In this study, the authors conducted an in-depth and comprehensive bioinformatics analysis of the expression of CXC chemokines in Renal cell carcinoma and evaluated their potential as therapeutic targets and prognostic biomarkers based on several large public databases.

Herein, I will recover all figures for this paper step by step.

In Brief for this paper

  • Publication

  • Keywords

    • Sixteen CXC chemokines (not including CXCL15)
      • CXCL1, CXCL2, CXCL3, CXCL4, CXCL5, CXCL6, CXCL7, CXCL8, CXCL9, CXCL10, CXCL11, CXCL12, CXCL13, CXCL14, CXCL16, CXCL17
    • Renal cell carcinoma(RCC), Kidney cancer

  • Methods

    • ONCOMINE
    • GEPIA
    • UALCAN
    • cBioPortal
    • GeneMANIA
    • DAVID 6.8
    • Metascape
    • TRRUST
    • LinkedOmics
    • TIMER

Figure 1. mRNA levels of CXC chemokines in RCC (ONCOMINE)

  • Methods: ONCOMINE.

  • Open Oncomine database fig1

  • Set up the parameters for CXCL1 fig12

  • Merge all CXC chemokines together with Google Slides

    • Red: the numbers of datasets with statistically significant mRNA over-expression (red)
    • Blue: downregulated expression of CXC chemokines.

    figure1

Table 1. The mRNA levels of CXC chemokines in different types of RCC tissues and normal renal tissues at transcriptome level (ONCOMINE)

  • Methods: ONCOMINE.

  • Set up the parameters for CXCL3 gene. tab3

  • Continue, set up the parameters for all CXC chemokines. tab6 tab7 tab91 tab92 tab93 tab94 tab101 tab102 tab103 tab104 tab111 tab112 tab113 tab131 tab132 tab161 tab162

TLR Type Fold change P-value t-test References
CXCL3 Papillary Renal Cell Carcinoma −2.244 1.000 -8.293 (26)
CXCL6 Clear Cell Renal Cell Carcinoma 30.664 4.80E-4 4.888 (27)
CXCL7 Clear Cell Renal Cell Carcinoma −9.410 0.991 −3.343 (27)
CXCL9 Clear Cell Renal Cell Carcinoma 2.997 1.41E-6 7.311 (28)
Clear Cell Renal Cell Carcinoma 31.985 1.81E-7 10.220 (27)
Clear Cell Renal Cell Carcinoma 4.648 1.26E-5 6.703 (30)
Clear Cell Renal Cell Carcinoma 7.115 2.22E-7 7.103 (29)
CXCL10 Clear Cell Renal Cell Carcinoma 12.873 3.10E-12 11.075 (27)
Clear Cell Renal Cell Carcinoma 5.447 5.90E-8 9.505 (30)
Hereditary Clear Cell Renal Cell Carcinoma 11.612 9.94E-11 9.867 (29)
Non-Hereditary Clear Cell Renal Cell Carcinoma 5.897 4.41E-7 6.000 (29)
CXCL11 Hereditary Clear Cell Renal Cell Carcinoma 2.994 9.61E-9 7.199 (29)
Clear Cell Renal Cell Carcinoma 6.303 1.26E-4 5.000 (30)
Clear Cell Renal Cell Carcinoma 20.691 8.45E-4 5.712 (27)
CXCL13 Clear Cell Renal Cell Carcinoma 9.934 0.002 3.633 (27)
Hereditary Clear Cell Renal Cell Carcinoma 1.921 7.84E-4 3.447 (29)
CXCL16 Clear Cell Renal Cell Carcinoma 5.797 5.82E-4 6.812 (27)
Clear Cell Renal Cell Carcinoma 2.212 7.86E-4 3.932 (28)

Figure 2. The transcription of CXC chemokines in RCC (UALCAN)

  • Methods: UALCAN.

  • Open UALCAN database fig21

  • Click on the button TCGA analysis and explore the result for CXCL1 gene. fig22

  • Click on the button Expression and generate the expression result of CXCL1 gene. fig23

fig24

fig25

  • Merge all CXC chemokines together with Google Slides

    • The transcriptional levels of CXCL12(G) were significantly reduced.

    figure2

Figure 3. The relative level of CXC chemokines in RCC

  • Open GEPIA fig31

  • Click on the button Multiple Gene Analysis and Multiple Gene Comparison

  • Set up the parameters and click the button Plot fig32

  • Save and edit plot in cloud fig33

  • Click on Heatmap to generate the final heatmap fig34

  • The relative expression of CXCL14 was the highest.

    figure3

Figure 4. Correlation between different expressed CXC chemokines and the pathological stage of RCC patients (GEPIA)

  • Methods: GEPIA.

  • Open GEPIA

  • Click on Expression DIY, choose Stage plot fig41

  • Set up the parameters for CXCL1 gene and click the button Plot

  • Generate result of CXCL1 gene. fig42

  • Merge all results of CXC chemokines together with Google Slides. figure4

Figure 5. The prognostic value of different expressed CXC chemokines in RCC patients in the disease free survival curve (GEPIA)

  • Methods: GEPIA.

  • Open GEPIA

  • Click Survival and choose Survival Plots fig51

  • Set up the parameters for CXCL1 gene and click the button Plot fig52

  • Generate result of CXCL1 gene fig531

  • Merge all results of CXC chemokines together with Google Slides. figure5

Figure 6. The prognostic value of CXC chemokines in RCC patients in the overall survival curve (GEPIA)

  • Methods: GEPIA.

  • Open GEPIA

  • Click Survival and choose Survival Plots fig61

  • Set up the parameters for CXCL1 gene and click the button Plot fig62

  • Generate result of CXCL1 gene fig631

  • Merge all results of CXC chemokines together with Google Slides. figure6

Figure 7. Genetic alteration, neighbor gene network, and interaction analyses of different expressed CXC chemokines in RCC patients

  • Methods: cBioportal.

  • Open cBioportal fig71

  • Set up the parameters and click the button Submit Query fig72

  • Generate result for Figure 7A. fig73

  • Download the mRNA expression z-scores relative to diploid samples (RNA Seq V2 RSEM) and save. fig732

  • To get the correlation heat map of different expressed CXC chemokines in RCC with R.Script. fig7B

  • Methods: STRING.

  • Opne STRING

  • Set up the parameters and click on the button SEARCH. fig74

  • Continue, click on the button CONTINUE>> fig75

  • Click on the button Exports fig76

  • Click on the button Exports and download the result of Fig7C. fig7C

  • Open GeneMANIA fig77

  • Enter gene list and click search icon fig78

  • Click on the style icon and then click on the save icon to get the result of Fig7D. fig7D

Table 2. Key regulated factor of CXC chemokines in RCC (TRRUST)

  • Methods: TRRUST.

  • Open TRRUST fig1

  • Entet query genes and setup parameters, and then click on the button Submit fig2

  • Generate the result fig3

  • Click on the relative number to check the details fig4

  • Edit the result and get the result of table2 table2

Table 3. The Kinase target networks of CXC chemokines in RCC (LinkedOmics)

  • Methods: LinkedOmics.

  • Open LinkedOmics fig1

  • Set up the parameters for CXCL1 gene. fig2

  • Click on the button LinkInterpreter fig3

  • Set up the parameters to query fig4

  • Download all results of CXCL1 gene. fig5

  • Similarly, get other CXC chemokines following the above steps.

Figure 8. The enrichment analysis of different expressed CXC chemokines and 50 most frequently altered neighboring genes in RCC (David 6.8)

  • Methods: David 6.8.

  • Open DAVID fig1

  • Follow the protocol step by step. fig2 fig3 fig4 fig5 fig6

  • Download data bp.txt,cc.txt,mf.txt and kegg.txt

  • Generate the result of bar plot with R.Script figure8

  • Alternate, generate the result of dot plot. figure82

Figure 9. The correlation between different expressed CXC chemokines and immune cell infiltration (TIMER)

  • Methods: TIMER.

  • Open TIMER fig1

  • Click on gene

  • Set up the parameters for CXCL1 gene and click the button Submit fig2

  • get the result of CXCL1 gene fig310

fig31

  • Merge all results of CXC chemokines together with Google Slides

    fig99

Table 4. The cox proportional hazard model of CXC chemokines and six tumor-infiltrating immune cells in RCC (TIMER)

  • Methods: TIMER.

  • Setup the cox proportional hazard of CXC chemokines fig1

Supplementary Figure 1. The enrichment analysis of different expressed CXC chemokines and 50 most frequently altered neighboring genes in RCC (Metascape)

  • Methods: Metascape.

  • Open Metascape fig1

  • Click on the button Express Analysis

  • Click on the button Analysis Report Page fig2

  • Generate the results of Supplementary Figure 1 supfigure1

In Summary

In this study, the authors conducted an in-depth and comprehensive bioinformatics analysis of the expression of CXC chemokines in Renal cell carcinoma and evaluated their potential as therapeutic targets and prognostic biomarkers based on several large public databases.

The present study is a straightforward and comprehensive bioinformatics method, which could be implied to different genes and diseases.