转移性黑色素瘤伴或不伴BRAF突变的蛋白质组学谱与计算机模拟分析

数据集获取（Figshare）

• 源站标题：Proteomic Profile and <i>In Silico</i> Analysis in Metastatic Melanoma with and without BRAF Mutation
• DOI：10.1371/journal.pone.0112025
• 发布日期：2014-12-01
• 文件数：6 个
• 文件总体积：177.84 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Proteomic_Profile_and_In_Silico_Analysis_in_Metastatic_Melanoma_with_and_without_BRAF_Mutation/1255533

命令行下载

curl -L -o "Table_S1.csv" "https://ndownloader.figshare.com/files/1811151"
curl -L -o "Table_S2.csv" "https://ndownloader.figshare.com/files/1811152"
curl -L -o "Table_S3.csv" "https://ndownloader.figshare.com/files/1811153"
curl -L -o "Table_S4.csv" "https://ndownloader.figshare.com/files/1811154"
curl -L -o "Table_S5.csv" "https://ndownloader.figshare.com/files/1811155"
curl -L -o "Table_S6.csv" "https://ndownloader.figshare.com/files/1811156"

文件清单

• Table_S1.csv（62.13 KB）
• Table_S2.csv（29.40 KB）
• Table_S3.csv（12.73 KB）
• Table_S4.csv（14.95 KB）
• Table_S5.csv（35.03 KB）
• Table_S6.csv（23.60 KB）

文件总体积 177.84 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Introduction
Selective inhibitors of BRAF, vemurafenib and dabrafenib are the standard of care for metastatic melanoma patients with BRAF V600, while chemotherapy continued to be widely used in BRAF wild type patients.
Materials and Methods
In order to discover novel candidate biomarkers predictive to treatment, serum of 39 metastatic melanoma vemurafenib (n = 19) or chemotherapy (n = 20) treated patients at baseline, at disease control and at progression, were analyzed using SELDI-TOF technology. In silico analysis was used to identify more significant peaks.
Results
In patients with different BRAF status, we found 5 peptides significantly deregulated, with the down-regulation of the m/z 9176 peak strongly associated with BRAF mutation. At baseline as predictive biomarkers we identified 2 peptides - m/z 6411, 4075 – as significantly up-regulated in responders to chemotherapy and 4 peaks - m/z 5900, 12544, 49124 and 11724 - significantly up-regulated in longer vs shorter responders to vemurafenib. After response, 3 peptides (m/z 4658, 18639, and 9307) resulted significantly down regulated while 3 peptides m/z 9292, 7765 and 9176 appeared up-regulated respectively in chemotherapy and vemurafenib responder patients. In vemurafenib treated patients, 16 peaks appeared deregulated at progression compared to baseline time. In silico analysis identified proteins involved in invasiveness (SLAIN1) and resistance (ABCC12) as well as in the pathway of detoxification (NQO1) and apoptosis (RBM10, TOX3, MTEFD1, TSPO2). Proteins associated with the modulation of neuronal plasticity (RIN1) and regulatory activity factors of gene transcription (KLF17, ZBTB44) were also highlighted.
Conclusion
Our exploratory study highlighted some factors that deserve to be further investigated in order to provide a framework for improving melanoma treatment management through the development of biomarkers which could act as the strongest surrogates of the key biological events in stage IV melanoma.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0112025（source: https://plos.figshare.com/articles/dataset/Proteomic_Profile_and_In_Silico_Analysis_in_Metastatic_Melanoma_with_and_without_BRAF_Mutation/1255533）。