Single-Cell Sequencing Analysis and Multiple Machine Learning Methods identified CDT1 as an oncogene in retinoblastoma

Abstract

BACKGROUND Retinoblastoma (RB) is a heterogeneous primary intraocular malignant tumor. OBJECTIVE This work attempted to reveal the significant gene-related to disulfidptosis in RB. METHODS The scRNA-seq data from RB samples and normal samples obtained from GSE159977 were analyzed to distinguish cone cells from malignant cone cells using inferCNV. Subsequently, AUCcell was used to assess the disulfidptosis levels in cone cells. Disulfidptosis-related genes in RB were analyzed through weighted gene co-expression network analysis and machine learning methods. Cell proliferation, migration, and invasion were examined through colony formation and Transwell experiments. RESULTS We obtained 7 annotated cell clusters. Among them, there was a significant increase in the proportion of cone cells in RB samples. Malignant cone cells exhibited a higher disulfidptosis score. Cell trajectory analysis indicated that the disulfidptosis process intensified as cone cells transition from normal cells to malignant cells. Through machine learning, disulfidptosis-related genes were screened, ultimately identifying CDT1 as a key gene. CDT1 was upregulated in WERI-Rb1 and Y79 cells. Silencing CDT1 significantly suppressed the proliferation, migration, and invasion of RB cells. CONCLUSIONS This work revealed that tumors contained cone cell states with distinct transcriptional programs, and provided a crucial RB-related gene CDT1 for tumor progression. Thus, this article is of great significance in formulating a molecular targeted therapy scheme to prevent the development of RB.