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pmid:34967576

Genomic Understanding Reveals the Important Role of FGFR2 as Paeoniflorin Target for Circumventing Breast Cancer Resistance to Tamoxifen.

Moh Arikus Wibowo · Eri Prasetyo Nugroho · Adam Hermawan

Asian Pacific journal of cancer prevention : APJCP · 2021 doi:10.31557/APJCP.2021.22.12.3949

Paper

complete

Experiment

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Data

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Analysis

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Execution

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:34967576
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science-record
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sha256:27f6066226cdfd8b25458f0d9b9eb51cf78d0ca43cbc29a9d60be61d6d097130
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https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json
created_at
2026-11-01T17:36:40Z
updated_at
2026-11-01T22:05:28Z
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paper object · 10 fields
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title
Genomic Understanding Reveals the Important Role of FGFR2 as Paeoniflorin Target for Circumventing Breast Cancer Resistance to Tamoxifen.
doi
10.31557/APJCP.2021.22.12.3949
pmid
34967576
authors array · 3 items
[0]
Moh Arikus Wibowo
[1]
Eri Prasetyo Nugroho
[2]
Adam Hermawan
journal
Asian Pacific journal of cancer prevention : APJCP
year
2021
abstract
OBJECTIVES: Paeoniflorin (PF), a compound found in Paeonia lactiflora and Paeonia suffruticosa, has anticancer potential, particularly in inhibiting migration and invasion, the resistant cancer cells hallmarks. To date, the mechanism of overcoming tamoxifen resistance in breast cancer is not yet elucidated. This research aims to explore the potential target of PF as a co-treatment for circumventing breast cancer resistance to tamoxifen with a genomic understanding-bioinformatics. METHODS: Microarray data originating from GSE67916 and GSE85871 in the NCBI GEO database was analyzed to obtain differentially expressed genes (DEGs). Further analyses were performed on DEGs using the DAVID v6.8, STRING-DB v11.0, the Cytoscape, and cBioportal. Gene expression analysis validation in breast cancer cells and tamoxifen-resistant breast cancer cells was accomplished using GEPIA and ONCOMINE databases. Survival rate analysis of selected genes was conducted using Kaplan-Meier. RESULTS: We obtained 175 DEGs from the two samples (tamoxifen-resistant and paeoniflorin-treated). DEG involves in 70 biological processes, 26 cellular components, and 18 molecular functions, and three pathways relevant to breast cancer. The PPI network analysis and hub genes selection obtained 10 genes with the highest degree scores. Genetic changes for selected genes, including IFNB1, CDK6, FGFR2, OAS1, BCL2, and STAT2 were found from 0.5% to 7% of the case population per patient case. Additional analysis using cBioportal revealed FGFR signaling pathway through Ras is important for the PF mechanism in circumventing breast cancer resistance to tamoxifen. ONCOMINE and GEPIA analysis emphasized the importance of selected genes in the tamoxifen-resistance mechanism. CONCLUSION: PF has potential to be used as a co-treatment for circumventing breast cancer resistance to tamoxifen by targeting FGFR2 signaling, but further validation is needed.
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The microarray data of the two GSE codes were further analyzed using the GEO2R to identify differentially expressed genes (DEGs) (Zhang et al., 2020).
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The microarray data of the two GSE codes were further analyzed using the GEO2R to identify differentially expressed genes (DEGs) (Zhang et al., 2020).
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The data that support the findings of this study are available in the supplementary files and available in http://ugm.id/PFRTA
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We obtained 175 DEGs from the two samples (tamoxifen-resistant and paeoniflorin-treated).
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We obtained 175 DEGs from the two samples (tamoxifen-resistant and paeoniflorin-treated).
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We obtained 175 DEGs from the two samples (tamoxifen-resistant and paeoniflorin-treated).
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Gene expression analysis in tamoxifen resistant ER+ breast cancer cell lines
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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 object · 4 fields
parent_id
pmid:34967576
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sha256:70c1f305afa457cad3cdabc66c3e6cd51dd9ed8dc4a100257013c8214b697e89
fork_of
null
gaps array · 9 items
[0]
asset:data:geo:GSE67916 licence not read
[1]
asset:data:geo:GSE67916 not downloaded
[2]
asset:data:geo:GSE85871 licence not read
[3]
asset:data:geo:GSE85871 not downloaded
[4]
execution.not_run
[5]
method.reader_named_unmapped
[6]
review.human_critique
[7]
review.independent_reader
[8]
review.machine_critique
assays array · 2 items
[0] object · 11 fields
role
data_generation
definition_id
microarray
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.99
evidence array · 4 items
[0]
nomic understanding-bioinformatics. METHODS: Microarray data originating from GSE67916 and GSE85871
[1]
Materials and Methods Data mining The microarray data were obtained from the NCBI Gene Expres
[2]
e series expressions were profiled using the microarray technology with Affymetrix Human Genome U133
[3]
uman Genome U133A 2.0 (Santa Clara, CA). The microarray data of the two GSE codes were further analy
order_basis
paper_text_order:587
id
assay:1
[1] object · 11 fields
role
analysis
definition_id
survival_analysis
definition_version
f2132e96ae6c7a4e
required_operations array · 1 item
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survival_km
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
Kaplan-Meier
order_basis
consumes_earlier_output
id
assay:2
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34967576", "kind": "science-record", "revision": 4, "content_hash": "sha256:27f6066226cdfd8b25458f0d9b9eb51cf78d0ca43cbc29a9d60be61d6d097130", "created_at": "2026-11-01T17:36:40Z", "updated_at": "2026-11-01T22:05:28Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Genomic Understanding Reveals the Important Role of FGFR2 as Paeoniflorin Target for Circumventing Breast Cancer Resistance to Tamoxifen.", "doi": "10.31557/APJCP.2021.22.12.3949", "pmid": "34967576", "authors": ["Moh Arikus Wibowo", "Eri Prasetyo Nugroho", "Adam Hermawan"], "journal": "Asian Pacific journal of cancer prevention : APJCP", "year": 2021, "abstract": "OBJECTIVES: Paeoniflorin (PF), a compound found in Paeonia lactiflora and Paeonia suffruticosa, has anticancer potential, particularly in inhibiting migration and invasion, the resistant cancer cells hallmarks. To date, the mechanism of overcoming tamoxifen resistance in breast cancer is not yet elucidated. This research aims to explore the potential target of PF as a co-treatment for circumventing breast cancer resistance to tamoxifen with a genomic understanding-bioinformatics.\nMETHODS: Microarray data originating from GSE67916 and GSE85871 in the NCBI GEO database was analyzed to obtain differentially expressed genes (DEGs). Further analyses were performed on DEGs using the DAVID v6.8, STRING-DB v11.0, the Cytoscape, and cBioportal. Gene expression analysis validation in breast cancer cells and tamoxifen-resistant breast cancer cells was accomplished using GEPIA and ONCOMINE databases. Survival rate analysis of selected genes was conducted using Kaplan-Meier.\nRESULTS: We obtained 175 DEGs from the two samples (tamoxifen-resistant and paeoniflorin-treated). DEG involves in 70 biological processes, 26 cellular components, and 18 molecular functions, and three pathways relevant to breast cancer. The PPI network analysis and hub genes selection obtained 10 genes with the highest degree scores. Genetic changes for selected genes, including IFNB1, CDK6, FGFR2, OAS1, BCL2, and STAT2 were found from 0.5% to 7% of the case population per patient case. Additional analysis using cBioportal revealed FGFR signaling pathway through Ras is important for the PF mechanism in circumventing breast cancer resistance to tamoxifen. ONCOMINE and GEPIA analysis emphasized the importance of selected genes in the tamoxifen-resistance mechanism.\nCONCLUSION: PF has potential to be used as a co-treatment for circumventing breast cancer resistance to tamoxifen by targeting FGFR2 signaling, but further validation is needed.", "claims": [{"id": "claim-method", "statement": "The microarray data of the two GSE codes were further analyzed using the GEO2R to identify differentially expressed genes (DEGs) (Zhang et al., 2020).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34967576", "locator": "sections[2] \"Materials and Methods\" paragraph 3", "quote": "The microarray data of the two GSE codes were further analyzed using the GEO2R to identify differentially expressed genes (DEGs) (Zhang et al., 2020)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data that support the findings of this study are available in the supplementary files and available in http://ugm.id/PFRTA", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34967576", "locator": "sections[7] \"Availability of data\" paragraph 2", "quote": "The data that support the findings of this study are available in the supplementary files and available in http://ugm.id/PFRTA"}], "state": "complete"}, {"id": "claim-target-result", "statement": "We obtained 175 DEGs from the two samples (tamoxifen-resistant and paeoniflorin-treated).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34967576", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We obtained 175 DEGs from the two samples (tamoxifen-resistant and paeoniflorin-treated)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We obtained 175 DEGs from the two samples (tamoxifen-resistant and paeoniflorin-treated).", "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:34967576"], "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:GSE67916"], "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:GSE67916"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34967576", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.31557/APJCP.2021.22.12.3949", "sha256": 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