GEO series

Epithelial barrier function and its underlying mechanical regulation affects the biology of Macro-Metastasis Parenchyma Interface (MMPI).

GSE243188 Homo sapiens (human) RNA-seq (gene expression) 28 samples Submitted 2024/11/30 Platform GPL11154

At a glance

RNA-seq (gene expression) · human · 28 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: 28 samples: 18 × BM vs 10 × HGroups 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)

Already years ago, the histopathological growth pattern (HGP) of liver metastases at the macro-metastases parenchyma interface (MMPIliver) was included in pathological guidelines. Brain metastases also differ in their HGPs at the MMPIbrain. However, there is no existing biological concept for these differences. Pathophysiological, the MMPI is an interface between an emerging metastatic tissue and the host organ, which both protect themselves from each other.

Read the full summary ↓

Thus, it would be not surprising that carcinomas reactivate their original function, the epithelial barrier at this interface. However, differences in epithelial barrier were never been taking into account for different HGPs at the MMPI. Here, we systematically compared models with epithelial infiltration (Epi-models) with diffuse infiltrating models (Diff-models) at the MMPIbrain. Using this stratification, we revealed fundamental differences in mechano-transduction via YAP, Ca2+ distribution and the epithelial barrier function. Moreover, these features have effects on immune cell infiltration and druggable immune checkpoints. Finally, we clustered human brain and liver metastases by using an Epi- and a Diff-gene signature into four clusters: the Epi-, Diff-, Mixed- and Unclassified-cluster. These also revealed enormous differences in pathways, responsible for epithelial barrier formation, extracellular matrix and immune response. Moreover, it also revealed YAP as an important transcriptional co-activator in this context. Taken together, we report about fundamental differences in barrier function and mechano-regulation at the MMPI, which could have implications for therapy decisions of metastasized patients in future.

This dataset

Open GSE243188 in finder → Find more human RNA-seq datasets →

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.

Series matrix ↓

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 GSE243188_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

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.

Raw reads on ENA ↗

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 PRJNA1017362 and SRA study SRP460516. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

18 × BM vs 10 × H

Supports a between-group comparison across 28 samples.

How we read this: 2 replicated groups read from 28 sample titles; they account for 28 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

GSM7780409 BM0001 BM
GSM7780410 BM0002 BM
GSM7780411 BM0007 BM
GSM7780412 BM1011 BM
GSM7780413 BM1014 BM
GSM7780414 BM1016 BM
GSM7780415 BM1078 BM
GSM7780416 BM1084 BM
GSM7780417 BM1096 BM
GSM7780418 BM1102 BM
GSM7780419 BM1103 BM
GSM7780420 BM1105 BM
GSM7780421 BM1117 BM
GSM7780422 BM1118 BM
GSM7780423 BM1135 BM
GSM7780424 BM1153 BM
GSM7780425 BM1167 BM
GSM7780426 BM1169 BM
GSM7780427 H100 H
GSM7780428 H1792 H
GSM7780429 H194 H
GSM7780430 H296 H
GSM7780431 H558 H
GSM7780432 H608 H
GSM7780433 H616 H
GSM7780434 H857 H
GSM7780435 H907 H
GSM7780436 H937 H

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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.