AUTHOR=Xiong Xi , Chen Chen , Li Xinxin , Yang Jun , Zhang Wei , Wang Xiong , Zhang Hong , Peng Min , Li Lili , Luo Pengcheng 

TITLE=Identification of a novel defined inflammation-related long noncoding RNA signature contributes to predicting prognosis and distinction between the cold and hot tumors in bladder cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 13 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.972558

DOI=10.3389/fonc.2023.972558

ISSN=2234-943X

ABSTRACT=Purpose：With the continuous development of bioinformatics, the molecular mechanism of pathogenesis of cancer have been partly understood at the genetic level. Molecular targets expressed in bladder cancer (BLCA) are usually used to develop targeted drugs for treatment. However, poor prognosis and poor immunotherapy effect are existed in BLCA, we will explore new potential possible biomarkers that could predict the cancer prognosis.
Methods：We downloaded bladder cancer transcriptional sequencing data from The Cancer Genome Atlas (TCGA) and searched for inflammation-related prognostic lncRNA by univariate Cox regression and co-expression analysis. We used the least absolute shrinkage and selection operator (LASSO) analysis to build the inflammation-related lncRNAs prognosis model. Then, we employed the following analysis to verify and assess this model, including the Kaplan-Meier analysis, univariate Cox (uniCox) regression, multivariate Cox (multiCox) regression, receiver operating characteristics (ROC), nomogram, and calibration curves. We further analyzed the risk group using gene set enrichment analysis (GSEA), immune cell analysis, ssGSEA analysis, tumor microenvironment (TME) analysis, immune analysis, principal component analysis (PCA), and half maximal inhibitory concentration (IC50). To distinguish the sensitivity of drug immunotherapy between cold tumors and hot tumors, all of the inflammation-related lncRNAs classified into three groups. 
Results: A model containing 7 inflammation-related lncRNAs was built in this study. The calibration map in the model was consistent with the prognosis prediction outcomes. The 1-, 3-, and 5-year areas under the curve (AUC) of ROC were 0.699, 0.689, and 0.699, respectively. Patients with the high-risk were related to tumor invasion and immunity, and had a higher state of immune infiltration. Immune cells and immune checkpoints were infiltrated and activated in the high-risk group. Hot tumors and cold tumors can be effectively distinguished by clusters. Among the three clusters, cluster 2 and cluster 3 were identified as hot tumors, which indicated they are more sensitive to immunotherapeutic drugs.
Conclusion: Our study provided evidence to support the hypothesis that inflammation-related lncRNAs can accurately predict patients' prognosis and differentiate between cold and hot tumors. As a result, individualized immunotherapy for BLCA patients will be improved, and patients will have access to new treatment options.