Original Article
Volume: 8 | Issue: 1 | Published: Jun 24, 2025 | Pages: 020 - 026 | DOI: 10.24911/JBCGenetics.11-2222
CFTR interactome may impact gastric cancer: an in silico system-level coexpression analysis
Authors:
Camila Sinimbú Forte
, Amanda Ferreira Vidal
, Pablo Diego do Carmo Pinto
, Gilderlanio Santana de Araújo
Article Info
Authors
Camila Sinimbú Forte
Institute of Technology, Federal University of Pará, Belém, Brazil
Amanda Ferreira Vidal
Vale Institute of Technology, Belém, Brazil
Pablo Diego do Carmo Pinto
Institute of Medical Sciences, Federal University of Pará, Belém, Brazil
Gilderlanio Santana de Araújo
Institute of Biological Sciences, Federal University of Pará, Belém, Brazil
Publication History
Received: May 18, 2025
Accepted: June 16, 2025
Published: June 24, 2025
Abstract
Background: The cystic fibrosis transmembrane conductance regulator (CFTR) dysfunction is linked to gastrointestinal inflammation and has been implicated in early-onset malignancies. However, its role in gastric cancer remains poorly understood.
Aims: To investigate the CFTR interactome and assess its potential functional involvement across different subtypes of gastric cancer.
Methods: We conducted a system-level in silico analysis using data from the Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD). CFTR expression and co-expression profiles were examined across molecular and histological subtypes of gastric cancer, including signet-ring cell carcinoma (Lauren’s classification). Differential gene expression (DGE) and co-expression analyses were integrated with protein-protein interaction networks, pathway enrichment, and gene ontology (GO) analysis to delineate CFTR’s functional associations.
Results: CFTR did not exhibit significant differential expression across gastric cancer subtypes. However, co-expression analysis identified CFTR as a key hub gene with a distinct interaction network, especially prominent in the signet-ring cell carcinoma subtype. Enrichment analyses revealed that CFTR's interactome is involved in regulatory pathways related to cellular homeostasis, ion transport, and immune modulation, suggesting a noncanonical yet critical role in tumor biology.
Conclusion: While CFTR expression remains stable across gastric cancer subtypes, its interactome reveals significant regulatory roles, particularly in signet-ring cell carcinoma. These findings highlight the potential contribution of CFTR to gastric cancer pathogenesis through its involvement in broader molecular networks rather than through expression changes alone.
Keywords: Gene co-expression network, CFTR, gastric cancer subtypes, signet-ring cell carcinoma
Pubmed Style
Camila Sinimbú Forte, Amanda Ferreira Vidal, Pablo Diego do Carmo Pinto, Gilderlanio Santana de Araújo. CFTR interactome may impact gastric cancer: an in silico system-level coexpression analysis. JBC Genetics. 2025; 24 (June 2025): 020-026. doi:10.24911/JBCGenetics.11-2222
Web Style
Camila Sinimbú Forte, Amanda Ferreira Vidal, Pablo Diego do Carmo Pinto, Gilderlanio Santana de Araújo. CFTR interactome may impact gastric cancer: an in silico system-level coexpression analysis. https://jbcgenetics.com/index.php/articles/2222 [Access: October 13, 2025]. doi:10.24911/JBCGenetics.11-2222
AMA (American Medical Association) Style
Camila Sinimbú Forte, Amanda Ferreira Vidal, Pablo Diego do Carmo Pinto, Gilderlanio Santana de Araújo. CFTR interactome may impact gastric cancer: an in silico system-level coexpression analysis. JBC Genetics. 2025; 24 (June 2025): 020-026. doi:10.24911/JBCGenetics.11-2222
Vancouver/ICMJE Style
Camila Sinimbú Forte, Amanda Ferreira Vidal, Pablo Diego do Carmo Pinto, Gilderlanio Santana de Araújo. CFTR interactome may impact gastric cancer: an in silico system-level coexpression analysis. JBC Genetics. (2025), [cited October 13, 2025]; 24 (June 2025): 020-026. doi:10.24911/JBCGenetics.11-2222
Harvard Style
Camila Sinimbú Forte, Amanda Ferreira Vidal, Pablo Diego do Carmo Pinto, Gilderlanio Santana de Araújo (2025) CFTR interactome may impact gastric cancer: an in silico system-level coexpression analysis. JBC Genetics, 24 (June 2025): 020-026. doi:10.24911/JBCGenetics.11-2222
Chicago Style
Camila Sinimbú Forte, Amanda Ferreira Vidal, Pablo Diego do Carmo Pinto, Gilderlanio Santana de Araújo. "CFTR interactome may impact gastric cancer: an in silico system-level coexpression analysis." 24 (2025), 020-026. doi:10.24911/JBCGenetics.11-2222
MLA (The Modern Language Association) Style
Camila Sinimbú Forte, Amanda Ferreira Vidal, Pablo Diego do Carmo Pinto, Gilderlanio Santana de Araújo. "CFTR interactome may impact gastric cancer: an in silico system-level coexpression analysis." 24.June 2025 (2025), 020-026. Print. doi:10.24911/JBCGenetics.11-2222
APA (American Psychological Association) Style
Camila Sinimbú Forte, Amanda Ferreira Vidal, Pablo Diego do Carmo Pinto, Gilderlanio Santana de Araújo (2025) CFTR interactome may impact gastric cancer: an in silico system-level coexpression analysis. , 24 (June 2025), 020-026. doi:10.24911/JBCGenetics.11-2222
Camila Sinimbú Forte et al. JBC Genetics. 2025;8(1):020-026
Journal of Biochemical and Clinical Genetics
CFTR interactome may impact gastric cancer: an in silico system-level co-expression analysis
Camila Sinimbú Forte1, Amanda Ferreira Vidal2, Pablo Diego do Carmo Pinto3, Gilderlanio Santana de Araújo4*
Correspondence to: Gilderlanio Santana de Araújo
*Institute of Biological Sciences, Federal University of Pará, Belém, Brazil.
Email: gilderlanio@ufpa.br
Full list of author information is available at the end of the article.
Received: 18 May 2025 | Revised: 09 June 2025 | Accepted: 21 June 2025
ABSTRACT
Background:
The cystic fibrosis transmembrane conductance regulator (CFTR) dysfunction is linked to gastrointestinal inflammation and has been implicated in early-onset malignancies. However, its role in gastric cancer remains poorly understood.
Aims:
To investigate the CFTR interactome and assess its potential functional involvement across different subtypes of gastric cancer.
Methods:
We conducted a system-level in silico analysis using data from the Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD). CFTR expression and co-expression profiles were examined across molecular and histological subtypes of gastric cancer, including signet-ring cell carcinoma (Lauren’s classification). Differential gene expression (DGE) and co-expression analyses were integrated with protein-protein interaction networks, pathway enrichment, and gene ontology (GO) analysis to delineate CFTR’s functional associations.
Results:
CFTR did not exhibit significant differential expression across gastric cancer subtypes. However, co-expression analysis identified CFTR as a key hub gene with a distinct interaction network, especially prominent in the signet-ring cell carcinoma subtype. Enrichment analyses revealed that CFTR’s interactome is involved in regulatory pathways related to cellular homeostasis, ion transport, and immune modulation, suggesting a noncanonical yet critical role in tumor biology.
Conclusion:
While CFTR expression remains stable across gastric cancer subtypes, its interactome reveals significant regulatory roles, particularly in signet-ring cell carcinoma. These findings highlight the potential contribution of CFTR to gastric cancer pathogenesis through its involvement in broader molecular networks rather than through expression changes alone.
Keywords:
Gene co-expression network, CFTR, gastric cancer subtypes, signet-ring cell carcinoma.
Introduction
In 2022, gastric cancer (GC) held the fifth position globally in terms of incidence and mortality [1]. Early detection and treatment of GC remains limited by our understanding of the molecular mechanisms that drives its heterogeneity [2,3]. Emerging evidence suggests a potential role for the cystic fibrosis transmembrane conductance regulator (CFTR) gene, in the development of various cancers, including GC [4-8]. Moreover, the precise biological role of CFTR in GC remains unclear. CFTR mutations, particularly the ΔF508 mutation, have been associated with an increased risk of developing GC [9,10]. Than et al. experimentally identified CFTR as a tumor suppressor gene in the intestinal tract; otherwise, its knockout caused high rates of tumors in both colon and small intestine in human and murine models [11]. Collobert et al. emphasize the importance of understanding the cis-regulatory elements that control the expression of the CFTR gene, as this knowledge could lead to novel therapeutic strategies aimed at modulating the activity of CFTR in GC [12].
Previous research has predominantly explored the effects of the CFTR gene in isolation, with limited attention to its system-level co-expression patterns or interactome in the context of GC subtypes. Here, we investigated the differential expression of CFTR across distinct GC subtypes, as well as its co-expression profiles and interaction networks within clinical classifications. Using systems biology approaches and comprehensive co-expression analysis, we identified distinct gene modules where CFTR emerges as a central interactor, providing novel insights into its functional relevance within the molecular architecture of GC.
Figure 1. CEMiTool gene co-expression results. A) Gene co-expression module detection for each TCGA-STAD subtype. The red circle with dot lines represents the module where the CFTR was found. B) Visualization of Module 1 in the signet ring cell carcinoma (SRCC-M1) subtype. C) Over-representation pathway in Module 1. D) Gene Ontology enrichment analysis of CFTR co-expressed genes in Module 1 for the SRCC subtype.
Methods and Results
Molecular heterogeneity in TCGA-STAD subtypes
We analyzed the differential expression of 19,932 transcripts across the six TCGA-STAD subtypes, and in Lauren’s clinical classification (Material and Methods in Supplementary Material and Supplementary Tables 1, 2). We observed that CFTR is not differentially expressed between the TCGA-STAD subtypes or even in Lauren subtypes (Supplementary Table 3).
CFTR-interactome is co-expressed in signet ring cell carcinoma in the TCGA-STAD classification
Co-expressed gene modules were identified for each TCGA-STAD subtype comparison (Figure 1). Notably, CFTR was identified as being co-expressed exclusively in Module 1 of the SRCC subtype (SRCC-M1), which comprises 123 genes (Figure 1A). CFTR is also classified as a hub gene and is co-expressed with 58 genes in the interaction network, supported by experimental studies cataloged for constructing the PPI network (Figure 1B). Over-representation pathway results for the SRCC-M1 module revealed statistical enrichment for pathways associated with glycosylation and protein modification defects, including the termination of O-glycan biosynthesis, dectin-2 family signaling, O-linked glycosylation of mucins, and diseases associated with protein glycosylation (Figure 1C). Biological processes enriched in the SRCC-M1 module are strongly linked to protein quality control, including responses to unfolded and topologically incorrect proteins. Molecular functions enriched in this module include protein-folding chaperone binding, unfolded protein binding, ion channel, and transporter inhibition. Furthermore, cellular component analysis identified enrichment in structures involved in vesicular trafficking (Figure 1D.1-D.3).
Figure 2. CEMiTool gene co-expression results for Lauren’s clinical classification. A) Module 11 in the intestinal type. B) Over-representation pathway analysis in Module 11 from Intestinal Type (IT). C) Module 10 in the Diffuse Type (DT). D) Over-representation pathway analysis in Module 10 from Diffuse Type (DT).
CFTR-interactome in Lauren’s classification for gastric cancer
CFTR is found co-expressed in module 11 of the intestinal type (IT-M11), which hosts 328 genes (Figure 2A). In the diffuse type, CFTR is found in module 10 (DT-M10) that comprises 557 genes (Figure 2C). The CFTR is co-expressed with 58 genes in both IT-M11 and DT-M10, similar to SRCC-M1 (Supplementary Table 4). As a result, the findings from the gene ontology analyses showed the same pathways (Figure 2B and 2D). ORA results for the DT-M10 module include cell signaling, cell-cell communication, junction and organization, and glycosylation processes.
Discussions
Single mutations and protein dysfunction of CFTR have been pointed to be a risk factor for gastrointestinal cancers [6-8, 10]. At the system level, we examined the differential expression and co-expression profiles of CFTR across TCGA-STAD subtypes and based on Lauren’s classification for GC. We identified a distinct gene module associated with CFTR in SRCC samples, which are largely encompassed within the DT type in Lauren’s classification. The CFTR-interactome includes 58 co-expressed genes with experimentally validated interactions. Notably, these genes exhibit consistent co-expression with CFTR across both the IT and DT subtypes. While IT and DT differ histologically, the convergence of CFTR-associated gene activity in both suggests a potential role of shared regulatory mechanisms rather than merely overlapping interactors. This convergence may contribute to reduced transcriptomic heterogeneity and positions CFTR as a promising therapeutic target across subtypes. However, we note that while Lauren’s classification supports this distinction, such subtype-specific observations were not conclusively established in the STAD-TCGA dataset, underscoring the need for further validation.
Pathway analysis and gene ontology revealed several pathways enrichment, biological processes, and molecular functions for SRCC-M1. Functional enrichment was prominent for glycosylation-related processes in GC. Glycosylation is essential for the proper CFTR protein maturation and localization, stabilizing it on the plasma membrane, regulating endocytosis rates, and is vital for preserving channel function over time [13]. Aberrant glycosylation contributes to tumor development by disrupting cell signaling, enabling immune evasion, and causing atypical glycan expression in normal tissues [14-16].
Biological processes related to the response to misfolded proteins and the regulation of protein folding are closely linked to cancer. When CFTR fails to fold correctly, as in the case of the common ΔF508 mutation, it accumulates in the endoplasmic reticulum, causing stress to this cellular compartment. This condition activates the unfolded protein response (UPR), a mechanism to restore cellular homeostasis. In cancer cells, the UPR can support cell survival under stress but may also trigger apoptosis if the stress becomes excessive [17]. Cancer cells often exploit this pathway to evade programmed cell death, enabling uncontrolled proliferation. Additionally, cancer cells frequently exhibit alterations in various signaling pathways that regulate the cell cycle and apoptosis, such as the PI3K-Akt pathway [18, 19]. CFTR dysfunction can modulate the activity of signaling pathways such as PI3K-Akt and MAPK, which are involved in cell proliferation and survival [9, 19] and may interact in cell cycle regulation and apoptosis [8].
Cellular component enrichment analysis showed an enrichment of structures involved in vesicular trafficking in the SRCC. This finding is consistent with the single-cell analysis by Zhao et al., which showed that SRCC cells exhibit decreased cell adhesion, which may facilitate metastasis [3]. A key feature of DT is the loss of cell adhesion, facilitating the migration and invasion of cancer cells into adjacent tissues [20]. Cell signaling pathways were prominent in the DT-M11 module, reflecting uncontrolled proliferation and apoptosis evasion, which drive oncogenesis through hyperactivation of pro-tumor signaling [21].
Although CFTR expression remains stable across GC subtypes, its interactome demonstrates significant regulatory activity, particularly in SRCC. These findings indicate that CFTR may influence tumor behavior through its integration in key molecular pathways, independent of expression levels. This suggests a potential role for CFTR as a functional biomarker or therapeutic target, especially in subtypes with limited prognostic or treatment options, supporting its clinical relevance in GC stratification and management.
Our study is based entirely on in silico analyses, which, while powerful for generating hypotheses, cannot fully capture the complexity of in vivo biological systems. TCGA datasets may introduce potential biases due to sample representation across cancer subtypes. These factors may affect robustness of our findings. Additionally, we acknowledge the experimental validation to confirm the functional significance of the predicted CFTR interactions and their downstream effects.
Conclusion
We found that CFTR-interactome may play a pivotal role in tumor biology by influencing the activity of co-expressed genes in signet ring cell carcinoma, and in Lauren’s subtype classification, DT and IT types. These findings suggest that CFTR and its interactions contributes to cancer progression at the system level, highlighting its potential as a key modulator of pathway dynamics in GC.
List of abbreviations
AC-NOS | Adenocarcinoma not otherwise specified |
DC | Diffuse type carcinoma |
DT | Diffuse type |
GC | Gastric cancer |
GCN | Gene co-expression network |
IA | Intestinal adenocarcinoma |
IT | Intestinal type |
MA | Mucinous adenocarcinoma |
SRCC | Signet ring cell carcinoma |
TA | Tubular adenocarcinoma |
UPR | Unfolded protein response |
Acknowledgment
We gratefully acknowledge the support of the scientific initiation scholarship PRO6982-2023, funded by UFPA/FAPESPA (00000.9.001490/2023).
Conflict of interests
The authors of this article have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.
Funding
None.
Consent for participation
Not applicable. Next-generation sequencing data for TCGA-STAD is publicly available.
Ethical approval
Not applicable.
Author contributions
All authors have read and agreed to the published version of the manuscript. Camila Sinimbú Forte: Software, Formal analysis, Investigation, Methodology, Writing - Original draft preparation; Amanda Ferreira Vidal: Writing - Review and Editing; Pablo Diego do Carmo Pinto: Writing - Review and Editing; Gilderlanio Santana de Araújo: Conceptualization, Formal analysis, Methodology, Writing - Original draft, Writing - Review and Editing, Supervision and Project administration.
Data availability
All next-generation sequencing data can be downloaded TCGABiolink (https://bioconductor.org/packages/release/bioc/html/TCGAbiolinks.html).
Author details
Camila Sinimbú Forte1, Amanda Ferreira Vidal2, Pablo Diego do Carmo Pinto3, Gilderlanio Santana de Araújo4
- Institute of Technology, Federal University of Pará, Belém, Brazil
- Vale Institute of Technology, Belém, Brazil.
- Institute of Medical Sciences, Federal University of Pará, Belém, Brazil
- Institute of Biological Sciences, Federal University of Pará, Belém, Brazil
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Supplementary Material
Supplementary Table 1. Sample distribution by gastric cancer subtype, gender, and therapy.
Samples (N) | ||
AC-NOS | 154 | |
IA | 79 | |
Stomach Adenocarcinoma Subtype | TA | 73 |
DC | 66 | |
MA | 17 | |
SRCC | 13 | |
Gender | Female | 146 |
Male | 264 | |
Pharmaceutic therapy | No | 180 |
Not reported | 43 | |
Yes | 187 | |
No | 293 | |
Radiation therapy | Not reported | 39 |
Yes | 78 |
Supplementary Table 2. Number of differential expressed genes between gastric cancer subtypes.
GC subtypes | Downregulated | Upregulated |
---|---|---|
IA x AC-NOS | 269 | 323 |
IA x MA | 803 | 1503 |
IA x SRCC | 28 | 274 |
IA x TA | 66 | 52 |
AC-NOS x MA | 181 | 621 |
AC-NOS x SRCC | 5 | 65 |
AC-NOS x TA | 1941 | 1221 |
DC x IA | 2449 | 2754 |
DC x AC-NOS | 742 | 1097 |
DC x MA | 1 | 23 |
DC x SRCC | 0 | 9 |
DC x TA | 2715 | 3040 |
SRCC x MA | 0 | 0 |
SRCC x TA | 347 | 20 |
TA x MA | 1535 | 1035 |
Supplementary Table 3. CFTR differential expression across cancer subtypes.
GC subtype comparison | logFC | logCPM | LR | PValue | FDR |
---|---|---|---|---|---|
IA x AC-NOS | −0.409398844 | 5.897554506 | 2.659509444 | 0.102932475 | 0.395321944 |
IA x MA | −0.630251909 | 6.093556777 | 1.657396598 | 0.197955197 | 0.448318444 |
IA x SRCC | −0.839613295 | 6.120387237 | 2.113141998 | 0.1460392 | 0.536723791 |
IA x TA | 0.157495399 | 6.22094543 | 0.282459633 | 0.595093599 | 0.910663028 |
AC-NOS x MA | −0.047149198 | 5.695396682 | 0.009438367 | 0.922606275 | 0.982430555 |
AC-NOS x SRCC | −0.533033943 | 5.708331706 | 0.947719736 | 0.330300399 | 0.866940061 |
AC-NOS x TA | 0.678384281 | 5.913692851 | 6.918452271 | 0.008531078 | 0.052643123 |
DC x IA | −0.369814438 | 5.994617951 | 1.403934055 | 0.236065991 | 0.413052745 |
DC x AC-NOS | 0.176246766 | 5.711767194 | 0.405116084 | 0.524458856 | 0.751540233 |
DC x MA | −0.113897304 | 5.684288315 | 0.045212479 | 0.831613805 | 0.999160178 |
DC x SRCC | −0.388583245 | 5.697880848 | 0.388989139 | 0.532831232 | 1 |
DC x TA | 0.541209778 | 6.020233795 | 3.079380275 | 0.079290733 | 0.186264388 |
SRCC x MA | −0.248120812 | 5.563251832 | 0.083362685 | 0.772791088 | 0.999985068 |
SRCC x TA | 0.681746318 | 6.175103141 | 1.456056929 | 0.227558548 | 0.698077473 |
TA x MA | 0.780575159 | 6.137801685 | 2.315979099 | 0.128050649 | 0.353607609 |
IT x DT | −0.4210756 | 6.028201 | 1.924078 | 0.1654078 | 0.3249337 |
Supplementary Table 4. CFTR gene network by CEMiTool.
# | GENE 1 | GENE 2 |
---|---|---|
1 | CFTR | COMMD1 |
2 | CFTR | HSPA8 |
3 | CFTR | MARCH2 |
4 | CFTR | PDZK1 |
5 | CFTR | PRKAA1 |
6 | CFTR | SLC9A3R1 |
7 | CFTR | STUB1 |
8 | CFTR | STX1A |
9 | CFTR | VIMP |
10 | CFTR | EZR |
11 | CFTR | HSP90AA1 |
12 | CFTR | ABCC11 |
13 | CFTR | AHSA1 |
14 | CFTR | AMFR |
15 | CFTR | CALM2 |
16 | CFTR | CAC-NOSX |
17 | CFTR | CAP1 |
18 | CFTR | CAPZB |
19 | CFTR | CLIC1 |
20 | CFTR | COPG1 |
21 | CFTR | DAB2 |
22 | CFTR | DERL1 |
23 | CFTR | DNAJA1 |
24 | CFTR | DNAJB1 |
25 | CFTR | DNAJC5 |
26 | CFTR | DRG1 |
27 | CFTR | DSTN |
28 | CFTR | FHL2 |
29 | CFTR | GLTSCR2 |
30 | CFTR | GNAS |
31 | CFTR | GNB2 |
32 | CFTR | GOPC |
33 | CFTR | HSPA4 |
34 | CFTR | HSPB1 |
35 | CFTR | HSPD1 |
36 | CFTR | KRT13 |
37 | CFTR | KRT31 |
38 | CFTR | MCCC2 |
39 | CFTR | MYO6 |
40 | CFTR | NRIP3 |
41 | CFTR | PDIA3 |
42 | CFTR | PSMD4 |
43 | CFTR | RAB5A |
44 | CFTR | SDHA |
45 | CFTR | SLC9A3R2 |
46 | CFTR | SNAP23 |
47 | CFTR | SǪSTM1 |
48 | CFTR | STAU1 |
49 | CFTR | TFG |
50 | CFTR | TMEM40 |
51 | CFTR | TRAFD1 |
52 | CFTR | TRIM5 |
53 | CFTR | USP10 |
54 | CFTR | VAPA |
55 | CFTR | VAPB |
56 | CFTR | VCP |
57 | CFTR | VDAC2 |
58 | CFTR | VPS4A |