Identification of hub genes with diagnostic values in pancreatic cancer by bioinformatics analyses and supervised learning methods

Pancreatic cancer is one of the most lethal tumors due to the poor prognosis, and now it is the fourth or fifth most common causes of cancer mortality in developed countries [1]. And it is estimated that by the year 2020, pancreatic cancer would move to the second leading cause of death [2]. Although some advances in understanding the molecular mechanisms of pancreatic cancer have been achieved, there still exist difficulties in early diagnosis due to non-specific symptoms and lacking effective testing identification, making it usually found in its late stage [3]. Until now, 1-year survival in pancreatic cancer patients is still not significantly improved [4], and the 5-year survival is less than 10% [5].

Numerous studies have focused on the investigation of biomarkers and molecular mechanisms of pancreatic cancers, and it is demonstrated that accumulated mutations in genes like oncogene Kras, and tumor-suppressor genes including P16 as well as TP53 resulted in the occurrence of pancreatic cancer [4]. One study performed the whole-genome sequencing and copy number variation (CNV) analyses showed that several genes including TP53, SMAD4, CDKN2A, ARID1A, ROBO2, PREX2, and KDM6A were disrupt resulting from chromosomal rearrangements in pancreatic ductal adenocarcinomas patients [6]. Molecular mechanisms researches demonstrated that overexpression of protein-coupled receptor GPR87 enhanced pancreatic cancer aggressiveness by activating NF-κB signaling pathway [7]. Moreover, Zhong and colleges have found that functional P38 MAPK activity contributed to overall survival through suppressing JNK signaling in pancreatic cancer [8]. In addition, aberrant expressions of some microRNAs have emerged as an important hallmark of cancer recently [9]. It was reported that microRNA-21 was overexpressed in pancreatic cancer, and could serve as a potential predictor of survival [10]. One study has found that miR-506 facilitated pancreatic cancer progression and chemoresistance via SPHK1/Akt/NF-κB signaling pathway [11]. Another study demonstrated that suppressing microRNA-34 expression downregulated Bcl-2 and Notch1/2 in pancreatic cancer cells, as well as significantly inhibited cell growth and invasion, induced apoptosis and G1 and G2/M arrest in cell cycle, and sensitized the cells to chemotherapy and radiation [12].

However, traditional experimental methods as mentioned above could only identify single gene or a few genes at once, which limits large-scale investigation of hub genes and pathways in the systematic biology level. Development of microarray and sequencing technologies provides better methods for biomarker screening and molecular mechanism discovery in cancer research. Recent years with the accessibility of multi-omics database like Gene Expression Omnibus (GEO) [13] as well as The Cancer Genome Atlas (TCGA) [14] and so on, it is now possible to acquire multi-sample data and compare cancer profiles with normal profiles in multiple omics dimensions. On one hand, omics data in multiple dimensions leading to the system biology- and/or network-based approach, which could better understand the dysregulated molecular mechanisms in cancer development and progression [15]. On the other hand, biology- and/or network-based method can not only identify critical genes but also can detect corresponding pathways and/or interactive network, which may provide better insights into molecular mechanisms investigation than dysregulated gene analysis individually [16]. For example, Kras was proved to be the most frequently mutated gene in pancreatic ductal adenocarcinoma [17], and the mutation of Kras was a hallmark of pancreatic cancer [18]. However, inhibitors targeting Kras gene were largely unsuccessful, while some omics-based strategies targeting Kras correlated pathways and interactive genes were proved to bear better therapeutic effects than targeting Kras individually [19].

To date, diagnosis of pancreatic cancer is mainly based on clinical signs and pathology confirmation. However, the specific symptoms and pathological imagines may only be detected unambiguously at the late stage of pancreatic cancer, which may lead to a limited therapies and poor prognosis. This raises an urgent need for the development of reliable biomarkers which can effectively differ tumor from normal tissues based on analyses of gene expression profiles. Herein, in order to identify novel diagnostic predictors and molecular markers, we integrated two microarray datasets from GEO database, and 11 candidate genes significantly differentially expressed between tumor and normal samples were screened by bioinformatics analyses. Then two hub genes, matrix metallopeptidase 7 (MMP7) and integrin, alpha 2 (ITGA2), were further identified by survival and cox analyses in TCGA database. These two hub genes were validated in another expression profile from GEO database, demonstrating that these hub genes can successfully differ normal tissues from tumor samples. The predictive accuracies of k-nearest neighbor (kNN) and random forest algorithms were almost 94% and almost 82%, respectively. Results in our study may provide an auxiliary evidence of pancreatic cancer diagnosis and therapy in the future.

Compared with other cancers, the occurrence of pancreatic cancer is relatively rare; however, it is still a lethal disease with poor prognosis. Until now, there still lacks effective therapies against pancreatic cancer, and many novel therapies are in the experimental stage. Therefore, it is important to find some potential hub genes playing crucial roles in regulating cancer occurrence and progression, which may become key targets in the treatment of pancreatic cancer in the future. In addition, these hub genes effectively differing cancer tissues from normal samples may provide novel auxiliary evidence in pancreatic cancer diagnosis. It is demonstrated that pancreatic cancer results from the accumulation of acquired mutations, which may lead to the upregulation of some oncogenes and downregulation of some tumor-suppressing genes and genomic maintenance genes [4]. Therefore, there might exist some DEGs between normal and tumor samples, and these DEGs may play important roles in regulating tumor occurrence, development, and progression. In the present study, two genes ITGA2 and MMP7 were screened from DEGs as hub genes by using a series of bioinformatics methods, and they could discriminate normal samples and tumor samples.

The matrix metalloproteinase (MMPs) is a family of enzymes, bearing the capability to cleave extracellular matrix substrates [26], as well as promotes the release of pro-TNF-α, Fas ligand, and some cytokines in various cancers cells [27]. One previous study has experimentally demonstrated that genes in matrix metallopeptidase family, collagen family, and integrin family were upregulated in pancreatic cancer, and they may correlate with cancer activity and poor prognosis [28]. MMPs also involved in proliferative, migrating, and differentiated processes in cells [29]. The interaction between MMPs and extracellular ligand induced a series of signaling cascade, and thus led to the functional regulation of intracellular and extracellular activities. The expression of MMP7 has been reported to be upregulated in several kinds of cancer, including colon cancer [27], pancreatic cancer [30], breast cancer [31], gastric cancer [32], and esophageal cancer [33]. One study has demonstrated that multiplex detection of pancreatic biomarkers CA19-9, MMP7, and MUC4 in sera samples were of high sensitivity, which may act as the critical biomarker in diagnosis of pancreatic cancer [34]. Another study compared tumor tissues with healthy control samples revealed that MMP7 was highly predictive for advanced stage of pancreatic cancer, which strongly associated with N1 status, T3/T4 stage, moderate/poor differentiation, and perineural invasion [35]. It has been reported that Stat3 was a critical factor to facilitate precursor formation and enforced MMP7 expression in pancreatic cancer cells, while MMP7 level was correlated with metastasis and survival in pancreatic cancer patients [36].

ITGA2 encoding by ITGA2 gene is the alpha subunit of the transmembrane receptor integrin, and it mainly exerts the adhesive roles in cell-cell interaction, also promotes the generation and adhesion of newly synthesized extracellular matrix [37, 38]. The polymorphisms of ITGA2 gene was related to the poor survival of nasopharyngeal carcinoma [39]. ITGA2 gene was reported to play migrating roles in colon cancer cells [40], and it expressed in colorectal cancer with liver metastasis tissues but absent in normal tissue [41]. In addition, epigenetic modifications such as DNA methylation were also important in tumorigenesis, and hypomethylation of ITGA2 with high gene expression was associated with poor survival in pancreatic cancer patients [42]. One research has found that ITGA2 was overexpressed in a variety of gastric cancer patients mainly playing pro-survival roles, and the blockage of ITGA2 could induce apoptosis and inhibit cell migration in gastric cancer [43]. Another research in gastric cancer revealed that HMGA2, FOXL2, and ITGA2 were increased in metastatic lymph nodes and distant metastases in gastric cancer, and suppressing the HMGA2-FOXL2-ITGA2 pathway could serve as a new strategy in further treatment in gastric cancer [44]. The transcriptional co-activators yes-associated protein (YAP) was considered as oncogene in many types of cancer; ITGA2 stimulating YAP activity was associated with unfavorable survival of pancreatic cancer patients [45].

In order to validate whether these genes were real hub genes, another mRNA expression profile GSE15471 from GEO database was utilized as testing set. Herein, kNN and random forest algorithms were performed to detect whether these hub genes could successfully distinguish tumor tissues from normal samples. We can see from Table 2 that hub genes selected by method 1 in this study represented the highest accuracy reaching to 94% approximately with 2.56% false negative and 3.84% false positive. In the cases of differing from tumor and normal samples, reduction of false negative results was more important than the reduction of false positive result. Since false negative results may lead to wrongly diagnose pancreatic cancer as normal condition, it may result in the delay of timely treatment, and further lead to more serious progression of disease as well as more waste of medical resources and costs. Bedsides, random forest algorithm also represented highly predictive accuracy of 81.31% after rerun for 100 times of method 1. Therefore, method 1 bear highly predictive accuracies in both of the two methods, and it could be inferred that these two hub genes were real hub genes, which could successfully discriminate normal and tumor samples. Another interesting result could be found in Table 2 that selection of genes with top 25% variance obviously increased the predictive accuracy from 70 to 94% (method 1 vs. method 3).

In addition, we can choose different minimum required interaction score when constructing PPI network. Minimum required interaction score is a threshold providing a score for each interactive pair, which is computed as the joint probability from different evidence (e.g., protein interaction, fusion, co-expression, text mining). Higher score may represent more confident interaction while lower score may lead to more false positives [22]. In order to elucidate whether setting different minimum required interaction score may have influence on hub gene selection, predictive accuracy was compared (shown in Table 2). It can be found that higher minimum required interaction score led to much higher predictive accuracies; method 5 bear the highest accuracy of 85% while the predictive accuracy of method 1 could reach to almost 95% by using kNN method.

Hub genes screened in this study were rational. Firstly, all the candidate genes and these two hub genes were closely correlated with the progression of tumor. As shown in GO enrichment, most of the candidate genes were implicated in the biological process of extracellular matrix, cell adhesion, cell proliferation, and signal transduction; they play important role in the progression of cancers. Moreover, both of the hub genes were implicated in the regulation of tumor microenvironment, including the regulation of tumor cells, stroma cells, extracellular matrix (ECM), and some extracellular molecules like cytokines as well as chemokines. It has been demonstrated that microenvironment was usually dysregulated and disorganized in cancer cells. Thus, disordered microenvironment may be favorable to tumor proliferation, progression, invasion and metastasis, and exert drug-hampering roles [46, 47], and now some treatment strategies have focused on the regulation of tumor microenvironment. Since pancreatic cancer was featured as uncontrolled and malignant invasion and migration, therefore we can infer that these hub genes implicated in tumor microenvironment might be core meditators in pancreatic cancer diagnosis and therapy. Secondly, two supervised learning methods were performed, and both of the predictive results of these two hub genes were good with lower false negative in discriminating tumor samples from normal samples.

However, there also exist some limitations in our study. Firstly, the number of samples in our study is not too much. According to the dataset screening criteria, three datasets were included in our study. There were 146 samples totally, of which 68 were training set and 78 were testing set. In the future, with more and more investigations about pancreatic cancer would be performed, more samples should be included. Secondly, in this study, we mainly focused on the genes in the pancreatic tissue not the genes from circulating tumor cells (CTC) nor circulating tumor DNA (ctDNA) in peripheral blood, since the genes in tissue are more accurate to analyze the important biomarkers. Moreover, the datasets about peripheral blood in GEO database are not enough to do the same research. In the future, the microarray analysis of DNA in peripheral blood of pancreatic cancer patients should be further proposed. Thirdly, in our study, all the hub genes were screened and validated only by bioinformatics method, and further exploration of the biological functions and molecular mechanisms of these hub genes both in vitro and in vivo are needed to be fulfilled.

In summary, we conducted a series of bioinformatics methods to find DEGs, further screened and validated hub genes. These two hub genes, ITGA2 and MMP7, may act as potential diagnostic and therapeutic biomarkers in pancreatic cancer patients. This study provides several useful hub genes for future in vitro and in vivo investigations of their molecular mechanisms in pancreatic cancer diagnosis and therapy. And profile data mining by bioinformatics analysis is an available method to find potential diagnostic and prognostic biomarkers systematically. Nevertheless, further molecular mechanisms investigations by biological experiments are still needed to be verified in pancreatic cancer cells.