A Novel Nine-lncRNA Risk Signature Correlates With Immunotherapy in Hepatocellular Carcinoma

Screening prognostic markers for hepatocellular carcinoma based on pyroptosis-related lncRNA pairs

BACKGROUND

Pyroptosis is closely related to cancer prognosis. In this study, we tried to construct an individualized prognostic risk model for hepatocellular carcinoma (HCC) based on within-sample relative expression orderings (REOs) of pyroptosis-related lncRNAs (PRlncRNAs).

METHODS

RNA-seq data of 343 HCC samples derived from The Cancer Genome Atlas (TCGA) database were analyzed. PRlncRNAs were detected based on differentially expressed lncRNAs between sample groups clustered by 40 reported pyroptosis-related genes (PRGs). Univariate Cox regression was used to screen out prognosis-related PRlncRNA pairs. Then, based on REOs of prognosis-related PRlncRNA pairs, a risk model for HCC was constructed by combining LASSO and stepwise multivariate Cox regression analysis. Finally, a prognosis-related competing endogenous RNA (ceRNA) network was built based on information about lncRNA-miRNA-mRNA interactions derived from the miRNet and TargetScan databases.

RESULTS

Hierarchical clustering of HCC patients according to the 40 PRGs identified two groups with a significant survival difference (Kaplan-Meier log-rank, p = 0.026). Between the two groups, 104 differentially expressed lncRNAs were identified (|log₂(FC)|> 1 and FDR < 5%). Among them, 83 PRlncRNA pairs showed significant associations between their REOs within HCC samples and overall survival (Univariate Cox regression, p < 0.005). An optimal 11-PRlncRNA-pair prognostic risk model was constructed for HCC. The areas under the curves (AUCs) of time-dependent receiver operating characteristic (ROC) curves of the risk model for 1-, 3-, and 5-year survival were 0.737, 0.705, and 0.797 in the validation set, respectively. Gene Set Enrichment Analysis showed that inflammation-related interleukin signaling pathways were upregulated in the predicted high-risk group (p < 0.05). Tumor immune infiltration analysis revealed a higher abundance of regulatory T cells (Tregs) and M2 macrophages and a lower abundance of CD8 + T cells in the high-risk group, indicating that excessive pyroptosis might occur in high-risk patients. Finally, eleven lncRNA-miRNA-mRNA regulatory axes associated with pyroptosis were established.

CONCLUSION

Our risk model allowed us to determine the robustness of the REO-based PRlncRNA prognostic biomarkers in the stratification of HCC patients at high and low risk. The model is also helpful for understanding the molecular mechanisms between pyroptosis and HCC prognosis. High-risk patients may have excessive pyroptosis and thus be less sensitive to immune therapy.

Tertiary lymphoid structures: Associated multiple immune cells and analysis their formation in hepatocellular carcinoma

The prognostic value of immune cells in tertiary lymphoid structures (TLSs) remains unclear in hepatocellular carcinoma (HCC). Here, 59 of 145 patients had TLSs in training set, 48 of 120 patients had TLSs in testing set. Immunohistochemistry (IHC) were used to label CD3+ T cells, CD20+ B cells, CD8+ T cells, CD208+ dendritic cells, and CD21+ follicular dendritic cells in TLSs. High CD20+, CD208+, and CD8+ cell densities were favorable prognostic factors for overall survival (OS). High CD3+, CD20+, CD208+, and CD8+ cell densities were significantly associated with reduced early recurrence. TLSs were divided into three grades (A, B, and C) based on immune cell density. Patients with grade C or B had significantly improved OS. Patients with grade C had the lowest recurrence rate, followed by those with grade B, while patients with grade A had the highest recurrence rate. The stromal, immune, and ESTIMATE scores derived from the ESTIMATE package were significantly higher and tumor purity was significantly lower in patients with TLSs. Patients with TLSs had significantly higher relative numbers of memory B cells, plasma cells, CD8+ T cells, NK cells, and dendritic cells and lower relative numbers of Treg cells, macrophages, and M2 macrophages according to the CIBERSORT assessment. Bioinformatics analysis and experiments confirmed that KLRK1 and GZMA expression are associated TLSs formation and can predict TLSs existence. Grade B and grade C were favorable prognostic factors for OS and recurrence and could represent immune-active tumors.

PSMB5 overexpression is correlated with tumor proliferation and poor prognosis in hepatocellular carcinoma

Aberrant expression of members of the proteasome subunit beta (PSMB) family (including PSMB2, PSMB4, PSMB7 and PSMB8) has been reported in hepatocellular carcinoma (HCC). However the role of PSMB5 in HCC is unclear. To address this issue, we examined the expression of PSMB5 in HCC tissues using the The Cancer Genome Atlas, International Cancer Genome Consortium and Gene Expression Omnibus databases. A quantitative real-time PCR and immunohistochemistry were performed to validate the expression of PSMB5 in HCC. The survival mutation status and immune cell infiltration of PSMB5 were also evaluated in HCC. We then examined the effect of knocking down PSMB5 expression through RNA interference in the HCC cell line Huh7. High expression of PSMB5 was observed in HCC tissues and was associated with poor prognosis. PSMB5 expression and clinical characteristics were then incorporated to build a prognostic nomogram. We observed that PSMB5 expression was closely related to the abundance of B cells, CD4⁺ T cells, CD8⁺ T cells, dendritic cell macrophages and neutrophils. Moreover silencing of PSMB5 in Huh7 significantly suppressed cell proliferation and migration at the same time as increasing apoptosis. Inhibition of the phosphatidylinositol-3-kinase/Akt/mechanistic target of rapamycin pathway was observed after PSMB5 downregulation in Huh7 cells. Our findings suggest that PSMB5 may promote the proliferation of HCC cells by inactivating the phosphatidylinositol-3-kinase/Akt/mechanistic target of rapamycin signaling pathway and thus PSMB5 may have potential as a biomarker for diagnosis and prognosis of HCC.

Tertiary Lymphatic Structures in Primary Hepatic Carcinoma: Controversy Cannot Overshadow Hope

Tertiary lymphoid structures (TLSs) are organized aggregates of immune cells found in the tumor microenvironment. TLS can influence primary hepatic carcinoma (PHC) occurrence and have an active role in cancer. TLS can promote or inhibit the growth of PHC depending on their location, and although available findings are controversial, they suggest that TLS have a protective role in PHC tissues and a non-protective role in paracancerous tissues. In addition, the cellular composition of TLS can also influence the outcome of PHC. As an immunity marker, TLS can act as a marker of immunotherapy to predict its effect and help to identify patients who will respond well to immunotherapy. Modulation of TLS formation through the use of chemokines/cytokines, immunotherapy, or induction of high endothelial vein to interfere with tumor growth has been studied extensively in PHC and other cancers. In addition, new tools such as genetic interventions, cellular crosstalk, preoperative radiotherapy, and advances in materials science have been shown to influence the prognosis of malignant tumors by modulating TLS production. These can also be used to develop PHC treatment.

Identification of an Epithelial-Mesenchymal Transition-Related Long Non-coding RNA Prognostic Signature to Determine the Prognosis and Drug Treatment of Hepatocellular Carcinoma Patients

Introduction

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. Epithelial–mesenchymal transition (EMT) is crucial for cancer progression and metastasis. Thus, we aimed to construct an EMT-related lncRNA signature for predicting the prognosis of HCC patients.

Methods

Cox regression analysis and LASSO regression method were used to build an EMT-related lncRNAs risk signature based on TCGA database. Kaplan-Meier survival analysis was conducted to compare the overall survival (OS) in different risk groups. ROC curves and Cox proportional-hazards analysis were performed to evaluate the performance of the risk signature. RT-qPCR was conducted in HCC cell lines and tissue samples to detect the expression of some lncRNAs in this risk model. Furthermore, a nomogram involving the risk score and clinicopathological features was built and validated with calibration curves and ROC curves. In addition, we explored the association between risk signature and tumor immunity, somatic mutations status, and drugs sensitivity.

Results

Twelve EMT-related lncRNAs were obtained to construct the prognostic risk signature for patients with HCC. The Kaplan-Meier curve analysis revealed that patients in the high-risk group had worse overall survival (OS) than those in low-risk group. ROC curves and Cox regression analysis suggested the risk signature could predict HCC survival exactly and independently. The prognostic value of the risk model was confirmed in the testing and entire groups. We also found AC099850.3 and AC092171.2 were highly expressed in HCC cells and HCC tissues. The nomogram could accurately predict survival probability of HCC patients. Gene set enrichment analysis (GSEA) and gene ontology (GO) analysis showed that cancer-related pathways and cell division activity were enriched in high-risk group. The SNPs showed that the prevalence of TP53 mutations was significantly different between high- and low-risk groups; the TP53 mutations and the high TMB were both associated with a worse prognosis in patients with HCC. We also observed widely associations between risk signature and drugs sensitivity in HCC.

Conclusion

A novel EMT-related lncRNAs risk signature, including 12 lncRNAs, was established and identified in patients with HCC, which can accurately predict the prognosis of HCC patients and may be used to guide individualized treatment in the clinical practice.

Construction and Validation of a Ferroptosis-Related lncRNA Signature as a Novel Biomarker for Prognosis, Immunotherapy and Targeted Therapy in Hepatocellular Carcinoma

Recently, immunotherapy combined with targeted therapy has significantly prolonged the survival time and improved the quality of life of patients with hepatocellular carcinoma (HCC). However, HCC treatment remains challenging due to the high heterogeneity of this malignancy. Sorafenib, the first-line drug for the treatment of HCC, can inhibit the progression of HCC by inducing ferroptosis. Ferroptosis is associated with the formation of an immunosuppressive microenvironment in tumours. Moreover, long non-coding RNAs (lncRNAs) are strongly associated with ferroptosis and the progression of HCC. Discovery of ferroptosis-related lncRNAs (FR-lncRNAs) is critical for predicting prognosis and the effectiveness of immunotherapy and targeted therapies to improve the quality and duration of survival of HCC patients. Herein, all cases from The Cancer Genome Atlas (TCGA) database were divided into training and testing groups at a 6:4 ratio to construct and validate the lncRNA signatures. Least Absolute Shrinkage and Selection Operator (LASSO) regression and Cox regression analyses were used to screen the six FR-lncRNAs (including MKLN1-AS, LINC01224, LNCSRLR, LINC01063, PRRT3-AS1, and POLH-AS1). Kaplan–Meier (K–M) and receiver operating characteristic (ROC) curve analyses demonstrated the optimal predictive prognostic ability of the signature. Furthermore, a nomogram indicated favourable discrimination and consistency. For further validation, we used real-time quantitative polymerase chain reaction (qRT-PCR) to analyse the expression of LNCSRLR, LINC01063, PRRT3-AS1, and POLH-AS1 in HCC tissues. Moreover, we determined the ability of the signature to predict the effects of immunotherapy and targeted therapy in patients with HCC. Gene set enrichment analysis (GSEA) and somatic mutation analysis showed that ferroptosis-related pathways, immune-related pathways, and TP53 mutations may be strongly associated with the overall survival (OS) outcomes of HCC patients. Overall, our study suggests that a new risk model of six FR-lncRNAs has a significant prognostic value for HCC and that it could contribute to precise and individualised HCC treatment.

Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients

Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. The tumor microenvironment (TME) plays a vital role in HCC progression. Thus, this research was designed to analyze the correlation between the TME and the prognosis of HCC patients and to construct a TME-related long noncoding RNA (lncRNA) signature to determine HCC patients’ prognosis and response to immunotherapy.

Methods: We assessed the stromal–immune–estimate scores within the HCC microenvironment using the ESTIMATE (Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data) algorithm based on The Cancer Genome Atlas database, and their associations with survival and clinicopathological parameters were also analyzed. Thereafter, differentially expressed lncRNAs were filtered out according to the immune and stromal scores. Cox regression analysis was performed to build a TME-related lncRNA risk signature. Kaplan–Meier analysis was used to explore the prognostic value of the risk signature. Furthermore, we explored the biological functions and immune microenvironment features in the high- and low-risk groups. Lastly, we probed the association of the risk model with treatment responses to immune checkpoint inhibitors (ICIs) in HCC.

Results: The stromal, immune, and estimate scores were obtained utilizing the ESTIMATE algorithm for patients with HCC. Kaplan–Meier analysis showed that high scores were significantly correlated with better prognosis in HCC patients. Six TME-related lncRNAs were screened to construct the prognostic model. The Kaplan–Meier curves suggested that HCC patients with low risk had better prognosis than those with high risk. Receiver operating characteristic (ROC) curve and Cox regression analyses indicated that the risk model could predict HCC survival exactly and independently. Functional enrichment analysis revealed that some tumor- and immune-related pathways were activated in the high-risk group. We also revealed that some immune cells, which were important in enhancing immune responses toward cancer, were significantly increased in the low-risk group. In addition, there was a close correlation between ICIs and the risk signature, which can be used to predict the treatment responses of HCC patients.

Conclusion: We analyzed the influence of the stromal, immune, and estimate scores on the prognosis of HCC patients. A novel TME-related lncRNA risk model was established, which could be effectively applied as an independent prognostic biomarker and predictor of ICIs for HCC patients.