A construction and comprehensive analysis of ceRNA networks and infiltrating immune cells in papillary renal cell carcinoma

• Published in: Cancer medicine (Malden, MA) Vol. 10; no. 22; pp. 8192 - 8209
• Main Authors: Fan, Yaqi, Dai, Fangfang, Yuan, Mengqin, Wang, Feiyan, Wu, Nanhui, Xu, Mingyuan, Bai, Yun, Liu, Yeqiang
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01-11-2021; John Wiley and Sons Inc; Wiley
• Subjects: Bioinfomatics; Biomarkers; Biomarkers, Tumor - genetics; Carcinoma, Renal Cell - genetics; Carcinoma, Renal Cell - pathology; CD4 antigen; Cell activation; Cell adhesion & migration; ceRNA network; Collagen (type I); Correlation analysis; Gene expression; Gene Regulatory Networks - genetics; Genomes; Humans; immune cells; Immunological memory; Kidney cancer; Kidney Neoplasms - genetics; Kidney Neoplasms - pathology; Lymphocytes T; Macrophages; Malignancy; Medical prognosis; Memory cells; Metastases; Metastasis; MicroRNAs; MicroRNAs - genetics; nomogram; Nomograms; Ontology; Papillary renal cell carcinoma; Patients; PRCC; Prognosis; Signal transduction; Survival analysis; Tumor-infiltrating lymphocytes; Tumorigenesis; Visualization; Wnt protein; immune cells; ceRNA network; nomogram; PRCC
• ISSN: 2045-7634; 2045-7634
• DOI: 10.1002/cam4.4309

Background
As the second most common malignancy in adults, papillary renal cell carcinoma (PRCC) has shown an increasing trend in both incidence and mortality. Effective treatment for advanced metastatic PRCC is still lacking. In this study, we aimed to establish competitive endogenous RNA (ceRNA) networks related to PRCC tumorigenesis, and analyze the specific role of differentially expressed ceRNA components and infiltrating immune cells in tumorigenesis.
Methods
CeRNA networks were established to identify the key ceRNAs related to PRCC tumorigenesis based on the 318 samples from The Cancer Genome Atlas database (TCGA), including 285 PRCC and 33 normal control samples. The R package, “CIBERSORT,” was used to evaluate the infiltration of 22 types of immune cells. Then we identified the significant ceRNAs and immune cells, based on which two nomograms were obtained for predicting the prognosis in PRCC patients. Finally, we investigated the co‐expression of PRCC‐specific immune cells and core ceRNAs via Pearson correlation test.
Results
COL1A1, H19, ITPKB, LDLR, TCF4, and WNK3 were identified as hub genes in ceRNA networks. Four prognostic‐related tumor‐infiltrating immune cells, including T cells CD4 memory resting, Macrophages M1, and Macrophages M2 were revealed. Pearson correlation test indicated that Macrophage M1 was negatively related with COL1A1 (p < 0.01) and LDLR (p < 0.01), while Macrophage M2 was positively related with COL1A1 (p < 0.01), TCF4 (p < 0.01), and H19 (p = 0.032). Two nomograms were conducted with favorable accuracies (area under curve of 1‐year survival: 0.935 and 0.877; 3‐year survival: 0.849 and 0.841; and 5‐year survival: 0.818 and 0.775, respectively).
Conclusion
The study constructed two nomograms suited for PRCC prognosis predicting. Moreover, we concluded that H19‐miR‐29c‐3p‐COL1A1 axis might promote the polarization of M2 macrophages and inhibit M1 macrophage activation through Wnt signaling pathway, collaborating to promote PRCC tumorigenesis and lead to poor overall survival of PRCC patients.
We constructed two nomograms for predicting the survival of papillary renal cell carcinoma patients.H19‐miR‐29c‐3p‐COL1A1 axis might promote the polarization of M2 macrophages and inhibit M1 macrophage activation through Wnt signaling pathway, collaborating to promote PRCC tumorigenesis and lead to poor overall survival of PRCC patients.