Prognostic Role of ceRNA Network in Immune Infiltration of Hepatocellular Carcinoma

Identification of prognosis value and immune microenvironment features of ceRNAs in NSCLC with distinct gene mutation

Non-small cell lung cancer (NSCLC), representing about 85% of all lung cancer (LC) cases, is by far the most common form of LC. High-throughput technology largely expands our ability to analyze the transcriptome data and a plethora of cancer-driving genes has been identified, paving the path to immune therapy, where the effects of cancer-causing mutations are countered with microenvironment complexity. Given that competing endogenous RNAs (ceRNAs) participate in diverse cellular processes by a broad array of mechanisms in cancer, we scrutinized the immune microenvironment and ceRNA signatures in mutation-specific NSCLC by integrating TCGA-NSCLC and NSCLS-associated GEO datasets. The results suggested that RASA1mutation clusters in LUSC had a better prognosis and immunity. Immune cell infiltration analysis indicated that the cluster with RASA1 mutation had a significantly high level of NK T cells and a low level of memory effector T cells. Further analysis of immune-related ceRNAs in LUSC showed that hsa-miR-23a was significantly associated with survival in RASA1-mutation samples, indicating that there may be specific ceRNAs in mutation-specific subgroups in NSCLC. In conclusion, this study verified the presence of complexity and diversity of NSCLC gene mutations and highlighted the intricate links between gene mutation and tumor environment features.

The diagnostic potential of a circRNA-miRNA network in non-small cell lung cancer

Increasing studies demonstrate the significant contributions of circRNA-related competitive endogenous RNA (ceRNA) regulatory networks to tumorigenesis and cancer progression. Here, we aimed to construct a non-small cell lung cancer (NSCLC)-specific circRNA-miRNA network and evaluate its diagnostic potential in NSCLC. MiRNA deep sequencing was performed to screen differentially-expressed serum miRNAs in NSCLC. Four bioinformatics databases (TargetScan, miRanda, starBase, and RNAhybrid) were used to analyze the integrated circRNA-miRNA interaction network. The circRNA-miRNA network, including hsa-miR-4482-3p, hsa-miR-146a-3p, hsa_circ_0008167 and hsa_circ_0003317 was constructed based on their interactions and preliminary testing in NSCLC cells. The relative levels of the selected non-coding RNAs (ncRNAs) were quantified using quantitative real-time polymerase chain reaction (qRT-PCR) in the healthy, pneumonia, benign lung tumor and NSCLC cohorts. The diagnostic power of the circRNA-miRNA network was evaluated using receiver operating characteristic (ROC) analyses. The serum levels of hsa-miR-4482-3p, hsa-miR-146a-3p, hsa_circ_0008167, and hsa_circ_0003317 were dysregulated in NSCLC. The combination of the four ncRNAs showed the highest diagnostic value to discriminate between benign lung tumors and NSCLC. Additionally, the upregulated levels of hsa_circ_0008167 were correlated to more aggressive features of NSCLC, such as lymph node metastasis, distant metastasis, and higher stage. Furthermore, the combination of hsa_circ_0008167 + hsa-miR-4482-3p, and hsa_circ_0008167 + hsa-miR-4482-3p + hsa-miR-146a-3p had the greatest diagnostic power to differentiate between lymph node +/- metastases and higher/lower stages, respectively, compared to circRNAs or miRNAs alone, and traditional tumor markers. In conclusion, we identified a specific circRNA-miRNA network with higher sensitivity and specificity to diagnose NSCLC, thereby providing a new strategy for further development of ceRNA-related tumor markers in other cancers. KEY MESSAGES: Serum miR-4482-3p, miR-146a-3p, circ_0008167 and circ_0003317 are dysregulated in NSCLC. Higher levels of serum circ_0008167 are associated with more malignant NSCLC. Multiple combinations of circRNAs and miRNAs show higher value to diagnose NSCLC.

Tumor Mutational Burden for Predicting Prognosis and Therapy Outcome of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC), the primary hepatic malignancy, represents the second-highest cause of cancer-related death worldwide. Many efforts have been devoted to finding novel biomarkers for predicting both patients' survival and the outcome of pharmacological treatments, with a particular focus on immunotherapy. In this regard, recent studies have focused on unravelling the role of tumor mutational burden (TMB), i.e., the total number of mutations per coding area of a tumor genome, to ascertain whether it can be considered a reliable biomarker to be used either for the stratification of HCC patients in subgroups with different responsiveness to immunotherapy, or for the prediction of disease progression, particularly in relation to the different HCC etiologies. In this review, we summarize the recent advances on the study of TMB and TMB-related biomarkers in the HCC landscape, focusing on their feasibility as guides for therapy decisions and/or predictors of clinical outcome.

Prognostic value of combined inflammatory and nutritional biomarkers in HCC within the Milan criteria after hepatectomy

Aims

This study aimed to evaluate the predictive value of the combined prognostic nutritional index (PNI) and GGT/ALT for the postoperative prognosis of patients with hepatocellular carcinoma (HCC) within Milan criteria undergoing radical hepatectomy.

Methods

This single-center retrospective study included 283 patients with HCC within the Milan criteria who underwent hepatectomy. The receiver operating characteristic (ROC) curve was used to calculate the optimal PNI and GGT/ALT cut-off values. Pre-treatment PNI, GGT/ALT, and PNI-GGT/ALT grades were calculated. Overall survival (OS) and recurrence-free survival (RFS) were estimated using the Kaplan–Meier method, and multivariate analysis was used to identify prognostic factors.

Results

Multivariate Cox regression analysis identified that the PNI, GGT/ALT, tumor number were significant prognostic markers for OS, and that the GGT/ALT, tumor number were significant prognostic markers for OS. The survival curves showed that low PNI, high GGT/ALT ratio, and high PNI-GGT/ALT grade were associated with poorer OS and DFS. With an area under the curve (AUC) of 0.690, PNI-GGT/ALT outperformed each individual score.

Conclusion

PNI-GGT/ALT, a new prognostic scoring model, qualifies as a novel prognostic predictor for patients with HCC within the Milan criteria after curative resection.

Predicting prognosis and immune responses in hepatocellular carcinoma based on N7-methylguanosine-related long noncoding RNAs

Background: Hepatocellular carcinoma (HCC), which has high rates of recurrence and metastasis and is the main reason and the most common tumor for cancer mortality worldwide, has an unfavorable prognosis. N7-methylguanosine (m7G) modification can affect the formation and development of tumors by affecting gene expression and other biological processes. In addition, many previous studies have confirmed the unique function of long noncoding RNAs (lncRNAs) in tumor progression; however, studies exploring the functions of m7G-related lncRNAs in HCC patients has been limited.

Methods: Relevant RNA expression information was acquired from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov), and m7G-related lncRNAs were identified via gene coexpression analysis. Afterward, univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate regression analyses were implemented to construct an ideal risk model whose validity was verified using Kaplan–Meier survival, principal component, receiver operating characteristic (ROC) curve, and nomogram analyses. In addition, the potential functions of lncRNAs in the novel signature were explored through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) analyses and gene set enrichment analysis (GSEA). At last, in both risk groups and subtypes classified based on the expression of the risk-related lncRNAs, we analyzed the immune characteristics and drug sensitivity of patients.

Results: After rigorous screening processes, we built a model based on 11 m7G-related lncRNAs for predicting patient overall survival (OS). The results suggested that the survival status of patients with high-risk scores was lower than that of patients with low-risk scores, and a high-risk score was related to malignant clinical features. Cox regression analysis showed that the m7G risk score was an independent prognostic parameter. Moreover, immune cell infiltration and immunotherapy sensitivity differed between the risk groups.

Conclusion: The m7G risk score model constructed based on 11 m7G-related lncRNAs can effectively assess the OS of HCC patients and may offer support for making individualized treatment and immunotherapy decisions for HCC patients.

Novel γδ T cell-based prognostic signature to estimate risk and aid therapy in hepatocellular carcinoma

BACKGROUND

Numerous studies have revealed that gamma delta (γδ) T cell infiltration plays a crucial regulatory role in hepatocellular carcinoma (HCC) development. Nonetheless, a comprehensive analysis of γδ T cell infiltration in prognosis evaluation and therapeutic prediction remains unclear.

METHODS

Multi-omic data on HCC patients were obtained from public databases. The CIBERSORT algorithm was applied to decipher the tumor immune microenvironment (TIME) of HCC. Weighted gene co-expression network analysis (WGCNA) was performed to determine significant modules with γδ T cell-specific genes. Kaplan-Meier survival curves and receiver operating characteristic analyses were used to validate prognostic capability. Additionally, the potential role of RFESD inhibition by si-RFESD in vitro was investigated using EdU and CCK-8 assays.

RESULTS

A total of 16,421 genes from 746 HCC samples (616 cancer and 130 normal) were identified based on three distinct cohorts. Using WGCNA, candidate modules (brown) with 1755 significant corresponding genes were extracted as γδ T cell-specific genes. Next, a novel risk signature consisting of 11 hub genes was constructed using multiple bioinformatic analyses, which presented great prognosis prediction reliability. The risk score exhibited a significant correlation with ICI and chemotherapeutic targets. HCC samples with different risks experienced diverse signalling pathway activities. The possible interaction of risk score with tumor mutation burden (TMB) was further analyzed. Subsequently, the potential functions of the RFESD gene were explored in HCC, and knockdown of RFESD inhibited cell proliferation in HCC cells. Finally, a robust prognostic risk-clinical nomogram was developed and validated to quantify clinical outcomes.

CONCLUSIONS

Collectively, comprehensive analyses focusing on γδ T cell patterns will provide insights into prognosis prediction, the mechanisms of immune infiltration, and advanced therapy strategies in HCC.

The Mechanism Underlying the ncRNA Dysregulation Pattern in Hepatocellular Carcinoma and Its Tumor Microenvironment

HCC is one of the most common malignant tumors and has an extremely poor prognosis. Accumulating studies have shown that noncoding RNA (ncRNA) plays an important role in hepatocellular carcinoma (HCC) development. However, the details of the related mechanisms remain unclear. The heterogeneity of the tumor microenvironment (TME) calls for ample research with deep molecular characterization, with the hope of developing novel biomarkers to improve prognosis, diagnosis and treatment. ncRNAs, particularly microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs), have been found to be correlated with HCC neogenesis and progression. In this review, we summarized the aberrant epigenetic and genetic alterations caused by dysregulated ncRNAs and the functional mechanism of classical ncRNAs in the regulation of gene expression. In addition, we focused on the role of ncRNAs in the TME in the regulation of tumor cell proliferation, invasion, migration, immune cell infiltration and functional activation. This may provide a foundation for the development of promising potential prognostic/predictive biomarkers and novel therapies for HCC patients.