• AdelineBoettcher

Oral Squamous Cell Carcinoma: A Minireview on Genomic and Proteomic Analysis

Updated: Mar 4


Oral squamous cell carcinoma (OSCC) is one of the variants of head and neck cancer with a 5-year survival rate of less than 50%. Despite rapid advancements in surgical and multimodality therapies, the prognosis of the disease remains poor. This is mostly due to the late diagnosis, misdiagnosis, or inappropriate referral for treatments. In determining the optimal treatment plan, the TNM (tumor, note, metastasis) classification needs to be determined by histopathology. Due to the low sensitivity issue of these diagnostic tests in the early stages, there is an immediate need to improve oral cancer detection at precancerous stages.

Before the complete progression of the disease into invasive squamous cell carcinoma, development initiates through a series of stages including hyperplasia to dysplasia and later into carcinoma in-situ. Risk factors that are most associated with the disease are tobacco use, alcohol consumption, and betel quid chewing. The anatomical sites that are affected are gingival cavity, tongue, lip, as well as the hard and soft palate.

According to the “field cancerization” theory, an injured area of the oral cavity can sustain initial repeated exposure to carcinogens, and then eventually proliferate to a premalignant stage. When additional genetic insults accumulate, there is a high probability that a carcinoma will develop. Hence, there is an important prerequisite in understanding various genetic and proteomic signatures involved in the disease.


There are several genes that are frequently mutated in OSCC, including TP53, PIK3CA, NOTCH1, RASA1, CDKN2A, and MET and some genes including EGFR, PIK3CA, and CCND1 incur a copy number gain. Among the OSCC driver genes analyzed, FAT1, CDKN2A, TP53, NOTCH1, CASP8, and HRAS exhibit significantly higher mutations. Table 1 (below) shows an overview of the function of some of these genes.

Some protein markers of OSCC include cytokines and growth factors such as IL-1α and β, IL-6, IL-8, and TNF-alpha. The analysis of proteomic assays done by pairwise t-tests revealed that these biomarkers are significantly overexpressed when compared to oral premalignant lesions and controls. The next section will describe some of the techniques that are used for genomic and proteomic analysis for the detection of OSCC.


Genomic Analysis Tools

The most widely used assay for the detection of oral cancer at the early stages is next generation sequencing (NGS). NGS aids in the identification of somatic mutations at a very early stage and hence provides results that have less than a 1% error rate. The process and the cost of NGS are also low compared to other sequencing techniques. Genetic variations are evaluated using NGS, PCR, and methylation assays.

Apart from these molecular techniques employed for genetic identification, some artificial intelligence (AI) algorithms were developed to evaluate oral lesions and categorize them to different stages of oral cancer. Due to the variability of lesions in different individuals leads to complications for the software to differentiate among various stages.

Proteomic Analysis Tools

Proteomic markers are also assessed in the detection and staging of OSCC. Proteomic markers can be identified by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), matrix-assisted laser desorption/ionization (MALDI-TOF), high-performance liquid chromatography (HPLC), and 2D-electrophoresis. The most common sample types that are used for protein assays are formalin-fixed paraffin embedded tissue samples (FFPE), blood, or plasma.


All of these processes are invasive for collection. While the world is moving towards the betterment of patient's health, it is important to develop easy, non-invasive, and accurate procedures for the identification and development of new assay techniques. Some non-invasive samples that can also be collected include saliva, oral rinse, or oral brushings that can be analysed for oral cancer detection.

With so many advancements in place, scientists are thriving for the best to detect and treat OSCC at a preliminary stage. “Point-of-care” techniques are quite challenging at this stage but can never be a failure when efforts are made!

Meghana Manjunath is a postgraduate student studying medical and molecular biosciences at PES University in India. She can be found on LinkedIn.

And of course, check out these references:

1. Ralhan, R. (2007) Diagnostic Potential of Genomic and Proteomic Signatures in Oral Cancer. Int. J. Hum. Genet., 7 (1), 57–66.

2. Jiang, X., Ye, J., Dong, Z., Hu, S., and Xiao, M. (2019) Novel genetic alterations and their impact on target therapy response in head and neck squamous cell carcinoma. Cancer Manag. Res., 11, 1321–1336.

3. Wang, M., Xiao, C., Ni, P., Yu, J.-J., Wang, X.-W., and Sun, H. (2018) Correlation of Betel Quid with Oral Cancer from 1998 to 2017: A Study Based on Bibliometric Analysis. Chin. Med. J. (Engl)., 131 (16), 1975–1982.

4. Rhodus, N.L., Ho, V., Miller, C.S., Myers, S., and Ondrey, F. (2005) NF-kappaB dependent cytokine levels in saliva of patients with oral preneoplastic lesions and oral squamous cell carcinoma. Cancer Detect. Prev., 29 (1), 42–45.

5. Speight, P.M., Elliott, A.E., Jullien, J.A., Downer, M.C., and Zakzrewska, J.M. (1995) The use of artificial intelligence to identify people at risk of oral cancer and precancer. Br. Dent. J., 179(10), 382–387.

6. Ariji, Y., Fukuda, M., Kise, Y., Nozawa, M., Yanashita, Y., Fujita, H., Katsumata, A., and Ariji, E. (2019) Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence. Oral Surg. Oral Med. Oral Pathol. Oral Radiol., 127 (5), 458–463.

7. Galvão-Moreira, L.V., and da Cruz, M.C.F.N. (2017) Saliva protein biomarkers and oral squamous cell carcinoma. Proc. Natl. Acad. Sci. U. S. A., 114 (2), E109–E110.

© 2023 by BoettcherBioBlog Proudly created with wix.com

Get Social

  • Twitter
  • Grey LinkedIn Icon