Exploring BIRC Family Genes as Prognostic Biomarkers and Therapeutic Targets in Prostate Cancer

doi:10.21203/rs.3.rs-4934589/v1

Download PDF

Research Article

Exploring BIRC Family Genes as Prognostic Biomarkers and Therapeutic Targets in Prostate Cancer

https://doi.org/10.21203/rs.3.rs-4934589/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

The potential oncogenic role of Baculoviral inhibitor of apoptosis (IAP) Repeat-Containing (BIRC) genes in prostate cancer (PCa) has yet to be fully investigated. Two genes associated with disease recurrence, BIRC5 and BIRC7, were identified through survival analysis, and prostate cancer patients were categorized into two subtypes, C1 and C2, based on these genes. We performed survival analyses to assess the relationship between subtypes and the prognosis of PCa. Single-cell dataset analysis was used to identify specific cell types with enriched expression of BIRC family genes. Our findings demonstrate that BIRC5 and BIRC7 exhibit higher expression in PCa tissues compared to non-cancerous tissues. High expression of BIRC5 and BIRC7 independently correlates with an adverse prognosis in PCa. The analysis of mechanisms reveals that the differentially expressed genes impact signaling pathways associated with cancer and immunity. BIRC5/BIRC7 correlate with several immune cells infiltrating levels including T cells and macrophages. Furthermore, our research indicates that elevated expression of BIRC5 diminishes the efficacy of immunotherapy in PCa. These findings highlight the potential of BIRC5/BIRC7 or C1 subtype as prognostic biomarkers, offering new insights into viable targets for the development of therapeutic biomarkers and immunotherapeutic drugs for PCa.

biomarker

BIRC

prognosis

prostate cancer

bioinformatics analysis

immune infiltration

The mortality rate of malignant tumors in China surpasses the global average, posing a serious threat to human health. As of 2020, prostate cancer holds the highest incidence rate among male cancers in 112 countries globally. It accounts for about 1.4 million new cases annually, leading to around 375,000 deaths each year. Prostate cancer is the second most common cancer among males in 2020, with a mortality rate ranking fifth [1]. Prostate cancer incidence exhibits considerable variation across regions and ethnic groups. It is most prevalent in Northern and Western Europe, the Caribbean, Australia/New Zealand, North America, and southern Africa, while Asia and North Africa experience comparatively lower rates [2, 3]. In China, the incidence of prostate cancer has markedly risen due to population aging and shifts in lifestyle habits. Consequently, prostate cancer is poised to impose a substantial health burden on men worldwide. Notwithstanding its clinical utility as a biomarker for diagnosing and prognosticating PCa, prostate-specific antigen (PSA) exhibits inherent limitations. For instance, it exhibits variations in sensitivity across different ethnic and racial groups and has low overall sensitivity. Given these limitations, there is a critical need to identify novel biomarkers that can aid in the diagnosis, development, and recurrence monitoring of PCa.

Apoptosis inhibitors, known as IAPs and expressed by BIRC genes, play a critical role in conferring apoptosis resistance in various cancer cells [4, 5]. These IAP proteins share significant structural and functional similarities. They exhibit two unique characteristics: first, they target both the initiating and executing caspases, resulting in a range of effects from anti-apoptotic to pro-apoptotic [4]. Secondly, IAPs exert influence over various cellular processes, including cell cycle regulation, immune system modulation, and DNA damage repair [6]. Furthermore, there is a significant link between IAPs and the NF-κB signaling pathway, underscoring their role in crucial cellular signaling cascades. IAPs exercise their function by counteracting proapoptotic signals, especially through interaction with the inhibitor of apoptosis-binding protein, thus aiding in cellular survival. [7, 8].

Given the limited therapeutic options available for PCa, the identification of novel biomarkers that can improve treatment outcomes and prognosis is imperative. Previous research has demonstrated that the BIRC gene family may have a significant impact on cancer treatment response and prognostic outcomes [9]. Thus, we hypothesized that the BIRC gene family could function as valuable prognostic indicators in PCa. To validate this hypothesis, we conducted a comprehensive bioinformatic analysis to evaluate the expression of BIRC family genes (BIRC 2/3/5/6/7) in PCa and their potential correlation with disease prognosis.

Acquisition of public data

The flow chart of this study design is shown in Fig. S1. RNA-seq count data for 553 samples (501 PCa tissue and 52 adjacent tissues were acquired from TCGA, along with corresponding clinical data. 466 samples with available RFS time were selected to perform downstream analysis. RNA-seq raw data underwent filtering to exclude genes with a median read count of zero. Subsequently, the varianceStabilizingTransformation (VST) function from DESeq2 was applied to the filtered matrix before further downstream analysis [10]. The expression profiles microarray data of BIRC genes from GEO were subsequently normalized using the limma package [11]. The GEO dataset was log2-transformed.

Bioinformatic analysis

GeneMANIA (http://genemania.org/) and the STRING (https://string-db.org/cgi/input.pl) were employed to create gene-gene and protein-protein interaction networks, respectively [12, 13]. A co-expression analysis of BIRC family genes was conducted using the PRAD cohort. To uncover potential gene regulatory mechanisms, we gathered data on gene mutations and copy number variations (CNVs) from cBioportal (https://www.cbioportal.org/) [14].

Survival analysis and cluster analysis

Kaplan-Meier (KM) analysis, and univariate and multivariate Cox regression were used to assess the prognostic significance of BIRC genes. Samples in both datasets were grouped into high and low expression categories using the median gene expression value. The KM analysis identified clinical factors associated with PCa prognosis. Clinical parameters meeting the significance threshold of P < 0.05 were included in the multivariate analysis. Afterwards, to explore the combined-predictive potential on relapse-free survival (RFS), we conducted a cluster analysis based on mRNA expression levels of recurrence-associated genes, and stratified prostate cancer patients into two subtypes (C1/C2) [15]. Additionally, we performed a stratified survival analysis, integrating various clinicopathological features, to assess the predictive significance of C1/C2 in PCa patients.

Prognostic Value and Clinical Relevance Analysis

We employed receiver operating characteristic (ROC) curves to assess the predictive accuracy of BIRC5/7 for patient survival. Additionally, it explored the association between BIRC5/7 expression and clinicopathological features in PCa.

Exploring the regulatory mechanisms of genes.

TCGA samples were classified into two subtypes based on the median expression values of BIRC genes related to prognosis. Differential expression genes (p < 0.05 and |log2foldchange| > 1) between C1 and C2 were screened using the DESeq2 package while visualizing the expression levels and differences by the heatmap package. Subsequently, The biological mechanism was investigated through GO and KEGG using the R package "ClusterProfiler"[16].

Exploration of tumor immune infiltration and anti-tumor therapy responses

CIBERSORT was used to explore the relationship between immune cell infiltration and genes related to prognosis. Furthermore, we investigated the relationship between the expression of prognosis-related genes and that of various immune checkpoint genes. The Cancer Immunome Atlas (TCIA) database was used to forecast the drug responsiveness of PCa patients to immune checkpoint inhibitors. Chemotherapeutic response to common drugs in PCa patients was assessed by the GDSC (Genomics of Drug Sensitivity in Cancer) website and the 'pRRophetic' package with TCGA datasets [17].

Single-cell RNA-seq expression analysis

Single-cell RNA-seq data (GSE153892) from human prostate cancer tissue was used to assess the cell-type-specific expression of BIRC family genes [18]. The GSE153892 dataset included 10,470 tumor cells in 3 prostate cancer samples. Seurat R package (R version 4.3.1) was used to filter gene expression matrices based on the raw data [19]. We first merged three tumor datasets, considering the cutoffs of > 3,>50, and < 5% respectively for min.cells, number of unique feature counts, and percent of mitochondrial content. The top 1,500 genes with the most variance were selected for further analysis. The subsequent analysis began with a principal component analysis (PCA), where the first 50 principal components were selected. Following this, Louvain clustering and UMAP were performed, resulting in the identification of 17 clusters based on a resolution level of 0.6. The criteria for identifying enrichment genes in clusters were |Log2FC|>1 and FDR < 0.05 (the Wilcox test). Re-clustering of T cell clusters was carried out to further subdivide T cell subtypes. The SingleR algorithm and manual annotation based on the websites of 'cell markers' and 'Cell Taxonomy' were used to assign clusters to cell types [20–22]. Monocle3.17 was used to identify and describe the continuous or discrete transitions between different cellular states and pathways [23].

Statistical analysis

Statistical analysis was conducted using SPSS 22.0. Survival disparities were detected through hazard ratios (HR) alongside their 95% CI. The comparison of continuous variables in either two or multiple groups was executed using the student t-test and one-way ANOVA, respectively. Correlation analysis across all measurements employed Spearman's correlation method. A statistical significance was set at P < 0.05.

Gene expression differential distributiion and Genomic alterations

BIRC2/3/5/6/7 expression in tumor tissues was higher compared to that in normal tissues in GEPIA (Fig. S2). The analysis of gene and protein interactions involving BIRC2/3/5/6/7 revealed a significant degree of interaction among these genes, along with protein homology. (Figs. 1A-B). Co-expression analysis of BIRC family genes was observed (Figs. 1C), such as BIRC5 vs BIRC7, BIRC2 vs BIRC3, BIRC2 vs BIRC6 (r = 0.31, 0.38 and 0.54). Differential expression analysis showed that BIRC5/7 expression is higher in PCa tissue while BIRC3 expression is lower. (Fig. 1D). The CNV data extracted from PCa samples indicated a potential correlation between the gene CNV and the expression levels of prognosis-related genes. (Figs. 1E, F). There was a 5.9% occurrence of BIRC family genes mutation carriers in PCa patients. (Fig. 1G).

Survival analysis and identification of PCa Subtypes Based on BIRC5/7

The TCGA dataset was initially analyzed, and a case lacking RFS time was excluded, resulting in the enrollment of 466 eligible PCa patients. Following KM survival analysis using clinicopathological indicators in PCa, significant clinical features (radical resection, Gleason score, and T/N stage) with a P < 0.05 were isolated. These features were subsequently integrated into Cox regression analysis for adjustment. (Table 1). Furthermore, the KM method was applied for the survival analysis of BIRC3/5/7 genes in TCGA and GSE46602 cohorts. It revealed significant associations of all genes, except BIRC3, with prognosis in PCa. High expression implied a shorter RFS. (Figs. 2A-D). Prognosis-related (BIRC5/7) genes were used to divide PCa patients into 2 subtypes (C1/C2) based on TCGA and GSE46602 datasets (Figs. 2E, F). We discovered that C1 exhibited higher expression levels of BIRC5/BIRC7 genes compared to C2 in TCGA. However, in GSE46602, only BIRC5 expression levels were found to be higher. The median recurrence-free survival time (MRFT) for low- and high- expression groups of BIRC genes were BIRC5 (118 months vs 106 months), BIRC7 (129 months vs 96 months), and C2/C1 (128 months vs 83 months) (Table 2). Similarly, the results of Cox regression analysis revealed that BIRC5 (adjusted P = 0.025; adjusted HR = 1.792 (1.077–2.981), BIRC7 (adjusted P = 0.033; adjusted HR = 1.707, 95% CI:1.045-2.787) and C1/C2 (adjusted P = 0.015; adjusted HR = 1.881, 95% CI:1.133‐3.122) were independent prognostic factors for PCa patients, suggesting high expression of these genes represents an increased risk of mortality (Table 2). The subtypes of C1/C2 better highlight PCa prognosis differences, enhancing prognostic predictability. (Figs. 2I, J). Stratified analysis showed that C1 suggested a poor RFS in PCa with T3/4 [HR (95%CI): 2.106(1.210,3.667)] and non-radical resection [HR (95%CI): 2.565(1.173,5.609)] (Table 3).

Table 1

Clinical characteristics of prostate cancer patients.
Variables	Events/total (n = 466)	MRFT (month)	HR (95% CI)	Log-rank P-value
Age(years)				0.336
<60	36/212	123	1
≥60	48/254	100	1.236(0.802,1.905)
Missing	0
Race				0.379
Asian	4/13	48	1
African	12/56	120	0.495(0.159,1.541)
White	65/383	104	0.496(0.180,1.364)
Missing	14
T stage				< 0.001
T2	12/181	147	1
T3/T4	72/279	79	4.09(2.218,7.544)
Missing	5
N stage				< 0.001
N0	54/325	127	1
N1	24/73	51	2.315(1.428,3.755)
Missing	68
Gleason score				< 0.001
6–7	24/277	146	1
8–10	60/189	466	4.239(2.639,6.808)
Missing	14
Zone of origin				0.975
Light/Right	9/49	78	1
Bilateral	74/410	122	0.989(0.494,1.980)
Missing	7
Radical resection				< 0.001
R0	38/295	133	1
R1/R2	41/142	81	2.593(1.666,4.034)
Missing	29

Table 2

Prognostic value of single and combined of Baculoviral IAP Repeat Containing genes expression in prostate cancer patient RFS from TCGA.
Gene	Events/total (n = 466)	MRFT (month)	Crude HR (95% CI)	Crude P-value	Adjusted HR (95% CI) ^a	Adjusted P-value^a
BIRC5
Low	27/233	118	1		1
High	57/233	106	2.104 (1.330,3.329)	0.001	1.792 (1.077,2.981)	0.025
BIRC7
Low	33/233	129	1		1
High	51/233	96	1.694 (1.093,2.627)	0.018	1.707	0.033
Cluster					(1.045,2.787)
C2	25/216	128	1		1
C1	59/250	83	2.344 (1.464,3.751)	< 0.001	1.881 (1.133,3.122)	0.015
Abbreviations: CI, confidence interval; HR, hazard ratio; MRFT, median recurrence free survival time; RFS, recurrence free survival.
^aAdjusted for radical resection, Gleason score and T/N stage.

Table 3

Stratified analysis of C1/C2 subgroups and RFS in PCa patients.
Group	C2		Crude HR (95% CI)	Crude P-value	Adjusted HR (95% CI)	Adjusted P-value^a
N stage
N0	116	92	1.221(0.506,2.948)	0.657
N1	23	44	1.291(0.493,3.376)	0.603
T stage
T 2	71	60	1.281(0.300,5.460)	0.738
T 3/4	93	148	2.106(1.210,3.667)	0.008	1.988(1.141,3.467)	0.015
Gleason score
6–7	116	92	1.221(0.506,2.948)	0.656
8–10	116	48	1.573 (0.809,3.059)	0.182
Radical resection
R0	119	130	1.474(0.740,2.938)	0.270
R1/R2	45	78	2.565(1.173,5.609)	0.018	2.441(1.113,5.353)	0.026

The clinical significance of prognosis-related genes.

The diagnostic ROC curve analysis results showed that BIRC5 and BIRC7 effectively distinguished between tumor tissue and normal tissue (BIRC5:0.889; BIRC7:0.763) (Fig. 2K). The time-dependent ROC curve of BIRC5 (1-, 2-, 3-years: 0.721, 0.656, 0.629) and BIRC7 (1-, 2-, 3-years: 0.619, 0.650, 0.617) at 1, 2, 3-years survival were medium in TCGA dataset (Figs. 3A-B). Next, the correlation between prognosis-related genes and clinicopathological factors was examined. In the TCGA dataset, the results revealed upregulation of BIRC5 in clinical features of age ≥ 60, T3/4, N1, Gleason score 8–10, PD/SD/PR, R1/R2, and patients who survive with tumor. Additionally, upregulation of BIRC7 was observed in clinical features of age ≥ 60, Gleason score 8–10, PD/SD/PR, R1/R2, and patients who survive with tumor. As for GSE46602, BIRC5 expression was significantly higher in PCa patients with T3 stage, Gleason score of 7–9, and tumor-positive resection margins (Figs. 3C-L). Moreover, we also found that BIRC5 may be associated with increased PSA (Figs. 3M, N).

Gene enrichment analysis

Next, we performed differential expression analysis based on the C1/C2 subtypes. The heatmap displays the differentially expressed genes (DEGs) between the two subtypes (Fig. 4A). GO and KEGG analyses were used to elucidate DEG functions. Biological process analyses showed that DEGs were enriched in nuclear division mitotic, nuclear division, and chromosome segregation. Cellular component analysis showed that immunoglobulin complex, sarcomere, myofibril, and contractile fiber were mainly enriched. Molecular function analysis highlighted DEGs primarily associated with antigen binding, actin binding, and immunoglobulin receptor binding (Fig. 4C). The KEGG pathway analysis unveiled their primary involvement in the cell cycle and drug metabolism (Fig. 4D).

Research on the Tumor Immune Microenvironment

Differential expression analysis revealed that DEGs modulate immunoglobulin receptor binding and immunoglobulin complex. Subsequently, the relationship between prognosis-related genes and immune cell infiltration was explored. The CIBERSORT algorithm was employed to assess whether BIRC5/7 expression could impact the distribution of infiltrating immune cells within tumor tissues. As indicated by the outcomes, Macrophages M2, T cells regulatory (Tregs) and T cells follicular helper were more enriched in the BIRC5 high expression PCa group, while T cells CD4 memory resting and Mast cells activated were more enriched in the BIRC5 low expression PCa group (Fig. 5A). Similarly, Macrophages M2, T cells regulatory (Tregs) and Macrophages M0 were more enriched in the BIRC7 high expression PCa group, while T cells CD4 memory resting, Macrophages M1, Monocyte and B cells naïve were more enriched in the BIRC7 low expression PCa group (Fig. 5B). Similar results were found in C1/C2 subtypes (Fig. 5C). The relationship between BIRC5/7 expression and immune-infiltrating cells in PCa was further explored. The outcomes showed BIRC5 exhibited a correlation with Macrophages M2 (r = 0.31), T cells regulatory (Tregs) (r = 0.24), T cells follicular helper (r = 0.17), and T cells CD4 memory resting (r = − 0.34) (Figs. 6A, B). Furthermore, BIRC7 was found to be related to Macrophages M2 (r = 0.33), T cells regulatory (Tregs) (r = 0.29), Macrophages M0 (r = 0.18), and T cells CD4 memory resting (r = − 0.37) (Figs. 6C, D).

Prediction of Immunotherapy and Chemotherapy Responses, and Co-expression Analysis of Immune Checkpoint Inhibitors

In recent years, immunotherapy has notably enhanced the prognosis for numerous cancer types. These medications mainly consist of antibodies that target CTLA-4 or PD-1/PD-L1. In order to investigate the responsiveness of BIRC5/7 expression to targeted inhibitors and immunotherapy, we acquired the International Prognostic Scores (IPSs) of PCa cases from the TCIA database (https://tcia.at/). Patients in the BIRC5 high expression group had a lower IPS (Figs. 7A-D, p < 0.01). However, this phenomenon was not observed in the BIRC7 high-expression group (Figs. 7E-H, p > 0.01). This observation suggests that cases within the high expression group of BIRC5 might exhibit increased sensitivity to CTLA4 and PD-1/PD-L1 blockade. Tumor cells employ diverse pathways to facilitate immune evasion, thereby promoting their progression. For instance, they may upregulate immunosuppressive checkpoint molecules, hindering anti-tumor immune responses. Then, the correlation between the expression of BIRC5/7 and several immune checkpoints was explored. The findings demonstrated a positive correlation between BIRC5/7 expression levels and various inhibitory checkpoint molecules, such as HAVCR2, PDCD1LG2, PDCD1, LAIR1, CD276, CTLA4, and CD274 (Figs. 7I, J). The drug sensitivity analysis showed a significant correlation between BIRC5/BIRC7 expression and anti-cancer drug sensitivity (Sepantronium bromide, Olaparib, Paclitaxel, Zoledronate, IAP_5620, 5-Fluorouracil, Cisplatin).

Cell-type specificity expression and functional trajectory inference

The results related to data preprocessing and the principal component analysis heatmap were placed in the supplementary files (Fig. S3). After standardizing the single-cell dataset, no significant batch effects were detected (Fig. 9A). Subsequently, we identified nine distinct cell types from the 15 clusters (Mainly including Monocyte, T cells, Resident natural killer cell, B cell, Macrophage, Mast cell, and Dendritic cell) (Figs. 9B-E). All genes in the BIRC gene family, except for BIRC7, exhibited cell-type-specific enrichment in PCa samples. The results showed that BIRC5 was mainly enriched in T cells (cluster 13), while BIRC3 exhibited enrichment in most cell types (Figs. 9F, G). Trajectory analysis was a bioinformatics method that inferred and constructed the developmental trajectory of cells within biological processes. This was achieved by examining gene expression changes in cells belonging to different clusters. This approach helps to understand transitions in cell states, changes in gene expression, and the dynamic nature of biological processes. There were three independent trajectory directions in different cell types, whereas the T cell crossed all three directions, suggesting that T cells played a significant role in shaping the immune microenvironment of prostate tumors (Fig. 9H). Additionally, monocytes likely served as the initiation point for the formation of the immune microenvironment (Fig. 9I). Cell trajectory analysis heatmaps revealed a positive correlation between BIRC5/ BIRC7 and pseudotime values, suggesting their distinct roles in the mid-to-late stages of tumor development (Fig. 9J). To further investigate the role of BIRC5 in T cells, we performed a re-clustering of the T-cell cluster, resulting in the identification of 10 clusters and 8 T-cell subtypes (Figs. 10A, B). We observed that BIRC5 was mainly enriched in proliferative T cells, whereas BIRC3 was enriched in regulatory T cells, and BIRC7 expression was undetected (Fig. 10C). Highly expressed BIRC5-characterized proliferative T cells resided at the origin of the cell trajectory map, suggesting their potential pivotal role in the evolution of T cells within the PCa immune microenvironment (Figs. 10D-G).

In recent years, molecular targeted therapy has become an important approach for the treatment of PCa. Traditional targeted therapy mainly focuses on androgen receptors, but patients are prone to developing metastatic castration-resistant prostate cancer (mCRPC), which seriously affects treatment efficacy. Therefore, identifying new therapeutic targets for PCa is imperative.

The genes BIRC2 and BIRC3 encode the cIAP1 and cIAP2 proteins, respectively, both of which are essential regulators of the NF-kappa-B (NFκB) signaling pathway [24]. Specifically, cIAP1 exerts its influence through its E3 ubiquitin-protein ligase activity, playing a crucial role in NFκB activation. This activation occurs primarily through the degradation of IκB inhibitors, a pivotal event in initiating NFκB-mediated cellular responses. Importantly, it's worth noting that cIAP1's involvement extends to both the canonical and noncanonical NFκB signaling pathways, underscoring its multifaceted role in shaping cellular signaling cascades [25]. However this study indicated that BIRC2/3 could not predict disease recurrence in PCa patients.

Survivin, a protein encoded by the BIRC5 gene, shares functional similarities with XIAP. Its expression profile varies during development, being prominently detected in the kidneys, brain, liver, lungs, and digestive tract during embryonic development. In fully developed organisms, survivin is found in limited amounts within tissues characterized by active proliferation and continuous regeneration (CD34 + stem cells). Earlier research has demonstrated that Survivin hampers cell death that is triggered by extrinsic and intrinsic apoptotic pathways. It imparts cells with anti-apoptotic prowess by directly or indirectly suppressing caspase activity [26]. Survivin is consistently expressed in various cancer types, including breast and colorectal cancer, largely due to the overexpression of its encoding gene, BIRC5. Elevated survivin levels are associated with poor prognoses in cancer patients [24, 25, 27–29]. An immunohistochemical analysis of 3000 patients with prostate cancer has revealed a significant upregulation of BIRC5 expression in tumor tissues, particularly in cases of metastatic disease. This elevation in expression is linked to increased tumor aggressiveness and advancement [30]. Our findings also suggest that elevated BIRC5 expression is associated with an adverse PCa prognosis. Additionally, BIRC5 is implicated in regulating the cell cycle, immunoglobulin complex, and immunoglobulin receptor binding. These processes have a substantial correlation with the advancement of tumors [31]. Our data also identified the correlation between elevated BIRC5 expression and advanced cancer characteristics, including T3/4 stages, N1, high Gleason scores, and patients who survived with tumors.

The APOLLON protein, encoded by the BIRC6 gene, holds a pivotal role in cancer. It possesses the ability to hinder the caspase cascade, thereby impeding programmed cell death [32]. Moreover, it serves a role in cell protection and participates in the regulation of cytokinesis [33, 34]. Despite this, we did not find a significant biological role of it in PCa.

Livin α/β, two splicing variants originating from the BIRC7, play an important role in tumors [35]. Livin α is linked to cellular resistance against staurosporin, whereas livin β prompts cellular resistance against TNF-α-induced apoptosis and etoposide [35, 36]. Livin overexpression is also observed in various cancers, such as bladder, colon cancer, hepatocellular carcinoma, and so on [37–39]. This study substantiates the association between elevated BIRC7 expression and poor clinical characteristics, including high Gleason scores and patients living with tumors.

In the TCGA bulk RNA-Seq dataset, prostate cancer samples were classified into two subtypes depending on the expression level of BIRC5/7. Then, we explore the tumor immune microenvironment in BIRC5/7 and C1/C2. The outcomes indicated that high–risk groups (high expression of BIRC5/7 & C1 subtype) positively associated with several immune cells, such as Macrophages M2/M0 and T cells regulatory (Tregs), and negatively associated with CD4 memory resting and Macrophages M1. T cell regulatory (Tregs) is a subset of T cells that inhibits the immune response of the body [40]. Although it only accounts for 1–2% of peripheral blood lymphocytes, it plays a crucial role in maintaining self-immune tolerance and long-term immune balance[41]. However, Treg cells are also the main obstacle to anti-tumor immunity. The formation and development of tumors require evasion of the body's immune surveillance, and Treg cells are the most important component involved in this process [42]. Treg cells, together with other cells and the extracellular matrix, create a microenvironment suitable for tumor growth. Many studies have shown that the high enrichment of Treg cells in tumors is associated with unfavorable prognostic outcomes [40, 43]. In the tumor microenvironment, the number of Treg cells is significantly increased, and these elevated Treg cells can suppress anti-tumor immunity and reduce the tumor immunotherapy effectiveness. Our research has found that there is generally higher T cell regulatory (Tregs) infiltration in the high-risk group, indicating that this cell may be responsible for the poor effectiveness of tumor immunotherapy. Macrophages have a key role in the body's defense against tumors [44]. However, under certain conditions, they can also promote tumor growth and development. Macrophages can be broadly divided into two types (M1& M2). M1 macrophages are activated by signals from the immune system and play a key role in the early stages of immune responses against tumors [44, 45]. They produce cytokines and chemokines that attract other immune cells to the site of the tumor, and they can also directly kill tumor cells. In addition, M2 macrophages are activated by signals from the tumor itself, and they promote tumor growth and metastasis [46]. They produce factors that stimulate angiogenesis (the growth of new blood vessels) and tissue remodeling, which can help the tumor to spread to other parts of the body. They can also suppress the activity of T cells, which are another type of immune cell that can attack tumors [47]. In our result, Macrophages M2, which are associated with a poor prognosis, are more prevalent in high-risk groups. This is consistent with the results of the sensitivity analysis of immunotherapy. In the outcomes of drug sensitivity analysis, Sepantronium bromide inhibits the expression of certain proteins, such as survivin (BIRC5) and livin (BIRC7). It showed a low estimated IC50 in the high BIRC5 expression group but had no significant effect in the BIRC7 group. All of these suggest that it may be more suitable for PCa patients with high expression of BIRC5. In single-cell RNA-seq analysis, we identified that BIRC5 was primarily expressed in proliferative T cells. Proliferative T cells (CDK1 + and MKI67 + T cells), also known as Dividing T cells or pro T cells, belonged to the lymphocyte category and were characterized by the expression of a T cell receptor complex [48]. These cells represented a precursor or developing stage of T cells, which were a type of white blood cell integral to the immune system. They were in the early stages of maturation and differentiation, undergoing multiple developmental phases to ultimately become fully functional T cells with the capacity to recognize and attack infected or abnormal cells [48, 49]. Our hypothesis was that BIRC5 might have influenced proliferative T cells in a manner that redirected their development toward a direction favorable for tumor progression. This redirection could have led to the formation of an aberrant microenvironment that supported tumor immune evasion, resulting in an unfavorable prognosis for PCa patients. More in vivo and in vitro experiments are required to validate our hypothesis. In conclusion, BIRC5 shows great promise as a novel target for immunotherapy in prostate cancer.

Our study conducted a comprehensive assessment of BIRC gene expression and their potential prognostic significance in PCa. Additionally, we established two subtypes based on TCGA data and validated them using the GEO dataset. The discovery of these immune infiltration-related genes or C1/C2 subtypes may be good news for the "cold winter" of immunotherapy for PCa. Decreasing the expression of these genes could markedly improve the efficacy of immunotherapy. These discoveries highlight the critical importance of these genes within the immune environment.

There are certain limitations in this study. Firstly, the data for this study was mainly sourced from retrospective collection, necessitating further acquisition of prospective real-world data to validate the outcomes. Secondly, our results are rooted in bioinformatics analysis. Additional research is essential to uncover the mechanism, both in vitro and in vivo.

Collectively, high expression of BIRC5/7 or C1 subtype predicts an unfavorable prognosis in PCa and hence holds promise as potential disease biomarkers. These genes could regulate cancer-related signaling pathways and modulate immune infiltration in PCa. Additional prospective investigations are necessary to validate these molecular mechanisms.

Competing interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author Contributions

RL and YL performed the data analysis work and aided in writing the manuscript. XY, WC, and YL designed the study and assisted in writing the manuscript. ZM edited the manuscript. All authors read and approved the final manuscript.

Funding

This study was supported by grants from the Guangxi Natural Science Foundation (2014GXNSFBA118201) and the Self-Raised Scientific Research Fund of the Health Commission of Guangxi Zhuang Autonomous Region (Z-A20231089), and the Key Special Project of China's Key R&D Program "Active Health and Scientific Response to Aging (2021YFC2009300).

Acknowledgments

This is a short text to acknowledge the contributions of specific colleagues, institutions, or agencies that aided the efforts of the authors.

Data availability

All the data used in this study are available in TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/), which are public data repositories.

Code availability

Access to all code data for this study will be granted upon the reasonable request of the corresponding author.

Ethics approval and consent to participate Not applicable.

Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70(1):7–30.
Hofman MS, Violet J, Hicks RJ, et al. [(177)Lu]-PSMA-617 radionuclide treatment in patients with metastatic castration-resistant prostate cancer (LuPSMA trial): a single-centre, single-arm, phase 2 study. Lancet Oncol. 2018;19(6):825–33.
von Eyben FE, Roviello G, Kiljunen T, et al. Third-line treatment and (177)Lu-PSMA radioligand therapy of metastatic castration-resistant prostate cancer: a systematic review. Eur J Nucl Med Mol Imaging. 2018;45(3):496–508.
Lopez J, Meier P. To fight or die - inhibitor of apoptosis proteins at the crossroad of innate immunity and death. Curr Opin Cell Biol. 2010;22(6):872–81.
Low CG, Luk IS, Lin D, et al. BIRC6 protein, an inhibitor of apoptosis: role in survival of human prostate cancer cells. PLoS ONE. 2013;8(2):e55837.
Liang J, Zhao W, Tong P, et al. Comprehensive molecular characterization of inhibitors of apoptosis proteins (IAPs) for therapeutic targeting in cancer. BMC Med Genom. 2020;13(1):7.
Gyrd-Hansen M, Meier P. IAPs: from caspase inhibitors to modulators of NF-kappaB, inflammation and cancer. Nat Rev Cancer. 2010;10(8):561–74.
Deveraux QL, Takahashi R, Salvesen GS, et al. X-linked IAP is a direct inhibitor of cell-death proteases. Nature. 1997;388(6639):300–4.
Adinew GM, Messeha S, Taka E et al. The Prognostic and Therapeutic Implications of the Chemoresistance Gene BIRC5 in Triple-Negative Breast Cancer. Cancers (Basel). 2022;14(21).
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.
Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Statistical applications in genetics and molecular biology. 2004;3:Article3.
Warde-Farley D, Donaldson SL, Comes O et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 2010;38(Web Server issue):W214–20.
von Mering C, Huynen M, Jaeggi D, et al. STRING: a database of predicted functional associations between proteins. Nucleic Acids Res. 2003;31(1):258–61.
Mermel CH, Schumacher SE, Hill B, et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 2011;12(4):R41.
Witten DM, Tibshirani R. A framework for feature selection in clustering. J Am Stat Assoc. 2010;105(490):713–26.
Yu G, Wang LG, Han Y, et al. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–7.
Geeleher P, Cox N, Huang RS. pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS ONE. 2014;9(9):e107468.
Masetti M, Carriero R, Portale F et al. Lipid-loaded tumor-associated macrophages sustain tumor growth and invasiveness in prostate cancer. J Exp Med. 2022;219(2).
Butler A, Hoffman P, Smibert P, et al. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411–20.
Aran D, Looney AP, Liu L, et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol. 2019;20(2):163–72.
Jiang S, Qian Q, Zhu T, et al. Cell Taxonomy: a curated repository of cell types with multifaceted characterization. Nucleic Acids Res. 2023;51(D1):D853–60.
Zhang X, Lan Y, Xu J, et al. CellMarker: a manually curated resource of cell markers in human and mouse. Nucleic Acids Res. 2019;47(D1):D721–8.
Trapnell C, Cacchiarelli D, Grimsby J, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32(4):381–6.
Saleem M, Qadir MI, Perveen N, et al. Inhibitors of apoptotic proteins: new targets for anticancer therapy. Chem Biol Drug Des. 2013;82(3):243–51.
Estornes Y, Bertrand MJ. IAPs, regulators of innate immunity and inflammation. Semin Cell Dev Biol. 2015;39:106–14.
Li C, Wu Z, Liu M, et al. Chemically synthesized human survivin does not inhibit caspase-3. Protein Sci. 2008;17(9):1624–9.
Aird KM, Ghanayem RB, Peplinski S, et al. X-linked inhibitor of apoptosis protein inhibits apoptosis in inflammatory breast cancer cells with acquired resistance to an ErbB1/2 tyrosine kinase inhibitor. Mol Cancer Ther. 2010;9(5):1432–42.
Falkenhorst J, Grunewald S, Muhlenberg T, et al. Inhibitor of Apoptosis Proteins (IAPs) are commonly dysregulated in GIST and can be pharmacologically targeted to enhance the pro-apoptotic activity of imatinib. Oncotarget. 2016;7(27):41390–403.
Hingorani P, Dickman P, Garcia-Filion P, et al. BIRC5 expression is a poor prognostic marker in Ewing sarcoma. Pediatr Blood Cancer. 2013;60(1):35–40.
Hennigs JK, Minner S, Tennstedt P, et al. Subcellular Compartmentalization of Survivin is Associated with Biological Aggressiveness and Prognosis in Prostate Cancer. Sci Rep. 2020;10(1):3250.
Xie J, Li Y, Jiang K, et al. CDK16 Phosphorylates and Degrades p53 to Promote Radioresistance and Predicts Prognosis in Lung Cancer. Theranostics. 2018;8(3):650–62.
Bartke T, Pohl C, Pyrowolakis G, et al. Dual role of BRUCE as an antiapoptotic IAP and a chimeric E2/E3 ubiquitin ligase. Mol Cell. 2004;14(6):801–11.
Pohl C, Jentsch S. Final stages of cytokinesis and midbody ring formation are controlled by BRUCE. Cell. 2008;132(5):832–45.
Hao Y, Sekine K, Kawabata A, et al. Apollon ubiquitinates SMAC and caspase-9, and has an essential cytoprotection function. Nat Cell Biol. 2004;6(9):849–60.
Crnkovic-Mertens I, Muley T, Meister M, et al. The anti-apoptotic livin gene is an important determinant for the apoptotic resistance of non-small cell lung cancer cells. Lung Cancer. 2006;54(2):135–42.
Ashhab Y, Alian A, Polliack A, et al. Two splicing variants of a new inhibitor of apoptosis gene with different biological properties and tissue distribution pattern. FEBS Lett. 2001;495(1–2):56–60.
Myung DS, Park YL, Chung CY, et al. Expression of Livin in colorectal cancer and its relationship to tumor cell behavior and prognosis. PLoS ONE. 2013;8(9):e73262.
Kempkensteffen C, Hinz S, Christoph F, et al. Expression of the apoptosis inhibitor livin in renal cell carcinomas: correlations with pathology and outcome. Tumour Biol. 2007;28(3):132–8.
Altieri B, Sbiera S, Della Casa S, et al. Livin/BIRC7 expression as malignancy marker in adrenocortical tumors. Oncotarget. 2017;8(6):9323–38.
Tanaka A, Sakaguchi S. Regulatory T cells in cancer immunotherapy. Cell Res. 2017;27(1):109–18.
Sawant DV, Vignali DA. Once a Treg, always a Treg? Immunol Rev. 2014;259(1):173–91.
Sawant DV, Yano H, Chikina M, et al. Adaptive plasticity of IL-10(+) and IL-35(+) T(reg) cells cooperatively promotes tumor T cell exhaustion. Nat Immunol. 2019;20(6):724–35.
Curiel TJ, Coukos G, Zou L, et al. Specific recruitment of regulatory T cells in ovarian carcinoma fosters immune privilege and predicts reduced survival. Nat Med. 2004;10(9):942–9.
Mantovani A, Allavena P, Marchesi F, et al. Macrophages as tools and targets in cancer therapy. Nat Rev Drug Discov. 2022;21(11):799–820.
Cassetta L, Pollard JW. Targeting macrophages: therapeutic approaches in cancer. Nat Rev Drug Discov. 2018;17(12):887–904.
Coussens LM, Zitvogel L, Palucka AK. Neutralizing tumor-promoting chronic inflammation: a magic bullet? Science (New York, NY). 2013;339(6117):286 – 91.
Mantovani A, Allavena P, Sica A, et al. Cancer-related inflammation. Nature. 2008;454(7203):436–44.
Du Y, Cai Y, Lv Y, et al. Single-cell RNA sequencing unveils the communications between malignant T and myeloid cells contributing to tumor growth and immunosuppression in cutaneous T-cell lymphoma. Cancer Lett. 2022;551:215972.
Chen Z, Xu H, Li Y, et al. Single-Cell RNA sequencing reveals immune cell dynamics and local intercellular communication in acute murine cardiac allograft rejection. Theranostics. 2022;12(14):6242–57.

No competing interests reported.

Download PDF

Editorial decision: Revision requested
09 Sep, 2024
Reviews received at journal
08 Sep, 2024
Reviews received at journal
06 Sep, 2024
Reviews received at journal
06 Sep, 2024
Reviewers agreed at journal
04 Sep, 2024
Reviewers agreed at journal
04 Sep, 2024
Reviewers agreed at journal
01 Sep, 2024
Reviewers agreed at journal
01 Sep, 2024
Reviewers agreed at journal
30 Aug, 2024
Reviewers agreed at journal
30 Aug, 2024
Reviewers invited by journal
30 Aug, 2024
Editor assigned by journal
28 Aug, 2024
Submission checks completed at journal
26 Aug, 2024
First submitted to journal
18 Aug, 2024

You are reading this latest preprint version

Exploring BIRC Family Genes as Prognostic Biomarkers and Therapeutic Targets in Prostate Cancer

Status:

Version 1

Abstract

Figures

1 INTRODUCTION

2 MATERIALS AND METHODS

3 Results

4 Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1