VIDEO: Gene expression algorithm helps identify basal cell carcinoma biomarkers
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Key takeaways:
- Biomarkers of basal cell carcinoma (BCC) were identified using a gene expression algorithm.
- Researchers hope this will help physicians diagnose BCC and differentiate from squamous cell carcinoma.
NEW ORLEANS — A machine learning algorithm was able to differentiate between basal cell and squamous cell carcinomas by identifying biomarkers, according to a poster presented here.
In this Healio video exclusive, Steven Stone, PhD, senior vice president of research and development at DermTech, discusses the data presented at the American Academy of Dermatology Annual Meeting.
“We’ve identified a set of markers that seem quite robust. They worked in sequential independent cohorts and they do consistently express between [basal cell carcinoma (BCC)] and other types of clinical stimulators,” Stone said. “The idea is that we can use those to develop a signature going forward that would have clinical utility in helping physicians diagnose BCC.”
Researchers conducted RNA sequencing of 354 patients — 94 with BCC, 87 with squamous cell carcinoma and 173 with non-cancerous skin diseases — and used this to identify 161 different expressed genes in BCC vs. the other groups.
They then conducted a targeted RNA AmpliSeq analysis of these genes.
The top five biomarkers of BCC were TAGLN, FDCSP, LINC02167, FOXI3 and CASC15.
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Bahrami-Samani E, et al. ePoster. Non-invasive detection of RNAs associated with basal cell carcinoma (BCC). Presented at: American Academy of Dermatology Annual Meeting; March 17-21, 2023; New Orleans.
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