Transcriptome Analysis Reveals the Immune Infiltration Profiles in Cervical Cancer and Identifies KRT23 as an...

Cervical cancer (CC) is one of the most common malignancies in women worldwide. Dismal prognosis rates have been associated with conventional therapeutic approaches, emphasizing the need for new strategies. Recently, immunotherapy has been used to treat various types of solid tumors, and different subtypes of the tumor microenvironment (TME) are associated with diverse responses to immunotherapy. Accordingly, understanding the complexity of the TME is pivotal for immunotherapy. Herein, we used two methods, "ssGSEA" and "xCell," to identify the immune profiles in CC and comprehensively assess the relationship between immune cell infiltration and genomic alterations. We found that more adaptive immune cells were found infiltrated in tumor tissues than in normal tissues, whereas the opposite was true for innate cells. Consensus clustering of CC samples based on the number of immune cells identified four clusters with different survival and immune statuses. Then, we subdivided the above four clusters into "hot" and "cold" tumors, where hot tumors exhibited higher immune infiltration and longer survival time. Enrichment analyses of differentially expressed genes (DEGs) revealed that the number of activated immune signaling pathways was higher in hot tumors than that in cold tumors. Keratin, type I cytoskeletal 23 (KRT23), was upregulated in cold tumors and negatively correlated with immune cell infiltration. In vitro experiments, real-time reverse transcription-quantitative polymerase chain reaction, cytometric bead arrays, and ELISA revealed that knockdown of KRT23 expression could promote the secretion of C-C motif chemokine ligand-5 and promote the recruitment of CD8⁺ T cells. We also constructed a model based on DEGs that exhibited a high predictive power for the survival of CC patients. Overall, our study provides deep insights into the immune cell infiltration patterns of CC. Moreover, KRT23 has huge prospects for application as an immunotherapeutic target. Finally, our model demonstrated a good predictive power for the prognosis of CC patients and may guide clinicians during immunotherapy.