A panel of Transcription factors identified by data mining can predict the prognosis of head and neck squamous cell carcinoma

CRISPR-Cas-based biosensors for the detection of cancer biomarkers

Along with discovering cancer biomarkers, non-invasive detection methods have played a critical role in early cancer diagnosis and prognostic improvement. Some traditional detection methods have been used for detecting cancer biomarkers, but they are time-consuming and involve materials and human costs. With great flexibility, sensitivity and specificity, the clustered regularly interspaced short palindromic repeats (CRISPR)-associated system provides a wide range of application prospects in this field. Herein, we introduce the background of the CRISPR-Cas (CRISPR-associated) system and comprehensively summarize the diagnosis strategies of cancer mediated by the CRISPR-Cas system, including four kinds of biochemical-based markers: nucleic acid, enzyme, tumor-specific protein and exosome. Furthermore, we discuss the challenges in implementing the CRISPR-Cas system in clinical applications.

A bioinformatics approach of specificity protein transcription factors in head and neck squamous cell carcinoma

Background and purpose

The seventh most common type of cancer with increasing diagnosis rates around the world is head and neck squamous cell carcinoma (HNSCC). Specificity proteins (SPs) have been known for their role in the regulation of cellular division, growth, and apoptotic pathways in various cancers. In this work, we analyzed the expression levels of SPs in HNSCC to assess their diagnostic and prognostic biomarker potential.

Experimental approach

Differential gene expression and correlation analysis methods were used to determine the top dysregulated genes in HNSCC. Functional enrichment and protein-protein interaction analyses were done with the DAVID database and Cytoscape software to understand their function and biological processes. Receiver operating test, logistic regression, and Cox regression analyses were performed to check SP genes' diagnostic and prognostic potential.

Findings/Results

SP1 (LogFC = -0.27, P = 0.0013) and SP2 (LogFC = -0.20, P = 0.0019) genes were upregulated in HNSCC samples, while SP8 (LogFC = 2.57, P < 0.001) and SP9 (LogFC = 2.57, P < 0.001) genes were downregulated in cancer samples. A moderate positive correlation was observed among the expression levels of SP1, SP2, and SP3 genes. The SP8 and SP9 genes with AUC values of 0.79 and 0.75 demonstrated diagnostic potential which increased to 0.84 when both genes were assessed by logistic regression test. Also, the SP1 gene held a marginally significant prognostic potential.

Conclusion and implications

Our findings clarify the potential of SP transcription factors as candidate diagnostic and prognostic biomarkers for early screening and treatment of HNSCC.