Integrative Analysis of Differential Gene Expression, Transcript Usage, and RNA Secondary Structure in Early- and Late-Onset Colorectal Cancer
by Salomon Marquez
11/02/2025
This site contains a summary of my bioinformatics master's thesis developed at CIMA University of Navarra under the supervision of Fernando Pastor Rodriguez and Igor Ruiz de los Mozos.
For a detailed overview of the project, you can check the following resources:
Abstract
Colorectal cancer is increasingly diagnosed in individuals younger than 50 years, a trend referred to as early-onset colorectal cancer. This study applied an integrative transcriptomic workflow to characterize molecular differences between early-onset and late-onset colorectal cancer. A total of 86 matched tumor and normal samples were analyzed using a combination of differential gene expression, differential transcript usage, and RNA secondary structure prediction.
Differential gene expression analysis identified a 40-gene signature uniquely deregulated in early-onset colorectal cancer, enriched in immune-related, inflammatory, and cell-matrix adhesion processes. Differential transcript usage analysis revealed substantial differences in transcript-level complexity between cohorts, with late-onset colorectal cancer showing extensive isoform remodeling, whereas early-onset tumors displayed a more restricted pattern of transcript usage. Despite these global differences, six conserved isoform switches were consistently detected in both cohorts, including TPM2, LMNB2, MAP3K20, and the long non-coding RNA HOXB-AS3. RNA secondary structure prediction further characterized significant isoforms, revealing a predominance of stem motifs.
These results indicate that gene-level and isoform-level analyses capture complementary aspects of colorectal cancer transcriptomic variation. The integrative framework presented here provides a methodological basis for future large-scale studies and supports the exploration of RNA structural features as potential biomarkers and therapeutic targets in colorectal cancer.
keywords: colorectal-cancer, deseq2-analysis, dtu-analysis, rna-structure-prediction, nf-core-rnaseq, isoformSwitchAnalyzeR, hpc-cluster, nextflow
Objective
The primary goal of this project is to identify genes and transcripts that are differentially regulated in early-onset and late-onset colorectal cancer, and to evaluate whether changes in isoform usage are associated with differences in their RNA secondary structure through computational prediction.
Proposed solution
We adopted an integrative transcriptomic strategy to investigate molecular differences between early-onset and late-onset colorectal cancer (EOCRC and LOCRC). Building upon a previously conducted gene-level study from Marx et al., we extend the analysis by incorporating isoform-level regulation and RNA secondary structure prediction, thereby moving beyond expression changes alone to explore post-transcriptional and structural layers of regulation. The overall integrative analysis workflow is summarized below:
Schematic overview of the integrative transcriptomic analysis workflow implemented in this study.
From concept to implementation
The main milestones of this project were tracked using a dedicated 🏁 roadmap on GitHub projects, which facilitated version control and documentation of analytical decisions. Among these decisions, the main technical and methodological challenges encountered during the development of this project and the mitigation strategies adopted are listed below:
Summary of products obtained
This project produced the following deliverables.
- A master’s thesis manuscript documenting the analytical workflow, results, and their biological interpretation.
- A configured and validated nf-core/rnaseq Nextflow pipeline for reproducible RNA-seq analysis on high-performance computing (HPC) clusters.
- A Zenodo repository hosting primary and processed data generated in this study.
- A GitHub repository containing primary scripts and documentation.
Results
This is a summary of results after the implementation of the integrative transcriptomic workflow for the analysis of EOCRC and LOCRC samples.
1. nf-core/rnaseq pipeline — EOCRC
SRA study SRP357925
21 EOCRC patient pairs
2. nf-core/rnaseq pipeline — LOCRC
SRA study SRP479528
22 LOCRC patient pairs
3. Differential Gene Expression (DGE)
4. Differential Transcript Usage (DTU)
- IsoformSwitchAnalyzeR Analysis
Show R script
# Install and load modules and libraries
.libPaths("~/R/x86_64-pc-linux-gnu-library/4.4")
# Check if package is installed, if not then install
if (!require("IsoformSwitchAnalyzeR", quietly = TRUE)) {
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("IsoformSwitchAnalyzeR")
}
library(scales)
library(ggplot2)
library(tidyr)
library(dplyr)
library(S4Vectors)
library(reshape2)
library(corrplot)
library(factoextra)
library(SummarizedExperiment)
library(DESeq2)
library(IsoformSwitchAnalyzeR)
library(tidyverse)
library(dbplyr)
library(BiocParallel)
library(Biostrings)
## ------------------------------------------------------------------------------------------------
# Adjust workers according to cluster cores
ncores <- 16
register(MulticoreParam(workers = ncores))
# Import quantifications
salmonQuant <- importIsoformExpression(
parentDir = "dtu_data/salmon"
)
head(salmonQuant$abundance, 2)
head(salmonQuant$counts, 2)
saveRDS(salmonQuant, file = "dtu_data/salmonQuant_42_44.rds")
## ------------------------------------------------------------------------------------------------
# Read 'se' object
se_42 <- readRDS("dtu_data/gene.SummarizedExperiment.metadata_42.rds")
se_44 <- readRDS("dtu_data/gene.SummarizedExperiment.metadata_44.rds")
# Display 'se' object
se_42
se_44
# Display colNames
colnames(se_42)
colnames(se_44)
# Display condition
colData(se_42)$condition
colData(se_44)$condition
# Display condition in salmonQuant
colnames(salmonQuant$abundance)
## ------------------------------------------------------------------------------------------------
# Define design matrix
myDesign <- data.frame(
sampleID = colnames(salmonQuant$abundance)[-1], # Exclude isoform_id column and include all sample names
condition = c(colData(se_42)$condition, colData(se_44)$condition) # Combine conditions from both se_42 and se_44
)
myDesign
# Define comparisonsToMake
mycomparisonsToMake <- data.frame(
condition_1 = c("normal_eocrc", "normal_locrc"),
condition_2 = c("tumor_eocrc", "tumor_locrc")
)
mycomparisonsToMake
## ------------------------------------------------------------------------------------------------
cat("1. Create switchAnalyzeRlist...\n")
# Create switchAnalyzeRlist
aSwitchList <- importRdata(
isoformCountMatrix = salmonQuant$counts,
isoformRepExpression = salmonQuant$abundance,
designMatrix = myDesign,
comparisonsToMake = mycomparisonsToMake,
isoformExonAnnoation = "dtu_data/gencode.v49.primary_assembly.annotation.gtf.gz",
isoformNtFasta = "dtu_data/gencode.v49.transcripts.fa.gz",
fixStringTieAnnotationProblem = TRUE,
showProgress = TRUE
)
cat("View aSwitchList summary...\n")
# View summary report
summary(aSwitchList)
saveRDS(aSwitchList, file = "dtu_outputs/aSwitchList_gencode_42_44.rds")
## ------------------------------------------------------------------------------------------------
cat("2. Prefiltering...\n")
cat("Prefiltering summary with geneExpressionCutoff = 1; isoformExpressionCutoff = 0; Isoform fraction = 0...\n")
# Prefilter
mySwitchList <- preFilter(aSwitchList)
summary(mySwitchList)
saveRDS(mySwitchList, file = "dtu_outputs/mySwitchList_gencode_42_44_filstandard.rds")
## ------------------------------------------------------------------------------------------------
cat("3. Differential Expression Analysis using TestSatuRn...\n")
# Identify differentially used isoforms with SatuRn
mySwitchList <- isoformSwitchTestSatuRn(
mySwitchList,
reduceToSwitchingGenes = TRUE, # focus on switching genes relevant to CRC
showProgress = TRUE
)
# Summarize results
extractSwitchSummary(mySwitchList)
## ------------------------------------------------------------------------------------------------
cat("4. Obtain Open Reading Frames (ORFs)...\n")
# Obtain Open Reading Frames (ORFs)
mySwitchList <- analyzeORF(
mySwitchList,
showProgress = TRUE
)
## ------------------------------------------------------------------------------------------------
cat("5. Extracting sequences...\n")
# Extract Sequences
mySwitchList <- extractSequence(
mySwitchList,
outputPrefix = "dtu_42_44"
)
saveRDS(mySwitchList, file = "dtu_outputs/mySwitchList_42_44_nt.rds")
## ------------------------------------------------------------------------------------------------
cat("6. Alternative and intron retention analysis...\n")
# Analyze alternative splicing
mySwitchList <- analyzeAlternativeSplicing(
switchAnalyzeRlist = mySwitchList,
quiet = TRUE
)
## ------------------------------------------------------------------------------------------------
# Analyze Intron Retention
mySwitchList <- analyzeIntronRetention(
switchAnalyzeRlist = mySwitchList,
onlySwitchingGenes = TRUE,
alpha = 0.05,
dIFcutoff = 0.1,
showProgress = TRUE,
quiet = TRUE
)
saveRDS(mySwitchList, file = "dtu_outputs/mySwitchList_Asplicing_Iretention_42_44.rds")
# Extract 10 top switching genes (by q-value)
extractTopSwitches(
mySwitchList,
filterForConsequences = FALSE,
n = 10,
sortByQvals = TRUE
)
## ------------------------------------------------------------------------------------------------
cat("7. Analyze Consequences of Isoform Switches...\n")
# AnalyzeSwitchConsequences
# tss -> alternative 5'UTR
# tts -> alternative 3'UTR
consequencesOfInterest <- c(
"tss",
"tts",
"exon_number",
"intron_structure",
"intron_retention"
)
bioMechanismeAnalysis <- analyzeSwitchConsequences(
mySwitchList,
consequencesToAnalyze = consequencesOfInterest,
showProgress = TRUE
)
extractSwitchSummary(bioMechanismeAnalysis, filterForConsequences = FALSE)
extractSwitchSummary(bioMechanismeAnalysis, filterForConsequences = TRUE)
saveRDS(bioMechanismeAnalysis, file = "dtu_outputs/mySwitchList_consequences_42_44.rds")
class: SummarizedExperiment
dim: 78428 42
metadata(8): abstract Accession ... Submission Relevance
assays(5): counts counts_length_scaled counts_scaled lengths tpm
rownames(78428): ENSG00000000003.17 ENSG00000000005.6 ...
ENSG00000310592.1 ENSG00000310593.1
rowData names(3): transcript_id gene_id gene_name
colnames(42): srr17866817 srr17866818 ... srr17866857 srr17866858
colData names(32): age_at_surgery tissue ... source_name condition
class: SummarizedExperiment
dim: 78428 44
metadata(9): abstract Accession ... Submission Relevance
assays(5): counts counts_length_scaled counts_scaled lengths tpm
rownames(78428): ENSG00000000003.17 ENSG00000000005.6 ...
ENSG00000310592.1 ENSG00000310593.1
rowData names(3): transcript_id gene_id gene_name
colnames(44): srr27320655 srr27320656 ... srr27320697 srr27320698
colData names(33): age_at_surgery tissue ... source_name condition
[1] "srr17866817" "srr17866818" "srr17866819" "srr17866820" "srr17866821"
[6] "srr17866822" "srr17866823" "srr17866824" "srr17866825" "srr17866826"
[11] "srr17866827" "srr17866828" "srr17866829" "srr17866830" "srr17866831"
[16] "srr17866832" "srr17866833" "srr17866834" "srr17866835" "srr17866836"
[21] "srr17866837" "srr17866838" "srr17866839" "srr17866840" "srr17866841"
[26] "srr17866842" "srr17866843" "srr17866844" "srr17866845" "srr17866846"
[31] "srr17866847" "srr17866848" "srr17866849" "srr17866850" "srr17866851"
[36] "srr17866852" "srr17866853" "srr17866854" "srr17866855" "srr17866856"
[41] "srr17866857" "srr17866858"
[1] "srr27320655" "srr27320656" "srr27320657" "srr27320658" "srr27320659"
[6] "srr27320660" "srr27320661" "srr27320662" "srr27320663" "srr27320664"
[11] "srr27320665" "srr27320666" "srr27320667" "srr27320668" "srr27320669"
[16] "srr27320670" "srr27320671" "srr27320672" "srr27320673" "srr27320674"
[21] "srr27320675" "srr27320676" "srr27320677" "srr27320678" "srr27320679"
[26] "srr27320680" "srr27320681" "srr27320682" "srr27320683" "srr27320684"
[31] "srr27320685" "srr27320686" "srr27320687" "srr27320688" "srr27320689"
[36] "srr27320690" "srr27320691" "srr27320692" "srr27320693" "srr27320694"
[41] "srr27320695" "srr27320696" "srr27320697" "srr27320698"
[1] tumor_eocrc normal_eocrc tumor_eocrc normal_eocrc tumor_eocrc
[6] normal_eocrc tumor_eocrc normal_eocrc tumor_eocrc normal_eocrc
[11] tumor_eocrc normal_eocrc tumor_eocrc normal_eocrc tumor_eocrc
[16] normal_eocrc tumor_eocrc normal_eocrc tumor_eocrc normal_eocrc
[21] tumor_eocrc normal_eocrc tumor_eocrc normal_eocrc tumor_eocrc
[26] normal_eocrc tumor_eocrc normal_eocrc tumor_eocrc normal_eocrc
[31] tumor_eocrc normal_eocrc tumor_eocrc normal_eocrc tumor_eocrc
[36] normal_eocrc tumor_eocrc normal_eocrc tumor_eocrc normal_eocrc
[41] tumor_eocrc normal_eocrc
Levels: normal_eocrc tumor_eocrc
[1] normal_locrc tumor_locrc normal_locrc tumor_locrc normal_locrc
[6] tumor_locrc normal_locrc tumor_locrc normal_locrc tumor_locrc
[11] normal_locrc tumor_locrc normal_locrc tumor_locrc normal_locrc
[16] tumor_locrc normal_locrc tumor_locrc normal_locrc tumor_locrc
[21] normal_locrc tumor_locrc normal_locrc tumor_locrc normal_locrc
[26] tumor_locrc normal_locrc tumor_locrc normal_locrc tumor_locrc
[31] normal_locrc tumor_locrc normal_locrc tumor_locrc normal_locrc
[36] tumor_locrc normal_locrc tumor_locrc normal_locrc tumor_locrc
[41] normal_locrc tumor_locrc normal_locrc tumor_locrc
Levels: normal_locrc tumor_locrc
[1] "isoform_id" "srr17866817" "srr17866818" "srr17866819" "srr17866820"
[6] "srr17866821" "srr17866822" "srr17866823" "srr17866824" "srr17866825"
[11] "srr17866826" "srr17866827" "srr17866828" "srr17866829" "srr17866830"
[16] "srr17866831" "srr17866832" "srr17866833" "srr17866834" "srr17866835"
[21] "srr17866836" "srr17866837" "srr17866838" "srr17866839" "srr17866840"
[26] "srr17866841" "srr17866842" "srr17866843" "srr17866844" "srr17866845"
[31] "srr17866846" "srr17866847" "srr17866848" "srr17866849" "srr17866850"
[36] "srr17866851" "srr17866852" "srr17866853" "srr17866854" "srr17866855"
[41] "srr17866856" "srr17866857" "srr17866858" "srr27320655" "srr27320656"
[46] "srr27320657" "srr27320658" "srr27320659" "srr27320660" "srr27320661"
[51] "srr27320662" "srr27320663" "srr27320664" "srr27320665" "srr27320666"
[56] "srr27320667" "srr27320668" "srr27320669" "srr27320670" "srr27320671"
[61] "srr27320672" "srr27320673" "srr27320674" "srr27320675" "srr27320676"
[66] "srr27320677" "srr27320678" "srr27320679" "srr27320680" "srr27320681"
[71] "srr27320682" "srr27320683" "srr27320684" "srr27320685" "srr27320686"
[76] "srr27320687" "srr27320688" "srr27320689" "srr27320690" "srr27320691"
[81] "srr27320692" "srr27320693" "srr27320694" "srr27320695" "srr27320696"
[86] "srr27320697" "srr27320698"
sampleID condition
1 srr17866817 tumor_eocrc
2 srr17866818 normal_eocrc
3 srr17866819 tumor_eocrc
4 srr17866820 normal_eocrc
5 srr17866821 tumor_eocrc
6 srr17866822 normal_eocrc
7 srr17866823 tumor_eocrc
8 srr17866824 normal_eocrc
9 srr17866825 tumor_eocrc
10 srr17866826 normal_eocrc
11 srr17866827 tumor_eocrc
12 srr17866828 normal_eocrc
13 srr17866829 tumor_eocrc
14 srr17866830 normal_eocrc
15 srr17866831 tumor_eocrc
16 srr17866832 normal_eocrc
17 srr17866833 tumor_eocrc
18 srr17866834 normal_eocrc
19 srr17866835 tumor_eocrc
20 srr17866836 normal_eocrc
21 srr17866837 tumor_eocrc
22 srr17866838 normal_eocrc
23 srr17866839 tumor_eocrc
24 srr17866840 normal_eocrc
25 srr17866841 tumor_eocrc
26 srr17866842 normal_eocrc
27 srr17866843 tumor_eocrc
28 srr17866844 normal_eocrc
29 srr17866845 tumor_eocrc
30 srr17866846 normal_eocrc
31 srr17866847 tumor_eocrc
32 srr17866848 normal_eocrc
33 srr17866849 tumor_eocrc
34 srr17866850 normal_eocrc
35 srr17866851 tumor_eocrc
36 srr17866852 normal_eocrc
37 srr17866853 tumor_eocrc
38 srr17866854 normal_eocrc
39 srr17866855 tumor_eocrc
40 srr17866856 normal_eocrc
41 srr17866857 tumor_eocrc
42 srr17866858 normal_eocrc
43 srr27320655 normal_locrc
44 srr27320656 tumor_locrc
45 srr27320657 normal_locrc
46 srr27320658 tumor_locrc
47 srr27320659 normal_locrc
48 srr27320660 tumor_locrc
49 srr27320661 normal_locrc
50 srr27320662 tumor_locrc
51 srr27320663 normal_locrc
52 srr27320664 tumor_locrc
53 srr27320665 normal_locrc
54 srr27320666 tumor_locrc
55 srr27320667 normal_locrc
56 srr27320668 tumor_locrc
57 srr27320669 normal_locrc
58 srr27320670 tumor_locrc
59 srr27320671 normal_locrc
60 srr27320672 tumor_locrc
61 srr27320673 normal_locrc
62 srr27320674 tumor_locrc
63 srr27320675 normal_locrc
64 srr27320676 tumor_locrc
65 srr27320677 normal_locrc
66 srr27320678 tumor_locrc
67 srr27320679 normal_locrc
68 srr27320680 tumor_locrc
69 srr27320681 normal_locrc
70 srr27320682 tumor_locrc
71 srr27320683 normal_locrc
72 srr27320684 tumor_locrc
73 srr27320685 normal_locrc
74 srr27320686 tumor_locrc
75 srr27320687 normal_locrc
76 srr27320688 tumor_locrc
77 srr27320689 normal_locrc
78 srr27320690 tumor_locrc
79 srr27320691 normal_locrc
80 srr27320692 tumor_locrc
81 srr27320693 normal_locrc
82 srr27320694 tumor_locrc
83 srr27320695 normal_locrc
84 srr27320696 tumor_locrc
85 srr27320697 normal_locrc
86 srr27320698 tumor_locrc
1. Create switchAnalyzeRlist...
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comparison estimated_genes_with_dtu
1 normal_eocrc vs normal_locrc 1027 - 1712
2 normal_eocrc vs tumor_eocrc 214 - 357
3 tumor_eocrc vs tumor_locrc 858 - 1429
View aSwitchList summary...
This switchAnalyzeRlist list contains:
398518 isoforms from 58088 genes
6 comparison from 4 conditions (in total 86 samples)
Feature analyzed:
[1] "ORFs, ntSequence"
2. Prefiltering...
Prefiltering summary with geneExpressionCutoff = 1; isoformExpressionCutoff = 0; Isoform fraction = 0...
This switchAnalyzeRlist list contains:
119137 isoforms from 14067 genes
6 comparison from 4 conditions (in total 86 samples)
Feature analyzed:
[1] "ORFs, ntSequence"
[1] "SatuRn results with standard prefiltering...\n"
Comparison nrIsoforms nrSwitches nrGenes
1 normal_eocrc vs tumor_eocrc 14 10 10
2 normal_locrc vs tumor_locrc 133 122 104
3 Combined 140 128 109
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gene_ref gene_id gene_name condition_1 condition_2
1 geneComp_00055095 TPM2 TPM2 normal_eocrc tumor_eocrc
2 geneComp_00110879 SORBS2 SORBS2 normal_locrc tumor_locrc
3 geneComp_00113199 TPM2 TPM2 normal_locrc tumor_locrc
4 geneComp_00091701 FSIP1 FSIP1 normal_locrc tumor_locrc
5 geneComp_00102515 OSBPL1A OSBPL1A normal_locrc tumor_locrc
6 geneComp_00098800 MAP3K20 MAP3K20 normal_locrc tumor_locrc
7 geneComp_00061664 CD44 CD44 normal_locrc tumor_locrc
8 geneComp_00063393 CXCL12 CXCL12 normal_locrc tumor_locrc
9 geneComp_00101397 NLN NLN normal_locrc tumor_locrc
10 geneComp_00093642 HNF4A HNF4A normal_locrc tumor_locrc
gene_switch_q_value Rank
1 0.0001116680 1
2 0.0001544585 2
3 0.0001544585 3
4 0.0001622092 4
5 0.0002382206 5
6 0.0003502090 6
7 0.0004922797 7
8 0.0006191435 8
9 0.0006191435 9
10 0.0009308128 10
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Comparison nrIsoforms nrSwitches nrGenes
1 normal_eocrc vs tumor_eocrc 14 10 10
2 normal_locrc vs tumor_locrc 133 122 104
3 Combined 140 128 109
Comparison nrIsoforms nrSwitches nrGenes
1 normal_eocrc vs tumor_eocrc 14 10 10
2 normal_locrc vs tumor_locrc 132 122 103
3 Combined 140 128 109
5. RNA Secondary Structure Analysis (RSS)
Contributing
This is a thesis repository, but suggestions for improvements are welcome via GitHub issues.