Comprehensive characterization of immune- and inflammation-associated biomarkers based on multi-omics integration in kidney renal clear cell carcinoma

Abstract

Background: Kidney renal clear cell carcinoma (KIRC) is the most common type of kidney cancer in adults, and it is responsible for approximately 90-95% of cases. Although extensive evidence has suggested that many immune- and inflammation-related genes could serve as effective biomarkers in KIRC, the potential associations among immune-, inflammation- and KIRC-related genes has not been sufficiently understood.

Methods: Here, we integrated multiple levels of data to construct an immune-, inflammation- or KIRC-directed neighbour network (IIKDN network) and a KIRC-related gene-directed network (KIRCD network).

Results: Our analysis suggested that immune- and inflammation-related genes in the network have special topological characteristics and expression patterns related to KIRC. We further identified five core clusters that showed a tighter network structure and stronger correlation of expression from the KIRCD network. Specifically, multiple-level molecular characteristics were systematically portrayed, including somatic mutation, copy-number variant and DNA methylation for the genes in five core clusters. We discovered that the genes showed strong correlation with respect to the expression and methylation levels in these five core clusters. These five core clusters could become special prognostic biomarkers for KIRC, and functional analysis showed that they were associated with activation of the immune and inflammation systems and cancer progression.

Conclusions: Our findings highlighted the novel role of the immune and inflammation genes in KIRC.

Keywords: DNA methylation; Immune; Inflammation; Kidney renal clear cell carcinoma; Multi-omics molecular; Network modules.

Fig. 1

The properties of immune, inflammation or KIRC-directed neighbour network (IIKDN network). a The global IIKDN network. b The degree distribution of nodes. c The bar plot shows the number of diverse genes. d The Venn diagram shows the number of immune-, inflammation- and KIRC-related genes. e The bar plot shows the average degree of diverse kinds of genes. f The bar plot showed the average degree of immune-, inflammation- and KIRC-related genes. g The top five genes ranked by gene degrees including TP53, SRC, GRB2, ESR1 and SMAD3. The bar plot shows the degree of the five genes. h The pie chart shows the percent of immune- and inflammation-related genes associated with KIRC genes

Fig. 2

The characteristics of KIRC-related gene-directed network (KIRCD network) and identification of core clusters. a The global KIRCD network. b The number of diverse genes. c1–c3) The bar plots show the comparison of topological characteristics between KIRCD and IIKND. d The first pie chart shows the percent of significantly correlated interactions. The second pie chart shows the percent of positive and negative correlated interactions. e The density distribution curve of PCC values in the KIRCD network. f1–f5) The five core modules identified from the KIRCD network

Fig. 3

The expression patterns of five core clusters. a The pie charts show the percent of differentially expressed genes. b The bar plot shows the number of up- and down-regulated genes. c The bar plot shows the average PCC values of all interactions and interactions without other genes. d The pie charts show the percent of significantly correlated interactions. e–h The core clusters. The bar plots show the number of up-regulated genes and down-regulated genes for each core cluster. The point plots show the expression pattern between two significant gene interactions. The heat maps show the differential expression between KIRC patients and matched normal samples for all differentially expressed genes

Fig. 4

Complex genomic characteristics including somatic mutation, CNV and DNA methylation. a1–a5 The rose diagrams show the number of somatic mutations for each gene in the five core clusters. b The bar plot shows the average number of mutations in the five core clusters. c An example, ERBB4, is shown. d The CNV pattern in the first core cluster. Red and blue represent amplification and deletion. e The point plot shows the number of differential methylation sites in the first core cluster. f The co-methylation pattern of each core cluster; the larger circle represents stronger correlation, red represents positive correlation, and blue represents negative correlation

Fig. 5

Core clusters are associated with survival for KIRC patients. a1–a5 The Kaplan–Meier curve for the overall survival of two patient groups with high- and low-risk scores in the KIRC patient set. The difference between the two curves was evaluated by a two-sided log-rank test. b1–b5 The gene-based risk score distribution of the genes in each cluster. c1–c5 The patient survival status of the genes

Fig. 6

Functional analyses of five core clusters. a GO terms enriched for genes in the five core clusters, respectively, ranked by − log10(P) are presented as bar plots. b The KEGG pathway, the JAK/SAT signalling pathway and the genes in the core clusters are coloured red