ArticleTranslational cancer research2025
Construction of a prognostic model for lung adenocarcinoma based on necroptosis genes and its exploration of the potential for tumor immunotherapy.
Article in Translational cancer research, 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.
- Impact of TRMT6 on prognosis and immune microenvironment in ovarian cancer.Frontiers in oncology · 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Lung cancer ranks among the most prevalent malignancies globally, with lung adenocarcinoma (LUAD) being its most frequent histological subtype. Necroptosis is a newly defined mode of programmed cell death that is different from apoptosis and necrosis. However, the role of necroptosis in the occurrence and development of LUAD remains largely unexplored. This study aimed to construct a prognostic model of LUAD based on necroptosis-related genes (NRGs) and analyze the predictive value of this model on the prognosis of LUAD patients. Methods: The dataset of LUAD patients was downloaded from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database, and the NRGs were downloaded from inside GeneCards and Harmonizome databases. LUAD prognostic models were constructed by one-way Cox analysis, least absolute shrinkage and selection operator (LASSO) regression analysis and multifactorial Cox regression analysis. Differential analyses of immune function as well as common tumor drugs were performed between high and low risk groups. A ceRNA was constructed to explore the potential lncRNA-miRNA-mRNA regulatory axis in LUAD. In this study, we leveraged bioinformatics to pinpoint genes implicated in necroptosis within LUAD. Results: Two differentially expressed NRGs (DENRGs: KL, PLK1) were screened and used to construct the prognostic model and validate the RiskScore as an independent prognostic factor. Gene set variation analysis (GSVA) analysis showed that differentially expressed genes were mainly enriched in immune-related pathways. Additionally, we conducted experimental assays to validate the expression of these genes in LUAD cell lines. The GSVA analysis showed that differentially expressed genes were mainly enriched in immune-related pathways. Significant differences (P<0.05) were found between the high and low risk groups in terms of immune function and half-maximal inhibitory concentration (IC Conclusions: The survival prognosis model of NRGs constructed in this study can predict the prognosis and immune microenvironment of LUAD patients.
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.