ArticleDiscover oncology2025
Integration analysis of single-cell and spatial transcriptomics identifies prognostic genes associated with neddylation in 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
13 authors.
Funding
Abstract
backgroundNeddylation modifications in immune and tumor cells are linked to poor tumor prognosis. This study identifies prognostic genes associated with neddylation-related genes (NRGs) in colorectal cancer (CRC) using single-cell and spatial transcriptome (ST) sequencing, aiming to advance CRC treatment strategies.
methodsDatasets included TCGA-CRC (training/internal validation, 7:3 split), GSE28722 (external validation), GSE132257 (scRNA-seq), and GSE226997 (ST). Single-cell analysis annotated seven cell types, with epithelial cells identified as key. Differentially expressed genes (DEGs) from key cells [DEGs(sc)] and bulk analysis of TCGA-CRC [DEGs(bulk)] were intersected with 247 NRGs to yield candidate genes. Regression analyses screened prognostic genes for risk model construction, validated internally and externally. Pseudotime trajectory and ST mapping visualized gene expression, while molecular networks and drug predictions were generated.
resultsIn scRNA-seq dataset, seven cell types were annotated, and epithelial cells were the key cells. A sum of 32 candidate genes were obtained by intersecting 5,131 DEGs(sc)(key cells), 9,089 DEGs(bulk), and 247 NRGs to produce PSMD12, PSMB2, and FBXL5 as prognostic genes. Both prognostic risk model and nomogram model were predictive of CRC. At the ST samples, PSMD12 was expressed at a low level in all sections, whereas PSMB2 and FBXL5 were expressed at a slightly higher level in the sections. In addition, a lncRNA-miRNA-mRNA network and a drug-prognostic gene network were created, getting some potential drugs like bortezomib.
conclusionA novel three-gene prognostic model for CRC was developed and validated, offering therapeutic insights through molecular networks and drug predictions.
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.