ArticleDiscover oncology2025
Targeting NLRC4 for potential therapeutic strategies in lung adenocarcinoma.
Article in Discover oncology, 2025. 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.
- NLRC4 and NLRP3 in tissues as potential biomarkers for the diagnosis of non-small cell lung cancer in pulmonary tuberculosis.Frontiers in cellular and infection microbiology · 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
backgroundAutophagy, a vital cellular process, plays a significant role in the development of a spectrum of diseases, notably cancer. The objective of this study was to assess the prognostic significance and explore the possible roles of autophagy-related genes (ARGs) in lung adenocarcinoma (LUAD) and in 33 cancer types.
methodsIn this study, ARGs were sourced from the Human Autophagy Database (HADb), with gene expression data retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The LASSO Cox and multivariate Cox regression models were employed to identify ARGs with prognostic significance, leading to the development of the Autophagy-Related Gene Prognostic Signature (ARGPS), which was compared with previously established prognostic models. The associations between the ARGPS and clinical parameters were examined to identify independent prognostic factors. Additionally, a pan-cancer analysis underscored the role of the ARGPS in immune subtyping, the tumor immune context, survival outcomes, stemness scores, and sensitivity to antitumor drugs. Finally, a virtual drug screening was performed to predict potential target interactions.
resultsIn the GSE116959 dataset, we identified 14 DEARGs with statistical significance (p < 0.05 and |logFC|> 1). Using LASSO Cox and multivariate Cox regression, we developed an independent prognostic signature, identifying seven ARGs to form the ARGPS. LUAD patients were stratified into low-risk and high-risk groups. Pan-cancer analysis highlighted significant heterogeneity in ARGPS expression among various cancers. ARGPS members were significantly correlated with immune infiltration, drug resistance, and stemness in tumors. Virtual screening identified five potential NLRC4-targeting drugs for LUAD treatment.
conclusionsIn this study, we developed a predictive risk model based on seven ARGs. We comprehensively examined ARGPS expression and its correlation with immune infiltration and the tumor microenvironment. These findings could inform targeted immunotherapy and chemotherapy for LUAD and other cancers. The low expression of NLRC4 in LUAD indicated its potential as a therapeutic target.
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.