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pmid:37092360

Identification of immune-related genes contributing to head and neck squamous cell carcinoma development using weighted gene co-expression network analysis.

Qiaojuan Guo · Tianzhu Lu · Hanchuan Xu · Qingfeng Luo · Zhiliang Liu · Sicong Jiang · Jianji Pan · Shaojun Lin · Mengyao Lin · Fang Guo

Cancer reports (Hoboken, N.J.) · 2023 doi:10.1002/cnr2.1808

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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title
Identification of immune-related genes contributing to head and neck squamous cell carcinoma development using weighted gene co-expression network analysis.
doi
10.1002/cnr2.1808
pmid
37092360
authors array · 10 items
[0]
Qiaojuan Guo
[1]
Tianzhu Lu
[2]
Hanchuan Xu
[3]
Qingfeng Luo
[4]
Zhiliang Liu
[5]
Sicong Jiang
[6]
Jianji Pan
[7]
Shaojun Lin
[8]
Mengyao Lin
[9]
Fang Guo
journal
Cancer reports (Hoboken, N.J.)
year
2023
abstract
BACKGROUND: This study aimed to identify genes related to the degree of immune cell infiltration in head and neck squamous cell carcinoma (HNSCC), explore their new biological functions, and evaluate their diagnostic and prognostic value in HNSCC. METHODS: Transcriptomic data from The Cancer Genome Atlas (TCGA) HNSCC dataset was used to screen differentially expressed genes between tumors and normal tissues, followed by weighted correlation network analysis (WGCNA) to identify immune-related modules. Differential gene expression, immune cell infiltration, and survival analyses were performed to screen key genes. The expression of these key genes was validated in Oncomine and gene expression omnibus (GEO) datasets and by immunohistochemistry (IHC). RESULTS: 1869 and 1578 genes were significantly upregulated and downregulated in HNSCC. WGCNA showed that the brown module was associated with the most significant number of immune-related genes. PPI network analysis demonstrated that PPL, SCEL, KRT4, KRT24, KRT78, KRT13, SPRR3, TGM3, CRCT1, and CRNN were key components in the brown module. Furthermore, the expression levels of KRT4, KRT78, KRT13, and SPRR3 in HNSCC correlated with infiltration levels of CD8+ T cells and macrophages. Survival analyses revealed that the expression of KRT78, KRT13, and SPRR3 in HNSCC correlated with overall survival (OS). The IHC assay indicated that KRT13 (p = .042), KRT78 (p < .001), and SPRR3 (p = .022) protein expression levels in HNSCC were significantly lower than in normal tissues. Analysis of GSE65858 and GSE41613 datasets showed that a worse OS was associated with low expression of KRT78 (p = .0086, and p = .005) and SPRR3 (p = .017, and p = .02). CONCLUSIONS: Our findings suggest that KRT4, KRT78, KRT13, and SPRR3 are related to the occurrence and development of HNSCC. Importantly, KRT78 and SPRR3 might serve as diagnostic and prognostic biomarkers of HNSCC.
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We used DESeq2 13 to analyze the differential expression of genes in the transcriptomic data of TCGA HNSCC database. WGCNA 16 was used to identify co‐expressed gene modules in DEGs and convert the adjacency matrix into a topological overlap matrix (TOM). Survival curves were drawn using the Kaplan–Meier method, and differences between groups were assessed using the log‐rank test.
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We used DESeq2 13 to analyze the differential expression of genes in the transcriptomic data of TCGA HNSCC database.
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sections[4] "Weighted correlation network analysis" paragraph 2
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WGCNA 16 was used to identify co‐expressed gene modules in DEGs and convert the adjacency matrix into a topological overlap matrix (TOM).
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Survival curves were drawn using the Kaplan–Meier method, and differences between groups were assessed using the log‐rank test.
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The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
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Our findings suggest that KRT4, KRT78, KRT13, and SPRR3 are related to the occurrence and development of HNSCC. Importantly, KRT78 and SPRR3 might serve as diagnostic and prognostic biomarkers of HNSCC.
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Our findings suggest that KRT4, KRT78, KRT13, and SPRR3 are related to the occurrence and development of HNSCC. Importantly, KRT78 and SPRR3 might serve as diagnostic and prognostic biomarkers of HNSCC.
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Our findings suggest that KRT4, KRT78, KRT13, and SPRR3 are related to the occurrence and development of HNSCC. Importantly, KRT78 and SPRR3 might serve as diagnostic and prognostic biomarkers of HNSCC.
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method-3
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differential_expression
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deseq2
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Differential expression analysis
dsl_operations array · 3 items
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normalise_counts
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differential_expression_fit
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fdr_bh
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count_matrix_with_condition
evidence object · 3 fields
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0.8
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Network / pathway analysis
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build_graph
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centrality_scores
[2]
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evidence object · 3 fields
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0.7
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method.reader_differs_from_extractor
capability object · 3 fields
state
complete
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capability_id
capability:computational_analysis
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Computational analysis platform
assay_families array · 9 items
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meta_analysis
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pca
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molecular_docking
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differential_expression
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gwas
candidate
Automated Assays Laravel application and simulated adapters
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readiness object · 8 fields
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complete
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axes object · 3 fields
method object · 5 fields
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true
required_operations array · 1 item
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logrank_test
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: log_rank_test.
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
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accessions array · 2 items
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GSE65858
[1]
GSE41613
resolved_accessions array · 2 items
[0]
GSE65858
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GSE41613
statements array · 1 item
[0]
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
basis
The paper names GSE65858, GSE41613 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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dsl_operations array · 1 item
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logrank_test
assets array · 2 items
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source:pmid:37092360
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data:geo:GSE41613
steps array · 1 item
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data_gaps array · 1 item
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data:geo:GSE41613
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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
2026-11-01T18:37:43Z
independence
single_reader
evidence_check object · 2 fields
method
reordered
availability
verbatim
calls
5
usage object · 3 fields
prompt_tokens
8366
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2087
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128
stages array · 1 item
[0] object · 4 fields
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extraction
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not_run
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gaps array · 1 item
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pmid:37092360
type
paper
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pmid:37092360
[1] object · 3 fields
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source:pmid:37092360
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manuscript
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PMID 37092360 full text
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data:geo:GSE41613
type
dataset
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GEO GSE41613
activities array · 3 items
[0] object · 4 fields
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activity-reading-sci-2026-11-01
type
classification
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[0] object · 3 fields
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pmid:37092360
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paper
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pmid:37092360
[1] object · 3 fields
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source:pmid:37092360
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manuscript
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PMID 37092360 full text
[2] object · 3 fields
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data:geo:GSE41613
type
dataset
label
GEO GSE41613
agent
agent:corpus-assay-engine
[1] object · 4 fields
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activity-read-37092360
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extraction
used array · 1 item
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agent:minimax-reader
[2] object · 4 fields
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accession_resolution
used array · 1 item
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agent:corpus-assay-engine
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agent:minimax-reader
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dsl_catalogue_fingerprint
343fb30756ba6ec3
capability_table_fingerprint
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lineage object · 4 fields
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null
gaps array · 9 items
[0]
asset:data:geo:GSE41613 licence not read
[1]
asset:data:geo:GSE41613 not downloaded
[2]
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[3]
asset:data:geo:GSE65858 not downloaded
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[0] object · 11 fields
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data_generation
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partial
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99e1195c7d6f
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evidence array · 3 items
[0]
stic and prognostic value in HNSCC. METHODS: Transcriptomic data from The Cancer Genome Atlas (TCGA) HNS
[1]
ucted a comprehensive integrated analysis of transcriptomic and clinical data of HNSCC patients from TCG
[2]
rrence of HNSCC, we performed WGCNA based on transcriptomic data. Using a soft‐threshold of 4 (Figure 4A
order_basis
paper_text_order:369
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assay:1
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data_generation
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99e1195c7d6f
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[0]
ene expression omnibus (GEO) datasets and by immunohistochemistry (IHC). RESULTS: 1869 and 1578 genes were sig
[1]
(GEO) datasets and by immunohistochemistry (IHC). RESULTS: 1869 and 1578 genes were signific
[2]
C correlated with overall survival (OS). The IHC assay indicated that KRT13 (p = .042), KRT78
order_basis
paper_text_order:842
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assay:2
[2] object · 11 fields
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analysis
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f2132e96ae6c7a4e
evidence array · 1 item
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We used DESeq2 13 to analyze the differential expression of genes in the transcriptomic data of TCGA HNSCC database. WGCNA 16 was used to identify co‐expressed gene modules in DEGs and convert the adjacency matrix into a topological overlap matrix (TOM). Survival curves were drawn using the Kaplan–Meier method, and differences between groups were assessed using the log‐rank test.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37092360", "kind": "science-record", "revision": 4, "content_hash": "sha256:83afa6eb7da44286cf12a2c8f85a8d7f29867d7b8bff30062d929317d677a5c5", "created_at": "2026-11-01T19:01:46Z", "updated_at": "2026-11-01T22:51:46Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of immune-related genes contributing to head and neck squamous cell carcinoma development using weighted gene co-expression network analysis.", "doi": "10.1002/cnr2.1808", "pmid": "37092360", "authors": ["Qiaojuan Guo", "Tianzhu Lu", "Hanchuan Xu", "Qingfeng Luo", "Zhiliang Liu", "Sicong Jiang", "Jianji Pan", "Shaojun Lin", "Mengyao Lin", "Fang Guo"], "journal": "Cancer reports (Hoboken, N.J.)", "year": 2023, "abstract": "BACKGROUND: This study aimed to identify genes related to the degree of immune cell infiltration in head and neck squamous cell carcinoma (HNSCC), explore their new biological functions, and evaluate their diagnostic and prognostic value in HNSCC.\nMETHODS: Transcriptomic data from The Cancer Genome Atlas (TCGA) HNSCC dataset was used to screen differentially expressed genes between tumors and normal tissues, followed by weighted correlation network analysis (WGCNA) to identify immune-related modules. Differential gene expression, immune cell infiltration, and survival analyses were performed to screen key genes. The expression of these key genes was validated in Oncomine and gene expression omnibus (GEO) datasets and by immunohistochemistry (IHC).\nRESULTS: 1869 and 1578 genes were significantly upregulated and downregulated in HNSCC. WGCNA showed that the brown module was associated with the most significant number of immune-related genes. PPI network analysis demonstrated that PPL, SCEL, KRT4, KRT24, KRT78, KRT13, SPRR3, TGM3, CRCT1, and CRNN were key components in the brown module. Furthermore, the expression levels of KRT4, KRT78, KRT13, and SPRR3 in HNSCC correlated with infiltration levels of CD8+ T cells and macrophages. Survival analyses revealed that the expression of KRT78, KRT13, and SPRR3 in HNSCC correlated with overall survival (OS). The IHC assay indicated that KRT13 (p = .042), KRT78 (p < .001), and SPRR3 (p = .022) protein expression levels in HNSCC were significantly lower than in normal tissues. Analysis of GSE65858 and GSE41613 datasets showed that a worse OS was associated with low expression of KRT78 (p = .0086, and p = .005) and SPRR3 (p = .017, and p = .02).\nCONCLUSIONS: Our findings suggest that KRT4, KRT78, KRT13, and SPRR3 are related to the occurrence and development of HNSCC. Importantly, KRT78 and SPRR3 might serve as diagnostic and prognostic biomarkers of HNSCC.", "claims": [{"id": "claim-method", "statement": "We used DESeq2 13 to analyze the differential expression of genes in the transcriptomic data of TCGA HNSCC database. WGCNA 16 was used to identify co‐expressed gene modules in DEGs and convert the adjacency matrix into a topological overlap matrix (TOM). Survival curves were drawn using the Kaplan–Meier method, and differences between groups were assessed using the log‐rank test.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37092360", "locator": "sections[3] \"Data collection and preprocessing\" paragraph 2", "quote": "We used DESeq2 13 to analyze the differential expression of genes in the transcriptomic data of TCGA HNSCC database."}, {"asset": "source:pmid:37092360", "locator": "sections[4] \"Weighted correlation network analysis\" paragraph 2", "quote": "WGCNA 16 was used to identify co‐expressed gene modules in DEGs and convert the adjacency matrix into a topological overlap matrix (TOM)."}, {"asset": "source:pmid:37092360", "locator": "sections[10] \"Statistical analysis\" paragraph 2", "quote": "Survival curves were drawn using the Kaplan–Meier method, and differences between groups were assessed using the log‐rank test."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37092360", "locator": "sections[23] \"DATA AVAILABILITY STATEMENT\" paragraph 2", "quote": "The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Our findings suggest that KRT4, KRT78, KRT13, and SPRR3 are related to the occurrence and development of HNSCC. Importantly, KRT78 and SPRR3 might serve as diagnostic and prognostic biomarkers of HNSCC.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37092360", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Our findings suggest that KRT4, KRT78, KRT13, and SPRR3 are related to the occurrence and development of HNSCC. Importantly, KRT78 and SPRR3 might serve as diagnostic and prognostic biomarkers of HNSCC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Our findings suggest that KRT4, KRT78, KRT13, and SPRR3 are related to the occurrence and development of HNSCC. Importantly, KRT78 and SPRR3 might serve as diagnostic and prognostic biomarkers of HNSCC.", "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:37092360"], "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:GSE41613"], "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:GSE41613"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37092360", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1002/cnr2.1808", "sha256": 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WGCNA 16 was used to identify co‐expressed gene modules in DEGs and convert the adjacency matrix into a topological overlap matrix (TOM). Survival curves were drawn using the Kaplan–Meier method, and differences between groups were assessed using the log‐rank test."}}, {"id": "method-2", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Wilcoxon"}}, {"id": "method-3", "family": "differential_expression", "specific": "deseq2", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.8, "band": "high", "quote": "DESeq2"}}, {"id": "method-4", "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": 0.7, "band": "medium", "quote": "PPI"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "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": ["logrank_test"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: log_rank_test."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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METHODS: Transcriptomic data from The Cancer Genome Atlas (TCGA) HNS", "ucted a comprehensive integrated analysis of transcriptomic and clinical data of HNSCC patients from TCG", "rrence of HNSCC, we performed WGCNA based on transcriptomic data. Using a soft‐threshold of 4 (Figure 4A"], "order_basis": "paper_text_order:369", "id": "assay:1"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ene expression omnibus (GEO) datasets and by immunohistochemistry (IHC). RESULTS: 1869 and 1578 genes were sig", "(GEO) datasets and by immunohistochemistry (IHC). RESULTS: 1869 and 1578 genes were signific", "C correlated with overall survival (OS). The IHC assay indicated that KRT13 (p = .042), KRT78"], "order_basis": "paper_text_order:842", "id": "assay:2"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["logrank_test"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["We used DESeq2 13 to analyze the differential expression of genes in the transcriptomic data of TCGA HNSCC database. WGCNA 16 was used to identify co‐expressed gene modules in DEGs and convert the adjacency matrix into a topological overlap matrix (TOM). Survival curves were drawn using the Kaplan–Meier method, and differences between groups were assessed using the log‐rank test."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}