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pmid:36568397

The molecular basis of the associations between non-alcoholic fatty liver disease and colorectal cancer.

Ting Qiu · Weitao Hu · Zilan Rao · Taiyong Fang

Frontiers in genetics · 2022 doi:10.3389/fgene.2022.1007337

Paper

complete

Experiment

complete

Data

partial

Analysis

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title
The molecular basis of the associations between non-alcoholic fatty liver disease and colorectal cancer.
doi
10.3389/fgene.2022.1007337
pmid
36568397
authors array · 4 items
[0]
Ting Qiu
[1]
Weitao Hu
[2]
Zilan Rao
[3]
Taiyong Fang
journal
Frontiers in genetics
year
2022
abstract
Background: Given the ongoing research on non-alcoholic fatty liver disease (NAFLD) and colorectal cancer (CRC), the number of studies suggesting a strong link between NAFLD and CRC is on the rise, while its underlying pathological mechanisms remain uncertain. This study aims to explore the shared genes and mechanisms and to reveal the molecular basis of the association between CRC and NAFLD through bioinformatics approaches. Methods: The Gene Expression Omnibus (GEO) dataset GSE89632 is downloaded for NAFLD cases and healthy controls. Additionally, the GSE4107 and GSE9348 datasets are obtained for CRC cases and healthy controls. Differentially expressed genes (DEGs) are obtained for NAFLD and CRC datasets, as well as shared genes between the two disorders. GO and KEGG enrichment analyses are further conducted. Subsequently, the STRING database and Cytoscape software are utilized to establish the PPI network and identify the hub genes. Then, co-expression analysis is performed using GeneMANIA. Subsequently, ROC curves and external datasets validation were applied to further screen the candidate markers. Finally, NetworkAnalyst is available as a means to construct a miRNA-gene regulatory network. Results: Under the threshold of FDR ≤ 0.01, 147 common genes are obtained in NAFLD and CRC. Categorization of GO functions shows that DEGs are predominantly enriched in "response to organic substance", "cellular response to chemical stimulus", and "response to external stimulus". The predominant KEGG pathways in DEGs are the "IL-17 signaling pathway", the "TNF signaling pathway", "Viral protein interaction with cytokine and cytokine receptor", "Cytokine-cytokine receptor interaction", and the "Toll-like receptor signaling pathway". Additionally, MYC, IL1B, FOS, CXCL8, PTGS2, MMP9, JUN, and IL6 are identified as hub genes by the evaluation of 7 algorithms. With the construction of miRNA-gene networks, 2 miRNAs, including miR-106a-5p, and miR-204-5p are predicted to be potential key miRNAs. Conclusion: This study identifies possible hub genes acting in the co-morbidity of NAFLD and CRC and discovers the interaction of miRNAs and hub genes, providing a novel understanding of the molecular basis for the relevance of CRC and NAFLD, thus contributing to the development of new therapeutic strategies to combat NAFLD and CRC.
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The “limma” package from R software is used to identify the differentially expressed genes (DEGs) in the NAFLD and CRC datasets respectively.
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sections[4] "Differentially expressed genes selection" paragraph 2
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The “limma” package from R software is used to identify the differentially expressed genes (DEGs) in the NAFLD and CRC datasets respectively.
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sections[18] "Data availability statement" paragraph 2
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The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
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Under an FDR threshold of 0.01, 147 common genes were obtained in NAFLD and CRC.
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Under the threshold of FDR ≤ 0.01, 147 common genes are obtained in NAFLD and CRC.
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Under an FDR threshold of 0.01, 147 common genes were obtained in NAFLD and CRC.
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GSM2385739
[20]
GSM2385740
[21]
GSM2385741
[22]
GSM2385742
[23]
GSM2385743
[24]
GSM2385744
[25]
GSM2385745
[26]
GSM2385746
[27]
GSM2385747
[28]
GSM2385748
[29]
GSM2385749
[30]
GSM2385750
[31]
GSM2385751
[32]
GSM2385752
[33]
GSM2385753
[34]
GSM2385754
[35]
GSM2385755
[36]
GSM2385756
[37]
GSM2385757
[38]
GSM2385758
[39]
GSM2385759
[40]
GSM2385760
[41]
GSM2385761
[42]
GSM2385762
[43]
GSM2385763
[44]
GSM2385764
[45]
GSM2385765
[46]
GSM2385766
[47]
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[48]
GSM2385768
[49]
GSM2385769
[50]
GSM2385770
[51]
GSM2385771
[52]
GSM2385772
[53]
GSM2385773
[54]
GSM2385774
[55]
GSM2385775
[56]
GSM2385776
[57]
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[58]
GSM2385778
[59]
GSM2385779
[60]
GSM2385780
[61]
GSM2385781
[62]
GSM2385782
sample_count
63
group_sizes
null
group_sizes_basis
null
platform
14951
taxon
Homo sapiens
title
Genome-wide analysis of hepatic gene expression in patients with non-alcoholic fatty liver disease and in healthy donors in relation to hepatic fatty acid composition and other nutritional factors
resolution_checked_at
2026-11-01T15:39:55Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[5] object · 15 fields
id
data:geo:GSE9348
role
raw
format
geo-series
media_type
application/vnd.ncbi.geo-series+json
uri
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE9348
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE9348
version
null
note
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
access_statement_kind
data_availability_statement
sample_accessions array · 82 items
[0]
GSM237914
[1]
GSM237915
[2]
GSM237916
[3]
GSM237917
[4]
GSM237918
[5]
GSM237919
[6]
GSM237920
[7]
GSM237921
[8]
GSM237922
[9]
GSM237923
[10]
GSM237924
[11]
GSM237925
[12]
GSM237926
[13]
GSM237927
[14]
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[15]
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[16]
GSM237930
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GSM237931
[18]
GSM237932
[19]
GSM237933
[20]
GSM237934
[21]
GSM237935
[22]
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[23]
GSM237937
[24]
GSM237938
[25]
GSM237939
[26]
GSM237940
[27]
GSM237941
[28]
GSM237942
[29]
GSM237943
[30]
GSM237944
[31]
GSM237945
[32]
GSM237946
[33]
GSM237947
[34]
GSM237948
[35]
GSM237949
[36]
GSM237950
[37]
GSM237951
[38]
GSM237952
[39]
GSM237953
[40]
GSM237954
[41]
GSM237955
[42]
GSM237956
[43]
GSM237957
[44]
GSM237958
[45]
GSM237959
[46]
GSM237960
[47]
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[48]
GSM237962
[49]
GSM237963
[50]
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[51]
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[52]
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[53]
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[54]
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[55]
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[56]
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[57]
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[58]
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[59]
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[60]
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[61]
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[62]
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[65]
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[66]
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[67]
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[68]
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[70]
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[71]
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[72]
GSM237986
[73]
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[74]
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[75]
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[76]
GSM237990
[77]
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[78]
GSM237992
[79]
GSM237993
[80]
GSM237994
[81]
GSM237995
sample_count
82
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Expression data from healthy controls and early stage CRC patient's tumor
resolution_checked_at
2026-11-01T15:39:55Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
gaps array · 10 items
[0]
asset:data:geo:GSE163211 not downloaded
[1]
asset:data:geo:GSE163211 licence not read
[2]
asset:data:geo:GSE184093 not downloaded
[3]
asset:data:geo:GSE184093 licence not read
[4]
asset:data:geo:GSE4107 not downloaded
[5]
asset:data:geo:GSE4107 licence not read
[6]
asset:data:geo:GSE89632 not downloaded
[7]
asset:data:geo:GSE89632 licence not read
[8]
asset:data:geo:GSE9348 not downloaded
[9]
asset:data:geo:GSE9348 licence not read
analysis object · 4 fields
state
complete
methods array · 2 items
[0] object · 10 fields
id
method-1
family
differential_expression
specific
limma_voom
label
Differential expression analysis
dsl_operations array · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
count_matrix_with_condition
evidence object · 3 fields
score
1
band
verbatim
quote
The “limma” package from R software is used to identify the differentially expressed genes (DEGs) in the NAFLD and CRC datasets respectively.
[1] object · 10 fields
id
method-2
family
network_analysis
specific
ppi_network
label
Network / pathway analysis
dsl_operations array · 3 items
[0]
build_graph
[1]
centrality_scores
[2]
pathway_enrich
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
edge_list
evidence object · 3 fields
score
0.7
band
medium
quote
PPI
targets array · 0 items
gaps array · 0 items
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
capability:computational_analysis
label
Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
machine_learning
[2]
survival_analysis
[3]
statistical_regression
[4]
pca
[5]
molecular_docking
[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
gaps array · 0 items
readiness object · 8 fields
state
complete
tier
R1
blocked_on
nothing:replicable_now
axes object · 3 fields
method object · 5 fields
tier
R1
expressible
true
required_operations array · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
missing_operations array · 0 items
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom.
capability object · 4 fields
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 object · 6 fields
tier
R1
reachable
true
accessions array · 5 items
[0]
GSE89632
[1]
GSE4107
[2]
GSE9348
[3]
GSE163211
[4]
GSE184093
resolved_accessions array · 5 items
[0]
GSE89632
[1]
GSE4107
[2]
GSE9348
[3]
GSE163211
[4]
GSE184093
statements array · 1 item
[0]
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
basis
The paper names GSE89632, GSE4107, GSE9348, GSE163211, GSE184093 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
dsl_operations array · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
assets array · 2 items
[0]
source:pmid:36568397
[1]
data:geo:GSE163211
steps array · 1 item
[0]
run_analysis
must_build object · 5 fields
dsl_operations array · 0 items
converters array · 0 items
readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
[0]
data:geo:GSE163211
gaps array · 0 items
review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
reader object · 5 fields
model
MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-11-01T18:21:50Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
5
usage object · 3 fields
prompt_tokens
8597
completion_tokens
1544
cached_tokens
128
stages array · 1 item
[0] object · 4 fields
id
extraction
state
complete
reader
MiniMax-M3.1-Flash
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minimax
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[1]
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[2]
review.human_critique
execution object · 5 fields
state
absent
status
not_run
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null
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execution.not_run
provenance object · 7 fields
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id
pmid:36568397
type
paper
label
pmid:36568397
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id
source:pmid:36568397
type
manuscript
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PMID 36568397 full text
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id
data:geo:GSE163211
type
dataset
label
GEO GSE163211
activities array · 3 items
[0] object · 4 fields
id
activity-reading-sci-2026-11-01
type
classification
used array · 3 items
[0] object · 3 fields
id
pmid:36568397
type
paper
label
pmid:36568397
[1] object · 3 fields
id
source:pmid:36568397
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manuscript
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PMID 36568397 full text
[2] object · 3 fields
id
data:geo:GSE163211
type
dataset
label
GEO GSE163211
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-36568397
type
extraction
used array · 1 item
[0]
source:pmid:36568397
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE163211
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE163211
agent
agent:corpus-assay-engine
agents array · 2 items
[0] object · 3 fields
id
agent:corpus-assay-engine
type
software
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Corpus assay engine
[1] object · 3 fields
id
agent:minimax-reader
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software
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MiniMax literature reader
source_run_id
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dsl_catalogue_fingerprint
343fb30756ba6ec3
capability_table_fingerprint
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lineage object · 4 fields
parent_id
pmid:36568397
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sha256:65dcd455b05f044647319ec76614c40cec62ce04ea67958825b536bffedd7428
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null
gaps array · 14 items
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[5]
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[6]
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[7]
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[8]
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[9]
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[11]
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[12]
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[0] object · 11 fields
role
analysis
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differential_expression
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f2132e96ae6c7a4e
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[1]
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[2]
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dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
The “limma” package from R software is used to identify the differentially expressed genes (DEGs) in the NAFLD and CRC datasets respectively.
order_basis
analysis_only
id
assay:1
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36568397", "kind": "science-record", "revision": 4, "content_hash": "sha256:4d0e1a0bbb4707e8cc81294493f9267c868be125bab91eb4bf7e8ad073b8767a", "created_at": "2026-11-01T19:01:07Z", "updated_at": "2026-11-01T22:50:57Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The molecular basis of the associations between non-alcoholic fatty liver disease and colorectal cancer.", "doi": "10.3389/fgene.2022.1007337", "pmid": "36568397", "authors": ["Ting Qiu", "Weitao Hu", "Zilan Rao", "Taiyong Fang"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Background: Given the ongoing research on non-alcoholic fatty liver disease (NAFLD) and colorectal cancer (CRC), the number of studies suggesting a strong link between NAFLD and CRC is on the rise, while its underlying pathological mechanisms remain uncertain. This study aims to explore the shared genes and mechanisms and to reveal the molecular basis of the association between CRC and NAFLD through bioinformatics approaches. Methods: The Gene Expression Omnibus (GEO) dataset GSE89632 is downloaded for NAFLD cases and healthy controls. Additionally, the GSE4107 and GSE9348 datasets are obtained for CRC cases and healthy controls. Differentially expressed genes (DEGs) are obtained for NAFLD and CRC datasets, as well as shared genes between the two disorders. GO and KEGG enrichment analyses are further conducted. Subsequently, the STRING database and Cytoscape software are utilized to establish the PPI network and identify the hub genes. Then, co-expression analysis is performed using GeneMANIA. Subsequently, ROC curves and external datasets validation were applied to further screen the candidate markers. Finally, NetworkAnalyst is available as a means to construct a miRNA-gene regulatory network. Results: Under the threshold of FDR ≤ 0.01, 147 common genes are obtained in NAFLD and CRC. Categorization of GO functions shows that DEGs are predominantly enriched in \"response to organic substance\", \"cellular response to chemical stimulus\", and \"response to external stimulus\". The predominant KEGG pathways in DEGs are the \"IL-17 signaling pathway\", the \"TNF signaling pathway\", \"Viral protein interaction with cytokine and cytokine receptor\", \"Cytokine-cytokine receptor interaction\", and the \"Toll-like receptor signaling pathway\". Additionally, MYC, IL1B, FOS, CXCL8, PTGS2, MMP9, JUN, and IL6 are identified as hub genes by the evaluation of 7 algorithms. With the construction of miRNA-gene networks, 2 miRNAs, including miR-106a-5p, and miR-204-5p are predicted to be potential key miRNAs. Conclusion: This study identifies possible hub genes acting in the co-morbidity of NAFLD and CRC and discovers the interaction of miRNAs and hub genes, providing a novel understanding of the molecular basis for the relevance of CRC and NAFLD, thus contributing to the development of new therapeutic strategies to combat NAFLD and CRC.", "claims": [{"id": "claim-method", "statement": "The “limma” package from R software is used to identify the differentially expressed genes (DEGs) in the NAFLD and CRC datasets respectively.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36568397", "locator": "sections[4] \"Differentially expressed genes selection\" paragraph 2", "quote": "The “limma” package from R software is used to identify the differentially expressed genes (DEGs) in the NAFLD and CRC datasets respectively."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36568397", "locator": "sections[18] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Under an FDR threshold of 0.01, 147 common genes were obtained in NAFLD and CRC.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36568397", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Under the threshold of FDR ≤ 0.01, 147 common genes are obtained in NAFLD and CRC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Under an FDR threshold of 0.01, 147 common genes were obtained in NAFLD and CRC.", "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:36568397"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE163211"], "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": ["data:geo:GSE163211"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36568397", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.1007337", "sha256": "sha256:e71d2dbf87f1eee59fba72845b9a980c7cac6c80d7b042d929a1af5fe183a5a3", "bytes": 29148, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2022.1007337", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9780501", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE163211", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE163211", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE163211", "version": null, "note": "The original contributions presented in the study are included in the article/Supplementary Material, further 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