Identification of novel diagnostic and prognostic biomarkers for hepatocellular carcinoma
- Authors:
- Published online on: November 22, 2019 https://doi.org/10.3892/or.2019.7415
- Pages: 87-98
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Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Hepatocellular carcinoma (HCC) is the third-leading cause of malignant tumor mortality in the world. In China, approximately 466,100 new diagnoses of HCC and 422,100 deaths occurred in 2015 (1). Chronic hepatitis resulting from hepatitis B virus (HBV) or hepatitis C virus (HCV) infection is a predominant risk factor for HCC (2). HCC is commonly diagnosed at a late stage, resulting in extensive tumor invasion and/or distant metastases and a poor 5-year survival rate (3). Therefore, specific prognostic factors, which guide the choice of therapeutic strategies, are necessary for prolonging survival of HCC patients (4). Alpha fetoprotein (AFP) is currently the most extensively used biological marker for HCC, particularly in developing countries (5,6). However, AFP has poor reliability. Hence, the identification of novel diagnostic and prognostic biomarkers for HCC are urgently required (7).
Transcriptome analyses, using next-generation sequencing technologies (RNA-Seq), have facilitated the molecular classification and stratification of HCC tumors in relation to prognosis (8–10). RNA-Seq offers an integral view of the transcriptome and is an effective strategy for understanding complex pathways involved in metastasis and invasion of HCC (11). Furthermore, large-scale cancer genome projects, such as The Cancer Genome Atlas (TCGA), can be used to build comprehensive and multi-dimensional maps, highlighting key genomic changes in cancer (12,13). A better comprehension of the regulatory circuits underlying candidate marker genes is necessary to develop novel therapeutic strategies.
The aim of the present study was to identify novel diagnostic and prognostic biomarkers that predict the outcome of HCC based on analysis of the clinical and RNA-seq data presented in TCGA database. The differentially expressed genes (DEGs) in HCC patients with long vs. short-term survival and in normal vs. tumor tissues were assessed. Then, the critical processes and pathways related to the progression of HCC were analyzed. Changes in four genes (GABRR1, SOX11, COL24A1 and MYLK2) that were correlated with poor outcomes in HCC patients were identified. To validate these results, the expression levels of these genes were assessed in tumor tissues and paired normal tissues from HCC patients. Moreover, the diagnostic properties of these four genes were described via ROC analysis. The present results demonstrated that all four genes were associated with poor prognosis, indicating that this gene signature is a potential novel biomarker that can be used to guide targeted therapy in HCC patients.
Materials and methods
TCGA database
The clinical, follow-up, and RNA-seq information from TCGA database are available on the Cancer Genomics Browser of the University of California Santa Cruz website (https://genome-cancer.ucsc.edu/). The 423 subjects from TCGA enrolled in the present study included 373 HCC patients and 50 normal control patients (NCs). The data used in this study are accessible without limitation or restriction. Patients were divided into high and low expression groups based on the ROC cutoff values or median survival time: Long-term survivors (>805 days) and short-term survivors (≤805 days). Detailed information on the datasets is described in Table I.
Patients
The Institutional Review Board of the First Affiliated Hospital of Zhengzhou University approved the study and written informed consent was obtained from all patients according to the Declaration of Helsinki. From October 2013 to June 2014, 53 HCC patients, who underwent surgery at the First Affiliated Hospital of Zhengzhou University, were enrolled in the study. Patients with HCC were followed-up until May 2017. Histological characterization and tumor grading were assessed based on the current Union for International Cancer Control (UICC) criteria. Fresh tumor tissue samples (n=53) and paired normal tissues (n=20, >5 cm from the tumor edge) were obtained from operative specimens. Tissues were washed twice with PBS and flash frozen in liquid nitrogen immediately after dissection. Samples were stored at −80°C until further processing. These patients were divided into high and low-expression groups according to median values. Table II reveals the clinical characteristics of patients.
Quantitative real time-PCR
Total tissue RNA was isolated from frozen tissue samples using TRIzol reagent (Invitrogen; Thermo Fosher Scientific, Inc.). Quality and concentration of RNA were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Inc.). Total tissue RNA was reverse-transcribed using the Primescript RT reagent Kit (Takara Biotechnology Co., Ltd.). Amplification was achieved with a Real-Time PCR System (Agilent Technologies, Inc.) and the 2(-Delta Delta C(T)) was calculated as was mentioned in a previous protocol (14). The thermocycling conditions were as follows: An initial denaturation step at 95°C for 10 min, followed by 40 cycles of 95°C 5 sec, 60°C 30 sec and 72°C 10 sec. Primers for GAPDH, GABRR1, SOX11, COL24A1 and MYLK2 are presented in Table III.
Bioinformatics analysis
Distributions in DEGs and biological functions were determined using OmicsBean (http://www.omicsbean.com:88/), a multi-omics data analysis tool. The log2 (fold-change) is the log-ratio of a gene or transcript, when comparing two groups. Genes were considered differentially expressed when meeting the cut-off criterion of |fold change| ≥1.2 and P<0.05.
Gene Ontology analysis
To understand the biological processes contributing to HCC development based on genetic overexpression, we used the gene ontology method (GO), which classifies genes that share common biological properties. The Fisher's exact test was used to classify the GO category, and the false discovery rate (FDR) was calculated to correct the P-value. P<0.05 and FDR<0.05 were used as a threshold to select significant GO categories. The enrichment score reflects the enrichment level per GO category.
Statistical analysis
SPSS 17.0 (SPSS, Inc.) and GraphPad Prism 5.0 software (GraphPad Software, Inc.) were used for general statistical analyses and results. Data are presented as the mean ± standard deviation (SD). The association between these four genes and clinical parameters were analyzed by χ2 test (sex, age, tumor stage and grade stage and survival status) or Student's t-test [overall survival, (OS)]. The AUC of ROC curves were utilized to evaluate the diagnostic efficiency and predictive value of GABRR1, SOX11, COL24A1 and MYLK2. Overall survival and relapse-free survival (RFS) were calculated using the Kaplan-Meier method with the log-rank test and Cox proportional hazard model applied for comparison. A P-value of <0.05 was considered to indicate a statistically significant difference.
Results
Patient clinical characteristics
A total of 332 HCC patients with integrated overall survival (OS) information, clinical parameters, and complete RNA-seq information were enrolled in the study. As revealed in Table I, patient age ranged from 17 to 90 years, with a median age of 57, and 222 (66.9%) were male patients, while 110 (33.1%) were female. The median follow-up time was 805 days and 107 patients died in the follow-up period. Patients with HCC were divided into long-term (>805 days, n=106) and short-term (≤805 days, n=226) survivors. No significant differences in clinical covariates were detected. In our sample database, 53 frozen tumor tissues and 20 paired normal tissues from HCC patients were analyzed to detect DEGs. The demographic, clinical, survival status, and tumor pathological features of these patients are listed in Table II.
Identification of DEGs
Whole-genome expression profiling is widely used to identify genes and biological processes that contribute to the development and progression of liver cancer. RNA-seq data from HCC tissues collected from long-term and short-term survivors were examined to detect differentially expressed genes. As revealed in Fig. 1A and B, 615 genes (528 upregulated and 87 downregulated genes) were identified as DEGs between long-term and short-term survivors.
A GO analysis was conducted to identify significant associations of genes with specific biological processes. Processes that were significantly enriched based on GO terms included multicellular organismal process, single-multicellular organism process, multicellular organismal development, developmental process, single-organism developmental process, system process, system development, cell-cell signaling, anatomical structure development, and response to chemicals. These biological processes are related to the growth and migration of cancer cells (Fig. 1C and D).
Identification of four genes from the TCGA database
The gene-expression profiles from HCC tumor tissues and adjacent non-tumor tissues were compared to narrow down candidate DEGs from TCGA database. Compared to the normal tissues, it was revealed that 6,116 genes were upregulated and 5,224 genes were downregulated in tumor tissues. (Fig. 2A and B). GO analysis was performed to identify biological processes enriched in the upregulated genes. The most enriched GO biological processes included histone H3-K4 methylation, DNA replication process, and multicellular organismal process, which are associated with the development and progression of HCC (Fig. 2C and D). The intersection of the DEGs revealed 70 DEGs, which may have prognostic value (Fig. 2E). Four of these DEGs (GABRR1, SOX11, COL24A1 and MYLK2) were consistently overexpressed in HCC.
Diagnostic value of DEGs for HCC patients
The expression levels of these four genes in normal and tumor tissues were evaluated to calculate the cutoff values using ROC analyses. The corresponding areas under the ROC curve (AUCs) were 0.634, 0.722, 0.767 and 0.698 for GABRR1, SOX11, COL24A1 and MYLK2, respectively (Fig. 3A). Using a cutoff point of 0.143, GABRR1 had a sensitivity of 35.9%, a specificity of 90.0%, a positive predictive value (PPV) of 96.4%, and a negative predictive value (NPV) of 15.8%. The cutoff point of SOX11 was 0.224, which had a sensitivity of 64.8%, a specificity of 72.0%, a PPV of 94.5%, and an NPV of 21.6%. The optimal diagnostic cutoff for COL24A1 (sensitivity 56.6%, specificity 86.0%, PPV 96.3%, and NPV 20.6%) was 1.719 and for MYLK2 (sensitivity 37.0%, specificity 96.0%, PPV 98.6%, and NPV 17.0%) was 2.027.
The performance of the four-gene signature in 53 HCC patients was assessed using ROC analyses. The corresponding AUCs for GABRR1, SOX11, COL24A1 and MYLK2 were 0.655, 0.751, 0.858 and 0.655, respectively (data not shown). Based on these cutoff values, the OS of HCC patients with high levels of GABRR1, SOX11, COL24A1 or MYLK2 was significantly shorter compared to patients with low levels of these genes (Fig. 3B). However, the OS was not significantly different between the high and low cutoff value groups in the 53 HCC patients (data not shown).
Validation of survival value in patients with HCC using the four-gene signature
To the best of our knowledge, the association of the 4 genes (GABRR1, SOX11, COL24A1 and MYLK2) with HCC has not been reported, nor have they been associated with clinical parameters. Thus, the clinical significance of these four genes was determined in HCC patients. The selected clinical and pathological factors were as follows: Sex, age, disease stage, grade stage, and survival status (Table IV). GABRR1, SOX11 and MYLK2 were significantly associated with survival status (P<0.01). In addition, SOX11 and MYLK2 were also associated with tumor stage and grade. The association between the expression of these genes and patient survival was assessed by analyzing the prognostic significance of these genes using a Kaplan-Meier analysis of TCGA datasets. The four-gene signature classified HCC patients into low-mRNA and high-mRNA expression groups, which differed in OS and PFS (Table V). As revealed in Fig. 4A, upregulation of GABRR1, SOX11, COL24A1 and MYLK2 was significantly correlated with shorter OS and shorter median survival time. This demonstrates that patients with high levels of these mRNAs had worse clinical prognosis compared to other HCC patients. Moreover, overexpression of the four genes was negatively correlated with PFS (Fig. 4B). These results indicated that the four genes are effective biomarkers for predicting the prognosis of HCC patients.
 | Table IV.Association between GABRR1, SOX11, COL24A1 and MYLK2 expression and clinicopathological characteristics of HCC patients in TCGA. |
Validation of the four-gene signature in HCC patients
The four prognostic genes We were validated using the mRNA expression levels measured by qRT-PCR. Expression profiles were acquired from TCGA. The results revealed that the four genes were upregulated in tumor tissues compared to non-tumor tissues (data not shown). The expression of the four genes was also assessed in tumor and non-tumor samples from HCC patients. The levels of GABRR1, SOX11, COL24A1 and MYLK2 were significantly higher in the tumor tissues compared to normal tissues (Fig. 5). Associations between the expression of the four genes and clinical parameters were assessed. GABRR1, SOX11 and COL24A1 expression levels were associated with OS. Although OS was longer in the low level MYLK2 group, the difference was not significant. No associations were detected among target gene expression and patient age, sex, tumor stage, or grade stage (Table VI). Overall, the results indicated that GABRR1, SOX11, COL24A1 and MYLK2 may affect the progression of HCC.
| Table VI.Association between GABRR1, SOX11, COL24A1 and MYLK2 expression and clinicopathological characteristics of HCC patients. |
Validation of HCC survival value using the four genes
To evaluate the association between the expression of the validated DEGs and patient survival, the prognostic significance of the genes in the 53 HCC patients was assessed using Kaplan-Meier analysis. Patients were classified into either low-mRNA or high-mRNA expression groups, using the expression levels of the four-gene signature. The present results revealed that the patients with high mRNA expression in their tumors had a worse OS (Fig. 6A). Thus, this four-gene signature was effective in predicting the survival of HCC patients. These results revealed the clinical value of testing the expression of these genes to identify high-risk cases and guide additional medical interventions.
Next, computed tomography (CT) scans of HCC patients with or without elevated levels of the four-gene signature were compared (Fig. 6B). One of the patients with high gene expression was diagnosed with stage IV HCC using a CT-guided aspiration biopsy of the liver. Enhanced CT scanning revealed that this patient had metastases and multiple lesions on the liver. Moreover, the patient received four cycles of gemcitabine plus nedaplatin chemotherapy, which resulted in progressive disease (PD) and the OS was <4 months. The other patient, with stage IV HCC, was treated with the same chemotherapy regimens and had no expression of these four genes. The result of CT scanning revealed that this patient had fewer lesions on the liver. Collectively, these results indicated that high expression of the four-gene signature is indicative of a negative chemotherapy response.
Discussion
Currently, gene expression datasets have been used to classify patients according to known prognostic factors (15–17). However, most studies only focused on specific genes and few studies have explored DEGs in HCC using a comprehensive array-based approach (18,19). In the present study, it was revealed that four genes (GABRR1, SOX11, COL24A1 and MYLK2) were significantly increased in HCC tumor samples compared to normal tissues. ROC analysis demonstrated a relatively high sensitivity and specificity for the diagnosis of HCC. Moreover, the expression levels of these genes were negatively correlated with OS and PFS. These data support the feasibility of utilizing these genes as potential biomarkers for HCC.
GABRR1 is a member of the rho subunit family (20). GABRR1, which encodes for the GABA receptor subunit ρ1, is widely expressed in the brain and spinal cord. Family-based association analyses indicate that single nucleotide polymorphisms (SNPs) in GABRR1 are significantly associated with alcohol (21). GABRR1 gene expression was revealed to be upregulated in medullary thyroid carcinoma and contributed to disease progression (22). However, there are very few studies demonstrating a role of this gene in human cancers. In the present study, it was demonstrated that the elevated expression of GABRR1 in tumor tissues of HCC was associated with short OS. This result warrants investigation of the specific role of GABRR1 in HCC in our future study.
SOX11, a transcription factor, is a member of the SRY-related HMG-box family (sex determining region Y-related HMG-box family) (23,24). SOX11 is extensively expressed during organogenesis in mouse embryos and is associated with human developmental and differentiation processes. SOX11 is highly expressed in human cancers and can promote tumor cell survival, proliferation, and metastasis in cancer (25). SOX11 was revealed to regulate growth and invasion in breast cancer. Increased SOX11 expression was also an independent prognostic indicator of poor survival in breast cancer patients (26,27). SOX11 expression was correlated with poor outcome in small-cell lung cancer and large cell neuroendocrine carcinomas (28). SOX11 expression might slow cancer progression in a variety of human cancers, including glioma, mantle cell lymphoma, and prostate cancer (25). However, the function of this gene in HCC patients is unclear. The present results revealed that increased SOX11 expression was positively correlated with poor prognosis in HCC patients, indicating that SOX11 is a biomarker for HCC.
COL24A1, also referred to as collagen XXIV, is a relatively uncharacterized fibrillar collagen expressed in the developing skeleton of the mouse embryo (29). In a murine model, Matsuo et al revealed that COL24A1 transcripts accrue at ossification centers of the craniofacial, axial, and appendicular skeleton. Thus, this gene is likely involved in osteoblast differentiation and bone formation. The expression of COL24A1 is also expressed at low levels in non-skeletal tissues, indicating a potentially broader role in organogenesis (29). Pre-clinical evidence indicated that the mRNA levels of COL24A1 are associated with tumor size in squamous cell carcinoma of the head and neck (HNSCC). Moreover, overexpression of COL24A1 may play a crucial role in HNSCC progression, suggesting a prognostic value in HNSCC patients (30). Consistent with that study, the present results also suggest that COL24A1 mRNA could be a prognostic predictor of HCC.
MYLK2 encodes for a calcium/calmodulin-dependent serine/threonine kinase, which is exclusively expressed in adult skeletal muscle. The highly restricted distribution of MYLK2 in normal tissues (skeletal and cardiac muscles) suggests that the functions of this protein may be related to cell motility (31). This enzyme is also associated with a variety of disorders, including cerebral hypoxia and ovarian cancer. Hence, MYLK2 is a potential therapeutic target in a variety of diseases (30). Extensive studies have demonstrated that MYLK2 methylation is associated with better OS in ovarian cancer patients, which may help determine the response to surgery (32). The present results revealed that patients with high MYLK2 expression had a shorter OS. However, no significant differences were detected between high- and low-MYLK2 expression groups.
Bioinformatic analysis of potential genes and a subsequent Go analysis are promising approaches for identifying plausible biomarkers and key events in tumor development and progression (33,34). Specific biological processes, including single-multicellular organism and multicellular organismal processes, which are associated with the development and migration of cancer were identified. Further molecular biological experiments are required to validate the regulatory mechanisms of the four genes.
In summary, four genes that were upregulated in tumor tissues and were correlated with the progression of HCC were identified. The diagnostic values of these genes for HCC were confirmed using ROC analysis. A large proportion of patients with liver cancer from TCGA were analyzed and the patients that expressed high levels of GABRR1, SOX11, COL24A1 and MYLK2 had a poor prognosis. Furthermore, this observation was validated by comparing the expression of these genes in tumor and normal tissues from HCC patients. The present data indicated that these four genes represent a potentially valuable biomarker for HCC and can be utilized to predict poor prognosis.
Acknowledgements
Not applicable.
Funding
The present study was supported by grants from Henan Province Health and Family Planning Science and Technology Talents Overseas Research Project (2018010).
Availability of data and materials
The datasets used during the present study are available from the corresponding author upon reasonable request.
Authors' contributions
FW and JG designed the experiments. FW, YoZ and JD performed the experiments. SY, HG and PL analyzed the data. Yuz, YW and WZ collected and analyzed the clinical data. FW wrote the paper and revised the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
The Institutional Review Board of the First Affiliated Hospital of Zhengzhou University approved the study and written informed consent was obtained from all patients according to the Declaration of Helsinki.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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