Back

Identification of Potential Oral Cancer-Specific Biomarker in Oral Potentially Malignant Disorders for Early Detection of Malignancy: A Systematic Review

Dwivedi, D. D.; Tiwari, P.; Bano, Y.; Kumar, P.; Yadav, C. P.; Singhal, D. K.

2025-05-21 cancer biology
10.1101/2025.05.18.651790 bioRxiv
Show abstract

Oral malignancies are increasing and significantly contribute to cancer-related mortality. Oral Potentially Malignant Diseases (OPMDs) share several characteristics with Oral Cancer (OC) and have the potential to undergo malignant transformation. Beyond clinical and visual similarities, OC and OPMDs also exhibit common molecular alterations at the genomic and transcriptional levels. Identifying commonly mutated genes between OC and OPMDs may enhance early detection strategies. Therefore, we conducted a systematic review analyzing mutated genes from 25 studies (selected from 331 studies using the PRISMA flowchart) that investigated gene mutations in OC and OPMDs through whole-exome sequencing analysis. Across these studies, we identified a total of 7,564 mutated genes: 6,885 from OC studies and 579 from OPMD studies. Of these, 311 genes were found to be common between OCs and OPMDs. To further assess their relevance, these genes were categorized into High (N=5), Medium (N=20), and Low-frequency (N=386) groups, and their distribution was compared between OC and OPMD studies. Based on these comparisons, four genes: TP53, PIK3CA, FAT1, and TTN were identified as showing comparable mutation patterns between OC and OPMDs, suggesting their potential role as early biomarkers for malignant transformation. Furthermore, FAT1 and TTN may serve as promising candidates for assessing the likelihood of Oral Cancer development in OPMDs.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.