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A machine learning framework to predict cancer metabolomics from gene-expression data

GSE299429 Homo sapiens (human) RNA-seq (gene expression) 15 samples Submitted 2025/09/30 Platform GPL34284

At a glance

RNA-seq (gene expression) · human · 15 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)
Samples: 15 samples: 5 conditions, mostly in triplicateGroups 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)

Metabolomics provides a direct functional readout of a tumor’s physiology. Yet, it is lagging behind other omics technologies in facilitating disease monitoring and prognostication. This stems partly from the scarcity of large-scale metabolomic studies, but also the analytical complexities of detecting diverse metabolites with varying physicochemical properties and concentrations.

Read the full summary ↓

To address this, we developed a machine learning framework using both tumor tissue and cell line samples across multiple cancer types that allows prediction of metabolomics from gene expression data. Two different model types were selected and trained for tissues and cell lines with their generalization capacity validated on independent cohorts, accurately predicting as high as 70-80% of tested metabolites. This work offers a scalable and efficient machine learning pipeline to determine metabolic from transcriptomic signatures, opening avenues to reconstruct and study the metabolic landscape of samples across novel and existing datasets lacking direct metabolomics measurements.

This dataset

Open GSE299429 in finder → Find more human RNA-seq datasets →

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 GSE299429_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 PRJNA1274415 and SRA study SRP591042. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

5 conditions, mostly in triplicate

MCF7_WT ×3 MCF7_MUT ×3 MCF10A_WT ×3 MCF10A_E545K ×3 MCF10A_H1047 ×3

Supports a between-group comparison across 15 samples.

How we read this: 5 replicated groups read from 15 sample titles; they account for 15 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

GSM9039104 MCF7_WT_1 MCF7_WT
GSM9039105 MCF7_WT_2 MCF7_WT
GSM9039106 MCF7_WT_3 MCF7_WT
GSM9039107 MCF7_MUT_1 MCF7_MUT
GSM9039108 MCF7_MUT_2 MCF7_MUT
GSM9039109 MCF7_MUT_3 MCF7_MUT
GSM9039110 MCF10A_WT_1 MCF10A_WT
GSM9039111 MCF10A_WT_2 MCF10A_WT
GSM9039112 MCF10A_WT_3 MCF10A_WT
GSM9039113 MCF10A_E545K_1 MCF10A_E545K
GSM9039114 MCF10A_E545K_2 MCF10A_E545K
GSM9039115 MCF10A_E545K_3 MCF10A_E545K
GSM9039116 MCF10A_H1047R_1 MCF10A_H1047
GSM9039117 MCF10A_H1047R_2 MCF10A_H1047
GSM9039118 MCF10A_H1047R_3 MCF10A_H1047

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