ArticleScientific reports2025
Exploring the regulatory mechanisms of paraptosis-related prognostic genes in gastric cancer using single-cell sequencing and transcriptome analysis.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Integration of Multi-Omics Data To Understand the Multifaceted Role of RAMP1 across Different Cancer Types.Cell biochemistry and biophysics · 2026Review
- Crosstalk Between Autophagy and Paraptosis: A New Frontier in Cancer Therapy.International journal of molecular sciences · 2026Review
- Paraptosis-related genes regulate tumor immune microenvironment and predict prognosis in breast cancer.Journal of cell communication and signaling · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Previous studies have suggested a potential role for paraptosis-related genes (PRGs) in cancer pathogenesis, yet their prognostic significance and therapeutic efficacy in gastric cancer (GC) remain largely unexplored. This study aims to investigate the function of PRGs in GC and offer valuable insights for clinical assessment of patient prognosis. Single-cell RNA sequencing (scRNA-seq) and the cancer genome atlas (TCGA)-GC transcriptomic data were acquired from public databases, with PRGs derived from Literature. Differential expressed genes 1 (DEGs1) were identified through scRNA-seq differential analysis. Prognostic genes were determined via regression analysis, followed by construction of prognostic models and nomograms to validate risk scores as independent predictors. Immune infiltration analysis assessed prognostic genes' impact on the tumor microenvironment. Cell trajectory analysis elucidated critical differentiation patterns between GC and normal groups, while cell communication analysis revealed interactions between key cellular subpopulations. In this study, 3,177 DEGs were identified from scRNA-seq. A risk model using 8 prognostic genes predicted GC patient survival outcomes effectively. These genes were also validated as independent prognostic factors in a nomogram. High-risk patients showed significantly increased immunotherapy resistance. T cells and fibroblasts were more differentiated in GC patients, while neutrophils had the highest interaction intensity in both GC and normal groups, with the CXCL8-CXCR2 interaction being most significant (p < 0.01). This study identified 8 prognostic genes (ABRACL, RANBP1, RAMP1, CCT2, AKR1A1, NPTN, ITGA8, CLU) and combined single-cell and transcriptome analysis to offer new insights into potential GC 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.