ArticleFrontiers in genetics2025
Comprehensive analysis of mRNA and lncRNA expression for predicting lymph node metastasis in cervical cancer: a novel seven-gene signature approach.
Article in Frontiers in genetics, 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Lymph node metastasis (LNM) critically determines recurrence and survival in cervical cancer (CC), yet current imaging-based methods lack accuracy. Retroperitoneal lymph node dissection leads to many adverse events. This study aimed to develop a clinically actionable molecular signature to predict LNM, enabling personalized surgical planning and improved patient outcomes. Methods: Transcriptome profiles and clinical data from 193 CC patients, encompassing information on LNM from The Cancer Genome Atlas (TCGA) and an external cohort (GSE26511), were analyzed. The differential expression of mRNAs and lncRNAs was identified using DESeq2. Subsequently, dual machine learning strategies-LASSO regression and the Boruta algorithm-were applied to select robust biomarkers. Finally, the seven-mRNA-lncRNA gene cluster was verified in tumor tissues of CC patients with and without LNM using qRT-PCR. Results: The seven-mRNA-lncRNA gene cluster included four mRNAs (ART3, HRG, MAPT, and SYTL5) and three lncRNAs (AC011239.1, AC125616.1, and RUVBL1.AS1). The expression patterns of the seven DEGs align with their levels in CC tissues. The signature demonstrated high predictive accuracy (AUC: 0.855 in training and 0.807 in testing cohorts). External validation using the GSE26511 dataset confirmed its clinical applicability (AUC: 0.611). Patients with high LNM scores exhibited poorer survival outcomes than those with low LNM scores ( Conclusion: We constructed a reliable prediction model of LNM in CC patients with a seven-mRNA-lncRNA gene cluster. This model guides lymphadenectomy decisions, reduces overtreatment, and enhances patient survival. Our work bridges molecular insights with clinical practice and provides a foundation for further research into the management of CC.
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.