Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Single-cell RNAseq of Basal Cell Carcinoma (BCC) mouse models

ABSTRACT: Single-cell RNAseq of Basal Cell Carcinoma (BCC) mouse models

PROVIDER: PRJNA1226524 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR32426147.fastq.gz	Fastqsanger.gz
	SRR32426148.fastq.gz	Fastqsanger.gz
	SRR32426149.fastq.gz	Fastqsanger.gz
	SRR32426150.fastq.gz	Fastqsanger.gz
	SRR32426151.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 32

Similar Datasets

Bulk RNAseq of Basal Cell Carcinoma (BCC) mouse models

Project description:Bulk RNAseq of Basal Cell Carcinoma (BCC) mouse models

| PRJNA1225727 | ENA

Bulk RNAseq of Basal Cell Carcinoma (BCC) mouse models

Project description:RNA sequencing (RNAseq) of mouse tail keratinocytes from Basal Cell Carcinoma mouse models

2025-03-18 | GSE290008 | GEO

Single-cell RNAseq of Basal Cell Carcinoma (BCC) mouse models

Project description:Single-cell RNA sequencing of mouse tail epidermis from Basal Cell Carcinoma mouse models

2025-03-18 | GSE290180 | GEO

Expression data from mouse basal cell carcinoma (BCC) and normal epidermis, and BCC cell line treated with cyclopamine

Project description:Activation of the Sonic hedgehog (Shh) signaling pathway due to Patched 1 (PTCH1) mutation is a key event in sporadic and familial basal cell carcinoma (BCC) development. To find out the specific pathway for therapeutic intervention, we developed a mouse BCC model by skin specific Ptch1 inactivation, and sought to identify novel Shh targets. We used microarrays to identify up-regulated genes in mouse BCC. We used microarrays to identify down-regulated genes in mouse BCC cell line by cyclopamine.

2018-01-15 | GSE20065 | GEO

Single-cell RNA-Seq (scRNA-Seq) of mouse basal cell carcinoma (BCC)

Project description:Single-cell RNA-Seq (scRNA-Seq) of mouse basal cell carcinoma (BCC)

| PRJNA772789 | ENA

Single-cell RNA-Seq (scRNA-Seq) of mouse basal cell carcinoma (BCC) immune cells.

Project description:We used scRNA-Seq to examine BCC immune cell heterogeneity.

2023-03-29 | GSE204949 | GEO

Single-cell ATAC-Seq (scATAC-Seq) of mouse basal cell carcinoma (BCC) immune cells.

Project description:We used scATAC-Seq to examine BCC immune cell heterogeneity.

2023-03-29 | GSE204863 | GEO

Single-cell RNA-Seq (scRNA-Seq) of mouse basal cell carcinoma (BCC)

Project description:We used scRNA-Seq to examine BCC tumor heterogeneity.

2022-10-25 | GSE186183 | GEO

Deciphering dermatological distinctions: Cornulin as a discriminant biomarker between Basal Cell Carcinoma and Squamous Cell Carcinoma detected through E-biopsy and Machine Learning

Project description:Background: Clinical misdiagnosis between cutaneous squamous cell carcinoma (cSCC) and basal cell carcinoma (BCC) poses treatment challenges and carries risks of recurrence, metastases, and increased morbidity and mortality. Objective: We aimed to identify discriminant proteins markers for cSCC and BCC using a minimally invasive proteome sampling method called e-biopsy, employing electroporation for non-thermal cell permeabilization and machine learning. Methods: E-biopsy facilitated ex vivo proteome extraction from 21 cSCC and 21 BCC pathologically validated human cancers. LC/MS/MS profiling of 126 proteomes was followed by Machine Learning analysis to identify proteins distinguishing cSCC from BCC. For identified panel validation, we used proteomes sampled by e-biopsy from unrelated 20 cSCC and 46 BCC human cancers, and differential expression analysis of published transcriptomics. The most commonly chosen discriminant biomarker by machine learning models, cornulin, was also validated using fluorescent immunohistochemistry. Results: 192 proteomes sampled from 108 patients were analyzed. Machine Learning-based approaches resulted in a set of 11 potential biomarker proteins that can be used to construct a model with 95.2% average cross-validation accuracy, BCC precision of 93.6±14.5%, cSCC precision of 98.4±7.2%, specificity of 97.7±11.8%, and per-patient sensitivity 92.7±15.3%. Protein-protein interaction analysis revealed a novel interaction network connecting 10 of the 11 resulted proteins. Histological and transcriptomic validation confirmed cornulin as a discriminant marker significantly lower in cSCC than in BCC. Conclusions: E-biopsy combined with machine learning provides a novel approach to molecular biomarkers sampling from skin for biomarker detection and differential expression analysis between cSCC and BCC

2024-08-22 | PXD050713 | Pride

Single-cell RNA-Seq (scRNA-Seq) of mouse basal cell carcinoma (BCC) immune cells.

Project description:Single-cell RNA-Seq (scRNA-Seq) of mouse basal cell carcinoma (BCC) immune cells.

| PRJNA842760 | ENA