Engineered bacteria as exogenous organelle mimics restore PTEN and p53 tumor suppressor functions for cancer therapy
RNA-seq (gene expression) · mouse · 30 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Mus musculus (mouse)
- Samples
- 30 samples: 6 conditions, mostly with about 5 replicates eachGroups are read from the sample names, not from the paper. Check the list below.
- Data you can get
- A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.
Processed table
The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE322551_RAW.tar (it can run to tens of gigabytes).
Raw sequencing reads
The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.
What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA1430413 and SRA study SRP680214. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 30 samples.
How we read this: 6 replicated groups read from 30 sample titles; they account for 30 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM9554126 A-1 A
- GSM9554127 A-2 A
- GSM9554128 A-3 A
- GSM9554129 A-4 A
- GSM9554130 A-5 A
- GSM9554131 B-1 B
- GSM9554132 B-2 B
- GSM9554133 B-3 B
- GSM9554134 B-4 B
- GSM9554135 B-5 B
- GSM9554136 C-1 C
- GSM9554137 C-2 C
- GSM9554138 C-3 C
- GSM9554139 C-4 C
- GSM9554140 C-5 C
- GSM9554141 D-1 D
- GSM9554142 D-2 D
- GSM9554143 D-3 D
- GSM9554144 D-4 D
- GSM9554145 D-5 D
- GSM9554146 E-1 E
- GSM9554147 E-2 E
- GSM9554148 E-3 E
- GSM9554149 E-4 E
- GSM9554150 E-5 E
- GSM9554151 F-1 F
- GSM9554152 F-2 F
- GSM9554153 F-3 F
- GSM9554154 F-4 F
- GSM9554155 F-5 F
- GSE126789 Reactivation of PTEN tumor suppressor for cancer treatment through inhibition of a MYC-WWP1 inhibitory pathway 20 samples shares pten, suppressor
- GSE247772 Lethal giant larvae gene family functions as a tumor suppressor in skin epidermis 12 samples shares suppressor, functions
- GSE293700 Transcriptome analysis in the prefrontal cortex of PTEN KI heterozygous mice treated with drug 60 samples shares pten
- GSE249577 Research on the Mechanism of PTEN Mediated tumor-associated macrophages (TAMs) Polarization 46 samples shares pten
- GSE314411 p53 maintains healthy aging [P53KO_PTENKO_RNAseq] 31 samples shares pten
- GSE319618 Prefrontal cortex bulk RNA-seq analysis of drug-treated PTEN KI heterozygous mice at P40 30 samples shares pten
- GSE291912 Biobank of genetically defined murine prostate cancer tumoroids uncovers oncogenic pathways and drug vulnerabilities driven by PTEN-loss 24 samples shares pten
- GSE249266 Transcriptomics of PLXDC2 knockdown BMDM treated with PTEN 24 samples shares pten
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.