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. 2024 Oct 16;25(20):11113.
doi: 10.3390/ijms252011113.

In Silico Insights Reveal Fibronectin 1 as a Theranostic Marker in Gastric Cancer

Tatiana Millapán  1   2 , Álvaro Gutiérrez  1   2   3 , Krisnna Rosas  1   4 , Kurt Buchegger  1   2   5   6 , Carmen Gloria Ili  1   2   5 , Priscilla Brebi  1   2   5
Affiliations

In Silico Insights Reveal Fibronectin 1 as a Theranostic Marker in Gastric Cancer

Tatiana Millapán et al. Int J Mol Sci. .

Abstract

Gastric cancer (GC) is a complex and highly variable disease, ranking among the top five cancers diagnosed globally, and a leading cause of cancer-related deaths. Emerging from stomach lining cells amid chronic inflammation, it often advances to preneoplastic stages. Late-stage diagnoses and treatment challenges highlight the critical need for early detection and innovative biomarkers, motivating this study's focus on identifying theranostic markers through gene ontology analysis. By exploring deregulated biological processes, this study aims to uncover insights into cancer progression and associated markers, potentially identifying novel theranostic candidates in GC. Using public data from The Human Protein Atlas, this study pinpointed 299 prognostic genes, delineating 171 with unfavorable prognosis and 128 with favorable prognosis. Functional enrichment and protein-protein interaction analyses, supported by RNAseq results and conducted via Metascape and Cytoscape, highlighted five genes (vWF, FN1, THBS1, PCDH7, and F5) with promising theranostic potential. Notably, FN1 and THBS1 exhibited significant promise, with FN1 showing a 370% expression increase in cancerous tissue, and it is possible that FN1 can also indicate the stratification status in GC. While further validation is essential, these findings provide new insights into molecular alterations in GC and potential avenues for clinical application of theranostic markers.

Keywords: FN1 gene; gastric cancer; gene ontology; theranostic markers.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Gene ontology analysis of GC-associated genes in Metascape. (A) Bar chart of enriched terms correlated with the 171 genes associated with poor prognosis in GC. (B) Bar chart of enriched terms correlated to the 128 genes associated with good prognosis. Each term is sorted according to its p-value significance.
Figure 2
Figure 2
Cytoscape analysis of genes related to poor prognosis in GC. (A) Group A’s functional enrichment. The bars represent the percentage of genes involved in each enriched term, along with the p-value indicating the statistical significance of the enrichment. (B) Gene set network showing the relationships between genes associated with the enriched pathways. The highlighted groups demonstrate how these enriched pathways are interconnected. The asterisks in the bar chart indicate the level of statistical significance of the enriched pathways, where one asterisk (*) represents a p-value < 0.05 and two asterisks (**) indicate a p-value < 0.01.
Figure 3
Figure 3
Cytoscape analysis of genes related to good prognosis in GC. (A) Group B’s functional enrichment was obtained from Cytoscape, highlighting key biological processes linked to favorable outcomes. (B) Gene set network showing the interconnectedness of genes, related to good prognosis, within the enriched terms with the enriched pathways. The asterisks in the bar chart indicate the level of statistical significance of the enriched pathways, where one asterisk (*) represents a p-value < 0.05 and two asterisks (**) indicate a p-value < 0.01.
Figure 4
Figure 4
Biological process and highlighted biomarkers from TCGA transcriptional analysis. (A) Biological process dysregulation obtained from RNA seq analysis. All results are ordered by counts (number of genes in each pathway). (B) Volcano plot depicting differentially expressed genes in GC samples. Orange dots represent genes expressed at higher levels in GC samples while light-blue dots represent genes with lower expression levels in GC samples. The Y-axis denotes −log10 p-values while the X-axis shows log2 fold change values. Magenta and black dashed lines correspond to the range of −1 to 1 and −2 to 2 log2 fold change values.
Figure 5
Figure 5
Expression profiles of potential biomarker genes across different cancer types. Each graph displays the expression levels (TPM) of (A) vWF, (B) FN1, (C) THBS1, (D) PCDH7, and (E) F5 in both tumor and normal tissues. For each cancer type, two bars are presented, one for tumor tissue and one for normal tissue, allowing for direct comparison. The incidence of GC is related to STAD (stomach adenocarcinoma) in all the graphs. Data were obtained from GEPIA. Abbreviations: ACC: adrenocortical carcinoma, BLCA: bladder urothelial carcinoma, BRCA: invasive breast carcinoma, CESC: cervical squamous cell carcinoma and endocervical adenocarcinoma, CHOL: cholangiocarcinoma, COAD: colon adenocarcinoma, DLBC: diffuse large B-cell lymphoma, ESCA: esophageal carcinoma, GBM: glioblastoma multiforme, HNSC: head and neck squamous cell carcinoma, KICH: kidney chromophobe, KIRC: kidney renal clear cell carcinoma, KIRP: kidney renal papillary cell carcinoma, LAML: acute myeloid leukemia, LGG: lower grade glioma, LIHC: liver hepatocellular carcinoma, LUAD: lung adenocarcinoma, LUSC: lung squamous cell carcinoma, OV: ovarian serous cystadenocarcinoma, PAAD: pancreatic adenocarcinoma, PCPG: pheochromocytoma and paraganglioma, PRAD: prostate adenocarcinoma, READ: rectum adenocarcinoma, SARC: sarcoma, SKCM: skin cutaneous melanoma, STAD: stomach adenocarcinoma, TGCT: testicular germ cell tumor, THCA: thyroid carcinoma, THYM: thymoma, UCEC: uterine corpus endometrial carcinoma, UCS: uterine carcinosarcoma, UVM: uveal melanoma.
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