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Nuclear translocation of TfR1 facilitating tumor progression via p53-mediated gene regulation [RNA-seq]

GSE271109 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2024/12/27 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 12 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
Homo sapiens (human)
Samples
12 samples: 3 × HCT-116-shscr-RNA-seq vs 3 × HCT-116-shTfR1-RNA-seq vs 3 × HCT-116-shscr-CDDP-RNA-seq vs 3 × HCT-116-shTfR1-CDDP-RNA-seqGroups 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.
Summary (written by the submitters)
It is well-known that p53-mediated gene regulation exerts a profound impact on the occurrence, progression, metastasis, and various other facets of tumor. Wherein, activation of p53 downstream DNA damage repair-related genes is able to promote the survival of cancer cells during tumor progression.Notably, it has been substantiated that the transcriptional activity of p53 could be modulated by interactions with other proteins.Therefore, we asked whether the interaction with TfR1 was relevant to the transcriptional regulation of p53 target genes.
Read the full summary ↓
Our results support the positive regulation of nuclear TfR1 on DNA damage repair-related genes, especially for the NER pathway.
This dataset
Download · which file do I need?

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.

1 · Just have a look

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.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE271109_RAW.tar (it can run to tens of gigabytes).

3 · Start from the raw reads

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 PRJNA1129875 and SRA study SRP517026. Searching any of these in the dataset finder brings you back here.

Study design
3 × HCT-116-shscr-RNA-seq vs 3 × HCT-116-shTfR1-RNA-seq vs 3 × HCT-116-shscr-CDDP-RNA-seq vs 3 × HCT-116-shTfR1-CDDP-RNA-seq

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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.

Samples in this study
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