CTGF, FN1, IL-6, THBS1, and WISP1 genes and PI3K-Akt signaling pathway as prognostic and therapeutic targets in gastric cancer identified by gene network modeling

doi:10.21203/rs.3.rs-3339996/v1

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CTGF, FN1, IL-6, THBS1, and WISP1 genes and PI3K-Akt signaling pathway as prognostic and therapeutic targets in gastric cancer identified by gene network modeling

https://doi.org/10.21203/rs.3.rs-3339996/v1

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Background: Gastric cancer (GC) is one of the most common malignancies worldwide and it is considered as the fourth most common cause of cancer death. This study aimed to find critical genes/pathways in GC pathogenesis and therefore, discovery of drugs can be helpful in this case.

Methods: Differentially expressed genes were explored between human gastric cancer and noncancerous tissues in Gene Expression Omnibus dataset (GSE54129) using the GEO2R tool, and their functions were annotated with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analyses in the STRING database. Hub genes were identified based on the protein-protein interaction network constructed in the STRING database with Cytoscape software. The hub genes were selected for further analysis, and their expression patterns in gastric cancer patients were investigated using the GEPIA2 database.

Results: Ten overexpressed hub genes in GC were identified in the current study, including FN1, TP53, IL-6, CXCL5, ELN, ADAMTS2, WISP1, MMP2, CTGF, and THBS1. The study demonstrated the PI3K-Akt pathway's central involvement in GC, with pronounced alterations in essential components. Survival analysis revealed significant correlations between CTGF, FN1, IL-6, THBS1, and WISP1 overexpression and reduced overall survival times in GC patients. A mutual interplay emerged, where PI3K-Akt signaling could upregulate certain genes, forming feedback loops and intensifying cancer phenotypes.

Conclusions: The interconnected overexpression of genes and the PI3K-Akt pathway fosters gastric tumorigenesis, suggesting therapeutic potential. DrugBank analysis identified limited FDA-approved drugs, advocating for further exploration while targeting these hub genes could reshape GC treatment. These results might open up new insights into GC pathogenesis. The identified genes might be novel diagnostic/prognostic biomarkers or potential therapeutic targets for GC. This work, being based on bioinformatics analysis acts as a hypothesis generator that requires further clinical validation.

Biological networks

Biomarker

Diagnostic target

Differentially expressed genes

Gastric cancer

Gene ontology

Kyoto Encyclopedia of Genes and Genomes

Prognostic target

Protein-protein interactions

Therapeutic target

With a poor prognosis, gastric cancer (GC) is the fourth most frequent malignancy worldwide and the fifth most common cause of cancer-related mortality (1). The relationship between various risk factors and the development of GC has been looked into in several research (2), but the precise molecular network processes have not yet been thoroughly introduced. Understanding the molecular pathways behind GC development may therefore aid in identifying potential targets for early identification and categorization, extending patient survival..

The complex process of carcinogenesis involves numerous genetic and epigenetic changes (3). With 95% of cases being adenocarcinoma, this disease is the most prevalent kind of GC (4). Numerous studies on the epigenetic and genetic changes in various types of GC have focused on changes in oncogenes, tumor suppressor genes, DNA repair genes, and cell cycle regulators (5). In order to better understand the molecular pathophysiological mechanisms behind carcinogenesis and, consequently, to create new therapeutic approaches, it may be helpful to investigate the genes that are dysregulated in various pathways. Detection and categorization of cancer, patient response to treatment, and disease prognosis are only a few of the clinical uses of gene and network analysis employing high-throughput techniques (6, 7). Precision and personalized medicine can benefit from using systems biology and systems pharmacology techniques to better understand mechanisms and identify gene signatures (8). Using these kinds of systems-level insights, it is possible to create in silico models of biological circuits that control the responses of cells and tumors that are more precise (9, 10).

Numerous studies have been conducted on the altered genes in GC (11–14). Nevertheless, they are insufficient to paint a clear picture of the molecular etiology of GC (15). The public functional genomics data repository Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/) is one of the most useful databases for gene expression information. The array- and sequence-based data are registered in this database. The experiments can be downloaded, and gene expression profiles can be curated (16). With a specific focus on potential gene hubs that may play a role in gastric carcinogenesis, the current study examines one of the GEO datasets of GC using bioinformatics tools to identify the genes involved in the pathogenesis of GC and investigates key Gene Ontology (GO) terms, KEGG pathways, and protein-protein interaction (PPI) networks.

Dataset

The entire GEO database, including 848 series of human gastric cancer, was screened for relevant datasets based on the following criteria: (1) data based on expression profiling by microarray, (2) gastric cancer-related project, (3) analysis of gastric cancerous and normal tissues, (4) at least two samples analyzed, and (5) supplementary filter: CEL. After precise screening, the GSE54129 dataset was selected as the database of choice. A total of 132 specimens, including 111 gastric tumor tissue and 21 gastric normal tissue were available based on the HG-U133 plus 2.0 Platform. All of the samples were surgically resected from patients with gastric cancer.

Screening of differentially expressed genes (DEGs)

The GEO2R analysis tool was used to screen the differentially expressed genes between gastric cancerous and normal tissues in the GSE54129 dataset.

In order to provide immediate, Web-based, and user-driven analysis for GEO data, GEO2R was developed. GEO2R is a web-based tool that enables the analysis of GEO Series (16). It uses a Web-based program that employs the Bioconductor packages (17), GEOQuery (18) and limma in R (19), with the Benjamini-Hochberg false-discovery rate method (20) for multiple-testing correction as its default method. Genes with differences in expression levels between tissue types at adjusted p-value < 0.05 and log fold-change (LogFC) between − 1.5 and 1.5 were considered as differentially expressed genes. For further analysis of the overexpressed genes, LogFC was set to > 1.5.

Protein-protein interaction network construction and selection of hub genes

STRING is a database of predicted and known interactions of proteins. The interactions among the significantly overexpressed genes were analyzed in the web-based application of STRING version 11.5 (http://string-db.org) (21) with a minimum required interaction score of 0.15 (low confidence), and the PPI list was prepared. Subsequently, the PPI list was imported into Cytoscape (version 3.9.1), an open-source platform for the visualization of molecular interaction networks and data integration. CytoHubba software (version 0.1) was used to identify hub proteins between all nodes (22). Four topological analysis methods, including MCC, Degree, DMNC, and MNC, were used to evaluate the significance of the nodes in the biological network. Four high-ranked proteins were introduced as the hub nodes from the analysis by each method. Subsequently, the subnetwork showing the interactions among the hub nodes was drawn using the CytoHubba plugin.

Gene Ontology and pathway enrichment analysis of the hub genes

The enrichment analysis of subnetwork genes of the hub genes was done based on the KEGG and GO, including molecular function (MF), cellular components (CC), and biological process (BP) at the web-based application of STRING (23).

Cluster analysis of the network

CytoCluster (version 2.1.0) was used for the clustering of network nodes. The IPCA (Identifying Protein Complex Algorithm) algorithm was used for cluster analysis of the subnetwork. This algorithm is density-based, which identifies dense subgraphs in protein interaction networks. IPCA determines the weight of an edge by calculating the common neighbors of its connected nodes and calculates each node’s weight through a sum up of the weights of its incident edges. (24). The threshold was set to 0.8, and the genes of each cluster were further analyzed in STRING version 10 (23) to find the KEGG pathways in which these genes are involved.

Survival Analysis of the Hub Genes

To identify the prognostic value of the ten hub genes, the Kaplan-Meier overall survival curves of patients with gastric cancer based on differential expression of the ten hub genes were extracted from the Gene Expression Profiling Interactive Analysis (GEPIA2) database (http://gepia2.cancer-pku.cn/#index).

Drug bank

Five hub genes associated with decreased overall survival times of GC patients (p-value < 0.05) were searched in Drug Bank (https://go.drugbank.com/) to find the approved drugs that target these genes.

Screening of differentially expressed genes (DEGs)

There were two groups in this study, gastric tumor and normal tissues. GEO2R online analysis tool with default parameters was used to screen the DEGs, using adjusted p-value < 0.05 and |logFC| > 1.5. Figure 1 shows the results of GEO2R analysis on the selected dataset including the Volcano plot, Venn diagram, UMAP plot and Boxplot of DEGs between human gastric cancer tissues and noncancerous gastric tissues. A total of 2005 DEGs, including 1035 up-regulated and 970 down-regulated genes were identified in this study. To find drug targets, further analysis was done on the up-regulated genes.

Reconstruction of genes and PPI networks and the hub analysis

In the present study, the overexpressed genes were used for further analysis using STRING and Cytoscape. The reconstructed gene and PPI networks of the hub genes are shown in Fig. 2. The hub analysis resulted in the identification of 10 genes with the most interactions (Table 1).

Table 1

List of hub genes identified by using *CytoHubba*. *CytoHubba* software (version 0.1) was used to draw the PPI network and identify hub proteins between all nodes. The hub analysis resulted in the identification of 11 genes with the most interactions.
#	Node	Gene ID	Ensembl	Gene Description	Rank	Ranking Method	Other names	Location
1	FN1	498	ENSG00000152234	ATP synthase F1 subunit alpha	1, 3, 3	MCC, MNC, Degree	FN; CIG; FNZ; MSF; ED-B; FINC; GFND; LETS; GFND2; SMDCF	2q35
2	IL-6	506	ENSG00000110955	ATP synthase F1 subunit beta	4,4	Degree MNC	CDF; HGF; HSF; BSF2; IL-6; BSF-2; IFNB2; IFN-beta-2	7p15.3
3	TP53	7157	ENSG00000141510	tumor protein p53	2,2	MNC Degree	P53; BCC7; LFS1; BMFS5; TRP53	17p13.1
4	ACTB	60	ENSG00000075624	actin beta	1,1	MNC Degree	- BRWS1; PS1TP5BP1	7p22.1
5	CXCL5	6374	ENSG00000163735	C-X-C motif chemokine ligand 5	4	DMNC	SCYB5; ENA-78	4q13.3
6	ELN	2006	ENSG00000049540	Elastin	2	DMNC	ATPase8; MTATP8; ATP8	7q11.23
7	ADAMTS2	9509	ENSG00000087116	ADAM metallopeptidase with thrombospondin type 1 motif 2	3	DMNC	MTND4; ND4	5q35.3
8	WISP1	8840	ENSG00000104415	Cellular communication network factor 4	1	DMNC	WISP1; WISP1c; WISP1i; WISP1tc; WISP1-OT1; WISP1-UT1 CCN4	8q24.22
9	CTGF	1490	ENSG00000118523	Cellular communication network factor 2	4	MCC	CCN2, CTGF; NOV2; HCS24; IGFBP8	6q23.2
10	THBS1	7057	ENSG00000137801	thrombospondin 1	3	MCC	TSP; THBS; TSP1; TSP-1; THBS-1	15q14

An analysis of gene ontology and pathways enriched by specific genes

GO analysis is a well-known method for gene and gene products and identification of typical biological aspects of high-throughput genome or transcriptome data, including molecular function, cellular components, and biological process [21, 22]. The results of GO analysis and pathway enrichment are illustrated in Figs. 3 to 6.

The GO terms that are commonly observed in at least 50% of genes for BP are highly enriched in various areas, including cellular and biological processes, response to stimuli, organismal and anatomical development, regulation of metabolic processes, and cell communication. The details of this enrichment can be seen in Fig. 3.

From the analysis, I found that the most common GO terms were enriched in various regions such as Cellular anatomical entity, Intracellular, Organelle, Membrane-bounded organelle, and Cytoplasm. Additionally, they were also found in the Extracellular region, Cell periphery, Plasma membrane, Vesicle, and Endomembrane system among others. This information is presented in Fig. 4.

The predominant (> 90 observed genes) GO terms found for MF are significantly enriched in Binding, Ion binding, Protein binding, Cation binding, Metal ion binding, Molecular function regulator, Anion binding, Signaling receptor binding, Carbohydrate derivative binding, Identical protein binding, Protein-containing complex binding, and Enzyme binding (Fig. 5).

KEGG(http://www.genome.jp/) is a database for allocating certain pathways to groups of DEGs, therefore, linking omics data with higher-order functional data [23]. Based on KEGG(an abbreviation for Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis (Fig. 4), the prominent (≥ 20 observed genes) pathways are PI3K-Akt Focal adhesion, Pathways in cancer, signaling pathway, and Human papillomavirus infection. Figure 6 shows the most enriched pathways (≥ 15 observed genes) identified by KEGG analysis.

Cluster analysis of the network

One of the most important strategies for identifying the functional modules and predicting network biomarkers and complexes of proteins is cluster analysis of the biological networks. In addition, when the results of clustering are visualized, the biological network structure can be displayed. CytoCluster, which is used in this study, includes six clustering algorithms. The selection of the clustering algorithm depends on the user requirements (24). The IPCA algorithm used in this study is density-based, identifying dense subgraphs in protein interaction networks. IPCA determines the weight of an edge by calculating the common neighbors of its connected nodes and calculates each node’s weight through a sum up of the weights of its incident edges. A node needs to have a higher weight to be regarded as a seed. First, a seed is considered a cluster. IPCA then extends a cluster by adding vertices recursively from its neighbor regarding the nodes’ priority. Adding a node to a cluster depends on two parameters: 1) the probability of its interaction and 2) the shortest path between it and the nodes in the cluster (24). Two-hundreds and seventeen clusters were obtained in the cluster analysis of the subnetwork. Clusters with ranks 1 to 4 were selected to be discussed in this paper (Figs. 7 to 10).

As shown in Table 2. Summary of the clusters (rank 1 to 4) resulted from the cluster analysis of the subnetwork of the overexpressed genes in Gastric Cancer plus their known neighbors using the CytoCluster App., the common pathways among all four cluster ranks include PI3K-Akt signaling pathway, Focal adhesion, Protein digestion and absorption, Proteoglycans in cancer, Human papillomavirus infection, Pathways in cancer, ECM-receptor interaction, AGE-RAGE signaling pathway in diabetic complications, Relaxin signaling pathway, and MicroRNAs in cancer.

Table 2

Summary of the clusters (rank 1 to 4) resulted from the cluster analysis of the subnetwork of the overexpressed genes in Gastric Cancer plus their known neighbors using the CytoCluster App.
Cluster Rank	Nodes	Edges	Pathways
1	88	3425	PI3K-Akt signaling pathway Focal adhesion Protein digestion and absorption Proteoglycans in cancer Human papillomavirus infection Pathways in cancer ECM-receptor interaction AGE-RAGE signaling pathway in diabetic complications Relaxin signaling pathway Amoebiasis MicroRNAs in cancer
2	87	3330	PI3K-Akt signaling pathway Focal adhesion Proteoglycans in cancer Human papillomavirus infection Protein digestion and absorption ECM-receptor interaction Pathways in cancer AGE-RAGE signaling pathway in diabetic complications Relaxin signaling pathway MicroRNAs in cancer
3	80	2841	PI3K-Akt signaling pathway Pathways in cancer Focal adhesion Proteoglycans in cancer Human papillomavirus infection AGE-RAGE signaling pathway in diabetic complications ECM-receptor interaction MicroRNAs in cancer Relaxin signaling pathway Protein digestion and absorption Amoebiasis MAPK signaling pathway
4	80	2896	PI3K-Akt signaling pathway Focal adhesion Human papillomavirus infection ECM-receptor interaction Proteoglycans in cancer Protein digestion and absorption Pathways in cancer AGE-RAGE signaling pathway in diabetic complications Relaxin signaling pathway MicroRNAs in cancer

Survival Analysis of the Hub Genes

The Kaplan-Meier overall survival curves of patients with gastric cancer are shown in Fig. 11. These curves indicate that overexpression of CTGF, FN1, IL-6, THBS1 and WISP1 are associated with decreased overall survival times of GC patients (p-value < 0.05).

Drug bank

Five hub genes (CTGF, FN1, IL-6, THBS1 and WISP1) significantly associated with decreased overall survival times of GC patients (p-value < 0.05) were searched in DrugBank (https://go.drugbank.com/) to find the drugs that target these genes. A list of the drugs that target these genes is provided in Table 3.

Table 3

Approved drugs that target the five hub genes significantly associated with decreased survival times in GC patients based on Drugbank data.
Hub gene	Drug Name	Drug Group	Drug Category	Drug Targets	Drug background
FN1	Ocriplasmin (Jetrea®)	Approved	Serine Endopeptidases	Fibronectin, Alpha-2-macroglobulin, Alpha-2-antiplasmin	It is a thrombolytic agent (proteolytic enzyme) and a truncated form of human plasmin produced by rDNA technology. It is indicated for the treatment of symptomatic vitreomacular adhesion.
IL-6	Foreskin fibroblast (neonatal) (Gintuit®)	Approved	Allogeneic Cultured Cell Scaffold	VEGFR-1, IL-6, IL1R1, TGFBR2, FGF1, IFN-γ, FGFR2, GM-CSF subunit alpha, PDGFR-β, TGFβ-1, TNF	It is a treatment of cells used to treat full-thickness, diabetic foot ulcers that extend through the dermis without tendon, muscle, joint capsule, or bone exposure.
IL-6	Foreskin keratinocyte (neonatal) (Gintuit®)	Approved	Allogeneic Cultured Cell Scaffold	FGF1, EGFR, GM-CSF subunit alpha, IL-1β, IL-6, IFN-γ, PDGFRA , TGFBR2, TGFβ-1, TNF, VEGF-A, FGFR2	It is a treatment of skin cells used to accelerate wound closure and healing.
IL-6	Binimetinib (Mektovi®)	Approved, investigational	Antineoplastic and Immunomodulating Agents, Kinase Inhibitor	MAPKK kinase 1, MAP2K2, IL-6, TNF, IL-1β	Binimetinib, also known as Mektovi, is a potent and selective oral mitogen-activated protein kinase 1/2 (MEK 1/2) inhibitor that is combined with Encorafenib 4,8. On June 27, 2018, the Food and Drug Administration approved the combination of Encorafenib and binimetinib (BRAFTOVI and MEKTOVI, from Array BioPharma Inc.) in combination for patients with unresectable or metastatic melanoma with the BRAF V600E or V600K mutations, as detected by an FDA-approved test.8
IL-6	Siltuximab (Sylvant®)	Approved, investigational	Antineoplastic and Immunomodulating Agents, Agents reducing cytokine levels	IL-6	Siltuximab is an interleukin antagonist used to treat multicentric Castleman's disease (MCD) in patients who are HIV and HHV-8 negative.
VEGFR-1: Vascular endothelial growth factor receptor 1; IL-6: Interleukin-6; IL1R1: Interleukin 1 receptor, type I; TGFBR2: transforming growth factor beta receptor 2, FGF1: Fibroblast growth factor 1; IFN-γ: Interferon gamma; FGFR2: Fibroblast growth factor receptor 2; GM-CSF: Granulocyte-macrophage colony-stimulating factor receptor subunit alpha; PDGFR-β: platelet-derived growth factor receptor-β; TGFβ-1: transforming growth factor beta-1; TNF: Tumor necrosis factor; EGFR: Epidermal growth factor receptor; IL-1β: Interleukin-1 beta; PDGFRA: Platelet-derived growth factor receptor alpha; VEGF-A: Vascular endothelial growth factor A; MAPKK: Mitogen-activated protein kinase kinase; MAP2K2: Dual specificity mitogen-activated protein kinase kinase 2

Currently, the FDA has not authorized any medications, targeting CTGF, THBS1, or WISP1. However, as shown in Table 3, a few approved drugs do target FN1 and IL-6, such as Ocriplasmin (targeting FN1), and Foreskin fibroblast, Binimetinib, and Siltuximab (targeting IL-6). Among these approved drugs, only Siltuximab has a single target (IL-6), while the other drugs have multiple targets.

In recent years, different investigations in the area of multi-center genomics research from gene to systems level and next-generation sequencing, clarified the various mechanisms specifically involved in tumor progression. This study used bioinformatics analysis to further investigate the genes involved in GC pathogenesis (25).

Ten hub genes were identified in this study, including FN1, CTGF, CXCL5, IL6, ELN, ADAMTS2, MMP2, TP53, WISP1 and THBS1. The relationship between the overexpression of these genes and gastric cancer is an area of active scientific investigation. These genes have been implicated in various molecular and cellular pathways relevant to cancer initiation, progression, and metastasis. Overexpression of FN1, a key component of the extracellular matrix, may promote tumor cell adhesion, migration, and invasion, facilitating the metastatic potential of cancer cells (26) and may have the same roles in GC. Sun et al. did the same bioinformatics analysis and reported FN1 as one of the key genes involved in GC pathogenesis which confirms our results(27). CTGF, a growth factor associated with fibrosis and tissue remodeling, has been linked to the pathogenesis of gastric cancer, potentially influencing the tumor microenvironment, angiogenesis, and cell proliferation(28). CXCL5, an inflammatory chemokine, The presence of immune cells and inflammation in the microenvironment can potentially contribute to the development of gastric cancer, promoting tumor growth and metastasis(29). IL6, a pro-inflammatory cytokine, can stimulate various signaling pathways, leading to cell survival, proliferation, and immune modulation in gastric cancer, potentially contributing to tumor progression. Up-regulation of ELN, which encodes the elastin protein, may affect tissue integrity, elasticity, and angiogenesis, potentially impacting the invasive behavior of gastric cancer cells (30). ADAMTS2, involved in extracellular matrix remodeling, might influence tumor cell invasion and angiogenesis in gastric cancer (31). MMP2, a matrix metalloproteinase, has implications in extracellular matrix degradation, which is essential for tumor invasion and metastasis in gastric cancer (32). The preservation of genomic stability depends greatly on the TP53 gene, which functions as a tumor suppressor. Mutations or dysregulation of TP53 are commonly associated with gastric cancer, contributing to uncontrolled cell growth and reduced DNA repair (33). WISP1, which regulates the Wnt signaling pathway, may have a part in encouraging cell growth and movement in gastric cancer, which could aid in the progression of the tumor (34).

THBS1, an adhesive glycoprotein, is involved in angiogenesis and cell-matrix interactions, and its overexpression may contribute to tumor angiogenesis and metastasis in gastric cancer. In conclusion(35), the overexpression or up-regulation of these genes is interconnected with various cellular processes and molecular pathways relevant to gastric cancer. However, the specific roles of each gene in the context of gastric cancer initiation, progression, and metastasis are complex and multifaceted. Further comprehensive research is essential to elucidate the precise contributions of these genes, potentially leading to the development of targeted therapeutic strategies and the identification of biomarkers for gastric cancer management.

The results of our study showed that the PI3K-Akt signaling pathway is the prominent pathway affected in GC. The PI3K-Akt pathway is commonly disrupted in GC, with multiple essential components showing alterations. Activation of this pathway leads to enhanced tumor growth, survival, metastasis, angiogenesis, and resistance to treatments in GC(36). The primary genomic changes causing abnormal PI3K-Akt activation involve PIK3CA amplifications/mutations, loss of PTEN, and the overexpression of proteins that activate chemical signaling pathways called receptor tyrosine kinases (such as EGFR, HER2, cMET). Clinical investigations have linked active PI3K-Akt signaling to aggressive disease, advanced tumor stages, metastasis, and unfavorable prognoses in patients with GC. Experimental studies indicate that inhibiting PI3K-Akt signaling curbs GC cancer cell proliferation induces apoptosis, and heightens sensitivity to chemotherapy and radiotherapy(37). Preliminary results from early-stage trials of PI3K/Akt/mTOR inhibitors, either alone or combined, have demonstrated potential efficacy against advanced GC. Noteworthy challenges encompass treatment-related toxicity, acquired resistance, and the subset of patients who respond positively to PI3K pathway inhibition, thus emphasizing the need to explore predictive biomarkers.

According to research, an increased amount of CTGF, FN1, IL-6, THBS1, and WISP1 genes has been found to be linked to shorter survival times in patients with GC. Further investigations are warranted to elucidate their potential as therapeutic targets. In the context of GC, CTGF overexpression augments tumor growth, invasion, and resistance to chemotherapy, partly through the activation of PI3K-Akt signaling. This interaction is facilitated by the direct binding of CTGF to the p85 subunit of PI3K. FN1 upregulation triggers FAK/Src signaling, which cross-communicates and amplifies PI3K-Akt signaling in GC cells, thereby promoting migration and invasion processes. Similarly, IL-6 overexpression in GC fuels PI3K-Akt signaling activation via the GP130/JAK/STAT3 cascade, resulting in a heightened advantage in terms of proliferation and survival. THBS1 upregulation in GC leads to PI3K-Akt signaling activation through integrin-mediated pathways, fostering increased tumor growth and resistance to therapy. WISP1, contributing to GC progression, partially activates the PI3K-Akt/mTOR axis, consequently intensifying proliferation, invasion, and angiogenesis. Importantly, a reciprocal relationship emerges, wherein PI3K-Akt signaling can upregulate the expression of certain genes among this group, forming positive feedback loops that reinforce cancer-related phenotypes.

The overexpression of these genes and activation of the PI3K-Akt pathway are interconnected in promoting gastric tumorigenesis and progression through their abilities to upregulate each other and mediate downstream oncogenic effects. Targeting this signaling nexus may have therapeutic potential in GC. The results of our investigation into these genes using DrugBank indicate that there are currently no drugs approved by the FDA targeting CTGF, THBS1, or WISP1. However, a few approved drugs do target FN1 and IL-6, such as Ocriplasmin (targeting FN1), and Foreskin fibroblast, Binimetinib, and Siltuximab (targeting IL-6). These drugs are suggested for further investigation as potential new therapeutic options for EC patients. Among these approved drugs, only Siltuximab has a single target (IL-6), indicating specific action and potentially fewer side effects. Therefore, Siltuximab could be considered a suitable choice for EC patients.

The exploration of pharmacotherapeutic agents targeting hub genes holds promise for personalized treatment strategies. While limited FDA-approved drugs targeting FN1 and IL6 were identified, the lack of approved agents for CTGF, THBS1, and WISP1 underscores an unmet therapeutic need. The multitarget nature of the drugs introduced in this study necessitates in-depth evaluation to assess potential off-target effects. Our findings advocate for future studies that delve into the mechanistic underpinnings of the identified hub genes and associated pathways. The integration of multi-omics data, coupled with experimental validation, could shed light on their roles in GC initiation, progression, and therapeutic response. Developing targeted therapies addressing these hub genes could potentially revolutionize treatment strategies for GC patients. Despite the comprehensive nature of our study, limitations such as the reliance on bioinformatics predictions and the lack of experimental validation merit consideration. Future investigations should encompass functional assays, animal models, and clinical validation to confirm the roles of these hub genes in gastric cancer pathogenesis and treatment response

The study employed bioinformatics analysis to unveil ten hub genes—FN1, CTGF, CXCL5, IL6, ELN, ADAMTS2, MMP2, TP53, WISP1, and THBS1—in GC pathogenesis, expanding the understanding of their overexpression and its implications across critical molecular and cellular pathways for cancer initiation, progression, and metastasis. The study demonstrated the PI3K-Akt pathway's central involvement in GC, frequently disrupted in GC, with pronounced alterations in essential components. Survival analysis revealed significant correlations between CTGF, FN1, IL-6, THBS1, and WISP1 overexpression and reduced overall survival times in GC patients. A mutual interplay emerged, where PI3K-Akt signaling could upregulate certain genes, forming feedback loops and intensifying cancer phenotypes. The interconnected overexpression of genes and the PI3K-Akt pathway fosters gastric tumorigenesis, suggesting therapeutic potential. DrugBank analysis identified limited FDA-approved drugs, advocating for further exploration while targeting these hub genes could reshape GC treatment. While comprehensive, the study acknowledges limitations like bioinformatics reliance and calls for additional validation through functional assays, animal models, and clinical studies.

BP: biological process, CC: cellular components, DEGs: differentially expressed genes, GC: Gastric cancer, GO: Gene Ontology, IPCA: Identifying Protein Complex Algorithm, KEGG: Kyoto Encyclopedia of Genes and Genomes, MF: molecular function, PPI: protein-protein interaction.

Ethics approval and consent to participate

This study does not require informed consent.

Consent for publication

Not applicable.

Availability of data and materials

The data supporting the conclusions of the study are all provided in the manuscript. The GSE54129 dataset is extracted from GEO database (https://www.ncbi.nlm.nih.gov/geo/).

Competing interests

The individuals who wrote this article do not have any significant competing interests to disclose.

Funding

This article is extracted from a thesis with the ethical code of IR.IUMS.REC.1401.699.

Authors' Contribution

N.M.D. and M.S.R.R. made the concept. N.M.D. and F.K. and H.M. and B.G. provide the Data curation. N.M.D. and F.K. made the investigation and formal analysis. N.M.D. supervised the manuscript. N.M.D. and F.K. wrote the original draft of the manuscript. A.G., and H.M., and F.Y., and M.S.R.R., and S.S. reviewed and edited the manuscript. N.M.D. and F.K. prepared the figures. All authors reviewed the manuscript.

Acknowledgment

Our sincere appreciation goes out to the Iran University of Medical Sciences for their support.

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CTGF, FN1, IL-6, THBS1, and WISP1 genes and PI3K-Akt signaling pathway as prognostic and therapeutic targets in gastric cancer identified by gene network modeling

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Abstract

Figures

Background

Methods

Dataset

Screening of differentially expressed genes (DEGs)

Protein-protein interaction network construction and selection of hub genes

Gene Ontology and pathway enrichment analysis of the hub genes

Cluster analysis of the network

Survival Analysis of the Hub Genes

Drug bank

Results

Screening of differentially expressed genes (DEGs)

Reconstruction of genes and PPI networks and the hub analysis

An analysis of gene ontology and pathways enriched by specific genes

Cluster analysis of the network

Survival Analysis of the Hub Genes

Drug bank

Discussion

Conclusion

Abbreviations

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Ethics approval and consent to participate

Consent for publication

Availability of data and materials

Competing interests

Funding

Authors' Contribution

Acknowledgment

References

Additional Declarations

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