Thanks for your help. I have decided to present the data as a scatter plot/plot MDS (with an error bar) using expression values of the specific gene between the two tumour types (40 vs 52 samples) to show that it is differentially expressed. So 92 dots/points in total.

Do you know how I might do this, if I used these commands for microarray differential expression analysis.

library("arrayQualityMetrics")
library(GEOquery)
library(oligo)
library(Biobase)
library(affy)
library("splitstackshape")
library("tidyr")
library("dplyr")


celFiles <- list.celfiles()
affyRaw <- oligo::rma(affyraw)
eset <- oligo::rma(affyRaw)
library(limma)
pData(eset)
Groups <- c("DDLPS", "DDLPS", "WDLPS", "WDLPS")
design <- model.matrix(~factor(Groups))
colnames(design) <- c("DDLPS", "DDLPSvsWDLPS")
fit <- lmFit(eset, design)
fit <- eBayes(fit)
option (digits =2)
res <- topTable (fit, number = Inf, adjust.method = "none", coef = 1)
write.table(res, "diff_exp.txt", sep= "\t”)
require(hgu133a.db)
annotLookup <- select(hgu133a.db, keys = probes,
  columns = c('PROBEID', 'ENSEMBL', 'SYMBOL'))