Cirrhosis is frequently the final stage of disease preceding the development of hepatocellular carcinoma (HCC) and is one of the risk factors for HCC. Preventive surveillance for early HCC in patients with cirrhosis is advantageous for achieving early HCC prevention and diagnosis, thereby enhancing patient prognosis and reducing mortality. However, there is no highly sensitive diagnostic marker for the clinical surveillance of HCC in patients with cirrhosis, which significantly restricts its use in primary care for HCC. To increase the accuracy of illness diagnosis, the study of the effective and sensitive genetic biomarkers involved in HCC incidence is crucial. In this study, a set of 120 significantly differentially expressed genes (DEGs) was identified in the GSE121248 dataset. A protein–protein interaction (PPI) network was constructed among the DEGs, and Cytoscape was used to extract hub genes from the network. In TCGA database, the expression levels, correlation analysis, and predictive performance of hub genes were validated. In total, 15 hub genes showed increased expression, and their positive correlation ranged from 0.80 to 0.90, suggesting they may be involved in the same signaling pathway governing HBV-related HCC. The GSE10143, GSE25097, GSE54236, and GSE17548 datasets were used to investigate the expression pattern of these hub genes in the progression from cirrhosis to HCC. Using Cox regression analysis, a prediction model was then developed. The ROC curves, DCA, and calibration analysis demonstrated the superior disease prediction accuracy of this model. In addition, using proteomic analysis, we investigated whether these key hub genes interact with the HBV-encoded oncogene X protein (HBx), the oncogenic protein in HCC. We constructed stable HBx-expressing LO2-HBx and Huh-7-HBx cell lines. Co-immunoprecipitation coupled with mass spectrometry (Co-IP/MS) results demonstrated that CDK1, RRM2, ANLN, and HMMR interacted specifically with HBx in both cell models. Importantly, we investigated 15 potential key genes (CCNB1, CDK1, BUB1B, ECT2, RACGAP1, ANLN, PBK, TOP2A, ASPM, RRM2, NEK2, PRC1, SPP1, HMMR, and DTL) participating in the transformation process of HBV infection to HCC, of which 4 hub genes (CDK1, RRM2, ANLN, and HMMR) probably serve as potential oncogenic HBx downstream target molecules. All these findings of our study provided valuable research direction for the diagnostic gene detection of HBV-related HCC in primary care surveillance for HCC in patients with cirrhosis.
Background Recent studies have identified ribonucleotide reductase subunit M2 (RRM2) as a putative promoter of tumors. However, no systematic analysis of its carcinogenicity has been conducted. Methods The potential functions of RRM2 in various tumor types were investigated using data from the Genotype-Tissue Expression (GTEx), the Clinical Proteomic Tumor Analysis Consortium (CPTAC), the Cancer Genome Atlas (TCGA), the Human Protein Atlas (HPA), cBioPortal, GEPIA, String, and Gene Set Enrichment Analysis (GSEA). We analyzed the difference in mRNA and protein expression, pathological stage, survival, mutation, tumor microenvironment (TME), and immune cell infiltration in relation to RRM2. Meanwhile, using TCGA and the Tumor Immune Estimation Resource 2 (TIMER 2), the associations between RRM2 expression, immune infiltration, and immune-related genes were assessed. Additionally, CCK-8, Edu and RT-PCR assays were used to validate that RRM2 acts as an oncogene in liver cancer cells and its association with HBx. A cohort of liver hepatocellular carcinoma (LIHC) patients (n=154) from Huashan Hospital was analyzed for the expression of RRM2 and the association between RRM2 and immune infiltration. Results Using the GTEx and TCGA databases, we discovered that 28 tumors expressed RRM2 at significantly higher levels than the corresponding normal tissues. Increased RRM2 expression may be predictive of a poor overall survival (OS) in patients with seven different cancers. GO, KEGG, and GSEA analyses revealed that the biological process of RRM2 was associated with the regulation of carcinogenic processes and immune pathways in a variety of tumor types. The expression of RRM2 was highly correlated with maker genes involved in immune activation and immunosuppression, immune checkpoints, DNA mismatch repair system (MMR), and the infiltration levels of Tregs and macrophages (TAMs), suggesting that the carcinogenic effect of RRM2 may be achieved by regulating immune related genes. Moreover, as demonstrated by CCK-8 and Edu assays, RRM2 was an oncogene in liver cancer cells. We confirmed for the first time that RRM2 was significantly upregulated by HBx, suggesting that RRM2 may be a key regulator of LIHC induced by HBV. IHC analysis validated the upregulated expression of RRM2 protein and its correlation with immune infiltration makers in a LIHC patient cohort. Conclusion RRM2 may be a valuable molecular biomarker for predicting prognosis and immunotherapeutic efficacy in pan-cancer, particularly in LIHC.
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