how to use the enrichplot pakcage for the Gene Ontology (GO) enrichment analysis in RNA-seq analysis? please provide five case studies including their codes and steps

Enrichplot 包是一个用于可视化基因本体（GO）富集分析结果的 R 语言包。以下是五个案例研究及其代码和步骤：

1. Case 1: GO enrichment analysis of differentially expressed genes (DEGs) in breast cancer
Code:

library(enrichplot)
library(org.Hs.eg.db)
library(clusterProfiler)

# Load DEGs data
data <- read.csv("breast_cancer_DEGs.csv")

# Perform GO enrichment analysis
ego <- enrichGO(data = data$Symbol, OrgDb = org.Hs.eg.db, ont = "BP", pvalueCutoff = .05, qvalueCutoff = .05)

# Plot the results
barplot(ego, showCategory = 10)

2. Case 2: GO enrichment analysis of DEGs in liver cancer
Code:

library(enrichplot)
library(org.Hs.eg.db)
library(clusterProfiler)

# Load DEGs data
data <- read.csv("liver_cancer_DEGs.csv")

# Perform GO enrichment analysis
ego <- enrichGO(data = data$Symbol, OrgDb = org.Hs.eg.db, ont = "MF", pvalueCutoff = .05, qvalueCutoff = .05)

# Plot the results
dotplot(ego, showCategory = 10)

3. Case 3: GO enrichment analysis of DEGs in lung cancer
Code:

library(enrichplot)
library(org.Hs.eg.db)
library(clusterProfiler)

# Load DEGs data
data <- read.csv("lung_cancer_DEGs.csv")

# Perform GO enrichment analysis
ego <- enrichGO(data = data$Symbol, OrgDb = org.Hs.eg.db, ont = "CC", pvalueCutoff = .05, qvalueCutoff = .05)

# Plot the results
cnetplot(ego, showCategory = 10)

4. Case 4: GO enrichment analysis of DEGs in prostate cancer
Code:

library(enrichplot)
library(org.Hs.eg.db)
library(clusterProfiler)

# Load DEGs data
data <- read.csv("prostate_cancer_DEGs.csv")

# Perform GO enrichment analysis
ego <- enrichGO(data = data$Symbol, OrgDb = org.Hs.eg.db, ont = "BP", pvalueCutoff = .05, qvalueCutoff = .05)

# Plot the results
vennpie(ego, showCategory = 10)

5. Case 5: GO enrichment analysis of DEGs in colon cancer
Code:

library(enrichplot)
library(org.Hs.eg.db)
library(clusterProfiler)

# Load DEGs data
data <- read.csv("colon_cancer_DEGs.csv")

# Perform GO enrichment analysis
ego <- enrichGO(data = data$Symbol, OrgDb = org.Hs.eg.db, ont = "MF", pvalueCutoff = .05, qvalueCutoff = .05)

# Plot the results
enrichMap(ego, showCategory = 10)