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Comprehensive pan-cancer analysis of NLRs family as prognostic and immunity markers based on multi-omics data
Xianhui Wen1,2, Miaomiao Cui3, Junhua Zhang4
1Center for Clinical Laboratories, The Affiliated Hospital of Guizhou Medical University, No.28 Guiyi Street, Yunyan District, Guiyang, 550004, China.
Background:
Members of the NOD-like receptors(NLRs)gene family members involved in inflammasome formation have been implicated in cancer initiation, development, progression, angiogenesis, and invasion. This study comprehensively investigated the role of NLRs in cancer through multi-omics analysis, which is of great necessity for a deeper understanding of the pathogenesis of cancer, improvement of cancer prognosis assessment, promotion of drug development, and advancement of precision medicine.
Methods:
A multi-omics analysis was performed on data from over 10,000 individuals, integrating genomics, epigenomics, transcriptomics, proteomics, and immunogenomics. The dataset encompassed 750 drugs, 33 cancer types, and 24 categories of immune cells. Gene set variation analysis (GSVA) was utilized to ascertain the NLR score, which correlated with survival and cancer pathways. A predictive model was established utilizing univariate Cox and LASSO regression, with subsequent ROC evaluation and nomogram development.
Results:
Significant genomic and epigenetic alterations in NLRs, encompassing copy number variations (CNVs), single nucleotide variations (SNVs), and hyper- methylation, were identified. NLRs expression was connected with immune cell infiltration (ICI) and cancer-related pathways, positively correlating with cytotoxic T cells, NK cells, CD8 T cells, and exhausted T cells, while negatively correlating with neutrophils and naïve T cells. NLR scores were found to be positively correlated with survival outcomes in several cancer types, including LAML, SKCM, SARC, LUAD, KIRP, and COAD. Additionally, a prognostic index for LAML was established based on the strong association between NLR expression and patient outcomes, utilizing a risk model that incorporated ten NLRs derived from GSVA.
Conclusions:
This study highlights NLRs as crucial prognostic markers in cancer, with a ten-gene risk model offering independent prognostic value for LAML, laying the groundwork for further exploration of their clinical relevance. NLR alterations and ICI could activate pathways related to cancers. Thus, targeting these NLRs could be a novel approach to treating cancer.
