Dr Menglan Li, NHC Contraceptives Adverse Reaction Surveillance Center, Jiangsu Health Development Research Center, 277 Fenghuang West Street, Gulou, Nanjing, Jiangsu 210036, P.R. China
*Contributed equally
Biochemical recurrence (BCR) is a cause of concern in advanced prostate cancer (PCa). Thus, novel diagnostic biomarkers are required to improve clinical care. However, research on PCa immunotherapy is also scarce. Hence, the present study aimed to explore promising BCR-related diagnostic biomarkers, and their expression pattern, prognostic value, immune response effects, biological functions, and possible molecular mechanisms were evaluated. GEO datasets (GSE46602, GSE70768, and GSE116918) were downloaded and merged as the training cohort, and differential expression analysis was performed. Lasso regression and SVM-RFE algorithm, as well as PPI analysis and MCODE algorithm, were then applied to filter BCR-related biomarker genes. The CIBERSORT and estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) methods were used to calculate the fractions of tumor-infiltrating immune cells. GO/DO enrichment analyses were used to identify the biological functions. The expression of latent transforming growth factor β-binding protein 2 (LTBP2) was determined by RT-qPCR and western blotting. The role of LTBP2 in PCa was determined by CCK-8, Transwell, and the potential mechanism was investigated by KEGG and GSEA and confirmed by western blotting. In total, 44 BCR-related differentially expressed genes (DEGs) in the training cohort were screened. LTBP2 was found to be a diagnostic biomarker of BCR in PCa and was associated with CD4+ T-cell infiltration and response to anti-PD-1/PD-L1 immunotherapy. Subsequently, using the ESTIMATE algorithm, it was identified that LTBP2 was associated with the tumor microenvironment and could be a predictor of the clinical benefit of immune checkpoint blockade. Finally, the expression and biological function of LTBP2 were evaluated via cellular experiments. The results showed that LTBP2 was downregulated in PCa cells and inhibited PCa proliferation and metastasis via the PI3K/AKT signaling pathway
Prostate cancer (PCa) is the most pervasive tumor among solid male tumors, accounting for 26% of cases reported, and is also the second leading cause of tumor-associated deaths in men, accounting for 11% of cancer-specific deaths (
Immune cells are an essential component of the tumor microenvironment (TME) and play a crucial role in tumorigenesis and progression, which has been investigated in numerous studies (
Given the aforementioned reasons, the present study aimed to identify potential diagnostic biomarkers of BCR in PCa and validate their correlation with immunity and prognosis. In this present study, latent transforming growth factor β-binding protein 2 (LTBP2) was screened and determined as a diagnostic biomarker gene associated with BCR of PCa by different algorithms, which could be confirmed by other external datasets. Next, the association between LTBP2 and immunity and prognosis was evaluated. The results revealed that the LTBP2 expression was associated with CD4+ T-cell recruitment. Moreover, the present study emphasized the important role of LTBP2 in inhibiting PCa invasion and metastasis
Available public transcriptome data for PCa from the Gene Expression Omnibus (GEO) database (
Lasso regression analysis is a valuable method for identifying interpretable prediction rules in high-dimensional data, featuring a simultaneous selection of variables and elimination of high correlations among them to prevent overfitting (
GO (
Tumor cell TME infiltration level was estimated by immune score, stromal score and tumor purity for each sample using the estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) algorithm (
To elucidate the interaction of LTBP2 on the ICB, the association between LTBP2 expression and three well-known immune checkpoint genes was explored using the Tumor Immune Estimation Resource (TIMER) database (
The Search Tool for Retrieving Interacting Genes (STRING) database (
The ceRNA regulatory network is a common upstream regulatory mechanism of target genes (
To evaluate the predictive value of LTBP2 in immunotherapeutic response, two immunotherapy cohorts, including clinical and transcriptomic data, were downloaded. The GSE78220 cohort (
The human prostate cell (RWPE-1; cat. no. SCSP-5025) and human PCa cell lines (LNcap; cat. no. TCHu173), PC3 (cat. no. TCHu158) and DU145 cells (cat. no. TCHu222) were originally purchased from the Cell Bank of Shanghai Institute of Life Sciences, Chinese Academy of Sciences. RPMI-1640 medium (Procell Life Science & Technology Co., Ltd.) containing 10% fetal bovine serum (FBS; Thermo Fisher Scientific, Inc.), penicillin (25 U/ml) and streptomycin (25 mg/ml; Gibco; Thermo Fisher Scientific, Inc.), were used to culture prostate cells and PCa cells at 37˚C in a humidified 5% CO2 environment. The sequence of LTBP2 was cloned in to a pcDNA3.1-vector to generate overexpression plasmid constructs by Shanghai GeneChem Co., Ltd. Lipofectamine 3000 reagent (Vazyme Biotech Co., Ltd.) was used for cell transfection according to the manufacturer's protocol (
Total RNA was isolated and extracted from cells and clinical tissues using E.Z.N.A.® Total RNA Kit I (50 preps) (Omega Bio-Tek, Inc.). Reverse transcription was then achieved with the HiScript II Q RT SuperMix reagent kit according to the manufacturer's protocol (cat. no. R223-01; Vazyme Biotech Co., Ltd.). PCR was implemented to measure Cq values using the SYBR Green PCR kit (Vazyme Biotech Co., Ltd.) according to the manufacturer's protocol (
CCK-8 assay Kit (BioBIO EXCELLENCE) was used to perform the cell proliferation. In brief, the transfected LNcap and DU145 cells were seeded into 96-well plates at a density of 1,500 cells/well. Following seeding for 24, 48, 72 and 96 h, 10 µl CCK-8 reagent was added to each well and then incubated for another 3 h before detecting the optical density (OD) at 450 nm.
Cell migration and invasion assays were implemented in 8-µm pore size Transwell chambers, distinguishing that invasion assays required 0.5 mg/ml Matrigel pretreatment (37˚C for 1 h). Specifically, the transfected PCa cells (10x104) were resuspended in a serum-free medium and inoculated into the upper chamber, and 600 µl of medium containing 10% FBS was placed in the lower chamber and incubated at 37˚C for 8-20 h. Subsequently, migrating and invading cells were fixed in methanol (20 min at room temperature), stained with 0.1% crystal violet (20 min at room temperature), and photographed and counted using a light microscope at x10 magnification.
Western blotting was conducted using the same method previously reported in the literature (
The statistical analysis was undertaken with R software (version 4.0.3) and GraphPad Prism 7 software (GraphPad Software, Inc.). The Perl programming language (version 5.30.2) was used for data processing. The Kaplan-Meier (K-M) survival analysis and log-rank tests were utilized to analyze the overall survival (OS), progression-free interval (PFI) and disease-specific survival (DSS). The associations between LTBP2 expression and various clinicopathological covariates were examined using a chi-square test. Data were obtained from at least three independent experiments
The flow chart of the present study is presented in
To obtain key BCR-associated DEGs, two distinct algorithms for screening were implemented. First, the Lasso Cox regression algorithm was applied, and 20 key genes were filtered out from 44 BCR-related DEGs (
These 44 BCR-related DEGs were utilized to construct a PPI network using STRING software (
Subsequently, the ROC curve and its AUC value of GEO merged datasets (the training set) were calculated to assess the accuracy and sensitivity of LTBP2 as a BCR diagnostic gene for PCa. As indicated in
To assess the clinical value and application of LTBP2, the association between LTBP2 expression and clinicopathological traits and the impact on prognosis in TCGA-PRAD dataset were examined. The results revealed that LTBP2 was under-expressed in tumor tissues in the TCGA and Genotype-Tissue Expression (GTEx) database (
Furthermore, given the scarcity of LTBP2-associated cancer studies in solid tumors, as shown in
As reported in the literature, immunity plays an essential role in the development and treatment of tumors (
Additionally, the predictive role of LTBP2 was investigated in the immunotherapeutic response against PD-1/PD-L1 based on two immunotherapy cohorts. As shown in
Given the aforementioned results, it was hypothesized that LTBP2 was also correlated with the TME. The ESTIMATE algorithm was used to calculate the tumor cell stromal score, immune score and tumor purity for each patient in TCGA-PRAD dataset. Spearman correlation analysis was performed and revealed that LTBP2 expression was positively correlated with immune score and stromal score in TCGA-PRAD dataset (
Subsequently, GO, KEGG, DO enrichment analyses and GSEA analysis was conducted to identify the biological functions and signaling pathways of LTBP2. These enrichment analyses were performed for the BCR-associated DEGs. The GO profiles revealed that these DEGs were integrally correlated with transforming growth factor-β (TGF-β) receptor and cellular metabolic processes. The top ten GO terms of MF, CC, and BP associated with 44 BCR-related are presented in
To better evaluate the expression and biological function of LTBP2, cellular experiments
Furthermore, the potential mechanisms by which LTBP2 inhibited PCa progression were further explored. Based on previous literature (
Identification and characterization of the specific biomarkers for BCR of PCa may be important for the diagnosis and prognosis of prostate tumors. In the present study, it was demonstrated that LTBP2 could be a diagnostic biomarker for BCR of PCa, which is correlated with immune response. To identify diagnostic biomarkers associated with BCR of PCa, the screening algorithms, Lasso and SVM-RFE were used to screen 44 BCR-related DEGs and 19 differential genes associated with BCR were obtained via overlapping. PPI analysis and MCODE algorithm were also used to screen the 44 DEGs and 4 hub genes were identified. Finally, by overlapping the hub genes and key BCR-related DEGs, LTBP2 was revealed to be the only candidate diagnostic gene for BCR of PCa and was found to be associated with CD4+ T-cell recruitment and anti-PD-1/PD-L1 immunotherapy response. Subsequently, using the ESTIMATE algorithm, it was determined that LTBP2 was associated with the TME state and could predict the clinical benefit of ICB. Finally, the expression and biological function of LTBP2 were evaluated by cellular experiments. The results showed that LTBP2 was downregulated in PCa cells and inhibited PCa proliferation and metastasis via the PI3K/AKT signaling pathway. Collectively, the present study demonstrated that LTBP2 could be employed as a novel biomarker for diagnosing BCR in PCa and a potential immunotherapeutic tool, which could inhibit PCa proliferation and metastasis via the PI3K/AKT signaling pathway.
Several studies (
Undoubtedly, immunotherapy is a powerful treatment strategy for solid tumors, yet PCa appears to be excluded from the ongoing immunotherapy revolution. However, several studies have confirmed the value of immunotherapy in advanced PCa. Bilusic
Recently, LTBP2 was recently identified as an ECM glycoprotein, and its expression was associated with poor prognosis in several tumors. For example, Wang
lncRNAs play an important regulatory role in the malignant progression and BCR of PCa (
In conclusion, 44 BCR-related DEGs were screened using the GEO-merged datasets, and LTBP2 was then identified as a diagnostic biomarker for BCR of PCa based on Lasso, SVM-RFE algorithms, PPI analysis and MCODE algorithm. The stability and reliability of candidate genes were validated with the GEO validation dataset and TCGA-PRAD datasets. It was then determined that LTBP2 exerted a crucial role in CD4+ T-cell recruitment and TME state. Notably, LTBP2 expression enhanced the clinical benefit of immunotherapy for PCa patients with BCR. In addition, the upstream lncRNA-miRNA-LTBP2 ceRNA regulatory network was constructed by bioinformatics and the downstream signaling pathway and biological functions were validated by
Not applicable.
The datasets generated and/or analyzed during the current study are available in the TCGA and GEO repositories,
XZ and CT confirm the authenticity of all the raw data. All authors (XZ, CT, JC, WM, ML and MC) made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted, and agree to be accountable for all aspects of the work.
Not applicable.
Not applicable.
The authors declare that they have no competing interests.
Flow chart of the work on screening and identifying a key gene associated with biochemical recurrence of prostate cancer and validating some potential biological functions. PCa, prostate cancer; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DO, Disease Ontology; DEGs, differentially expressed genes; Lasso, least absolute shrinkage and selection operator; SVM-RFE, support vector machine with recursive feature elimination; PPI, protein-protein interaction; MCODE, molecular complex detection; GSEA, gene set enrichment analysis; ROC, receiver operating characteristic; TCGA, The Cancer Genome ATLAS; PRAD, prostate adenocarcinoma; LTBP2, latent transforming growth factor β-binding protein 2; ceRNA, competitive endogenous RNA; TME, tumor microenvironment.
Screening and identification of LTBP2 as a key DEG associated with BCR of PCa. (A) Lasso Cox regression algorithm was applied to screen DEGs associated with BCR of PCa in GEO-merged datasets. (B) SVM-RFE algorithm was performed to identify DEGs associated with BCR of PCa in GEO-merged datasets. (C) Venn diagram showing the overlap of 19 BCR-based DEGs between the two different algorithms. (D) PPI analysis of BCR-related DEGs in PCa using STRING database. (E) The STRING outcomes were uploaded to Cytoscape software to identify hub genes using MCODE algorithm in the PPI network. (F) Venn diagram revealed that LTBP2 was the only BCR-associated hub gene. LTBP2, latent transforming growth factor β-binding protein 2; DEG, differentially expressed gene; BCR, biochemical recurrence; PCa, prostate cancer; Lasso, least absolute shrinkage and selection operator; GEO, Gene Expression Omnibus; SVM-RFE, support vector machine with recursive feature elimination; PPI, protein-protein interaction; STRING, The Search Tool for Retrieving Interacting Genes; MCODE, molecular complex detection.
Evaluation of the accuracy and sensitivity of LTBP2 as a biomarker for BCR of PCa. (A and C) ROC curve and its AUC value in (A) GEO-merged datasets and (C) validation dataset (GSE70769). (B and D) The relative expression levels of LTBP2 in (B) TCGA-PRAD dataset and (D) a validation dataset (GSE70769). LTBP2, latent transforming growth factor β-binding protein 2; BCR, biochemical recurrence; PCa, prostate cancer; ROC, receiver operating characteristic; AUC, area under the curve; GEO, Gene Expression Omnibus; TCGA, The Cancer Genome ATLAS; PRAD, prostate adenocarcinoma; Pri-, primary.
Associations of LTBP2 expression with clinicopathological features and pan-cancer analysis. (A and B) Boxplots revealed that LTBP2 was under-expressed in tumor tissues compared with normal control tissues in (A) TCGA-PRAD dataset and (B) TCGA combined with GTEx dataset. (C-E) Boxplot indicating LTBP2 expression in different (C) Gleason-score, (D) AJCC T stage, (E) PSA-value of PCa samples from TCGA-PRAD dataset. (F) The ROC curves revealed the efficiency of LTBP2 expression levels to distinguish PCa tissues from normal prostate tissues. (G and H) Boxplots displaying the LTBP2 expression using pan-cancer analysis. (I and J) Boxplot indicating LTBP2 expression in various clinicopathological features using pan-cancer analysis. Subgroup comparison between different tumors in G-I were demonstrated using *P<0.05, **P<0.01 and ***P<0.001. LTBP2, latent transforming growth factor β-binding protein 2; TCGA, The Cancer Genome ATLAS; PRAD, prostate adenocarcinoma; GTEx, Genotype-Tissue Expression; AJCC, American Joint Committee on Cancer; PSA, prostate-specific antigen; PCa, prostate cancer; ROC, receiver operating characteristic.
mRNA expression of LTBP2 is correlated with the level of CD4+ T cells. (A) Violin plot displayed the fraction of tumor-infiltrating immune cells in primary PCa and BCR PCa in the GEO-merged dataset. (B) Lollipop chart revealed the correlation coefficient between LTBP2 expression and immune-infiltrating cells in the GEO-merged dataset. (C and D) The correlation scatter plot revealed that LTBP2 expression was (C) significantly positively correlated with T-cell CD4 memory resting, but (D) significantly negatively correlated with T-cell follicular helper in the GEO-merged dataset. (E) Correlation scatter plots showed a significant positive correlation between LTBP2 and CD4 T-cell expression validated in TCGA-PRAD dataset. (F and G) Boxplots revealed significant differences in LTBP2 expression between (F) different anti-PD-1 clinical response subgroups in the GSE78220 cohort and (G) other anti-PD-L1 clinical response groups in the IMvigor210 cohort. LTBP2, latent transforming growth factor β-binding protein 2; PCa, prostate cancer; BCR, biochemical recurrence; GEO, Gene Expression Omnibus; TCGA, The Cancer Genome ATLAS; PRAD, prostate adenocarcinoma; PD-1, programmed cell death protein 1.
mRNA expression of LTBP2 is correlated with the TME infiltration cell characteristics and immune checkpoint genes. (A and B) The correlation scatter plot revealed that LTBP2 expression was positively correlated with (A) immune score and (B) stromal score in TCGA-PRAD dataset. (C and D) The correlation scatter plot confirmed that LTBP2 expression was significantly positively correlated with T-cell CD4 memory resting, while it was significantly negatively correlated with T-cell follicular helper in TCGA-PRAD dataset. (E) The scatter plot showed that the mRNA expression of LTBP2 was significantly positively correlated with the relative expression of immune checkpoint genes, including PD-L1, CTLA4, PD-1, but negatively correlated with tumor purity in the TIMER database and GEPIA database (cor >0; P-value <0.001). LTBP2, latent transforming growth factor β-binding protein 2; TME, tumor microenvironment; TCGA, The Cancer Genome ATLAS; PRAD, prostate adenocarcinoma; PD-1, programmed cell death protein 1; TIMER, Tumor Immune Estimation Resource; GEPIA, Gene Expression Profiling Interactive Analysis.
Biological function and pathway annotation enrichment analysis. (A) Bubble plot of GO enrichment analysis of the 44 BCR-associated DEGs revealing the enriched BP, CC and MF. (B) DO enrichment analysis of the 44 BCR-associated DEGs showing the enriched top 10 diseases. (C) KEGG pathway analysis revealed the enriched signaling pathways of the 44 BCR-associated DEGs. (D) GSEA showed the top five KEGG signaling pathways in BCR-PCa of the GEO-merged dataset. (E) GSEA enrichment analysis showed the top six KEGG signaling pathways in BCR-PCa in TCGA-PRAD dataset. GO, Gene Ontology; BCR, biochemical recurrence; DEGs, differentially expressed genes; BP, biological processes; CC, cellular components; MF, molecular functions; DO, Disease Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; GSEA, gene set enrichment analysis; PCa, prostate cancer; GEO, Gene Expression Omnibus.
LTBP2 inhibits the proliferation, migration and invasion of PCa cells
Comparison of clinical characteristics of prostate cancer patients in TCGA-PRAD database.
Expression of LTBP2 | ||||
---|---|---|---|---|
Characteristics | Total | Low (%) | High (%) | P-value |
Total samples, n | 449 | 249 | 250 | |
Age, n (%) | 0.054 | |||
≤60 | 324 | 123 (24.6) | 101 (20.2) | |
>60 | 275 | 126 (25.3) | 149 (29.9) | |
T stage, n (%) | <0.001 | |||
T2 | 189 | 117 (23.8) | 72 (14.6) | |
T3 | 292 | 128(26) | 164 (33.3) | |
T4 | 11 | 2 (0.4) | 9 (1.8) | |
N stage, n (%) | 0.120 | |||
N0 | 347 | 168 (39.4) | 179(42) | |
N1 | 79 | 30(7) | 49 (11.5) | |
M stage, n (%) | 0.99 | |||
M0 | 455 | 228 (49.8) | 227 (49.6) | |
M1 | 3 | 2 (0.4) | 1 (0.2) | |
PSA (ng/ml), n (%) | 0.99 | |||
<4 | 415 | 207 (46.8) | 208 (47.1) | |
≥4 | 27 | 14 (3.2) | 13 (2.9) | |
Gleason score, n (%) | <0.001 | |||
6 | 46 | 33 (6.6) | 13 (2.6) | |
7 | 247 | 142 (28.5) | 105(21) | |
8 | 64 | 28 (5.6) | 36 (7.2) | |
9 | 138 | 45(9) | 93 (18.6) | |
10 | 4 | 1 (0.2) | 3 (0.6) |
TCGA, The Cancer Genome ATLAS; PRAD, prostate adenocarcinoma; LTBP2, latent transforming growth factor β-binding protein 2; T, tumor, N, node; M, metastasis; PSA, prostate-specific antigen.