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Safety and efficacy of neoadjuvant radiotherapy and immunotherapy in the treatment of esophageal squamous cell carcinoma

GSE289185 Homo sapiens (human) RNA-seq (gene expression) 29 samples Submitted 2026/02/07 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 29 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
Esophageal cancerThis series is part of our curated briefing on it.
Samples
29 samples
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)
Background: Immunotherapy has been employed in the neoadjuvant treatment of locally advanced esophageal squamous cell carcinoma. To assess the feasibility of neoadjuvant radiotherapy combined with immunotherapy (NRIT), this study compares the efficacy and effectiveness of neoadjuvant chemoradiotherapy combined with immunotherapy (NICRT) and NRIT, while also exploring the underlying mechanisms of NRIT.
Read the full summary ↓
Methods:This study retrospectively analyzed a cohort of 64 patients diagnosed with locally advanced esophageal squamous cell carcinoma (ESCC) who underwent neoadjuvant therapy followed by esophagectomy between January 2019 and September 2023. Of these, 26 patients received neoadjuvant radiotherapy and immunotherapy (NRIT), while 38 patients were treated with neoadjuvant chemoradiotherapy (NICRT). The final analysis included pathological complete response (pCR), major pathological response (MPR), tumor regression grade (TRG), the rate of positive lymph node pCR, toxicity, surgical outcomes, postoperative complications, disease-free survival (DFS), and overall survival (OS). Additionally, paraffin-embedded tissue sections obtained before and after neoadjuvant NRIT were subjected to whole-exome sequencing, transcriptome sequencing, and immunohistochemistry (IHC) for programmed death-ligand 1 (PD-L1) analysis. Results: There was no significant difference in the rates of MPR and PCR (28.95% vs 30.77%, P=0.873), TRG classification (P=0.338), surgical outcomes, or postoperative complications (P=0.148), as well as overall survival (OS) and disease-free survival (DFS) between the NICRT and NRIT groups. However, a notable difference was observed in grade 3 or higher adverse events during treatment, with the NICRT group experiencing a higher incidence (34.21% vs 7.69%, P = 0.017) and severe hematological toxicity (23.68% vs 0.00%, P = 0.008). In the NRIT group, patients with high CPS scores demonstrated improved pathological responses. Pre-treatment genomic analyses indicated no differences in tumor mutation burden (TMB), somatic mutations, and copy number variations between MPR and non-MPR patients post-surgery. Transcriptome analysis revealed a significant number of differentially expressed genes in patients before and after treatment, closely associated with key pathways related to immunity. Following treatment, MPR patients exhibited a marked upregulation of the type I interferon response and the MHC class I antigen presentation pathway. Additionally, the number of B cells and monocytes increased, while non-MPR patients showed a significant downregulation of dendritic cells (DCs) after treatment, alongside the activation of various immune cells, including CD8 naïve cells, NK cells, and B cells. Conclusion: There was no significant difference between the NRIT group and the NICRT group regarding pathological response, surgical outcomes, postoperative complications, and survival analysis; furthermore, NRIT demonstrated a better safety profile compared to NICRT during treatment. Additionally, the mechanism of action of NRIT has been elucidated through a comprehensive analysis of genomic, transcriptomic, immunohistochemical, and other methods in conjunction with clinical results, thereby establishing NRIT as a neoadjuvant treatment that is both effective and safe.
This dataset
Open GSE289185 in finder → Find more human RNA-seq datasets → Esophageal 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 GSE289185_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 PRJNA1221879 and SRA study SRP562970. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
A cohort of 29 individual samples

The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Sample titles run RNA1 … RNA29 and carry no group information.

Samples in this study
More Esophageal cancer datasets

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