Construction and validation of a scoring system for perineural invasion-mediated inflammation in gastric cancer
RNA-seq (gene expression) · human · 50 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
- 50 samples: 50 samples, each with a distinct title — no replicated groupsGroups 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.
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE214293_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA1334132. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 50 sample titles: 50 distinct titles with little repetition. 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.
- GSM6603270 tumor tissue of Patient 1 [T1]
- GSM6603271 tumor tissue of Patient 11 [T11]
- GSM6603272 tumor tissue of Patient 13 [T13]
- GSM6603273 tumor tissue of Patient 14 [T14]
- GSM6603274 tumor tissue of Patient 15 [T15]
- GSM6603275 tumor tissue of Patient 17 [T17]
- GSM6603276 tumor tissue of Patient 2 [T2]
- GSM6603277 tumor tissue of Patient 20 [T20]
- GSM6603278 tumor tissue of Patient 21 [T21]
- GSM6603279 tumor tissue of Patient 23 [T23]
- GSM6603280 tumor tissue of Patient 25 [T25]
- GSM6603281 tumor tissue of Patient 26 [T26]
- GSM6603282 tumor tissue of Patient 27 [T27]
- GSM6603283 tumor tissue of Patient 28 [T28]
- GSM6603284 tumor tissue of Patient 29 [T29]
- GSM6603285 tumor tissue of Patient 3 [T3]
- GSM6603286 tumor tissue of Patient 30 [T30]
- GSM6603287 tumor tissue of Patient 4 [T4]
- GSM6603288 tumor tissue of Patient 40 [T40]
- GSM6603289 tumor tissue of Patient 7 [T7]
- GSM6603290 adjacent normal tissues of Patient 1 [N1]
- GSM6603291 adjacent normal tissues of Patient 11 [N11]
- GSM6603292 adjacent normal tissues of Patient 13 [N13]
- GSM6603293 adjacent normal tissues of Patient 14 [N14]
- GSM6603294 adjacent normal tissues of Patient 15 [N15]
- GSM6603295 adjacent normal tissues of Patient 17 [N17]
- GSM6603296 adjacent normal tissues of Patient 2 [N2]
- GSM6603297 adjacent normal tissues of Patient 21 [N21]
- GSM6603298 adjacent normal tissues of Patient 23 [N23]
- GSM6603299 adjacent normal tissues of Patient 25 [N25]
- GSM6603300 adjacent normal tissues of Patient 26 [N26]
- GSM6603301 adjacent normal tissues of Patient 27 [N27]
- GSM6603302 adjacent normal tissues of Patient 28 [N28]
- GSM6603303 adjacent normal tissues of Patient 30 [N30]
- GSM6603304 adjacent normal tissues of Patient 4 [N4]
- GSM6603305 adjacent normal tissues of Patient 40 [N40]
- GSM6603306 adjacent normal tissues of Patient 7 [N7]
- GSM6603307 adjacent normal tissues of Patient 12 [N12]
- GSM6603308 adjacent normal tissues of Patient 19 [N19]
- GSM6603309 adjacent normal tissues of Patient 22 [N22]
+ 10 more — browse all 50 samples with per-sample file links →
- GSE134520 Dissecting the single-cell transcriptome network underlying gastric premalignant lesions and early gastric cancer [10x genomics] 13 samples same disease briefing · shares gastric
- GSE163558 Transcriptional heterogeneity in primary and metastatic gastric cancer revealed using single-cell RNA sequencing 10 samples same disease briefing · shares gastric
- GSE173195 Roles of LTBP1 in the gastric cancer cells 6 samples same disease briefing · shares gastric
- GSE182543 ALKBH5 cooperated with LINC00659 promote gastric cancer progression by JAK1 mRNA m6A-YTHDF2-dependent modification [polyA] 6 samples same disease briefing · shares gastric
- GSE103045 Genetic and epigenetic gene inactivation coupling with KRAS activation in human gastric cancer [RNA-Seq] 3 samples same disease briefing · shares gastric
- GSE119196 HOXC10 Overexpression Promotes Cell Proliferation and Migration through Regulation of CST1 Expression in Gastric Cancer 2 samples same disease briefing · shares gastric
- GSE171054 NINJ2 inhibition increased chemo-sensitivity by decreasing CICs in chemotherapy-resistant gastric cancer 2 samples same disease briefing · shares gastric
- GSE182542 ALKBH5 cooperated with LINC00659 promote gastric cancer progression by JAK1 mRNA m6A-YTHDF2-dependent modification [2-m6A] 2 samples same disease briefing · shares gastric
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.