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pmid:36247089

Exploring Potential Biomarkers, Ferroptosis Mechanisms, and Therapeutic Targets Associated with Cutaneous Squamous Cell Carcinoma via Integrated Transcriptomic Analysis.

Wenxing Su · Biao Huang · Qingyi Zhang · Wei Han · Lu An · Yi Guan · Jiang Ji · Daojiang Yu

Journal of healthcare engineering · 2022 doi:10.1155/2022/3524022

Paper

complete

Experiment

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Data

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Analysis

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:36247089
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science-record
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sha256:b2debcd1dd8c7f9dc66afde924b32a3107abbd1c38531600bb69d2737591be26
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https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json
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2026-11-01T18:15:38Z
updated_at
2026-11-01T22:50:23Z
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paper object · 10 fields
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title
Exploring Potential Biomarkers, Ferroptosis Mechanisms, and Therapeutic Targets Associated with Cutaneous Squamous Cell Carcinoma via Integrated Transcriptomic Analysis.
doi
10.1155/2022/3524022
pmid
36247089
authors array · 8 items
[0]
Wenxing Su
[1]
Biao Huang
[2]
Qingyi Zhang
[3]
Wei Han
[4]
Lu An
[5]
Yi Guan
[6]
Jiang Ji
[7]
Daojiang Yu
journal
Journal of healthcare engineering
year
2022
abstract
Background: Cutaneous squamous cell carcinoma (cSCC) is the leading cause of death in patients with nonmelanoma skin cancers (NMSC). However, the unclear pathogenesis of cSCC limits the application of molecular targeted therapy. Methods: Three microarray datasets (GSE2503, GSE45164, and GSE66359) were downloaded from the Gene Expression Omnibus (GEO). After identifying the differentially expressed genes (DEGs) in tumor and nontumor tissues, five kinds of analyses, namely, functional annotation, protein-protein interaction (PPI) network, hub gene selection, TF-miRNA-mRNA regulatory network analysis, and ferroptosis mechanism, were performed. Results: A total of 146 DEGs were identified with significant differences, including 113 upregulated genes and 33 downregulated genes. The enriched functions and pathways of the DEGs included microtubule-based movement, ATP binding, cell cycle, P53 signaling pathway, oocyte meiosis, and PLK1 signaling events. Nine hub genes were identified (CDK1, AURKA, RRM2, CENPE, CCNB1, KIAA0101, ZWINT, TOP2A, and ASPM). Finally, RRM2, AURKA, and SAT1 were identified as significant ferroptosis-related genes in cSCC. The differential expression of these genes has been verified in two other independent datasets. Conclusions: By integrated bioinformatic analysis, the hub genes identified in this study elucidated the molecular mechanism of the pathogenesis and progression of cSCC and are expected to become future biomarkers or therapeutic targets.
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The DEGs between cSCC and noncancerous samples were screened using GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r), which is an online tool for differential analysis of the original dataset based on the LIMMA software package [16].
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sections[4] "2.2. Identification of DEGs" paragraph 2
quote
The DEGs between cSCC and noncancerous samples were screened using GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r), which is an online tool for differential analysis of the original dataset based on the LIMMA software package [16].
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In this study, mRNA microarray datasets were downloaded from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo).
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sections[23] "Data Availability" paragraph 2
quote
In this study, mRNA microarray datasets were downloaded from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo).
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A total of 146 common DEGs, including 113 upregulated genes and 33 downregulated genes, were identified between cSCC tissues and normal tissues.
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qualitative
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sections[12] "3.1. Identification of DEGs in cSCC" paragraph 2
quote
As shown in Figure 3(a), a total of 146 common DEGs, including 113 upregulated genes and 33 downregulated genes, were finally identified between cSCC tissues and normal tissues.
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A total of 146 common DEGs, including 113 upregulated genes and 33 downregulated genes, were identified between cSCC tissues and normal tissues.
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In this study, mRNA microarray datasets were downloaded from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo).
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Novel Molecular Markers for non-melanoma skin cancer
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Transcription profiling of human skin squamous cell carcinoma (SCC)
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In this study, mRNA microarray datasets were downloaded from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo).
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GEO accession URL
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In this study, mRNA microarray datasets were downloaded from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo).
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Molecular classification of non-melanoma skin cancer by gene expression profiling
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In this study, mRNA microarray datasets were downloaded from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo).
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570
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Analysis of the gene expression profile in normal human epidermal keratinocytes and cutaneous squamous cell carcinoma cell lines
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2026-11-01T18:46:34Z
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GEO accession URL
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In this study, mRNA microarray datasets were downloaded from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo).
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570
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Homo sapiens
title
Gene Expression Patterns Involved in the Malignant Transformation and Progression of Metastatic Melanoma
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The DEGs between cSCC and noncancerous samples were screened using GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r), which is an online tool for differential analysis of the original dataset based on the LIMMA software package [16].
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[0]
source:pmid:36247089
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE2503
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE2503
agent
agent:corpus-assay-engine
agents array · 2 items
[0] object · 3 fields
id
agent:corpus-assay-engine
type
software
label
Corpus assay engine
[1] object · 3 fields
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 object · 4 fields
parent_id
pmid:36247089
parent_hash
sha256:fe567124b22bf4ad8c1959314edb912bbd354416b75a4c4d2ca39caae83f8ccd
fork_of
null
gaps array · 14 items
[0]
asset:data:geo:GSE2503 licence not read
[1]
asset:data:geo:GSE2503 not downloaded
[2]
asset:data:geo:GSE45164 licence not read
[3]
asset:data:geo:GSE45164 not downloaded
[4]
asset:data:geo:GSE53462 licence not read
[5]
asset:data:geo:GSE53462 not downloaded
[6]
asset:data:geo:GSE66359 licence not read
[7]
asset:data:geo:GSE66359 not downloaded
[8]
asset:data:geo:GSE7553 licence not read
[9]
asset:data:geo:GSE7553 not downloaded
[10]
execution.not_run
[11]
review.human_critique
[12]
review.independent_reader
[13]
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
0.93
evidence array · 1 item
[0]
f molecular targeted therapy. Methods: Three microarray datasets (GSE2503, GSE45164, and GSE66359) w
order_basis
paper_text_order:369
id
assay:1
[1] object · 11 fields
role
analysis
definition_id
differential_expression
definition_version
f2132e96ae6c7a4e
required_operations array · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
The DEGs between cSCC and noncancerous samples were screened using GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r), which is an online tool for differential analysis of the original dataset based on the LIMMA software package [16].
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:36247089", "kind": "science-record", "revision": 5, "content_hash": "sha256:b2debcd1dd8c7f9dc66afde924b32a3107abbd1c38531600bb69d2737591be26", "created_at": "2026-11-01T18:15:38Z", "updated_at": "2026-11-01T22:50:23Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploring Potential Biomarkers, Ferroptosis Mechanisms, and Therapeutic Targets Associated with Cutaneous Squamous Cell Carcinoma via Integrated Transcriptomic Analysis.", "doi": "10.1155/2022/3524022", "pmid": "36247089", "authors": ["Wenxing Su", "Biao Huang", "Qingyi Zhang", "Wei Han", "Lu An", "Yi Guan", "Jiang Ji", "Daojiang Yu"], "journal": "Journal of healthcare engineering", "year": 2022, "abstract": "Background: Cutaneous squamous cell carcinoma (cSCC) is the leading cause of death in patients with nonmelanoma skin cancers (NMSC). However, the unclear pathogenesis of cSCC limits the application of molecular targeted therapy.\nMethods: Three microarray datasets (GSE2503, GSE45164, and GSE66359) were downloaded from the Gene Expression Omnibus (GEO). After identifying the differentially expressed genes (DEGs) in tumor and nontumor tissues, five kinds of analyses, namely, functional annotation, protein-protein interaction (PPI) network, hub gene selection, TF-miRNA-mRNA regulatory network analysis, and ferroptosis mechanism, were performed.\nResults: A total of 146 DEGs were identified with significant differences, including 113 upregulated genes and 33 downregulated genes. The enriched functions and pathways of the DEGs included microtubule-based movement, ATP binding, cell cycle, P53 signaling pathway, oocyte meiosis, and PLK1 signaling events. Nine hub genes were identified (CDK1, AURKA, RRM2, CENPE, CCNB1, KIAA0101, ZWINT, TOP2A, and ASPM). Finally, RRM2, AURKA, and SAT1 were identified as significant ferroptosis-related genes in cSCC. The differential expression of these genes has been verified in two other independent datasets.\nConclusions: By integrated bioinformatic analysis, the hub genes identified in this study elucidated the molecular mechanism of the pathogenesis and progression of cSCC and are expected to become future biomarkers or therapeutic targets.", "claims": [{"id": "claim-method", "statement": "The DEGs between cSCC and noncancerous samples were screened using GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r), which is an online tool for differential analysis of the original dataset based on the LIMMA software package [16].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36247089", "locator": "sections[4] \"2.2. Identification of DEGs\" paragraph 2", "quote": "The DEGs between cSCC and noncancerous samples were screened using GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r), which is an online tool for differential analysis of the original dataset based on the LIMMA software package [16]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "In this study, mRNA microarray datasets were downloaded from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36247089", "locator": "sections[23] \"Data Availability\" paragraph 2", "quote": "In this study, mRNA microarray datasets were downloaded from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 146 common DEGs, including 113 upregulated genes and 33 downregulated genes, were identified between cSCC tissues and normal tissues.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36247089", "locator": "sections[12] \"3.1. Identification of DEGs in cSCC\" paragraph 2", "quote": "As shown in Figure 3(a), a total of 146 common DEGs, including 113 upregulated genes and 33 downregulated genes, were finally identified between cSCC tissues and normal tissues."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of 146 common DEGs, including 113 upregulated genes and 33 downregulated genes, were identified between cSCC tissues and normal tissues.", "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:36247089"], "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:GSE2503"], "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:GSE2503"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36247089", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/3524022", "sha256": 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