ArticleBMC biology2025
Transcriptional patterns of cancer-related genes in primary and metastatic tumours revealed by machine learning.
Article in BMC biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA key to understanding cancer is to determine the impact on the cellular pathways caused by the repertoire of DNA changes accrued in a cancer cell. Exploring the interactions between genomic aberrations and the expressed transcriptome can not only improve our understanding of the disease but also identify potential therapeutic approaches.
resultsUsing random forest models, we successfully identified transcriptional patterns associated with the loss of wild-type activity in cancer-related genes across various tumour types. While genes like TP53 and CDKN2A exhibited unique pan-cancer transcriptional patterns, others like ATRX, BRAF, and NRAS showed tumour-type-specific expression patterns. We also observed that genes like AR and ERBB4 did not lead to strong detectable patterns in the transcriptome when disrupted. Our investigation has also led to the identification of genes highly associated with transcriptional patterns. For instance, DRG2 emerged as the top contributor in classification of ATRX alterations in lower-grade gliomas and was significantly downregulated in ATRX mutant tumours. Additionally, transcriptional features important in classification of PTEN aberrations, such as CDCA8, AURKA, and CDC20, were found to be closely related to PTEN function.
conclusionsOur findings demonstrate the utility of machine learning in interpretation of cancer genomic data and provide new avenues for development of targeted therapies tailored to individual patients with cancer. Our analysis on the transcriptome revealed genes with expression levels strongly correlated with alterations in cancer-related genes. Additionally, we identified AURKA inhibitors as potential therapeutic option for tumours with alterations in tumour suppressors like FBXW7 or NSD1.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.