Pan-cancer analysis of DEFB1 as a candidate prognostic biomarker and associated with immune infiltration

doi:10.21203/rs.3.rs-4684975/v1

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Pan-cancer analysis of DEFB1 as a candidate prognostic biomarker and associated with immune infiltration

https://doi.org/10.21203/rs.3.rs-4684975/v1

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Despite many cell or animal experiments that support the close association of DEFB1 with cancer, no comprehensive pan-cancer analysis has been reported. The present research initially investigated its role in many cancers using The Cancer Genome Atlas (TCGA) data. To analyze DEFB1 in cancers, we utilized The Human Protein Atlas (HPA), TCGA, Genotype-Tissue Expression (GTEx), Tumor Immune Estimation Resource 2.0 (TIMER2.0), University of Alabama at Birmingham Cancer data analysis Portal (UALCAN), Gene Expression Profiling Interactive Analysis 2 (GEPIA2), and cBioPortal databases. The visualization of data was primarily accomplished through the use of the R language. Most cancers and their adjacent normal tissues exhibit differential expression of DEFB1.The prognosis of distinct cancers was notably impacted by DEFB1. High DEFB1 expression could induce a poorer overall survival (OS) in the lung adenocarcinoma (LUAD)and pancreatic adenocarcinoma (PAAD) cohorts; in contrast, it could lead to a significantly higher OS in the head-neck squamous cell carcinoma (HNSC) cohort (P < 0.05). Moreover, high DEFB1 expression could result in a poor disease-free survival (DFS) in the cholangiocarcinoma (CHOL) cohort (P < 0.05). Notably, Liver hepatocellular carcinoma (LIHC)samples demonstrated the highest DEFB1 mutation frequency among all cancer types. Furthermore, there was a close relationship of DEFB1 expression with the extent of cancer-associated fibroblast infiltration in LIHC, thyroid carcinoma (THCA), colon adenocarcinoma (COAD), head-neck squamous cell carcinoma (HNSC), and stomach adenocarcinoma (STAD), while neutrophil infiltration was revealed in other malignancies, including bladder carcinoma (BLCA), diffuse large B-cell (DLBC), lung squamous cell carcinoma (LUSC), PAAD, as well as uterine corpus endometrial carcinoma (UCEC). This initial pan-cancer research can help comprehensively understand the carcinogenesis of DEFB1 in many malignancies.

DEFB1

malignancy

bioinformatics

prognosis

immune infiltration

Performing a comprehensive investigation of specific gene expression across multiple types of cancer and evaluating their molecular mechanisms is crucial in the face of tumorigenesis' complexity. The publicly funded The Cancer Genome Atlas (TCGA) project comprises functional genomics datasets for many cancers [1, 2], enabling us to conduct a comprehensive analysis across multiple cancer types.

Most structures of the tiny cationic peptides known as defensins is made up of a central β-sheet, which is consisted of six cysteine residues that are interconnected by three intramolecular disulfide bonds [3]. Three subfamilies have been identified. The human β-defensin family exhibits a considerable level of complexity, as evidenced by the discovery of approximately 40 distinct human β-defensin genes [4]. It is noteworthy that the presence of β-defensins was initially observed in cows' tracheal mucosa [5], and subsequent investigations led to the discovery of more β-defensins derived from bovine neutrophils [6]. The initial identification and isolation of human β-defensin, known as human β-defensin-1 (DEFB1), took place in 1995 [7]. Generally, DEFB1 is down-regulated in multiple cancers, indicating that it may inhibit tumor progression [8–10]. In comparison to healthy tissues, a notable decrease in DEFB1 levels has been observed in several malignancies including other types such as lung cancer, breast cancer, ovarian cancer, pancreatic cancer, and liver cancer [11–18]. Despite extensive searching, there is yet to be found any comprehensive evidence that supports the association of DEFB1 with various malignancies.

Our study presents a pioneering examination of DEFB1 across various types of cancer utilizing data from the TCGA project. To elucidate the molecular mechanism that underlies the role of DEFB1 in cancer pathogenesis and clinical prognosis, we incorporated several factors into our analysis, including transcriptomic profiles, patient outcomes, genomic alterations, immune cell infiltration, and associated genes.

2.1. Gene Expression Analysis

As an open-access database, the Human Protein Atlas (HPA) (https://www.proteinatlas.org/) [19] that maps all human proteins and employs the integration of various omics technologies. From HPA, scatterplots were obtained for the DEFB1 mRNA level.

The “Gene_DE” module of the tumor immune estimation resource version 2 (TIMER2) web (http://timer.cistrome.org/) (2) was employed to analyze the expression variations of DEFB1 between tumor and adjacent normal tissues in different tumors or specific tumor subtypes from the TCGA project. For tumors where there were limited or unavailable normal tissues (e.g., TCGA-diffuse large B-cell (DLBC), TCGA-LAML (Acute Myeloid leukemia), etc.), the “Expression analysis-Box Plots” module of the Gene Expression Profiling Interactive Analysis, version 2 (GEPIA2) web server (http://gepia2.cancer-pku.cn/#analysis) (3). By leveraging the GEPIA2, we obtained box plots that compared DEFB1 expressions of these tumor tissues and their corresponding normal tissues sourced from the Genotype-Tissue Expression (GTEx) database. Our analysis used a P-value cutoff of 0.01, a log2FC (fold change) cutoff of 1, and we selected the option to "Match TCGA normal and GTEx data". This approach allowed us to derive insightful visualizations of DEFB1 expression through violin plots at various pathological stages (I-IV) across all TCGA tumors using the "Pathological Stage Plot" module of GEPIA2. Throughout both types of plots, the expression data underwent a logarithmic transformation using log2 [TPM (Transcripts per million) + 1] to facilitate appropriate analysis and interpretation.

The University of Alabama at Birmingham Cancer data analysis Portal (UALCAN) portal (http://ualcan.path.uab.edu/analysis-prot.html) could allow us to obtain cancer Omics data and analyze gene expression from the Clinical proteomic tumor analysis consortium (CPTAC) database (4). This present study investigated the difference in DEFB1 (NM_005218) expression of primary tumor and adjacent healthy tissue via entering “DEFB1”. Six cancers were chosen, including ovarian cancer, clear cell renal cell carcinoma (RCC), uterine corpus endometrial carcinoma (UCEC), lung adenocarcinoma (LUAD), HNSC, and liver cancer.

2.2. Survival Analysis

The "Survival Map" module of GEPIA2 was utilized to collect data on the significance of overall survival (OS) as well as disease-free survival (DFS) associated with DEFB1 across all TCGA tumors. To categorize the cohorts into high- and low-expression groups, the expression threshold of 50% were employed as the cutoff value. The log-rank test was applied to test the hypothesis,, and the survival plots were generated utilizing the "Survival Analysis" module of GEPIA2.

2.3. Gene Alteration Analysis

Upon accessing the cBioPortal website (https://www.cbioportal.org/) (5), the study selected the “TCGA Pan Cancer Atlas Studies” within the “Quick select” section, utilizing the input “DEFB1” to conduct an inquiry into the genetic attributes associated with DEFB1. The alteration frequency, mutation type as well as copy number alteration (CNA) across the entire range of TCGA tumor types were investigated using the “Cancer Types Summary” module. Besides, the DEFB1 mutated site information was visualized in the schematic diagram of the protein structure or the 3D structure provided by the “Mutations” module.

2.4. Immune Infiltration Analysis

The study applied the “Immune-Gene” module of the TIMER2 website to identify the association of DEFB1 with immune infiltration. Cancer-associated fibroblasts and neutrophils were selected. Immune infiltration was estimated via TIMER, EPIC, QUANTISEQ, XCELL, MCPCOUNTER, CIBERSORT, as well as CIBERSORT-ABS. The P-values together with partial correlation (cor) values were acquired utilizing the purity-adjusted Spearman’s rank correlation test. All findings was presented as a heatmap along with a scatter plot.

2.5. DEFB1-related Gene Enrichment Analysis

In this study, the "Similar Gene Detection" module of GEPIA2 was utilized to identify a selection of 100 genes that are strongly associated with DEFB1. This analysis was performed using comprehensive datasets encompassing both tumor and normal tissues from the TCGA database. The investigation additionally employed the "correlation analysis" module to conduct Pearson correlation analysis of DEFB1 and its associated genes in a pairwise manner. The log2 TPM was done for the dot plot. The P-value and the correlation coefficient (R) were indicated. Additionally, the "Gene_Corr" module was utilized to obtain the heatmap data of the chosen genes, as well as the partial correlation coefficient and P-value in the purity-adjusted Spearman’s rank correlation test.

This study also applied the “ggplot2” (https://cran.r-project.org/web/packages/ggplot2/index.html) to conduct enrichment analysis through the R language software (version 3.6.3) (https://www.r-project.org/). P < 0.05 was deemed to have statistically significance.

3.1. Result of Gene Expression Analysis

The present research intended to identify the oncogenic role of DEFB1 (NM_005218.4 for mRNA). Thus, an initial analysis into DEFB1 expressions among various non-neoplastic tissues was carried out in it. By combining the HPA, GTEx, as well as Functions annotation of the mammalian genome 5 (FANTOM5) datasets, it has been determined that DEFB1 has the highest mRNA expression in the salivary gland, which is followed by the kidney and pancreas (Fig. 1A). Furthermore, the single-cell expression result (Fig. 1B) found a high DEFB1 expression level in distal tubular, mucus glandular, and salivary duct cells. At the same time, DEFB1 was highly expressed in various cell lines, including CACO-2, Hep-G2, and OE19 (Fig. 1C).

Figure 1. DEFB1 expression pattern in cancer and adjacent normal tissues. DEFB1 expression level in mRNA (A), single-cell (B), and cell lines (C).

We utilized the TIMER2 website and TCGA data to examine the DEFB1 expression level in diverse cancers. Figure 2A demonstrates that the DEFB1 level was higher in tissues from cholangiocarcinoma (CHOL), kidney chromophobe (KICH), skin cutaneous melanoma (SKCM), esophageal carcinoma (ESCA), lung squamous cell carcinoma (LUSC), and UCEC than in the corresponding normal tissues (P < 0.05). However, DEFB1 expressions from cancer tissues of BRCA, colon adenocarcinoma (COAD), HNSC, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, prostate adenocarcinoma, as well as rectum adenocarcinoma (P < 0.001) showed a significant lower level.

We set the normal tissue of the GTEx dataset as controls and demonstrated that DEFB1 levels were different in LAML, Ovarian serous cystadenocarcinoma (OV), Sarcoma (SARC), Testicular germ cell tumors (TGCT), as well as Uterine carcinosarcoma (UCS) with statistical significance (Fig. 2B, P < 0.05).

According to the data obtained from CPTAC, it was observed that there is a significant upregulation of DEFB1 in samples from ovarian cancer, UCEC, LUAD, as well as liver cancer (Fig. 2C, P < 0.001) compared to that in the matched normal tissues. In contrast, the primary tissues of clear cell RCC and HNSC exhibited a relatively lower DEFB1 protein expression in contrast with normal tissues (Fig. 2C, P < 0.001).

This study utilized the “Pathological Stage Plot” module of GEPIA2 to clarify the association of DEFB1 with other cancers, including KICH, OV, pancreatic adenocarcinoma (PAAD) and SKCM (Fig. 3A, P < 0.01).

Figure 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).

3.2. Survival Data

Cases with cancer were classified into two groups, namely high- and low-expression groups, based on DEFB1 expression levels. Subsequently, the correlation of DEFB1 expression with the prognosis of patients with tumors based on the datasets primarily obtained from TCGA. Figure 3B finds that high DEFB1 level could induce poor OS for LUAD and PAAD (P < 0.05). Besides, low DEFB1 level could lead to poor OS for HNSC (Fig. 3B, P = 0.017). Figure 3C concludes that high DEFB1 level could induce poor DFS for CHOL cases (P = 0.027).

Figure 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.

3.3. Genetic Alteration Data

The condition of DEFB1 expression was obtained from the TCGA cohorts. Figure 4A demonstrates that patients with the primary type Liver hepatocellular carcinoma (LIHC) with “deep deletion” had the highest alteration frequency of DEFB1 (> 6%). In stomach adenocarcinoma (STAD), the predominant form of CNA is characterized by "amplification", exhibiting an alteration frequency exceeding 3%. SKCM cases with the alteration frequency of 1% had copy number mutation of DEFB1 (Fig. 4A). The types, sites, as well as case number of the DEFB1 genetic alteration are shown in Fig. 4B. The result indicated missense mutation to be the primary alteration type. In one case of UCEC and one case each of cervical squamous cell carcinoma & endocervical adenocarcinoma (Fig. 4B), an H34D/N alteration in the Defensin_beta domain led to a missense or DEFB1 gene mutation, causing a subsequent change in the DEFB1 protein. The DEFB1 protein's 3D structure is visually illustrated in Fig. 4C.

Figure 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).

3.4. Data of Immune Infiltration

To analyze the underlying association of immune cell infiltration levels with the expressions of DEFB1 across various cancer types in TCGA, we employed the TIMER, EPIC, QUANTISEQ, XCELL, MCPCOUNTER, CIBERSORT, as well as CIBERSORT-ABS algorithms. A positive association of the DEFB1 levels with the estimated infiltration values of cancer-associated fibroblasts among the LIHC and thyroid carcinoma (THCA) but concluded a negative relationship for COAD, HNSC, and STAD with statistical significance was observed (Fig. 5). The result showed a statistical positive association of the infiltration degree of neutrophil with DEFB1 for bladder carcinoma (BLCA), DLBC, LUSC, PAAD, and UCEC (Fig. 6). The scatterplot data are presented in Figs. 5–6. For instance, utilizing the CIBERSORT algorithm, a positive association of DEFB1 expression level in BLCA with neutrophil infiltration level was observed(Fig. 6, cor = 0.247, P = 1.62e-06)

Figure 5. Correlation of DEFB1 level with infiltration degree of cancer-associated fibroblasts. Different algorithms were conducted to investigate the association of DEFB1 with the infiltration degree of fibroblasts.

Figure 6. Association of DEFB1 with the infiltration degree of neutrophil. Different algorithms were conducted to clarify the relationship of DEFB1 level with the infiltration degree of neutrophil in TCGA tumors.

3.5. DEFB1-related Partners

In an effort to delve into the molecular mechanisms behind DEFB1 gene's role in tumorigenesis, this research aimed to clarify its related proteins and genes. The study utilized GEPIA2 to find out the top 100 genes that related to DEFB1 level. Figure 7 reveals that the DEFB1 level was positively related to KLK1 ( kallikrein 1) (R = 0.68), BSND (barttin CLCNK type accessory subunit beta) (R = 0.60), FXYD2 (FXYD domain containing ion transport regulator 2) (R = 0.60), EMX1 (empty spiracles homeobox 1) (R = 0.59), and CLCNKB (empty spiracles homeobox chloride voltage-gated channel Kb) (R = 0.59) genes (all P < 0.001). Among most of specific cancer types, the heatmap data exhibited a positive association of DEFB1 with the aforementioned 5 genes.

Figure 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.

Recent publications have indicated a close correlation between DEFB1 and many diseases, particularly tumors [8–10]. There still remains an unresolved question regarding the potential involvement of DEFB1 in tumorigenesis across various types of tumors via shared molecular mechanisms. We conducted a literature review and could not find any publication offering an analysis of DEFB1 across all the tumors. Therefore, this research comprehensively examined the role of the DEFB1 gene among 33 tumors through TCGA and CPTAC, along with molecular characteristics of gene expression / genetic alteration.

DEFB1 was substantially expressed in the majority of tumors. Despite this, the analysis data for the DEFB1 gene’s survival prognosis suggested distinct conclusion between DEFB1 level and survival prognosis for various tumors. Recent investigations have revealed that DEFB1 functions as a gene that suppress the tumors. DEFB1 expressions among cell lines of prostate cancer unequivocally would result in the attenuation of cellular proliferation [24]. DEFB1 loss was observed in 95.6% of cases with prostate adenocarcinoma characterized by Gleason Patterns 3 & 4. Besides, loss of DEFB1 expression among basal cells served as an efficient biomarker within a cohort exhibiting false-negative outcomes, enabling the identification of high-risk patients despite their initial negative biopsy results. [25]. Our results align with prior research. Hence, comprehending the molecular mechanism of DEFB1 in the progression of prostate cancer will be of immense significance.

The E-cadherin was the top gene positively related to the significantly reduced DEFB1 level, whereas the substrate of tyrosine kinase regulated by hepatocyte growth factor demonstrated the highest negative correlation. This suggests that it can protect liver cancer development [26]. DEFB1 is significantly downregulated in human hepatocellular carcinoma. The intervention of its rescue exerts an inhibitory effect on cellular proliferation and the formation of colonies. When evaluated in an experimental model of nude mouse hepatocellular carcinoma, the expression of DEFB1 suppresses tumor growth through the promotion of protein degradation and the induction of endoplasmic reticulum stress. Besides, the c-Jun N-terminal kinase pathway could be activated to mediate the inhibitory effect of DEFB1 [27]. Our results align with prior research. To obtain comprehensive insights into the onset and progression of hepatic carcinoma holds significant significance within the academic realm.

Interestingly, DEFB1 has the ability to inhibit tumor migration and invasion, and it could potentially serve as a promising prognostic biomarker in oral squamous cell carcinoma [28]. A lower DEFB1 expression in OSCC is associated with a worse prognosis. The enrichment analysis found that its anti-tumor effect is linked to the following cellular processes and pathways: remodeling of the extracellular matrix, activation of receptor tyrosine kinase/phosphoinositide 3-kinase/Akt/mechanistic target of rapamycin signaling, promotion of keratinization, as well as modulation of cytokine pathways [29]. Our results are in agreement with previous studies. The initial findings suggested DEFB1 to be significant in the progression and development of OSCC.

According to datasets obtained from patient cohorts, there is a reduction in DEFB1 transcription in colorectal cancer. By inhibiting the epidermal growth factor receptor (EGFR) in human cell lines of colon cancer as well as organoids of normal human colonic primary cells, the expression of DEFB1 increases; however, it is reduced when EGFR is activated via the MAPK kinase 1/2 (MEKK1/2)-ERK1/2 pathway [30]. Our results were consistent with previous studies. Hence, acquiring a profound comprehension regarding the molecular pathways related to the initiation, advancement, as well as manifestation of colorectal cancer is of paramount importance.

We applied the TCGA-LUSC and TCGA-LUAD databases and discovered that high DEFB1 level could induce a poor OS in LUAD (P = 0.016) but not in LUSC. The results are depicted in Fig. 3B. Therefore, it is necessary to conduct a larger study to validate the influence of DEFB1 on the survival outcome of patients with diverse lung cancer types.

Tumor-infiltrating immune cells, integral constituents of tumor microenvironment, have exhibited a strong association with the commencement, advancement, or dissemination of malignant neoplasms [31]. DEFB1 has the ability to draw in cells that have underwent transfection by C-C chemokine receptor 6, implying its potential in recruiting Th cells as well as neutrophils [32, 33]. Moreover, we utilized various immune deconvolution techniques and discovered a significant negative association of DEFB1 level with the infiltration degree of fibroblasts in the tumors of LIHC, THCA, COAD, HNSC, and STAD. Furthermore, our results indicated a potential relationship of DEFB1 level with the degree of neutrophil infiltration among specific tumor types.

However, the study had some limitations: (I) Gene expression omnibus (GEO) analysis was needed; (II) there were limited samples to analyze the DEFB1 mechanisms underlying the development and progression of tumors; (III) further verification of DEFB1 function at the cellular level was needed; (IV) further research needed to promote the DEFB1 as a prognostic indicator or therapeutic target for tumors.

This initial pan-cancer analyses showed a significant association of DEFB1 level with the patient's prognosis, immune infiltration degree, which can help understand its role in tumorigenesis.

Conflicts of Interest

The authors declare no conflict of interest.

Author Contribution

Conceptualization and writing, L.W.; software, H.Y.; methodology and funding acquisition, L.C; analysis, Y.Y.; visualization, R.D. All authors have read and agreed to the published version of the manuscript.

Acknowledgments

This work was supported by National Science Foundation of China (82002813); Tianjin Key Medical Discipline(Specialty) Construction Project(TJYXZDXK-012A)

Data Availability

The datasets analyzed for this study can be found in the HPA, TCGA, GTEx, TIMER2.0, UALCAN, CCLE, GEPIA2, and cBioPortal web resources, and requests to further access to datasets can be directed to [email protected].

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Pan-cancer analysis of DEFB1 as a candidate prognostic biomarker and associated with immune infiltration

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Abstract

Figures

1. Introduction

2. Methods

2.1. Gene Expression Analysis

2.2. Survival Analysis

2.3. Gene Alteration Analysis

2.4. Immune Infiltration Analysis

2.5. DEFB1-related Gene Enrichment Analysis

3. Results

3.1. Result of Gene Expression Analysis

3.2. Survival Data

3.3. Genetic Alteration Data

3.4. Data of Immune Infiltration

3.5. DEFB1-related Partners

4. Discussion

Declarations

Conflicts of Interest

Author Contribution

Acknowledgments

Data Availability

References

Additional Declarations

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