DATABASE ANALYSIS
e-ISSN 1643-3750 © Med Sci Monit, 2020; 26: e920943 DOI: 10.12659/MSM.920943
Received: 2019.10.24 Accepted: 2020.01.27 Multi-Omics Analysis Reveals the Pan-Cancer Available online: 2020.02.12 Published: 2020.04.05 Landscape of Bone Morphogenetic Proteins
Authors’ Contribution: AB Wen-Li Luo Department of Orthopedics, Ningbo Hangzhou Bay Hospital, Ningbo, Zhejiang, Study Design A ABCDEFG Ming-Xing Luo P.R. China Data Collection B Statistical Analysis C ABCD Rong-Zhen He Data Interpretation D ACD Lv-Fang Ying Manuscript Preparation E A Jian Luo Literature Search F Funds Collection G
Corresponding Author: Ming-Xing Luo, e-mail: [email protected], [email protected] Source of support: Departmental sources
Background: Bone morphogenetic proteins (BMPs) are widely involved in cancer development. However, a wealth of con- flicting data raises the question of whether BMPs serve as oncogenes or as cancer suppressors. Material/Methods: By integrating multi-omics data across cancers, we comprehensively analyzed the genomic and pharmacoge- nomic landscape of BMP genes across cancers. Results: Surprisingly, our data indicate that BMPs are globally downregulated in cancers. Further genetics and epi- genetics analyses show that this abnormal expression is driven by copy number variations, especially hetero- zygous amplification. We next assessed the BMP-associated pathways and demonstrated that they suppress cell cycle and estrogen hormone pathways. Bone morphogenetic protein interacts with 58 compounds, and their dysfunction can induce drug sensitivity. Conclusions: Our results define the landscape of the BMP family at a systems level and open potential therapeutic oppor- tunities for cancer patients.
MeSH Keywords: Bone Morphogenetic Proteins • Epigenomics • Gene Expression Profiling
Full-text PDF: https://www.medscimonit.com/abstract/index/idArt/920943
1946 — 9 17
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Background Survival analysis and cancer subtype analysis
Bone morphogenetic proteins (BMPs) were first identified as We used sample barcodes to match gene expression and clin- proteins that are responsible for bone formation. They were ical survival data. We used the median RSEM value to divide first reported to induce the formation of cartilage in vivo in samples into high and low gene expression groups. Then, we 1988 [1] and were believed to be members of the transform- used the R package survival to assess to the survival time and ing growth factor b (TGF-b) family [1]. BMPs are highly con- survival status of the 2 groups. We constructed a Cox ratio-haz- served and have existed for over 700 million years [2], which ard model for each gene and plotted the Kaplan-Meier curve indicates the importance of BMPs in vertebrate physiology. using the log-rank test for each gene. We performed ANOVA They were later shown to regulate a broad spectrum of bio- to find subtype-specific genes. logical processes such as cancer development [3]. The BMP family consists of over 10 components [4], some of which are Single-nucleotide variation analysis currently in clinical use. Perturbations of BMPs gives rise to a wide range of clinical traits, including tumorigenesis. We obtained single-nucleotide variation data from The Cancer Genome Atlas, and we used maftools (https://bioconductor.org/ In the context of cancer, a few studies have shown that BMPs packages/release/bioc/html/maftools.html) to analyze the SNV have widely altered expression in tumor tissues [3]. For exam- data. SNV percentage refers to the number of mutated sam- ple, BMP2 is downregulated in prostate cancer samples and ples divided by the number of cancer samples. is correlated with cancer progression [5]. In contrast, BMP2 is significantly upregulated in head and neck squamous cell car- Copy number variation analysis cinoma [6]. BMP4 was reported by various research groups to have a dual role, acting as an oncogene or a tumor suppres- CNVs are divided into 2 subtypes – heterozygous CNV and ho- sor [7,8]. A wealth of conflicting studies indicated that the mozygous CNV. These 2 subtypes represent only 1 chromo- same BMP ligand can act differently depending on the cancer some or 2 chromosomes simultaneously with CNV. Based on type, and it appears that multiple members in the BMP family the percentage statistics of CNV subtypes, GISTIC (http://soft- have distinct functions [3,9]. These seemingly conflicting re- ware.broadinstitute.org/cancer/software/genepattern/modules/ sults raise the critical question of how they are involved and docs/GISTIC_2.0) was used to process CNV data, and the cal- how they influence cancer progression. There is a pressing culations used original CNV data and RNA-seq data. need to define the role of the BMP family across cancer types. Methylation analysis Here, by using high-throughput sequencing data from a large cohort of patients, we explored the genomic and pharmacoge- We retrieved methylation data from the NCI Genomic Data nomic interactions of BMP genes across cancers to assess how Commons (https://gdc.cancer.gov/). Only cancers paired with BMP family genes are upregulated or downregulated and to normal data were included for analysis. The mRNA expression determine the cancer hallmark signaling pathways they are and methylation data were merged according to their TCGA associated with. Our work helps define the landscape of the barcode. We used the methylation of the promoter region of BMP family at the systems level and provides potential ther- matched genes. We examined the relationship between gene apeutic opportunities for cancer patients. expression and methylation level according to the correla- tion coefficient of humans. Genes with FDR less than 0.05 were retained. Material and Methods Pathway activity quantification Gene expression analysis RPPA data from The Cancer Proteome Atlas (TCPA) were used We limited our analysis to cancer types with over 10 pairs of to calculate the scores of 10 cancer-related pathways. Reversed matched tumors and normal samples. We conducted paired dif- Phase Protein Array (RPPA) is a technology with a process sim- ferential gene expression analysis of tumors and their matched ilar to Western blot analysis. The RPPA data were centered normal samples. The fold change shows the mean gene ex- and the relative protein levels were obtained by normalizing pression of tumors by dividing the mean gene expression of the standard deviations for all samples of each component. normal samples. P values were adjusted by FDR. Genes with fold change (FC >2) and significance (FDR >0.05) were used for further analysis.
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A
BMP8A log2 FC BMP1 ≥3 1.5 BMP8B 0 GDF11 –1.5 BMP4 ≤–3 BMP15 BMP10 FDR GDF2 0.05 10–5 BMP2 10–10 BMP7 <10–15 BMP3 GDF7 BMP6 BMP5 A CA AD AD LIHC KIRC KIRP KICH BL ST THC BRCA CO PRAD
B C
Heterozygous apmli cationHeterozygous deletion Hete CNV% Heterozygous apmli cationHeterozygous deletion Homo CNV% GDF7 5 GDF7 5 10 10 GDF2 20 GDF2 20 40 40 GDF11 60 GDF11 60 BMP8A 100 BMP8A 100 BMP7 BMP7 BMP6 BMP6 BMP5 BMP5 BMP4 BMP4 BMP3 BMP3 BMP2 BMP2 BMP15 BMP15 BMP10 BMP10 BMP1 BMP1 A A A A CA CA AD AD AD AD CA CA AD AD AD AD SCNA Type LIHC LIHC KIRC KIRC KIRP KIRP KICH KICH LIHC LIHC KIRC KIRC KIRP KIRP KICH KICH BL BL ST ST UCEC UCEC BL BL THC THC ST ST BRCA BRCA UCEC UCEC CO CO PRAD PRAD THC THC BRCA BRCA CO CO PRAD PRAD Deletion Ampli cation
Figure 1. Bone morphogenetic protein family genes are commonly dysregulated in human cancers. (A) The CDK gene is downregulated in a wide range of cancers. Copy number variation of the CDK gene includes (B) heterozygous amplification and (C) homozygous amplification.
Drug response analysis with a family error rate of less than 0.025 per tail. The Pearson correlation coefficient of the labeled drug target pair was com- To analyze the correlation between gene expression and drug pared to the same number of correlation coefficients produced sensitivity, we downloaded the region under the drug dose- by random sampling correlation. response curve (AUC) values and the gene expression profiles of all cancer cell lines. Fisher’s Z transform was used to nor- malize the transcription level to the Pearson correlation coeffi- cient of AUCs, with a Bonferroni-corrected 2-tailed distribution
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A B BMP7 BMP2 BMP8B GDF2 BMP3 BMP15 GDF7 BMP5 BMP2 BMP1 BMP4 GDF11 GDF11
Symbol Symbol BMP3 BMP8A BMP8A BMP1 BMP6 GDF7 GDF2 BMP6 BMP5 BMP8B BMP15 BMP4 BMP10 BMP7 A A A A CA CA AD AD AD LIHC LIHC KIRC KIRC KIRP KIRP KICH BL BL ST UCEC THC THC BRC BRC CO CO PRAD PRAD Cancer types Cancer types
Methylation Di (T–N) –Log10 (FDR) 10 20 30 Spearman correlation coecient –Log10 (P. value) 10 20 30 40 50 –2.0 0.0 0.2 0.4
Figure 2. The BMP family is epigenetically demethylated. (A) Differential methylation of the BMP genes in human cancers. (B) Correlation between methylation and BMP gene expression.
Results Promoters of the bone morphogenetic protein family are consistently epigenetically methylated The bone morphogenetic protein family shows globally downregulated expression across human cancers DNA methylation, especially gene promoter methylation, con- tributes to downregulation of mRNA expression [11]. To deter- An unanswered question about BMPs is whether these mole- mine why BMP molecules are consistently downregulated, we cules are downregulated or upregulated [3]. We thus queried next explored whether methylation is involved in this process. the BMP expression landscape across 10 cancers in The Cancer We retrieved the methylome data of pan-cancer patients in Genome Atlas with matched normal samples. We directly used The Cancer Genome Atlas and searched their matched RNA-seq paired differential gene expression analysis. Surprisingly, we data. As expected, most of the BMP family molecules showed observed that BMP molecules are consistently downregulat- an epigenetically silenced trend (Figure 2A). For example, pro- ed across cancers (Figure 1A and Supplementary Figure 1). For motors of BMP6 are consistently methylated, and this is in example, BMP5 shows globally decreased expression in most agreement with their expression at the RNA level. Next, we cancers. In kidney renal clear cell carcinoma, BMPs show the directly computed the spearman Rho estimate to confirm the most alteration, but in bladder urothelial carcinoma only BMP8A negative correlation between methylation and gene expres- is upregulated in tumor samples. To explore the origin of ex- sion. As expected, methylation level was negatively associat- pression alteration, we assessed the copy number variations ed with mRNA expression (Figure 2B). Collectively, our data (CNVs) of the BMP family [10] and surprisingly found that CNVs demonstrate that methylation also contributes to BMP fami- importantly contribute to expression alteration (Figure 1B, 1C, ly downregulation. and Supplementary Figure 2). Specifically, heterozygous am- plification, but not heterozygous deletion, is directly correlat- Mutation also affects the dysfunctional bone ed with mRNA expression. BMP family dysregulation is also morphogenetic protein landscape across cancers associated with patient survival (Supplementary Figure 3). In summary, the BMP family shows globally downregulated ex- To further understand how BMPs are altered across cancers, we pression across human cancers, in which CNV contributes to analyzed the mutation profile of BMPs that result in dysfunc- expression alteration. tional protein function but do not induce expression change. We first assessed the mutation frequency and unexpectedly observed that BMPs, especially BMP1, are frequently mutated (Figure 3A). In 338 analyzed patients, 372 (90.86%) patients showed at least 1 mutation. Particularly in uterine corpus en- dometrial carcinoma, ~32% samples showed mutated BMP1.
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A 14 12 10 8 6 4 2 010203040506070 0 21 BMP1 18 BMP5 17 GDF2 16 BMP7 % 13 BMP3 12 BMP2 12 BMP15 12 BMP4 10 BMP6 10 BMP10
Cancer types BRCA BLCA Nonsense mulation Splice site THCA KICH Missense mulation Multi hit COAD KIRC Frame shift ins Frame shift del LIHC KIRP STAD PRAD UCEC
B AD (n=368) AD (n=290) UCEC (n=249) ST CO LIHC (n=199) B:CA (N=395) BRCA (n=976) KICH (n=67) KIRC (n=442) KIRP (n=162) PRAD (n=426) THCA (n=402)
BMP1 Mutation frequency (%) BMP5 65 55 GDF2 45 35 BMP7 25 20 BMP3 15 10 5 BMP2 0 BMP15
BMP4
BMP6
BMP10
GDF11
GDF7
BMP8B
BMP8A
Figure 3. Mutations can also lead to a BMP family that is dysfunctional in cancer. (A) Frequency of CDK gene mutation. (B) A summary of the variation of each sample.
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A
GDF7
BMP8B
BMP8A
BMP7
BMP6 Symbol BMP5
BMP4
BMP3
BMP2
BMP1 I I I I A A A A T I R I T A R A TK I cle I TK A cle A R TO EM R TO EM ell cy esponse I ell cy esponse A SC/m C PI3K/AKT I SC/m C Apoptosis PI3K/AKT A T Apoptosis RAS/MAPK T RAS/MAPK Hormone ER Hormone AR Hormone ER Hormone AR Percent DNA damage r B DNA damage r –2002040 Pathway (A: Activity; I: Inhibit) esponse cle T K K C ell cy Apoptosis C DNA damage r EM Hormone AR Hormone ER PI3K/AKT RAS/MAPK RT RT TS
BMP1 Activation BMP2 Inhibition None
BMP3
BMP4
BMP5
BMP6
BMP7
BMP8A
BMP8B
GDF11
GDF7
Figure 4. The BMP genes are widely associated with the hallmark cancer pathways. (A) A gene heat map with function (inhibition or activation) in at least 5 cancer types. Pathway_a represents the activation of this pathway in a manner similar to pathway_i. (B) The pathway activation or inhibition percentage of all BMP genes.
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XAV939 SB 216763 UNC0638 Tubastatin A Vorinostat CUDC-101 CAY10603 AR-42 PIK-93 PI-103 KIN001-244 BX-912 ZSTK474 TGX221 KIN001-102 PHA-793887 THZ-2-102-1 AT-7519 RO-3306 FK866 I-BET-762 Dasatinib WH-4-023 Saracatinib NPK76-II-72-1 GSK1070916 Ispinesib mesylate Navitoclax Obatoclax mesylate YM201636 QL-X-138 JW-7-24-1 T0901317 XMD13-2 TPCA-1 TL-1-85 SNX-2112 QL-XII-61 NG-25 KIN001-260 XMD8-85 WZ-1-84 MG-132 Bortezomib A-770041 17-AAG Methotrexate Bleomycin (50uM) 5-Fluorouracil SB590885 PLX4720 Dabrafenib Vinblastine Docetaxel –lod10 (FDR) 10 20 30 40 OSI-027 Erlotinib WZ3105 Spearman correlation Midostaurin –0.4 –0.2 0.0 0.2 BMP1 BMP3 BMP4 GDF11
Figure 5. BMP family genes have a broad impact on drug sensitivity in cancer. Dots indicate the relationship between BMP gene expression and drug sensitivity. A positive correlation indicates that the high expression of this gene is resistant.
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We next explored which type of mutation contributes to it and human cancers. A number of conflicting studies suggested that interestingly observed that SNP predominates (Figure 3B and the same BMP genes act differently depending on the cancer Supplementary Figure 4). Most of the mutations are missense type, and it appears that multiple members of the BMP fam- and most mutations are C>T. To summarize, besides transcrip- ily have distinct functions [3,9]. For example, BMP2 was pre- tional dysfunction, DNA mutation also contributes to abnor- viously reported to be downregulated in breast cancer sam- mal BMPs across cancers. ples [13], which is consistent with our finding. Our results provide more evidence showing that not only BMP2, but also Bone morphogenetic protein is associated with certain key BMP3, BMP5, and BMP6, are downregulated in breast cancer hallmark cancer pathways samples. We provide the full map of BMP family expression profile across human tumors. BMPs are globally downregulated in cancers, but it remains unclear how this is associated with certain signaling path- Second, we showed the reason why BMPs are downregulated. ways at the systems level. We thus compared the expression We screened potential expression-drivers from genetics fac- difference between distinct pathway activity groups (activa- tors to epigenetics factors. We found that copy number varia- tion and inhibition). We selected 10 hallmark cancer path- tions, especially heterozygous amplification, drive the abnor- ways and compared them across cancer types (Figure 4A). mal expression of BMP genes. At the epigenetics level, most Interestingly, our results demonstrated that BMP molecules, BMP gene promoters are globally methylated. For example, especially BMP1, are associated with epithelial-mesenchymal our results demonstrated that BMP6 is methylated in breast transition (EMT). These data are consistent with results of an- cancer samples, which is in line with previous reports [14]. other independent study [12]. Further, we showed that high ex- Further, single-nucleotide variants can also contribute to the pression of BMPs is associated with inhibited cell cycle path- abnormal function of BMP genes across cancers. Our compu- way (Figure 4A, 4B). We determined the pathways in the BMP tational results not only confirm previously reported observa- family that are most associated, including EMT, cell cycle, and tions, but also generate new data and hypotheses about ab- apoptosis at the systems level. normal BMP gene expression.
Bone morphogenetic protein dysfunction induces drug Third, we defined BMP-associated signaling pathways. BMP sensitivity genes are TGF-b pathway modulators and are widely involved in TGF-b-associated biological processes. To fully define the To further investigate the potential effects of BMP genes on BMP-associated pathways, we comprehensively analyzed 10 drug sensitivity, we examined the correlation between mRNA hallmark cancer pathways and quantified their correlation expression of BMP genes in the GDSC dataset (see Methods). with BMP family expression. Our results show that high ex- After removing negative molecules, we found that 4 BMP genes pression of BMP triggers cell cycle inhibition across cancers. were associated with drug sensitivity (Figure 5). Among 481 Other independent research groups also reported similar re- compounds, we identified 58 compounds that have at least sults, showing that BMP genes are broadly involved in regu- 1 significant correlation with BMP genes. For example, BMP4 lating the cell cycle [15,16]. shows mostly positive correlations with compounds. These re- sults show the association of BMP genes with drug response. Our study has some limitations. We did not calculate the hazard ratio of each gene in survival analysis due to techni- cal limitations, although it is important for clinicians to inter- Discussion pret those results [17]. We hope future studies will be able to calculate the hazard ratio of each gene. To the best of our BMPs are involved in a wide range of biological processes knowledge, this study is the first to investigate the effect of such as oncogenesis, but whether they promote or inhibit can- BMP genes at the systems level. Further research is needed cer development remains controversial. A wealth of conflict- to confirm our results. ing studies indicate that the same BMP ligand can act differ- ently depending on the cancer type, and it seems that various members of the BMP family have distinct functions [3,9]. We Conclusions integrated multi-omics data on thousands of patients and as- sessed how they are involved and how they affect cancer pro- We comprehensively analyzed alterations in BMP genes across gression at the systems level. multiple human cancers. Our data demonstrate that the BMP family is transcriptionally dysfunctional and shows strong in- First, we demonstrated that the bone morphogenetic pro- teractions between BMP genes and cancer hallmark pathways, tein family shows globally downregulated expression across which highlights the importance of BMPs in cancer biology.
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Supplementary Data
A B BMP8A GDF7
BMP8B BMP7
GDF7 BMP6 Survival worse Cancer type BMP5 H BMP5 BLCA L BRCA BMP3 BMP3 KIRC STAD BMP7 –log10 (P-value) BMP1 –log10 (P-value) 2.5 BMP6 GDF11 5 5.0 10 7.5 15 BMP4 BMP8B 20 25 BMP2 BMP8A
BMP10 BMP4
BMP1 BMP2 A CA AD AD LIHC KIRC KIRC KIRP BL ST UCEC THC BRCA BRCA CO C GDF7 TMP BMP2 200 150 BMP11 100 50 0 BMP8B
BMP8A
BMP7
BMP6
GeneName BMP5
BMP4
BMP3
BMP2
BMP15
BMP10
BMP1 t y y e er oid rv ar eas stis ung east Skin gina olo n Liv essel L yr Brain Te Hear C Blood Ov Ne Br ncr Uterus Spleen Va Kidney Muscle y gland Th Bladder Pituiar vix uteri Prostate Stomach Pa r Esophagus Ce Blood v allopian tube Adipole tissue Adrenal gland Salivar F Small intestine Tissue
Supplementary Figure 1. The BMP gene is dysfunctional in cancer. (A) Prognostic value of the BMP genes. (B) Expression of BMP genes is different in cancer subtypes. C( ) Expression of BMP genes in normal samples.
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A BMP1
GDF11
BMP7
BMP8B
GDF7 Pearson BMP2 corelation 0.0 0.2 0.4 Symbol BMP4 –Log10 (FDR) 51015 BMP8A
BMP6
BMP5
BMP3
BMP15 A CA AD AD LIHC KIRC KIRP KICH BL ST UCEC THC BRCA CO PRAD A AD CA B AD UCEC ST KIRP BL CO THC BRCA LIHC KIRC PRAD KICH BMP1
BMP8A
BMP8B
BMP3
BMP4
GDF2
BMP15 Hete amp Homo amp BMP6 hete del Homo del None BMP5
BMP10
GDF7
GDF11
BMP2
BMP7
Supplementary Figure 2. Copy number variation affects CDK gene expression. A( ) Correlation between copy number variation and gene expression. (B) Copy number variant subtype of CDK genes in cancer. Hete Amp: heterozygous amplification; Hete Del: heterozygous deletion; Homo Amp: homozygous amplification; Homo Del: homozygous deletion; no: no CNV.
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Supplementary Figure 3. The prognostic value of CDK gene GDF2 methylation in different cancer types. BMP8B BMP7 BMP6 BMP1 Symbol GDF11 BMP8A BMP4 BMP3 BMP2 CA AD LIHC KIRC KIRP KICH BL UCEC THCA BRCA CO PRAD
Hypermethy worse logrank pvalue High Low 0.05 10–5 10–10
A B Variant type Variant classication
Missense mulation
SNP Nonsense mulation
Frame shift del INS Frame shift ins
Splice site DEL Nonstop mulation
0 200 400 600 0 200 400 600 N N
C SNV class D Variant classication
T>G
T>C 10
T>A
C>T % of variants 5
C>G
C>A 0 0 200 400 600 N Samples
Variant classication summary Frequently mutated genes
8 BMP1
BMP5 Indexed in: [Current Contents/Clinical Medicine] [SCI Expanded] [ISI Alerting System] This work is licensed under Creative Common Attribution- [ISI Journals Master List] [Index Medicus/MEDLINE] [EMBASE/Excerpta Medica] NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) e920943-11 6 [ChemicalGDF2 Abstracts/CAS] BMP7 N BMP3 4 BMP2
BMP15
2 BMP4
BMP6
0 BMP10 020 40 6080 Variant classication N Variant type Variant classication
Missense mulation
SNP Nonsense mulation
Frame shift del INS Frame shift ins
Splice site DEL Nonstop mulation
0 200 400 600 0 200 400 600 N N
SNV class Variant classication
T>G
T>C 10
T>A
C>T % of variants 5 Luo W.-L. et al.: DATABASE ANALYSIS The landscape of bone morphogenetic proteins in cancers C>G © Med Sci Monit, 2020; 26: e920943
C>A 0 0 200 400 600 N Samples
E Variant classication summary F Frequently mutated genes
8 BMP1
BMP5
6 GDF2 BMP7 N BMP3 4 BMP2
BMP15
2 BMP4
BMP6
0 BMP10 020 40 6080 Variant classication N
Supplementary Figure 4. A review of CDK gene variants in human cancers. (A) variant type, (B) variant classification, C( ) single- nucleotide variation, (D) variation per sample, (E) mutation classification, F( ) mutation of CDK gene between cancer types frequency.
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