| Background:The biomarkers research is one of the core research fields in preventive medicine. Biomarker is an indicator for objective measuring and evaluating biological process, pathological process, and drug responses, which involve various changes in cellular structure and functions, abnormal physiological activity, biochemical metabolic process, and abnormal manifestations on individual or entire ecosystem. The biomarkers research provides theoretical references for preventing and controlling health hazards effectively. This research has become a common concerned focus of preventive medicine both home and abroad in recent years. The reason is due to the development of molecular biology and advanced life sciences, which is called as significant milestone on the road to the molecular level in environmental medicine, and it plays an important role in molecular epidemiology, environmental hygiene, labor hygiene, and molecular toxicology. In the research of environmental epidemiology, tumor molecular biomarkers plays an important role in the prediction of cancer risk, assessment of treatment effects, prognosis and tertiary prevention, and hence is widely used.Primary liver cancer is one of the most common malignant tumors. The statistics of it shows an upward trend in the morbidity and mortality of liver cancer in recent years. The morbidity and mortality are ranked as fifth and second in male malignant cancer, and they are ranked as seventh and sixth in feminine malignant cancer worldwide, respectively. The new cases and deaths of liver cancer in China are counted to be nearly half of the amount all around the world. The mortality of liver cancer is next to gastric carcinoma in China. According to China Cancer Registration in 2010, the morbidity and mortality of liver cancer in Guangxi was 4.376 and 3.582 per million, which were higher than the national averages. The morbidity and mortality of liver cancer will continually rise with an increasingly aging population, which are expected to increase 60% by 2030. Higher grade malignancy, difficult therapy, fast infiltrating growth, and poor prognosis are the typical characteristics of hepatocellular carcinoma(HCC). Five-year survival rates of HCC is less than 10%. The clinical manifestations of HCC in the early stage are atypical and not apparent. Only 30%-40% HCC patients can be prompt diagnosis and effective treatment at present due to the lack of effective early diagnosis for liver cancer. Therefore, identifying novel and reliable biomarkers to distinguish, predict and treat HCC are urgently needed.The occurrence and development of tumor involve multiple levels of pathological process, such as genome, transcriptome, proteome, and metabolome. The single-omics analysis, as the traditional strategy of tumor biomarkers research, only reflects one levels of pathological changes and has limitations with tumor biomarkers research. Integrative multi-omics analysis will be helpful for understanding biological behavior of tumor in a systematic and comprehensive way, and it will provide a new clue for studying tumor biomarkers and investigating tumor mechanism. Therefore, it has become the common concerned focus of tumor research by integrative multi-omics analysis.In this study, we screened and identified HCC biomarkers by integrative multi-omics analysis:(1) we detected mutual differentially expressed genes(DEGs) between HCC cells and tissues by integrating with cell transcriptomics and tissue transcriptomics data;(2) we identified mutual differentially expressed factors(DEFs) between HCC serum and cells by integrating with serum proteome and cell transcriptomics data. Then, their biological characteristics and functions were analyzed by bioinformatics. Meanwhile, the expression of candidate biomarkers was verified in HCC cells, tissues and serum using q RTPCR, immunohistochemical staining and western blotting. Furthermore, the correlation analysis between candidate biomarker expression and clinicopathological parameters and survival analysis were performed to find valuable tumor molecular biomarkers for the prevention and the treatment of liver cancer.Part 1 Identification of NEK2 as HCC potential biomarker by transcriptome sequencingObjectives:To detect mutual DEGs between HCC cells and tissues by integrating with cell transcriptomics and tissue transcriptomics data, and verify their expressions in HCC cells and tissues. Furthermore, the analysis of clinicopathological parameters and signal pathway correlation were performed to investigate the mechanisms of tumor marker in HCC occurrence and development.1. The DEGs between HCC cell line SMMC-7721 and normal liver cell line L-02 were detected by Ion Proton transcriptome sequencing.Methods:2. Mutual DEGs were obtained by integrating with cell transcriptomics and tissue transcriptomics data, and the DEG with the most obvious difference was identified to be HCC candidate biomarker.3. Validating candidate biomarker NEK2 expression in HCC cell line SMMC-7721 and the normal liver cell line L-02, and in 5 cases of HCC tissues and adjacent non-tumorous liver tissues by q RT-PCR; Validating NEK2 expression level in 63 cases of HCC tissues and adjacent non-tumorous liver tissues by immunohistochemical staining.4. The correlation between candidate biomarker NEK2 expression and clinicopathological parameters of 63 of HCC patients was analyzed.5. The correlation analysis between candidate biomarker and critical proteins p-AKT and MMP-2 in pathway that candidate biomarker may involve.Results:1. Transcriptome sequence assays showed that 611 DEGs were detected in HCC cell line SMMC-7721 and normal liver cell line L-02, which included 368 up-regulated and 243 down-regulated DEGs in HCC cell.2. 12 mutual DEGs were found by integrating with cell transcriptomics and tissue transcriptomics data, among which 10 were up-regulated and 2 were down-regulated in HCC. NEK2, as the most obvious difference DEG in cell transcriptomics and tissue transcriptomics, was identified to be HCC candidate biomarker.3. The validation results showed that NEK2 mRNA level was 0.024±0.0026 in HCC cell line SMMC-7721, which was 1.71 times higher than the NEK2 m RNA level 0.014±0.0003 in normal liver cell line L-02(P = 0.002); NEK2 m RNA level in the HCC tissues was 5.60±3.69, which was 16.47 times higher than the NEK2 m RNA level 0.34±0.38 in adjacent non-tumorous liver tissues(P = 0.013); Furthermore, the expression of NEK2 protein in the HCC tissues was significantly higher than that in the adjacent non-tumorous liver tissues(P = 0.000).4. HCC clinicopathological parameters analysis found that NEK2, p-AKT, and MMP-2 expressions in HCC tumor size ≤ 10 cm group was 1.58 times(P = 0.024), 2.24 times(P = 0.041) and 2.29 times(P = 0.013) higher than that in HCC tumor size > 10 cm group, respectively. The NEK2 and MMP-2 expressions in HCC diolame incomplete group was 1.91 times(P = 0.000) and 2.04 times(P = 0.009) higher than that in HCC diolame complete group. The NEK2, p-AKT, and MMP-2 expressions in HCC multinodular group was 1.62 times(P = 0.012), 2.40 times(P = 0.046) and 2.04 times(P = 0.024) higher than that in HCC uninodular group, respectively. The NEK2 and p-AKT expressions in HCC recurrence group was 2.09 times(P = 0.004) and 3.24 times(P = 0.045) higher than that in HCC nonrecurrence group.5. Correlation analysis showed there was a significant positive correlation between NEK2 with p-AKT and MMP-2 in HCC tissues, the correlation coefficient was r = 0.832(P < 0.01) and r = 0.763(P < 0.01), respectively.Conclusions:NEK2 was probably an important HCC biomarker with significant functions, and its expression was linked to HCC invasion and metastasis. NEK2 may promote the invasion and metastasis by activating PI3K/AKT signal pathway.Part 2 Identification of HSP90α as HCC potential biomarker by integrating with proteome and transcriptomicsObjectives:To detect mutual DEFs between HCC serum and cells by integrating with serum proteome and cell transcriptomics data, and verify their expressions in HCC cells, tissues and serum. Furthermore, the correlation between mutual DEFs and clinicopathological parameters were analyzed.Methods:1. The HCC-derived DEFs were detected between 10 cases of HCC serum and 10 cases of normal serum by i TRAQ-LC-MALDI-TOF/TOF.2. Mutual DEFs were obtained by integrating with serum proteome and cell transcriptomics data, and the DEFs with the most obvious difference was identified to be HCC candidate biomarker.3. Validating candidate biomarker HSP90α expression in HCC cell line SMMC-7721 and the normal liver cell line L-02 by q RT-PCR; Validating HSP90α expression level in 7 cases of HCC serum and 7 cases of normal serum by Western blotting and ELISA; Validating HSP90α expression level in 76 cases of HCC tissues and adjacent non-tumorous liver tissues by immunohistochemical staining.4. The correlation between candidate biomarker HSP90α expression and clinicopathological parameters of 76 of HCC patients was analyzed.Results:1. Serum proteome result showed that 31 differentially expressed proteins were detected in HCC serum and normal serum, which included 17 up-regulated and 14 down-regulated differentially expressed proteins in HCC serum.2. 3 mutual DEFs were found by integrating with serum proteome and cell transcriptomics data, among which 2 were up-regulated and 1 was downregulated in HCC. HSP90α, as the most obvious difference DEF in serum proteome and cell transcriptomics, was listed as HCC candidate biomarker.3. The validation results showed that HSP90α m RNA level in HCC cell line SMMC-7721 was 4.18 times higher than that in normal liver cell line L-02(P < 0.05); Western blotting results showed that HSP90α protein level in HCC serum was 7.26 times higher than that in normal serum(P < 0.05); ELISA results showed that HSP90α protein level was 273.6±20.3 μg/ml in HCC serum, which was significantly higher than the HSP90α protein level 186.2±18.3 μg/ml in normal serum(P < 0.05); Furthermore, the expression of HSP90α protein in the HCC tissues was significantly higher than that in the adjacent nontumorous liver tissues(P < 0.05).4. HCC clinicopathological parameters analysis found that HSP90α expressed proportion was considered to br intense positive was 66.7% and 25% in HCC metastasis and non-metastasis group. HSP90α expression was increased significantly in HCC metastasis group when compared with non- metastasis group(P = 0.010).Conclusions:HSP90α was probably an important HCC biomarker, and its expression was linked to HCC invasion and metastasis.Part 3 Diagnostic value and survival analysis identify NEK2 and HSP90α as HCC candidate biomarkers in HCCObjectives:To assess the diagnostic value and analyze survival rates of NEK2 and HSP90α in HCC based on TCGA database, and evaluate their feasibility for clinical application.Methods:1. HCC RNASeq V2 data was downloaded from TCGA database. The differential expression of NEK2 and HSP90α were analyzed between 359 cases of HCC tissues and adjacent non-tumorous liver tissues.2. The best cut-off point, AUC, sensitivity and specificity of NEK2 and HSP90α in HCC diagnosis were obtained by ROC curve analysis.3. The HCC patients were divided into high expression group and low expression group of NEK2 and HSP90α according to the best cut-off points from ROC curve. The differences of survival rate between two groups were estimated by Kaplan-Meier curve analysis.Results:1. The expressions of NEK2 and HSP90α were increased significantly in HCC tissues, which the expression in HCC tissues was 19.036 and 1.399 times(P < 0.001) higher than adjacent non-tumorous liver tissues. Compared with HSP90α, the overexpression of NEK2 in HCC tissues was even more obvious.2. NEK2 and HSP90α had compelling accuracies in the diagnosis of HCC,and the accuracy of NEK2 was higher than HSP90α. AUC of NEK2 and HSP90α were 0.960(P = 0.003) and 0.703(P = 0.029), and the best cut-off points of NEK2 and HSP90α were 52.73 and 17971.78, respectively. The sensitivity and specificity of NEK2 in the diagnosis of HCC were 0.98 and 0.82, and in HSP90α were 0.88 and 0.55, respectively. Compared with HSP90α, NEK2 had a higher sensitivity and specificity in the diagnosis of HCC.3. Survival curve results showed that the expressions of NEK2 and HSP90α were related to the survival rate of patients(P = 0.0145 and 0.0032), and the survival rate of high expressed group was lower than the low expressed group.Conclusions:NEK2 and HSP90α were valuable in the diagnosis of HCC. The high expressions of NEK2 and HSP90α were associated with poor outcomes. Compared with AFP, NEK2 had a higher sensitivity and specificity in the diagnosis of HCC, and it was more valuable for clinical application. |
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