Dissecting the single-cell transcriptome network underlying gastric premalignant lesions and early gastric cancer [10x genomics]
Intestinal-type gastric cancer is preceded by premalignant lesions including chronic atrophic gastritis and intestinal metaplasia. In this study, we performed a scRNA-seq survey of 56,440 cells from thirteen gastric antral mucosa biopsies from nine patients with Non-atrophic gastritis (NAG), CAG, IM or early gastric cancer (EGC), and constructed a single-cell transcriptome atlas for gastric premalignant and early-malignant lesions. The thirteen biopsies, including three wild superficial gastriti...
Provenance — who produced it, who reused it
Linked to 29 papers in the literature. Roles are inferred factual signals (who deposited the data vs who reused it), with counts — never a judgement about any author.
- Single cell RNA sequencing of 13 human tissues identify cell typ... 2020 · 912 cites
- Mouse Heterochromatin Adopts Digital Compaction States without S... 2020 · 309 cites
- METTL3 promotes oxaliplatin resistance of gastric cancer CD133+ ... 2022 · 133 cites
- Systematic investigation of mitochondrial transfer between cance... 2023 · 114 cites
- Single-Cell RNA Sequencing Unifies Developmental Programs of Eso... 2023 · 90 cites
- m6A RNA methylation-mediated NDUFA4 promotes cell proliferation... 2022 · 89 cites
23 further papers cite this accession but reuse could not be confirmed.
Deep data QC
metadata only · no data-level QC for this typeStandardized, field-standard QC computed by touching the data — every metric states how it was obtained
No quantitative QC rubric exists for this data type yet, so it is deliberately left unscored — this is an honest "not applicable", not a poor rating.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently
Scientific quality
Based on hands-on reproduction of the papers that use this dataset. A reproducible paper that stands on this data is positive evidence; a flagged one is a prompt to look closer — never a verdict on the dataset itself without the evidence.