Integrative genomic pan-cancer analysis reveals the prognostic significance of DEFB1 in tumors

Abstract

Background: Defensin beta 1 (DEFB1) is a key immune response gene, but its role in cancer remains unclear. This study aims to explore DEFB1 expression, genetic alterations, immune infiltration, and prognostic significance across various cancer types.

Methods: We analyzed DEFB1 expression and its association with cancer prognosis using data from public platforms, including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Human Protein Atlas (HPA). Additionally, we examined DEFB1 genetic alterations, immune cell infiltration, and its molecular partners using various bioinformatics tools.

Results: DEFB1 expression was highest in salivary glands, kidneys, and pancreas. In cancers, DEFB1 was upregulated in cholangiocarcinoma, kidney chromophobe, and melanoma, but downregulated in breast, colon, and rectal cancers. High DEFB1 expression was linked to poorer overall survival in lung adenocarcinoma and pancreatic adenocarcinoma, but better survival in head and neck squamous cell carcinoma. Genetic analysis revealed alterations in liver and gastric cancers. Immune infiltration analysis showed a correlation between DEFB1 and cancer-associated fibroblasts in liver cancer, while neutrophil infiltration was linked to bladder carcinoma, diffuse large B-cell lymphoma, and lung squamous cell carcinoma. Key genes associated with DEFB1 included KLK1, BSND, and CLCNKB.

Discussion: This study highlights DEFB1's potential as a prognostic biomarker and its influence on the tumor immune microenvironment across different cancers. These findings suggest DEFB1 could be a target for future cancer therapies, although further studies are needed to validate these results.

Keywords: Bioinformatics; DEFB1; Immune infiltration; Malignancy; Prognosis.

Fig. 1

DEFB1 expression pattern in cancer and adjacent normal tissues. DEFB1 expression level in mRNA (A), single-cell (B), and cell lines (C)

Fig. 2

DEFB1 level in cancer tissues as well as cell lines. A DEFB1 levels in different cancers was identified via TIMER2. (^*P < 0.05; ^**P < 0.01; ^***P < 0.001). B The controls for the LAML, OV, SARC, TGCT, and UCS samples in the TCGA project were established using the corresponding normal tissues sourced from the GTEx database. The box plot data were supplied. (^*P < 0.05). C The study utilized the CPTAC dataset to clarify the DEFB1 expression in ovarian cancer, clear cell RCC, UCEC, LUAD, HNSC, as well as liver cancer. (^***P < 0.001)

Fig. 3

Association of DEFB1 expression with prognosis in TCGA data. A The study used the TCGA data to investigate the association of the DEFB1 level with the different stages of KICH, OV, PAAD as well as SKCM. Log2 (TPM + 1) was used for log-scale. The present research applied the GEPIA2 tool to obtain results on overall survival (B) as well as disease-free survival (C). The positive survival map together with Kaplan–Meier curves were presented

Fig. 4

Mutation characteristics associated with DEFB1. We applied the cBioPortal tool for the purpose of elucidating the mutation characteristics pertaining to DEFB1. The mutation type (A), mutation site (B), and the mutation site with the highest alteration frequency (H34D/N) are displayed. We also display its 3D structure (C)

Fig. 5

Study on correlation between fibroblasts and DEFB1. A Correlation analysis between DEFB1 and fibroblasts in pan-cancer analysis. B Scatter plot of correlation between DEFB1 and fibroblasts

Fig. 6

Study on correlation between neutrophils and DEFB1. A Analysis of the correlation between DEFB1 and neutrophils in pan-cancer analysis. B Scatter diagram of the correlation between DEFB1 and neutrophils

Fig. 7

DEFB1-related gene analysis. The GEPIA2 approach was conducted to get the top 100 genes related with DEFB1 in TCGA and explored the association of DEFB1 expressions with specific targeting genes, such as KLK1, BSND, FXYD2, EMX1, and CLCNKB. The heatmap data for cancer types are displayed