인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous tumor that is highly aggressive and ranks fifth among the most common cancers worldwide. Although, the researches that attempted to construct a diagnostic model were deficient in HNSCC. Currently, the gold standard for diagnosing head and neck tumors is pathology, but this requires a traumatic biopsy. There is still a lack of a noninvasive test for such a high-incidence tumor. In order to screen genetic markers and construct diagnostic model, the methods of random forest (RF) and artificial neural network (ANN) were utilized. The data of HNSCC gene expression was accessed from Gene Expression Omnibus (GEO) database; we selected three datasets totally, and we combined 2 datasets (GSE6631 and GSE55547) for screening differentially expressed genes (DEGs) and chose another dataset (GSE13399) for validation. Firstly, the 6 DEGs (CRISP3, SPINK5, KRT4, MMP1, MAL, SPP1) were screened by RF. Subsequently, ANN was applied to calculate the weights of 6 genes. Besides, we created a diagnostic model and nominated it as neuralHNSCC, and the performance of neuralHNSCC by area under curve (AUC) was verified using another dataset. Our model achieved an AUC of 0.998 in the training cohort, and 0.734 in the validation cohort. Furthermore, we used the Cell-type Identification using Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm to investigate the difference in immune cell infiltration between HNSCC and normal tissues initially. The selected 6 DEGs and the constructed novel diagnostic model of HNSCC would make contributions to the diagnosis.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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