2.1 Expression pattern of CFAP45 mRNA in DKD
The characteristics of DKD patients in GSE30122 have been reported previously.10 As shown in Fig. 1A, we compared the expression levels of CFAP45 in 19 DKD tissues and 50 normal tissues using the Wilcoxon’s rank sum test. The expression levels of CFAP45 mRNA in DKD tissues were significantly lower than those in normal tissues (p = 7.59e-05) (Fig. 1A). Next, we performed receiver operating characteristic (ROC) analysis to measure the discriminatory ability of CFAP45. The area under curve (AUC) of the ROC curve was 0.811, thus representing a moderate ability to discriminate cases of DKD from a normal kidney (Fig. 1B). In addition, we used the CCLE database to analyze the expression levels of CFAP45 mRNA in different cell lines from various tissues (Fig. 1C). We observed high expression levels of CFAP45 in cell lines associated with meningioma, breast, and the urinary tract. Low levels of CFAP45 expression were identified in soft tissue and cell lines associated with B-cell_ALL and neuroblastoma.
2.2 The correlation between CFAP45 expression and different biological processes
Analysis of the GeneCards database identified 178 apoptosis-related genes, 152 autophagy-related genes, and 117 senescence-related genes. Pathway enrichment analysis was then performed with ssGSEA. High expression levels of CFAP45 were significantly associated with higher levels of apoptosis (P < 0.01, Fig. 2A) and higher senescence scores (P < 0.05, Fig. 2C). The expression levels of CFAP45 were not significantly correlated with the extent of autophagy (Fig. 2B).
2.3 The correlation between CFAP45 expression and immune infiltration
Pearson correlation was used to analyze the correlation between CFAP45 expression levels and the relative abundance of 22 immune cell subtypes (quantified by CIBERSORT) in DKD samples (Fig. 3A). Analysis showed that CFAP45 expression was positively correlated with activated mast cells (Pearson’s correlation = 0.855, P < 0.001, Fig. 3B) and resting dendritic cells (Pearson’s correlation = 0.491, P = 0.033, Fig. 3C), and negatively correlated with resting mast cells (Pearson’s correlation = -0.514, P < 0.017, Fig. 3D).
2.4 Identification of DEGs in between high- and low- CFAP45 groups
PCA analysis (Fig. 4A) identified a large difference between high- and low- CFAP45 groups. Next, R package limma was performed to compare the gene expression profiles between the two groups. A total of 349 DEGs, including 59 up-regulated genes and 290 down-regulated genes, were detected (adjusted P-value < 0.05 and |logFC| > 1.0). The relative expression levels of DEGs were illustrated by Volcano plot (Fig. 4B) and Heat map (Fig. 4C).
2.5 Functional enrichment of CFAP45 related DEGs in DKD patients
To predict the functional enrichment information of CFAP45-associated DEGs in DKD, we performed GO enrichment analysis using the ClusterProfiler R package. GO results showed that CFAP45-associated DEGs were mainly located in the mitochondrial matrix and the lysosomal membrane (Fig. 5A and Table 1). The biological processes (BPs) and molecular functions (MFs) of CFAP45-associated genes included the regulation of protein modification by small protein conjugation or removal, the tricarboxylic acid cycle, proteasome-mediated ubiquitin-dependent protein catabolic processes, oxidoreductase activity acting on NAD(P)H, phospholipid transporter activity, intramembrane lipid transporter activity, and I-SMAD binding (Fig. 5B-C and Table 1). Next, we performed KEGG pathway enrichment analysis and identified several pathways that were influenced by CFAP45, including the citrate cycle (TCA cycle), carbon metabolism, valine, leucine and isoleucine degradation, fatty acid metabolism, endocytosis, glycolysis and gluconeogenesis, amino sugar and nucleotide sugar metabolism, the PPAR signaling pathway, and peroxisomes (Fig. 5D and Table 2).
Table 1
Gene Ontology (GO) analysis of differentially expressed genes (DEGs)
ONTOLOGY
|
ID
|
Description
|
Count
|
P value
|
Gene
|
BP
|
GO:1903320
|
regulation of protein modification by small protein conjugation or removal
|
15
|
8.46E-06
|
UFL1/HMG20A/BAG5/RWDD3/HSP90AB1/TRIP12/CTNNB1/BAG2/
ISG15/TOPORS/HSPA5/DCUN1D1/RAB1A/NDFIP1/HERPUD1
|
BP
|
GO:0050792
|
regulation of viral process
|
14
|
1.13E-05
|
LGALS1/IFI27/STAU1/IGF2R/ISG15/IFITM2/NELFCD/IFITM3/
SNF8/MPHOSPH8/POLR2J/CCL4/IFITM1/FCN3
|
BP
|
GO:0006099
|
tricarboxylic acid cycle
|
6
|
1.88E-05
|
DLAT/NNT/DLD/SUCLA2/SDHA/SUCLG2
|
CC
|
GO:0005759
|
mitochondrial matrix
|
21
|
3.25E-05
|
NME4/CBR4/CLPX/DLAT/IARS2/OXCT1/DLD/HSPA9/VDAC1/
SUCLA2/NARS2/HSPD1/ACAD8/ETFA/TFB2M/SUCLG2/ACSM3/
MRPL19/GLUD1/ALDH7A1/MIPEP
|
CC
|
GO:0005925
|
focal adhesion
|
19
|
4.14E-05
|
LIMA1/KLF11/HNRNPK/PPP1CB/TRIOBP/RRAS/CTNNB1/
TGFB1I1/HSPA9/RAB21/IGF2R/CPNE3/CYBA/ZNF185/
FLRT3/HSPA5/EHD3/DPP4/CNN3
|
CC
|
GO:0030055
|
cell-substrate junction
|
19
|
5.21E-05
|
LIMA1/KLF11/HNRNPK/PPP1CB/TRIOBP/RRAS/CTNNB1/
TGFB1I1/HSPA9/RAB21/IGF2R/CPNE3/CYBA/ZNF185/
FLRT3/HSPA5/EHD3/DPP4/CNN3
|
MF
|
GO:0043903
|
regulation of interspecies interactions between organisms
|
14
|
2.34E-05
|
LGALS1/IFI27/STAU1/IGF2R/ISG15/IFITM2/NELFCD/
IFITM3/SNF8/MPHOSPH8/POLR2J/CCL4/IFITM1/FCN3
|
MF
|
GO:0043161
|
proteasome-mediated ubiquitin-dependent protein catabolic process
|
19
|
8.73E-05
|
UFL1/TBL1XR1/BAG5/HSP90AB1/CD2AP/IFI27/WWP1/
CTNNB1/DNAJB14/RNF14/MTM1/TOPORS/UBE2W/PTTG1/
HSPA5/NEDD4L/HERPUD1/PSMB10/PSMA4
|
MF
|
GO:0046596
|
regulation of viral entry into host cell
|
5
|
0.000128
|
LGALS1/IFITM2/IFITM3/IFITM1/FCN3
|
Table 2
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of differentially expressed genes (DEGs)
ID
|
Description
|
Count
|
P value
|
Gene
|
hsa00020
|
Citrate cycle (TCA cycle)
|
5
|
0.00019
|
DLAT/DLD/SUCLA2/SDHA/SUCLG2
|
hsa01200
|
Carbon metabolism
|
8
|
0.001409
|
DLAT/DLD/SUCLA2/PGK1/SDHA/SUCLG2/EHHADH/GLUD1
|
hsa00650
|
Butanoate metabolism
|
4
|
0.001575
|
OXCT1/L2HGDH/ACSM3/EHHADH
|
hsa00280
|
Valine, leucine and isoleucine degradation
|
5
|
0.001755
|
OXCT1/DLD/ACAD8/EHHADH/ALDH7A1
|
hsa00640
|
Propanoate metabolism
|
4
|
0.003277
|
DLD/SUCLA2/SUCLG2/EHHADH
|
hsa01212
|
Fatty acid metabolism
|
5
|
0.003762
|
CBR4/SCP2/ACSL1/CPT2/EHHADH
|
hsa04144
|
Endocytosis
|
11
|
0.005765
|
ARFGAP3/WWP1/SNX2/AGAP1/IGF2R/
RAB11FIP1/EHD3/SNF8/NEDD4L/ARPC1A/SNX4
|
hsa00010
|
Glycolysis / Gluconeogenesis
|
5
|
0.007501
|
MINPP1/DLAT/DLD/PGK1/ALDH7A1
|
Gene set enrichment analysis was also used to identify signaling pathways that were involved in DKD by comparing between data sets with high and low levels of CFAP45 expression. GSEA revealed significant differences (|NES| > 1) in the enrichment of MSigDB collections (c2.cp.kegg. v6.2.symbols). Four pathways showed significant differential enrichment in the low- CFAP45 expressed phenotype, including nicotinate and nicotinamide metabolism, tryptophan metabolism, selenoamino acid metabolism, and the adipocytokine signaling pathway (Fig. 6A, 6B and Table 3), thus indicating the potential role of CFAP45 in the metabolic disorder associated with DKD.
Table 3
Gene set enrichment analysis (GSEA) analysis parameters.
Name
|
Size
|
Enrichment
Score
|
NES
|
P value
|
Leading edge
|
KEGG_NICOTINATE_AND_NICOTINAMIDE_METABOLISM
|
16
|
-0.6427
|
-1.48595
|
0.017682
|
tags = 50%, list = 15%, signal = 59%
|
KEGG_TRYPTOPHAN_METABOLISM
|
33
|
-0.6979
|
-1.39764
|
0.087891
|
tags = 67%, list = 16%, signal = 79%
|
KEGG_SELENOAMINO_ACID_METABOLISM
|
19
|
-0.5416
|
-1.36996
|
0.095703
|
tags = 63%, list = 24%, signal = 83%
|
KEGG_BETA_ALANINE_METABOLISM
|
21
|
-0.73829
|
-1.36531
|
0.116142
|
tags = 71%, list = 16%, signal = 85%
|
KEGG_ADIPOCYTOKINE_SIGNALING_PATHWAY
|
62
|
-0.38756
|
-1.36028
|
0.057769
|
tags = 23%, list = 11%, signal = 25%
|
2.6 PPI Network analysis and Transcriptional Regulatory Network (TRN) construction
All CFAP45-associated DEGs were then imported into the STRING database for protein-protein interaction network analysis; the confidence score was set to 0.4 (Fig. 7A). Next, the top ten hub genes were ranked using the MCC method, including WWP1, FBXW2, NEDD4L, HERC6, RNF14, TRIP12, PJA2, UBE2W, RNF138, and UFL1 (Fig. 7B).
Changes in the patterns of gene expression are known to be affected by regulatory events at the transcriptional and post-transcriptional levels. Therefore, we selected potential transcription factor (TF) targets to further explore the mechanisms that might underlie the progression of DKD. As shown in Fig. 7C and Table S1, there was a strong correlation between TFs and DEGs (Pearson’s correlation > 0.8, P < 0.0001). We introduced this data into Cytoscape and created a transcriptional regulatory network (TRN) featuring key transcription factors, including CTNNB1, KLF11, RCOR1, SMAD4, SNAPC2, and TBL1XR1.