Zhang et al. BMC Gastroenterol (2021) 21:284 https://doi.org/10.1186/s12876-021-01869-4

RESEARCH Open Access Characterization of TGFβ‑associated molecular features and drug responses in gastrointestinal adenocarcinoma Qiaofeng Zhang1,2,3†, Furong Liu1,2,3†, Lu Qin4, Zhibin Liao1,2,3, Jia Song1,2,3, Huifang Liang1,2,3, Xiaoping Chen1,2,3, Zhanguo Zhang1,2,3* and Bixiang Zhang1,2,3*

Abstract Background: Gastrointestinal adenocarcinoma (GIAD) has caused a serious disease burden globally. Targeted ther- apy for the transforming growth factor beta (TGF-β) signaling pathway is becoming a reality. However, the molecular characterization of TGF-β associated signatures in GIAD requires further exploration. Methods: Multi-omics data were collected from TCGA and GEO database. A pivotal unsupervised clustering for TGF-β level was performed by distinguish status of TGF-β associated . We analyzed diferential mRNAs, miRNAs, mutations and copy number variations in both clusters for comparison. Enrichment of pathways and gene sets were identifed in each type of GIAD. Then we performed diferential mRNA related drug response by col- lecting data from GDSC. At last, a summarized deep neural network for TGF-β status and GIADs was constracted. Results: The TGF-βhigh group had a worse prognosis in overall GIAD patients, and had a worse prognosis trend in gastric cancer and colon cancer specifcally. Signatures (including mRNA and proteins) of the TGF-βhigh group is highly correlated with EMT. According to miRNA analysis, miR-215-3p, miR-378a-5p, and miR-194-3p may block the efect of TGF-β. Further genomic analysis showed that TGF-βlow group had more genomic changes in gastric cancer, such as TP53 mutation, EGFR amplifcation, and SMAD4 deletion. And drug response dataset revealed tumor-sensitive or tumor-resistant drugs corresponding to TGF-β associated mRNAs. Finally, the DNN model showed an excellent predic- tive efect in predicting TGF-β status in diferent GIAD datasets. Conclusions: We provide molecular signatures associated with diferent levels of TGF-β to deepen the understand- ing of the role of TGF-β in GIAD and provide potential drug possibilities for therapeutic targets in diferent levels of TGF-β in GIAD. Keywords: Gastrointestinal adenocarcinoma, Transforming growth factor beta, Multi-omics signatures, Drug susceptibility, Deep neural network

Introduction Cancer of the digestive tract share a large quantity of global cancer incidences. gastrointestinal adenocarci- *Correspondence: [email protected]; [email protected] nomas (GIADs), including esophageal adenocarcinoma †Qiaofeng Zhang and Furong Liu contributed equally to this work 1 Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, (ESAD), stomach adenocarcinoma (STAD), colon adeno- Huazhong University of Science and Technology, Hubei Province (COAD), rectum adenocarcinoma (READ), for the Clinical Medicine Research Center of Hepatic Surgery, 1095 Jiefang revealed a nonnegligible global health burden in recent Avenue, Wuhan 430030, China Full list of author information is available at the end of the article years [1]. Previous studies have established that all types

© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat​ iveco​ mmons.​ org/​ licen​ ses/​ by/4.​ 0/​ . The Creative Commons Public Domain Dedication waiver (http://creat​ iveco​ ​ mmons.org/​ publi​ cdoma​ in/​ zero/1.​ 0/​ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Zhang et al. BMC Gastroenterol (2021) 21:284 Page 2 of 15

of GIADs share the same features of DNA hypermethyla- of 4 type of gastrointestinal adenocarcinoma (ESAD, tion, mRNA expression and biomarkers, which STAD, COAD, READ) from Xena Hub. Genes with aver- confrmed GIAD in possession of exclusive character- age expression of less than 1 in RNA-seq and miRNA-seq istics [2, 3]. Apart from traditional histologic classifca- as well as synonymous mutations in the mutation data tion, an increasing number of molecular signatures were were fltered. Level 4 Copy number variation (CNV) data found across cancer. Models and clinical cohort were processed by GISTIC2.0 was obtained from Firehouse. built to identify the function of molecular biomarkers. Additional sequencing data for gastrointestinal adeno- Tus, a dramatic increasing number of potential bio- carcinoma were downloaded from NCBI GEO, including markers were found and reported, which made clinical GSE19417, GSE62254, GSE17536, GSE45404, along with diagnosis, prognostic and immunotherapy more efective GSE62254 and GSE17536 contained survival data. and reliable. Transforming growth factor beta (TGF-β) family Classifcation of TGF‑β status in GIAD consists great many of activation and inhibition fac- 39 core TGF-β associated genes were selected as TGF-β tors which participate in a variety of cellular biological signatures, including TGF-β ligands, processes [4]. As a prototypical factor in TGF-β fam- receptors, receptor substrates, Co-SMAD, inhibitory ily proteins, encoded by 33 genes in mammals, TGF-β Smads, adaptors, and Smad chaperones. Te specifc is a multifunctional regulator involved in cell prolifera- details are as follows: 43 TGF-β core genes were selected tion and diferentiation [5], even in immune suppression from previous study [11], and the genes with low expres- within tumor microenvironment [6]. Some cell-surface sion level were further removed from the expression data transmembrane receptors with serine or threonine of TCGA’s gastrointestinal adenocarcinoma (average activity can interact with activated TGF-βs, fol- count value less than 1), and 39 TGF-β core genes were lowing of SMAD proteins, which then fnally obtained. In order to evaluate diferent TGF-β regulate the expression of TGF-β target genes [7]. Addi- levels, two evaluation methods were used: (1) Unsuper- tionally, the activation of TGF-β signaling pathways can vised K-means clustering analysis based on the 39 mRNA be negatively correlated with the development of COAD of TGF-β-related signatures for each sample in GIAD; in multiple mechanisms [8]. And target genes of TGF-β (2) Gene set variation analysis based on single sample are upregulated in ESAD samples while tumor progres- gene-set enrichment analysis (ssGSEA) method was used sion is suppressed by TGF-β knockdown [9]. to calculate the TGF-β score of each sample in GIAD. With the TGF-β cascades being Unsupervised clustering was performed by R package able to promote the growth and diferentiation of tumor ‘ConsensuClusterPlus’ [12], which is repeated 1000 times cells, and to inhibit cell proliferation in diferent tumor to ensure the stability of the results as in previous report stages [4, 7, 10], it is still difcult to defne the function [13]. of TGF-β signaling pathways in digestive tract adenocar- cinomas. Moreover, TGF-β signaling targeted drugs have Analysis of alterations between diferent TGF‑β status bright prospect in clinical application. Hence, more pro- For the mRNA, miRNA, and RPPA data in TGF-βhigh and spective research for TGF-β associated molecular signa- TGF-βlow group in each GIAD, we used the permuta- tures and possibility of targeted therapy in GIADs are in tion test for diferential analysis as previously described need. [14]. For RNA-seq, we further screened mRNAs with the We focused on multi-omics study across 4 cancer types absolute value of logFC (log­ 2 (Fold change) = mean value in Te Cancer Genome Atlas (TCGA) to elaborate dif- in TGF-βhigh / mean value in TGF-βlow) > 1; for miRNA, ferent molecular pattern between TGF-β high expres- we set the absolute value of logFC > 0.5 as the threshold; sion (TGF-βhigh) and low expression (TGF-βlow) cluster. for RPPA data, we set the diference greater than 0.2 as An array of transcription, post-modifcation, proteomic the signifcantly changed proteins. For the mutation data, change, gene mutation, genomic alteration and what reg- we selected high frequency mutations of more than 10% ulating function they induced were analyzed in cancers. in the TGF-βhigh or TGF-βlow group for analysis. Also, for And the potential efects of anticancer drugs targeted on CNV data, amplifcation or deletion regions exceeding TGF-β signaling were assessed. 10% in the TGF-βhigh or TGF-βlow group were selected for analysis. Fisher’s exact test was used to evaluate signif- Methods and materials cantly mutated genes, amplifed and deleted chromosomal TCGA and GEO data collection and processing regions. We used FDR correction for all multiple tests, We collected multi-omics data (including count data of with FDR < 0.05 set as the threshold. Additionally, miRNA- RNA-seq and miRNA-seq, Reverse phase protein lysate targeted mRNAs were extracted from miRTarBase [15] microarray (RPPA), mutation maf fles and survival data) and miRDB [16]. Te mRNAs targeted by miRNA in two Zhang et al. BMC Gastroenterol (2021) 21:284 Page 3 of 15

databases was intersected as the fnal targeted mRNAs. [l] = [l] [l−1] + [l] [0] = = A max W A b ,0, A X, l (1, 2, 3) In order to analyze the correlation between miRNA and [l] [l] mRNA in diferent TGF-β status groups, principal compo- where A is the output matrix of the hidden layer, W [l] nent analysis (PCA) was used to reduce dimensionality to is the weight matrix, b is a column vector, and X is the obtain the frst principal component (PC1), and then Pear- normalized matrix of input layer including the samples son correlation evaluation was conducted for the PC1. Te and gene signatures. Also, for the fnal output layer, we advantage of this approach is that it concentrates on scores used the sigmoid activation function, that are correlated (or inversely correlated) between gene 1 sets. Y = eA[3] + 1 Enrichment analysis of pathways and gene set functions We respectively performed Kyoto Encyclopedia of Genes where Y is the probability vector of the output layer, A[3] and Genomes (KEGG) pathway analysis of the diferen- and is the output matrix of the third hidden layer. We tially expressed mRNAs in each type of GIAD, including chose the loss function in logistic regression to measure genes upregulated in the TGF-βhigh or TGF-βlow group. At the operation of the algorithm: the same time, gene-set enrichment analysis (GSEA) anal- 1 m J(W , b) = L yˆ(i), y(i) ysis was also used to analyze the signifcant up- or down-   m = regulated pathways and functions in TGF-βhigh group i 1 1 m involved in each GIAD. Both KEGG and GSEA analyses = −y(i)logyˆ(i) − 1 − y(i) log 1 −ˆy(i) m      are implemented on the R package ‘clusterProfler’ [17]. Te i=1 classic ‘Hallmark gene sets’ was selected as signatures for where J(W , b) is the cost function, m is the sample GSEA analysis. FDR < 0.05 was considered as signifcant (i) (i) number, L yˆ , y is the lost function of a sample, and enrichment. (i) (i )  yˆ and y are output value of DNN and the real label Analysis of drug‑targeted TGF‑β associated with gene value of sample i respectively. Ten, in order to minimize signatures (W , b) , we used the stochastic gradient descent training [l] [l] Te Pearson coefcient (r) between the expression of model to learn W and b . Learning rate (α) was set to TGF-β specifc mRNAs and the drug response (IC50) were be between 0.0005 and 0.005, and the epoch was set to be evaluated in digestive tract cell lines. Te drug response 100 to evaluate the best model. Te RMSprop algorithm data of cell lines was downloaded from Genomics of Drug was used to optimize the model. TCGA data were ran- Sensitivity in Cancer (GDSC) [18]. Pearson correlation domly divided into training set and testing set according coefcient and FDR were calculated in GDSC, in which to 3:1. 75% of the TCGA samples were used for training, |r|> 0.3 and FDR < 0.05 were considered to be signifcant. and the remaining 25% were used to test the performance We also further evaluated the estimated drug responses of the model, which was evaluated by receiver operating of 138 drugs in the TCGA sample from a previous study characteristic (ROC) curve. Finally, we used this model to [19]. For these estimated drug responses, we used the Wil- evaluate and analyze the TGF-β status on multiple GEO coxon signed rank test, and FDR < 0.05 was considered to datasets. be diferential. Other information for analysis Construction of deep neural network (DNN) All analysis was done on R (Version: 3.6.1) or Python Using genomic data, we trained a deep neural network (Version: 3.6). Due to diferent types of survival data, sur- (DNN) model to predict the TGF-β status of diferent vival analysis in TCGA refers to overall survival (OS) and GIAD datasets (Fig. 7a). In addition to the input layer, we progression-free interval (PFI), and in GEO datasets to construct a four-layer neural network, including three hid- OS and disease-free survival (DFS). In addition to the sta- den layers and an output layer. We labeled the TGF-βhigh tistical analysis mentioned above, the log-rank test was group as 1 and the TGF-βlow group as 0. For a given hid- used for survival analysis, and all multiple statistical tests den layer l, we use ReLu activation function. According to were FDR corrected. the output layer of the previous layer as the input of the next layer A[l−1], we can carry out forward propagation as follows: Zhang et al. BMC Gastroenterol (2021) 21:284 Page 4 of 15

(a) Tumor type

ACVR1 TGFB2 (b) TGFBR1 high BMPR1B TGFB1 INHBA TGFB3 BMP3 TGFBR2 *** *** *** *** *** BMPR2 TGFBR3 Tumor type SMAD3 2 COAD SMAD7 1 STAD SMAD6 0 READ BMP4 ESAD INHBB TGF−beta score BMP7 2 INHBE 1 INHBC 0 INHA NODAL 0 GDF11 ACVR2B Cluster ACVR1B 1 BMP2 2 BMP5 BMP6 ACVRL1 ACVR1C ACVR2A SMAD5 BMPR1A SMAD9 ZFYVE9 ESAD STAD COAD READ GIAD SMAD1 SMAD4 SMAD2 SPTBN1 TGFBRAP1 (c) (d) (e)

1.00 +++ GIAD OS 1.00 +++ GIAD PFIS1.00 +++ TAD OS +++ +++ ++ ++ +++ ++ +++ ++++ ++ ++++ +++ ++ ++++ ++++ ++ ++++ + low (n=569) +++ + low (n=570) +++ + low (n=206) ++++ ++++ +++ ++++++ high +++ high + high +++++++ + (n=483) +++++ + (n=484) + + (n=162) 0.75 ++++++++ 0.75 ++++++ 0.75 ++ +++++++++++ +++++++ +++ ++ +++ ++ + ++++ ++ +++++ +++++++++++ ++++ ++++++ ++++ +++ +++ ++++ ++++ ++ ++++++ ++++ +++++ ++++ ++++ ++ +++ ++ + + +++++++ ++++ +++++++++++++ + ++++++++ ++ ++++ + ++++ ++++++++++++ 0.50 + ++++ 0.50 ++ ++ 0.50 ++++++ +++ +++ ++++++++ + ion of OS tion of OS + ++ tion of PF I ++++++ ++ ++ +++++++++++ ++

r ++++ + ++ ++++++++++++ r +++++++++ + +++++ ++++ + ++ ++ + rank +++ rank rank + + Propo Propo rt Propo +++ 0.25 ++ ++++ 0.25 0.25 p = 0.0058 p = 0.012 p = 0.012 + ++

0.00 0.00 0.00 050100 150 050 100 150 0255075 100 125 Months Months Months

(f) (g) (h) STAD PFI COAD OS COAD PFI 1.00 ++++ 1.00 +++ 1.00 ++ ++ ++ ++ +++ ++++ +++ +++++ ++++ ++++ +++ ++ + ++++ ++ ++ + low ++++++++++++ + low ++++ + + (n=207) +++++++++ (n=233) +++++ +++ high ++ +++ high +++ ++ + (n=163) ++++++++++++++ + (n=217) ++++++++++ + 0.75 ++ 0.75 +++ 0.75 ++++++++++ +++ ++++++++++++++++ +++ +++ +++ + +++ +++++ ++++ + +++ ++++ ++++++++++ + +++++++++++++++++ +++ + + + ++ +++ ++ +++++++++++++++ + +++ + ++++++ ++ +++++++++ +++ ++++++++++ ++ 0.50 ++ +++ 0.50 0.50 tion of OS tion of PF I ++++++ +++++ r r ++++ ++ ++++ ++ + ++++ + ++++ rank rank Propo Propo 0.25 0.25 ++ + 0.25 + p = 0.034 p = 0.058 p = 0.1

0.00 0.00 0.00 0255075100 125 050 100 150 050 100 150 Months Months Fig. 1 Unsupervised classifcation of TGF-β status and prognosis in gastrointestinal adenocarcinoma (GIAD). a Heatmap based on cluster of 39 TGF-β core genes. b Comparison of TGF-β scores in two clusters across diferent tumors. c–h Kaplan-survival curves (including overall survival and progression-free interval) for diferent TGF-β groups in GIAD. *P < 0.05, **P < 0.01, ***P < 0.001 Zhang et al. BMC Gastroenterol (2021) 21:284 Page 5 of 15

Result 95% CI 1.095–1.723, P = 0.0058; PFI: HR = 1.329, 95% Unsupervised classifcation of TGF‑β status and overall CI 1.065–1.659, P 0.012) (Fig. 1c, d). Specifc to each high = patterns of multi‑omics signatures between TGF‑β tumor, although only STAD showed a signifcant difer- low and TGF‑β groups ence between two groups (Fig. 1e, f), a trend of poor Unsupervised clustering was used to identify the acti- prognosis still shown in TGF-βhigh-specifc COAD and vation of respective driver genes between two clusters ESAD patients (Fig. 1g, h, Additional fle 1: Fig. S1b–e), characterized by low and high distribution of TGF-β which partially elucidated the causality of poor progno- core genes (Fig. 1a). We used single-sample gene set sis and TGF-β pathway activity. enrichment analysis (ssGSEA) to further exhibit that To further investigate TGF-β associated molecular cluster2 had higher TGF-β scores not only in various features across GIAD, a procedure was designed with categories of GIAD but also in general (Fig. 1b). Cor- analytic pipeline outlined in Fig. 2a. Ten, we analyzed relation analysis was performed to exhibit that TGF-β various molecular signatures between previous cluster core genes were correlated to TGF-β score in a positive groups, including mRNA expression (approximately tendency (Additional fle 1: Fig. S1a), which explained 18,000 genes), miRNA expression (approximately 600 the consistency of gene activation and cluster outcome. miRNAs), protein expression (198 proteins), somatic Terefore, we set cluster1 as the TGF-βlow group and mutation (frequency > 5% in at least one cluster) and cluster2 as the TGF-βhigh group. copy number variation (CNV) (Additional fle 2). And To analyze the prognosis of diferent TGF-β groups, the most striking signatures turned out to be mRNA, we performed a survival analysis. As a whole, GIAD miRNA and protein expression (Fig. 2b). Te number patients with high activation of TGF-β pathway had of mRNA genes ranged from 1865 in STAD to 5606 in statistically signifcant poor prognosis (OS: HR = 1.374, READ, most of which surprisingly belonged to high

(a) Functional mRNA analysis high TGF−β miRNA Unsupervised TGF−βspecific Protein Drug reponse Stratification signatures TGF−βlow mutation CNV DNN model construction (b) ESAD STAD COAD Normalized fraction READ 0.25 0.50 020004000 050100 150200 0.75 mRNA CNV miRNAProteinMutation mRNA miRNA 1.00

Molecular levels ESAD TGF−βhigh STAD TGF−βlow COAD READ 0510 15 20 25 0510 15 0255075 Protein Mutation CNV Fig. 2 TGF-β specifc multi-omics signatures in GIAD. a Overview of analysis process in this study. b The relative abundance and the number of each molecular signature in TGF-βhigh and TGF-βlow group. The dot plot represents the proportion of molecular characteristics of each tumor in all GIADs, and the bar plot represents the number of molecular signatures of each tumor in the TGF-βhigh and TGF-βlow group (FDR < 0.05) Zhang et al. BMC Gastroenterol (2021) 21:284 Page 6 of 15

score group. Te number of miRNAs showing signif- lot of post-transcriptional modifcations in these clas- cant expression level ranged from 47 in READ to 276 sical proteins, including phosphorylation, ubiquitin, in STAD. Alterations of protein expression exhibited glycosylation and the SUMOylation, which would afect the largest number in STAD, and signifcant altera- the overall diference between mRNA and protein. We tions of low score group were more than high score also found that STAD had a large amount of alterations group across cancer types. However, the diferences in in somatic mutation and CNV, while both clusters of protein and mRNA expression between the two groups other cancer types showed non-diferential signatures. were not consistent. Te possible reason is that, unlike In aggregate, STAD got the most TGF-β associated sig- the sequencing of all the genes encoding the transcrip- natures, including 1865 mRNA genes, 276 miRNAs, 46 tome, only 198 classical proteins were included in the proteins, 18 gene mutation and nearly 90 CNV regions. study in the sequencing of proteins, and there were a

(a) (b) THBS4 PCDH10 NRK Cell adhesion molecules (CAMs) KERA Focal adhesion SCN7A CHRDL1 Proteoglycans in cancer OGN TCEAL2 Calcium signaling pathway GPM6A OMD Ras signaling pathway PTGIS GREM1 cAMP signaling pathway HTR2A ASB5 Rap1 signaling pathway NEXMIF SLIT2 CDH19 Hippo signaling pathway CILP Count HMCN1 50 SFRP1 Gap junction NALCN 100 EPHA7 LogFC Hedgehog signaling pathway 150 CHRM2 200 5 HAND2 AD AD FGF7 4 ESADST CO READ SYT4 p.adjust FGF14 3 (c) CYP1B1 2 0.01 NUDT10 EPITHELIAL MESENCHYMAL TRANSITION 0.02 FNDC1 TNFA SIGNALING VIA NFKB 0.03 BNC2 IL2 STAT5 SIGNALING GUCY1A1 INFLAMMATORY RESPONSE 0.04 MGP COL8A1 KRAS SIGNALING UP MYOC APICAL JUNCTION LRRC4C COMPLEMENT PCDH9 ALLOGRAFT REJECTION TRARG1 CASQ2 HYPOXIA PLXNA4 MYOGENESIS CPEB1 KRAS SIGNALING DN KCNK2 APOPTOSIS ISM1 ITGBL1 UV RESPONSE DN PLCXD3 COAGULATION FLRT2 IL6 JAK STAT3 SIGNALING CARTPT PANCREAS BETA CELLS GAS1 HEDGEHOG SIGNALING SFRP4 FABP4

AD AD AD AD ESADST CO READ ESADST CO READ Fig. 3 TGF-β efects on mRNA and functional pathways. a The heatmap shows the most signifcantly upregulated 50 mRNAs in the TGF-βhigh group across each GIAD. b KEGG pathways [54] enriched by signifcantly upregulated mRNA in the TGF-βhigh group (FDR < 0.05). c Overall gene-set enrichment analysis (GSEA) pathway enrichment analysis was performed for the diferentially expressed mRNAs (TGF-βhigh/TGF-βlow) Zhang et al. BMC Gastroenterol (2021) 21:284 Page 7 of 15

TGF‑β efects on mRNA and functional pathways their predicted downstream regulation targets, mRNAs. To understand the function of TGF-β associated mRNA And the results testifed a negative regulation function signatures in digestive tract adenocarcinoma, we com- of high expressed miRNAs in TGF-βlow group (TGF-βlow pared mRNA expression levels for each gene. Ten a specifc miRNAs) by showing a signifcant negative cor- robust rank aggregation (RRA) analysis was performed relation for 4 types of cancers (Fig. 4a). Meanwhile, the to rank top 50 genes with the highest expression in TGF- same trend was also found in TGF-βhigh specifc miRNAs βhigh group as previous research did [20] (Fig. 3a). For with low expression and its targeted mRNAs in TGF-βhigh example, thrombospondin 4 [21] (THBS4), 1 [22] group (Additional fle 1: Fig. S3a). Tese indicated that (GREM1), slit guidance ligand 2 [23] (SLIT2) and hemi- TGF-β associated mRNAs are regulated by the associated centin-1 [24] (HMCN1, also known as fbulin-6) was able TGF-β specifc miRNAs. to mediate the function of cell proliferation, invasion and Subsequently, we arranged the top 20 reduced miR- migration induced by TGF-β1 (Fig. 3a). Enriched KEGG NAs, with 3 (miR-215-3p, miR-378a-5p, and miR-194-3p) pathway of diferentially expressed mRNAs revealed of them signifcantly downregulated in TGF-βhigh cluster that genes involved in signaling, cell across cancer type (Fig. 4b). Specifcally, miR-215-3p was adhesion, classic PI3K/AKT signaling pathway and pro- reported to be a co-inhibitor of cell migration induced teoglycans expression were up-regulated in high TGF-β by TGF-β1 [27]. And the overexpression of miR-194-3p score samples (Fig. 3b). Meanwhile, due to the small signifcantly inhibited RUNX2 [28] and PRC1 [29] sign- number of down-regulated genes in the TGF-βhigh group aling pathway which was crucial to cell proliferation and across cancer types, there were fewer common genes and migration. Nevertheless, the efects of miR-378a-5p on enriched pathways in the four tumors, with the result of cell proliferation, migration and invasion were contro- pathway enrichment focused on ESAD and STAD (Addi- versial according to recent studies [30, 31]. Te network tional fle 1: Fig. S2a–b). To further manifest the enriched of these representative miRNAs and enriched target functions of diferentially expressed mRNAs, we per- mRNAs were exhibited in Fig. 4c. Te expression level of formed gene set enrichment analysis (GSEA) on all of miR-215-3p, miR-194-3p and miR-378a-5p were nega- them (Fig. 3c). Some of the enriched functions across tively correlated with TGF-β pathway scores across can- cancer types were associated with TGF-β induced bio- cer types, which substantiated performed results (Fig. 4d, logical process, such as epithelial mesenchymal transition Additional fle 1: Fig. S3c–d). Tough the regulatory (EMT) and infammatory response, which was involved relationships were not clear between TGF-βhigh-specifc in tumor genesis and metastasis [6, 25]. We also noticed expressing miRNAs and corresponding mRNAs, which that signaling pathways of immune regulation and cellu- might owe to few samples of TGF-βlow-specifc mRNAs lar metabolism were signifcantly enhanced. In general, (Additional fle 1: Fig. S3a–b). A recent research sug- the results were unsurprisingly consistent with previ- gested that expression of miR-100 and miR-125b, which ous fndings. It is noteworthy that the EMT showed the got involved in EMT, tumorigenesis and poor prognosis, most signifcant enrichment in our analysis, suggesting was induced and upregulated by TGF-β [32]. that TGF-β signaling pathway in GIAD may promote the Standardized diferences of protein expression levels occurrence of EMT and tumor metastasis, which is also between two groups were displayed in Fig. 4e. And we consistent with previous research reports [26]. focused on STAD, COAD and READ due to unappar- ent alterations in ESAD samples. For example, down- Comparison of miRNA, and protein expression in diferent regulated E-cadherin in TGF-βhigh group suggested its TGF‑β status inhibiting efect of metastasis in cancer, which was con- As we all known that miRNA is the key regulator in cordant with previous studies [33, 34]. On the contrary, post-transcription of gene expression [13]. In our study, IV [35] and fbronectin [36] were reported as diferentially expressed miRNAs were detected across pro-metastasis factors and found unsurprisingly overex- cancer types. Principal component analysis and correla- pressed in TGF-βhigh group. We also found that proteins tion analysis were implemented to describe the correla- associated with cell cycle (such as cyclinb1 and cycline1) tion in between diferentially expressed miRNAs and were downregulated in TGF-βhigh cluster, which exactly

(See fgure on next page.) Fig. 4 Comparison of miRNA, and protein expression in diferent TGF-β status. a Correlation between the frst principal component (PC1) of the expression of TGF-βlow-specifc miRNAs and their predicted targeted TGF-βhigh-specifc mRNAs. b The heatmap shows the most signifcantly down-regulated 20 miRNAs in the TGF-βhigh group across each GIAD. c A network Schematic diagram of miR-215-3p, miR-378a-5p, and miR-194-3p and their predicted targeting TGF-βhigh-specifc mRNA in stomach adenocarcinoma. d Negative correlation between miR-194-3p expression and TGF-β score in 4 types of GIAD (Pearson correlation). e The signifcantly diferential proteins in TGF-βhigh and TGF-βlow groups; Red indicates high protein expression in the TGF-βhigh group, and blue indicates high protein expression in the TGF-βlow group Zhang et al. BMC Gastroenterol (2021) 21:284 Page 8 of 15

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(b) (c)

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Fig. 4 (See legend on previous page.) Zhang et al. BMC Gastroenterol (2021) 21:284 Page 9 of 15

(a) (b) 15 **** 600 ****** **

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Amplification Deletion Fig. 5 Signifcant mutations and CNVs between the TGF-β subgroups in GIAD. a, b Comparison of tumor mutation burden (TMB) and CNV burden in diferent TGF-β cluster. c Mutation frequency of signifcant mutation gene in two TGF-β subtypes in stomach adenocarcinoma (Fisher’s exact test, FDR < 0.05). d Chromosome map shows signifcant amplifcation and deletion of chromosome regions in stomach adenocarcinoma (Fisher’s exact test, FDR < 0.05); The bar plot on the right panel shows the amplifcation of the chromosomal region where EGFR is located. *P < 0.05, **P < 0.01, ***P < 0.001

verifed the inhibitory efect of TGF-β on proliferation expression in gastroenteric tumor [37]. Terefore, we [4]. analyzed the diferences between low and high TGF-β expression at the genomic level. Overall, poor genomic Signifcant mutations and CNVs between the TGF‑β stability was found in TGF-βlow cluster on account of a subgroups in GIAD higher level of tumor mutation burden (TMB) and CNV, Various patterns of TGF-β signaling were historically with STAD the most signifcant one across cancer types found to be associated with genomic stability and gene (Fig. 5a–b). Zhang et al. BMC Gastroenterol (2021) 21:284 Page 10 of 15

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VI1 u A2 PD AN T m TE 4 T PH 4 4 Z Dabra A Selumetinib GFRA 9 RO Axitin ZD1332 STK32B 3 AC M 0 PEBP4 N A A 6 ST8S M4 CLMP b ASTN1 COL24 AZD4547 stat BNC2 AZD ani SLC6A1 f48 iv lino I e A C1R CRISP Br B 2 A1 C8or bozantinib SCN2B 4orf132 b 7 Ca C1 1 LD 2 rizotini S A C CN1B CCL1 BC ib GL A9 CLIP3 Foretin I RO ABCA 1 CAY10603 RB 38877605 CNRIP1 6 JNJ RHO A1 LO J XHD COL6 1 REEP 2 2 COL6A CACNA1H RCVR Linifanib N COL10A1 FBL TNFAIP6 N2 Ten PTX3 DACT3 PD173074 o PTPRN DDR2 RSPO2 DGKI PTGFR P Quiza GR rtinib DLC1 PPFIA2 Tiv EVC AM180A ozan T A4 F ib CFH ric PLXN AD hos FAM20C AMTS1 tatin_A PIANP ATP10A FAP 2 ATP1A2 DHA3 PC 0 FGF5 STMN CHRD FN HC4 S I PCDH1 A2 C L1 GSF P GFPT 1 9 OL1A AP E XHD1 P JA DNR LO 1 3 PCDH 2 1 BPC5 KCNMA1M2 2 NR PA Gene M LRFN5 80 OL A CADM2 K M NTRK LRRC C NT V3 S LSAM F P TA U A LZTS1 N1 K M UN ELN ML D K 1 1 SD N 1 M LH O5A 4 CR Z AG DCX N YL 2B 3 P 5

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TGF-beta score

Fig. 6 Analysis of TGF-β associated gene signatures on drug susceptibility in GIAD. a TGF-β associated gene responses to drugs involved in diferent pathways; The red dots represent genes, the blue dots represent drugs, the green line indicates high gene expression is sensitive to drugs, and the pink line indicates high gene expression is resistant to drugs. b Volcano plot showed imputed drug response diferences in the TGF-β subgroups in GIAD (most samples are ESAD samples); The blue dots indicate signifcant drug-sensitivity, and the red dots indicate signifcant drug-resistance (FDR < 0.05). c The correlation between lenalidomide (or IPA-3) and TGF-β score

Subsequently, we focused on signifcant gene muta- region 7p11.2 amplifed more frequently in TGF-βlow tions in individual tumors, and only in COAD and samples (56.0%) leading to an increased sensitivity STAD did we fnd signifcant mutations (FDR < 0.05). to targeted therapy [41, 42]. Whereas SMAD4 from In addition to more EP300 mutation in the TGF-βhigh 18q21.2, as a key mediator of TGF-β signaling pathway, group in COAD, we found more interesting results in exhibited a signifcant deletion (52.6%). STAD. Specifcally, there were a total of 18 mutated genes with signifcant diference in 2 STAD subtypes, Analysis of TGF‑β associated gene signatures on drug all of which had more high-frequency mutations in the susceptibility in GIAD TGF-βlow group (Fig. 5c). As an outstanding tumor sup- We wanted to further investigate the TGF-β signaling pressor gene activated by oncogenic stress, TP53 muta- specifc sensitivity of targeted drugs in digestive tract tion occurred frequently in various tumors [38], and adenocarcinoma. By identifying a total of 760 diferen- showed the highest frequency of 52.6% in our study. tially expressed mRNA genes associated with TGF-β, we Recent studies identifed a high correlation between used Pearson correlation test between gene expression TP53 mutation and tumor immune response [39] with and 50% inhibiting concentration (IC50) for 345 anti- relevant TGF-β signaling induced immune evasion and cancer drugs to assess drug response across 106 diges- immunotherapeutic resistance [40]. Signifcant CNVs tive tract cancer cell lines from the Genomics of Drug of STAD in TGF-βlow cluster showed that amplifca- Sensitivity in Cancer (GDSC). We focused on these sig- tion and deletion of genes were concentrated on sev- nifcant correlations between 311 genes and 73 drugs, eral (Fig. 5d). In particular, EGFR from targeting on ERK, JNK and p38 signaling, RTK signaling, Zhang et al. BMC Gastroenterol (2021) 21:284 Page 11 of 15

chromatin signature, cell cycle, apoptosis and PI3K/ DNN classifers demonstrated excellent performance, mTOR signaling (FDR < 0.05, Fig. 6a and Additional fle 1: with the area under roc curve (AUC) of receiver operat- Fig. S4a–h). And dabrafenib got the largest quantity of ing characteristic (ROC) curve across all tumors above associated genes (62 genes), such as FAP, FN1 and MMP2 86% in both the training set and the test set. For example, ­(rFAP = − 0.51, ­rFN1 = − 0.42, ­rMMP2 = − 0.51), which pro- in STAD, AUC was 96.8% and 94.6% in training and test moted cell invasion, metastatic and EMT [43–45]. Ten sets, respectively (Fig. 7b). we calculated imputed tumor response as quantifed Further, we analyzed other GIAD datasets. In the other sensitivity of 138 anticancer drugs in 79 TCGA patient datasets of four types of GIAD, we used the DNN model samples (mainly ESAD) to assess the association between to identify TGF-β subtypes. Obviously, TGF-β scores drug response and activation level of TGF-β signaling were higher in the DNN-identifed high TGF-βhigh group pathway. As a member of mutant BRAF-V600 inhibitors, (Fig. 7c). Meanwhile, we evaluated prognosis in two of debrafenib was reported be one favourable option for the datasets (GSE17536 (COAD) and GSE62254 (STAD)) BRAF-V600 mutant malignances [46]. BRAF is one high with survival data. In both datasets, the TGF-βhigh group frequency mutated kinase in human cancer and V600 a had a poorer prognosis, including OS and DFS (Fig. 7d– canonical mutation site in conserved domain, suggesting g), although there was no statistical diference in OS for a signifcant ultilizing on clinical treatment and prognosis GSE17536 (P = 0.066). Finally, we performed GSEA anal- prediction. In melanoma, activation of TGF-β signaling ysis for all TGF-β subgroups identifed by DNN model in directly upregulates the expression of EGFR and platelet- four types of GIAD, in which EMT and TGF-β signaling derived growth factor receptor-β, which leads to BRAF pathways were signifcantly enriched (all P < 0.05, Fig. 7h, inhibitor resistance [47]. And BRAF-V600 inhibitor can Additional fle 1: Fig. S5). Tese results indicate that our suppress the activation of TGF-β signaling via dephos- DNN model is a good predictor of TGF-β status and porylation of TFEB, while TFEB phosphorylation and patient prognosis in COAD and STAD. TGF-β upregulation play a pro-tumorigenic role in BRAF inhibitor resistant melanoma [48]. Our analyzed enriched Discussion genes associated with debrafenib provides a way to fur- As a large and classic family of molecules, TGF-βs induce ther understanding of underlying mechanism. However, a series biological processes involved in tumorigenesis the application of BRAF-V600 inhibitors in the treatment and metastasis, which can be sensitive to targeted drug of digestive tract adenocarcinoma is almost limited to therapy. Te main function of TGF-β signaling pathway colorectal cancer. Our result suggests the clinical poten- varies from diferent cancer types and diferent stages of tial of BRAF-V600 inhibitors in other GIACs. Signifcant cancer. First of all, we performed an unsupervised clus- imputed response diferences of anticancer drugs were tering to characterized expression status of TGF-β signal- identifed with 4 of them resistant but 5 of them sensitive ing and associated molecular diferences in each cancer to digestive tract adenocarcinoma (Fig. 6b). In our study, type. By identifying the relevancy between TGF-β high correlations between TGF-β score and drug sensitivity expression and poor prognosis, our method used permu- that were most signifcant were exclusively analyzed with tation test to implement multi-omics variation analysis. a positive result for lenalidomide (r = -0.51) but quite In this way, we managed to comprehensively evaluate the opposite for IPA-3 (r = 0.40) (Fig. 6c). functions of TGF-β signaling by analyzing diferentially expressed miRNAs and its negatively regulated mRNAs. Identifcation of two TGF‑β subtypes based on the deep In the meantime, identifed gene mutations and CNVs neural network (DNN) associated with TGF‑β signatures demonstrated genetic approaches to a wide range of bio- In order to better examine the application of TGF-β asso- logical processes, such as cell cycle, angiogenesis, EMT, ciated signatures in the classifcation of TGF-β subtypes, cell invasion and other downstream pathways. Consider- we developed a DNN model to identify TGF-β subtypes ing that TGF-β associated gene and protein expression (Fig. 7a). Te advantage of DNN is that it can extract the get involved in cancer progression (metastasis in espe- characteristics of the data as much as possible and clas- cial), we then used the method from a previous study to sify the nonlinear data. We used the aforesaid top 10 gene identify drug response targeted at aforesaid signatures for signatures with smallest p value in Fig. 3a as input data, further research of targeted therapy [14]. Several drugs which could which can fully represent the characteristic suggested a great sensitivity to biological processes of information of diferent groups of TGF-β. Trough the tumor cells induced by TGF-β and related molecules. GIAD data marked in TCGA, we trained in 75% of the Moreover, some classic oncogenes were found to be tar- data and tested in the remaining data. We found that in geted by dozens of anticancer drugs, which improved TCGA, our model was able to distinguish between the the feasibility of targeted therapy. Also, based on TGF-β- groups with TGF-βhigh and TGF-βlow status. Overall, the specifc transcriptional signatures, we developed a deep Zhang et al. BMC Gastroenterol (2021) 21:284 Page 12 of 15

Model TrainTest STAD model (a) gene Hidden layer (b) signaures 1.00 1.00

0.75 0.75

Output: P(Y=1|X) e Rat C 0.50 0.50 sitiv AU ... ue Po

0.25 Tr 0.25 STAD training AUC: 96.8% ... STAD testing AUC: 94.6% .. .

... Random AUC: 51.9% 0.00 0.00 ESAD STAD COAD READ 0.00 0.25 0.50 0.75 1.00 False Positive Rate

(c) high (d) (e) (f) GSE62254 OS GSE62254 DFS GSE17536 OS * *** *** *** 1.00 1.00 + 1.00 ++ ++ +++ +++ ++ + + ++ ++ ++++ +++ + ++ + + ++ 0.75 0.75 + 0.75 + ++ ++++++++ ++ +++ + + ++ + ++++ ++ + ++++++++++++++++++++++++++++++++++++++++ + + ++++++++++++++++++++++++++++ ++++ +++++++++++++++ + + ++ +++ +++++++++++++++ 0.50 0.50 ++++ +++ ion of OS 0.50 +++ ++ tion of OS ++++++++++++++++ +++ tion of DF S rt +++ r 0 ++++++++ ++++ ++++++ + +++++++++++++ ++++++++++ +++ +++++++++ + ++++++ +++ rank rank rank + Prop o 0.25 0.25 Prop o 0.25 Propor + p < 0.0001 p = 0.00014 + Lo + + p = 0.066 + Lo + Lo

0.00 0.00 0.00 0255075100 0255075 100 050 100 150 Months Months Months (ESAD) (STAD) (COAD) (READ)

(g) GSE17536 DFS (h) GSE19417 GSE62254 1.00 ++++ + +++++ 0.8 + ++ 0.6 + EPITHELIAL MESENCHYMAL TRANSITION EPITHELIAL_MESENCHYMAL_TRANSITION +++ + +++++++++++++++++++++++++++++ ++++ TGF BETA SIGNALING 0.6 TGF_BETA_SIGNALING 0.4 0.75 ++ ichment Score

+++ ichment Score

+ r 0.4 ++ ++++++++++++++++++ 0.2 + 0.2 0.50 ++++++ +

tion of DF S 0.0 Running Enr Running En 0.0

rank + 0.25 Propor p = 0.013 + Lo ic ic r

0.00 0.0 0.0 d list me t d list metr

050100 150 ke Months Ran Ran ke

Rank in Ordered Dataset Rank in Ordered Dataset Fig. 7 Identifcation of two TGF-β subtypes based on the deep neural network (DNN) associated with TGF-β signatures. a Schematic diagram of a deep neural network for predicting TGF-β subtypes. b The performance of our DNN classifer on training and testing sets; Left: area under roc curve (AUC) of receiver operating characteristic (ROC) curve across each tumor. Right: ROC curve of the DNN classifer in stomach adenocarcinoma. c TGF-β score in diferent TGF-β subtypes for each other dataset classifed by the DNN model. d–g Kaplan-survival curves (including overall survival and disease-free survival) for diferent TGF-β groups identifed by DNN model in GSE62254 and GSE17536. h Gene-set enrichment analysis showed that TGF-β and EMT pathways were signifcantly enriched in diferent TGF-β groups identifed by DNN model. *P < 0.05, **P < 0.01, ***P < 0.001

neural network classifer that could well predict TGF-β promoting proteins (such as collagen IV, fbronectin) levels in diferent datasets of GIAD. were upregulated in TGF-βhigh samples with inhibiting In our study, TGF-βhigh patients displayed a high pro- proteins (such as E-cadherin, FASN) downregulated. In pensity of overexpressed mRNAs which are relevant to line with our fnding, a previous research comprehen- extracellular matrix organization and cellular biologi- sively identifed the function of TGF-β family in both cal process. Tese cell behaviors included proliferation, transcriptional and molecular level [5]. Genomic analy- adhesion, invasion and migration, which can be inhib- sis led to an impressed result that occurrence of signif- ited by regulations at transcriptional level for TGF-βlow cant alterations was tendentious across multiple cancer patients. We also noticed that a number of metastasis types (especially STAD). It is worth noting that in our Zhang et al. BMC Gastroenterol (2021) 21:284 Page 13 of 15

mutation and CNV analysis, both signifcant mutation Conclusions and CNV loci were present only in STAD; however, simi- Our study provided a comprehensive analysis of the lar trends were observed in other gastrointestinal adeno- molecular characteristics associated with TGF-β and (Additional fle 1: Fig. S6), but there was no provides possible therapeutic targets in GIAD. statistically signifcant diference. However, in the overall analysis, there was more mutation load and copy number low Abbreviations load in the TGF-β group (Fig. 5a, b), which indicated GIAD: Gastrointestinal adenocarcinoma; ESAD: Esophageal adenocarcinoma; the consistency of gastrointestinal adenocarcinoma to STAD: Stomach adenocarcinoma; COAD: Colon adenocarcinoma; READ: some extent. Specifcally, a high level of TMB and SCNA Rectum adenocarcinoma; TGF-β: Transforming growth factor beta; CNV: Copy low number variation; ssGSEA: Single sample gene-set enrichment analysis; DNN: lead to modifcation of coding proteins in TGF-β sam- Deep neural network; EMT: Epithelial mesenchymal transition; TMB: Tumor ples, which can activate abundant anti-tumor immune mutation burden; OS: Overall survival; PFI: Progression-free interval; DFS: response as tumor-specifc neoantigens for TGF-βlow Disease-free survival; SCNA: Somatic copy number alteration. GIADs. Recently, immune responses were found to be Supplementary Information suppressed due to TGF-β induced abnormal function The online version contains supplementary material available at https://​doi.​ of immune cells within tumor microenvironment [6, 49, org/​10.​1186/​s12876-​021-​01869-4. 50]. As for our observation, immune evasion induced by high TGF-β expression and lack of immune response in Additional fle 1. Supplementary fgures. TGF-βhigh GIAD interpreted the poor prognosis of TGF- Additional fle 2. Molecular characterization of TGF-beta in GIACs. βhigh patients, which was just the opposite situation in low TGF-β group. Acknowledgements Our analysis about drug sensitivity revealed that dif- We are grateful to Master Huang Xin (Hunan University, college of electrical and information engineering) for his help in the schematic drawing of deep ferentially altered gene were targeted by anticancer drugs neural network. for various clinical therapies. Te most representative drug dabrafenib, inhibitor of BRAF-V600 mutation, is Authors’ contributions BXZ and ZGZ conceived this study. BXZ, ZGZ and XPC provided fnancial and sensitive to genes related to cell invasion, metastatic and administrative support. QFZ and FRL did most of the data analysis. LQ, ZBL, EMT, such as FAP, FN1 and MMP2. We predict dab- and JS collected the public data. XPC provided the advices of this study. All rafenib one potential anticancer drug for GIAD patients. authors discussed the results, explained them further and prepared the panels for these fgures. QFZ and FRL wrote the manuscript. All authors read and Lenalidomide, an immunomodulator, can facilitate acti- approved the fnal manuscript. vation and function of immune cells and participates in induction of tumor cell apoptosis. Due to imputed Funding response diferences, lenalidomide may be a valid anti- This study was funded by the State Key Project on Infection Disease of China high (No. 2018ZX10723204-003); the Chinese Ministry of Public Health for Key Clini- cancer drug for TGF-β patients. Tough several trials cal Project (No. [2010] 493-51); National Natural Science Foundation of China of targeted drugs for TGF-β associated signatures have (81502530 and 81874189). been implemented and reviewed, the molecular mecha- Availability of data and materials nisms of targeted drugs remained unspecifc [7, 51–53]. Data in this study are from the TCGA, GDSC, and GEO database. To view addi- Te role of clinically actionable genes in cancer progres- tional sequencing data involved in this article please visit websites at: https://​ www.​ncbi.​nlm.​nih.​gov/​geo/​query/​acc.​cgi?​acc ​GSE19​417, https://​www.​ncbi.​ sion and metastasis deserves further explanation so that nlm.​nih.​gov/​geo/​query/​acc.​cgi?​acc ​GSE62​254=, https://​www.​ncbi.​nlm.​nih.​ rational combination therapies can be exploitable to pro- gov/​geo/​query/​acc.​cgi?​acc ​GSE17=​536, https://​www.​ncbi.​nlm.​nih.​gov/​geo/​ = mote prognosis in cancer patients. Hence, our system- query/​acc.​cgi?​acc ​GSE45​404, https://​www.​ncbi.​nlm.​nih.​gov/​geo/​query/​acc.​ cgi?​acc ​GSE62​254=, https://​www.​ncbi.​nlm.​nih.​gov/​geo/​query/​acc.​cgi?​acc ​ atic identifcation of TGF-β molecular signatures and GSE17​536= . To view multi-omics data of ESAD, STAD, COAD and READ please= sensitivity analysis can beneft clinical targeted therapies visit websites at: https://​xenab​rowser.​net/​datap​ages/. Data downloading and which are expected to be more efcient in the future. processing are as described in Methods and Materials. All data generated or analyzed during this study are included in the manuscript and its additional Tis study has certain limitations. Despite these cau- fles. salities and correlations elaborated by our and many pre- vious studies, further experimentations and analyses can Declarations be helpful to extend these conclusions and clinical viabil- ity. Retrospective studies and large-scale clinical trials Ethics approval and consent to participate are necessary for experimental verifcation. Finally, our Not applicable. combination of multi-omics is comprehensive but not Consent for publication exhaustive. Potential regulatory pathways and molecu- Not applicable. lar mechanisms need further investigation. In addition, Competing interests prospective cohort studies are called to manifest clinical The authors declare that they have no competing interests. benefts for patients with tumor. Zhang et al. BMC Gastroenterol (2021) 21:284 Page 14 of 15

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