Comprehensive application of AI algorithms with TCR NGS data for glioma diagnosis

• Published in: Scientific reports Vol. 14; no. 1; pp. 15361 - 12
• Main Authors: Zhou, Kaiyue, Xiao, Zhengliang, Liu, Qi, Wang, Xu, Huo, Jiaxin, Wu, Xiaoqi, Zhao, Xiaoxiao, Feng, Xiaohan, Fu, Baoyi, Xu, Pengfei, Deng, Yunyun, Xiao, Wenwen, Sun, Tao, Da, Lin
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 04.07.2024; Nature Publishing Group UK; Nature Portfolio
• Subjects: Algorithms; Artificial intelligence techniques; Brain Neoplasms - diagnosis; Brain Neoplasms - genetics; Classification; Datasets; Diagnosis; Diversity indices; DNA sequencing; Feature selection methods; Glioma; Glioma - diagnosis; Glioma - genetics; Glioma cells; High-Throughput Nucleotide Sequencing - methods; Humans; Immune response; Immune response (cell-mediated); Lymphocytes T; Next-generation sequencing; Nucleotide sequence; Prognosis; Receptors, Antigen, T-Cell - genetics; T cell receptors; T-cell repertoire (TCR); Artificial intelligence techniques; T-cell repertoire (TCR); Prognosis; Glioma; Feature selection methods
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-65305-9

T-cell receptor (TCR) detection can examine the extent of T-cell immune responses. Therefore, the article analyzed characteristic data of glioma obtained by DNA-based TCR high-throughput sequencing, to predict the disease with fewer biomarkers and higher accuracy. We downloaded data online and obtained six TCR-related diversity indices to establish a multidimensional classification system. By comparing actual presence of the 602 correlated sequences, we obtained two-dimensional and multidimensional datasets. Multiple classification methods were utilized for both datasets with the classification accuracy of multidimensional data slightly less to two-dimensional datasets. This study reduced the TCR β sequences through feature selection methods like RFECV (Recursive Feature Elimination with Cross-Validation). Consequently, using only the presence of these three sequences, the classification AUC value of 96.67% can be achieved. The combination of the three correlated TCR clones obtained at a source data threshold of 0.1 is: CASSLGGNTEAFF_TRBV12_TRBJ1-1, CASSYSDTGELFF_TRBV6_TRBJ2-2, and CASSLTGNTEAFF_TRBV12_TRBJ1-1. At 0.001, the combination is: CASSLGETQYF_TRBV12_TRBJ2-5, CASSLGGNQPQHF_TRBV12_TRBJ1-5, and CASSLSGNTIYF_TRBV12_TRBJ1-3. This method can serve as a potential diagnostic and therapeutic tool, facilitating diagnosis and treatment of glioma and other cancers.