Expression and clinical significance of FANCI gene in pan-cancer: a comprehensive analysis based on multi-omics data

Abstract

Introduction: The FANCI gene, an essential element of the Fanconi anemia pathway, has been associated with a variety of cancer types. This investigation seeks to clarify the expression profiles, prognostic relevance, and diagnostic capabilities of FANCI across multiple malignancies, along with its links to immune cell infiltration, genetic alterations, protein-protein interactions, and functional roles.

Methods: By utilizing data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, we conducted a comprehensive analysis of FANCI mRNA expression using R software and visualized the results with the ggplot2 package. Prognostic and diagnostic evaluations were conducted using Xiantao tools to produce survival and receiver operating characteristic (ROC) curves. The examination of genetic variation was facilitated through cBioPortal, while DNA methylation and mRNA modifications were analyzed utilizing UALCAN and SangerBox 3.0. Correlations with immune responses were assessed via the EPIC platform and SangerBox 3.0. Additionally, we constructed protein-protein interaction networks employing the STRING database and Cytoscape software. Functional enrichment analyses encompassed Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). The CancerSEA database was also utilized for single-cell level investigation of FANCI's association with the functional states of cancer.

Results: Our findings reveal that FANCI is significantly upregulated in the majority of tumor types when compared to normal tissues, with increased protein levels observed in several cancers, including colorectal adenocarcinoma (COAD) and pancreatic adenocarcinoma (PAAD). Elevated FANCI expression is associated with unfavorable prognoses in cancers such as adrenocortical carcinoma (ACC) and liver hepatocellular carcinoma (LIHC). Methylation assessments demonstrated a robust inverse correlation between FANCI promoter methylation and its expression in LIHC. Moreover, FANCI expression was found to be connected to immune cell infiltration and tumor mutation burden in select cancers.

Discussion: In summary, FANCI presents as a promising biomarker for cancer prognosis and diagnosis, with potential implications for therapeutic interventions. Subsequent investigations should concentrate on elucidating the mechanistic functions of FANCI in cancer development and assessing its viability as a therapeutic target.

Keywords: DNA damage repair; FANCI; biomarker; clinical significance; gene expression; pan-cancer.

FIGURE 1

FANCI Expression Levels and Localization. (A) Comparison of FANCI mRNA expression in tumors versus normal tissues from TCGA and GTEx. (B) FANCI expression in tumors and paired adjacent normal tissues from TCGA (n = 15,043). (C,D) Subcellular localization of FANCI in A-431 and U-251MG cells from the HPA dataset, with green representing the target protein and red representing microtubules. (E–K) Comprehensive proteomic analysis showing FANCI expression across various cancers, providing a protein-level perspective. (L) Protein expression of FANCI in cancer and normal tissues detected by immunohistochemistry in the HPA dataset. (M–T) Correlations between FANCI expression and clinicopathological characteristics. *p < 0.05, **p < 0.01, ***p < 0.001.

FIGURE 2

Correlation of FANCI Expression with Pan-Cancer Prognosis and Diagnosis. (A) Forest plot showing the association between FANCI expression and overall survival in various cancers, highlighting significant results (p < 0.05). (B–I) Kaplan-Meier survival curves illustrating the impact of FANCI expression on overall survival in specific cancers: ACC, KIRP, LGG, LIHC, MESO, PAAD, SARC, and SKCM. (J) ROC curve analysis assessing the diagnostic value of FANCI expression in pan-cancer, with AUC values indicating low (0.5-0.7), medium (0.7-0.9), and high (>0.9) accuracy. (K,L) ROC curves for LIHC (AUC = 0.909) and PAAD (AUC = 0.978).

FIGURE 3

Analysis of the correlation between FANCI expression and regulatory factors involved in mRNA modification methylation: (A) m1A, (B) m5C, (C) m6A. The associations are evaluated using Pearson’s correlation coefficient and assessed for statistical significance (*p < 0.05).

FIGURE 4

Analysis of FANCI Methylation in Liver Cancer. (A,B) Analysis of FANCI methylation in hepatic carcinoma and normal tissues utilizing the UALCAN database. (C) A heatmap depicting FANCI DNA methylation in LIHC, sourced from MethSurv. (D,E) Investigation of FANCI methylation status in LIHC was conducted using the OncoDB database. (F) MEXPRESS was employed to visualize methylation sites within the LIHC DNA sequence related to gene expression. FANCI expression is shown by the blue line. Pearson’s correlation coefficients and p-values for methylation sites and gene expression are indicated on the right. **P < 0.01, ***P < 0.001.

FIGURE 5

Correlation analysis of FANCI expression and immune infiltration. (A) FANCI expression and immune cell infiltration. (B) Correlation of FANCI expression and tumor mutation burden (TMB) in pan-cancer tissues. (C–J) Correlations between FANCI expression and immune subtypes in eight cancers. (K–N) Correlations between FANCI expression and molecular subtypes in four cancers. (*P < 0.05, **P < 0.01, ***P < 0.001).

FIGURE 6

Integrated Analysis of FANCI’s Molecular Interaction Network, Functional Enrichment, and Transcriptional Regulation (A) The PPI network for FANCI. (B) The top ten hub genes within the PPI network. (C) A heatmap showing the association of hub genes with FANCI across eight cancers. (D) Heatmap showing the expression distribution of the top 10 core genes in LIHC tumor tissues and adjacent normal tissues. (E,F) GO and KEGG pathway enrichment for FANCI and closely interacting genes. (G) Predict the potential transcription factors of FANCI and screen out eight high-confidence regulatory factors: HINFP, POU2F2, ELK1, TFAP2A, NFYA, JUND, FOXC1, and NKX3-2. (*P < 0.05, **P < 0.01, ***P < 0.001).

FIGURE 7

GSEA functional enrichment analysis of FANCI expression in 8 cancers. The top 10 GSEA functional enrichment pathways of FANCI in (A) ACC, (B) KIRP, (C) LGG, (D) LIHC, (E) MESO, (F) PAAD, (G) SARC, (H) SKCM. The Y-axis represents one gene set and the X-axis is the distribution of logFC corresponding to the core molecules in each gene set.

FIGURE 8

The correlation of FANCI with functional states in cancers is illustrated as follows: (A) An interactive bubble chart displaying the correlation of FANCI with functional states across 16 cancers. The correlation of FANCI with functional states in (B) AML, (C) MEL, (D) Glioma, and (E) LUAD. The X-axis represents different gene sets. (*P < 0.05, **P < 0.01, ***P < 0.001).

FIGURE 9

Construction of a FANCI-associated ceRNA regulatory network in LIHC. (A) Venn diagram illustrating the overlap of FANCI-targeted miRNAs predicted by DIANA-microT, miRWalk, and miRcode. (B) Sankey diagram depicting the relationship between target miRNAs and their corresponding target lncRNAs. (C) A lncRNA-miRNA-FANCI interaction network was established for LIHC using Cytoscape.

FIGURE 10

Knockdown of FANCI suppresses cell migration and invasion. (A,B) Western blot and qPCR were performed to detect the expression level of FANCI in LIHC tissues. (C,D) Quantitative analysis of FANCI mRNA and protein in Huh7 cells following treatment with FANCI siRNA to verify the silencing efficiency of different siRNAs. (E,F) Wound healing assay and Transwell migration assay were used to evaluate the impact of FANCI downregulation on the migratory or invasive abilities of Huh7 cells. Scale bars, 100 × = 400 μm. All experiments were implemented three times, and data are presented as mean ± SD.