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 genes. We analyzed diferential mRNAs, miRNAs, proteins gene 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 carcinoma (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. 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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 protein 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 gene expression 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 kinase activity can interact with activated TGF-βs, fol- count value less than 1), and 39 TGF-β core genes were lowing phosphorylation 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 signal transduction 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