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pmid:33823876

Screening of gene markers related to the prognosis of metastatic skin cutaneous melanoma based on Logit regression and survival analysis.

Guoliang Jia · Zheyu Song · Zhonghang Xu · Youmao Tao · Yuanyu Wu · Xiaoyu Wan

BMC medical genomics · 2021 doi:10.1186/s12920-021-00923-0

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Experiment

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Data

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title
Screening of gene markers related to the prognosis of metastatic skin cutaneous melanoma based on Logit regression and survival analysis.
doi
10.1186/s12920-021-00923-0
pmid
33823876
authors array · 6 items
[0]
Guoliang Jia
[1]
Zheyu Song
[2]
Zhonghang Xu
[3]
Youmao Tao
[4]
Yuanyu Wu
[5]
Xiaoyu Wan
journal
BMC medical genomics
year
2021
abstract
BACKGROUND: Bioinformatics was used to analyze the skin cutaneous melanoma (SKCM) gene expression profile to provide a theoretical basis for further studying the mechanism underlying metastatic SKCM and the clinical prognosis. METHODS: We downloaded the gene expression profiles of 358 metastatic and 102 primary (nonmetastatic) CM samples from The Cancer Genome Atlas (TCGA) database as a training dataset and the GSE65904 dataset from the National Center for Biotechnology Information database as a validation dataset. Differentially expressed genes (DEGs) were screened using the limma package of R3.4.1, and prognosis-related feature DEGs were screened using Logit regression (LR) and survival analyses. We also used the STRING online database, Cytoscape software, and Database for Annotation, Visualization and Integrated Discovery software for protein-protein interaction network, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses based on the screened DEGs. RESULTS: Of the 876 DEGs selected, 11 (ZNF750, NLRP6, TGM3, KRTDAP, CAMSAP3, KRT6C, CALML5, SPRR2E, CD3G, RTP5, and FAM83C) were screened using LR analysis. The survival prognosis of nonmetastatic group was better compared to the metastatic group between the TCGA training and validation datasets. The 11 DEGs were involved in 9 KEGG signaling pathways, and of these 11 DEGs, CALML5 was a feature DEG involved in the melanogenesis pathway, 12 targets of which were collected. CONCLUSION: The feature DEGs screened, such as CALML5, are related to the prognosis of metastatic CM according to LR. Our results provide new ideas for exploring the molecular mechanism underlying CM metastasis and finding new diagnostic prognostic markers.
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With univariate and multivariate Cox regression analysis in the survival package of R3.4.1, we screened 61 independent prognostic DEGs, and then using LR and cluster analysis, we successfully screened 11 feature DEGs: ZNF750, NLRP6, TGM3, KRTDAP, CAMSAP3, KRT6C, CALML5, SPRR2E, CD3G, RTP5, and FAM83C.
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sections[16] "Discussion" paragraph 3
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With univariate and multivariate Cox regression analysis in the survival package of R3.4.1, we screened 61 independent prognostic DEGs, and then using LR and cluster analysis, we successfully screened 11 feature DEGs: ZNF750, NLRP6, TGM3, KRTDAP, CAMSAP3, KRT6C, CALML5, SPRR2E, CD3G, RTP5, and FAM83C.
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The datasets supporting the conclusions of this article are available in the [GSE65904] and [TCGA] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65904] and [https://xenabrowser.net/datapages/?cohort=GDC%20TCGA%20Melanoma%20(SKCM)&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443].
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The datasets supporting the conclusions of this article are available in the [GSE65904] and [TCGA] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65904] and [https://xenabrowser.net/datapages/?cohort=GDC%20TCGA%20Melanoma%20(SKCM)&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443].
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title
Whole-genome expression analysis of melanoma tumor biopsies from a population-based cohort.
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Gene expression profile data and clinical information
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List of important prognostic feature DEGs screened by Logit model
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[0]
CAMSAP3
[1]
− 1.18449
[2]
0.52553
[3]
1
[4]
0.0242
[6] array · 5 items
[0]
KRT6C
[1]
− 0.96555
[2]
0.44870
[3]
1
[4]
0.03141
[7] array · 5 items
[0]
CALML5
[1]
1.47087
[2]
0.57774
[3]
1
[4]
0.0109
[8] array · 5 items
[0]
SPRR2E
[1]
1.59208
[2]
0.53097
[3]
1
[4]
0.00271
[9] array · 5 items
[0]
CD3G
[1]
4.12233
[2]
1.77857
[3]
1
[4]
0.02046
[10] array · 5 items
[0]
RTP5
[1]
7.35929
[2]
2.91801
[3]
1
[4]
0.01167
[11] array · 5 items
[0]
FAM83C
[1]
9.35699
[2]
3.95983
[3]
1
[4]
0.01813
locator
section[12]/table-wrap[1]#Tab3
[5] object · 16 fields
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asset:table:pmid:33823876:4
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table
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jats-table
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application/vnd.jats.table+json
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null
sha256
sha256:87d14b555f0fe92386db6ef490abeaeb0cd80a021c9dc93e847383619e2c766f
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access
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verified
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read_table
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section[12]/table-wrap[2]#Tab4
version
null
note
Fuzzy matrix of LR classification result
structure object · 5 fields
pmcid
PMC8022370
section_index
12
section_title
Screening of feature DEGs by LR
row_count
10
column_count
5
table object · 8 fields
label
Table 4
caption
Fuzzy matrix of LR classification result
rows array · 10 items
[0] array · 2 items
[0]
Dataset name
[1]
Predict
[1] array · 4 items
[0]
Class
[1]
Metastatic
[2]
Primary
[3]
Percent
[2] array · 5 items
[0]
TCGA dataset
[1]
Observed
[2]
[3]
[4]
[3] array · 5 items
[0]
[1]
Metastatic
[2]
350
[3]
8
[4]
97.77
[4] array · 5 items
[0]
[1]
Primary
[2]
25
[3]
77
[4]
75.49
[5] array · 5 items
[0]
Overall percent
[1]
[2]
[3]
[4]
92.83
[6] array · 5 items
[0]
GSE65904 microarray dataset
[1]
Observed
[2]
[3]
[4]
[7] array · 5 items
[0]
[1]
Metastatic
[2]
72
[3]
11
[4]
86.75
[8] array · 5 items
[0]
[1]
Primary
[2]
27
[3]
40
[4]
59.70
[9] array · 5 items
[0]
Overall percent
[1]
[2]
[3]
[4]
74.67
locator
section[12]/table-wrap[2]#Tab4
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GO biological processes and KEGG signaling pathways in which genes are significantly related in the interaction network
structure object · 5 fields
pmcid
PMC8022370
section_index
15
section_title
GO and KEGG signaling pathway enrichment analysis
row_count
31
column_count
5
table object · 8 fields
label
Table 5
caption
GO biological processes and KEGG signaling pathways in which genes are significantly related in the interaction network
rows array · 31 items
[0] array · 5 items
[0]
Category
[1]
Term
[2]
Count
[3]
Gene
[4]
P value
[1] array · 5 items
[0]
Biology Process
[1]
GO:0,031,424 ~ keratinization
[2]
20
[3]
TGM3, HRNR
[4]
3.18E−35
[2] array · 5 items
[0]
[1]
GO:0,008,544 ~ epidermis development
[2]
22
[3]
CALML5, ZNF750
[4]
9.46E−34
[3] array · 5 items
[0]
[1]
GO:0,030,216 ~ keratinocyte differentiation
[2]
19
[3]
TGM3, TP63
[4]
1.04E−28
[4] array · 5 items
[0]
[1]
GO:0,018,149 ~ peptide cross-linking
[2]
14
[3]
LCE5A, EVPL
[4]
8.98E−22
[5] array · 5 items
[0]
[1]
GO:0,061,436 ~ establishment of skin barrier
[2]
9
[3]
HRNR, FLG
[4]
2.25E−16
[6] array · 5 items
[0]
[1]
GO:0,045,104 ~ intermediate filament cytoskeleton organization
[2]
4
[3]
KRT6C, KRT3
[4]
2.92E−06
[7] array · 5 items
[0]
[1]
GO:0,043,163 ~ cell envelope organization
[2]
3
[3]
HRNR, TGM3
[4]
4.98E−06
[8] array · 5 items
[0]
[1]
GO:0,016,337 ~ single organismal cell–cell adhesion
[2]
6
[3]
PKP1, DSG1
[4]
5.90E−06
[9] array · 5 items
[0]
[1]
GO:0,042,060 ~ wound healing
[2]
5
[3]
NLRP6, KRT6A
[4]
3.26E−05
[10] array · 5 items
[0]
[1]
GO:0,045,109 ~ intermediate filament organization
[2]
3
[3]
KRT17, KRT2
[4]
1.92E−04
[11] array · 5 items
[0]
[1]
GO:0,030,855 ~ epithelial cell differentiation
[2]
4
[3]
KRT14, KRT3
[4]
2.99E−04
[12] array · 5 items
[0]
[1]
GO:0,007,010 ~ cytoskeleton organization
[2]
5
[3]
KRT6B, KRT5
[4]
4.34E−04
[13] array · 5 items
[0]
[1]
GO:0,031,069 ~ hair follicle morphogenesis
[2]
3
[3]
KRT17, TGM3
[4]
5.47E−04
[14] array · 5 items
[0]
[1]
GO:0,010,482 ~ regulation of epidermal cell division
[2]
2
[3]
TP63, SFN
[4]
1.23E−03
[15] array · 5 items
[0]
[1]
GO:0,045,110 ~ intermediate filament bundle assembly
[2]
2
[3]
PKP1, KRT14
[4]
1.23E−03
[16] array · 5 items
[0]
[1]
GO:0,051,546 ~ keratinocyte migration
[2]
2
[3]
KRT16, KRT2
[4]
2.04E−03
[17] array · 5 items
[0]
[1]
GO:0,045,087 ~ innate immune response
[2]
6
[3]
NLRP6,SH2D1A
[4]
3.18E−03
[18] array · 5 items
[0]
[1]
GO:0,010,838 ~ positive regulation of keratinocyte proliferation
[2]
2
[3]
TGM1, TP63
[4]
3.64E−03
[19] array · 5 items
[0]
[1]
GO:0,003,334 ~ keratinocyte development
[2]
2
[3]
KRT2, SFN
[4]
4.04E−03
[20] array · 5 items
[0]
[1]
GO:0,031,581 ~ hemidesmosome assembly
[2]
2
[3]
KRT, KRT14
[4]
4.82E−03
[21] array · 5 items
[0]
[1]
GO:0,043,616 ~ keratinocyte proliferation
[2]
2
[3]
TP63, KRT2
[4]
4.82E−03
[22] array · 5 items
[0]
KEGG Pathway
[1]
hsa04020:Calcium signaling pathway
[2]
3
[3]
CALML3, CALML5
[4]
9.31E−03
[23] array · 5 items
[0]
[1]
hsa04070:Phosphatidylinositol signaling system
[2]
2
[3]
CALML3, CALML5
[4]
1.98E−02
[24] array · 5 items
[0]
[1]
hsa04640:Hematopoietic cell lineage
[2]
2
[3]
CD3G, IL7R
[4]
2.26E−02
[25] array · 5 items
[0]
[1]
hsa04912:GnRH signaling pathway
[2]
2
[3]
CALML3, CALML5
[4]
2.53E−02
[26] array · 5 items
[0]
[1]
hsa04916:Melanogenesis
[2]
2
[3]
CALML3, CALML5
[4]
2.56E−02
[27] array · 5 items
[0]
[1]
hsa04114:Oocyte meiosis
[2]
2
[3]
CALML3, CALML5
[4]
2.80E−02
[28] array · 5 items
[0]
[1]
hsa04270:Vascular smooth muscle contraction
[2]
2
[3]
CALML3, CALML5
[4]
2.84E−02
[29] array · 5 items
[0]
[1]
hsa04722:Neurotrophin signaling pathway
[2]
2
[3]
CALML3, CALML5
[4]
3.10E−02
[30] array · 5 items
[0]
[1]
hsa04910:Insulin signaling pathway
[2]
2
[3]
CALML3, CALML5
[4]
3.32E−02
locator
section[15]/table-wrap[1]#Tab5
gaps array · 2 items
[0]
asset:data:geo:GSE65904 not downloaded
[1]
asset:data:geo:GSE65904 licence not read
analysis object · 4 fields
state
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methods array · 6 items
[0] object · 10 fields
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method-1
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survival_analysis
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Survival analysis
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quote
With univariate and multivariate Cox regression analysis in the survival package of R3.4.1, we screened 61 independent prognostic DEGs, and then using LR and cluster analysis, we successfully screened 11 feature DEGs: ZNF750, NLRP6, TGM3, KRTDAP, CAMSAP3, KRT6C, CALML5, SPRR2E, CD3G, RTP5, and FAM83C.
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evidence object · 3 fields
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quote
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[2] object · 10 fields
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quote
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evidence object · 3 fields
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method-6
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evidence object · 3 fields
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medium
quote
protein-protein interaction
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complete
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capability:computational_analysis
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Automated Assays Laravel application and simulated adapters
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basis
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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
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GSE65904
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GSE65904
statements array · 1 item
[0]
The datasets supporting the conclusions of this article are available in the [GSE65904] and [TCGA] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65904] and [https://xenabrowser.net/datapages/?cohort=GDC%20TCGA%20Melanoma%20(SKCM)&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443].
basis
The paper names GSE65904 in GSE65904 and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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With univariate and multivariate Cox regression analysis in the survival package of R3.4.1, we screened 61 independent prognostic DEGs, and then using LR and cluster analysis, we successfully screened 11 feature DEGs: ZNF750, NLRP6, TGM3, KRTDAP, CAMSAP3, KRT6C, CALML5, SPRR2E, CD3G, RTP5, and FAM83C.
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33823876", "kind": "science-record", "revision": 7, "content_hash": "sha256:71e9fcc819e743b850482a4e42bd54dd9985280d53e70b970073a94e89d497c3", "created_at": "2026-11-01T15:41:31Z", "updated_at": "2026-11-01T22:03:29Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Screening of gene markers related to the prognosis of metastatic skin cutaneous melanoma based on Logit regression and survival analysis.", "doi": "10.1186/s12920-021-00923-0", "pmid": "33823876", "authors": ["Guoliang Jia", "Zheyu Song", "Zhonghang Xu", "Youmao Tao", "Yuanyu Wu", "Xiaoyu Wan"], "journal": "BMC medical genomics", "year": 2021, "abstract": "BACKGROUND: Bioinformatics was used to analyze the skin cutaneous melanoma (SKCM) gene expression profile to provide a theoretical basis for further studying the mechanism underlying metastatic SKCM and the clinical prognosis.\nMETHODS: We downloaded the gene expression profiles of 358 metastatic and 102 primary (nonmetastatic) CM samples from The Cancer Genome Atlas (TCGA) database as a training dataset and the GSE65904 dataset from the National Center for Biotechnology Information database as a validation dataset. Differentially expressed genes (DEGs) were screened using the limma package of R3.4.1, and prognosis-related feature DEGs were screened using Logit regression (LR) and survival analyses. We also used the STRING online database, Cytoscape software, and Database for Annotation, Visualization and Integrated Discovery software for protein-protein interaction network, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses based on the screened DEGs.\nRESULTS: Of the 876 DEGs selected, 11 (ZNF750, NLRP6, TGM3, KRTDAP, CAMSAP3, KRT6C, CALML5, SPRR2E, CD3G, RTP5, and FAM83C) were screened using LR analysis. The survival prognosis of nonmetastatic group was better compared to the metastatic group between the TCGA training and validation datasets. The 11 DEGs were involved in 9 KEGG signaling pathways, and of these 11 DEGs, CALML5 was a feature DEG involved in the melanogenesis pathway, 12 targets of which were collected.\nCONCLUSION: The feature DEGs screened, such as CALML5, are related to the prognosis of metastatic CM according to LR. Our results provide new ideas for exploring the molecular mechanism underlying CM metastasis and finding new diagnostic prognostic markers.", "claims": [{"id": "claim-method", "statement": "With univariate and multivariate Cox regression analysis in the survival package of R3.4.1, we screened 61 independent prognostic DEGs, and then using LR and cluster analysis, we successfully screened 11 feature DEGs: ZNF750, NLRP6, TGM3, KRTDAP, CAMSAP3, KRT6C, CALML5, SPRR2E, CD3G, RTP5, and FAM83C.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33823876", "locator": "sections[16] \"Discussion\" paragraph 3", "quote": "With univariate and multivariate Cox regression analysis in the survival package of R3.4.1, we screened 61 independent prognostic DEGs, and then using LR and cluster analysis, we successfully screened 11 feature DEGs: ZNF750, NLRP6, TGM3, KRTDAP, CAMSAP3, KRT6C, CALML5, SPRR2E, CD3G, RTP5, and FAM83C."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets supporting the conclusions of this article are available in the [GSE65904] and [TCGA] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65904] and [https://xenabrowser.net/datapages/?cohort=GDC%20TCGA%20Melanoma%20(SKCM)&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33823876", "locator": "sections[22] \"Availability of data and materials\" paragraph 2", "quote": "The datasets supporting the conclusions of this article are available in the [GSE65904] and [TCGA] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65904] and [https://xenabrowser.net/datapages/?cohort=GDC%20TCGA%20Melanoma%20(SKCM)&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443]."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:33823876"], "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:GSE65904"], "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:GSE65904"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33823876", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12920-021-00923-0", "sha256": 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[https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65904] and [https://xenabrowser.net/datapages/?cohort=GDC%20TCGA%20Melanoma%20(SKCM)&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443].", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets supporting the conclusions of this article are available in the [GSE65904] and [TCGA] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65904] and [https://xenabrowser.net/datapages/?cohort=GDC%20TCGA%20Melanoma%20(SKCM)&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443].", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1608593", "GSM1608594", "GSM1608595", "GSM1608596", "GSM1608597", "GSM1608598", "GSM1608599", "GSM1608600", "GSM1608601", "GSM1608602", "GSM1608603", "GSM1608604", "GSM1608605", "GSM1608606", "GSM1608607", "GSM1608608", "GSM1608609", "GSM1608610", "GSM1608611", "GSM1608612", "GSM1608613", "GSM1608614", 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metastatic(N = 102)", "P value"], ["Age (years, mean ± sd)", "56.26 ± 15.73", "64.53 ± 13.91", "7.10E−07a"], ["Gender (Male/Female)", "226/132", "60/42", "0.488b"], ["Pathologic_M (M0/M1/–)", "313/21/24", "97/3/2", "0.317b"], ["Pathologic_N (N0/N1/N2/N3/–)", "172/65/39/44/38", "57/8/10/11/16", "0.0785b"], ["Pathologic_T (T0/T1/T2/T3/T4/–)", "23/40/72/80/67/76", "0/1/5/10/84/2", "2.2E−16b"], ["Pathologic_stage (I/II/III/IV/–)", "81/74/142/20/41", "2/65/27/3/5", "1.133E−15b"], ["Radiotherapy (Yes/No/–)", "47/310/1", "2/100/0", "4.395E−04b"], ["Tumor recurrence (Yes/No/–)", "69/135/154", "20/72/10", "0.0402b"], ["Dead (Death/Alive)", "193/165", "29/73", "0.0715b"], ["Overall survival time (months, mean ± sd)", "74.10 ± 67.91", "47.15 ± 10.04", "5.279E−22a"]], "row_count": 11, "column_count": 4, "numeric_cells": 32, "header_rows": 1, "locator": "section[3]/table-wrap[1]#Tab1"}}, {"id": "asset:table:pmid:33823876:2", "role": "table", "format": "jats-table", "media_type": 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["Pathologic_N (N0/N1/N2/N3/–)", "118/42/28/33/26", "111/31/21/22/28", "0.599b"], ["Pathologic_T (T0/T1/T2/T3/T4/–)", "20/33/51/54/44/45", "3/8/26/36/107/33", "2.893E−13b"], ["Pathologic_stage (I/II/III/IV/–)", "66/43/97/14/27", "17/96/72/9/19", "6.568E−12b"], ["Radiotherapy (Yes/No/–)", "35/212/0", "14/198/1", "0.00975b"], ["Tumor recurrence (Yes/No/–)", "45/107/95", "44/100/69", "0.899b"], ["Tumor metastatic (Yes/No)", "235/12", "123/90", "2.200E−16b"], ["Dead (Death/Alive)", "111/136", "111/102", "0.149b"], ["Overall survival time (months, mean ± sd)", "78.21 ± 71.12", "42.07 ± 49.53", "4.269E−10a"]], "row_count": 12, "column_count": 4, "numeric_cells": 35, "header_rows": 1, "locator": "section[10]/table-wrap[1]#Tab2"}}, {"id": "asset:table:pmid:33823876:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:794d0671121e30a92382592a188603662a8fe2f46adadee4940f6043b1a9b368", "bytes": 759, "access": "open", "state": 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["", "Metastatic", "350", "8", "97.77"], ["", "Primary", "25", "77", "75.49"], ["Overall percent", "", "", "", "92.83"], ["GSE65904 microarray dataset", "Observed", "", "", ""], ["", "Metastatic", "72", "11", "86.75"], ["", "Primary", "27", "40", "59.70"], ["Overall percent", "", "", "", "74.67"]], "row_count": 10, "column_count": 5, "numeric_cells": 15, "header_rows": 2, "locator": "section[12]/table-wrap[2]#Tab4"}}, {"id": "asset:table:pmid:33823876:5", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:6d962b543c14bd65d6156a4c95f17a5b2c32b48042561aab433f1f37b28c39cb", "bytes": 2747, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[15]/table-wrap[1]#Tab5", "version": null, "note": "GO biological processes and KEGG signaling pathways in which genes are significantly related in the interaction network", "structure": {"pmcid": "PMC8022370", "section_index": 15, "section_title": "GO and KEGG signaling pathway enrichment analysis", "row_count": 31, "column_count": 5}, "table": {"label": "Table 5", "caption": "GO biological processes and KEGG signaling pathways in which genes are significantly related in the interaction network", "rows": [["Category", "Term", "Count", "Gene", "P value"], ["Biology Process", "GO:0,031,424 ~ keratinization", "20", "TGM3, HRNR", "3.18E−35"], ["", "GO:0,008,544 ~ epidermis development", "22", "CALML5, ZNF750", "9.46E−34"], ["", "GO:0,030,216 ~ keratinocyte differentiation", "19", "TGM3, TP63", "1.04E−28"], ["", "GO:0,018,149 ~ peptide cross-linking", "14", "LCE5A, EVPL", "8.98E−22"], ["", "GO:0,061,436 ~ establishment of skin barrier", "9", "HRNR, FLG", "2.25E−16"], ["", "GO:0,045,104 ~ intermediate filament cytoskeleton organization", "4", "KRT6C, KRT3", "2.92E−06"], ["", "GO:0,043,163 ~ cell envelope organization", "3", "HRNR, TGM3", "4.98E−06"], ["", "GO:0,016,337 ~ single organismal cell–cell adhesion", "6", "PKP1, DSG1", "5.90E−06"], ["", "GO:0,042,060 ~ wound healing", "5", "NLRP6, KRT6A", "3.26E−05"], ["", "GO:0,045,109 ~ intermediate filament organization", "3", "KRT17, KRT2", "1.92E−04"], ["", "GO:0,030,855 ~ epithelial cell differentiation", "4", "KRT14, KRT3", "2.99E−04"], ["", "GO:0,007,010 ~ cytoskeleton organization", "5", "KRT6B, KRT5", "4.34E−04"], ["", "GO:0,031,069 ~ hair follicle morphogenesis", "3", "KRT17, TGM3", "5.47E−04"], ["", "GO:0,010,482 ~ regulation of epidermal cell division", "2", "TP63, SFN", "1.23E−03"], ["", "GO:0,045,110 ~ intermediate filament bundle assembly", "2", "PKP1, KRT14", "1.23E−03"], ["", "GO:0,051,546 ~ keratinocyte migration", "2", "KRT16, KRT2", "2.04E−03"], ["", "GO:0,045,087 ~ innate immune response", "6", "NLRP6,SH2D1A", "3.18E−03"], ["", "GO:0,010,838 ~ positive regulation of keratinocyte proliferation", "2", "TGM1, TP63", "3.64E−03"], ["", "GO:0,003,334 ~ keratinocyte development", "2", "KRT2, SFN", "4.04E−03"], ["", "GO:0,031,581 ~ hemidesmosome assembly", "2", "KRT, KRT14", "4.82E−03"], ["", "GO:0,043,616 ~ keratinocyte proliferation", "2", "TP63, KRT2", "4.82E−03"], ["KEGG Pathway", "hsa04020:Calcium signaling pathway", "3", "CALML3, CALML5", "9.31E−03"], ["", "hsa04070:Phosphatidylinositol signaling system", "2", "CALML3, CALML5", "1.98E−02"], ["", "hsa04640:Hematopoietic cell lineage", "2", "CD3G, IL7R", "2.26E−02"], ["", "hsa04912:GnRH signaling pathway", "2", "CALML3, CALML5", "2.53E−02"], ["", "hsa04916:Melanogenesis", "2", "CALML3, CALML5", "2.56E−02"], ["", "hsa04114:Oocyte meiosis", "2", "CALML3, CALML5", "2.80E−02"], ["", "hsa04270:Vascular smooth muscle contraction", "2", "CALML3, CALML5", "2.84E−02"], ["", "hsa04722:Neurotrophin signaling pathway", "2", "CALML3, CALML5", "3.10E−02"], ["", "hsa04910:Insulin signaling pathway", "2", "CALML3, CALML5", "3.32E−02"]], "row_count": 31, "column_count": 5, "numeric_cells": 101, "header_rows": 1, "locator": 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