Identification of Convergent regulatory networks in colorectal Cancer:

A Boolean modeling analysis of driver Mutation networks

Anshu Thakur^1,2, Abhilasha Sharma³, Mehul R. Chorawala²*

¹Research Scholar, Gujarat Technological University, Ahmedabad, India.

²L. M. College of Pharmacy, Opp. Gujarat University, Navrangpura,

Ahmedabad-380009, Gujarat, India. ORCID ID: 0000-0002-4824-7673

³BRIC-National Agri-Food and Biomanufacturing Institute (NABI),

Mohali, Punjab, 140306, India. ORCID ID: 0000-0002-2438-6556

*Corresponding Author E-mail: mchorawalaresearch@gmail.com; mehul.chorawala@lmcp.ac.in

ABSTRACT:

Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide. The complex aetiology of the disease is closely associated with genetic predispositions and lifestyle factors. In this study, we employ Boolean modeling to identify convergent signaling pathways in CRC that drive disease progression regardless of the initial genetic mutation. Initially, through a gene regulatory network (GRN), we integrate the WNT, EGF, TGF-𝛽, and IL-6 pathways via BioTapestry, followed by Boolean analysis for networking probabilities and attractor states, using the BoolNet R package. Our findings reveal that irrespective of the diverse driver mutations⎯such as those involving APC, KRAS, SMADs, and IL6⎯pathways, consistently converge upon a core set of downstream effectors, specifically VEGF, PI3K/mTOR, and c-myc. With the high probability of VEGF activation across simulated states, its role is underscored as a pivotal regulator of neoangiogenesis and metastasis, serving as a functional bottleneck in the transition from primary tumor to systemic spread. Our study provides a system-biology rationale for the efficacy of broad-spectrum therapeutic targets, particularly the VEGF axis, for treating CRC across different mutational profiles.

KEYWORDS: Colorectal cancer, Gene Regulatory Network, BioTapestry, BoolNet, Boolean analysis.

INTRODUCTION:

Colorectal cancer(CRC) ranks third globally in terms of incidence and second in terms of mortality among various cancers. It is more prevalent in males than in females.¹ It has a higher prevalence in developed countries compared to developing countries, with the highest incidence rates in North America, Western Europe, and Oceania, while Africa, Asia, and South America have the lowest incidence rates.² Although earlier considered a disease of the old is now grappling younger population as well.^3,4

Although the etiology of the CRC remains unclear and is considered a multifactorial disorder, it is related to multiple factors, including genetic predisposition, dietary factors, non-cancerous diseases like colorectal polyps, ulcerative colitis, and Crohn’s disease.⁵ Some of the prominent genetic factors are Familial Adenomatous Polyposis (FAP)⁶, Lynch Syndrome⁷, the Mismatch Repair Gene (MMR)⁸. Additionally, various lifestyle factors, such as sedentary lifestyle, a diet rich in high-fat and high-calorie foods, and excessive consumption of highly processed foods, also contribute to the increased risk of CRC.^9-12

As the literature suggests, multiple driver genes are involved in the genesis and pathophysiology of CRC. In this research work, we have selected some of the most prevalent driver mutations reported in various databases and literature. Subsequently, we have constructed a Boolean operable gene regulatory network and conducted a Boolean analysis to identify the most commonly affected pathways resulting from different types of mutations.

MATERIAL AND METHOD:

To determine the crucial genes and pathways involved in the pathogenesis of CRC, we searched GeneCards¹³ – MalaCards¹⁴, PathCards¹⁵, and other published journal articles.¹⁶ The following Gene Regulatory Networks (GRNs) were selected, which were most commonly found in CRC pathogenesis (downregulated and upregulated genes):

· WNT pathway (APC gene downregulation)

· EGF pathway (KRas gene upregulation)

· TGF-β pathway (SMAD 2,3,4 downregulation)

· IL-6 pathway (IL-6 upregulation)

After the selection of crucial genes and pathways for CRC, we draw the pathways and inter-pathways between the genes involved – using ‘BioTapestry’^17,18, a freely available online interactive software tool for building, visualizing, and sharing gene regulatory network models that embed Logical functions in genes (Fig. 1). Of note, this file format is supported by the R package “Boolnet”.

Figure 1: The figure is the gene regulatory Networks (GRNs) obtained from the BioTapestry, an online interactive software tool. One of the common genes and its mutational role with the associated downstream pathway has been used to embed logical functions into each gene. Different pathways and their interaction with each other further affect the downstream genes. The functional role of the affected downstream genes was indicated at the bottom.

At this juncture, we have successfully constructed the rudimentary framework of the pertinent signal transduction pathway, encompassing its logical function. Consequently, we are equipped to execute Boolean operations utilising the R package ‘BoolNet’¹⁹. These operations have been employed to assess the probabilities of networking and the states of associated genes (Fig. 2). The Networking probabilities serve as indicators of the gene-gene associations and the extent of these associations, thereby elucidating their relevance within the GRN. Conversely, the Attractors states represent stable patterns of gene expression or system behaviour within the Boolean network

RESULT AND DISCUSSION:

Through this computational based study, we tried to scale the intracellular element of the cell such the intracellular microenvironment of the cell signal transduction pathways associated with CRC can be replicated and thus we can get the clue for most affected pathways, that gets involved, regardless of driver mutation. This insight will help us to screen most critical pathways to target for design of chemotherapies related to CRC.

Networking probabilities of the genes suggest that Kras, Akt, NF𝜅𝛽, and beta catenin shows highest degree of association and can be influenced by multiple pathways (Fig. 3).

Figure 3: This figure illustrates the extent of correlation between specific genes that play a pivotal role in the etiology of colorectal cancer. The data was obtained by calculating the network probabilities on the gene regulatory network using the “Boolnet” software.

We have conducted synchronous Boolean network simulations and analysed their operation within the “Boolnet” simulator to obtain the gene regulatory state during metastatic colorectal cancer. These networks comprise a set of Boolean variables (genes), denoted as X, and corresponding set of transition functions, each tailored to a specific variable. The subsequent state of the network is subsequently determined by applying all transition functions to the current state (Fig. 4). Our findings suggest that there is:

· A very high probability of VEGF activation – This activation will induce cell proliferation and neoangiogensis

· A high probability of PI3K and mTOR activation – These activations will further enhance the migration and metastasis of tumor cells

· A high probability of c-myc activation – one of the key driver genes for cell proliferation and tumor progression

The identification of the above gene networks through this boolean operation suggests that VEGF is a pivotal gene in the spread of colorectal cancer, driving cell proliferation and neoangiogenesis. Therefore, through our study we have found that, irrespective of initial driver mutations that initiates transforms normal epithelial cells of colon or rectum to colorectal cancer cells, through various signal transduction pathways, they route towards a certain gene, and advance the disease.

Consistent with the available studies that mention that VEGF biomarker has an important role in the induction of angiogenesis to meet the systemic and local nutrition and oxygen demands in a normal tissue, but under the cancer condition with metastasis spread, VEGF levels are associated with excessive angiogenesis to meet the increased demand of tumor.^20,21 Therefore, several drugs have been studied and are being used to prevent angiogenesis. Notably, including monoclonal antibodies targeting VEGF, like bevacizumab, aflibercept, Ramucirumab, etc.²²

CONCLUSION:

In our current study, employing a Boolean analysis (in silico method), we investigated the activation states of VEGF in prevalent CRC mutations. Our findings revealed that, irrespective of the initiating mutation gene, distinct pathways result in VEGF activation.

ACKNOWLEDGEMENT:

The authors are grateful to Prof. Gaurang B. Shah, Department of Pharmacology and Pharmacy Practice, L. M. College of Pharmacy, Ahmedabad, Gujarat, India, for kind support and guidance in manuscript preparation. The authors also extend their appreciation to the L. M. College of Pharmacy, Ahmedabad, India, for providing continuous library and resource support throughout the literature survey and data collection.

FUNDING:

This study was supported by a grant received from SHODH - ScHeme of Developing High Quality Research, Education Department, Government of Gujarat, India (KCG/SHODH/2022-23/ 2021013712).

ETHICS APPROVAL:

As this is a review study and did not use preclinical or clinical-level data, no Ethics Committee approval is required.

CREDIT AUTHORSHIP CONTRIBUTION STATEMENT:

Anshu Thakur: Conceptualization, Methodology, Literature review, Data collection, Validation, Visualization, Formal analysis, Writing – original draft. Mehul R. Chorawala: Conceptualization, Methodology, Resources, Writing – review and editing, Visualization, Supervision, Project administration

DECLARATION OF COMPETING INTEREST:

The author declares that they have no known competing financial and/or non-financial interests or personal relationships that could have appeared to influence the work reported in this paper.

CONFLICT OF INTEREST:

The author declares no conflict of interest.

DECLARATION FOR USE OF GENERATIVE AI OR AI-ASSISTED TECHNOLOGIES:

All the authors declare that no generative AI or AI-assisted technologies have been utilized while drafting this manuscript.

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Received on 15.01.2026 Revised on 09.04.2026

Accepted on 06.06.2026 Published on 01.07.2026

Available online from July 04, 2026

Research J. Pharmacy and Technology. 2026;19(7):3084-3088.

DOI: 10.52711/0974-360X.2026.00438

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.