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miR-BART1-3p inhibits proliferation and invasion of gastric cancer cells by regulating STAT3/pSTAT3 and PI3K/AKT/pAKT signaling pathways

GSE241046 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2024/10/01 Platform GPL16791
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)
Topic
Gastric cancerThis series is part of our curated briefing on it.
Samples
12 samples: 3 × AGS cells, NC vs 3 × AGS cells, OE vs 3 × SNU719 cells, S vs 3 × AGS cells, WTGroups 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)
[Aim] EBVaGC accounts for about 10% of gastric cancer and has a better prognosis than EBVnGC, but its molecular mechanism is still unclear. We investigated the role of EBV-miR-BART1-3p in the occurrence and development of EBVaGC by applying bioinformatics methods, high-throughput sequencing, in vitro experiments, target gene screening and verification, differential gene protein interaction network and gene regulatory network construction.
Read the full summary ↓
[Materials and Methods] The data set GSE51575 was normalized, and the outlier samples were deleted. Finally, gene expression profiles of 11 EBVaGC and 14 EBVnGC tumor tissues and non-tumor control tissues were obtained. Differential gene function analysis was performed, and molecular regulatory network was constructed. The mRNA expression levels of SNU-719 (EBV-positive gastric cancer cell line), AGS, AGS-NC (EBV-negative gastric cancer cell line) and AGS-OE (lentivirus-transfected miR-BART1-3p overexpression cell line) were detected by high-throughput next-generation sequencing technology, and differential gene expression characteristics were analyzed. To investigate the effect of miR-BART1-3p overexpression on mRNA expression level of gastric cancer cells and construct the protein interaction network of differential genes and the regulatory network of miR-BART1-3p gene. DIANA-MR micro-T, miRnada, miRDB, TarBase v.8, and ViRBase online databases were used to predict the target genes of miR-BART1-3p and GO enrichment analysis of target gene function was performed. Real-time fluorescence quantitative PCR was used to detect the expression levels of miR-BART1-3p and target genes in SNU719, AGS-OE and AGS gastric cancer cells. The effects of overexpression of EBV-miR-BART1-3p on proliferation, apoptosis, invasion, and migration of gastric cancer cells were investigated by CCK-8, cell cloning, cell scratch and Transwell. [Results] By analyzing the gene expression profile of the GSE51575 dataset, we found that EBVaGC up-regulated the expression of immune cell membrane protein receptors and chemokine family, and was involved in inducing tumor immunity, suggesting a better prognosis. EBVnGC significantly up-regulated the expression of AURK, BUB1B, CCNA2 and CDC20, suggesting the risk of malignant progression and recurrence. High-throughput next-generation sequencing of SNU719 and AGS to detect differences in gene expression revealed that EBVaGC cells (SNU719) up-regulated the expression of genes that inhibit malignant progression as well as potential tumor immunotherapy targets. These results are consistent with those found in the GSE51575 dataset. Fifteen miR-BART1-3p target genes, including FAM168A, TET3, HBP1, ZBTB5, ATXN7L3, KMT5C, SPARC and BMF, were predicted and screened by online tools. After the overexpression of miR-BART1-3p in AGC(EBVnGC) cells, the proliferation, migration, and invasion ability were significantly inhibited, and the signaling pathway promoting tumor progression was also limited in activation. [Conclusions] The overexpression of miR-BART1-3p may inhibit the proliferation and invasion and migration of gastric cancer cells by inhibiting the activation of STAT3/ pSTAT3 and PI3K/AKT signaling pathways and may become a target for the treatment of EBVaGC, which is worthy of further study.
This dataset
Open GSE241046 in finder → Find more human RNA-seq datasets → Gastric cancer landscape →
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 GSE241046_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 PRJNA1006300 and SRA study SRP455522. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
3 × AGS cells, NC vs 3 × AGS cells, OE vs 3 × SNU719 cells, S vs 3 × AGS cells, WT

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
More Gastric cancer datasets

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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.