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posted on 02.06.2022, 04:02 authored by Zhifeng Wang, Zihao Chen, Tengyun Guo, Menglin Hou, Junpeng Wang, Yanping Guo, Tao Du, Xiaoli Zhang, Ning Wang, Degang Ding, Xiqing Li
Background

According to clinical study results, immune checkpoint blockade (ICB) treatment enhances the survival outcome of patients with clear cell renal cell carcinoma (ccRCC). Previous research has divided ccRCC patients into immune subtypes with distinct ICB response rates. However, the study on the association between lncRNAs and ccRCC immune subtypes is lacking.

Methods

Differentially expressed lncRNAs/mRNAs between two major immune subgroups were calculated. A weighted gene co-expression network analysis (WGCNA) was conducted to establish the lncRNA-mRNA co-expression network and select the key lncRNAs. Then, prognostic lncRNAs were selected from the network by the bioinformatics method. Next, the risk-score was estimated by lncRNA expression and their coefficients. Finally, a nomogram based on lncRNAs and clinical parameters was created to predict the prognosis of ccRCC.

Results

LncRNAs and mRNAs associated with ccRCC immune subtypes were identified. The lncRNAs and mRNAs from a gene module closely linked to the immune subtype were used to construct a network. The KEGG pathways enriched in the network were related to immune system activation processes. These 8 lncRNAs (AL365361.1, LINC01934, AC090152.1, PCED1B-AS1, LINC00426, AC007728.2, AC243829.4, and LINC00158) were found to be positively correlated with immune cells of the tumor microenvironment. The C-index of the nomogram was 0.777, and the calibration curve data suggests that the nomogram has a high degree of discriminating capacity.

Conclusion

In summary, we discovered core lncRNAs linked with immune subtypes and created corresponding lncRNA–mRNA networks. These lncRNAs are anticipated to have predictive significance for ccRCC and may provide insight into novel biomarkers for the disease.

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