ArticleHereditas2026
A prognostic model for gastric cancer based on histamine-associated prognostic genes.
Article in Hereditas, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Deciphering MMRN1 diagnostic and therapeutic implications in the substantia nigra of Parkinson's disease patients via integrative bioinformatic analysis and multi-omics studies.Frontiers in aging neuroscience · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundHistamine has various associations with gastric cancer (GC); however, the mechanisms underlying histamine functions in GC have not been established. This study aimed to examine histamine-linked prognostic genes and mechanisms in GC. Relevant data were sourced from public databases, and differential expression, multivariate Cox, univariate Cox, and machine learning regression analyses were performed to identify prognostic genes associated with histamine in GC. Subsequently, a prognostic model was constructed. Independent prognostic factors linked to GC prognosis were determined through regression analyses and used for nomogram model construction. Furthermore, Gene Set Enrichment Analysis (GSEA) and drug sensitivity and immune infiltration analyses were conducted to explore the potential function of these genes in GC from various perspectives. Finally, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to validate the expression of GRP, NPPB, SERPINE1, GAMT, MMRN1, and SLC22A16 in GC and paracarcinoma tissue.
resultsGRP, NPPB, SERPINE1, GAMT, MMRN1, and SLC22A16 were identified as putative prognostic genes, and a prognostic model was constructed. Compared with the low-risk group (LRG), the survival rates in the high-risk group (HRG) were reduced. Moreover, only M, N, age, and risk scores could be used to construct the nomogram model, which could precisely predict the survival status of patients with GC. GSEA indicated that the development of GC may be associated with certain metabolic pathways. Furthermore, there were 23 distinct infiltrating immune cells between the HRG and LRG, including activated B cells. HRG and LRG showed remarkable variation in sensitivity to 100 drugs (e.g., AP.24534, pazopanib, and AZD8055). RT-qPCR revealed that GRP, NPPB, SERPINE1, GAMT, MMRN1, and SLC22A16 expression was markedly upregulated in GC tissues compared with paracarcinoma tissue.
conclusionWe identified six prognostic genes and constructed a prognostic model, which provides a theoretical basis for the relationship between histamine and GC as well as potential therapeutic targets for GC.
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.