Unveiling Novel Regulatory Mechanisms of BEX1 in Breast, Gastric, and Colorectal Cancer via a Systems Biology Approach: The Roles of lncRNAs COLCA1 and GAS6-AS1 and Their Interactions

doi:10.21203/rs.3.rs-5118033/v1

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Unveiling Novel Regulatory Mechanisms of BEX1 in Breast, Gastric, and Colorectal Cancer via a Systems Biology Approach: The Roles of lncRNAs COLCA1 and GAS6-AS1 and Their Interactions

https://doi.org/10.21203/rs.3.rs-5118033/v1

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Background: This study aimed to explore novel regulatory networks involving the BEX1 gene and its interaction with non-coding RNAs (ncRNAs) in breast cancer (BC), gastric cancer (GC), and colorectal cancer (CRC). BEX1 has been linked to tumor suppression, but its role in signaling pathways and its interactions with regulatory RNAs in these cancers has not been fully elucidated.

Methods: High-throughput microarray datasets (GSE10810, GSE54129, and GSE208099) were analyzed to investigate BEX1 expression in breast cancer, gastric cancer, and colorectal cancer. The expression analysis and survival outcomes for BEX1 and selected lncRNAs were validated using the ENCORI platform. Regulatory interactions of BEX1 with proteins and microRNAs were identified using STRING and miRWalk, respectively, while lncRNA interactions were examined through lncRRIsearch. Final validation of differential expression analysis and biomarker potential was conducted using qRT-PCR, along with ROC analysis to assess diagnostic capability.

Results: BEX1, identified as a tumor suppressor with low expression in breast, gastric, and colorectal cancer, demonstrated potential as a diagnostic biomarker, particularly in breast cancer (AUC: 0.8025, p = 0.0011). The lncRNAs COLCA1 and GAS6-AS1 were found to potentially regulate BEX1 expression. BEX1 exhibited significant interactions with two key proteins involved in cancer-related signaling pathways: CALML3 and LMO2. Moreover, BEX1 and these proteins demonstrated competitive interactions with miR-3616-3p, which was found to suppress BEX1 expression by targeting its 3'UTR. COLCA1 and GAS6-AS1 also exhibited dysregulated expression across breast, gastric, and colorectal cancers, suggesting their potential as diagnostic biomarkers.

Conclusion: The lncRNAs GAS6-AS1 and COLCA1, alongside miR-3616-3p, may play pivotal roles in regulating cancer-related pathways, including gastric acid secretion, insulin signaling, and homeostasis. These regulatory processes occur through direct and indirect interactions between the non-coding RNAs and BEX1, further highlighting the potential of these molecules as therapeutic targets and diagnostic biomarkers.

Bioinformatics

Systems Biology

long non-coding RNA

Bioinformatics

Systems Biology

Cancer Genetics

The Brain-Expressed X-linked (BEX) gene family, particularly BEX1, plays a crucial role in various cellular processes that are linked to cancer development, including breast, gastric, and colorectal cancers. BEX1, along with its homologs, is involved in regulating cell survival, apoptosis, and cell cycle progression, all of which are critical processes in tumorigenesis (1).

In breast cancer, BEX1 is often associated with tumor suppression. Its expression is correlated with the modulation of apoptosis pathways and the inhibition of cell proliferation. Specifically, BEX1 interacts with the p75 neurotrophin receptor (p75NTR) to influence NF-kappaB signaling, a key pathway involved in regulating cell survival and apoptosis. Loss of BEX1 expression has been linked to poor response to therapeutic agents like tamoxifen, highlighting its potential role as a prognostic marker and therapeutic target in breast cancer (1). In the context of gastric and colorectal cancers, BEX1 and its related proteins are involved in autophagy and apoptosis regulation. For instance, BEX2, a closely related homolog, has been shown to regulate the JNK/c-Jun signaling pathway, which is crucial for cell proliferation and survival in colorectal cancer cells. Knockdown of BEX2 leads to a significant reduction in cancer cell proliferation and tumor growth, demonstrating the importance of BEX family proteins in these malignancies (2).

High-throughput genomics experiments have revolutionized cancer research by enabling the rapid and precise analysis of vast genetic datasets, which is crucial for identifying novel cancer biomarkers. These advanced technologies facilitate the discovery of specific genetic alterations and expression patterns that are essential for early diagnosis, prognosis, and personalized treatment. For instance, integrating high-throughput proteomics with artificial intelligence significantly enhances the identification and validation of cancer biomarkers by analyzing both genomic and protein data (3). Additionally, high-throughput liquid biopsy methods that detect multiple biomarkers in single extracellular vesicles have improved the accuracy and efficiency of cancer diagnostics (4). Moreover, targeted next-generation sequencing assays provide comprehensive genomic profiling of solid tumors, offering critical insights into molecular alterations and supporting the development of precision therapies (5). These high-throughput genomics techniques are indispensable tools in the ongoing quest to discover and validate new cancer biomarkers, ultimately improving patient outcomes through personalized medicine.

Long noncoding RNAs (lncRNAs) are emerging as valuable biomarkers for both inflammation and cancer due to several distinct characteristics. First, many lncRNAs exhibit tissue-specific expression, making them ideal for identifying disease origins. Second, their expression levels are often upregulated in response to specific inflammatory or oncogenic signals. Third, lncRNAs released from cells are encapsulated and safeguarded within extracellular vesicles, ensuring their stability. Finally, circulating lncRNAs in the bloodstream can be efficiently detected through various RNA sequencing techniques, enhancing their potential as non-invasive molecular markers for disease (6). The selection of hub lncRNAs, which are central to these regulatory networks, is particularly important for understanding cancer mechanisms and developing targeted therapies. In papillary thyroid cancer, for example, identifying key lncRNAs has led to the development of prognostic models that predict patient outcomes based on the interactions of these lncRNAs with miRNAs and mRNAs (7). Similarly, in ovarian cancer, constructing lncRNA-miRNA-mRNA networks has provided insights into novel therapeutic targets and potential biomarkers for early detection (8). Also, in hepatocellular carcinoma, it is demonstrated that EZH2 mRNA is regulated by different lncRNAs, including lncRNA HAGLR (9). In GC, CCDC144NL-AS1 and LINC01094 lncRNAs can act as two potential diagnostic biomarkers (10)Understanding the roles of these hub lncRNAs across different cancer types is essential for advancing precision oncology and improving patient outcomes.

RNA interaction and network analysis are integral to a systems biology approach, significantly enhancing cancer diagnosis and therapy by providing a comprehensive understanding of the molecular interactions within cancer cells. By mapping the intricate networks of coding and non-coding RNAs, researchers can identify key regulatory RNAs that drive cancer progression and pinpoint potential therapeutic targets. For example, ceRNA networks, which include mRNAs, lncRNAs, and miRNAs, reveal how disruptions in these interactions contribute to tumorigenesis and can highlight biomarkers for early diagnosis and targeted therapy (11). Additionally, integrating RNA-Seq data with network analysis has been shown to improve the prediction of key cancer drivers and therapeutic responses, providing a more personalized approach to cancer treatment (12). Furthermore, understanding the connectivity between non-coding RNAs and protein-coding genes enhances the identification of potential intervention points within the cancer regulatory networks, offering new avenues for therapeutic development (13). These systems biology strategies not only facilitate the discovery of novel biomarkers but also pave the way for innovative treatments that target the complex regulatory networks of cancer cells.

In this study, we aimed to find novel regulatory lncRNAs in gastric cancer (GC), breast cancer (BC), and colorectal cancer (CRC) patients. We used several systems biology approaches to find potential correlations and interactions between the expression levels of different coding and non-coding RNAs in the patients. Furthermore, the diagnostic possibility of each hub RNA is evaluated in this study. All in all, we tried to find novel regulatory functions of BEX1 in the development of GC, BC, and CRC to find novel and general functionality for BEX1.

2.1. Gene Expression Analyses

First, to evaluate the expression level of BEX1 in high-throughput experiments, the following microarray datasets were analyzed for BC, GC, and CRC, respectively: GSE10810 (14), GSE54129, and GSE208099 (15). The significance level of microarray analyses is set on adj. P. Value < 0.05, and considering the significance criteria, |logFC| > 1 was considered as the dysregulation threshold. Using the GEOquery (16) package, the raw gene expression matrix of all datasets was downloaded, and the limma (17) package performed the statistical analyses for finding the differentially expressed genes (DEGs) in all datasets. Visualization of the plots was performed using the ggplot2 (18) package. The expression level of BEX1 in BC, GC, and CRC was evaluated using ENCORI (19) (comparing to control samples) for the validation of microarray analysis. Survival analyses were conducted using ENCORI to evaluate the relation of expression changes in selected mRNA and lncRNAs with the survival rate of cancer patients.

2.2. RNA interaction and functional enrichment analysis

In this study, several online platforms were employed to identify emerging non-coding regulatory biomarkers in BC, GC, and CRC samples. The lncRRIsearch (20) web application (http://rtools.cbrc.jp/LncRRIsearch) was used to select novel regulatory lncRNAs. To map the protein interaction networks associated with the identified mRNA, the STRING (21) database (https://string-db.org/) was utilized. Novel regulatory miRNAs were identified using the miRWalk (22, 23)platform (http://mirwalk.umm.uni-heidelberg.de/). MicroRNAs with the following criteria were selected for the network: binding probability (score): 1, interaction in the seed region (3’UTR), and ordered from lower binding energy to higher. RNA interaction networks were visualized through the Cytoscape (24, 25) software. Pathway enrichment analysis was performed using the Reactome (26–28) (https://reactome.org/) web database.

2.3. qRT-PCR sample preparation

The Ethics Committee of Al-Zahra Hospital, under the jurisdiction of Isfahan University of Medical Sciences, provided ethical approval for all research protocols involving human specimens used in this study. Written informed consent was obtained from all participating patients. In this case-control study, the expression profiles of selected mRNAs and lncRNAs were examined in 20 tissue samples from patients with BC, CRC, and GC. These were compared with 20 adjacent non-tumorous tissue samples for each cancer type. Importantly, none of the subjects had previously received chemotherapy or radiation therapy. The detailed clinicopathological characteristics of the human samples are presented in Tables 1–3.

Table 1

Clinical characteristics of BC samples.
Variable	Status	Number	%
Stage	I	0	0
	II	6	30
	III	12	60
	IV	0	0
	Unknown	2	10
Age	< 45	10	50
	> 45	8	40
	Unknown	2	10
Tumor size (TS)	< 5cm	12	60
	> 5cm	6	30
	Unknown	2	10
Menopausal status	Yes	18	90
	No	2	10
	Unknown	0	0
Lymph node	Yes	16	80
	No	2	10
	Unknown	2	10
ER receptor	Positive	8	40
	Negative	7	35
	Unknown	5	25
PR receptor	Positive	6	30
	Negative	9	45
	Unknown	5	25
HER2/neu receptor	Positive	10	50
	Negative	5	25
	Unknown	5	25

Table 2

Clinicopathological characteristics of GC samples.
Variable	Status	Number	%
Age	< 50	8	40
Age	> 50	12	60
Sex	Male	18	90
Sex	Female	2	10
Tumor Size	< 5 cm	10	50
Tumor Size	> 5 cm	10	50
Histology	Adenocarcinoma	18	90
	Mucinous Adenocarcinoma	1	5
	Signet Ring Carcinoma	1	5
Perineural Invasion	No	6	30
Perineural Invasion	Yes	14	70
Nodal Extension	No	16	80
Nodal Extension	Yes	4	20
TNM Staging	I	1	5
	II	6	30
	IIIA	2	10
	IIIB	4	20
	IV	7	35
Family History	No	14	70
Family History	Yes	6	30
Smoking	DX-Smoker at Diagnosis but Discontinued	2	10
	Ex-Smoker	2	10
	Non-Smoker	15	75
	smoker	1	5

Table 3

Clinicopathological table of colorectal cancer patients.
Variable	Status	Number	%
Stage	I	2	10
	II	3	15
	III	7	35
	IV	8	40
	Unknown	0	0
Age	< 50	5	25
	> 50	15	75
	Unknown	0	0
Tumor size (TS)	< 5cm	9	45
	> 5cm	11	55
	Unknown	0	0
Lymphatic Invasion	Yes	8	40
	No	11	55
	Unknown	1	5
Perineural Invasion	Yes	12	60
	No	8	40
	Unknown	0	0
Smoking	Non-smoker	17	85
	Smoker	3	15
	Unknown	0	0
Sex	Female	11	55
	Male	9	45
	Unknown	0	0

2.4. RNA extraction, cDNA synthesis, and qRT-PCR programs

RNA extraction from both tumor and normal tissues was carried out using TRIZOL reagent (Invitrogen, Carlsbad, CA, USA). Following extraction, cDNA synthesis was performed with the TaKaRa cDNA synthesis kit (TaKaRa, Tokyo, Japan), following the manufacturer’s protocol. Real-time PCR was subsequently conducted using Sybergreen (Amplicon Company, Denmark) on a MIC Real-Time PCR machine (Australia). The PCR reactions included an initial denaturation at 95°C for 15 minutes, followed by 40 cycles of 95°C for 15 seconds, 60°C for 20 seconds, and 72°C for 20 seconds. Primers were designed using Oligo 7 software and synthesized by TAQ Copenhagen Company (Denmark), and their sequences are detailed in Table 4.

Table 4

The table of primer sequences.
gene	RNA type	Primer (5' -> 3')	Forward/Reverse	Tm	GC	Product length
BEX1	mRNA	TCGGGAGAAGGAGGAGACTAC	F	59.79	57.14	84
BEX1	mRNA	TCCATGCTGAGACTGTTTACTG	R	58.07	45.45	84
GAS6-AS1	lncRNA	TGGCTGCATTCGTTGACATCTG	F	61.76	50	125
GAS6-AS1	lncRNA	CTGGTCCTCGTTTCCTCGTAAC	R	60.67	54.55	125
COLCA1	lncRNA	GGTGCAACTGGGTCTGAAAG	F	59.05	55	77
COLCA1	lncRNA	GCCTGCTTCACGGTGATATTC	R	59.4	52.38	77

2.5. Statistical analysis

The statistical analysis for the qRT-PCR experiment was performed using GraphPad Prism 8 software. Both paired and unpaired t-tests were employed to determine the significance levels. Additionally, receiver operating characteristic (ROC) analysis was conducted to evaluate the diagnostic potential of the tumor specimens. The area under the curve (AUC) was carefully examined in the ROC results. An AUC value between 0.7 and 0.8 indicates an acceptable biomarker, a value between 0.8 and 0.9 denotes a good biomarker, and an AUC value between 0.9 and 1 represents an excellent diagnostic biomarker. Pearson correlation was performed for an initial validation of lncRNA-mRNA interaction. A correlation (r) higher than 0.7 was considered a strong correlation.

3.1. Low expression of BEX1 in BC, GC, and CRC and correlation with the survival rate

Principal component analysis (PCA) was performed to assess the quality of microarray samples (Fig. 1). Separation of tumor samples from control samples in PCA plots shows the suitable quality of microarray samples in BC (Fig. 1a, GC (Fig. 1b), and CRC (Fig. 1c) datasets. In the microarray datasets, BEX1 has significant low expression in BC (logFC: -3.23432, adj. P. Value < 0.0001), GC (logFC: -2.56432, adj. P. Value < 0.0001), and CRC (logFC: -1.724, adj. P. Value < 0.0001). Volcano plot shows the up and down-regulated DEGs in BC (Fig. 2a), GC (Fig. 2b), and CRC (Fig. 2c). ENCORI expression analysis also validated microarray analysis results (Fig. 3). Survival analysis shows no significant correlation between the expression of BEX1 and the survival rate of patients. However, there was a slight and non-significant correlation between the low expression of BEX1 and the higher death rate of BC and GC patients (Fig. 4).

3.2. BEX1 indirectly modulates cancer-related signaling pathways

Figure 5 shows the protein interaction network of BEX1, which led the study to demonstrate related signaling pathways that are regulated by this regulatory pathway. Based on the protein interaction network, the BEX1 protein is involved in 4 signaling pathways by interacting with other regulatory proteins. Through the regulation of CALML3-6, BEX1 regulates the following signaling pathways: Gastric Acid Secretion (Red), Insulin signaling pathway (Blue), and Pathways in Cancer (Green). Also, BEX1 regulates Transcriptional Misregulation in Cancer (Yellow) signaling pathway through interaction with LDB1 and LMO2.

Direct pathway analysis of BEX1 through the Reactome database revealed the potential role of BEX1 in Hemostasis (Fig. 6). Direct interaction of BEX1 with PICK1 regulates the binding of PICK1 to TSPAN7 at the homeostasis signaling pathway. Specifically, this binding is part of the “cell surface interaction at the vascular wall.”

3.3. Non-coding interactions with BEX1

To find novel potential regulatory non-coding RNAs affecting the homeostasis signaling pathways in cancer development, miRNA and lncRNA interaction analysis was performed with respect to the central protein-coding gene in this study (BEX1). Among all potential lncRNAs that have interaction with BEX1, two lncRNAs with significant dysregulation in BC, GC, and CRC were selected: GAS6-AS1 and COLCA1 (Fig. 7). Survival analysis revealed no significant correlation between GAS6-AS1 and CLCA1. However, due to the p-value of survival analysis for GAS6-AS1 in STAD and COAD and COLCA1 in BRCA, performing the same analysis with a higher sample size or in different populations might result in novel information about the correlation of mentioned dysregulated lncRNAs with the survival rate of BC, GC, and CRC (Fig. 8).

miRNA interaction analysis revealed the potential role of miR-3616-3p in the regulation of BEX1. Among all miRNAs that have interaction with BEX1, 17 miRNAs had the criteria described in Materials and Methods (Table 5). Among those 17 miRNAs, miR-3616-3p has the strongest interaction with the 3’UTR region of mRNA BEX1 (energy: -25 kcal/mol). Furthermore, miR-3616-3p interacts with CALML3 and LMO2, two important nodes in the protein interaction networks (Fig. 5). Figure 9 illustrates the interaction network in this study.

Table 5

miRNA-BEX interaction analysis revealed that miR-3616-3p has the strongest interaction with BEX mRNA in the 3’UTR (seed) region.
miRNA	symbol	score	energy (kcal/mol)	seed	position
miR-3616-3p	BEX1	1	-25	1	3UTR
miR-6792-5p	BEX1	1	-23	1	3UTR
miR-6880-5p	BEX1	1	-22.7	1	3UTR
miR-4697-5p	BEX1	1	-22.2	1	3UTR
miR-10401-5p	BEX1	1	-21.7	1	3UTR
miR-3141	BEX1	1	-21.3	1	3UTR
miR-6131	BEX1	1	-20.8	1	3UTR
miR-492	BEX1	1	-20.7	1	3UTR
miR-2278	BEX1	1	-20.7	1	3UTR
miR-4713-3p	BEX1	1	-20	1	3UTR
miR-711	BEX1	1	-19.4	1	3UTR
miR-15b-5p	BEX1	1	-18.7	1	3UTR
miR-1973	BEX1	1	-18.4	1	3UTR
miR-4443	BEX1	1	-18.3	1	3UTR
miR-4708-3p	BEX1	1	-16.3	1	3UTR
miR-300	BEX1	1	-16.2	1	3UTR
miR-4300	BEX1	1	-16.2	1	3UTR

3.4. qRT-PCR experiment

To assess the biomarker potential of BEX1, GAS6-AS1, and COLCA1 in BC, GC, and CRC samples, a qRT-PCR experiment was conducted. Initially, differential expression analysis was performed on the qRT-PCR data to validate the gene expression results from bioinformatics analyses. The experiment revealed that BEX1 exhibited low expression in both BC and GC samples. GAS6-AS1 showed low expression in BC samples but high expression in GC and CRC samples. Similarly, COLCA1 was found to be expressed at low levels in GC samples, while its expression was elevated in CRC samples (Fig. 10). Additionally, BEX1 in BC (AUC: 0.8025, p-value: 0.0011) and GAS6-AS1 (AUC: 0.8375, p-value: 0.0003) as well as COLCA1 (AUC: 0.8150, p-value: 0.0007) in CRC are identified as promising diagnostic biomarkers (Fig. 11). Pearson correlation analysis demonstrated a strong correlation between the lncRNAs GAS6-AS1 and COLCA1 with BEX1 in GC samples. A strong correlation between GAS6-AS1 and BEX1 was also observed in CRC samples (Fig. 12). Statistical parameters of the qRT-PCR experiment are provided in Table 6. Detail of correlation analysis is provided in Table 7.

Table 6

Details of the gene expression analysis conducted in this study.
		expression		ROC
gene	disease	logFC	p-value	AUC	p-value
BEX1	BC	-2.516	0.0407	0.7350	0.0110
	GC	-3.075	0.0079	0.7450	0.0080
	CC	-1.571	0.1100	0.6375	0.1368
COLCA1	BC	-1.673	0.0844	0.5900	0.1441
	GC	-2.960	0.0361	0.7650	0.0041
	CC	2.949	0.0016	0.8150	0.0007
GAS6-AS1	BC	-3.286	0.0307	0.7250	0.0149
	GC	3.525	0.0136	0.7650	0.0041
	CC	3.796	0.0010	0.8375	0.0003

Table 7

Details of the correlation analysis between lncRNAs and BEX1 mRNA in this study.
	BC		GC		CRC
	r	p-value	r	p-value	r	p-value
COLCA1	0.6638	0.0014	0.8382	< 0.0001	0.6244	0.0033
GAS6-AS1	0.6894	0.0008	0.9379	< 0.0001	0.7296	0.0003

In this study, we sought to identify novel non-coding RNA interactions involving the mRNA BEX1 and explore the regulatory roles of these interactions in the progression of breast, gastric, and colorectal cancers. As a tumor suppressor with low expression, BEX1 interacts with the lncRNAs GAS6-AS1 and COLCA1 and is further suppressed by the miRNA miR-3616-3p. This miRNA also regulates the expression of CALML3 and LMO2, two proteins that have significant interactions with BEX1. Additionally, through its interaction with PICK1, BEX1 indirectly influences the homeostasis signaling pathway. Given its involvement in "cell surface interactions at the vascular wall," reduced expression of BEX1 could contribute to a more aggressive tumor phenotype by disrupting normal vascular responses, ultimately promoting tumor growth and metastasis. Therefore, the downregulation of BEX1 in these cancers suggests its critical role in modulating interactions within the tumor microenvironment, particularly within the vascular niche, which may inform future therapeutic approaches aimed at restoring its function (29).

Homeostasis is crucial in maintaining the stability of biological systems, especially in the vascular wall, where it regulates processes such as cell growth, immune response, and endothelial integrity. In the context of cancer, disruptions to homeostatic mechanisms can lead to an altered tumor microenvironment that supports cancer progression. The vascular wall is a critical site where cancer cells interact with endothelial cells and bypass immune surveillance, a process that is highly dependent on maintaining homeostatic balance. When this balance is disturbed, the endothelial barrier becomes compromised, allowing cancer cells to enter the bloodstream and metastasize to distant organs. This highlights the importance of vascular homeostasis in controlling the spread of cancer cells and the potential for therapeutic interventions aimed at restoring this balance (30, 31).

Cell surface interactions at the vascular wall are also key to cancer development and progression. Tumor cells exploit these interactions to invade and migrate through the vascular endothelium. Molecules such as integrins and selectins play significant roles in this process, facilitating the adhesion of cancer cells to the vascular wall. Once adhered, cancer cells can cross the endothelial barrier and disseminate throughout the body, leading to metastasis. Targeting these cell surface interactions offers a promising strategy for cancer therapies, as blocking the adhesion of cancer cells to the vascular wall could prevent their spread and improve patient outcomes (32). This approach not only addresses the local impact of tumor growth but also the systemic spread of cancer, making it a crucial area of research (33).

LncRNA COLCA1 has emerged as a key regulator in colorectal cancer (CRC) development, significantly influencing immune responses within the tumor microenvironment. COLCA1 is particularly associated with genetic variants, such as rs3802842, which has been linked to increased CRC susceptibility (34). Studies show that COLCA1 co-localizes with eosinophils, macrophages, and other immune cells, suggesting a role in modulating the immune response against tumor growth. This lncRNA is thought to function by affecting the expression of miRNAs, particularly miR-371a-5p, which plays a pivotal role in inflammation and immune regulation (35). By suppressing the tumor-suppressive activities of miR-371a-5p, COLCA1 likely contributes to immune evasion and tumor proliferation (36).

Further evidence points to COLCA1's involvement in key signaling pathways associated with cancer progression, such as the Wnt/β-catenin and mTORC1 pathways (34). Through these interactions, COLCA1 influences cell proliferation, angiogenesis, and immune cell infiltration, which are crucial for both tumor growth and metastasis. COLCA1's presence in eosinophilic granules within immune cells, along with its interaction with proteins such as major basic protein (MBP) and eosinophil peroxidase (EPO), highlights its potential role in the immune modulation of cancer (35). This complex interplay between genetic variants, immune response, and cancer signaling pathways suggests that COLCA1 may serve as a therapeutic target for CRC and other cancers where it is dysregulated (34, 35).

GAS6-AS1 (Growth Arrest-Specific 6 Antisense 1) plays a key role in various cancers by acting as a competitive endogenous RNA (ceRNA), influencing oncogenic processes. For instance, in lung adenocarcinoma (LUAD), GAS6-AS1 functions by sponging miR-24-3p, a microRNA involved in regulating cancer cell proliferation and invasion. Through this interaction, GAS6-AS1 inhibits the activity of miR-24-3p, thereby upregulating its target gene GTPase IMAP Family Member 6 (GIMAP6), which promotes cancer progression (37). Moreover, GAS6-AS1 is expressed mainly in the cytoplasm of LUAD cells and serves as a vital player in regulating cellular proliferation, migration, and apoptosis (37). Studies have shown that downregulation of GAS6-AS1 is associated with poor prognosis in LUAD, while its overexpression leads to reduced cell proliferation and invasion, making it a potential diagnostic biomarker and therapeutic target in LUAD.

In hepatocellular carcinoma (HCC), GAS6-AS1 has been shown to sponge miR-585, thereby releasing the oncogene EIF5A2 from inhibition, which in turn enhances tumor growth and metastasis (38). This pathway highlights GAS6-AS1's oncogenic potential in driving cancer progression through its regulation of the PI3K/AKT signaling pathway (39). Additionally, in gastric cancer, GAS6-AS1 promotes cell proliferation and invasion by interacting with AXL signaling, a pathway that is crucial in several cancers due to its role in epithelial-to-mesenchymal transition (EMT) and metastasis (39). Collectively, these studies suggest that GAS6-AS1 serves as a pivotal regulatory molecule in cancer development, interacting with both proteins and miRNAs to influence key signaling pathways, and it holds promise as a therapeutic target across various malignancies.

In this study, we sought to identify novel regulatory networks involved in the development of BC, GC, and CRC. Our previous research focused on uncovering new regulatory lncRNAs and miRNAs across various cancer types. For instance, we explored the potential regulatory roles of IGF1 (40)and XBP1 (41)in BC, as well as UHRF1 (42)and MEG9 (43) in BC, GC, and CRC. However, further investigation is required to achieve more precise validation of RNA interactions and to assess the differential expression of these RNAs under varying pathological conditions.

Based on this investigation, lncRNAs COLCA1 and GAS6-AS1 might regulate cancer-related signaling pathways through the regulation of BEX1 mRNA. Furthermore, the mentioned lncRNAs can be considered as the two potential cancer diagnostic biomarkers (specifically in BC, GC, and CRC). miR-3616-3p is also one of the hub nodes in the interaction network, suppressing the expression level of BEX1.

Ethics approval: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of the Ethics Committee of Isfahan University of Medical Sciences.
Consent for publication: Informed consent was obtained from all individual participants included in the study.
Availability of data and materials: The datasets generated or analyzed during the current study are available in the GEO repository, GSE10810, GSE54129, and GSE208099.
Conflicts of interest: The authors declare that they have no competing interests.
Financial support and sponsorship: Not applicable.
Authors’ contribution: Mehrnoosh Tavakoli, Parnian Salehipour, Seyedeh Saba Hosseinipouya, Shima Asgari, Mohammadreza Rezaei, Dorsan Vatani, Maryam Mousavi, Younes Poudineh: Software, Formal Analysis, Investigation, Data Curation, Writing – Original Draft, Visualization; Mohammad Rezaei and Seyedeh Zahra Shirdeli: Writing – Review & Editing, Conceptualization, Methodology, Validation, Supervision; Reza Ghelich: Writing – Original Draft; Mansoureh Azadeh: Writing – Review & Editing, Conceptualization, Methodology, Validation, Resources, Project Administration, Resources. Mehrnoosh Tavakoli, Parnian Salehipour, Seyedeh Saba Hosseinipouya, and Shima Asgari equally contributed to this study as the first authors. Mohammadreza Rezaei, Dorsan Vatani, Maryam Mousavi, and Younes Poudineh equally contributed to this study as second authors. Mohammad Rezaei and Seyedeh Zahra Shirdeli equally contributed to this study as supervisors and third authors.

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The authors declare no competing interests.

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Unveiling Novel Regulatory Mechanisms of BEX1 in Breast, Gastric, and Colorectal Cancer via a Systems Biology Approach: The Roles of lncRNAs COLCA1 and GAS6-AS1 and Their Interactions

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Abstract

Figures

1. Introduction

2. Materials and Methods

2.1. Gene Expression Analyses

2.2. RNA interaction and functional enrichment analysis

2.3. qRT-PCR sample preparation

2.4. RNA extraction, cDNA synthesis, and qRT-PCR programs

2.5. Statistical analysis

3. Results

3.1. Low expression of BEX1 in BC, GC, and CRC and correlation with the survival rate

3.2. BEX1 indirectly modulates cancer-related signaling pathways

3.3. Non-coding interactions with BEX1

3.4. qRT-PCR experiment

4. Discussion

5. Conclusion

Declarations

References

Additional Declarations

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