The transcriptional landscape and mutational profile of lung adenocarcinoma
Understanding the molecular signatures of cancer is important to apply appropriate targeted therapies. Here we present the first large scale RNA sequencing study of lung adenocarcinoma demonstrating its power to identify somatic point mutations as well as transcriptional variants such as gene fusions, alternative splicing events and expression outliers. Our results reveal the genetic basis of 200 lung adenocarcinomas in Koreans including deep characterization of 87 surgical specimens by transcri...
Provenance — who produced it, who reused it
Linked to 35 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.
- A statistical approach for identifying differential distribution... 2016 · 329 cites
- SHMT1 knockdown induces apoptosis in lung cancer cells by causin... 2014 · 133 cites
- Targeting HSP90 Inhibits Proliferation and Induces Apoptosis Thr... 2022 · 99 cites
- Two-pass alignment improves novel splice junction quantification 2015 · 81 cites
- Feature Selection and Cancer Classification via Sparse Logistic... 2016 · 72 cites
30 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.