Bioinformatics Analysis Of Microsatellite Instability And Tumor Immune Features In Colorectal Cancer

Background Colorectal cancer has been one of the leading gut malignant diseases with resilient incidence and mortality.The tumorigenesis and progression of the colorectal cancer was reflected by the distinct heterogeneity and mutations.Meanwhile,the inherit characteristics and interactions with external environment impact the early stage and biological features of colorectal cancer.Microsatellite instability,reflected by mismatch repair(MMR),is one of the key representatives for genomic analysis in colorectal clinical practices.However,currently there remains limited bioinformatics analysis focusing on the genomic features of MSI and tumor immunology.Methods This study included GSE13067,GSE4554,GSE25071,GSE18088,GSE13067,GSE78220 and the cancer genome atlas(TCGA)for genome profiles analysis.The differentially expressed genes(DEGs)were enriched by GO analysis,including biological processes(BP),cellular components(CC)and molecular functions(MF)as well as Kyoto Encyclopedia of Genes and Genomes(KEGG).The protein-protein-interaction(PPI) networks were further established by the Search Tool for the Retrieval of Interaction Genes/Proteins(STRING).The prognostic signature of hub genes was performed via the Surv Express.Further the gene set enrichment analysis(GSEA)and weighted gene correlation network analysis(WGCNA)were performed.The clinical data of 407 colorectal cancer patients was retrospectively collected from the department of general surgery,Ruijin Hospital,Shanghai Jiao Tong University,School of Medicine from September 2017 to September 2018.GSE56386,containing four samples with cetuximab non-responder and four samples with cetuximab responder,and GSE77346,containing trastuzumab sensitive and resistant gastric cancer cell lines,were used for bioinformatics analysis.Results 771 DEGs were identified in GSE13067 and 1266 were identified in GSE4554.357 overlapped DEGs were retrieved.The GO terms included cell migration,negative regulation of cell differentiation and regulation of cell differentiation in BP.Nucleoplasm?apical part of cell and nucleoplasm part were in CC.Double-stranded DNA binding?nucleotide binding and nucleoside phosphate binding were in MF.KEGG pathway included hsa04062: chemokine signaling pathway?hsa05169: Epstein-Barr virus infection and hsa04670: leukocyte transendothelial migration.The hub genes included CTNNB1(catenin beta 1),CDC42(cell division cycle 42),GSR(glutathione-disulfide reductase),CDK2(cyclin dependent kinase 2),SYK(spleen associated tyrosine kinase),ENO2(enolase 2),CXCR4(C-X-C motif chemokine receptor 4),HSPA1A(heat shock protein family A(Hsp70)member 1A),MDM2(MDM2 proto-oncogene)and CFTR(cystic fibrosis transmembrane conductance regulator).The prognostic signature indicated high risk group with poor outcomes(Hazard ratio=2.52,95%CI:1.88-3.38,p=7.07e-10).In GSE25071,a total of 49 DEGs were identified,including 6 upregulated and 43 downregulated genes.A total of 15 biological processes BP,3 CC and 5 MF were identified.No significant enriched KEGG pathway.Five hub genes were identified through gene coexpression network,including Zinc finger protein(ZNF)813,ZNF426,ZNF611,ZNF320 and ZNF573.Only the mammalian target of rapamycin complex 1(m TORC1)showed significant enrichment.WGCNA identified 25 gene modules,with the top module significantly enriched in ribosome-associated terms.The hub genes included ribosomal protein L12(RPL12),ribosomal protein S3A(RPS3A),ribosomal protein S9(RPS9),ribosomal protein L27a(RPL27a),ribosomal protein L7(RPL7),ribosomal protein L28(RPL28),ribosomal protein L14(RPL14),ribosomal protein S17(RPS17),mitochondrial ribosomal protein L16(MRPL16),G elongation factor,mitochondrial 2(GFM2).MSI-H showed comparably higher immune-related scores compared to MSS.In TCGA,a total of 564 DEGs were identified between MSI-H and MSS,with 304down-regulated and 260 up-regulated.MLH1 was the most differentially expressed.Cytokine-cytokine receptor interaction(hsa04060,adj.p=0.00134),chemokine signaling pathway(hsa04062,adj.p=0.0036)and fat digestion and absorption(hsa04975,adj.p=0.017)were the top listed KEGG pathways.The top three pathways enriched in GSEA included complement,interferon-gamma-response,allograft rejection.The hub genes included Interleukin 6(IL6),C-X-C motif chemokine ligand 8(IL8),Interferon gamma(IFNG),C-C motif chemokine ligand 5(CCL5),C-C motif chemokine receptor 5(CCR5),Jun proto-oncogene,AP-1 transcription factor subunit(JUN),Toll like receptor 2(TLR2),Leucine rich repeat kinase 2(LRRK2),C-X-C motif chemokine receptor 4(CXCR4),C-X-C motif chemokine ligand 9(CXCL9).Different tumor immune infiltrates correlations were noticed between colon cancer and rectal cancer.CCR5 showed the highest correlation with dendritic cells both in colon and rectal cancer.Only the cg03405026 of CRC methylation showed significant prognosis.Furthermore,no significant DEGs was found between long term MSS and short term MSS.The tumor position varied significantly between MMR deficient and proficient groups.69.6% MMR deficient group were right-sided colon cancer whereas 75.6% in MMR proficient group were left-sided colon cancer.MMR deficient cases featured larger tumor size(6.57±3.00cm)than MMR proficient(4.28±1.74cm).However,CA199 and CA125 were significantly upregulated in MMR proficient group than MMR deficient group.In lymph node stage,82.6% of MMR deficient were in N0,significantly differed than MMR proficient(N0,54.6%).73.9% of MMR deficient were mainly in stage II whereas only32.7% in MMR proficient were in stage II.No significant correlation was found between EGFR/ Ki67 and MMR status. KRT family genes are potentially the key genes involved in the cetuximab insensitivity in CRC.Three genes were identified in the overlapped DEGs between GSE56386 and GSE77346,including DSG3,H2AFY2 and KRT6 A.Conclusion This study identified key DEGs between MSI-H and MSS using multiple bioinformatics strategies and characterized target drugs insensitivity in both gastric cancer and colorectal cancer.