, and SOX4) as crucial prognostic factors; these factors were then used to construct a survival specific ceRNA network. Patients with high risk scores exhibited significantly worse overall survival than patients with low risk scores, and the AUC for 5-year survival was 0.801. 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We also analyzed the clinical data of 349 GA patients with a nonzero survival time from the TCGA database and drew the survival curves of all lncRNAs and mRNAs in the ceRNA network using the R package “survival”."}}, {"id": "method-2", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.7, "band": "medium", "quote": "Cox regression"}}, {"id": "method-3", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.8, "band": "high", "quote": "limma"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["survival_km"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: kaplan_meier."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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