ArticleGenes & genomics2025
Comprehensive analysis based on IFN-γ and SASP related genes, bulk RNA and single-cell sequencing to evaluate the prognosis and immune landscape of stomach adenocarcinoma.
Article in Genes & genomics, 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStomach adenocarcinoma (STAD) represents the predominant subtype of gastric cancer, known for its drug resistance, unfavorable prognosis, and low cure rates. IFN-γ serves as a cytokine generated by immune cells, instrumental in tumor immune clearance and essential to the tumor microenvironment. The aging-associated secretory phenotype (SASP) can modify the local tissue environment, facilitating gastric cancer progression and chemotherapy resistance.
objectiveThis study intends to identify STAD subtypes based on IFN-γ and SASP-related genes and to develop a risk prognostic model for predicting patient survival, tumor immune microenvironment, and responses to drug treatment.
methodsThe genomic and clinical datasets originate from the Cancer Genome Atlas (TCGA) database, while the genes associated with IFN-γ and SASP come from pertinent scholarly articles. We discovered the prognostic genes linked to IFN-γ and SASP in STAD using Cox regression analysis. Next, we applied non-negative matrix factorization (NMF) to categorize LIHC into distinct molecular subtypes, identifying differentially expressed genes across these subtypes. Following this, we developed a predictive model using Cox and LASSO regression analyses to stratify patients into specific risk categories, validating the model to assess the prognostic significance of the identified signatures. Lastly, we integrated single-cell data to elucidate the immune landscape of STAD and identified potential drugs along with their sensitivity profiles.
resultsWe identified 17 prognostic genes related to IFN-γ and SASP, successfully classifying patients into two distinct molecular subtypes. These subtypes exhibited notable differences in immune profiles and prognostic outcomes. We pinpointed three differentially expressed genes to establish risk characteristics and created a prognostic model capable of accurately predicting patient outcomes. Our findings revealed a strong association between STAD and the extracellular matrix, low-risk group exhibited favorable prognosis, and may derive greater benefits from immunotherapy.
conclusionWe developed a risk model using IFN-γ and SASP-associated genes to predict the prognosis of STAD patients more accurately. Additionally, we assessed the immune landscape of STAD by integrating bulk RNA and single-cell sequencing analyses. This approach may yield valuable insights for clinical decision-making and immunotherapy strategies in STAD.
Indexed as
Identifiers
40293675What 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.