Development and validation of an individualized immune-related gene pairs prognostic signature in papillary renal cell carcinoma

Abstract

Background: Papillary renal carcinoma (PRCC) is one of the important subtypes of kidney cancer, with a high degree of heterogeneity. At present, there is still a lack of robust and accurate biomarkers for the diagnosis, prognosis and treatment selection of PRCC. Considering the important role of tumor immunity in PRCC, we aim to construct a signature based on immune-related gene pairs (IRGPs) to estimate the prognostic of patients with PRCC.Methods: We obtained gene expression profiling and clinical information of patients with PRCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), which were divided into discovery and validation cohorts, respectively. The immune-related genes in the samples were used to construct gene pairs, and the immune-related genes pairs (IRGPs) with robust impact for overall survival (OS) were screened out to construct the signature by univariate analysis, multivariate Cox analysis, and least absolute shrinkage and selection operator (Lasso) analysis. Then we verified the prognostic role of the signature, and assessed the relationship between this signature with tumor immune infiltration and functional pathways.Results: A total of 315 patients were included in our study, and divided to discovery (n=287) and validation (n=28) cohorts. Finally, we selected 14 IRGPs with a panel of 22 unique genes to construct the prognostic signature. According to the signature, we stratified patients into high-risk group and low-risk group. In both discovery and validation cohorts, the results of Kaplan-Meier analysis showed that there were significant differences in OS between the two groups (p<0.001). Combined with multiple clinical pathological factors, the results of multivariate analyses confirmed that this signature was an independent predictor of OS (HR, 3.548; 95%CI, 2.096−6.006; p<0.001). The results of immune infiltration analysis demonstrated that the abundance of multiple tumor-infiltration lymphocytes such as CD8+ T cells, Tregs, and T follicular cell helper were significantly higher in the high-risk group. Functional analysis showed that multiple immune-related signaling pathways were enriched in the high-risk group.Conclusions: We successfully established an individualized prognostic immune-related gene pairs signature, which can accurately and independently predict the OS of patients with PRCC.

A novel tailored immune gene pairs signature for overall survival prediction in lower-grade gliomas

“…Gene pair is an expression of the targeted and reference genes from the one sample. Signatures that resemble gene pairs have been used in prognostic evaluations of various cancers [30] , [31] , [32] , [33] . Moreover, the concordance between the 1p/19 codeletion-associated immune prognostic signature developed in our earlier study [6] and the current gene pairs signature was greatly improved than that of the EORTC and RTOG criteria [5] .…”

A united risk model of 11 immune‑related gene pairs and clinical stage for prediction of overall survival in clear cell renal cell carcinoma patients

“…Feng et al analyzed the expression of CASR, COL4A1, GPR4, MMP2, DCN, UTS2, and LDLR genes, which may be applied to improve RCC diagnosis and considered potential treatment targets, using information from Gene Expression Omnibus database with the help of Gene Set Enrichment Analysis, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Protein-protein Interaction method [32]. There is also a study based on the bioinformatic analysis of pRCC in which a prognostic model was constructed based on IRGPs, but this model does not include clinical factors [17]. The methods used for other cancers, such as cutaneous melanoma, are similar to our methods.…”

“…IRGs were matched to compare with each other to obtain IRGPs. If the first IRG's expression level was higher than the second one, their IRGP value was 1; otherwise, the value was 0 17 . In TCGA and E-MTAB-1980, IRGPs with an IRGP score of 0 or 1 and a ratio of less than 80% were retained as candidate IRGPs for prognostic prediction.…”

“…However, this platform lacks validation data from other platforms and it has platform bias [16]. There is also a study based on the bioinformatic analysis of pRCC in which a prognostic model was constructed based on immune-related gene pairs (IRGPs), but this model does not include clinical factors [17]. Therefore, we need to build a comprehensive and accurate immune-related gene prognostic evaluation system for ccRCC.…”

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“…The authors wish to thank all researchers and staff who uploaded and maintained data in TCGA and GEO databases for their great efforts. This manuscript has been released as a pre-print at Research Square (Zhou et al, 2020).…”