ArticleAdvances in therapy2022
Identification of Metastasis-Associated Genes in Cutaneous Squamous Cell Carcinoma Based on Bioinformatics Analysis and Experimental Validation.
Article in Advances in therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed, 6 citations in OpenAlex.
- Multi-omic discovery of SCG5 as a shared driver and prognostic biomarker in head-neck and non-small cell lung cancers.Discover oncology · 2026Article
- Identification of PI3K/AKT/mTOR-related genes as diagnostic biomarkers for cutaneous squamous cell carcinoma.Biochemistry and biophysics reports · 2026Article
- The potential of secretogranin V as a prognostic biomarker in non-small cell lung cancer.Scientific reports · 2025Article
- Identification and validation of mBMC oral health · 2023Article
- An explainable machine learning-driven proposal of pulmonary fibrosis biomarkers.Computational and structural biotechnology journal · 2023Article
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 at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionCutaneous squamous cell carcinoma (cSCC) is a global malignant tumor with a high degree of malignancy. Once metastasis occurs, it will lead to poor prognosis and even death. This study attempts to find out the central genes closely related to cSCC metastasis, so as to clarify the molecular regulatory mechanism of cSCC metastasis and open up new ideas for clinical treatment.
methodsFirstly, cSCC data set GSE98767 was used to establish a tumor metastasis model via clustering analysis. The key module and hub genes associated with cSCC metastasis were analyzed by weighted gene co-expression analysis (WGCNA). Next, the prognostic functions of hub genes were identified by functional and pathway enrichment analysis, pan-cancer analysis, and receiver operating characteristic-area under the curve (ROC-AUC) validation. Finally, the key genes were verified by clinical sample detection and biological in vitro test.
resultsA total of 19 hub genes related to cSCC metastasis were identified. They were highly expressed in cSCC metastatic tissues and were mainly enriched in cellular material and energy metabolism pathways. Overall survival (OS) and disease-free survival (DFS) results from pan-cancer analysis showed that eight and six highly expressed genes, respectively, with PAPSS2 and SCG5 had highly reliable ROC-AUC validation values and were poor prognostic factors. Clinical and biological tests also confirmed the upregulation of PAPSS2 and SCG5 in cSCC. Deletion of PAPSS2 and SCG5 resulted in decreased viability, migration, and invasion of A-431 cells.
conclusionPAPSS2 and SCG5 may be important factors for cSCC metastasis, and they are involved in the regulation of cSCC cell viability, migration, and invasion.
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.