ArticleBrazilian journal of otorhinolaryngology
A bioinformatics analysis revealing autophagy related DEGs in head and neck squamous cell carcinoma: expanding insights into lipid metabolism.
Article in Brazilian journal of otorhinolaryngology. 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo identify value of prognostic model constructed based on tumor module-associated differential autophagy genes in Head and Neck Squamous Cell Carcinoma (HNSC), and verified through in vitro and tissue experiments.
methodsAfter obtaining the relevant information of HNSC patients from the GEO and TCGA databases, we used Weighted Correlation Network Analysis (WGCNA), multivariate Cox regression analysis, and survival analysis to construct a prognostic prediction model, and validated the prognostic value of the model. We used RT-qPCR and cell scratch assays to validate in vitro for the biological results with TU686 and CAL-27 cell lines. We collected pathological sections from 30 patients with laryngeal cancer, hypopharyngeal cancer, and 10 patients with vocal cord polyps, and immunohistochemical DAB staining was performed to detect the expression of GSK3B in these tissues.
resultsA total of 6163 differentially expressed genes were jointly identified between HNSC and control samples. WGCNA analysis revealed that the genes clustered in the brown module had the highest correlation with HNSC. Using the formula derived from the inclusion of 7 genes in the prognostic model, the risk scores were calculated for the training and validation sets of HNSC disease samples. Survival analysis results showed a significant difference in Overall Survival (OS) between the high and low-risk groups in both the training and validation sets (p < 0.05). The one-year, three-year, and five-year AUC values of the risk scores obtained from the prognostic model in the training and validation sets were all greater than 0.6. After knocking down GSK3B, the relative expression levels of si-GSK3B in the si-GSK3B1 and si-GSK3B2 groups were significantly lower than those in the NC group (both p < 0.05). We compared with the NC group, the migration ability of TU686 and CAL-27 cells was significantly decreased (both p < 0.05). We used immunohistochemical DAB method to detect and found that the content of GSK3B in laryngeal cancer was significantly higher than that in vocal cord polyps (p < 0.05), and the content of GSK3B also in pharyngeal cancer was significantly higher than that in vocal cord polyps (p < 0.05).
conclusionPrognostic model constructed based on tumor module-associated differential autophagy genes in HNSC holds prognostic value. LEVEL OF EVIDENCE: Level 3.
Indexed as
Identifiers
What Socratic 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 Socratic graph.