Exploration of the Immune-Related Long Noncoding RNA Prognostic Signature and Inflammatory Microenvironment for Cervical Cancer

Hui Yao; Xiya Jiang; Hengtao Fu; Yinting Yang; Qinqin Jin; Weiyu Zhang; Wujun Cao; Wei Gao; Senlin Wang; Yuting Zhu; Jie Ying; Lu Tian; Guo Chen; Zhuting Tong; Jian Qi; Shuguang Zhou

doi:10.3389/fphar.2022.870221

Exploration of the Immune-Related Long Noncoding RNA Prognostic Signature and Inflammatory Microenvironment for Cervical Cancer

Front Pharmacol. 2022 May 19:13:870221. doi: 10.3389/fphar.2022.870221. eCollection 2022.

Authors

Hui Yao^{1

2}, Xiya Jiang^{1

2}, Hengtao Fu³, Yinting Yang^{1

2}, Qinqin Jin^{1

2}, Weiyu Zhang^{1

2}, Wujun Cao⁴, Wei Gao^{1

2}, Senlin Wang⁴, Yuting Zhu^{1

2}, Jie Ying^{1

2}, Lu Tian^{1

2}, Guo Chen^{1

2}, Zhuting Tong⁵, Jian Qi⁶, Shuguang Zhou^{1

2}

Affiliations

¹ Department of Gynecology, Anhui Medical University Affiliated Maternity and Child Healthcare Hospital, Hefei, China.
² Department of Gynecology, Anhui Province Maternity and Child Healthcare Hospital, Hefei, China.
³ Department of Pharmacy, North China University of Science and Technology, Tangshan, China.
⁴ Department of Clinical Laboratory, Anhui Medical University Affiliated Maternity and Child Healthcare Hospital, Anhui Province Maternity and Child Healthcare Hospital, Hefei, China.
⁵ Department of Radiation Oncology, the First Affiliated Hospital of Anhui Medical University, Hefei, China.
⁶ Anhui Province Key Laboratory of Medical Physics and Technology, Center of Medical Physics and Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.

Abstract

Purpose: Our research developed immune-related long noncoding RNAs (lncRNAs) for risk stratification in cervical cancer (CC) and explored factors of prognosis, inflammatory microenvironment infiltrates, and chemotherapeutic therapies. Methods: The RNA-seq data and clinical information of CC were collected from the TCGA TARGET GTEx database and the TCGA database. lncRNAs and immune-related signatures were obtained from the GENCODE database and the ImPort database, respectively. We screened out immune-related lncRNA signatures through univariate Cox, LASSO, and multivariate Cox regression methods. We established an immune-related risk model of hub immune-related lncRNAs to evaluate whether the risk score was an independent prognostic predictor. The xCell and CIBERSORTx algorithms were employed to appraise the value of risk scores which are in competition with tumor-infiltrating immune cell abundances. The estimation of tumor immunotherapy response through the TIDE algorithm and prediction of innovative recommended medications on the target to immune-related risk model were also performed on the basis of the IC50 predictor. Results: We successfully established six immune-related lncRNAs (AC006126.4, EGFR-AS1, RP4-647J21.1, LINC00925, EMX2OS, and BZRAP1-AS1) to carry out prognostic prediction of CC. The immune-related risk model was constructed in which we observed that high-risk groups were strongly linked with poor survival outcomes. Risk scores varied with clinicopathological parameters and the tumor stage and were an independent hazard factor that affect prognosis of CC. The xCell algorithm revealed that hub immune-related signatures were relevant to immune cells, especially mast cells, DCs, megakaryocytes, memory B cells, NK cells, and Th1 cells. The CIBERSORTx algorithm revealed an inflammatory microenvironment where naive B cells (p < 0.01), activated dendritic cells (p < 0.05), activated mast cells (p < 0.0001), CD8⁺ T cells (p < 0.001), and regulatory T cells (p < 0.01) were significantly lower in the high-risk group, while macrophages M0 (p < 0.001), macrophages M2 (p < 0.05), resting mast cells (p < 0.0001), and neutrophils (p < 0.01) were highly conferred. The result of TIDE indicated that the number of immunotherapy responders in the low-risk group (124/137) increased significantly (p = 0.00000022) compared to the high-risk group (94/137), suggesting that the immunotherapy response of CC patients was completely negatively correlated with the risk scores. Last, we compared differential IC50 predictive values in high- and low-risk groups, and 12 compounds were identified as future treatments for CC patients. Conclusion: In this study, six immune-related lncRNAs were suggested to predict the outcome of CC, which is beneficial to the formulation of immunotherapy.

Keywords: TCGA; cervical cancer; immune-related; inflammation; lncRNA; prognosis; risk score; tumor inflammatory microenvironment.