ArticleDiscover oncology2026
Multifaceted bioinformatic analysis uncover links m5C-related ferroptosis gene SLC2A1 to prognosis and immune infiltration in lung adenocarcinoma.
Article in Discover oncology, 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.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecent research highlights the pivotal role of 5-methylcytosine (m5C) modification and ferroptosis in the progression of various cancers. However, the prognostic value of m5C-related ferroptosis genes in lung adenocarcinoma (LUAD) remains unclear. This study aims to establish a prognostic framework centered on m5C-related ferroptosis genes to improve the accuracy of prognosis prediction in LUAD patients, thereby optimizing targeted therapeutic strategies.
methodsThe mRNA expression profiles, along with clinicopathological information of LUAD patients were obtained from The Cancer Genome Atlas (TCGA). Differential expression and weighted gene co-expression network analysis (WGCNA) identified prognosis-related modules. Ferroptosis-related genes and m5C regulators were integrated to identify m5C-associated ferroptosis genes. Machine learning and Cox regression were applied to construct a prognostic model. Finally, functional enrichment, immune infiltration, and drug sensitivity analysis were performed.
resultsTwo key gene modules significantly correlated with LUAD prognosis were identified, yielding 29 m5C-related ferroptosis genes. Ten hub genes were selected via machine learning, and the prognostic risk model achieved strong predictive performance. Immune infiltration profiling revealed close associations between the model and tumor microenvironment features. Among the hub genes, SLC2A1, RRM2, and KIF20A were markedly overexpressed in tumor tissues and associated with poor survival. Notably, SLC2A1 expression correlated with enhanced immunotherapy response. Drug sensitivity analysis and molecular docking identified nine small-molecule compounds with favorable binding affinities to SLC2A1.
conclusionsThis study delineates the critical prognostic significance of m5C-related ferroptosis genes in LUAD and establishes a clinically relevant prognostic model. The identification of candidate SLC2A1 inhibitors offers promising avenues for targeted therapy and personalized treatment strategies in LUAD.
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.