Integrative bioinformatics and experiments identify RIBC2 as a key regulator in the esophageal cancer

Abstract

Early detection of esophageal cancer (EC) remains a major challenge due to the limited understanding of its initial molecular alterations. Therefore, this study aimed to identify the key molecular drivers involved in EC carcinogenesis. Human normal esophageal epithelial cells were subjected to chronic malignant transformation, followed by assessment of their morphological changes, proliferative capacity, clonogenic potential, migration, and invasion abilities. To elucidate the molecular mechanisms underlying tumorigenesis, transcriptome sequencing was performed and integrated with clinical datasets from two independent EC cohorts. Machine learning algorithms were then applied to pinpoint diagnostic and prognostic gene signatures, which were further validated through comprehensive in vitro and in vivo experiments. Differential expression analysis and machine learning identified RIB43A domain with coiled-coils 2 (RIBC2) as a strong diagnostic and prognostic biomarker for EC. RIBC2 expression was markedly upregulated in chronically transformed epithelial cells, established EC cell lines, and clinical tumor specimens, and its elevation was associated with unfavorable clinicopathological characteristics. Functional studies revealed that silencing RIBC2 significantly inhibited cell proliferation, migration, and invasion in both transformed and EC cells. Moreover, immune profiling indicated that high RIBC2 expression was linked to an immune-excluded tumor microenvironment, implying a potential role in modulating responsiveness to immunotherapy. These findings reveal RIBC2 as a novel driver of EC initiation and progression, highlighting its potential as a biomarker for early diagnosis and as a promising target for therapeutic intervention.