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Comprehensive analysis of the correlation between circulating biomarkers and tumor-immune microenvironment in advanced or recurrence non-small cell lung cancer in various treatment options, i.e., immunotherapy, EGFR TKI, and chemotherapy

GSE311200 Homo sapiens (human) RNA-seq (gene expression) 41 samples Submitted 2026/07/01 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 41 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
Lung cancerThis series is part of our curated briefing on it.
Samples
41 samples: 16 × NSCLC, Pretreatment, mEGFR, Ph21 vs 13 × NSCLC, Pretreatment, mEGFR, Ph20 vs 9 × NSCLC, Pretreatment, mEGFR, Ph22 vs 3 × NSCLC, Pretreatment, mEGFR, MLC21Groups 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)
The prospective cohort of advanced or recurrence non-small cell lung cancer in 2 academic centers in Bangkok, Thailand. We enrolled participants who received systemic treatment based on biomarker results, for example, EGFR TKI for sensitizing EGFR mutations. Pretreatment specimens and blood were collected and analyzed for circulating biomarkers, including TIME, i.e., CD8, Treg/FOXP3, and PD-L1 (22C3). Advanced omics platforms, such as RNA sequencing and whole-exome sequencing, are also employed to explain biologic meaningful.
This dataset
Open GSE311200 in finder → Find more human RNA-seq datasets → Lung 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 GSE311200_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 PRJNA1275863 and SRA study SRP609488. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
16 × NSCLC, Pretreatment, mEGFR, Ph21 vs 13 × NSCLC, Pretreatment, mEGFR, Ph20 vs 9 × NSCLC, Pretreatment, mEGFR, Ph22 vs 3 × NSCLC, Pretreatment, mEGFR, MLC21

Supports a between-group comparison across 41 samples.

How we read this: 4 replicated groups read from 41 sample titles; they account for 41 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

+ 1 more — browse all 41 samples with per-sample file links →

More Lung cancer datasets

Search all human RNA-seq datasets in GEO →

Share this dataset

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.