Significance of tumor-infiltrating immunocytes for predicting prognosis of hepatitis B virus-related hepatocellular carcinoma

Pan-Cancer Analysis of the Prognostic and Immunological Role of SMG5: A Biomarker for Cancers

INTRODUCTION

SMG5 is involved in tumor cell development and viewed as a potential target for immunotherapy. The purpose of this study was to systematically analyze the expression level, function, and prognostic value of SMG5 in pan-cancers.

METHODS

Differential expression of SMG5 in normal and tumor tissues was analyzed using The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression Database (GTEx) data. Survival analysis was performed by Kaplan-Meier method and Cox risk regression. The relationship between SMG5 expression and lymphocyte abundance, tumor cell immune infiltration level, molecular and immune subtypes as well as immune checkpoints was analyzed by tumor-immune system interactions database (TISIDB), Tumor Immune Estimation Resource (TIMER), and Sangerbox databases. The correlation between SMG5 and immune scores was studied using the Estimation of Stromal and Immune Cells in Malignant Tumours using Expression (ESTIMATE) data algorithm. Further, drug sensitivity analysis of SMG5 with low-grade glioma (LGG) was conducted using the CellMiner database.

RESULTS

SMG5 was highly expressed in 23 tumors and only had a significant impact on the prognosis of patients with LGG only. In addition, in tumor microenvironment and tumor immune analysis, we found that the level of immune infiltration, tumor mutational load, microsatellite instability, and immune checkpoints of LGG were significantly correlated with SMG5 expression. Furthermore, SMG5 was significantly associated with immune scores, stromal scores, and sensitivity of some drugs in LGG.

CONCLUSION

SMG5 is differentially expressed in several cancers and is significantly associated with prognosis, immune microenvironment, and immune checkpoints in LGG patients. Therefore, SMG5 could be a potential pan-cancer biomarker and an immunotherapeutic target for LGG.

A novel risk score based on immune-related genes for hepatocellular carcinoma as a reliable prognostic biomarker and correlated with immune infiltration

BACKGROUND

Immunological-related genes (IRGs) play a critical role in the immune microenvironment of tumors. Our study aimed to develop an IRG-based survival prediction model for hepatocellular carcinoma (HCC) patients and to investigate the impact of IRGs on the immune microenvironment.

METHODS

Differentially expressed IRGs were obtained from The Genomic Data Commons Data Portal (TCGA) and the immunology database and analysis portal (ImmPort). The univariate Cox regression was used to identify the IRGs linked to overall survival (OS), and a Lasso-regularized Cox proportional hazard model was constructed. The International Cancer Genome Consortium (ICGC) database was used to verify the prediction model. ESTIMATE and CIBERSORT were used to estimate immune cell infiltration in the tumor immune microenvironment (TIME). RNA sequencing was performed on HCC tissue specimens to confirm mRNA expression.

RESULTS

A total of 401 differentially expressed IRGs were identified, and 63 IRGs were found related to OS on the 237 up-regulated IRGs by univariate Cox regression analyses. Finally, five IRGs were selected by the LASSO Cox model, including SPP1, BIRC5, STC2, GLP1R, and RAET1E. This prognostic model demonstrated satisfactory predictive value in the ICGC dataset. The risk score was an independent predictive predictor for OS in HCC patients. Immune-related analysis showed that the immune infiltration level in the high-risk group was higher, suggesting that the 5-IRG signature may play an important role in mediating immune escape and immune resistance in the TIME of HCC. Finally, we confirmed the 5-IRG signature is highly expressed in 65 HCC patients with good predictive power.

CONCLUSION

We established and verified a new prognosis model for HCC patients based on survival-related IRGs, and the signature could provide new insights into the prognosis of HCC.

Immune cell infiltration and immunotherapy in hepatocellular carcinoma

Hepatocellular carcinoma is a highly malignant tumor and patients yield limited benefits from the existing treatments. The application of immune checkpoint inhibitors is promising but the results described in the literature are not favorable. It is therefore urgent to systematically analyze the immune microenvironment of HCC and screen the population best suited for the application of immune checkpoint inhibitors to provide a basis for clinical treatment. In this study, we collected The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC)-related data sets to evaluate the immune microenvironment and immune cell infiltration (ICI) in HCC. Three independent ICI subtypes showing significant differences in survival were identified. Further, TCGA-LIHC immunophenoscore (IPS) was used to identify the differentially expressed genes between high- and low-IPS in HCC, so as to identify the immune gene subtypes in HCC tumors. The ICI score model for HCC was constructed, whereby we divided HCC samples into high- and low-score groups based on the median ICI score. The differences between these groups in genomic mutation load and immunotherapy benefit in HCC were examined in detail to provide theoretical support for accurate immunotherapy strategy in HCC. Finally, four genes were screened, which could accurately predict the subtype based on the tumor immune infiltration score. The findings may provide a basis and simplify the process for screening clinical drugs suitable for relevant subgroups.

NLRP3 promotes immune escape by regulating immune checkpoints: A pan-cancer analysis

NLRP3 plays a pathogenic role in tumorigenesis by regulating innate and acquired immunity, apoptosis, differentiation, and intestinal microbes in tumors. Our research aimed to investigate the role of NLRP3 in pan-cancers based on multi-omics data in the TCGA database. Most types of tumors showed increased expression of NLRP3. Among them, the overexpressed NLRP3 in liver hepatocellular carcinoma (LIHC) and ovarian cancer (OV) indicated worse overall survival (OS). Further analysis also confirmed overexpressed NLRP3 in colon cancer (COAD) indicated a high probability of microsatellite instability (MSI) and low tumor mutational burden (TMB), which indicated a better response to immune checkpoint inhibitors (ICIs). Interestingly, overexpression of NLRP3 was closely related to high infiltration of immune cells (T cells, B cells, etc.) and overexpressed immune checkpoints (PD-1, PD-L1, LAG3, etc.). These results demonstrated NLRP3 promoted immune escape in cancers. Finally, we investigated the expression of various immune checkpoints by treating NLRP3 inhibitor MCC950 during the co-culture of peripheral blood mononuclear cells (PBMC) and LIHC cell line Hep3B. MCC950 significantly repressed the expression of PD-L1 and LAG3, and promoted the apoptosis rate of Hep3B. In conclusion, our research demonstrated the role of NLRP3 in pan-cancer, especially in LIHC. Inhibition of NLRP3 promoted the killing effect of T cells to cancer cells by repressing the expression of immune checkpoints.

Factors associated with liver cancer prognosis after hepatectomy: A retrospective cohort study

This article was to investigate risk factors influencing liver cancer prognosis after hepatectomy.Patients undergoing hepatectomy after being diagnosed with liver cancer in Zhongshan Hospital Affiliated to Xiamen University were collected in the retrospective cohort study between January 2012 and December 2017, and divided into disease progression and non-progression groups based on their prognostic status. Univariate analysis was performed on the patients' baseline and laboratory test data, with multivariate logistic regression further conducted to investigate the independent risk factors for liver cancer progression after hepatectomy.Among the 288 subjects, 159 had adverse outcomes (death or cancer recurrence). Hepatitis B and high levels of aspartate aminotransferase, gamma-glutamyltransferase, alkaline phosphatase (ALP), direct bilirubin, and total bilirubin as well as low level of lymphocyte (LYM) were found to be associated with disease progression in the univariate analysis, and were introduced into the multivariate logistic regression. The results indicated that patients with high ALP level (odds ratio [OR] = 1.004, 95%CI: 1.002-1.007, P = .003) and with a history of hepatitis B (OR = 2.182, 95%CI: 1.165-4.086, P = .015) had a higher risk of liver cancer progression compared with those of lower ALP level and those without hepatitis B respectively, whereas the elevated level of LYM (OR = 0.710, 95%CI: 0.516-0.978, P = .034) had favorable progression.The elevated ALP level and a history of hepatitis B may increase the risk of death or cancer recurrence, whereas high LYM level may decrease poor progression among liver cancer patients after hepatectomy. More importance should be attached to the improvement of the liver function and treatment of hepatitis B to enable a better outcome for the patients.

Profiles of immune infiltration in the tumor microenvironment of hepatocellular carcinoma

Background

Thus far, few studies have systematically analyzed the profiles of immune cells infiltrated in the tumor microenvironment (TME) of hepatocellular carcinoma (HCC). Therefore, the purpose of our study was to comprehensively analyze the 22 tumor-infiltrating immune cells (TIICs) and the immune subtypes of HCC, as well as the factors associated with the prognosis of HCC patients.

Methods

In this study, we evaluated the abundance of 22 tumor-infiltrating immunocytes of 371 HCC patients from The Cancer Genome Atlas (TCGA) database by using the CIBERSORT algorithm, and defined immune subtypes of HCC according to unsupervised cluster analysis. The immune score of HCC patients was calculated by the prognostic regression model, while the survival analysis was evaluated by the Kaplan-Meier method. In addition, the consistency index of TIICs and principal component analysis (PCA) of immunomodulator genes were estimated.

Results

The results of this study showed that three distinct immune subtypes of HCC were stratified, and the C1 subtype and C3 subtype were correlated with a good prognosis. The cellular composition of three immune subtypes was different. Moreover, immunomodulator gene and programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) expression in the C1 subtype was significantly higher (P<0.05).

Conclusions

This suggested that the low immune score of HCC patients is associated with better clinical outcomes. In addition, the interaction network of cluster of differentiation CD8+ T cells was mainly concentrated in the C1 subtype. Taken together, this study showed that tumor-infiltrating immune cells can perhaps be an important determinant of clinical outcomes of patients with HCC and may provide biomarkers to reflect the immunotherapy response. Notably, the C1 subtype of HCC may be used as an important predictive factor for immunotherapy response.

Identification of hepatocellular carcinoma prognostic markers based on 10-immune gene signature

Background: Due to the heterogeneity of hepatocellular carcinoma (HCC), hepatocelluarin-associated differentially expressed genes were analyzed by bioinformatics methods to screen the molecular markers for HCC prognosis and potential molecular targets for immunotherapy.

Methods: RNA-seq data and clinical follow-up data of HCC were downloaded from The Cancer Genome Atlas (TCGA) database. Multivariate Cox analysis and Lasso regression were used to identify robust immunity-related genes. Finally, a risk prognosis model of immune gene pairs was established and verified by clinical features, test set and Gene Expression Omnibus (GEO) external validation set.

Results: A total of 536 immune-related gene (IRGs) were significantly associated with the prognosis of patients with HCC. Ten robust IRGs were finally obtained and a prognostic risk prediction model was constructed by feature selection of Lasso. The risk score of each sample is calculated based on the risk model and is divided into high risk group (Risk-H) and low risk group (Risk-L). Risk models enable risk stratification of samples in training sets, test sets, external validation sets, staging and subtypes. The area under the curve (AUC) in the training set and the test set were all >0.67, and there were significant overall suvival (OS) differences between the Risk-H and Risk-L samples. Compared with the published four models, the traditional clinical features of Grade, Stage and Gender, the model performed better on the risk prediction of HCC prognosis.

Conclusion: The present study constructed 10-gene signature as a novel prognostic marker for predicting survival in patients with HCC.

Identification of a five-immune gene model as an independent prognostic factor in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a common malignant tumor with a poor prognosis. We aimed to identify a new prognostic model of HCC based on differentially expressed (DE) immune genes.

Methods

The DE immune genes were identified based on an analysis of 374 cases of HCC and 50 adjacent non-tumor specimens from the Cancer Genome Atlas (TCGA) database. Univariate Cox analysis, Lasso regression, and multivariate Cox analysis were used to construct the model based on the training group. Survival analysis and the receiver operating characteristic (ROC) curves were used to evaluate model performance. The testing group and the entire group were subsequently used for validation of the model.

Results

A five-immune gene model consisted of HSPA4, ISG20L2, NDRG1, EGF, and IL17D was identified. Based on the model, the overall survival was significantly different between the high-risk and low-risk groups (P = 7.953e-06). The AUCs for the model at 1- and 3-year were 0.849 and 0.74, respectively. The reliability of the model was confirmed using the validation groups. The risk score was identified as an independent prognostic parameter and closely related to the content of immune cells from human HCC specimens.

Conclusion

We identified a five-immune gene model that can be used as an independent prognostic marker for HCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08012-2.

Machine Learning for Building Immune Genetic Model in Hepatocellular Carcinoma Patients

BACKGROUND

Hepatocellular carcinoma (HCC) is the leading liver cancer with special immune microenvironment, which played vital roles in tumor relapse and poor drug responses. In this study, we aimed to explore the prognostic immune signatures in HCC and tried to construct an immune-risk model for patient evaluation.

METHODS

RNA sequencing profiles of HCC patients were collected from the cancer genome Atlas (TCGA), international cancer genome consortium (ICGC), and gene expression omnibus (GEO) databases (GSE14520). Differentially expressed immune genes, derived from ImmPort database and MSigDB signaling pathway lists, between tumor and normal tissues were analyzed with Limma package in R environment. Univariate Cox regression was performed to find survival-related immune genes in TCGA dataset, and in further random forest algorithm analysis, significantly changed immune genes were used to generate a multivariate Cox model to calculate the corresponding immune-risk score. The model was examined in the other two datasets with recipient operation curve (ROC) and survival analysis. Risk effects of immune-risk score and clinical characteristics of patients were individually evaluated, and significant factors were then used to generate a nomogram.

RESULTS

There were 52 downregulated and 259 upregulated immune genes between tumor and relatively normal tissues, and the final immune-risk model (based on SPP1, BRD8, NDRG1, KITLG, HSPA4, TRAF3, ITGAV and MAP4K2) can better differentiate patients into high and low immune-risk subpopulations, in which high score patients showed worse outcomes after resection (p < 0.05). The differentially enriched pathways between the two groups were mainly about cell proliferation and cytokine production, and calculated immune-risk score was also highly correlated with immune infiltration levels. The nomogram, constructed with immune-risk score and tumor stages, showed high accuracy and clinical benefits in prediction of 1-, 3- and 5-year overall survival, which is useful in clinical practice.

CONCLUSION

The immune-risk model, based on expression of SPP1, BRD8, NDRG1, KITLG, HSPA4, TRAF3, ITGAV, and MAP4K2, can better differentiate patients into high and low immune-risk groups. Combined nomogram, using immune-risk score and tumor stages, could make accurate prediction of 1-, 3- and 5-year survival in HCC patients.

An integrated pan-cancer analysis of TFAP4 aberrations and the potential clinical implications for cancer immunity

Studies have shown that transcription factor activating enhancer binding protein 4 (TFAP4) plays a vital role in multiple types of cancer; however, the TFAP4 expression profile is still unknown, as is its value within the human pan‐cancer analysis. The present study comprehensively analysed TFAP4 expression patterns from 33 types of malignancies, along with the significance of TFAP4 for prognosis prediction and cancer immunity. TFAP4 displayed inconsistent levels of gene expression across the diverse cancer cell lines, and displayed abnormal expression within most malignant tumours, which closely corresponded to overall survival. More importantly, the TFAP4 level was also significantly related to the degree of tumour infiltration. TFAP4 was correlated using gene markers in tumour‐infiltrating immune cells and immune scores. TFAP4 expression was correlated with tumour mutation burden and microsatellite instability in different cancer types, and enrichment analyses identified TFAP4‐associated terms and pathways. The present study comprehensively analysed the expression of TFAP4 across 33 distinct types of cancers, which revealed that TFAP4 may possibly play a vital role during cancer formation and development. TFAP4 is related to differing degrees of immune infiltration within cancers, which suggests the potential of TFAP4 as an immunotherapy target in cancers. Our study demonstrated that TFAP4 plays an important role in tumorigenesis as a prognostic biomarker, which highlights the possibility of developing new targeted treatments.

A prognostic risk model based on immune-related genes predicts overall survival of patients with hepatocellular carcinoma

BACKGROUND AND AIMS

Hepatocellular carcinoma (HCC) is one of the most common heterogeneous tumors that occurs after chronic liver diseases and hepatitis virus infection. Immune-related genes (IRGs) and their ligands regulate the homeostasis of tumor microenvironment, which is essential for the treatment of HCC and its prognosis. This study aimed to investigate the clinical value of IRGs in predicting the prognosis of HCC.

METHODS

We downloaded RNA-seq data and clinical information from TCGA database. Samples were randomly divided into training cohort and testing cohort. The "limma" R package was performed to identify differentially expressed IRGs (DEIRGs) between HCC group and normal group. Prognostic DEIRGs (PDEIRGs) were obtained by univariate Cox analysis. LASSO and multivariate Cox analysis were used, and a prognostic risk model was constructed. In order to better demonstrate the clinical value of our model in predicting overall survival rate, a nomogram was constructed. To further investigate the molecular mechanism of our model, gene set enrichment analysis (GSEA) was performed.

RESULTS

Compared with the low-risk group, the high-risk group had a significantly worse prognosis. Moreover, our prognostic risk model can accurately stratify tumor grade and TNM stage. Importantly, in our model, not only immune checkpoint genes were well predicted, but also human leucocyte antigen-I molecules were revealed. GSEA suggested that "MAPK signaling pathway," "mTOR signaling pathway," "NOD like receptor signaling pathway," "Toll like receptor signaling pathway," "VEGF signaling pathway," "WNT signaling pathway" had significant correlations with the high-risk group.

CONCLUSION

Overall, our study showed that our prognostic risk model can be used to assess prognosis of HCC, which may provide a certain basis for the survival rate of patients with HCC.

Molecular subtypes based on immune-related genes predict the prognosis for hepatocellular carcinoma patients

BACKGROUND

Hepatocellular carcinoma (HCC) is a malignancy exhibiting the highest lethality. The present study aimed to identify different immune-related clusters in HCC and a robust tumor gene signature to facilitate the prognosis prediction for HCC patients.

METHODS

For the 375 HCC cases collected from the dataset of Cancer Genome Atlas (TCGA), their overall survival (OS) and immune-related genes (IRGs) expression patterns were collected. Thereafter, consensus clustering was employed for grouping and functional enrichment, whereas the ESTIMATE algorithm and the CIBERSORT algorithm were used in subsequent assessment. Immunohistochemistry (IHC) was conducted to verify the protein expression of model genes in HCC and adjacent tissues.

RESULTS

According to consensus clustering with 93-survival related IRGs, a total of five subgroups were found. These five clusters had different prognoses, immune statuses, and expression of immune checkpoints. Afterwards, 11 genes were enrolled for constructing the OS-related prediction model for TCGA HCC cases, which was then validated using the database of International Cancer Genome Consortium (ICGC). The protein expression of LCN2, S100A10, FABP6, PLXNA1, KITLG and OXTR were enhanced in HCC tissues relative to that in normal hepatic tissues, while the protein expression of S100A1, CCL26, CMTM4, IL1RN and RARG were reduced in HCC compared with normal tissues. In addition, different immunocyte infiltration levels between low- and high- groups were further examined.

CONCLUSIONS

According to our results, the IRGs-based classifications assist in explaining the HCC heterogeneity, which may help to develop the more efficient individualized treatments.

Evaluating Distribution and Prognostic Value of New Tumor-Infiltrating Lymphocytes in HCC Based on a scRNA-Seq Study With CIBERSORTx

Hepatocellular carcinoma (HCC) is a commonly diagnosed cancer with high mortality rates. The immune response plays an important role in the progression of HCC. Immunotherapies are becoming an increasingly promising tool for treating cancers. Advancements in scRNA-seq (single-cell RNA sequencing) have allowed us to identify new subsets in the immune microenvironment of HCC. Yet, distribution of these new cell types and their potential prognostic value in bulk samples from large cohorts remained unclear. This study aimed to investigate the tumor-infiltration and prognostic value of new cell subsets identified by a previous scRNA-seq study in a TCGA HCC cohort using CIBERSORTx, a machine learning method to estimate cell proportion and infer cell-type-specific gene expression profiles. We observed different distributions of tumor-infiltrating lymphocytes between tumor and normal cells. Among these, the CD4-GZMA cell subset showed association with prognosis (log-rank test, p < 0.05). We further analyzed CD4-GZMA cell specific gene expression with CIBERSORTx, and found 19 prognostic genes (univariable cox regression, p < 0.05). Finally, we applied Least absolute shrinkage and selection operator (LASSO) Cox regression to construct an immune risk score model and performed a prognostic assessment of our model in TCGA and ICGC cohorts. Taken together, the immune landscape in HCC bulk samples may be more complex than assumed, with heterogeneity and different tumor-infiltration relative to scRNA-seq results. Additionally, CD4-GZMA cells and their characteristics may yield therapeutic benefits in the immune treatment of HCC.

Functional and Clinical Characterization of Tumor-Infiltrating T Cell Subpopulations in Hepatocellular Carcinoma

Tumor-infiltrating T-lymphocytes are defined as T-lymphocytes that infiltrated into tumor tissues; however, their composition, clinical significance, and underlying mechanism in hepatocellular carcinoma (HCC) and adjacent non-tumor tissues are still not completely understood. Herein, we collected marker genes of T cell subpopulations from a previous study and estimated their relative infiltrating levels in HCC and adjacent non-tumor tissues. Specifically, the infiltrating levels of all the T cells were significantly reduced in HCC as compared with non-tumor tissues. Unsupervised clustering of the HCC samples by the T cell infiltrating levels revealed that the HCC samples could be clearly classified into two groups. The driver genes, including PTK2B, ATM, PIK3C2B, and KIT, and several CNAs were observed to be associated with reduced T cell infiltrating levels. Particularly, deletion of TP53 more frequently occurred in low T cell infiltration HCC samples and resulted in its downregulation and cell cycle progression, indicating that cell cycle progression was closely associated with reduced T cell infiltration. In contrast, for the samples with high infiltration T cells, its immune evasion might be regulated by the immune checkpoint regulators, such as PD-1/PD-L1 and CTLA4. Moreover, Olaparib, one of the PARP inhibitors, and immune checkpoint inhibitors might be therapeutic candidates for the samples from the two T cell infiltrating clusters. Clinically, the tumor-infiltrating levels of cytotoxic CD4 cell, Mucosal associated invariant T (MAIT) cell, and exhausted CD8⁺ T cell might be used as predictors for vascular invasion, recurrence, and overall survival. Collectively, we systematically evaluated the clinical significance and potential molecular mechanisms of tumor-infiltrating T cell subpopulations in hepatocellular carcinoma, which might broaden our insights into the immunological features of HCC and provide potential immunotherapeutic targets.

Clinical Implications of Aberrant PD-1 and CTLA4 Expression for Cancer Immunity and Prognosis: A Pan-Cancer Study

Combination therapy with inhibitors of cytotoxic T lymphocyte-associated protein (CTLA)4 and programmed death (PD)-1 has demonstrated efficacy in cancer patients. However, there is little information on CTLA4 and PD-1 expression levels and their clinical significance across diverse cancers. In this study, we addressed this question by analyzing PD-1 and CTLA4 levels in 33 different types of cancer along with their prognostic significance using The Cancer Genome Atlas (TCGA) and Cancer Cell Line Encyclopedia datasets. Liver hepatocellular carcinoma (LIHC) patients receiving cytokine-induced killer cell (CIK) immunotherapy at Sun Yat-sen University cancer center were enrolled for survival analysis. The correlation between PD-1/CTLA4 expression and cancer immunity was also analyzed. The results showed that PD-1 and CTLA4 transcript levels varied across cancer cell lines, with aberrant expression detected in certain cancer types; Kaplan–Meier analysis with the Cox proportional hazards model showed that this was closely related to overall survival in breast invasive carcinoma, glioblastoma multiforme, head and neck squamous cell carcinoma, acute myeloid leukemialymphoma, uterine corpus endometrial carcinoma, and uveal melanoma in TCGA. High serum PD-1 and CTLA4 levels predicted better survival in LIHC patients receiving CIK therapy. PD-1 and CTLA4 levels were found to be significantly correlated with the degree of tumor cell infiltration using Tumor Immune Estimation Resource, Estimating Relative Subsets of RNA Transcripts, and Estimation of Stromal and immune Cells in Malignant Tumor Tissues Using Expression Data as well as with tumor-infiltrating lymphocyte marker expression; they were also related to tumor mutation burden, microsatellite instability, mismatch repair, and the expression of DNA methyltransferases in some cancer types. Gene set enrichment analysis of 33 cancer types provided further evidence for associations between PD-1/CTLA4 levels and cancer development and immunocyte infiltration. Thus, PD-1 and CTLA4 play important roles in tumorigenesis and tumor immunity and can serve as prognostic biomarkers in different cancer types.

Immune system‑associated genes increase malignant progression and can be used to predict clinical outcome in patients with hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most malignant types of cancer, and is associated with high recurrence rates and a poor response to chemotherapy. Immune signatures in the microenvironment of HCC have not been well explored systematically. The aim of the present study was to identify prognostic immune signatures and build a nomogram for use in clinical evaluation. Using bioinformatics analysis, RNA‑seq data and overall survival (OS) information on 370 HCC cases from TCGA and 232 HCC cases from ICGC were analyzed. The differential expression of select immune genes, based on previously published studies, between HCC and adjacent tissue were analyzed using the limma package in R. Enrichment of pathways and gene ontology analysis was performed using clusterProfiler. Subsequently, univariate Cox regression analysis, Lasso penalty linear regression and multivariate Cox regression models were used to construct a model for immune risk score (IRS). The R packages, survival and survivalROC, were used to plot survival and the associated receiver operating characteristic curves. Infiltration of immune cells was calculated using Tumor IMmune Estimation Resource, with significance examined using a Pearson's correlation test. P<0.05 was considered significant. Based on the analysis, expression of 200 immune genes were upregulated and 47 immune genes were downregulated immune genes. In the multivariate Cox model, 5 genes (enhancer of zest homology 2, ferritin light chain, complement factor H related 3, isthmin 2, cyclin dependent kinase 5) were used to generate the IRS. By stratifying according to the median IRS, it was shown that patients with a high IRS had poor OS rates after 1, 2, 3 and 5 years, and this result was consistent across the testing, training and independent validation cohorts. Additionally, the IRS was correlated with the abundance of infiltrating immune cells. The nomogram built using IRS and clinical characteristics, was able to predict 1, 3 and 5 year OS with area under the curve values of >0.8. These results suggest that the model developed to calculate the IRS may be used to monitor the effectiveness of treatment strategies and for prognostic prediction.

Profiles of m6A RNA methylation regulators for the prognosis of hepatocellular carcinoma

N6-methyladenosine (m⁶A) RNA methylation, which is related to cancer initiation and progression, is dynamically regulated by the m⁶A RNA methylation regulators (including ‘writers’, ‘erasers’ and ‘readers’). However, the prognostic value of m⁶A RNA methylation regulators involved in hepatocellular carcinoma (HCC) carcinogenesis and progression remains to be elucidated. The aim of the present study was to determine the prognostic score in predicting the prognosis of HCC patients based on these regulators. In The Cancer Genome Atlas, most of the 13 major m⁶A RNA methylation regulators were found to be differentially expressed between HCC and normal samples (P<0.001). In addition, two subgroups (clusters 1/2) had also been identified by applying consensus clustering in the m⁶A RNA methylation regulators. As compared with the cluster 1 subgroup, the cluster 2 subgroup was correlated with a poorer prognosis, as shown by the Kaplan-Meier method (P=6.197e-4). A risk signature was constructed based on these findings using six m⁶A RNA methylation regulators, which could not only predict the clinicopathological features of HCCs, but also serve as an independent prognostic marker, as shown by Cox regression analysis (hazard ratio=1.219, 95% confidence interval: 1.143–1.299; P<0.001). Data from the International Cancer Genome Consortium were used for external validation. In addition, gene set enrichment analysis identified several pathways that m⁶A RNA methylation regulators were closely associated with. In conclusion, the m⁶A RNA methylation regulators are the crucial participants in the malignant progression of HCCs, which are potentially useful for prognosis stratification and therapeutic strategy development for HCC.