Integrative Transcriptomics Analysis of Early-Onset and Late-Onset Colorectal Cancer

doi:10.21203/rs.3.rs-2865282/v1

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Integrative Transcriptomics Analysis of Early-Onset and Late-Onset Colorectal Cancer

https://doi.org/10.21203/rs.3.rs-2865282/v1

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Colorectal cancer (CRC) is the third most common type of cancer as it accounts for approximately 7% of all cancer types. The goal of this study is to identify and analyze the differentially expressed genes (DEGs) in early-onset CRC (EOCRC) and late-onset CRC (LOCRC). The RNA-seq data was downloaded from GEO database and analyzed using GEO2R tool. This was followed by gene and pathway enrichment, detection of protein-protein interactions, predicting the governing transcriptional factors. Besides, survival rate and response to chemotherapy were also assessed. 250 DEGs were obtained, 235 down-regulated and 15 up-regulated. Extracellular structure organization, collagen-containing extracellular matrix, platelet-derived growth factor and protein digestion and absorption were the most common biological process, cellular compartment, molecular function and KEGG pathway. Moreover, ten hub genes were filtered, namely COL1A1, VWF, COL3A1, EGF, IGF1, COL1A2, ITGB3, COL11A2, COL6A1, CD163 as the top-ten hub genes while FOXC1, GATA2, YY1, TFAP2A and PPARG were predicted to be the most significant transcriptional factors controlling them. Only EGF was considered as prognostic biomarker while COL1A1 as predictive biomarker. In conclusion, the shortlisted hub genes account for the discrimination between EOCRC and LOCRC which should be further explored in-depth.

colorectal cancer

biomarker

gene enrichment

transcriptional factors

survival rate

It has been estimated that approximately 1.9 million new colorectal cancer cases were recorded worldwide in 2020, among which more than 900.000 deaths (Sung et al., 2021). Colorectal cancer (CRC) is one of the leading causes of morbidity and mortality globally. It is the third most common cancer type and considered as the second cancer with highest deaths worldwide (Bray et al., 2018). The incidence of CRC is not the same in all populations and is influenced by various factors such as age, family history, lifestyle, and environmental factors (Haggar & Boushey, 2009; Mattiuzzi et al., 2019). Indeed, a complex interplay of genomic, epigenomic, as well as environmental factors contribute to the etiology of CRC. Some of the key molecular pathways implicated in the initiation and progression of CRC include the TGF-β signaling pathway, Wnt/β-catenin pathway, and the PI3K/Akt/mTOR pathway (Currais et al., 2022). It is well established that defected regulation of those pathways can result in uncontrolled cell growth, resistance to apoptosis, and metastasis ensues (Ruffinelli et al., 2021). As of all cancer types, screening for CRC is substantial for the early diagnosis and the corresponding management/treatment. Various screening methods are available, including fecal occult blood testing, colonoscopy, flexible sigmoidoscopy, in addition to stool DNA examination (Biller & Schrag, 2021). The American Cancer Society recommends the early screening at age 45, particularly for those with high risk factors (Wolf et al., 2018). CRC is a heterogeneous disease that can be divided into two categories: early-onset colorectal cancer (EOCRC) and late-onset colorectal cancer (LOCRC). EOCRC is more often diagnosed in people younger than fifty years, whilst LOCRC is usually detected in patients that are 50 years of age or older (Bailey et al., 2015). Table 1 summarizes the main differences between the two types of CRC (REACCT Collaborative, 2021). The higher incidence of EOCRC in certain populations suggests potential disparities in access to healthcare and differences in environmental exposures and lifestyle factors. The variation in the molecular attributes of EOCRC and LOCRC denotes the distinguished signaling pathways of tumorigenesis and the corresponding treatment and therapy response (Nfonsam et al., 2022; Rho et al., 2017).

Table 1

Comparison of EOCRC and LOCRC
Differences	Variables	EOCRC	LOCRC
Epidemiological differences	Incidence	< 50 years	> 50 years
	Ethnic risk	African Americans, Hispanics	Caucasians
	Socioeconomic status	poor	Good
Clinical manifestations	Disease stage at diagnosis	Advanced	Early-stage
	Spatial distribution	Distal (mainly rectal)	Proximal (mainly colon)
	Metastasis rate	Higher	Lower
Molecular differences	Associated mutations	DNA mismatch repair deficiency, BRAF mutations, CpG island methylator phenotype, Microsatellite instability-high tumors	Chromosomal instability (CIN), KRAS mutations, TP53 mutations
Molecular differences	Prognosis	Poor	Acceptable

The available treatment options for CRC include radiation, surgery, chemotherapy, therapy, and targeted immunotherapies (Van Cutsem et al., 2016). The choice of treatment depends upon several factors, such as the progression stage of cancer, the patient's overall health, and the co-presence of other morbidities. In recent years, there have been significant advances in the development of targeted therapies for CRC. VEGF and EGFR pathways are the hotspots of targeted therapy using the highly-specific monoclonal antibodies (Folprecht et al., 2022). The present study was purposed to detect and analyze the differentially-expressed genes in EOCRC and LOCRC.

Identification of differentially expressed genes

Gene expression omnibus (GEO) dataset (Edgar et al., 2002) (https://www.ncbi.nlm.nih.gov/geo/) was utilized for downloading the raw data having the accession code GSE213092 (accessed on 22 April 22, 2023). The transcriptome of two types of data, i.e. early-onset CRC and late-onset CRC each with same number of included samples (N = 30). The data was processed with GEO2R embedded tool (Clough & Barrett, 2016) (https://www.ncbi.nlm.nih.gov/geo/geo2r/) which can be used for the comparison of two or more transcriptomic samples to identify differentially expressed genes (DEGs) with the assistance of GEO query and limma R packages. The shortlisting is based upon log2FC<-1 for sorting down-regulated genes and log2FC > 1 for up-regulated genes with a p value < 0.05.

Gene enrichment and pathway analysis

In order to predict the potential biological processes as well as pathways DEGs involved in, Enrichr platform (Kuleshov et al., 2016) (https://maayanlab.cloud/Enrichr/) was used. This involved biological processes, cellular compartments, molecular function as well as Kyoto encyclopedia of genes and genomes (KEGG). An adjusted p value less than 0.05 was a cut of criteria for statistical significance.

Hub gene network analysis

The next step was to shortlist the DEGs to detect Hub genes. STRINGdb (Szklarczyk et al., 2015) (https://string-db.org/) was firstly employed for detecting the probable protein-protein interaction (PPI) of DEGs. Then, sorting the PPI according to a combined score > 0.4 was performed. Then, the remaining PPI network was entered to Cytoscape program (Kohl et al., 2011) and the 10 hub genes was determined via CytoHubba plugin.

Identification of Hub genes-regulating transcriptional factors

The transcriptional factors that are probable to control the hub genes were discovered through Networkanalyst server (G. Zhou et al., 2019) (https://www.networkanalyst.ca/). The chosen database was JASPAR and the obtained network was further processed using Cytoscape.

Prediction of survival rates

KM plotter (Lánczky & Győrffy, 2021) (https://kmplot.com/analysis/) was used for the association between the hub genes and the survival tests such as overall survival (OS) and relapse-free survival (RFS). High-expression and low-expression impact of hub genes on survival rates were compared. The hazard ratio with 95% confidence intervals and the log-rank P < 0.05 were calculated.

Response to chemotherapy prediction

The best two hub genes with poor and good clinical prognosis values were subjected to the potential response to chemotherapy prediction using ROC plotter (Fekete & Győrffy, 2019) (https://www.rocplot.org/). The ROC plotter is a powerful tool capable of linking gene expression and response to therapy using transcriptome-level data of breast, ovarian, and colorectal cancer patients and glioblastomas.

Identification of differentially expressed genes

The first step was to identify DEGs in the two compared groups. Of the shortlisted table provided using GEO2R tool, there were 235 under-expressed genes (UEG) and 15 over-expressed genes (OEG) (250 in total). The volcano plot of the processed data was shown in Fig. 1. However, the top 15 DRG as well as the top 15 URG were listed in Table 1.

Table 2

DEGs of from the EOCRC group and LOCRC
Gene name	logFC	p-value	Gene name	logFC	p-value
Over-expressed genes			Under-expressed genes
C20orf204	2.884	6.72E-08	IGHA2	-4.064	3.03E-13
CPS1	3.409	1.75E-07	IGKV3D-15	-4.293	1.03E-12
VENTX	2.965	1.94E-07	IGKV3-15	-4.097	8.81E-12
MBIP	1.075	6.85E-07	IGKV1-27	-3.962	6.20E-11
EGF	2.558	2.57E-06	IGHV3-21	-4.218	1.21E-10
KRT17	2.464	6.51E-06	RASD2	-2.116	2.37E-10
OLFM1	2.107	1.06E-05	IGLV7-46	-3.531	3.48E-10
VGF	1.787	1.18E-05	IGHV1-18	-4.231	4.79E-10
BNIP3	1.796	1.27E-05	IGHV4-34	-3.781	5.48E-10
FGFBP1	1.805	2.89E-05	IGKV1-16	-4.056	9.10E-10
CST4	2.265	3.44E-05	IGHV5-51	-4.031	1.19E-09
ABCC2	2.027	6.66E-05	IGHV3-23	-3.854	1.74E-09
EPHX3	1.7	6.69E-05	IGHV4-59	-3.554	3.84E-09
ALOX12B	1.783	9.20E-05	IGLV3-25	-3.548	4.17E-09
IGFL2	2.39	1.29E-04	IGKV2D-30	-3.701	5.07E-09

Gene enrichment and pathway analysis

The next step was to analyze the enriched gene ontology and pathways of the DEGs (Fig. 2). The enriched biological processes ranked extracellular structure organization first followed by external encapsulating structure organization and collagen fibril organization. With respect to cellular compartmentation, the most enriched compartment was collagen-containing extracellular matrix followed by endoplasmic reticulum lumen and platelets granules. On the other hand, platelet-derived growth factor binding, immunoglobulin receptor binding and ABC-transporter binding were the top molecular functions of the enriched DEGs. KEGG enrichment suggested processes like protein digestion and absorption (most common), extracellular matrix-receptor interaction in addition to focal adhesion.

Hub gene network analysis

The protein-protein interactions (PPI) of the obtained DEGs were investigated using StringDB platform and depicted in Fig. 3. The network had 149 nodes, 196 edges with PPI enrichment p-value < 1.0E^− 16 denoting the highly significant interactions. The PPI network was inputted to cytoscape in order to sort the top ten hub genes. It was found that COL1A1, VWF, COL3A1, EGF, IGF1, COL1A2, ITGB3, COL11A2, COL6A1, CD163 were the top 10 hub genes ranked according to the degrees of interactions as elucidated in Fig. 4.

Prediction of transcriptional factors

The discovered top ten hub genes were searched for the potential corresponding transcriptional factors via Networkanalyst server. The interaction revealed 231 nodes, 1085 edges. The most important transcriptional factors that control the DEGs are FOXC1, GATA2, YY1, TFAP2A and PPARG with corresponding hits of 121, 103, 59, 55 and 53, respectively. The 10 hub transcriptional factors that play the most crucial role in DEGs expression are viewed in Fig. 5.

Prediction of survival rates

The hub genes were then predicted for their prognostic outcome as significant biomarkers of CRC in terms of OS (Fig. 6) as well as RFS (Fig. 7). It turned out that over-expression of the hub genes had poor prognostic outcome of OS. Similarly, RFS of the over-expressed hub genes was of poor prognostic outcome as well. Notably, EGF gene which had good prognostic outcomes upon overexpression was the only exception in terms of OS and RFS.

Response to chemotherapy prediction

The last step was to check the potential response to chemotherapy of top-ranked hub gene (COL1A1) and the only hub gene having good prognostic outcome (EGF). As displayed in Fig. 8, the level of expression of EGF is slightly but significantly different (p < 0.01) between responder group and non-responder group. Similarly, responder group is highly significant in its expression level of COL1A1 in comparison with non-responder group (p < 5.6e-9). Also, area under curve (AUC) in COL1A1 was 0.627 compared to EGF (0.548) denoting the suitability of the former as a predictive biomarker of CRC. In short, COL1A1 is substantially better than EGF as a predictive biomarker in all aspects.

CRC is a complex cancer type that takes place in the lower gastrointestinal tract (colon and rectum). The transformed cells therein are the glandular epithelial cells. CRC is divided into three classes: hereditary, sporadic, and colitis-associated. Plenty of risk factors account for the increased susceptibility to CRC with long-standing ulcerative colitis and Crohn’s disease with increasing age is the most important (Saraiva et al., 2023). The goal of this work is to identify and explore the DEGs in the EOCRC and LOCRC.

Upon shortlisting the DEGs and investigating the corresponding protein-protein interactions, ten hub genes were obtained, namely COL1A1, VWF, COL3A1, EGF, IGF1, COL1A2, ITGB3, COL11A2, COL6A1, CD163. The obtained hub genes were predicted to be regulated through several transcriptional factors including FOXC1, GATA2, YY1, TFAP2A and PPARG.

COL1A1 is a gene encoding for the alpha 1 chain of type I collagen forming the network of connective tissue as well as extracellular matrix. It has been recently many reports linked of COL1A1 to important processes such as cancer progression, wound healing, and tissue engineering (Barnes et al., 2019; Liu et al., 2021).

VWF gene encodes for von Willebrand factor, one of the key proteins involved in blood coagulation cascades. However, its role is not restricted in blood coagulation since the latest researches have focused on its participation in cardiovascular disease, with studies suggesting that elevated levels of VWF are associated with increased risk of thrombosis and heart disease (Favaloro et al., 2021; Kotronia et al., 2021).

COL3A1 (alpha 1 chain of type III collagen-encoding gene) which is distributed many tissues like skin and blood vessels, among other tissues. Recent studies have linked the role of COL3A1 differential expression in tissue repair, wound healing, and fibrosis (Qian et al., 2021; Wang et al., 2021).

EGF, on the other hand, encodes for epidermal growth factor, the prominent growth factor involved in the cellular growth, division and proliferation. As a consequence, it has been implicated in various types of cancer types as well as progression and therapy. Hence, numerous EGF receptor antagonists were FDA-approved as potential treatments for several types of cancer (Esmaeili et al., 2020; She et al., 2021).

Insulin-like growth factor 1 (IGF1), is a hormone that resembles insulin in its structure and the liberated connected peptide. Being a growth factors, it promotes growth and development of different tissues and wound healing. Therefore, there were multiple reports suggesting the association of IGF1 in crucial processes including aging, neurodegenerative diseases, and cancer. Also, IFG1 signaling interruption might halt disease progression (Feik et al., 2010; Guo et al., 2022).

It is obvious that six out of ten hub genes encode for collagen synthesis and assembly as confirmed by the gene enrichment results. This indicates the important role especially in EOCRC which is a prerequisite event during its metastasis considering its more aggressive than LOCRC. In other words, disorganized extracellular matrix, in which collagen is a mainstay, aids the disintegration of connected cells and, thus, facilitates metastatic invasion (Kaushik et al., 2019; Najafi et al., 2019). Additionally, two growth factors (EGF and IGF1) were in the ten hub genes differentially expressed in EOCRC and LOCRC. These factors promote the cellular growth, development and proliferation which may interpret their differential expression. Accordingly, many guided therapies have focused on these growth factors to block CRC (Gligorijević et al., 2022). About 25–82% of CRC cases exhibit EGF overexpression (Janani et al., 2022). It has been proved that VWF can mediate tumour cell adhesion to endothelial cells. Besides, it enhances platelets recruitment into the tumour microenvironment, where tumour-platelet aggregates aids in the spreading of cancer (Colonne et al., 2022).

Concerning survival rate of the obtained hub genes, EGF was the only one displaying good prognostic outcome. This is in an agreement with the previous reports of CRC (Schölch et al., 2019). Nonetheless, it failed to be predictive biomarker as resistance to multi-drug entities of CRC was well-established (J. Zhou et al., 2021). COL1A1, albeit predicted to be poor prognostic biomarker, is a potential predictive biomarker which needs further in-depth investigation.

In summary, EOCRC and LOCRC differ in their clinical presentation, molecular profile, and epidemiological characteristics. The differences in the molecular profile of EOCRC and LOCRC suggest different pathways of tumorigenesis and potential differences in treatment response. Understanding these differences is important for improving the management and outcomes of CRC patients. The present study explored the discrimination between EOCRC and LOCRC in terms of DEGs. About 250 genes were found to be differentially-expressed. The further shortlisting to obtain the most significant interactions found COL1A1, VWF, COL3A1, EGF, IGF1, COL1A2, ITGB3, COL11A2, COL6A1, CD163 as the top-ten hub genes while FOXC1, GATA2, YY1, TFAP2A and PPARG as the most prominent transcriptional factors controlling them. Whereas EGF was predicted to be the only hub gene to have good prognostic survival rates (OS and RFS), it failed to be a predictive biomarker. Instead, COL1A1 was a potential predictive biomarker of CRC which needs further analysis.

Ethics approval and consent to participate

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Availability of data and material

All data are available within the manuscript.

Competing interests

The author declares that they have no competing interests

Funding

No funding was acquired for this study

Authors contributions

HAA has conceptualized, designed and written the manuscript.

Acknowledgments

Not applicable.

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Integrative Transcriptomics Analysis of Early-Onset and Late-Onset Colorectal Cancer

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