ArticleDiscover oncology2025
Integration of microbiome and transcriptome information in helping diagnosis of colorectal cancer.
Article in Discover oncology, 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
10 authors.
Funding
Abstract
backgroundColorectal cancer (CRC) is a major global public health issue, with over 1.8 million new cases and 881,000 deaths in 2018. This study aims to explore the application of integrating microbiome and transcriptome analysis in the diagnosis of colorectal adenocarcinoma (COAD), focusing on its potential in identifying cancer biomarkers and predicting disease progression.
methodsBy utilizing COAD transcriptome and microbiome data from TCGA, diversity and differential expression analyses were conducted to identify microbiome composition differences between primary tumors (PT) and solid tissue normal (STN) samples, and the role of mRNA in prognosis. Mediation analysis was used to identify interactions between microbiome, transcriptome, in COAD tumors. The Multi-Omics Graph convolutional NET works (MOGONET) framework was employed to combine these data for COAD tumor prediction.
resultsSignificant changes in microbiome composition and specific mRNA expression patterns were closely related to COAD development. It was observed that the Simplex virus genus can mediate the abundance of the BRAF transcripts, thereby affecting the risk of COAD. The MOGONET model demonstrated high accuracy in predicting COAD tumors, achieving 0.977 accuracy, 0.988 F1 score, and 1.0 AUROC.
conclusionIntegrating microbiome and transcriptome analysis shows significant potential in COAD diagnosis and prognostic assessment. These findings provide important insights for further clinical applications and cancer treatment strategies.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.