GEO data mining and TCGA analysis reveal altered branched chain amino acid metabolism in pancreatic cancer patients

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive tumor of the digestive system which has a less than 1% 5-year survival rate. The pathogenesis of PDAC development is incompletely understood. Genetic predisposition, disease history of chronic pancreatitis and diabetes elevate the risk of PDAC while environmental and dietary factors including smoking, alcohol abuse, high fat/protein intake as well as air pollution exacerbate PDAC progression. BCAAs, consisting of leucine, isoleucine and valine are essential amino acids that are obtained from food and play versatile roles in carcinogenesis. Recent studies have demonstrated that BCAA metabolism affects PDAC development but the results are controversial. To explore the possible engagement of BCAA metabolism in PDAC, we took advantage of the GEO and TCGA database and discovered that BCAA uptake is closely related to PDAC development while BCAA catabolism is down-regulated in PDAC tissue. Besides, NOTCH and MYC are differentially involved in BCAA metabolism in tumor and muscle, and enhanced lipid synthesis is independent of BCAA catabolism. Altogether, we highlight BCAA uptake as a promising target for PDAC treatment.

AGING fuel the DNA replication process [3]. Then, the α-keto acids are further decarboxylated and combined with a CoA group under the enzymatic action of branchedchain ketoacid dehydrogenase (BCKDH) complex. Finally, after a series of irreversible enzymatic actions, the succinyl-CoA and acetyl-CoA are generated to take part in the tricarboxylic acid (TCA) cycle and serve as a carbon source [3,4]. Free, untransformed intracellular BCAAs are potent stimulators of mTORC1 signaling pathway activation, which leads to enhanced protein synthesis [5,6]. All these characteristics of BCAA metabolism make it a potential candidate for tumor intervention.
Indeed, BCAA metabolism was found to be critically involved in the progression of multiple cancer types [7][8][9], especially for the pancreatic ductal adenocarcinoma (PDAC) with controversial results being reported [10,11]. Some research has shown that BCAA uptake is down-regulated in PDAC tissue [10], while other research has demonstrated an elevated BCAA uptake along with enhanced expression of corresponding solute carrier (SLC) transporters (SLC7A5, SLC1A5, SLC3A2, SLC43A1, SLC43A2) [12]. In Lee's study, BCAT2 was found elevated in mouse PDAC model and human patients. Pancreatic tissue specific knockout of BCAT2 impeded progression of pancreatic intraepithelial neoplasia (PanIN) in LSL-KrasG12D/+ mice [13]. However, Mayers and colleagues found decreased SLC7A5, BCAT1, BCAT2 and BCKDH expression in PDAC tissue, and loss of BCAT2 does not affect PDAC tumor formation [14]. Similarly, PDAC and non-small cell lung carcinoma (NSCLC) obtain BCAAs as a nitrogen source, whereas deletion of BCAT1 and BCAT2 could prevent NSCLC tumor formation in vivo but has limited effect on PDAC tumor growth [10,14]. Nonetheless, the increased plasma BCAA level associates with worse prognosis of pancreatic cancer, and BCAT2 expression in PDAC tissue was independently correlated to high mortality rate [11]. Lines of evidence consistently indicated an elevated level of BCAA in the plasma of PDAC patients, which may result from accelerated muscle wasting in individuals suffering from cachexia [10]. Despite the fact that the mechanisms underlying how PDAC cells utilize the BCAAs remain enigmatic, lipid biosynthesis that is pivotal for unlimited proliferation and invasive metastasis of cancer cells is gaining much attention [12,15]. Adipocyte and lipid metabolism are also responsible for the drug resistance in cancer [16]. BCAA degrading enzymes promote lipogenesis pathway but do not affect glucose metabolism and mitochondrial respiration [12]. In addition, BCKDHA knockdown significantly inhibited fatty acid synthesis while having no effect on levels of tricarboxylic acid cycle (TCA) intermediates or the oxygen consumption rate (OCR) [12].
Taken together, the BCAA uptake, expression of BCAA metabolic enzymes and their functional effect in PDAC tumor cell progression are still under debate. To unveil the role of BCAA metabolism in human PDAC and its relationship with lipid metabolism, we took advantage of three human PDAC datasets acquired from public GEO database to explore the alteration of BCAA metabolism related genes, and the TCGA database to carry out survival analysis. These results comprehensively provide clinicians and researchers with a grand landscape of BCAA metabolism in PDAC development.

Identification of differentially expressed genes (DEGs) between PDAC and normal pancreatic tissue
GSE62165, comprised of 118 PDAC and 13 normal control pancreatic samples, was used to identify the DEGs between PDAC and normal tissue. The dataset conformed to sample homogeneity and PCA analysis could clearly discriminate PDAC group from control group ( Figure 1A-1B). A total of 909 DEGs were obtained in GSE62165 (the cut-off value of logFC is 1.816), among which 271 genes were down-regulated and 638 genes up-regulated ( Figure 1C). The top 25 DEGs exhibited by heatmap were displayed in Figure  1D. We further analyzed the functional enrichment of the top 25 DEGs ( Figure 1E-1F). GO analysis showed that cell skeleton components and protein phosphorylation events are closely related to their effect, and KEGG analysis indicates the involvement of pathways like cell cycle regulation, cellular senescence, PPAR signaling, P53 signaling, chemokine signaling and cytokine-cytokine receptor signaling pathway, which are all well-appreciated in tumor development. Therefore, this set of data is eligible for further investigation.

Correlation and survival analysis
To further understand the relationship between BCAA catabolism and fatty acid biosynthesis/oxidation, correlation analysis was conducted. ACACA gene expression is in moderate negative correlation with ACADS and ACADSB expression (r = -0.383, r = -0.344) and FAS was in moderate negative correlation with BCAT1 and BCAT2 (r = -0.305, r = -0.348) ( Figure 3A). The rest of genes displays no obvious correlation; thus, contrary to what has been anticipated, there does not exist a positive link between BCAA catabolism and lipid biosynthesis. Finally, we used the pancreatic cancer dataset in TCGA for survival analysis. We first check the BCAA carriers. Although none of them reached statistic difference, the tendency showed that high expression of SLC1A5 and SLC7A5 is linked to low survival rate while high expression of SLC3A2, SLC43A1 and SLC43A2 is related to high survival rate ( Figure 3B-3C). Additionally, we found that high expression of BCAT1 (HR = 0.605, CI: 0.402-0.911, P = 0.016) and FAS (HR = 0.620, CI: 0.411-0.936, P = 0.023) indicates low survival rate ( Figure 3D-3E), while among the rest of associated genes examined, none of them displayed statistical significance (data not shown). These results reflected the flexibility of the BCAA transporter usage, and their role in predicting PDAC prognosis deserves large sample investigations.

Experimental verification
To further verify the aforementioned results, we took advantage of three distinct human pancreatic duct epithelial cell lines: the HPDE6-E6E76c7 (H6c7) cell line was immortalized from normal human pancreatic duct epithelial (HPDE) cells, PANC-1 derived from a human carcinoma of exocrine pancreas, and SW-1990 was established in 1978 from a spleen metastasis of a grade II pancreatic adenocarcinoma. SW-1990 possesses the highest degree of malignancy followed by PANC-1, while H6c7 could serve as a normal control. Regarding the BCAA transporters, SLC7A5 exhibited considerably increased expression level in both PANC-1 and SW-1990 cells. SLC1A5, SLC3A2 and SLC43A2 were mostly enriched in PANC-1 cell line, whereas SLC43A1 was slightly decreased in both PANC-1 and SW-1990 cells ( Figure 4A). These results corroborated the flexibility of BCAA transporter usage, which brought challenge for therapies aimed at limiting BCAA uptake in PDAC. The similar flexibility was also observed in the upstream regulator NOTCH family proteins: as NOTCH4 was highly expressed in both PANC-1 and SW-1990 cells, NOTCH2 and NOTCH3 were highly expressed only in PANC-1 while NOTCH1 was enriched in H6c7 cell line. Contrary to the bioinformatic analysis, MYC displayed an increased expression level in both PANC-1 and SW-1990 cell lines ( Figure 4B), and the enhanced expression of MYC and NOTCH4 ICD (intracellular domain) was confirmed at the protein level ( Figure 4C). Consistently, the decreased BCAT2 expression indicated a diminished BCAA catabolism while the elevated CPT1A implied strengthened fatty acid oxidation; however, we were unable to detect an increase of FASN expression in PANC-1 and SW-1990 cell lines ( Figure 4D-4E).

MYC mainly regulates BCAA uptake in myocyte of PC patients
Muscle is another important place where BCAAs are produced or deposited [25,26]. To unveil the BCAA metabolism in muscle of pancreatic cancer patients, GSE130563 dataset was additionally applied to do further investigation. We discovered that gene expression of NOTCHs does not vary between groups AGING while MYC (5.428 ± 0.394 vs 4.333 ± 0.214, P = 0.023) expression in muscles of cachectic PDAC patients was significantly up-regulated ( Figure 5A-5E). Most of the SLC transporters exhibit no change between groups, except for SLC7A5 (6.813 ± 0.638 vs 6.355 ± 0.109, P = 0.015) whose expression in muscles of cachectic PDAC patients is slightly up-regulated compared to that of normal control ( Figure 5F). At the same time, BCAA catabolism related genes exhibited no significant difference ( Figure 5G-5M). Correlation analysis showed that SLC7A5 expression was in moderate correlation with MYC expression in muscle of PDAC patients (r = 0.639) ( Figure 5N). Since SLC7A5 is upregulated both in PDAC tissue and muscle of PDAC patients, fold-change (relative to normal counterparts) of SLC7A5 expression in muscle and tumor was calculated to determine which one potentially acquires more BCAAs from circulation. These results indicate that the relative fold-change of SLC7A5 expression in GSE62165 (tumor) and GSE102238 (tumor) are higher than that in GSE130563 (muscle) (Figure 5O), which means PDAC tumor cells might be more capable than myocytes regarding to BCAA uptake. Thus, distinct from tumor cells, MYC instead of NOTCH is a possible upstream transcriptional regulator of SLC transporters in muscle. During PDAC cancer development, cachectic condition in particular, pancreatic tissue robustly enhance the BCAA absorptive activity with only minor change in skeletal muscles.

DISCUSSION AND CONCLUSION
Metabolic pathways are increasingly being appreciated in tumor intervention [27,28]. With the aim to clarify the relationship between BCAA metabolism and PDAC development, we utilize the open accessed databases (GEO and TCGA) to address some of the most concerned issues. Intriguingly, we found BCAA uptake elevated while BCAA catabolism decreased in PDAC tissue, implying that the tumor-favoring effect is a major consequence of free BCAAs which act as potent stimulators of mTOR signaling pathway. Lipid metabolism including fatty acid biosynthesis and beta oxidation is enhanced in PDAC, but no positive associative link between lipid metabolism and BCAA catabolism is observed. Most likely, BCAAs does not serve as a supplying source for lipid metabolism in pancreatic cancer, which is consistent with a downregulated BCAA catabolic pathway. Therefore, the key to BCAA targeted therapy for PDAC falls into blockade of BCAA uptake. NOTCH and MYC are critical for the transcription of SLC carriers that are responsible for BCAA transportation [18][19][20]. Notably, NOTCH appears to be important in PDAC tissue given its higher expression and the positive correlation with SLC7A5 and SLC3A2. On the other hand, in skeletal muscle, MYC plays a pivotal role but the only identified differentially expressed BCAA transporter, SLC7A5, shows merely subtle change. BCAA is substantial to the maintenance of muscle mass [26,29]. During cancer-induced cachexia, muscle proteins would degrade and release BCAAs into peripheral blood [29]. However, it seems that circulating BCAAs prefer to flux into PDAC tissue rather than recycling back to the muscle, which possibly explain the unrestrained tumor progression. This discovery is in consistent with another study suggesting that loss of skeletal muscle, but not adipose tissue, is associated with shorter survival time of patients with advanced pancreatic cancer [30]. Overall, targeting on specific SLC carriers or NOTCH signaling pathway represents a promising strategy in PDAC treatment, and may also help to ameliorate the cachectic syndrome.
The shortcomings of this study stem from the relatively small sample size and lack of enough experimental data. For instance, the possible role of BCAA metabolic enzymes serving as PDAC prognostic biomarkers and the correlation with survival rate are inadequately revealed. Nonetheless, our work provides a meaningful view on the relationship between BCAA metabolism and PDAC development, which sheds novel light on future clinical translational studies.

Gene expression data downloaded from GEO
Using the keywords "pancreatic ductal adenocarcinoma GEO accession" to search on the GEO database, the gene expression profiles of GSE62165 and GSE102238 (Cheng et al, unpublished data, 2015) were downloaded. The GSE62165 profile consisted of 118 PDAC samples and 13 corresponding normal tissues. GSE102238 contained 100 cases of paired PDAC tissue and the adjacent normal tissue. GSE130563 dataset included muscle samples of pancreatic cancer patients. The R software package was applied to process the downloaded files and to reject the unqualified data. The data were calibrated, standardized, and log2 transformed.

Principal component analysis (PCA) and correlation analysis
Principal component analysis (PCA) was performed with R package to the degree of aggregation of pancreatic samples. Correlation values were calculated via Speaman's correlation to evaluate the relationship between the selected genes.

Identification of differentially expressed genes (DEGs)
The downloaded platform and series of matrix file(s) were converted using the R software. The ID corresponding to the probe name was converted into an international standard name for genes (gene symbol) and saved in a txt file. Genes with the parameter of false discovery rate (FDR) below 0.05 (a corrected P-value < 0.05) and log fold-change (log2FC) values >2 were considered as differentially expressed genes (DEGs).

Gene ontology (GO) and KEGG enrichment analysis
All DEGs were mapped to GO terms in the Gene Ontology database. Gene numbers were calculated for every term and GO terms with an FDR < 0.05 and Pvalue < 0.05 were defined as significantly enriched terms. KOBAS online analysis database was employed for KEGG pathway analysis of DEGs. Significantly enriched metabolic pathways or signal transduction pathways in DEGs were identified by comparing them with the whole genome background. Pathways meeting the condition of an FDR < 0.05 (P-value < 0.05) were considered statistically significant.

Gene set enrichment analysis
GSEA software (Broad Institute) was used to determine the statistically significant gene sets in the comparison between different subgroups. Then, annotated gene sets from the HALMARK collection v7.1 were used as enrichment input. Significantly enriched gene sets were defined as gene sets with a normalized enrichment score NES) > 1 and FDR< 0.05.

Western blot analysis
Cell lysates were prepared using the radioimmunoprecipitation assay (RIPA) buffer (Servicebio, Wuhan, China) containing a protease inhibitor cocktail (Roche, IN, USA). Western blot analysis of target proteins was conducted as described using the indicated primary antibodies, followed by probing to the corresponding HRP-conjugated secondary antibody. The reactive bands were visualized using ECL plus reagents (Servicebio, Wuhan, China). GAPDH was used as an internal control to analyze the results. The detailed information about the antibodies was as following: Anti-BCKDE1a (#90198), anti-BCAT2 (#79764), anti-NOTCH4 (#2423), anti-c-myc (#14819) and anti-fatty acid synthase (FAS, #3180) antibodies were obtained from the Cell Signaling Technology (Danvers, MA, USA). Anti-CPT1a antibody (#15184-1) was purchased from Proteintech Group (Wuhan, China). Antibody against GAPDH was purchased from Santa Cruz Biotechnology (Santa Cruz, CA, USA).

Survival analysis of the hub genes
Kaplan-Meier (KM) Plotter, one of the publicly available datasets for cancer microarray with clinical annotations, was employed to evaluate the prognostic value of the indicated genes in PDCA. PDCA patients were divided into high and low expression groups using the median level. The selected genes were inputted as a query and the data were collected for overall survival (OS) analysis. The log rank p values and the hazard ratio (HR) with 95% confidence intervals (95% CI) were calculated and displayed.

Statistics
The statistical significance between the tumor and normal groups was calculated by Student's t test. The statistical analysis and illustration were performed by GraphPad Prism 6 (San Diego, CA, USA) and R software (Version 3.5.1). Adjusted p value < 0.05 and log2FC > 2 were set up as the significant cut-off for DEGs only.

AUTHOR CONTRIBUTIONS
JX and HF were the overall principal investigators who conceived the study and were responsible for the study design, and supervised the entire study. JL, FS, CY, HZ, QZ, HC, MG, JX and HF drafted the paper. JL, FS, CY, QZ and HC did the statistical analyses. HZ, MG, JX and HF did the data analysis and interpreted the results. JL, FS, CY, HZ, QZ, HC, JX and HF reviewed the manuscript. All authors contributed to data interpretation, manuscript writing, and review of the manuscript.

CONFLICTS OF INTEREST
No potential conflicts of interest are to be disclosed by authors.