Introduction

Inguinal hernia is a prevalent malady in elderly man, and hernia repair is the most commonly performed general surgical procedure around the world. An inguinal hernia occurs in the abdomen near the groin area. It is a protrusion of contents that originated from the abdominal-cavity through the inguinal canal [1]. Though the pathogenesis is weakly understood, the probabilities of the lifetime risk of inguinal hernia are very higher in men than that in women [2]. What’s more, the symptoms of inguinal hernia included that the bulges can appear to increase in size when standing up or cough, in addition, it also comprised pain, burning sensations, swelling of the scrotum in men. There are many risks for inguinal hernia, which include: heredity, having a prior inguinal hernia, being male, premature birth, being overweight or obese, pregnancy, cystic fibrosis, chronic cough, and chronic constipation [3, 4].

Currently, an inguinal hernia has classified into indirect or direct, incarcerated, or strangulated categories [5, 6]. The former type of hernia can occur at any of time during your lifetime. However, a direct inguinal hernia more likely occurs in adults owing to their ages. Moreover, when tissue in the groin becomes stuck and can’t be reducible, an incarcerated inguinal hernia could be happened. Besides, a strangulated inguinal hernia is the most severe type of inguinal hernia, usually could be life-threatening and require emergency medical care [7].

To date, the diagnosing an inguinal hernia usually depends on a physical exam, such as asking you to cough while standing so that the doctor could check the hernia. However, there is no much researches about genetics of hernia that has been focused on the molecular mechanisms of pathogenesis, and the specific molecular mechanisms have not been disclosed.

In this study, using comprehensive bioinformatics analyses is a proper strategy to uncover the potential genes and signaling pathways in the mimic human inguinal hernia model. Firstly, we downloaded the GSE92748 gene expression datasets [8], which contained humanized aromatase transgenic mice to mimic human inguinal model, from the National Center for Biotechnology Information (NCBI) [9]. Then dysregulated genes were identified between different groups. Furthermore, gene ontology, signaling pathway enrichment annotation and protein–protein interaction were performed among these dysregulated genes through different bioinformatics methods [10, 11]. Finally, we could find the potential gene biomarkers and correlated pathways, which might be associated with inguinal hernia and could be give us a new sight to explore the molecular mechanism of inguinal hernia hidden.

Methods and materials

Microarray data analysis

GSE92748 expression profile (.txt format files) and correlated clinical information data (.soft format file) have been acquired from NCBI-GEO website [9, 12], which was done on the GPL6887 platform. GSE92748 datasets contain 4 high expression of humanized aromatase transgenic mice (marked AromhumH), 4 low expression of humanized aromatase transgenic mice (marked AromhumL), and 4 wild type mice (marked WT). We filter out 8 samples from the GSE92748 to identify different expression genes (DEGs) between AromhumH and AromhumL groups.

Data preprocessing

Probe identification numbers were transformed into official gene symbols based on the information built in GPL6887 platform, the mRNA probes were retained and the other non-mRNA probes were abandoned, and the multiple probes to the same gene were assigned the significant value as the gene expression level. Then, using limma package to detect gene expression matrix, processed by affy, affyPLM packages, and obtain differentially expressed genes (DEGs) in AromhumH group and AromhumL group [13,14,15,16]. DEGs with the fold change (FC) ≥ 1.4 & adjust P value < 0.05, corrected by the Benjamini–Hochberg method [17], as the cut-off criteria were selected for the follow-up analyses.

Gene ontology and pathway enrichment analysis

The gene ontology (GO) analysis is a general and useful method for annotating gene products and their characteristics of functional features [10]. Gene Ontology annotation is defined into three classes (biological process, cellular component, molecular function). The Kyoto Encyclopedia of Genes and Genomes (KEGG) database is an open access informatic source from Japan for interpreting biological function and characteristics of the organic system, produced by the microarray and RNA-seq experiments [11]. The GO and KEGG enrichment of DEGs were analyzed using an online tool DAVID, a functional annotation bioinformatics microarray analysis website, used to gene annotation, visualization. FDR (false discovery rate) < 0.05 was considered as statistically significance [18, 19].

Protein interaction and module analysis

The online database STRING (version 11.0), covering about 24.6 M proteins and more than 3.1 billion interactions originated from 5.09 K organisms, was known as the primary source to describe and display the interaction among various proteins, encoded by corresponding genes [20]. Firstly, we uploaded DEGs into the STRING website, and the minimum interaction score > 0.4 (low confidence) was recognized as significant. Then the TSV format file of protein–protein interaction (PPI) information was downloaded, and PPI networks were constructed through Cytoscape software [21]. Subsequently, the Molecular Complex Detection (MCODE) and STRING app built in Cytoscape was used to classify the significant gene modules (clusters), which have highly interconnected clusters in the PPI network [22, 23]. All parameters in MCODE were executed by default. The genes/nodes in gene modules were performed drug-gene interaction analysis.

Drug-gene interaction and functional analysis of potential genes

To get interaction between genes and the existing drugs and explore the potential application of the new drug indications for human hernia. The drug-gene interaction database (DGIdb: https://www.dgidb.org) is an open-source and supports searching, browsing and filtering of information on drug-gene interactions based on over thirty trusted sources [24]. The module genes, as the potential targets, were pasted into the drug-gene database to search for existing drugs or compounds. These potential genes which have matched drugs were obtained and also performed functional enrichment analysis.

Statistics analysis

The moderate t-test was applied to identify DEGs; Fisher’s Exact test was used to analyzed GO and KEGG annotation enrichments [25]. All statistical analyses were executed in R version 3.6.1 software Fig. 1.

Fig. 1
figure 1

The framework of data analyses

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Results

Identification of DEGs

There are 64 DEGs identified in AromhumH compared with AromhumL group, according to the criteria: fold change (FC) ≥ 1.4 & adjust P value < 0.05. Among them, 43 up-regulated genes and 21 down-regulated genes (Table 1).

Gene ontology and pathway enrichment analysis

To outline gene ontology and signal pathway enrichments of DEGs, we used DAVID website to visualize functional annotations. As shown in Fig. 2 and Table 2, it showed that the significant enrichment terms for BP, CC of DEGs. In BP annotation, it was mainly involved in the muscle system process, actomyosin structure organization, and muscle structure development. In CC annotation, it was significantly involved in the extracellular exosome, extracellular vesicle, and extracellular organelle. There no signal pathway enrichment terms can be available with FDR < 0.05.

Fig. 2
figure 2

All available significant gene ontology enrichment terms of the differentially expressed genes (DEGs)

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As for up-regulated DEGs, the GO annotation was significantly involved in the muscle system process, extracellular exosome etc. (Table 3), while the down-regulated DEGs haven’t annotated any GO and signaling pathway terms.

Protein interaction and module analysis

The 64 DEGs were input into the STRING database and then analyzed with STRING APP built in Cytoscape software. A total of 29 genes/nodes with 38 edges were participated in the construction of the PPI networks, and 35 genes haven’t fallen into the PPI networks (Fig. 3a). Furthermore, a significant gene module was selected to cluster all genes using the MCODE APP built in Cytoscape. Module 1 consists 6 genes/nodes with 12 edges/interactions, which 4 up-regulated genes (Cmya1, Casq2, Csrp3 and Actc1) and 2 down-regulated genes (Cmya5, Ttn) (Fig. 3b).

Fig. 3
figure 3

a The protein–protein interaction (PPI) networks of differentially expressed genes (DEGs); b the significant gene module in the PPI networks

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Drug-gene interaction and functional analysis of potential genes

The 6 potential genes clustered in the significant gene module 1 were selected for drug-gene interaction analysis. In human species, we found that there was just ACTC1 target to one potential existing drug, namely DEXAMETHASONE.

Discussion

Inguinal hernia as one of the common symptoms often occurs in the groin in elderly men. Some symptoms can affect the quality of people’s life. As you found out, you can’t prevent the birth defect that makes you vulnerable to an inguinal hernia. We can, however, reduce strain on our abdominal muscles and tissues. Such as keep moving and sustain a healthy weight.

Currently, a large number of researches have revealed that inguinal hernial have associated with the status of muscle tissues. In this paper, we used Aromhum mice to mimic the human inguinal model and expected to discover potential gene markers and some existing drugs. Aromhum mice represent a pathologically and unique relevant experimental model to study the molecular mechanism behind inguinal hernia. Comprehensive analyses of gene expression profiling allowed us to identify a number of potential molecular biomarkers (Cmya1, Cmya5, Casq2, Csrp3, Ttn and Actc1) and new drug indications of the existing drug (DEXAMETHASONE).

Cmya1 (Cardiomypathy-associated gene 1) have involved in embryonic cardiac development, postnatal cardiac remodeling and myocardial injury repair, and its abnormal gene expression have correlated with cardiac hyperplasia and primary myocardiopathy [26]. Cmya5 (Cardiomyopathy-associated gene 5) encodes myospryn, could be recognized as a biomarker for some diseases affecting striated muscle and associated with aschizophrenia [27, 28]. Casq2 (Calsequestrin 2), generally referring to its mutation, is considered to be the crucial sarcoplasmic reticulum (SR) Ca2 + storage protein needed for SR Ca2 + release in the mammalian heart [29, 30]. Crsp3 (cardiac LIM protein cysteine and glycine-rich protein 3) is thought to crucial component mediating cardiac mechanotransduction and stress responses within cells [31]. Ttn (titin) is familiar with its high expression in human cardiomyocytes and necessary for normal sarcomere function [32].

Based on the above illustration, we found that most of these genes are related to cardiac events. It is well known that muscle tissues include myocardial tissue and skeletal muscle tissue, and the components of the heart are myocardial tissue. Nevertheless, abdominal muscle is composed of skeletal muscle and the occurrence of hernia is closely associated with the status of muscle tissues. Studies have shown that Cyma1 and Cyma5 represent high expression levels in myocardial and skeletal muscle, as well as in injury muscle [27, 33]. Casq2 has involved in cardiovascular physiology, skeletal muscle phenotypes, hematopoiesis and metabolism [34]. Csrp3 is also a skeletal muscle-specific LIM-only factor expressed in skeletal muscle, and crucial to maintain the structure and function of skeletal normal muscle [35]. The variants of Ttn could be recognized as the current classification criteria to Ttn-associated skeletal muscle disorders [36]. As a result, we come to the conclusion that these five genes are linked to skeletal muscle. On the one hand, these facts gave us reason to believe that they could be treated as potential targets of inguinal hernia for further research. On the other hand, we also proposed another hypothesis, whether the inguinal hernia is a systemic disease, which is a supplement to the previous point: a locally occurring disease.

As to Actc1 (actin alpha cardiac muscle), Mazzarotto et al. have suggested that Actc1 has associated with Dilated cardiomyopathy (DCM) and recognized as a diagnostic testing biomarker [37]. Meanwhile, Alliot-Licht et al. have shown that dexamethasone, target to Actc1, increased the proportion of multipotential mesenchymal progenitor cells [38]. Inder et al. have described that dexamethasone drug administration inhibits skeletal muscle expression of androgen receptor (AR), which have associated with fibrosis, skeletal muscle atrophy, and the development of hernias [8, 39]. However, the expression levels of androgen receptor target genes in the lower abdominal muscle of the inguinal hernia were decreased [8]. These studies seem to remind us that dexamethasone might play some unknown role in the occurrence of the inguinal hernia.

Up to date, all these genes and drugs haven’t been found in the research and application of inguinal hernia. Though we used comprehensive analyses to identify 6 genes and one existing drug through the mimic mice model of humanized inguinal hernia, it is needed some molecular experiments to support our results. What’s more, the humanized inguinal hernia datasets were not as easily acquired and collected as cancer datasets from the online database and reality, so this paper gives us a clue to explore the molecular mechanism of the inguinal hernia.

Conclusions

Through applying a series of bioinformatics methods to gene expression profiling, we acquired 6 potential biomarkers (Cmya1, Cmya5, Casq2, Csrp3, Ttn and Actc1) and one existing drug (DEXAMETHASONE), which will provide insight for new study targets and new drug indications.

Table 1 The DEGs in AromhumH versus AromhumL transgenic mice group
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Table 2 The significant gene ontology and signal enrichment terms of DEGs. (* Terms that do not meet the FDR < 0.05 conditions are not shown)
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Table 3 The significant gene ontology and KEGG enrichment terms of Up-regulated and down-regulated DEGs, respectively.(*Terms that do not meet the FDR < 0.05 conditions are not shown)
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