Data files used for exploring BRAF mutation-associated gene risk model for predicting the prognosis of melanoma
doi:10.4121/21532917.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future.
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doi: 10.4121/21532917
doi: 10.4121/21532917
Datacite citation style:
Xiang Huang (2022): Data files used for exploring BRAF mutation-associated gene risk model for predicting the prognosis of melanoma. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/21532917.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
CC BY 4.0by
Xiang Huang
We identified significantly enriched KEGG pathways and corresponding genes in the BRAF mutant group via GSEA. For the first time, a prognostic signature based on seven BRAF-related genes with an independent prognostic value for melanoma patients and related to immune cell infiltration and checkpoint was constructed and validated. Furthermore, we established a nomogram based on the prognostic signature and independent clinical characteristics, providing a tool for the objective assessment of the survival rate of melanoma patients.
history
- 2022-11-14 first online, published, posted
publisher
4TU.ResearchData
format
*.gmt; .txt; *.tsv; *.xlsx
organizations
Guizhou Medical University
DATA
files (32)
- 3,036 bytesMD5:
d0dfa29d8ef81292d76866c34b1d30acfig.2-Group-BRAF.cls.txt - 21,094 bytesMD5:
c6e7c22a25e241a2e9243550db18e253fig.2-gsea_report_for_MUT_1663369829866.tsv - 6,316 bytesMD5:
24607071e4fb72c5d32a0cb50761d1edfig.2-gsea_report_for_WILD_1663369829866.tsv - 89,517 bytesMD5:
a2c8ba8056e10dd40ccd8b6fe73acc1cfig.2-KEGG-gene-sets.gmt - 733,979 bytesMD5:
74865529b02a625f60e400945ceb97e8fig.2-ranked_gene_list_MUT_versus_WILD_1663369829866.tsv - 151,206,608 bytesMD5:
3780ac84229defd0016a9689f865e901fig.2-total-exp.gct.txt - 64,441 bytesMD5:
b0a9d9f874ebc448e13215a56387a152fig.3-Lasso-Gene-list2.txt - 18,598 bytesMD5:
d0fab5a4525d2fc335247e122b12ac72fig.3-Lasso-riskscore.txt - 6,678 bytesMD5:
63f0db1d4a9b92e77d194d0f0baa7a78fig.3-univariate result.txt - 319,730 bytesMD5:
abbf7c6015e76ca9529f817c61d3a72bfig.3-univariate-Gene-list.txt - 10,741 bytesMD5:
7dfe85d7d828b2cabddf7084c24430c8fig.3-univariate-survival.tsv - 128,199 bytesMD5:
5fd90009499d745c17a990aa9d399acafig.4-ICGC-data-heat map.xlsx - 27,761 bytesMD5:
070552601b39765673c124816666f9aafig.4-ICGC-data-KM.xlsx - 83,783 bytesMD5:
150c1d9ee3180f0ee16c50aa53f341f1fig.4-ICGC-data-ROC.xlsx - 66,779 bytesMD5:
d4bd7e7ca6b4ea23ccc8b27c70e18afcfig.4-TCGA-data-heat map.xlsx - 18,573 bytesMD5:
6b96f9094e0bf9cbfda9df74cffca75cfig.4-TCGA-data-KM.txt - 18,400 bytesMD5:
8030ab8b1192d06f4926c2b26a9b0db8fig.4-TCGA-data-ROC.txt - 34,683 bytesMD5:
e11e45c525d0b6c73568617675b86369fig.5-data.xlsx - 100,209 bytesMD5:
fd47a0f62e40002b64fa146d5112b907fig.6-data.xlsx - 18,400 bytesMD5:
8030ab8b1192d06f4926c2b26a9b0db8fig.6-riskscore.txt - 401,578 bytesMD5:
9f6ac951602796475092ac9bce6d44dffig.6-TCGA-SKCM.GDC_phenotype.tsv - 18,500 bytesMD5:
d5fe2732b69016c8a98be377ac20fff1fig.7-nomagram.txt - 22,194 bytesMD5:
be4d8e4d7773f018a3b29db790f8ed8efig.7-TCGA-SKCM-clinical.tsv - 10,741 bytesMD5:
7dfe85d7d828b2cabddf7084c24430c8fig.7-TCGA-SKCM.survival.tsv - 145,986,200 bytesMD5:
ead22d184b6f9226a205f559b0557273fig.8-FPKM-Non-nor.txt - 36,987 bytesMD5:
db3ddfecfe0f5d46cc61e811bc358c4cfig.8-Group-riskscore.xlsx - 50,523 bytesMD5:
e6e004d82c2a0b21b730d3cb08a09abbfig.8-gsea_report_for_risk_high_1664072176472.tsv - 681,128 bytesMD5:
26bd4a557787c5ac77aaf5d6ec2ba7f1fig.8-gsea_report_for_risk_low_1664072176472.tsv - 733,471 bytesMD5:
34126a89278f8fdadbd691b3c22ee9a9fig.8-ranked_gene_list_risk_high_versus_risk_low_1664072176472.tsv - 151,206,608 bytesMD5:
3780ac84229defd0016a9689f865e901fig.8-total-exp.gct.txt - 294,229 bytesMD5:
857ef930c5e35f6747416d122bc71f07fig.9-immueScore.xlsx - 68,648 bytesMD5:
3d1680273d5020bf574dc119655e3c0dfig.9-immune checkpoints.xlsx -
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