Comprehensive analysis of Transcriptome Profiles in Hepatocellular Carcinoma patients
Purpose: The goal of this study is to get a comprehensive evaluation of the transcriptome profile of HCC patients Methods:Transcriptome profiles of paired Tumor and adjacent non-tumour from 25 HCC patients were generated by deep sequencing using the Illumina HiSeq 2000 platform. Paired sample T-test was used to identify differentially expressed genes between Tumours and adjacent non-Tumours. Results: A total of 4462 genes are differentially expressed genes between tumours and adjacent non-tumour...
Provenance — who produced it, who reused it
Linked to 15 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.
- MYC-targeted WDR4 promotes proliferation, metastasis, and sorafe... 2021 · 235 cites
- METTL5 stabilizes c‐Myc by facilitating USP5 translation to repr... 2023 · 208 cites
- NOP2-mediated m5C Modification of c-Myc in an EIF3A-Dependent Ma... 2023 · 90 cites
12 further papers cite this accession but reuse could not be confirmed.
Deep data QC
100/100 · AStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
Human bulk-RNA-seq with 90 bp reads at 97.9% Q20 and 92.8% Q30 across 789 million bases maintains high base quality across full read length. The 49.6% GC and mean quality 36.3 with minimal adapter contamination (0.02%) support confident transcript mapping and expression quantification in mammalian tissues.
The A grade is a transparent weighted average. Each metric below scored from 0–100% against the published bulk-RNA-seq thresholds, weighted by its importance; nothing is hidden or subjective.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0
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.