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pmid:32149133

Identification of Three lncRNAs as Potential Predictive Biomarkers of Lung Adenocarcinoma.

Donghui Jin · Yuxuan Song · Yuan Chen · Peng Zhang

BioMed research international · 2020 doi:10.1155/2020/7573689

Paper

complete

Experiment

partial

Data

partial

Analysis

complete

Execution

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pmid:32149133
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2026-11-01T15:40:57Z
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paper object · 10 fields
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title
Identification of Three lncRNAs as Potential Predictive Biomarkers of Lung Adenocarcinoma.
doi
10.1155/2020/7573689
pmid
32149133
authors array · 4 items
[0]
Donghui Jin
[1]
Yuxuan Song
[2]
Yuan Chen
[3]
Peng Zhang
journal
BioMed research international
year
2020
abstract
BACKGROUND: Lung cancer is the most common cancer and the most common cause of cancer-related death worldwide. However, the molecular mechanism of its development is unclear. It is imperative to identify more novel biomarkers. METHODS: Two datasets (GSE70880 and GSE113852) were downloaded from the Gene Expression Omnibus (GEO) database and used to identify the differentially expressed genes (DEGs) between lung cancer tissues and normal tissues. Then, we constructed a competing endogenous RNA (ceRNA) network and a protein-protein interaction (PPI) network and performed gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and survival analyses to identify potential biomarkers that are related to the diagnosis and prognosis of lung cancer. RESULTS: A total of 41 lncRNAs and 805 mRNAs were differentially expressed in lung cancer. The ceRNA network contained four lncRNAs (CLDN10-AS1, SFTA1P, SRGAP3-AS2, and ADAMTS9-AS2), 21 miRNAs, and 48 mRNAs. Functional analyses revealed that the genes in the ceRNA network were mainly enriched in cell migration, transmembrane receptor, and protein kinase activity. mRNAs DLGAP5, E2F7, MCM7, RACGAP1, and RRM2 had the highest connectivity in the PPI network. Immunohistochemistry (IHC) demonstrated that mRNAs DLGAP5, MCM7, RACGAP1, and RRM2 were upregulated in lung adenocarcinoma (LUAD). Survival analyses showed that lncRNAs CLDN10-AS1, SFTA1P, and ADAMTS9-AS2 were associated with the prognosis of LUAD. CONCLUSION: lncRNAs CLDN10-AS1, SFTA1P, and ADAMTS9-AS2 might be the biomarkers of LUAD. For the first time, we confirmed the important role of lncRNA CLDN10-AS1 in LUAD.
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Due to lack of clinical information, we performed Kaplan–Meier (KM) analyses of lncRNAs in the ceRNA network and genes in the PPI network through the Kaplan–Meier Plotter (http://kmplot.com/analysis/), respectively [18].
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sections[7] "2.5. Online Survival Analyses in LUAD and LUSC" paragraph 2
quote
Due to lack of clinical information, we performed Kaplan–Meier (KM) analyses of lncRNAs in the ceRNA network and genes in the PPI network through the Kaplan–Meier Plotter (http://kmplot.com/analysis/), respectively [18].
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All data can be obtained from GEO (https://www.ncbi.nlm.nih.gov/gds/), TCGA (https://portal.gdc.cancer.gov/), and THPA (https://www.proteinatlas.org/).
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sections[19] "Data Availability" paragraph 2
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All data can be obtained from GEO (https://www.ncbi.nlm.nih.gov/gds/), TCGA (https://portal.gdc.cancer.gov/), and THPA (https://www.proteinatlas.org/).
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3.1. Gene Expression Profile Data
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Yuan J., Yue H, Luo J., Chen R.
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3.1. Gene Expression Profile Data
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Differentially expressed lncRNAs and mRNAs of merged dataset.
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lncRNAs
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MNX1-AS1, FEZF1-AS1, CLDN10-AS1, LINC00511, LINC01635, LINC02544, LINC02362, AC105760, AC100861, LINC02483, AC108134, AL391056, CASC9, LINC01876
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3.6. Expression Levels and Diagnostic Values of Four lncRNAs in LUAD Confirmed through TCGA
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Diagnostic values of the four lncRNAs.
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[0] array · 5 items
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0.078
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Due to lack of clinical information, we performed Kaplan–Meier (KM) analyses of lncRNAs in the ceRNA network and genes in the PPI network through the Kaplan–Meier Plotter (http://kmplot.com/analysis/), respectively [18].
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the highest connectivity in the PPI network. Immunohistochemistry (IHC) demonstrated that mRNAs DLGAP5, MCM7,
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ty in the PPI network. Immunohistochemistry (IHC) demonstrated that mRNAs DLGAP5, MCM7, RACGA
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ials and Methods 2.2. Integrated Analysis of Microarray Datasets We merged the two datasets not only
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Due to lack of clinical information, we performed Kaplan–Meier (KM) analyses of lncRNAs in the ceRNA network and genes in the PPI network through the Kaplan–Meier Plotter (http://kmplot.com/analysis/), respectively [18].
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32149133", "kind": "science-record", "revision": 7, "content_hash": "sha256:5d9938098186050f4b4a0ddd7eba2b234fa00cebc7c18c7c2049d06febac2e90", "created_at": "2026-11-01T15:40:57Z", "updated_at": "2026-11-01T22:01:21Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Three lncRNAs as Potential Predictive Biomarkers of Lung Adenocarcinoma.", "doi": "10.1155/2020/7573689", "pmid": "32149133", "authors": ["Donghui Jin", "Yuxuan Song", "Yuan Chen", "Peng Zhang"], "journal": "BioMed research international", "year": 2020, "abstract": "BACKGROUND: Lung cancer is the most common cancer and the most common cause of cancer-related death worldwide. However, the molecular mechanism of its development is unclear. It is imperative to identify more novel biomarkers.\nMETHODS: Two datasets (GSE70880 and GSE113852) were downloaded from the Gene Expression Omnibus (GEO) database and used to identify the differentially expressed genes (DEGs) between lung cancer tissues and normal tissues. Then, we constructed a competing endogenous RNA (ceRNA) network and a protein-protein interaction (PPI) network and performed gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and survival analyses to identify potential biomarkers that are related to the diagnosis and prognosis of lung cancer.\nRESULTS: A total of 41 lncRNAs and 805 mRNAs were differentially expressed in lung cancer. The ceRNA network contained four lncRNAs (CLDN10-AS1, SFTA1P, SRGAP3-AS2, and ADAMTS9-AS2), 21 miRNAs, and 48 mRNAs. Functional analyses revealed that the genes in the ceRNA network were mainly enriched in cell migration, transmembrane receptor, and protein kinase activity. mRNAs DLGAP5, E2F7, MCM7, RACGAP1, and RRM2 had the highest connectivity in the PPI network. Immunohistochemistry (IHC) demonstrated that mRNAs DLGAP5, MCM7, RACGAP1, and RRM2 were upregulated in lung adenocarcinoma (LUAD). Survival analyses showed that lncRNAs CLDN10-AS1, SFTA1P, and ADAMTS9-AS2 were associated with the prognosis of LUAD.\nCONCLUSION: lncRNAs CLDN10-AS1, SFTA1P, and ADAMTS9-AS2 might be the biomarkers of LUAD. For the first time, we confirmed the important role of lncRNA CLDN10-AS1 in LUAD.", "claims": [{"id": "claim-method", "statement": "Due to lack of clinical information, we performed Kaplan–Meier (KM) analyses of lncRNAs in the ceRNA network and genes in the PPI network through the Kaplan–Meier Plotter (http://kmplot.com/analysis/), respectively [18].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32149133", "locator": "sections[7] \"2.5. Online Survival Analyses in LUAD and LUSC\" paragraph 2", "quote": "Due to lack of clinical information, we performed Kaplan–Meier (KM) analyses of lncRNAs in the ceRNA network and genes in the PPI network through the Kaplan–Meier Plotter (http://kmplot.com/analysis/), respectively [18]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data can be obtained from GEO (https://www.ncbi.nlm.nih.gov/gds/), TCGA (https://portal.gdc.cancer.gov/), and THPA (https://www.proteinatlas.org/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32149133", "locator": "sections[19] \"Data Availability\" paragraph 2", "quote": "All data can be obtained from GEO (https://www.ncbi.nlm.nih.gov/gds/), TCGA (https://portal.gdc.cancer.gov/), and THPA (https://www.proteinatlas.org/)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "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:32149133"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:32149133", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2020/7573689", "sha256": "sha256:fcbb00152ed31b48c61f828cfeb2ad928b2d92fb74dc38957c0de6d26cb6ef77", "bytes": 29808, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/2020/7573689", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7053454", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "asset:table:pmid:32149133:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:51c12eb88d8ef22704ed085da184274b53638d3b5caec0f402e83b18c70d5d71", "bytes": 414, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[10]/table-wrap[1]#tab1", "version": null, "note": "Details of datasets from Gene Expression Omnibus database.", "structure": {"pmcid": "PMC7053454", "section_index": 10, "section_title": "3.1. Gene Expression Profile Data", "row_count": 3, "column_count": 5}, "table": {"label": "Table 1", "caption": "Details of datasets from Gene Expression Omnibus database.", "rows": [["Series accession", "Platform", "Tumor", "Normal", "Contributors"], ["GSE70880", "GPL19748", "20", "20", "Yuan J., Yue H, Luo J., Chen R."], ["GSE113852", "GPL16847", "27", "27", "Polycarpou-Schwarz M., Roth A, et al."]], "row_count": 3, "column_count": 5, "numeric_cells": 8, "header_rows": 1, "locator": "section[10]/table-wrap[1]#tab1"}}, {"id": "asset:table:pmid:32149133:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:cd9368a5538edc047c9660d135bd1d4ba3737ab19d1702949841a02ff958baae", "bytes": 6578, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[10]/table-wrap[2]#tab2", "version": null, "note": "Differentially expressed lncRNAs and mRNAs of merged dataset.", "structure": {"pmcid": "PMC7053454", "section_index": 10, "section_title": "3.1. 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