Research ArticleArticles
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Potential Common Molecular Mechanisms Between Periodontitis and Hepatocellular Carcinoma: A Bioinformatic Analysis and Validation

XIAOMIAO FAN, ZIMIN SONG, WENGUANG QIN, TING YU, BAOGANG PENG and YUQIN SHEN
Cancer Genomics & Proteomics November 2023, 20 (6) 602-616; DOI: https://doi.org/10.21873/cgp.20409

Abstract

Background/Aim: Hepatocellular carcinoma (HCC) is the most common primary liver cancer and has a poor prognosis. Periodontitis, or tooth loss, is considered to be related to hepatocarcinogenesis and its poor prognosis. This study aimed to explore potential associations and cross-talk mechanisms between periodontitis and HCC. Materials and Methods: Periodontitis and HCC microarray datasets were acquired from the Gene Expression Omnibus (GEO) database and were analyzed to obtain differentially expressed (DE) lncRNAs, miRNAs and mRNAs. Functional enrichment analysis was used to detect the functions of these mRNAs. Then, a ceRNA network of periodontitis-related HCC was constructed. Least absolute shrinkage and selection operator (LASSO) regression, random forest algorithm, and support vector machine-recursive feature elimination (SVM-RFE) were performed to explore the diagnostic significance of mRNAs in periodontitis-related HCC. Cox regression analyses were conducted to screen mRNAs with prognostic significance in HCC. Quantitative real-time PCR (qRT–PCR) and immunohistochemistry (IHC) were conducted to validate the expression of these mRNAs in HCC tissues. Results: A ceRNA network was constructed. Functional enrichment analysis indicated that the network is associated with immune and inflammatory responses, the cell cycle and liver metabolic function. LASSO, random forest algorithm and SVM-RFE showed the diagnostic significance of DE mRNAs in HCC. Cox regression analyses revealed that MSH2, GRAMD1C and CTHRC1 have prognostic significance for HCC, and qRT–PCR and IHC validated this finding. Conclusion: Periodontitis may affect the occurrence of HCC by changing the immune and inflammatory response, the cell cycle and liver metabolic function. MSH2, GRAMD1C and CTHRC1 are potential prognostic biomarkers for HCC.

Key Words

Chronic periodontitis (CP) is a chronic infectious disease that leads to destruction of periodontal support tissue, including alveolar bone, cementum, gingiva and periodontal ligament (1, 2). It affects nearly 50% of the global population and is a highly prevalent disease worldwide (3). Periodontal pathogens induce the host immune response and play a crucial role in the progression of periodontitis. Periodontitis poses a serious threat to public health, not only leading to tooth loss but also to a variety of systemic diseases, including type 2 diabetes, Alzheimer’s disease, cardiovascular diseases, rheumatoid arthritis and even certain digestive system cancers, including hepatocellular carcinoma (4), colorectal cancer (5), esophageal cancer (6) gastric cancer (7) and pancreatic cancer (8). Periodontitis and these systemic diseases share the characteristic of being long-lasting immune disorders.

Hepatocellular carcinoma (HCC) is the most common primary liver cancer and the third leading cause of mortality worldwide (9). There are approximately 800,000 newly diagnosed cases annually, resulting in more than 700,000 deaths worldwide (10). Although therapeutic approaches for HCC have achieved great advances in recent decades, the prognosis remains poor, with five-year survival rates less than 20% (11). The pathogenesis of HCC is mediated by multiple risk factors, including alcohol abuse, aflatoxin exposure, hepatitis B virus, hepatitis C virus, nonalcoholic steatohepatitis and diabetes (12-16).

Recently, studies have suggested that periodontitis or tooth loss may be related to hepatocarcinogenesis and its poor prognosis (16, 17). A Finnish cohort study, including 29,096 male smokers followed up for seventeen years, showed that tooth loss was associated with the occurrence of primary liver cancer with HRs of 1.42 (1.01-1.98) and 1.45 (1.00-2.10) after adjusting for confounding factors (18). Consistent with this finding, a positive relationship between tooth loss and liver cancer mortality was reported in an 80-year-old community-dwelling Japanese population in a twelve-year prospective study (19). Patients with periodontitis had a poorer stage of HCC and higher circulating reactive oxygen species (ROS) levels than patients without periodontitis according to the Japan Integrated Stage (JIS) score (17). Certain mechanisms, such as systemic inflammation, oxidative stress and the oral-gut-liver axis, are responsible for the role of periodontitis in HCC (17, 20, 21).

Even so, these studies have limited power to confirm an association between periodontitis and HCC. More mechanistic studies are needed to elucidate the potential relationship between periodontitis and poor prognosis in HCC. Recently, bioinformatics analysis has been widely used to explore the molecular mechanism between periodontitis and various systemic diseases, such as rheumatoid arthritis and Alzheimer’s disease (22, 23). However, bioinformatics analysis of the mechanism of interaction between periodontitis and HCC remains limited. The competing endogenous RNA (ceRNA) hypothesis, proposed by Salmena et al., showed the mechanism of interaction between lncRNAs, miRNAs, and mRNAs and plays a crucial role in studying pathological conditions of diseases (24). LncRNAs can competitively bind to miRNA targets, influencing the negative regulation of miRNAs on mRNAs (24). Some studies have constructed periodontitis- or HCC-related ceRNA networks and revealed the role of ceRNA networks in the occurrence and progression of diseases (25, 26). No ceRNA network relative to periodontitis-related HCC was constructed.

In this study, we employed bioinformatics analyses to construct a ceRNA network, reveal the cross-talk genes between periodontitis and HCC and explored the correlation between these cross-talk genes and the prognosis of HCC. Then, mRNAs correlated with the prognosis of HCC were validated using quantitative real-time PCR (qRT–PCR) and immunohistochemistry (IHC). Our study indicated that the cross-talk genes (MSH2, GRAMD1C and CTHRC1) may be potential prognostic biomarkers for HCC. Periodontitis may affect the occurrence of HCC by changing the immune and inflammatory responses, the cell cycle and liver metabolic function. This study is the first to explore the potential mechanisms between periodontitis and HCC. These findings are expected to lay a theoretical foundation for the exploration of the correlation between periodontitis and HCC and shine a fresh light on targeted therapy for HCC.

Materials and Methods

Data acquisition. Periodontitis and HCC microarray datasets were acquired from the Gene Expression Omnibus (GEO) database in NCBI (http://www.ncbi.nlm.nih.gov/geo/). GSE16134 (GPL570) (27) with 241 periodontitis samples and 69 healthy samples was used to obtain DE lncRNAs of periodontitis. DE miRNAs of periodontitis were acquired from GSE54710 (GPL15159) (28), and 159 periodontitis samples and 41 healthy samples were included. The GSE10334 (GPL570) (29) dataset, including 183 periodontitis samples and 63 healthy samples, was randomly divided into a training cohort (n=123) and a validation cohort (n=123) and was used to disclose DE mRNAs of periodontitis. GSE45267 (GPL570) (30) included 46 HCC samples and 41 healthy samples. Samples from GSE45267 were randomly divided into a training cohort (n=44) and a validation cohort (n=43). DE mRNAs of HCC were obtained from GSE45267. Before differential analysis, the four datasets were normalized using “normalizeBetweenArrays”.

Identification of differentially expressed lncRNAs, miRNAs and mRNAs of periodontitis. Differentially expressed (DE) lncRNAs, miRNAs and mRNAs of periodontitis were analyzed using the R (v.4.0.3) “limma” package. |log2-fold-change (FC)| >0.5 and adjusted p<0.05 were the thresholds to identify DE lncRNAs, DE miRNAs and DE mRNAs. miRNAs that DE lncRNAs target were predicted by the MiRcode database (http://www.mircode.org/). Next, the VennDiagram R package was used to obtain the intersecting miRNAs between DE miRNAs and target miRNAs. mRNAs targeted by intersecting miRNAs were obtained from the miRDB (http://www.mirdb.org/), miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/) and TargetScan (http://www.targetscan.org/) databases, and the VennDiagram R package was used to explore the intersections between DE mRNAs and target mRNAs.

Identification of interrelated mRNAs between periodontitis and HCC and construction of a ceRNA network of periodontitis-related HCC. DE mRNAs of HCC were analyzed using the R (v.4.0.3) package “limma” with thresholds |log2-fold-change (FC)| >1.5 and adjusted p-value <0.05. The intersections between DE mRNAs of HCC and intersecting mRNAs of periodontitis were visualized using the VennDiagram R package. According to the DE lncRNAs, intersecting miRNAs and intersecting mRNAs between periodontitis and HCC obtained above, a ceRNA network of periodontitis-related HCC was constructed and visualized by Cytoscape software (v3.9.1). To explore the functional annotation of mRNAs of periodontitis-related HCC, gene ontology (GO) term enrichment analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed with the Clusterprofiler R package.

Diagnostic significance of mRNAs of periodontitis-related HCC. RNA sequencing (RNAseq) data and corresponding clinical information of HCC were downloaded from the TCGA database (https://portal.gdc.cancer.gov/). Fragments per kilobase million (FPKM)-formatted mRNA data were obtained, including 50 healthy samples and 374 HCC samples. To determine the diagnostic markers, least absolute shrinkage and selection operator (LASSO) regression, random forest algorithm, and support vector machine-recursive feature elimination (SVM-RFE) were conducted. LASSO was conducted by running the “glmnet” package. The “randomForest” package was applied to construct the random forest model. SVM-RFE, an efficiently applied supervised machine learning algorithm, was implemented by running the “e1071” package. The intersecting genes of the three machine learning methods were obtained as the diagnostic markers of HCC by running the VennDiagram R package. Subsequently, based on these diagnostic markers, a nomogram was created by running the “RMS” package. Then, the predictive accuracy of the nomogram was assessed with a calibration curve. In addition, decision curve analysis (DCA) and clinical impact curve (CIC) were drawn to evaluate the clinical applicability of the nomogram.

Prognostic significance of mRNAs of periodontitis-related HCC. To evaluate the prognostic significance of mRNAs of periodontitis-related HCC, univariate and multivariate Cox regression analyses were conducted. The clinical information of HCC came from the TCGA database, including survival time and survival status. First, univariate Cox regression analysis was conducted. Then, statistically significant mRNAs of periodontitis-related HCC (p-value <0.05) were included in multivariate Cox regression analysis to acquire statistically significant risk genes. The risk score was calculated for each patient according to the formula:

Risk score=Sni Expi × Coefi (Expi: the expression of each risk gene; Coefi: correlation coefficient)

All patients were divided into high-risk and low-risk groups according to the median risk score in the training set. Receiver operating characteristic (ROC) curve analysis and Kaplan–Meier (K-M) curves of the two groups were obtained by running “survivalROC” and “survival”. The prognostic model was validated using an external dataset obtained from the GEO (GSE116174) database. Survival time of each patient, scatter plots of risk scores and heatmaps of risk genes are given. The overall survival (OS) difference between the high-risk and low-risk groups in each risk gene was compared by the K–M curves generated by running the “survival” and “survminer” R packages.

Validation of mRNAs of periodontitis-related HCC. Ten HCC tissues and ten tumor-adjacent tissues were selected from the Center of Hepato-Pancreato-Biliary Surgery, the First Affiliated Hospital of Sun Yat-sen University. This study was conducted according to the ethical guidelines of the Declaration of Helsinki and was approved by the Research Medical Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University [(2022) 003]. Total RNA was extracted from HCC tissues and tumor-adjacent tissue using TRIzol (TaKaRa, Kyoto, Japan) and reverse transcribed into complementary DNA (cDNA) using a PrimeScript TMRT kit (TaKaRa). The reaction was set at 95°C for 30s and was carried out for 40 cycles (95°C for 5s and 60°C for 30s). The expression of mRNAs was validated by qRT–PCR using SYBR Green Premix Pro Taq (TaKaRa). qRT–PCR was performed on a LightCycler 480 system (Roche, Basel, Switzerland). The following primer sequences were used: GAPDH, forward, 5′-GACCTGACCTGCCGTCTAG-3′ and reverse, 5′-AGGAGTGGGTGTCGCTGT-3′; MSH2, forward, 5′-GTTTCAAAGACAAGCAGCAAAC-3′ and reverse, 5′-GGAGA AGTCAGAACGAAGATCA-3′; GRAMD1C, forward, 5′-GGCA CTGAGCTAGGTTTAAATG-3′ and reverse, 5′-GGTCTGTTT CTCATTTTGGGAC-3′; CTHRC1, forward, 5′-TTGGACCAAG GAAGCCCTGAAATG-3′ and reverse, 5′-ACCAACCCAGATA GCAACATCCAC-3′. The relative gene expression was determined using the 2−ΔΔCt method.

The paraffin-embedded tissue microarrays were deparaffinized in xylene. Endogenous peroxidase activity was blocked with 3% catalase for ten min. Tissue microarrays were immersed in citrate buffer (pH 6.0) and heated in a microwave oven at 100°C for 15 min for antigen retrieval. After blocking treatment with 10% goat serum for 30 min, the tissue microarrays were incubated with monoclonal anti-MSH2 antibody (1:1,000, Abcam, Cambridge, MA, USA), anti-GRAMD1C antibody (1:200, Abcam) or anti-CTHRC1 antibody (1:500, Abcam) at 4°C overnight, followed by secondary antibody (1:50, GTVisionTM III Detection System/Mo&Rb) for 1 h at 37°C and colored with 3,3′ diaminobaniline hydrochloride (GTVisionTM III Detection System/Mo&Rb). Nuclei were stained with hematoxylin. The intensity of staining was assessed based on the following scales: zero, one, two and three were defined as negative, weak, moderate and strong positive, respectively. Finally, the average score of the five fields was obtained as the final score. Scoring was performed independently by two observers.

Results

Identification of DE lncRNAs, DE miRNAs and DE mRNAs of periodontitis. A flow chart of this study is shown in Figure 1. The “limma” R package was used to obtain differentially expressed lncRNAs, miRNAs and mRNAs. A total of three up-regulated and five down-regulated DE lncRNAs were found in the GSE16134 dataset. Volcano plots and heatmaps of DE lncRNAs of periodontitis are shown in Figure 2A. Fifty-six DE miRNAs were obtained from periodontitis samples in the GSE54710 dataset, including 36 up-regulated and 20 down-regulated miRNAs. Volcano plots and heatmaps of DE miRNAs are shown in Figure 2B. Furthermore, only for three DE lncRNAs (MIAT, SOX21-AS1 and LINC00525) their target miRNAs could be found from the miRcode database, and a total of 74 target miRNAs were predicted. The VennDiagram R package was used to visualize the intersections between 56 DE miRNAs and 74 target miRNAs. Finally, 14 intersecting miRNAs were obtained (Figure 2E). A total of 966 DE mRNAs were found in periodontitis samples from the GSE10334 dataset; 575 were up-regulated and 391 were down-regulated, and volcano plots and heatmaps of DE mRNAs are shown in Figure 2C. A total of 6,634 mRNAs targeted by intersecting miRNAs were predicted from the miRDB, miRTarBase and TargetScan databases. Then, by the VennDiagram R package, 336 intersecting mRNAs between DE mRNAs and target mRNAs were acquired (Figure 2F).

Figure 1.
Figure 1.

Flow chart of the study. Periodontitis and HCC microarray datasets from the GEO database were analyzed to obtain DE lncRNAs, DE miRNAs and DE mRNAs. Then, a ceRNA network was constructed. Functional enrichment analysis was used to detect the functions of these mRNAs. LASSO, random forest algorithm, and sSVM-RFE, and Cox regression analyses were performed to explore the diagnostic and prognostic significance of intersection mRNAs between CP and HCC. qRT–PCR and IHC were used to validate the expression of these mRNAs in HCC tissues. GEO: Gene Expression Omnibus; DE: differentially expressed; LASSO: least absolute shrinkage and selection operator; SVM-RFE: support vector machine-recursive feature elimination; CP: chronic periodontitis; HCC: hepatocellular carcinoma; qRT–PCR: quantitative real-time PCR; IHC: immunohistochemistry.

Figure 2.
Figure 2.

Screening out DE lncRNAs, DE miRNAs and DE mRNAs of periodontitis and DE mRNAs of HCC. (A) DE lncRNAs, (B) DE miRNAs and (C) DE mRNAs of periodontitis. (D) DE mRNAs of HCC. (E) Intersection miRNAs. (F) Intersection mRNAs. (G) Intersection mRNAs between CP and HCC. The red points in the volcano plots represent up-regulated genes, and the green points represent down-regulated genes. The color in the heatmaps from green to red shows the expression from low to high. DE: Differentially expressed; CP: chronic periodontitis; HCC: hepatocellular carcinoma.

Identification of intersection mRNAs between periodontitis and HCC and construction of the ceRNA network of periodontitis-related HCC. By using the “limma” R package, 403 mRNAs were found to be differentially expressed between HCC samples and healthy samples in the GSE45267 dataset. Volcano plots and heatmaps are shown in Figure 2D. Then, 26 intersecting mRNAs were identified between 336 mRNAs in periodontitis and 403 DE mRNAs in HCC by the VennDiagram R package (Figure 2G). According to the above results, 26 mRNAs of periodontitis-related HCC were targeted by five up-regulated miRNAs and six down-regulated miRNAs, and eleven miRNAs were targeted by two up-regulated lncRNAs (MIAT and LINC00525). The detailed relationship among lncRNAs, miRNAs and mRNAs is in the ceRNA network of periodontitis-related HCC (Figure 3A-C). Among them, 16 mRNAs of periodontitis-related HCC targeted by five up-regulated miRNAs are shown in Figure 3B. Twelve mRNAs of periodontitis-related HCC targeted by six down-regulated miRNAs are shown in Figure 3C.

Figure 3.
Figure 3.

Construction of ceRNA networks and GO and KEGG enrichment analyses. (A) The ceRNA network based on intersection mRNAs between CP and HCC. (B) A subceRNA network based on 5 up-regulated miRNAs. (C) A subceRNA network based on 6 down-regulated miRNAs. Purple represents lncRNAs, yellow represents miRNAs, and green represents mRNAs. (D-E) Functional annotations of intersection mRNAs between CP and HCC. GEO: Gene Expression Omnibus; KEGG: Kyoto Encyclopedia of Genes and Genomes; CP: chronic periodontitis; HCC: hepatocellular carcinoma.

Functional annotation of mRNAs of periodontitis-related HCC. To confirm the biological features of mRNAs of periodontitis-related HCC, GO and KEGG enrichment analyses were conducted for mRNAs. GO enrichment analyses revealed that mRNAs were enriched in biological processes, such as acute inflammatory response, immunoglobulin-mediated immune response, cell cycle G1/S phase transition and B-cell-mediated immunity. KEGG enrichment analyses showed that mRNAs were involved in tryptophan metabolism and the cell cycle (Figure 3D-E).

Diagnostic significance of mRNAs of periodontitis-related HCC. To identify the diagnostic significance of mRNAs of periodontitis-related HCC, LASSO, random forest and SVM-RFE were conducted to determine the diagnostic markers of HCC. Seven, fifteen and nineteen genes were separately screened by LASSO, random forest and SVM-RPE (Figure 4A-C). After overlapping, five genes were identified, including MSH2, CTHRC1, GHR, GRAMD1C and RRAGD (Figure 4D). Then, a nomogram was established to facilitate the clinical performance of the genes (Figure 4E). The calibration curve demonstrated that the predictive accuracy of the nomogram was close to the actual condition. DCA illustrated that the decision curve was higher than the two reference curves. The CIC showed that the predicted model was close to the actual condition. DCA and CIC indicated that the nomogram possessed preferred clinical applicability (Figure 4F).

Figure 4.
Figure 4.

Diagnostic significance of intersection mRNAs between CP and HCC. (A-C) LASSO, random forest algorithm and SVM-RFE were conducted to determine the diagnostic markers. (D) The intersecting genes of three machine learning methods. (E) The nomogram based on the intersecting genes of three machine learning methods. (F) Calibration curve, DCA and CIC were used to assess the predictive accuracy and practicability of the nomogram. LASSO: Least absolute shrinkage and selection operator; SVM-RFE: support vector machine-recursive feature elimination; CP: chronic periodontitis; HCC: hepatocellular carcinoma.

Prognostic significance of mRNAs of periodontitis-related HCC. To further explore the prognostic significance of mRNAs of periodontitis-related HCC, univariate and multivariate Cox regression analyses were performed on mRNAs of periodontitis-related HCC. According to the multivariate Cox regression analysis, MSH2, GRAMD1C and CTHRC1 (p<0.05) were statistically significant (Figure 5A). Utilizing the adjusted regression coefficients of these mRNAs, we calculated the risk scores of mRNAs for each patient. Patients were assigned to the high- and low-risk groups according to the median risk score. The K-M curve showed that the overall survival time of patients in the low-risk group was longer than that in the high-risk group (p<0.05) (Figure 5B). In addition, the ROC curve showed that the AUC of the risk score model was 0.754 (Figure 5C). Then, we used an external dataset (GSE116174) to validate the prognostic model. The K-M curve and ROC curve are shown in Figure 5J-K. The K-M curve revealed that patients in the high-risk group (32 HCC patients) had shorter overall survival times than those in the low-risk group (32 HCC patients) (p<0.05). The AUCs at one, three, and five years for HCC patients were 0.692, 0.601 and 0.722, respectively. These results demonstrated the convincing performance of the prognostic model. The heatmap demonstrated that the expression levels of MSH2 and CTHRC1 were higher in the high-risk group and that the expression of GRAMD1C was higher in the low-risk group (Figure 5D). The risk scores and survival statuses of each patient are illustrated in Figure 5E-F. By constructing a K-M curve based on the three mRNAs, the results showed that the overall survival time of patients in the low-risk group was longer than that in the high-risk group based on MSH2 and CTHRC1. In contrast, the overall survival time in the high-risk group was longer than that in the low-risk group based on GRAMD1C (Figure 5G-I). The results suggested that MSH2, CTHRC1 and GRAMD1C are related to the prognosis of HCC. The high expression of GRAMD1C was associated with favorable prognosis, and the high expression of MSH2 and CTHRC1 was associated with unfavorable prognosis of HCC. Based on these prognostic biomarkers, a ceRNA network (lncRNA MIAT/hsa-miR-205-5p/MSH2, lncRNA MIAT/hsa-miR-205-5p/GRAMD1C and lncRNA MIAT/hsa-miR-155-5p/CTHRC1) of periodontitis-related HCC was found. The results still need to be further verified.

Figure 5.
Figure 5.
Figure 5.

Prognostic significance of intersection mRNAs between CP and HCC. (A) MSH2, GRAMD1C and CTHRC1 were screened by multivariate Cox regression analysis. (B-C) K-M curve and ROC curve analysis of risk scores of HCC patients. (D-F) Risk scores, survival statuses for each patient and distributions of mRNA expression. (G-I) K-M survival analysis of overall survival of MSH2, GRAMD1C and CTHRC1. (J) K-M curve of HCC patients in the validation dataset. (K) ROC curve of HCC patients at 1, 3 and 5 years in the validation dataset. CP: Chronic periodontitis; HCC: hepatocellular carcinoma; K-M: Kaplan-Meier; ROC: receiver operating characteristic.

Validation of mRNAs of periodontitis-related HCC. To validate the expression levels of MSH2, GRAMD1C and CTHRC1 in tissues, qRT–PCR and immunohistochemistry were conducted. The qRT–PCR results demonstrated that the expression of MSH2 and CTHRC1 was higher in HCC tissues and that GRAMD1C was higher in tumor-adjacent tissues (Figure 6A-C). The results of immunohistochemistry demonstrated that MSH2 and CTHRC1 were more highly expressed in HCC tissues than in tumor-adjacent tissues and that GRAMD1C had higher expression in tumor-adjacent tissues, which was consistent with the results of bioinformatics analysis (Figure 6D-F).

Figure 6.
Figure 6.

Validation of mRNAs in periodontitis-related HCC. (A-C) The relative expression of MSH2, GRAMD1C and CTHRC1 was examined by qRT–PCR. (D-F) The expression of MSH2, GRAMD1C and CTHRC1 at the protein level. (A) The expression level of MSH2 was higher in HCC tissues. (B) The expression level of CTHRC1 was higher in HCC tissues. (C) The expression level of GRAMD1C was higher in tumor-adjacent tissues. (D) MSH2 was highly expressed in HCC tissues. (E) The expression of CTHRC1 was higher in HCC tissues. (F) The expression of GRAMD1C was higher in tumor-adjacent tissues. *p<0.05. HCC: Hepatocellular carcinoma; qRT–PCR: quantitative real-time PCR.

Discussion

Periodontitis is a chronic inflammatory condition and has been linked to various systemic diseases, including cancer. Recently, emerging evidence has shown that tooth loss and periodontitis may be risk factors for HCC. Periodontal infection can induce chronic persistent inflammation by activating inflammatory mediators (31, 32). Chronic inflammation is a crucial factor in the occurrence, growth and dissemination of cancer (33, 34). Although some plausible theories have been proposed, the specific mechanism linking hepatocarcinogenesis and periodontitis remains elusive. This study revealed the crosstalk genes between periodontitis and HCC and indicated that these crosstalk genes may be potential prognostic biomarkers for HCC. Our study provides new insights into the potential correlation between periodontitis and HCC.

The oral-gut-liver axis plays a crucial role in inducing liver inflammation. The gut-liver axis acts as an important pathway for the interaction between extraintestinal organs and gut microbiota, which is established through the portal vein. Gut-derived products are transported directly to the liver via the portal vein, and the liver feeds bile and antibodies back to the intestine (35). Periodontitis can induce dysbiosis of gut microbiota (36). This dysbiosis weakens the intestinal barrier, thereby increasing mucosal permeability, enabling bacterial metabolites, microbe-associated molecular patterns (MAMPs), pathogen-associated molecular patterns (PAMAs), or even bacteria themselves to travel to the liver via the portal vein. These molecules interact directly with hepatocytes, Kupffer cells, and stellate cells, triggering downstream proinflammatory cascades (37, 38) (Figure 7). Additionally, periodontal pathogens, such as Fusobacterium nucleatum (F. nucleatum) and Porphyromonas gingivalis (P. gingivalis) have been isolated from some digestive system tumor tissues, indicating that these periodontal pathogens may promote carcinogenesis (20, 39, 40). These periodontal pathogens produce metabolic byproducts, enzymes and other bacterial virulence factors, such as endotoxins, that are toxic to tissues (41). Some studies have proven their abilities to inhibit apoptosis, increase cell proliferation, enhance angiogenesis, activate epithelial-to-mesenchymal transition, and produce carcinogenic metabolites (42, 43). Finally, some epidemiological studies have indicated that periodontitis may be a risk factor for chronic liver diseases (CLDs), such as nonalcoholic fatty liver disease (NAFLD) and cirrhosis (44, 45), and that these CLDs are precursors of HCC (46, 47).

Figure 7.
Figure 7.

Oral-gut-liver axis. Periodontitis can induce dysbiosis of the gut microbiota. This dysbiosis weakens the intestinal barrier and accordingly increases mucosal permeability, and then bacterial metabolites, microbe-associated molecular patterns (MAMPs), pathogen-associated molecular patterns (PAMAs), or even bacteria themselves travel to the liver via the portal vein. These molecules activate hepatocytes and Mac1 Kupffer cells, resulting in the secretion of proinflammatory cytokines. The activation of inflammasome pathways in hepatocytes can trigger apoptosis and release damage-associated molecular patterns (DAMPs). Proinflammatory cytokines, DAMPs and Mac2 Kupffer cells upon gut-derived molecule exposure activate stellate cells and further exacerbate fibrogenesis. The figure was created using BioRender.

In this study, a ceRNA network was constructed based on DE lncRNAs, DE miRNAs and DE mRNAs, including two lncRNAs, eleven miRNAs and 26 mRNAs. After further machine-learning algorithm (LASSO, random forest and SVM-RFE) and Cox regression analysis, three mRNAs (MSH2, GRAMD1C and CTHRC1) were considered to be related to the prognosis of HCC and were verified to be differentially expressed between human adjacent tissues and HCC tissues by qRCR and IHC. Based on these three prognostic biomarkers, we finally screened out a ceRNA network (lncRNA MIAT/hsa-miR-205-5p/MSH2, lncRNA MIAT/hsa-miR-205-5p/GRAMD1C and lncRNA MIAT/hsa-miR-155-5p/CTHRC1) of periodontitis-related HCC. Although some studies have reported that the lncRNAs MIAT, hsa-miR-205-5p, hsa-miR-155-5p, MSH2 and CTHRC1 serve as risk factors during hepatocarcinogenesis and are involved in various pathways, our study is the first to show that the lncRNA MIAT/hsa-miR-205-5p/MSH2, lncRNA MIAT/hsa-miR-205-5p/GRAMD1C and lncRNA MIAT/hsa-miR-155-5p/CTHRC1 axis is related to HCC progression. The potential cross-talk mechanisms between periodontitis and HCC appear to offer a deeper understanding of the two diseases.

According to the results of differential analysis, lncRNA MIAT was up-regulated in periodontitis samples. A bioinformatics analysis related to periodontitis has proven that lncRNA MIAT has a positive association with activated B cells and may participate in the immune response during periodontitis progression (48). Additionally, some studies have shown that the lncRNA MIAT plays a role in HCC development. The lncRNA MIAT may be involved in immune escape during the development of HCC, regulate the epithelial-to-mesenchymal transition (EMT) process of HCC and is connected with the sensitivity of many anticancer drugs (49, 50). This study demonstrated that hsa-miR-205-5p was down-regulated in periodontitis samples. Some studies also verified that the expression level of hsa-miR-205-5p was lower in the serum and gingival tissues of periodontitis patients using qPCR (51, 52) and that P. gingivalis could activate JAK/STAT signaling by inhibiting hsa-miR-205-5p expression and promoting periodontitis progression (53). Similarly, the expression level of hsa-miR-205-5p was lower in liver cancer tissues and plasma or serum samples of patients with HBV-related HCC (54-56). Hsa-miR-205-5p could suppress tumor growth and invasion by targeting vascular endothelial growth factor A (VEGFA) and SEMA4C (55, 57) and mediate the abnormal lipid metabolism of liver cancer by targeting acyl-CoA synthetase long-chain family member 1 (ACSL1) mRNA (58). As a biomarker of periodontitis, hsa-miR-155-5p has been reported in some studies on periodontitis (59, 60). Hsa-miR-155-5p was highly expressed in tissue samples of periodontitis patients, such as saliva, peripheral blood mononuclear cells (PBMCs) and gingival tissues (52, 61, 62). Likewise, hsa-miR-155-5p, as a biomarker of HCC, has been involved in a variety of regulatory networks to promote HCC progression (63), such as circTP63/miR-155-5p/ZBTB18, LINC01189/hsa-miR-155-5p and AURKAPS1/hsa-miR-155-5p (64-66).

MSH2, GRAMD1C and CTHRC1 are the cross-talk genes between periodontitis and HCC. The bioinformatics analysis results showed that MSH2 and CTHRC1 were up-regulated in HCC samples and GRAMD1C was highly expressed in tumor-adjacent tissues, and the results were verified by qRCR assay and immunohistochemistry. The Cox regression analysis and survival analysis results revealed that MSH2 and CTHRC1 were correlated with poor prognosis of HCC, and the low-risk group survived longer than the high-risk group. In contrast, GRAMD1C was associated with a better prognosis of HCC, and the low-risk group survived for a shorter period than the high-risk group. By analyzing 1,021 HCC samples and 1,021 non-HCC samples, Zhu X et al. found that MSH2 is a risk factor for HCC and could reduce the survival time of HCC patients (67). The dysregulation of MSH2 is triggered by proinflammatory cytokines during hepatocarcinogenesis (68). Some studies have indicated that CTHRC1 may serve as a prognostic biomarker for hepatocellular carcinoma (69) and could negatively regulate the progression and metastasis of HCC (70). The low expression of GRAMD1C was reported to be related to the prognosis of kidney renal clear cell carcinoma, while studies referring to its association with HCC prognosis are still limited. This study is the first to show that GRAMD1C is related to a better prognosis of HCC.

GO and KEGG enrichment analyses were performed on mRNAs to detect their biological functions. The results from enrichment analyses indicated that the ceRNA regulatory networks we acquired were biologically functional. As shown by GO and KEGG enrichment analyses, acute inflammatory response, cell cycle G1/S phase transition, immunoglobulin-mediated immune response, B-cell-mediated immunity, coenzyme biosynthetic process, response to cAMP, chemokine activity, chemokine receptor binding, tryptophan metabolism and cell cycle may play vital roles between periodontitis and HCC. The results indicate that periodontitis may affect the occurrence of HCC by inducing immune and inflammatory responses and changing the cell cycle and liver metabolic function.

According to the above analysis, an association between periodontitis and poor prognosis of HCC likely exists. The cross-talk genes (MSH2, GRAMD1C and CTHRC1) between periodontitis and HCC are potential prognostic biomarkers for HCC. The findings can provide a basis for future research. However, there are still some limitations. The study is based on a bioinformatics analysis. Periodontitis and HCC patients come from public datasets and different groups of individuals. In addition, there is insufficient information, especially the patient characteristics like age, sex and comorbidities, and inclusion/exclusion criteria. Those parameters might have influenced the current findings. Therefore, the interpretation of the results is restricted to a hypothetical level. The potential mechanisms found in this study still need to be further verified in animal models and clinical settings.

Conclusion

The cross-talk genes (MSH2, GRAMD1C and CTHRC1) between periodontitis and HCC were associated with prognosis of HCC. Periodontitis may affect the occurrence of HCC by changing the immune and inflammatory responses, the cell cycle and liver metabolic function.

Acknowledgements

This work was supported by National Key Research and Development Program of China (2021YFE0108000), Medical Support Program of the Jilin University (no. 20170311032YY), Science and Technology Project of the Jilin Provincial Department of Finance (no. jcsz2020304-9 and no. jsz2018170-12) and Traditional Chinese Medicine Bureau of Guangdong Province (no. 20222131).

Footnotes

  • Conflicts of Interest

    There are no potential conflicts of interest to disclose.

  • Authors’ Contributions

    XMF and ZMS contributed to the conception of the study and manuscript writing. WGQ and TY contributed to performing the literature search and analyze the data. BGP and YQS performed the analysis and quality assessment of the study. All authors contributed to the article and approved the submitted version.

  • Received July 5, 2023.
  • Revision received August 28, 2023.
  • Accepted September 1, 2023.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) 4.0 international license (https://creativecommons.org/licenses/by-nc-nd/4.0).

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Potential Common Molecular Mechanisms Between Periodontitis and Hepatocellular Carcinoma: A Bioinformatic Analysis and Validation
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Cancer Genomics & Proteomics Nov 2023, 20 (6) 602-616; DOI: 10.21873/cgp.20409
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