ArticleAging2024
Development and validation of glycosyltransferase related-gene for the diagnosis and prognosis of head and neck squamous cell carcinoma.
Article in Aging, 2024. 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, 2 citations in OpenAlex.
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
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHead and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous cancer characterized by difficulties in early diagnosis and outcome prediction. Aberrant glycosylated structures produced by the aberrant expression of glycosyltransferases are prevalent in HNSCC. In this study, we aim to construct glycosyltransferase-related gene signatures with diagnostic and prognostic value to better stratify patients with HNSCC and improve their diagnosis and prognosis.
methodsBioinformatic tools were used to process data of patients with HNSCC from The Cancer Genome Atlas (TCGA) database. The prognostic model was formatted using univariate and multivariate Cox regression methods, while the diagnostic signature was constructed using support vector machine (SVM) and LASSO analysis. The results were verified using the Gene Expression Omnibus (GEO) cohort. The tumor microenvironment and benefits of immune checkpoint inhibitor (ICI) therapy in subgroups defined by glycosyltransferase-related genes were analyzed. Molecular biology experiments, including western blotting, cell counting kit (CCK)-8, colony formation, wound healing, and Transwell assays, were conducted to confirm the oncogenic function of beta-1,4-galactosyltransferase 3 (B4GALT3) in HNSCC.
resultsWe established a five-gene prognostic signature and a 15-gene diagnostic model. Based on the median risk score, patients with low risk had longer overall survival than those in the high-risk group, which was consistent with the results of the GEO cohort. The concrete results suggested that high-risk samples were related to a high tumor protein (TP)53 mutation rate, high infiltration of resting memory cluster of differentiation (CD)4 T cells, resting natural killer (NK) cells, and M0 macrophages, and benefited from ICI therapy. In contrast, the low-risk subgroup was associated with a low TP53 mutation rate; and high infiltration of naive B cells, plasma cells, CD8 T cells, and resting mast cells; and benefited less from ICI therapy. In addition, the diagnostic model had an area under curve (AUC) value of 0.997 and 0.978 in the training dataset and validation cohort, respectively, indicating the high diagnostic potential of the model. Ultimately, the depletion of B4GALT3 significantly hindered the proliferation, migration, and invasion of HNSCC cells.
conclusionsWe established two new biomarkers that could provide clinicians with diagnostic, prognostic, and treatment guidance for patients with HNSCC.
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.