Construction of a Prognostic Model for Cervical Cancer Related to lncRNA Based on Differential Co-expression Network...

Cervical cancer is a common gynecological malignancy, and the average age of onset is decreasing gradually. Therefore, an effective predictive model is urgently needed to improve the personalized treatment of cervical cancer patients. Long non-coding RNAs (lncRNAs) play crucial roles in the occurrence, development, and prognosis of malignant tumors. In this study, we used cervical cancer multi-omics data and single-cell sequencing data for analysis, and established a 33-lncRNA-CESC model by using the random forest algorithm in ensemble learning and mRNA and lncRNA co-expression network technology. The results demonstrated that the model exhibited strong discriminative ability, accuracy, and clinical utility. Furthermore, we investigated the relationship between the model and immune cell infiltration. Enrichment analysis revealed associations between the model and cellular proliferation as well as epidermal growth factor receptor (EGFR) signaling pathways. Subsequently, attention was directed toward the gene EGFR-AS1 in the model, which was identified within the co-expression network and exhibited a significant association with patient prognosis. Additionally, EGFR-AS1 was found to be specifically associated with FAM83B. Analysis of single-cell data confirmed that FAM83B plays a role in the late stage of cervical cancer development mainly through the EGFR signaling pathway. Functional experiments showed that knockdown of either EGFR-AS1 or FAM83B inhibited cervical cancer cell proliferation and migration capabilities, and the phosphorylated ERK and AKT levels. In addition, there was a mutual regulatory effect between EGFR-AS1 and FAM83B expression. In conclusion, this study identifies that EGFR-AS1 served as a key factor in our 33-lncRNA-CESC model and potentially interacted with FAM83B to regulate the EGFR pathway which significantly impacting cervical cancer development.