Cervical cancer (CC) is a malignant neoplasm distinguished by invasive and widespread growth, resulting in a substantial global incidence and recurrence rate. The effectiveness of conventional treatment for advanced stage CC is constrained. Over the past few decades, notable progress in molecular genotyping and phenotype has considerably propelled the advancement of targeted therapy and immunotherapy for CC. Nevertheless, the mortality rate continues to escalate, and the available treatment alternatives remain restricted.
This study utilized the TCGA database to conduct survival analysis on the expression of 43 NRLs. The results revealed significant disparities in the expression levels of 12 NRLs between tumor and normal tissues. To establish prognostic markers, LASSO Cox regression analysis was employed. The 12 NRLs exhibiting significant differences in expression levels were identified as AC022137.3, AC024270.3, AL596214.1, AC004847.1, LINC01943, AC010542.5, AL109614.1, AC010536.2, U91328.1, AL021978.1, AL021368.2, and AL354833.2. Following this, the patients were categorized into high-risk or low-risk groups based on the median risk score. The outcomes of our study revealed a notable disparity in the overall survival rate (OS) between the high-risk and low-risk groups. Through univariate and multivariate analysis, AC022137.3, AC024270.3, AC010542.5, AC010536.2, U91328.1, and AL021978.1 were identified as independent factors influencing patient prognosis. These findings propose that our prognostic markers have the potential to serve as an autonomous prognostic indicator capable of predicting OS in patients with CC. Furthermore, subject operating characteristic (ROC) curves were generated to assess the independence of prognostic markers as indicators. The findings demonstrate the model's substantial capacity to accurately forecast overall survival rates over one, three, and five years. In conclusion, the identification of NRL as a novel feature of CC presents potential novel perspectives for prognostic prediction in CC patients.
The present study has uncovered a noteworthy association between the expression levels of six predictive NRLs and the infiltration of immune cells in CC. The outcomes of our investigation propose that these six NRLs may possess a crucial function in the regulation of immune cell infiltration in CC. Furthermore, we have observed a significant correlation between the expression levels of AL596214.1, AC004847.1, LINC01943, AL021978.1, and AL354833.2 and immune checkpoints. These findings indicate that the six NRL signals may contribute to tumor immune evasion and anti-tumor immunity, thereby influencing the carcinogenic process in CC. Our research has the potential to make valuable contributions to the development of CC immunotherapy strategies.
Tumor-infiltrating immune cells play a crucial role in the tumor immune microenvironment and contribute to the response to immunotherapy (15). Immune scoring is commonly employed to guide immunotherapy and assess prognosis. As cited in the references, cancer patients belonging to the high immune score group exhibited significantly prolonged disease-free survival and overall survival compared to those in the low immune score group (16). Given the intricacy and constraints associated with individual immune penetration algorithms (17), we performed a correlation analysis between risk score and immune score utilizing seven algorithms. The findings of this study demonstrate a significant negative correlation between risk scores and the quantities of B cells, CD8 + T cells, T_ Helper_ cells, Tfh cells, and TIL cells. These results suggest that the integration of the seven algorithms employed in this research provides a more comprehensive understanding of the immune landscape, surpassing the insights offered by individual algorithms alone.
Immune checkpoint molecular targeted therapy has emerged as a promising approach in the realm of cancer treatment (18). The primary objective of researchers is to attenuate the activity of these checkpoints, thereby reinstating the immune system's capacity to identify and combat cancerous cells. Noteworthy instances of immune checkpoint molecules encompass CTLA-4, PD-1, and PD-L1 (19). Tumor cells can elude immune detection through a phenomenon known as immune editing, wherein PD-L1, a ligand responsible for programmed cell death, may undergo alterations. The inhibitory interaction between programmed cell death protein 1 (PD-1) and programmed death ligand 1 (PD-L1) expressed on tumor cells impedes the elimination of malignant cells by inhibiting the activation and proliferation of T lymphocytes[20]. Consequently, the utilization of PD-L1/PD-1 immune checkpoint blockade therapy has demonstrated favorable outcomes in clinical contexts[21]. Nevertheless, the immunosuppressive characteristics of the tumor microenvironment restrict the effectiveness of these interventions for the majority of patients. Hence, to enhance the efficacy of anti PD-1/PD-L1 therapy, a comprehensive comprehension of the molecular mechanism governing PD-L1 regulation is imperative. Our investigation unveiled a noteworthy association between PD-L1 expression and NRL. Nonetheless, in order to attain a thorough understanding of these intricate interrelationships, additional inquiry is warranted.
The findings of our study suggest a potential correlation between specific immune cell populations, namely aDC, macrophages, B cells, CD8 + T cells, TIL, and Treg cells, and susceptibility to disease. Notably, heightened levels of these immune cells were observed in the low-risk subgroup. The inverse relationship between risk scores and these immune cell populations implies their potential involvement in disease occurrence.