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pmid:39364312

Identification of potential biomarkers for lung adenocarcinoma: a study based on bioinformatics analysis combined with validation experiments.

Chuchu Zhang · Ying Liu · Yingdong Lu · Zehui Chen · Yi Liu · Qiyuan Mao · Shengchuan Bao · Ge Zhang · Ying Zhang · Hongsheng Lin · Haiyan Li

Frontiers in oncology · 2024 doi:10.3389/fonc.2024.1425895

Paper

complete

Experiment

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Data

partial

Analysis

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Execution

absent

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title
Identification of potential biomarkers for lung adenocarcinoma: a study based on bioinformatics analysis combined with validation experiments.
doi
10.3389/fonc.2024.1425895
pmid
39364312
authors array · 11 items
[0]
Chuchu Zhang
[1]
Ying Liu
[2]
Yingdong Lu
[3]
Zehui Chen
[4]
Yi Liu
[5]
Qiyuan Mao
[6]
Shengchuan Bao
[7]
Ge Zhang
[8]
Ying Zhang
[9]
Hongsheng Lin
[10]
Haiyan Li
journal
Frontiers in oncology
year
2024
abstract
Background: The prognosis for lung adenocarcinoma (LUAD) remains dismal, with a 5-year survival rate of <20%. Therefore, the purpose of this study was to identify potentially reliable biomarkers in LUAD by machine learning combination with Mendelian randomization (MR). Methods: TCGA-LUAD, GSE40791, and GSE31210 were employed this study. Key module differential genes were identified through differentially expressed analysis and weighted gene co-expression network analysis (WGCNA). Furthermore, candidate biomarkers were derived from protein-protein interaction network (PPI) and machine learning. Ultimately, biomarkers were confirmed using MR analysis. In addition, immunohistochemistry was used to detect the expression levels of genes that have a causal relationship to LUAD in the LUAD group and the control group. Cell experiments were conducted to validate the effect of screening genes on proliferation, migration, and apoptosis of LUAD cells. The correlation between the screened genes and immune infiltration was determined by CIBERSORT algorithm. In the end, the gene-related drugs were predicted through the Drug-Gene Interaction database. Results: In total, 401 key module differential genes were obtained by intersecting of 5,702 differentially expressed genes (DEGs) and 406 key module genes. Thereafter, GIMAP6, CAV1, PECAM1, and TGFBR2 were identified. Among them, only TGFBR2 had a significant causal relationship with LUAD (p=0.04, b=-0.06), and it is a protective factor for LUAD. Subsequently, sensitivity analyses showed that there were no heterogeneity and horizontal pleiotropy in the univariate MR results, and the results were not overly sensitive to individual SNP loci, further validating the reliability of univariate Mendelian randomization (UVMR) results. However, no causal relationship was found between them by reverse MR analysis. Meanwhile, TGFBR2 expression was decreased in LUAD group through immunohistochemistry. TGFBR2 can inhibit proliferation and migration of lung adenocarcinoma cell line A549 and promote apoptosis of A549 cells. Immune infiltration analysis suggested a potential link between TGFBR2 expression and immune infiltration. Finally, Irinotecan and Hesperetin were predicted through DGIDB database. Conclusion: In this study, TGFBR2 was identified as a biomarker of LUAD, which provided a new idea for the treatment strategy of LUAD and may aid in the development of personalized immunotherapy strategies.
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In this study, “TwoSampleMR” R package was employed to conduct two-sample MR analysis between exposures and outcome.
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sections[8] "Analysis of the causal relationship between exposure factors and outcome through univariate Mendelian randomization analysis" paragraph 3
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In this study, “TwoSampleMR” R package was employed to conduct two-sample MR analysis between exposures and outcome.
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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 authors.
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Among them, only TGFBR2 had a significant causal relationship with LUAD (p=0.04, b=−0.06), and it is a protective factor for LUAD.
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Among them, only TGFBR2 had a significant causal relationship with LUAD (p=0.04, b=−0.06), and it is a protective factor for LUAD.
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Among them, only TGFBR2 had a significant causal relationship with LUAD (p=0.04, b=−0.06), and it is a protective factor for LUAD.
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Among them, only TGFBR2 had a significant causal relationship with LUAD (p=0.04, b=−0.06), and it is a protective factor for LUAD.
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GSM1001703
sample_count
194
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null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Usp44 binds centrin to regulate centrosome positioning and suppress tumorigenesis
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
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gaps array · 4 items
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[2]
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[3]
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Genetic association (GWAS / linkage / MR)
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In this study, “TwoSampleMR” R package was employed to conduct two-sample MR analysis between exposures and outcome.
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Network / pathway analysis
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evidence object · 3 fields
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protein-protein interaction
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id
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genetic_association
specific
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label
Genetic association (GWAS / linkage / MR)
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gwas_locus_scan
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genotype_phenotype_table
evidence object · 3 fields
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claim-target-result
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The threshold the paper applied to its own result, as the paper states it. A run that reproduces the effect is judged against this threshold.
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capability object · 3 fields
state
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[0] object · 6 fields
capability_id
capability:computational_analysis
label
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gwas
candidate
Automated Assays Laravel application and simulated adapters
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readiness object · 8 fields
state
complete
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required_operations array · 1 item
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The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: mendelian_randomisation.
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tier
R1
capability_id
capability:computational_analysis
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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
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true
accessions array · 2 items
[0]
GSE40791
[1]
GSE31210
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[0]
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GSE31210
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[0]
The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.
basis
The paper names GSE40791, GSE31210 in Gene Expression Omnibus (GEO) database and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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mr_estimate
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[0]
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[1]
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data:geo:GSE31210
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partial
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extraction_review
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MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-11-01T19:34:12Z
independence
single_reader
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method
verbatim
availability
verbatim
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5
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17052
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extraction
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MiniMax-M3.1-Flash
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pmid:39364312
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paper
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pmid:39364312
[1] object · 3 fields
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source:pmid:39364312
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manuscript
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PMID 39364312 full text
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data:geo:GSE31210
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GEO GSE31210
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paper
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pmid:39364312
[1] object · 3 fields
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source:pmid:39364312
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manuscript
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PMID 39364312 full text
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data:geo:GSE31210
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dataset
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GEO GSE31210
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agent:corpus-assay-engine
[1] object · 4 fields
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activity-read-39364312
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extraction
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[0]
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agent:minimax-reader
[2] object · 4 fields
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activity-resolve-GSE31210
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used array · 1 item
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null
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99e1195c7d6f
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[0]
re confirmed using MR analysis. In addition, immunohistochemistry was used to detect the expression levels of
[1]
pression was decreased in LUAD group through immunohistochemistry. TGFBR2 can inhibit proliferation and migrat
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paper_text_order:769
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[1] object · 11 fields
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99e1195c7d6f
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[0]
ening genes on proliferation, migration, and apoptosis of LUAD cells. The correlation between the s
[1]
ng adenocarcinoma cell line A549 and promote apoptosis of A549 cells. Immune infiltration analysis
order_basis
paper_text_order:1028
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d migration of lung adenocarcinoma cell line A549 and promote apoptosis of A549 cells. Immune
[1]
noma cell line A549 and promote apoptosis of A549 cells. Immune infiltration analysis suggeste
[2]
red from Procell (CL-0016). These cells were cultured in Ham’s F-12K medium (21127-022, Gibco, USA) s
[3]
n LUAD. Cell culture Human lung cancer cells A549 were acquired from Procell (CL-0016). These
order_basis
paper_text_order:2134
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In this study, “TwoSampleMR” R package was employed to conduct two-sample MR analysis between exposures and outcome.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39364312", "kind": "science-record", "revision": 3, "content_hash": "sha256:1994e40e80d592177225d97de9be95efccea75a1638744fb2949c6a54e8af6f4", "created_at": "2026-11-01T20:29:34Z", "updated_at": "2026-11-01T22:12:50Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of potential biomarkers for lung adenocarcinoma: a study based on bioinformatics analysis combined with validation experiments.", "doi": "10.3389/fonc.2024.1425895", "pmid": "39364312", "authors": ["Chuchu Zhang", "Ying Liu", "Yingdong Lu", "Zehui Chen", "Yi Liu", "Qiyuan Mao", "Shengchuan Bao", "Ge Zhang", "Ying Zhang", "Hongsheng Lin", "Haiyan Li"], "journal": "Frontiers in oncology", "year": 2024, "abstract": "Background: The prognosis for lung adenocarcinoma (LUAD) remains dismal, with a 5-year survival rate of <20%. Therefore, the purpose of this study was to identify potentially reliable biomarkers in LUAD by machine learning combination with Mendelian randomization (MR).\nMethods: TCGA-LUAD, GSE40791, and GSE31210 were employed this study. Key module differential genes were identified through differentially expressed analysis and weighted gene co-expression network analysis (WGCNA). Furthermore, candidate biomarkers were derived from protein-protein interaction network (PPI) and machine learning. Ultimately, biomarkers were confirmed using MR analysis. In addition, immunohistochemistry was used to detect the expression levels of genes that have a causal relationship to LUAD in the LUAD group and the control group. Cell experiments were conducted to validate the effect of screening genes on proliferation, migration, and apoptosis of LUAD cells. The correlation between the screened genes and immune infiltration was determined by CIBERSORT algorithm. In the end, the gene-related drugs were predicted through the Drug-Gene Interaction database.\nResults: In total, 401 key module differential genes were obtained by intersecting of 5,702 differentially expressed genes (DEGs) and 406 key module genes. Thereafter, GIMAP6, CAV1, PECAM1, and TGFBR2 were identified. Among them, only TGFBR2 had a significant causal relationship with LUAD (p=0.04, b=-0.06), and it is a protective factor for LUAD. Subsequently, sensitivity analyses showed that there were no heterogeneity and horizontal pleiotropy in the univariate MR results, and the results were not overly sensitive to individual SNP loci, further validating the reliability of univariate Mendelian randomization (UVMR) results. However, no causal relationship was found between them by reverse MR analysis. Meanwhile, TGFBR2 expression was decreased in LUAD group through immunohistochemistry. TGFBR2 can inhibit proliferation and migration of lung adenocarcinoma cell line A549 and promote apoptosis of A549 cells. Immune infiltration analysis suggested a potential link between TGFBR2 expression and immune infiltration. Finally, Irinotecan and Hesperetin were predicted through DGIDB database.\nConclusion: In this study, TGFBR2 was identified as a biomarker of LUAD, which provided a new idea for the treatment strategy of LUAD and may aid in the development of personalized immunotherapy strategies.", "claims": [{"id": "claim-method", "statement": "In this study, “TwoSampleMR” R package was employed to conduct two-sample MR analysis between exposures and outcome.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39364312", "locator": "sections[8] \"Analysis of the causal relationship between exposure factors and outcome through univariate Mendelian randomization analysis\" paragraph 3", "quote": "In this study, “TwoSampleMR” R package was employed to conduct two-sample MR analysis between exposures and outcome."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary Material . 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Further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Among them, only TGFBR2 had a significant causal relationship with LUAD (p=0.04, b=−0.06), and it is a protective factor for LUAD.", "kind": "quantitative", "target": {"value": 0.04, "unit": "p-value", "tolerance": null, "source": "Among them, only TGFBR2 had a significant causal relationship with LUAD (p=0.04, b=−0.06), and it is a protective factor for LUAD."}, "evidence": [{"asset": "source:pmid:39364312", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Among them, only TGFBR2 had a significant causal relationship with LUAD (p=0.04, b=−0.06), and it is a protective factor for LUAD."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Among them, only TGFBR2 had a significant causal relationship with LUAD (p=0.04, b=−0.06), and it is a protective factor for LUAD.", "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:39364312"], "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:GSE31210"], "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:GSE31210"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39364312", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2024.1425895", "sha256": "sha256:f32620c122c122e21ea7282f3fb17227d73bb43d8eecb37ce2a0c5d6b49d7070", "bytes": 62724, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fonc.2024.1425895", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11446723", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE31210", "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=GSE31210", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE31210", "version": null, "note": "The original contributions presented in the study are included in the article/ Supplementary Material . 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