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pmid:28588348

Analysis of Microarray-Identified Genes and MicroRNAs Associated with Idiopathic Pulmonary Fibrosis.

Lichao Fan · Xiaoting Yu · Ziling Huang · Shaoqiang Zheng · Yongxin Zhou · Hanjing Lv · Yu Zeng · Jin-Fu Xu · Xuyou Zhu · Xianghua Yi

Mediators of inflammation · 2017 doi:10.1155/2017/1804240

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Experiment

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title
Analysis of Microarray-Identified Genes and MicroRNAs Associated with Idiopathic Pulmonary Fibrosis.
doi
10.1155/2017/1804240
pmid
28588348
authors array · 10 items
[0]
Lichao Fan
[1]
Xiaoting Yu
[2]
Ziling Huang
[3]
Shaoqiang Zheng
[4]
Yongxin Zhou
[5]
Hanjing Lv
[6]
Yu Zeng
[7]
Jin-Fu Xu
[8]
Xuyou Zhu
[9]
Xianghua Yi
journal
Mediators of inflammation
year
2017
abstract
The aim of this study was to identify potential microRNAs and genes associated with idiopathic pulmonary fibrosis (IPF) through web-available microarrays. The microRNA microarray dataset GSE32538 and the mRNA datasets GSE32537, GSE53845, and GSE10667 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DE-miRNAs)/genes (DEGs) were screened with GEO2R, and their associations with IPF were analyzed by comprehensive bioinformatic analyses. A total of 45 DE-microRNAs were identified between IPF and control tissues, whereas 67 common DEGs were determined to exhibit the same expression trends in all three microarrays. Furthermore, functional analysis indicated that microRNAs in cancer and ECM-receptor interaction were the most significant pathways and were enriched by the 45 DE-miRNAs and 67 common DEGs. Finally, we predicted potential microRNA-target interactions between 17 DE-miRNAs and 17 DEGs by using at least three online programs. A microRNA-mediated regulatory network among the DE-miRNAs and DEGs was constructed that might shed new light on potential biomarkers for the prediction of IPF progression.
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This tool is based on a t-test (ANOVA) or analysis of variance, and it is useful for comparing two or more groups of samples across the same experimental conditions to characterize differentially expressed microRNAs or genes.
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sections[4] "2.2. Inclusion Criteria for Differentially Expressed MicroRNAs and Genes" paragraph 2
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This tool is based on a t-test (ANOVA) or analysis of variance, and it is useful for comparing two or more groups of samples across the same experimental conditions to characterize differentially expressed microRNAs or genes.
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We downloaded the microRNA expression microarray dataset GSE32538 [14] and the mRNA expression microarray datasets GSE32537 [14], GSE53845 [15], and GSE10667 [16–18]. The samples used to generate the GSE32538 dataset were lung tissues from 167 subjects with IIP and 50 nondiseased controls.
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sections[3] "2.1. Acquisition and Analysis of Datasets" paragraph 2
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We downloaded the microRNA expression microarray dataset GSE32538 [14] and the mRNA expression microarray datasets GSE32537 [14], GSE53845 [15], and GSE10667 [16–18].
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sections[6] "3.1. Microarray Datasets That Met the Inclusion Criteria" paragraph 2
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The samples used to generate the GSE32538 dataset were lung tissues from 167 subjects with IIP and 50 nondiseased controls.
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The study used GEO2R to screen microarrays downloaded from GEO, identifying 45 DE-miRNAs in IPF and 67 common DEGs across three mRNA datasets.
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The microRNA microarray dataset GSE32538 and the mRNA datasets GSE32537, GSE53845, and GSE10667 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DE-miRNAs)/genes (DEGs) were screened with GEO2R, and their associations with IPF were analyzed by comprehensive bioinformatic analyses.
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A total of 45 DE-microRNAs were identified between IPF and control tissues, whereas 67 common DEGs were determined to exhibit the same expression trends in all three microarrays.
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MMP1 and MMP7 as potential peripheral blood biomarkers in Idiopathic Pulmonary Fibrosis
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GSM806612
sample_count
192
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null
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null
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8786
taxon
Homo sapiens; synthetic construct
title
Molecular phenotyping of the idiopathic interstitial pneumonias [miRNA]
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2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
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false
licence_read
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[4] object · 15 fields
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null
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We downloaded the microRNA expression microarray dataset GSE32538 [14] and the mRNA expression microarray datasets GSE32537 [14], GSE53845 [15], and GSE10667 [16–18]. The samples used to generate the GSE32538 dataset were lung tissues from 167 subjects with IIP and 50 nondiseased controls.
structure object · 15 fields
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GEO accession URL
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We downloaded the microRNA expression microarray dataset GSE32538 [14] and the mRNA expression microarray datasets GSE32537 [14], GSE53845 [15], and GSE10667 [16–18]. The samples used to generate the GSE32538 dataset were lung tissues from 167 subjects with IIP and 50 nondiseased controls.
access_statement_kind
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GSM1302077
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GSM1302078
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GSM1302079
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GSM1302080
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48
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null
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null
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6480
taxon
Homo sapiens
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40 IPF patients and 8 healthy controls
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2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
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microRNA and gene expression microarray datasets related to idiopathic pulmonary fibrosis.
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PMC5446886
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6
section_title
3.1. Microarray Datasets That Met the Inclusion Criteria
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6
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9
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label
Table 1
caption
microRNA and gene expression microarray datasets related to idiopathic pulmonary fibrosis.
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[0]
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IPF
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MicroRNA
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GSE32538 [14]
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University of Colorado
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GPL8786
[4]
Public on June 21, 2013
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Homo sapiens
[6]
Noncoding RNA profiling by array
[7]
106
[8]
50
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[0]
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GSE32537 [14]
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Homo sapiens
[6]
Expression profiling by array
[7]
119
[8]
50
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[1]
GSE53845 [15]
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Genentech, Inc.
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GPL6480
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Homo sapiens
[6]
Expression profiling by array
[7]
40
[8]
8
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[0]
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University of Pittsburgh
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GPL4133
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Public on Feb. 20, 2009
[5]
Homo sapiens
[6]
Expression profiling by array
[7]
23
[8]
15
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section[6]/table-wrap[1]#tab1
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Top 10 logFc of differentially expressed miRNAs obtained from the GSE32538 dataset.
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pmcid
PMC5446886
section_index
7
section_title
3.2. Differentially Expressed MicroRNAs and Pathway Enrichment
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12
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4
table object · 8 fields
label
Table 2
caption
Top 10 logFc of differentially expressed miRNAs obtained from the GSE32538 dataset.
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[0] array · 4 items
[0]
Dysfunction
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miRNA
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logFc
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adj. p value
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hsa-miR-205
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1.8093192
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2.79E-08
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hsa-miR-34c-3p
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2.2856359
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1.91E-08
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[0]
hsa-miR-34c-5p
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2.3310863
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3.36E-07
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2.3422275
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1.28E-08
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hsa-miR-532-5p
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−1.9021268
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5.03E-18
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8.44E-15
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5.39E-11
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3.16E-15
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[0]
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−1.6402524
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4.57E-15
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section[7]/table-wrap[1]#tab2
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asset:data:geo:GSE32538 licence not read
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asset:data:geo:GSE53845 not downloaded
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This tool is based on a t-test (ANOVA) or analysis of variance, and it is useful for comparing two or more groups of samples across the same experimental conditions to characterize differentially expressed microRNAs or genes.
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edge_list
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pathway enrichment
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complete
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The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: anova.
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GSE32538
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[0]
GSE32538
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GSE53845
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GSE10667
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[0]
We downloaded the microRNA expression microarray dataset GSE32538 [14] and the mRNA expression microarray datasets GSE32537 [14], GSE53845 [15], and GSE10667 [16–18]. The samples used to generate the GSE32538 dataset were lung tissues from 167 subjects with IIP and 50 nondiseased controls.
basis
The paper names GSE32538, GSE32537, GSE53845, GSE10667 in National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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group_summary
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data:geo:GSE10667
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MiniMax-M3.1-Flash
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minimax
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minimax
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same_provider
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verbatim
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[0]
Analysis of Microarray-Identified Genes and MicroRNAs Associated wi
[1]
ough web-available microarrays. The microRNA microarray dataset GSE32538 and the mRNA datasets GSE32
[2]
ds 2.1. Acquisition and Analysis of Datasets Microarray data from IPF-related microRNA and mRNA expr
[3]
ctly the same expression trends in all three microarray sets (the list of the 67 differentially expr
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[0]
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:28588348", "kind": "science-record", "revision": 9, "content_hash": "sha256:3fced6a4ecdfef356413be7535ba7b4dd7945b5e507e5ed211c1bad024960106", "created_at": "2026-11-01T15:40:26Z", "updated_at": "2026-11-01T22:43:00Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Analysis of Microarray-Identified Genes and MicroRNAs Associated with Idiopathic Pulmonary Fibrosis.", "doi": "10.1155/2017/1804240", "pmid": "28588348", "authors": ["Lichao Fan", "Xiaoting Yu", "Ziling Huang", "Shaoqiang Zheng", "Yongxin Zhou", "Hanjing Lv", "Yu Zeng", "Jin-Fu Xu", "Xuyou Zhu", "Xianghua Yi"], "journal": "Mediators of inflammation", "year": 2017, "abstract": "The aim of this study was to identify potential microRNAs and genes associated with idiopathic pulmonary fibrosis (IPF) through web-available microarrays. The microRNA microarray dataset GSE32538 and the mRNA datasets GSE32537, GSE53845, and GSE10667 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DE-miRNAs)/genes (DEGs) were screened with GEO2R, and their associations with IPF were analyzed by comprehensive bioinformatic analyses. A total of 45 DE-microRNAs were identified between IPF and control tissues, whereas 67 common DEGs were determined to exhibit the same expression trends in all three microarrays. Furthermore, functional analysis indicated that microRNAs in cancer and ECM-receptor interaction were the most significant pathways and were enriched by the 45 DE-miRNAs and 67 common DEGs. Finally, we predicted potential microRNA-target interactions between 17 DE-miRNAs and 17 DEGs by using at least three online programs. A microRNA-mediated regulatory network among the DE-miRNAs and DEGs was constructed that might shed new light on potential biomarkers for the prediction of IPF progression.", "claims": [{"id": "claim-method", "statement": "This tool is based on a t-test (ANOVA) or analysis of variance, and it is useful for comparing two or more groups of samples across the same experimental conditions to characterize differentially expressed microRNAs or genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:28588348", "locator": "sections[4] \"2.2. 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Differentially Expressed MicroRNAs and Pathway Enrichment", "row_count": 12, "column_count": 4}, "table": {"label": "Table 2", "caption": "Top 10 logFc of differentially expressed miRNAs obtained from the GSE32538 dataset.", "rows": [["Dysfunction", "miRNA", "logFc", "adj. p value"], ["Upregulated", "hsa-miR-205", "1.8093192", "2.79E-08"], ["hsa-miR-34c-3p", "2.2856359", "1.91E-08"], ["hsa-miR-34c-5p", "2.3310863", "3.36E-07"], ["hsa-miR-31", "2.3422275", "1.28E-08"], [""], ["Downregulated", "hsa-miR-532-5p", "−1.9021268", "5.03E-18"], ["hsa-miR-652", "−1.8713939", "8.44E-15"], ["hsa-miR-130a", "−1.6770333", "5.48E-11"], ["hsa-miR-210", "−1.6699558", "5.39E-11"], ["hsa-miR-500", "−1.6698889", "3.16E-15"], ["hsa-miR-193a-5p", "−1.6402524", "4.57E-15"]], "row_count": 12, "column_count": 4, "numeric_cells": 30, "header_rows": 1, "locator": "section[7]/table-wrap[1]#tab2"}}], "gaps": ["asset:data:geo:GSE10667 not downloaded", "asset:data:geo:GSE10667 licence not read", "asset:data:geo:GSE32537 not downloaded", "asset:data:geo:GSE32537 licence not read", "asset:data:geo:GSE32538 not downloaded", "asset:data:geo:GSE32538 licence not read", "asset:data:geo:GSE53845 not downloaded", "asset:data:geo:GSE53845 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "group_comparison", "specific": "anova", "label": "Group comparison", "dsl_operations": ["group_summary", "welch_t", "bootstrap_mean_difference", "group_comparison_anova"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "This tool is based on a t-test (ANOVA) or analysis of variance, and it is useful for comparing two or more groups of samples across the same experimental conditions to characterize differentially expressed microRNAs or genes."}}, {"id": "method-2", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "PPI"}}, {"id": "method-3", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "pathway enrichment"}}], "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": ["group_summary", "welch_t", "bootstrap_mean_difference", "group_comparison_anova"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: anova."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE32538", "GSE32537", "GSE53845", "GSE10667"], "resolved_accessions": ["GSE32538", "GSE32537", "GSE53845", "GSE10667"], "statements": ["We downloaded the microRNA expression microarray dataset GSE32538 [14] and the mRNA expression microarray datasets GSE32537 [14], GSE53845 [15], and GSE10667 [16–18]. The samples used to generate the GSE32538 dataset were lung tissues from 167 subjects with IIP and 50 nondiseased controls."], "basis": "The paper names GSE32538, GSE32537, GSE53845, GSE10667 in National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["group_summary", "welch_t", "bootstrap_mean_difference", "group_comparison_anova"], "assets": ["source:pmid:28588348", "data:geo:GSE10667", "asset:table:pmid:28588348:1", "asset:table:pmid:28588348:2"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE10667"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-10-31T18:31:19Z", "independence": "same_provider"}, "evidence_check": {"method": "verbatim", "availability": null}, "calls": 8, "usage": {"prompt_tokens": 73287, "completion_tokens": 3617, "cached_tokens": 2080}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:28588348", "type": "paper", "label": "pmid:28588348"}, {"id": "source:pmid:28588348", "type": "manuscript", "label": "PMID 28588348 full text"}, {"id": "data:geo:GSE10667", "type": "dataset", "label": "GEO GSE10667"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:28588348", "type": "paper", "label": "pmid:28588348"}, {"id": "source:pmid:28588348", "type": "manuscript", "label": "PMID 28588348 full text"}, {"id": "data:geo:GSE10667", "type": "dataset", "label": "GEO GSE10667"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-28588348", "type": "extraction", "used": ["source:pmid:28588348"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE10667", "type": "accession_resolution", "used": ["data:geo:GSE10667"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:28588348", "parent_hash": "sha256:03e3c3dc50c24a6d5960c7c82a80078e77a003b5abbd4e6a463037a35072b196", "fork_of": null, "generation": 8}, "gaps": ["asset:data:geo:GSE10667 licence not read", "asset:data:geo:GSE10667 not downloaded", "asset:data:geo:GSE32537 licence not read", "asset:data:geo:GSE32537 not downloaded", "asset:data:geo:GSE32538 licence not read", "asset:data:geo:GSE32538 not downloaded", "asset:data:geo:GSE53845 licence not read", "asset:data:geo:GSE53845 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["Analysis of Microarray-Identified Genes and MicroRNAs Associated wi", "ough web-available microarrays. The microRNA microarray dataset GSE32538 and the mRNA datasets GSE32", "ds 2.1. Acquisition and Analysis of Datasets Microarray data from IPF-related microRNA and mRNA expr", "ctly the same expression trends in all three microarray sets (the list of the 67 differentially expr"], "order_basis": "paper_text_order:0", "id": "assay:1"}, {"role": "data_generation", "definition_id": "apoptosis_assay", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.69, "evidence": ["r, extracellular matrix, cell migration, and apoptosis are shown in Figure 4(b). 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