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

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

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2022.870221

DOI=10.3389/fphar.2022.870221

ISSN=1663-9812

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 chemotherapeutics therapies.
Methods  The RNA-seq data and clinical information of CC were collected from the TCGA TARGET GTEx database and the TCGA database. lncRNA and immune-related signatures were obtained from GENCODE database and the Import database respectively. We screened out immune-related lncRNAs signatures through univariate Cox, LASSO and multivariate Cox regression method. 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 proceeded to appraise the value of risk scores which are in competition with tumor-infiltrating immune cells abundances. Estimation of tumor immunotherapy response through TIDE algorithm and prediction of innovative recommended medications on target to immune-related risk model on the basis of 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 which we observed that high-risk groups were strongly linked with poor survival outcomes. Risk scores varied with clinicopathological parameters and 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, DC, megakaryocyte, memory B-cells, NK-cells, Th1-cells. The CIBERSORTx algorithm revealed inflammatory microenvironment that naive B cells (p<0.01), Activated dendritics cells (p<0.05), Activated mast cells (p<0.0001), CD8+ T-cells (p<0.001), Regulatory T-cells (p<0.01) were significant lower in high-risk group, while Macrophages M0 (p<0.001), Macrophages M2 (p<0.05), Resting mast cells (p<0.0001), Neutrophils (p<0.01) were higher 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, Compared differential IC50 predictive values in high- and low-risk groups, and 12 compounds were identified as future treatments for CC patients.