Gastrointestinal Tract EBV pathologies with Gene relations in Acute Mono and Active EBV

In this document we explore the Tableau dashboard we created earlier from many gene study analysis extracting top genes by fold change from gastrointestinal tract (GIT) pathologies strictly associated with Epstein-Barr Virus (EBV) compared to the top fold change genes we extracted from the Acute Infectious Mono (AIM) and Chronic Active Epstein-Barr Virus (CAEBV). These are all found in gene studies on the National Center for Bioinformatics Information or NCBI database online. We took 5 stimulated genes and 5 inhibited genes in the top genes of the AIM and CAEBV study under GSE85599 to compare to the 5 GIT studies on NPC with mutated LMP1 gene under GSE271486, NPC metastatic compared to normal and non-metastatic GSE299775, Gastric carcinoma or stomach cancer GSE308231 compared to peritoneal metastatic gastric carcinoma, colorectal cancer compared to knockout of the AURKA gene GSE302491, and intrahepatic cholangiocarcinoma or biliary duct cancer with hypoxic state and hypoxic state knocking out the SLC2A1 gene in GSE316921.

We compared gene relations using the filter to select the most up and down regulated or simulated and inhibited genes by foldchange values using the top genes in the pathology dataset from GSE85599 in the top middle right side of the dashboard and compared those results per selected gene of which five up and five down that are also in all the studies’ datasets. There are five studies comparing these GIT EBV associated pathology genes with.

Results:

GIT EBV associated pathology gene relationships to AIM & CAEBV

• ASPM is 2.17 same as 217% in AIM & 2.39 in NPCmeta to normal But only 115% in NPCmeta/NPC stimulated and 121% in CAEBV & 118% CRC with shAURKA gene and 49% stimulated in shSLC2A1 hypoxic condition of ICC, an inverse relationship with ICC hypoxia of 0.35.

• KIF11 is 260% stimulated in AIM and 296% stimulated in metastatic NPC to normal, 130% in CAEBV similar to 121% stimulated in hypoxic shSLC2A1 of ICC and 128% stimulated in NPCmeta/NPC, inversely related in ICC to hypoxic only state with 33% of gene expression or 67% inhibited. Not much change in GCPM of 113% or CRC shAURKA of 4% downregulated.

• MKI67 is 221% upregulated in AIM and 278% in metaNPC to normal and 102% stimulated in metaNPC/NPC, very inhbited in ICC hypoxia with 24%, slightly upregulated in ICC with shSLC2A1 gene of 107%, and upregulated 136% in CAEBV and in GCPM by 136%.

• TOP2A gene is 219% stimulated in AIM and 484% in metaNPC to normal. This gene is 125% upregulated in CAEBV and ICC hypoxic with shSLC2A1 and 130% stimulated in NPCmeta/NPC. Inhibited to 92% in GCPM, 85% in NPC LMP1mutation, ICC hypoxic only and in CRC shAURKA.

• CCNA2 gene is 233% stimulated in AIM and 211% in NPCmeta/normal, and 118% upregulated in NPCmeta/NPC, 120% in CAEBV, and 114% in ICC hypoxia with shSLC2A1. Inverse relation with NPC mutated LMP1 of 83%, GCPM of 64%, and hypoxic ICC is 59%. Slight upregulation of 107% in CRC with shAURKA.

Now for five genes that are inhibited greatly in AIM or CAEBV.

• GPR84 is 36% downregulated in AIM and also 50% in CRC with shAURKA, and 72% in CAEBV, and 81% in NPCmeta/NPC. But this gene is stimulated in NPCmeta/normal 249%, 180% in GCPM, 200% in hypoxic ICC and 225% in hypoxic ICC with shSLC2A1 and normal in NPC with mutated LMP1 gene.

• IL1A is 31% inhibited in AIM, 46% in CAEBV, 36% in CRC with shAURKA, 59% in NPC with mutated LMP1, and 69% in NPCmeta/NPC. But inverse relationship with NPCmeta/normal with 137% stimulated, 144% in hypoxic ICC and 223% stimulated in hypoxic ICC with shSLC2A1, and close to normal with 96% downregulation in GCPM.

• OLR1 is 37% inhibited in AIM and 52% in CAEBV, 43% in GCPM, 58% in hypoxic ICC with shSLC2A1, 68% in hypoxic ICC, 73% in metaNPC/NPC, and 88% in NPC with mutated LMP1. Upregulated in NPCmeta/normal, and no change at 100% in CRC with shAURKA.

• PI3 is 50% downregulated in AIM, 55% in CAEBV, 41% in NPCmeta/NPC, 20% in NPCmeta/normal, and 58% in GCPM. Inverse relationship with ICC hypoxic 138% stimulated, ICC hypoxic with shSLC2A1 is 120%, 150% in CRC with shAURKA, and only 106% in NPC with mutated LMP1 gene.

• PLAU is 58% inhibited in CAEBV, 42% in AIM, and ICC hypoxia 84% with shSLC2A1 is 82%. All others are inversely related by being stimulated 112% in CRC shAURKA, 203% in GCPM, 418% in NPCmeta/normal, and 110% in NPCmeta/NPC, and 107% in NPC with mutated LMP1 gene.

Now we want to read in all six total data sets of these gene studies that we found genes after filtering and combining with other genes in some of those studies.

library(rmarkdown)
library(randomForest)

## randomForest 4.7-1.2

## Type rfNews() to see new features/changes/bug fixes.

Lets read in the AIM & CAEBV.

pathMono <- "path to this file on AIM and CAEBV"

setwd(pathMono)

monoCAEBV <- read.csv("CAEBV_genes_32670_FCs.csv")

paged_table(monoCAEBV[1:10,]) # 32670X24

Now lets read in the NPC with LMP1 mutation data.

pathNPC_LMP1 <- "path to this file on NPC with LMP1 mutation"

setwd(pathNPC_LMP1)

NPC_LMP1 <- read.csv("NPCEBV_filtered.csv")

paged_table(NPC_LMP1) # 21932 X 19

Now lets read in the NPC metastatic compared to normal and NPC without metastasis.

pathNPC_metastatic <- "path to this file of NPC metastasis compared to normal and non-metastatic"

setwd(pathNPC_metastatic)

NPC_metastatic <- read.csv("EBV_nasopharyngealCarcinoma_NPC_GSE299775_foldchanges.csv")
# 18677 X 35

paged_table(NPC_metastatic[1:10,])

Now read in the gastric carcinoma peritoneal metastasis dataset.

pathGCPM <- "path to your file on the GCPM dataset"

setwd(pathGCPM)

GCPM <- read.csv("allGenes_ready_GCPM_23552k_noNaNsInfs_ordered.csv")
# 23552 X 10

paged_table(GCPM[1:10,])

Now lets read in the colorectal carcinoma with the short hairpin knockout of the AURKA gene as shAURKA.

pathCRC <- "path to your CRC file"

setwd(pathCRC)

CRC <- read.csv("colorectal cancer GSE302491 all genes/Data1_allGenesAndFCs.csv")
# 16122 X 28

paged_table(CRC[1:10,])

And now for the biliary tract cancer or ICC for intrahepatic cholangiocarcinoma.

pathICC <- "your path to the ICC file"

setwd(pathICC)

ICC <- read.csv("allData_FCs_36242X19.csv")
# 36242 X 19

paged_table(ICC[1:10,])

Lets make a character string vector of the 10 genes we want to extract from each of these data sets.

relationalGenes <- c(
  "ASPM", "KIF11","MKI67","TOP2A","CCNA2",
  "GPR84","IL1A","OLR1","PI3","PLAU"
)

relationalGenes

##  [1] "ASPM"  "KIF11" "MKI67" "TOP2A" "CCNA2" "GPR84" "IL1A"  "OLR1"  "PI3"  
## [10] "PLAU"

Get these 10 genes from each dataset.

monoCAEBV10 <- monoCAEBV[which(monoCAEBV$gene %in% relationalGenes),] #10X24

NPC_metastatic10 <- NPC_metastatic[which(NPC_metastatic$ID_REF %in% relationalGenes),] #10X35

NPC_LMP1_10 <- NPC_LMP1[which(NPC_LMP1$gene_name %in% relationalGenes),] #10X19

ICC10 <- ICC[which(ICC$Gene %in% relationalGenes),] #10X19

GCPM10 <- GCPM[which(GCPM$Gene_ID %in% relationalGenes),] #10X10

CRC10 <- CRC[which(CRC$Gene_Symbol %in% relationalGenes),] #10X28

Next we want only the gene symbol ID and the actual samples. We will do this to each of these datasets of 10 genes by removing all other columns like the means and fold changes and for the CRC dataset selecting only the counts, tpm, or fpkm samples that were used. We used all for CRC but lets just select one since they run the same in magnitude for the most part, we will choose the fpkm for fragments per kilomillion.

colnames(CRC10)

##  [1] "Gene_Symbol"         "fpkm_PL1"            "fpkm_PL2"           
##  [4] "fpkm_PL3"            "fpkm_SH1"            "fpkm_SH2"           
##  [7] "fpkm_SH3"            "tpm_PL1"             "tpm_PL2"            
## [10] "tpm_PL3"             "tpm_SH1"             "tpm_SH2"            
## [13] "tpm_SH3"             "read_count_PL1"      "read_count_PL2"     
## [16] "read_count_PL3"      "read_count_SH1"      "read_count_SH2"     
## [19] "read_count_SH3"      "fpkm_baseline_mean"  "fpkm_AURKA_ko_mean" 
## [22] "tpm_baseline_mean"   "tpm_AURKA_ko_mean"   "count_baseline_mean"
## [25] "count_AURKA_ko_mean" "fpkm_FC"             "tpm_FC"             
## [28] "counts_FC"

CRC10_a <- CRC10[,c(1:7)]

paged_table(CRC10_a)

colnames(GCPM10)

##  [1] "GC1"           "GC2"           "GC3"           "PM1"          
##  [5] "PM2"           "PM3"           "Gene_ID"       "GC_mean"      
##  [9] "PM_mean"       "FC_PM_over_GC"

GCPM10_a <- GCPM10[,c(1:7)]

paged_table(GCPM10_a)

colnames(ICC10)

##  [1] "Gene"                    "CAFSHNCCTRL_1"          
##  [3] "CAFSHNCCTRL_2"           "CAFSHNCCTRL_3"          
##  [5] "CAFSHNCHYPO_1"           "CAFSHNCHYPO_2"          
##  [7] "CAFSHNCHYPO_3"           "CAFSHSLCCTRL_1"         
##  [9] "CAFSHSLCCTRL_2"          "CAFSHSLCCTRL_3"         
## [11] "CAFSHSLCHYPO_1"          "CAFSHSLCHYPO_2"         
## [13] "CAFSHSLCHYPO_3"          "WT_noHypoxia_Mean"      
## [15] "WT_Hypoxia_Mean"         "shSLC2A1_noHypoxia_Mean"
## [17] "shSLC2A1_Hypoxia_Mean"   "WT_Foldchange"          
## [19] "shSLC2A1_Foldchange"

ICC10_a <- ICC[,c(1:13)]

paged_table(ICC10_a)

colnames(monoCAEBV10)

##  [1] "ID"                 "gene"               "GSM2279022_AIM"    
##  [4] "GSM2279023_AIM"     "GSM2279024_AIM"     "GSM2279025_CAEBV"  
##  [7] "GSM2279026_AIM"     "GSM2279027_CAEBV"   "GSM2279028_CAEBV"  
## [10] "GSM2279029_CAEBV"   "GSM2279030_CAEBV"   "GSM2279031_healthy"
## [13] "GSM2279032_healthy" "GSM2279033_healthy" "GSM2279034_healthy"
## [16] "GSM2279035_healthy" "GSM2279036_healthy" "GSM2279037_AIM"    
## [19] "GSM2279038_AIM"     "AIM_mean"           "CAEBV_mean"        
## [22] "healthy_mean"       "FC_AIM_healthy"     "FC_CAEBV_healthy"

monoCAEBV10_a <- monoCAEBV10[,c(2:19)]

paged_table(monoCAEBV10_a)

colnames(NPC_LMP1_10)

##  [1] "gene_id"                "gene_name"              "description"           
##  [4] "locus"                  "HNE_1_MUT_LMP1_1_count" "HNE_1_MUT_LMP1_2_count"
##  [7] "HNE_1_MUT_LMP1_3_count" "HNE_1_WT_LMP1_1_count"  "HNE_1_WT_LMP1_2_count" 
## [10] "HNE_1_WT_LMP1_3_count"  "HNE_1_MUT_LMP1_1_FPKM"  "HNE_1_MUT_LMP1_2_FPKM" 
## [13] "HNE_1_MUT_LMP1_3_FPKM"  "HNE_1_WT_LMP1_1_FPKM"   "HNE_1_WT_LMP1_2_FPKM"  
## [16] "HNE_1_WT_LMP1_3_FPKM"   "EBV_mean"               "baseline_mean"         
## [19] "FoldchangeEBV_baseline"

Also, only use fpkm samples in the NPC mutated LMP1 gene.

NPC_LMP1_10_a <- NPC_LMP1_10[,c(2,11:16)]

paged_table(NPC_LMP1_10_a)

colnames(NPC_metastatic10)

##  [1] "ID_REF"                   "GSM9045905_MET"          
##  [3] "GSM9045906_MET"           "GSM9045909_MET"          
##  [5] "GSM9045910_MET"           "GSM9045911_MET"          
##  [7] "GSM9045912_MET"           "GSM9045913_MET"          
##  [9] "GSM9045914_MET"           "GSM9045915_MET"          
## [11] "GSM9045918_MET"           "GSM9045919_MET"          
## [13] "GSM9045928_MET"           "GSM9045929_MET"          
## [15] "GSM9045930_MET"           "GSM9045907_nonMET"       
## [17] "GSM9045908_nonMET"        "GSM9045916_nonMET"       
## [19] "GSM9045917_nonMET"        "GSM9045923_nonMET"       
## [21] "GSM9045924_nonMET"        "GSM9045926_nonMET"       
## [23] "GSM9045927_nonMET"        "GSM9045931_nonMET"       
## [25] "GSM9045932_nonMET"        "GSM9045920_Normal"       
## [27] "GSM9045921_Normal"        "GSM9045922_Normal"       
## [29] "GSM9045925_Normal"        "MET_mean"                
## [31] "nonMET_mean"              "Normal_mean"             
## [33] "MET_Normal_FoldChange"    "nonMET_Normal_FoldChange"
## [35] "MET_nonMET_FoldChange"

NPC_metastatic10_a <- NPC_metastatic10[,c(1:29)]

paged_table(NPC_metastatic10_a)

We are going to stop here due to the timing of this project but it will be resaved, this was 3 hours to find genes on Tableau dashboard and prepare the document this much. We will return to this document later and make the matrices of each of these data tables of 10 genes and order the genes alphabetically, and then test the accuracy in prediction of class of each of these study classes including grouping all normal or healthy samples together. After combining all matrices together with row bind after alphabatized and adding a class feature to those matrices adjusting names of the healthy or normal samples where we had them available. We will see if some of these pathologies using these 10 genes of 5 stimulated and 5 inhibited in common with the AIM and CAEBV data shows shifting in classification towards other pathologies or one other pathology or only to itself or healthy. We will break down the samples in each class for each dataset as well to see how balanced these samples will be when classifying.

Thanks so much.