Identifcation of DEGs in colorectal cancer .There were 65 CRC tissues and normal tissues in our present study. Via GEO2R online tools, we extracted1643,1269༌3497and2180 DEGs from GSE74602༌GSE110223༌GSE113513 and GSE 141174, respectively. Then, we used Venn diagram software to identify the commonly DEGs in the four datasets. Results showed that a total of 171commonly DEGs were detected, including 148 up-regulated genes(logFC > 0)and 23 down-regulated genes (logFC < 0) in the CRC tissues (Table 1 & Fig. 1).
Table 1
All 171 commonly differentially expressed genes (DEGs) were detected from four profile datasets in the CRC tissues compared to normal tissues
DEGs
|
Genes Name
|
Up-
regulated
|
PLCE1 PTPRR HSD17B2 EPB41L4B ZG16 DHRS11 HSD11B2 PAPSS2 TRPM6 CES2 PYY VWA5A MAOA SLCO2A1 PDZD3 ZZEF1 GNA11 HHLA2 FGFR3 AHCYL2 CDHR5 PBLD PTPRH KLF4 MT1G AQP8 SGK1 AOC1 C1orf115 ACADVL LGALS4 ADH1C SGK2 SLC17A4 BMP2 UGT2A3 LRRC19 SST SCNN1B CLIC5 EPB41L3 PPP1R14D CWH43 MEP1A ACAA2 MALL GPD1L CA12 GUCA2A TP53I3 SLC22A5 MT1E CA4 APPL2 NAAA NAT2 KRT20 SLC26A2 TST ADTRP MT1F PLPP1 GCNT3 MS4A12 CEACAM1 HIST1H1C ABCA5 CDA SLC4A4 SI IQGAP2 SQRDL PHLPP2 PRDX6 AKR1B10 TUBAL3 DEFB1 ARL14 PIGZ CKB SLC26A3 CHGA CAPN9 DSC2 ABCB1 VILL PDE9A C4orf19 SMPD1 EDN3 SPON1 ITPKA PCK1 SMPDL3A ITM2C CASP7 SCGB2A1 TMPRSS2 ACADS SEMA6A DHRS9 PNLIPRP2 CA7 ENTPD5 MMP28 GUCA2B MGLL CHP2 CLCA4 UGDH MYO1A SLC36A1 NXPE4 FABP1 ETFDH MXI1 SLC1A1 FAM46A GDPD3 NR5A2 RBM47 FMO5 CA2 BCAS1 STAP2 ANPEP CEACAM7 ALPI ARHGAP44 TSPAN1 GCG CHST5 P2RX4 UGP2 CLDN8 EGLN3 MT1H VIPR1 PLAC8 GPA33 CA1 RETSAT SULT1B1 FXYD3 SELENBP1 GRAMD3 PARM1 CDHR2
|
Down-
regulated
|
TRIB3 CDC25B PHLDA1 PRDX4 TCFL5 MYC NAP1L1 MMP11 LRP8 CEMIP CTPS1 COL4A1 SHMT2 PLAU GTF3A MTHFD2 SCD IFITM3 SLC7A5 FBL PPAT TESC TGFBI
|
DEGs gene ontology and KEGG pathway analysis in colorectal cancer
All 171 DEGs were analyzed by DAVID software and the results of GO analysis indicated that 1) The main genes enriched in biological processes (BP), up-regulated DEGs were particularly enriched in oxidation-reduction process, bicarbonate transport, digestion, ion transmembrane transport and one-carbon metabolic process, and down-regulated DEGs in positive regulation of cell proliferation, cellular response to insulin stimulus and extracellular matrix organization; 2) The main genes enriched in cell component (CC),up-regulated DEGs were enriched in extracellular exosome, integral component of membrane, plasma membrane, extracellular spacea and integral component of plasma membrane, and down-regulated DEGs in cytosol, extracellular region and extracellular matrix; 3) The main genes enriched in molecular function (MF),up-regulated DEGs were enriched in zinc ion binding, oxidoreductase activity, transporter activity and hormone activity(Table 2).
Table2 Gene ontology analysis of differentially expressed genes in colorectal cancer
KEGG analysis results were shown in Table 3which demonstrated that up-regulated DEGs were particularly enriched in Metabolic pathways, Mineral absorption, Nitrogen metabolismans and Pancreatic secretion while down-regulated DEGs in One carbon pool by folate.
Table 3 KEGG pathway analysis of differentially expressed genes in colorectal cancer
![](https://myfiles.space/user_files/83064_0857a92044b57365/83064_custom_files/img1625138826.png)
Protein–protein interaction network (PPI)
A total of 171 DEGs were imported into the DEGs PPI network complex which included 170 nodes and 204 edges, including 23 down-regulated and 143 up-regulated genes (Fig.2a). Then we applied STRING analysis and results showed that 87 central nodes were identified among the 160 nodes (Fig.2b).
Analysis of core genes by the Kaplan Meier plotter and GEPIA
Kaplan Meier plotter (http://kmplot.com/analysis) was utilized to identify 88 core genes survival data. It was found that 30 genes had a significantly worse survival while 58 had no significant (P < 0.05, Table 4 & Fig. 3). Then, GEPIA was used to dig up the 30 gene expression level between cancerous and normal people. Results reported that 13 of 30 genes reflected high expressed in CRC samples contrasted to normal samples (P< 0.05,Table 5 & Fig. 4).
Table 4 The prognostic information of the 88 key candidate genes
Category
|
Genes
|
Genes with significantly
worse survival (P < 0.05)
|
MYC CEMIP MTHFD2 SCD TGFBI PLCE1 HSD17B2 ZG16
TRPM6 CES2 AHCYL2 FGFR3 BMP2 SLC22A5 KRT20 TST
CDA SI PHLPP2 AKR1B10 TMPRSS2 DHRS9 MGLL MYO1A SLC36A1 NXPE4 SLC1A1 ANPEP CEACAM7 ALPI
|
Genes without significantly
worse survival (P > 0.05)
|
TRIB3 CDC25B CTPS1 COL4A1 SHMT2 PLAU SLC7A5 FBL
PYY MAOA PDZD3 GNA11 CDHR5 KLF4 AQP8 SGK1
ACADVL LGALS4 ADH1C SGK2 UGT2A3 SST SCNN1B
MEP1A ACAA2 CA12 GUCA2A CA4 NAAA MS4A12 SLC4A4
SQRDL DEFB1 SLC26A3 CHGA ABCB1 PDE9A EDN3 ITPKA
PCK1 CASP7 ACADS PNLIPRP2 CA7 ENTPD5 GUCA2B
CLCA4 UGDH FABP1 ETFDH MXI1 CA2 GCG UGP2 VIPR1
CA1 RETSAT CDHR2
|
Table 5 Vadidation of 30 genes via GEPIA
Category
|
Genes
|
Genes with high expressed
in COAD (P < 0.05)
|
CEMIP CDA FGFR3 KRT20 MTHFD2 MYC MYO1A PLCE1 SCD TGFBI TMPRSS2 TST ZG16
|
Genes without high
expressed in COAD (P > 0.05)
|
HSD17B2 TRPM6 CES2 AHCYL2 BMP2 SLC22A5
SI PHLPP2 AKR1B10 DHRS9 MGLL SLC36A1
NXPE4 SLC1A1 ANPEP CEACAM7 ALPI
|
Re-analysis of 13 selected genes via KEGG pathway enrichment
To understand the possible pathway of these 13 selected DEGs, KEGG pathway enrichment was re-analyzed via DAVID (P<0.05). Results showed that two genes(MYC,FGFR3) markedly enriched in the Bladder cancer pathway (P = 0.041, Table 6 & Fig. 5).
Table 6 Re-analysis of 13 selected genes via KEGG pathway enrichment
Pathway ID and Name
|
Count
|
%
|
p-Value
|
FDR
|
Genes
|
hsa05219:Bladder cancer
|
2
|
15.3846153846153
|
0.0410001159215224
|
1
|
MYC, FGFR3
|
To identify more useful prognostic biomarkers in CRC cancer, this study used bioinformatical methods on the basis of four profile datasets(GSE74602,GSE110223,GSE113513 and GSE 141174), Sixty-five CRC specimens and normal specimens were enrolled in the present research. Via GEO2R and Venn software, we revealed a total of 171 commonly changed DEGs (|logFC| > 2 and adjust P value < 0.05) including 148 up-regulated (LogFC > 0) and 23 down-regulated DEGs (Log FC < 0). Then, Gene Ontology and Pathway Enrichment Analysis using DAVID methods showed that up-regulated DEGs were particularly enriched in oxidation-reduction process, in extracellular exosome, in zinc ion binding, in Metabolic pathways, Mineral absorption; and down-regulated DEGs in positive regulation of cell proliferation, in cytosol, in One carbon pool by folate. Next, DEGs PPI network complex of 170 nodes and 204 edges was constructed via the STRING online database. Furthermore, for the analysis of overall survival among those genes, Kaplan–Meier analysis was implemented and 30 of 88 genes had a significantly worse prognosis. For validation in Gene Expression Profiling Interactive Analysis (GEPIA), 13 of 30 genes were discovered highly expressed in CRC tissues compared to normal tissues. Furthermore, two genes(MYC,FGFR3) markedly enriched in the Bladder cancer pathway. In conclusion, we have identified two significant up-regulated DEGs with poor prognosis in CRC on the basis of integrated bioinformatical methods, which could be potential therapeutic targets for CRC patients.
MYC (oncogene) is highly expressed in colorectal cancer tissues, and is widely activated in colorectal cancer. It is involved in the regulation of growth, invasion and metastasis of colorectal cancer, and has been used as a research target for anti-tumor therapy for many years。Guo[15] found that polyamine biosynthesis is often disordered in colorectal cancer, and there is a close relationship between polyamine metabolism pathway and oncogenic signaling pathway in the process of tumor development. Inhibition of SMS and Myc simultaneously has synergistic effects, and combined inhibition of SMS and Myc expression may provide a new therapeutic target and therapeutic strategy for the treatment of colorectal cancer。Bian[16]shows that MYC family oncogene and DNA damage repair protein PARP 1 play an important role in the development and development of small cell lung cancer. MYC family genes are amplified or highly expressed in some small cell lung cancer cell lines and patient tissues. Combined inhibition of PARP and BET can effectively inhibit the proliferation and survival of MI℃ family gene-dependent small cell lung cancer tumor tissue.Fibroblast growth factor receptor (FGFR) is a receptor protein binding to Fibroblast growth factor family member protein ligand (FGF),A member of the tyrosine kinase family, it is involved in cell proliferation, stem cell differentiation, embryo development, migration, survival, angiogenesis and organogenesis through multiple signaling pathways.Studies have shown that the activation of FGFR signaling pathway is related to the occurrence and development of a variety of cancers. FGFR2 can also be phosphorylated, but the way and sequence of its phosphorylation and activation remain unclear. The phosphorylation of FGFR3 and FGFR4 is different from that of FGFR1.
Zhao[17] have shown that effective components from the rhizome of R. sinicum inhibit the proliferation of colorectal cancer cells by targeting FGFR1 and down-regulating the expressions of p-jake,p-STAT3 and p-MEK1/2, and inhibit the growth of human colorectal cancer xenograft tumors with high FGFR1 expression。Fibroblast growth factor receptor 3(FGFR3), Wang[18] have shown that miR-99a-SP has target gene binding sites with downstream FGFR3. MiR-99a-sp mimics significantly down-regulated FGFR3 after overloading; MiR-99a-SP can negatively regulate FGFR3, and the proliferation, metastasis and invasion of cancer cells are all decreased after targeted knockdown of FGFR3.Numerous studies have proved that MYC and FGFR genes were related to various types of cancer’s progression,In this study, MYC and FGFR genes were also enriched in bladder cancer, and the mechanism of action of the two genes was also studied. MYC and FGFR genes, as important indicators for detection, could be used as effective targets for diagnosis and treatment of digestive tract tumors.