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. 2021 Oct 7;14(1):241.
doi: 10.1186/s12920-021-01092-w.

Identification of immunization-related new prognostic biomarkers for papillary renal cell carcinoma by integrated bioinformatics analysis

Ping Wu  1 , Tingting Xiang  2 , Jing Wang  3 , Run Lv  4 , Shaoxin Ma  4 , Limei Yuan  4 , Guangzhen Wu  5 , Xiangyu Che  6
Affiliations

Identification of immunization-related new prognostic biomarkers for papillary renal cell carcinoma by integrated bioinformatics analysis

Ping Wu et al. BMC Med Genomics. .

Abstract

Background: Despite papillary renal cell carcinoma (pRCC) being the second most common type of kidney cancer, the underlying molecular mechanism remains unclear. Targeted therapies in the past have not been successful because of the lack of a clear understanding of the molecular mechanism. Hence, exploring the underlying mechanisms and seeking novel biomarkers for pursuing a precise prognostic biomarker and appropriate therapies are critical.

Material and methods: In our research, the differentially expressed genes (DEGs) were screened from the TCGA and GEO databases, and a total of 149 upregulated and 285 downregulated genes were sorted. This was followed by construction of functional enrichment and protein-protein interaction (PPI) network, and then the top 15 DEGs were selected for further analysis. The P4HB gene was chosen as our target gene by repetitively validating multiple datasets, and higher levels of P4HB expression predicted lower overall survival (OS) in patients with pRCC.

Results: We found that P4HB not only connects with immune cell infiltration and co-expression with PD-1, PD-L2, and CTLA-4, but also has a strong connection with the newly discovered hot gene, TOX.

Conclusion: We speculate that P4HB is a novel gene involved in the progression of pRCC through immunomodulation.

Keywords: Biological markers; Carcinoma; Computational biology; Immunotherapy; Prolyl hydroxylases; Renal cell.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Identification of overlapping DEGs in the GEO database and the TCGA database. a Heatmap and volcano plots of DEGs in the GEO database using the R language (GES11151). b Heatmap and volcano plots of DEGs in the GEO database using the R language (GES15641). c Screening for differential genes in the TCGA database via the GEPIA website. d Venn plots of DEGs across the GEO database and the TCGA database
Fig. 2
Fig. 2
GO and KEGG analysis of DEGs. a, b GO analysis of DEGs using the metascape online website. c, d PPI analysis of DEGs using the metascape online website. e GO (BP. MF.CC) pathway analysis of DEGs using Webgestalt online website. f KEGG pathway analysis via DAVID online tool and bubble chart display via R language. g KEGG pathway analysis of DEGs via clugo plugin in Cytoscape software
Fig. 3
Fig. 3
PPI analysis of DEGs, screening of Hub gene, and pathway analysis of Hub gene. a PPI analysis of DEGs through the STRING website tool. b, c Screening the top 15 Hub genes by MCC operation through the cytohubba plugin in Cytoscape software. d GO (BP. MF.CC) analysis of hub genes using the webgestalt online website. e KEGG pathway analysis of DEGs via clugo plugin in Cytoscape software. f GO pathway analysis via DAVID online tool and bubble chart display via R language
Fig. 4
Fig. 4
The difference in mRNA expression of Hub gene in PRCC and normal kidney tissues by TCGA database (GEPIA online tool)
Fig. 5
Fig. 5
Survival analysis of Hub genes in PRCC. a The differential expression of the hub genes between pRCC and normal kidney tissue. b, c Meta-analysis of four different databases in the TCGA database and revalidation of seven upregulated genes and eight downregulated genes
Fig. 6
Fig. 6
Analysis of P4HB information in PRCC through the TCGA database. a Analysis of the differential expression of P4HB between PRCC and normal kidney tissue in multiple tumors via TIMER online tool. b Analysis of P4HB expression differences in PRCC and normal kidney tissues. c Analysis relationship of different P4HB expressions and overall survival time. d Immunohistochemical images of P4HB in kidney cancer and normal tissues. e H score was performed to assess protein levels of gene P4HB in five normal tissues and five papillary renal cell carcinoma samples. P values < 0.0001 were considered statistically significant. All results are expressed as mean ± standard deviation (SD)
Fig. 7
Fig. 7
Immunological correlation analysis of P4HB. a, b P4HB expression and immune cell infiltration analysis. c Survival analysis of P4HB and immune cells in PRCC. d Co-expression analysis of P4HB and immunological checkpoint related genes
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