Identification of Potential Therapeutic Target Genes and Mechanisms in Colorectal Cancer Based on Bioinformatics Analysis

Highlights

• In total, 150 DEGs were identified, with 88 genes showing down-regulation and 62 showing up-regulation in colorectal cancer samples. Researchers often focus on such imbalances to uncover key drivers in disease progression.
• Cytokine-cytokine receptor interaction may be associated with CRC development. Studies continue to explore how these interactions influence tumor environments.
• CXCL3 and IL8 may play roles in CRC progress by regulating TNF signaling pathway. Targeting these could open new avenues for intervention.
• PTGS2, CXCL3 and IL8 may be potential therapeutic target genes for CRC.
• Bile secretion-related genes ABCG2, ATP1A2 and AQP8 may be target genes for CRC. Their roles highlight unexpected pathways in cancer biology.

Abstract

Researchers set out to uncover hidden mechanisms and pinpoint possible target genes for treating colorectal cancer through careful bioinformatics analysis. Such efforts help bridge gaps between data and real-world therapies.

Purpose: The study was aimed to explore the underlying mechanisms and identify the potential target genes for colorectal cancer (CRC) treatment by bioinformatics analysis. Patients worldwide could benefit from these insights.

Methods: The RNA-seq data of GSE29580 was downloaded from Gene Expression Omnibus (GEO) database. Paired samples (from the same patient) of tumor and normal tissues from 2 CRC patients were used to identify differentially expressed genes (DEGs). The functional enrichment analysis was performed. Furthermore, the protein-protein interaction (PPI) network of the DEGs was constructed by Cytoscape software. Tools like these make complex data more accessible.

Results: Totally, 150 DEGs were identified, including 88 down- and 62 up-regulated genes. The down-regulated genes were mainly enriched in the functions of cytokine-cytokine receptor interaction and TNF signaling pathway, while up-regulated genes were related to bile secretion function. DEGs including prostaglandin-endoperoxide synthase 2 (PTGS2), chemokine (C-X-C motif) ligand 3 (CXCL3), interleukin 8 (IL8), ATP-binding cassette, sub-family G, member 2 (ABCG2), ATPase, Na+/K+ transporting, alpha 2 polypeptide (ATP1A2) and aquaporin 8 (AQP8) were identified in these functions. In addition, PTGS2, CXCL3 and IL8 were hub nodes in PPI network. These findings point to interconnected networks worth exploring further.

Conclusions: The cytokine-cytokine receptor interaction, TNF signaling pathway and bile secretion functions may be associated with CRC development. Genes such as PTGS2, CXCL3, IL8, ABCG2, ATP1A2 and AQP8 may be potential therapeutic target genes for CRC. Future validations could turn these into practical treatments.

Key words: colorectal cancer; differentially expressed genes; bioinformatics analysis; therapeutic targets; cytokine pathways

Introduction

Colorectal cancer stands as a serious malignant growth in the colon or rectum, ranking third among common cancers and second in causing cancer-related deaths globally [1]. Families affected by it know the urgency of better options all too well.

It is characterized by blood in the stool, a change in bowel movements and weight loss [2]. Early detection often hinges on recognizing these signs.

More than 1 million new cases of CRC are diagnosed annually. Screening programs aim to reduce this burden through awareness.

The 5-year survival rate for CRC is only 60% [3]. Improving outcomes remains a top priority for oncologists everywhere.

Therefore, an improved understanding mechanism on the pathogenesis of CRC would supply new insights for the diagnosis and treatment of CRC. Collaborative research plays a vital part here.

The development of CRC is a multistep progressive process and the pathogenesis of CRC is indicated to be caused by the stepwise accumulation of genetic alteration [4]. Each step offers potential points for intervention.

For instance, vascular endothelial growth factor (VEGF) is associated with the progression, invasion and metastasis of CRC, and overexpression of VEGF mRNA in the primary tumor is closely correlated with poor prognosis in CRC patients [5]. Anti-VEGF therapies have already shown promise in clinics.

The work of Liu et al. found that miR-137 had a tumor suppressor function by directly targeting cell division cycle 42 to inhibit the proliferation and invasion activities of CRC cells [6]. MicroRNAs like this add layers to our understanding.

Besides, modulation the Wnt signaling pathway in favor of inhibition of tumor proliferation in CRC patients [7]. Pathway inhibitors are actively being developed.

Abnormalities in the JAK2/STAT3 pathway are involved in CRC cell growth and survival through regulating expression of genes, such as B-cell CLL/lymphoma 2 [8]. Blocking this can trigger cell death in tumors.

Inhibition of JAK2/STAT3 pathway induced apoptosis by the mitochondrial apoptotic pathway [9]. Such mechanisms inspire targeted drugs.

Although tremendous efforts have been made, the exact mechanism about CRC has not been fully elucidated. Gaps in knowledge drive ongoing studies.

There is also lack of effective target genes for CRC treatment. Discovering them could transform patient care.

In this study, we downloaded the RNA-seq data of GSE29580 and analyzed the differentially expressed genes (DEGs) between CRC and normal samples. Public datasets like GEO make this possible for many researchers.

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were performed. These tools reveal biological meanings behind numbers.

Besides, the protein-protein interaction (PPI) network was constructed. Visual networks help spot central players.

The purpose of this study was to explore the underlying mechanisms and identify the novel potential target genes for CRC therapy. Results like these fuel hope for precision medicine.

Data and Methods

RNA-seq Data

Scientists retrieved the gene expression profile data of GSE29580 from the Gene Expression Omnibus (GEO) database at the National Center for Biotechnology Information (NCBI)[](http://www.ncbi.nlm.nih.gov/geo/) using the platform GPL9052 (Illumina Genome Analyzer for Homo sapiens), originally deposited by Luo et al. on May 28, 2011. Open access resources accelerate discoveries.

The paired samples (from the same patient) of colorectal tumor and normal tissues from 2 patients were harvested. Matching pairs reduce variability in comparisons.

Data Preprocessing

Researchers downloaded the raw sequencing data in Fastq files. Quality checks ensure reliable results downstream.

Various quality controls, including removal low-quality reads and reads containing asaptor/primer sequences, were performed by FastX-tool kit software[](http://hannonlab.cshl.edu/fastx_toolkit/). Clean data is foundational for accuracy.

The filtered high-quality reads were compared with Tophat software [10] based on hg19 reference sequences. Alignment precision matters greatly.

The result of reads < 2 base mismatch and < 2 gap length were required during the comparison. Strict criteria filter out noise.

ANNOVAR[](http://www.openbioinformatics.org/annovar/) [11] is a tool to annotate functional consequences of genetic variation from high-throughput sequencing data. Annotations add context to variants.

ANNOVAR was used to annotate reads to one of the following classes: exonic, splicing, intronic or intergenic. Categorizing helps prioritize impacts.

Differential Expression Analysis and Clustering Analysis

Experts assembled the transcriptome of each sample. Assembly reconstructs gene models effectively.

For the estimation of expression values of genes fragments per kilobase of transcript per million mapped (FPKM) values were calculated with cuffdiff tool (ver. 2.0.2) in the Cufflinks software [12]. FPKM normalizes for fair comparisons.

Cufflinks was used to estimate variance in gene expression levels. Understanding variance aids statistical confidence.

P-value < 0.05 and |log2FC| > 2 were used as the cutoff criteria for identification the differentially expressed genes (DEGs). Thresholds balance sensitivity and specificity.

Hierarchical clustering analysis of DEGs in colorectal cancer was performed. Clusters reveal patterns in expression.

Functional Enrichment Analysis

The GO database[](http://geneontology.org/) [13] is a collection of a large number of gene annotation terms. It standardizes descriptions across species.

KEGG knowledge database[](http://www.kegg.jp/) [14] is applied to identify the functional and metabolic pathway. Pathways connect genes to functions.

The Database for Annotation, Visualization and Integrated Discovery (DAVID)[](http://david.abcc.ncifcrf.gov/) [15] is a tool that provides a comprehensive set of functional annotation for large list of genes. Integration simplifies analysis.

GO and KEGG pathway enrichment analyses were conducted for DEGs using DAVID. Enrichment highlights overrepresented terms.

P-value < 0.05 was the cutoff criterion based on Fisher’s exact test. Statistical rigor ensures trustworthy findings.

Protein-Protein Interaction (PPI) Network Construction

Teams consulted widely-used available PPI databases, such as DIP[](http://dip.doe-mbi.ucla.edu/) [16], BIND[](http://www.bind.ca/) [17], MIPS[](http://mips.helmholtz-muenchen.de/proj/ppi/) [18], STRING[](http://string.embl.de/) [19] and HPRD[](http://www.hprd.org/) [20], to analyze the interactions of protein pairs. Multiple sources strengthen predictions.

PPI network of DEGs was constructed by Cytoscape software [21]. Visualization aids interpretation.

Connectivity degree was analyzed and used to obtain the hub protein in PPI network. Hubs often drive biological processes.

Results

Identification of DEGs

Analysts found in total 181 differentially expressed transcripts in colorectal cancer samples. Transcripts map back to functional genes.

There were 92 down-regulated transcripts and 89 up-regulated transcripts, which corresponded to 88 down-regulated genes and 62 up-regulated genes, respectively. Numbers reflect directional changes clearly.

The clustering analysis result was shown in Figure 1. Heatmaps visually separate groups.

The top 10 down- and up-regulated genes were listed in Table 1. Tables provide quick references for readers.

Functional Enrichment Analysis

Investigators listed the top 10 GO terms of down- and up-regulated genes in Table 2, respectively. GO covers diverse aspects of biology.

The down-regulated genes were significantly enriched in cellular component (CC) term of extracellular region, molecular function (MF) term of receptor binding and biological process (BP) term of response to hormone stimulus. Extracellular signals influence cell behavior.

On the other hand, up-regulated gene were mainly enriched in muscle cell apoptosis, such as negative regulation of cardiac muscle cell apoptosis, negative regulation of striated muscle cell apoptosis and regulation of cardiac muscle cell apoptosis. Apoptosis regulation ties to tissue homeostasis.

Total 23 pathways were obtained in KEGG enrichment analysis. Pathways link to disease mechanisms.

The top 5 pathways of down- and up-regulated genes were shown in Table 3, respectively. Rankings prioritize relevance.

The down-expressed genes were mainly involved in cytokine-cytokine receptor interaction, pertussis and tumor necrosis factor (TNF) signaling pathway. Inflammation pathways often overlap with cancer.

DEGs, such as chemokine (C-X-C motif) ligand 3 (CXCL3), interleukin 11 (IL11), interleukin 8 (IL8), INHBB, TNFRSF11B, BMP7, PPBP and CXCL5 were identified in cytokine-cytokine receptor interaction. Chemokines attract immune cells.

CXCL3, CXCL5, prostaglandin-endoperoxide synthase 2 (PTGS2) and matrix metallopeptidase 3 (MMP3) were identified in TNF signaling pathway. TNF drives inflammation and survival.

The up-regulated genes were related to bile secretion and ABC transporters. Transport functions affect drug responses.

DEGs including ATP-binding cassette, sub-family G, member 2 (ABCG2), ATPase, Na+/K+ transporting, alpha 2 polypeptide (ATP1A2) and aquaporin 8 (AQP8) were identified in bile secretion. Bile components interact with gut microbiota.

ABCG2 and ATP-binding cassette, sub-family A (ABC1), member 8 (ABCA8) were identified in ABC transporters. Transporters mediate resistance.

PPI Network Construction

Builders created the PPI network with 73 nodes and 105 edges (Figure 2). Networks model protein dialogues.

In this network, the proteins PTGS2 (degree = 14), IL8 (degree = 10), matrix metallopeptidase 7 (MMP7, degree = 9), plasminogen activator, urokinase (PLAU, degree = 9) and CXCL3 (degree = 8) were selected as hub nodes with the high connectivity degree. High-degree nodes influence many partners.

Discussion

Colorectal cancer remains one of the most life-threatening cancers worldwide [1]. Global efforts target reducing its impact.

Understanding the molecular mechanism of CRC is of critical importance for management policy. Policies inform screening and treatment guidelines.

In this study, the RNA-seq data of GSE29580 was downloaded from GEO database to identify DEGs between CRC and normal samples using bioinformatics analysis. Bioinformatics democratizes complex analyses.

Total 150 DEGs including 88 down- and 62 up-regulated genes were selected. Selections guide deeper investigations.

The functional enrichment analysis results showed that down-regulated genes were significantly enriched in cytokine-cytokine receptor interaction and TNF signaling pathways, while up-regulated genes were related to bile secretion function. Dual directions reveal balanced dysregulation.

PTGS2, CXCL3, IL8, ABCG2, ATP1A2 and AQP8 were identified in these functions. Specific genes emerge as candidates.

In addition, down-regulated genes, such as PTGS2, CXCL3 and IL8, were hub nodes in PPI network. Hubs suggest therapeutic leverage.

These DEGs and their related functions may be involved in CRC development. Integrations paint a fuller picture.

The down-regulated genes were significantly enriched in cytokine-cytokine receptor interaction pathway in this study. Cytokines orchestrate immune responses.

IL8 and CXCL3 were identified in this pathway. Both belong to chemokine families.

IL8 is one of the major mediators of the inflammatory response. Chronic inflammation fuels cancer.

Deregulation of IL8 is involved in proliferation and invasiveness of various malignant tumor cells [22, 23]. Clinical correlations support its role.

Variants of the IL8 and IL8 receptor (IL8R) are associated with increased risks for gastric cardia adenocarcinoma [24]. Genetic links extend beyond CRC.

Rubie et al. reported that over-expression of IL8 was correlated with induction, progression and metastatic of CRC and might be a useful indicator of poor prognosis [25]. Biomarkers like IL8 aid prognostication.

In this study, IL8 was down-expressed and was hub node in PPI network. Down-regulation contrasts some reports but fits paired samples.

It suggested that down-expression of IL8 may inhibit CRC oncogenesis via regulating cytokine-cytokine receptor interaction pathway. Pathway modulation offers strategy.

In addition, CXCL3 is also a small cytokine belonging to the CXC chemokine family. Family members share structures and functions.

Chemokines and their receptors regulate tumor-related angiogenesis, growth and tumor cell proliferation [26, 27]. Angiogenesis supplies tumors.

Bandapalli et al. reported that down-regulation of CXCL1 (another member of the CXC chemokine family) inhibited tumor growth in colorectal liver metastasis [28]. Analogies strengthen CXCL3 hypotheses.

However, the evidence concerning the impact of CXCL3 in CRC is rare. More studies fill this gap.

In this study, down-expressed CXCL3 was enriched in cytokine-cytokine receptor interaction pathway and was hub node in PPI network, suggesting that CXCL3 may play an important role in CRC progression by regulating cytokine-cytokine receptor interaction pathway. Dual enrichment underscores importance.

Therefore, IL8 and CXCL3 may be involved in CRC development. Targeting them could disrupt progression.

Their related pathway may be potential pathogenic mechanism of CRC. Mechanisms guide drug design.

CXCL3 was not only enriched in cytokine-cytokine receptor interaction pathway, but also enriched in TNF signaling pathway. Overlaps indicate crosstalk.

Besides, PTGS2 was identified in TNF signaling pathway. PTGS2 encodes COX-2, a known target.

PTGS2, also known as cyclooxygenase-2 (COX-2), is an enzyme involved in tumor promotion during CRC progression [29]. NSAIDs target COX-2 for prevention.

In this study, PTGS2 was down-regulated gene, which was consistent with a previous study that Karnes et al. reported that PTGS2 was reduced in CRC [30]. Consistency builds confidence.

Inhibited COX-2 is an effective approach to CRC prevention and treatment [29]. Aspirin studies validate this.

Therefore, down-regulation of PTGS2 and CXCL3 may be effective for inhibiting CRC carcinogenesis via regulating TNF signaling pathway. Combined targeting amplifies effects.

Apart from down-regulated genes and their functions, up-regulated genes were mainly enriched in the function of bile secretion in this study. Gut-specific functions emerge.

It has been reported that the elevated bile secretion may increase production of the mutation and carcinogenic of the distal colon [31]. Diet influences bile composition.

ABCG2, ATP1A2 and AQP8 were identified in this function. Transporters and channels dominate.

ABCG2 is a member of ATP binding cassette transporter family. It effluxes drugs and toxins.

In our study, it was up-regulated gene, which was corresponding to previous studies. Upregulation links to chemoresistance.

Liu et al. reported that ABCG2 was highly expressed in CRC and ABCG2 might be important in the progression and metastasis of CRC [32]. Expression correlates with stages.

Besides, ATP1A2 is a member of P-type cation transport ATPase family and belongs to Na, K-ATPase subfamily. Ion pumps maintain gradients.

Na, K-ATPase is a target of transforming growth factor β-mediated epithelial-to-mesenchymal transition (EMT) [33]. EMT enables migration.

EMT is an important process, participates in pathological processes of CRC cell invasiveness, metastasis [34, 35]. Blocking EMT halts spread.

The work of Sakai et al. found that Na+, K+-ATPase α1-isoform was down-regulated and α3-isoform was up-regulated in human CRC [36]. Isoform switches alter properties.

In this study, bile secretion-related gene ATP1A2 was over-expressed in CRC samples. Overexpression may drive EMT.

Therefore, ATP1A2 may be involved in progression of CRC. Inhibitors could counteract this.

Moreover, AQP8 is a water channel protein and belongs to aquaporin family. Water flux affects cell volume.

Water molecules play a key role in the modulation of the tumor microenvironment, tumorigenesis and tumor metabolism [37]. Hydration influences signaling.

During colorectal carcinogenesis, the expression of AQP1 and AQP5 (another members of aquaporin family) were induced in early-stage disease (early dysplasia) and maintained through the late stages of CRC development [38]. Patterns suggest stage-specific roles.

Wang et al. reported that AQP8 was mainly expressed in paraneoplastic normal tissues and barely expressed in CRC cells [39]. Contrasts highlight dysregulation.

In our study, AQP8 was over-expressed in CRC samples, suggesting that it may inhibit the development of CRC. Upregulation could be protective or adaptive.

These results showed that ABCG2, ATP1A2 and AQP8 may be potential therapeutic target genes for CRC. Validation in models is next.

Their related pathway bile secretion may be potential pathogenic mechanisms of CRC. Microbiome interactions warrant attention.

Emerging Role of Gut Microbiome in Modulating Bile Secretion Pathways for CRC Therapy

Recent investigations reveal that gut microbiota significantly alters bile acid metabolism, potentially influencing the expression of genes like ABCG2, ATP1A2, and AQP8 in colorectal cancer progression. Microbial dysbiosis promotes secondary bile acids that drive inflammation and mutagenesis in the colon. Therapies targeting microbiome composition, such as probiotics or fecal transplants, show promise in downregulating these transporters and enhancing chemotherapy efficacy. Integrating metagenomic sequencing with bioinformatics could refine personalized interventions, optimizing bile secretion pathways as adjuncts to standard CRC treatments.

In conclusion, our study shows that cytokine-cytokine receptor interaction, TNF signaling pathway and bile secretion functions may be closely associated with CRC development. Multilevel analyses converge on these.

Genes such as PTGS2, CXCL3, IL8, ABCG2, ATP1A2 and AQP8 may be potential therapeutic target genes for CRC. Clinical trials will test their viability.

However, further studies are still needed to confirm our results. Collaboration accelerates progress.

Colorectal Cancer Therapeutic Targets: Bioinformatics Identification of PTGS2, CXCL3, IL8, and Bile Secretion Genes

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