Identification of chromatin organization-related gene signature for hepatocellular carcinoma prognosis and predicting immunotherapy response

Identification of hub genes within the CCL18 signaling pathway in hepatocellular carcinoma through bioinformatics analysis

Introduction

Hepatocellular carcinoma (HCC) is an aggressive malignancy, and CCL18, a marker of M2 macrophage activation, is often associated with tumor immune suppression. However, the role of CCL18 and its signaling pathway in HCC is still limited. Our study focuses on investigating the prognostic impact of CCL18 and its signaling pathway in HCC patients and biological functions in vitro.

Methods

HCC-related RNA-seq data were obtained from TCGA, ICGC, and GEO. The 6 hub genes with the highest correlation to prognosis were identified using univariate Cox and LASSO regression analysis. Multivariate Cox regression analysis was performed to assess their independent prognostic potential and a nomogram was constructed. In vitro experiments, including CCK8, EdU, RT-qPCR, western blot, and transwell assays, were conducted to investigate the biological effects of exogenous CCL18 and 6 hub genes. A core network of highly expressed proteins in the high-risk group of tumors was constructed. Immune cell infiltration was evaluated using the ESTIMATE and CIBERSORT packages. Finally, potential treatments were explored using the OncoPredict package and CAMP database.

Results

We identified 6 survival-related genes (BMI1, CCR3, CDC25C, CFL1, LDHA, RAC1) within the CCL18 signaling pathway in HCC patients. A nomogram was constructed using the TCGA_LIHC cohort to predict patient survival probability. Exogenous CCL18, as well as overexpression of BMI1, CCR3, CDC25C, CFL1, LDHA, and RAC1, can promote proliferation, migration, invasion, stemness, and increased expression of PD-L1 protein in LM3 and MHCC-97H cell lines. In the high-risk group of patients from the TCGA_LIHC cohort, immune suppression was observed, with a strong correlation to 21 immune-related genes and suppressive immune cells.

Conclusion

Exogenous CCL18 promotes LM3 and MHCC-97H cells proliferation, migration, invasion, stemness, and immune evasion. The high expression of BMI1, CCR3, CDC25C, CFL1, LDHA, and RAC1 can serve as a biomarkers for immune evasion in HCC.

Development of a novel lncRNA-derived immune gene score using machine learning-based ensembles for predicting the survival of HCC

BACKGROUND

Long noncoding RNAs (lncRNAs) are implicated in the tumor immunology of hepatocellular carcinoma (HCC).

METHODS

HCC mRNA and lncRNA expression profiles were used to extract immune-related genes with the ImmPort database, and immune-related lncRNAs with the ImmLnc algorithm. The MOVICS package was used to cluster immune-related mRNA, immune-related lncRNA, gene mutation and methylation data on HCC from the TCGA. GEO and ICGC datasets were used to validate the model. Data from single-cell sequencing was used to determine the expression of genes from the model in various immune cell types.

RESULTS

With this model, the area under the curve (AUC) for 1-, 3- and 5-year survival of HCC patients was 0.862, 0.869 and 0.912, respectively. Single-cell sequencing showed EREG was significantly expressed in a variety of immune cell types. Knockdown of the EREG target gene resulted in significant anti-apoptosis, pro-proliferation and pro-migration effects in HepG2 and HUH7 cells. Moreover, serum and liver tissue EREG levels in HCC patients were significantly higher than those of healthy control patients.

CONCLUSION

We built a prognostic model with good accuracy for predicting HCC patient survival. EREG is a potential immunotherapeutic target and a promising prognostic biomarker.

A robust six-gene prognostic signature based on two prognostic subtypes constructed by chromatin regulators is correlated with immunological features and therapeutic response in lung adenocarcinoma

Accumulating evidence has demonstrated that chromatin regulators (CRs) regulate immune cell infiltration and are correlated with prognoses of patients in some cancers. However, the immunological and prognostic roles of CRs in lung adenocarcinoma (LUAD) are still unclear. Here, we systematically revealed the correlations of CRs with immunological features and the survival in LUAD patients based on a cohort of gene expression datasets from the public TCGA and GEO databases and real RNA-seq data by an integrative analysis using a comprehensive bioinformatics method. Totals of 160 differentially expressed CRs (DECRs) were identified between LUAD and normal lung tissues, and two molecular prognostic subtypes (MPSs) were constructed and evaluated based on 27 prognostic DECRs using five independent datasets (p =0.016, <0.0001, =0.008, =0.00038 and =0.00055, respectively). Six differentially expressed genes (DEGs) (CENPK, ANGPTL4, CCL20, CPS1, GJB3, TPSB2) between two MPSs had the most important prognostic feature and a six-gene prognostic model was established. LUAD patients in the low-risk subgroup showed a higher overall survival (OS) rate than those in the high-risk subgroup in nine independent datasets (p <0.0001, =0.021, =0.016, =0.0099, <0.0001, =0.0045, <0.0001, =0.0038 and =0.00013, respectively). Six-gene prognostic signature had the highest concordance index of 0.673 compared with 19 reported prognostic signatures. The risk score was significantly correlated with immunological features and activities of oncogenic signaling pathways. LUAD patients in the low-risk subgroup benefited more from immunotherapy and were less sensitive to conventional chemotherapy agents. This study provides novel insights into the prognostic and immunological roles of CRs in LUAD.

Seven chromatin regulators as immune cell infiltration characteristics, potential diagnostic biomarkers and drugs prediction in hepatocellular carcinoma

Treatment is challenging due to the heterogeneity of hepatocellular carcinoma (HCC). Chromatin regulators (CRs) are important in epigenetics and are closely associated with HCC. We obtained HCC-related expression data and relevant clinical data from The Cancer Genome Atlas (TCGA) databases. Then, we crossed the differentially expressed genes (DEGs), immune-related genes and CRs to obtain immune-related chromatin regulators differentially expressed genes (IRCR DEGs). Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to select the prognostic gene and construct a risk model for predicting prognosis in HCC, followed by a correlation analysis of risk scores with clinical characteristics. Finally, we also carried out immune microenvironment analysis and drug sensitivity analysis, the correlation between risk score and clinical characteristics was analyzed. In addition, we carried out immune microenvironment analysis and drug sensitivity analysis. Functional analysis suggested that IRCR DEGs was mainly enriched in chromatin-related biological processes. We identified and validated PPARGC1A, DUSP1, APOBEC3A, AIRE, HDAC11, HMGB2 and APOBEC3B as prognostic biomarkers for the risk model construction. The model was also related to immune cell infiltration, and the expression of CD48, CTLA4, HHLA2, TNFSF9 and TNFSF15 was higher in high-risk group. HCC patients in the high-risk group were more sensitive to Axitinib, Docetaxel, Erlotinib, and Metformin. In this study, we construct a prognostic model of immune-associated chromatin regulators, which provides new ideas and research directions for the accurate treatment of HCC.

An immune and epigenetics-related scoring model and drug candidate prediction for hepatic carcinogenesis via dynamic network biomarker analysis and connectivity mapping

Hepatocellular carcinoma (HCC) is a malignant tumor with high mortality. This study aimed to build a prognostic signature for HCC patients based on immune-related genes (IRGs) and epigenetics-related genes (EPGs). RNA-seq data from Gene Expression Omnibus were used for dynamic network biomarker (DNB) analysis to identify 56 candidate IRG-EPG-DNBs and their first-neighbor genes. These genes were screened using LASSO-Cox regression analysis to finally obtain five candidate genes-RNF2, YBX1, EZH2, CAD, and PSMD1-which constituted the prognostic signature panel. According to this panel, patients in The Cancer Genome Atlas and International Cancer Genome Consortium were divided into high- and low-risk groups. The prognosis, clinicopathological features, and immune cell infiltration significantly differed between the two risk groups. The prognostic ability of the signature panel and expression profiling were further validated using online databases. We used an independent cohort of patients to validate the expression profiles of the five genes using reverse transcription-PCR. CMap and CellMiner predicted four small molecule drug-protein pairs based on the five prognostic genes. Of them, two market drugs approved by the Food and Drug Administration (AT-13387 and KU-55933) have emerged as candidates for HCC study. This new signature panel may serve as a potential prognostic marker, engendering the possibility of novel personalized therapy with classification of HCC patients.

The Heterogeneity of Tumour-Associated Macrophages Contributes to the Recurrence and Outcomes of Glioblastoma Patients

Cellular heterogeneity and immune cell molecular phenotypes may be involved in the malignant progression of glioblastoma (GBM). In this study, we aimed to know whether the heterogeneity of tumour-associated macrophages contributes to the recurrence and outcomes of glioblastoma patients. Single-cell RNA sequencing (scRNA-Seq) data were used to assess the heterogeneity of CD45 + immune cells in recurrent GBM and analyse differentially expressed genes (DEGs) in master cells. Then, a prognostic signature based on the identified DEGs was established and validated, the correlation between risk score and tumour microenvironment (TME) was explored. The correlation between immune infiltration and LGMN, an important DEG in GBM tumour-associated macrophages (TAMs) was illuminated, using integrated bioinformatics analyses. Finally, immunohistochemistry and multiplex immunohistochemistry (mIHC) were used to analyse the expression of LGMN in GBM tissues from our hospital. scRNA-Seq analysis showed that the heterogeneity of recurrent GBM mainly comes from TAMs, which can be divided into 8 cell subclusters. Among these subclusters, TAM1 (markers: CXCL10, ADORA3), TAM3 (markers: MRC1, CFP), TAM4 (markers: GPNMB, PLTP), and TAM5 (markers: CCL4, IRAK2) were specifically present in recurrent GBM. After 342 DEGs in TAMs were identified, a prognostic signature was established based on 13 TAM-associated DEGs, and this signature could serve as an excellent prognostic predictor for patients with GBM. LGMN, one of 13 TAM-associated DEGs, was an important gene in lysosome pathway, we found that macrophage infiltration levels were higher after LGMN upregulation. GBM tissues from our hospital were collected for histopathologic validation, then LGMN was co-expressed with CD68, which is associated with the immune regulation of GBM. In conclusion, cell heterogeneity of TAMs is important for recurrent GBM, a prognostic signature based on 13 TAM-related DEGs can predict the survival outcome of GBM patients. An important DEG, LGMN may regulate the immune cell infiltration of GBM.

Four-gene signature predicting overall survival and immune infiltration in hepatocellular carcinoma by bioinformatics analysis with RT‒qPCR validation

Background

Hepatocellular carcinoma (HCC) is one of the most lethal cancers, with a poor prognosis. Prognostic biomarkers for HCC patients are urgently needed. We aimed to establish a nomogram prediction system that combines a gene signature to predict HCC prognosis.

Methods

Differentially expressed genes (DEGs) were identified from publicly available Gene Expression Omnibus (GEO) datasets. The Cancer Genome Atlas (TCGA) cohort and International Cancer Genomics Consortium (ICGC) cohort were regarded as the training cohort and testing cohort, respectively. First, univariate and multivariate Cox analyses and least absolute shrinkage and selection operator (LASSO) regression Cox analysis were performed to construct a predictive risk score signature. Furthermore, a nomogram system containing a risk score and other prognostic factors was developed. In addition, a correlation analysis of risk group and immune infiltration was performed. Finally, we validated the expression levels using real-time PCR.

Results

Ninety-five overlapping DEGs were identified from four GEO datasets, and we constructed a four-gene-based risk score predictive model (risk score = EZH2 * 0.075 + FLVCR1 * 0.086 + PTTG1 * 0.015 + TRIP13 * 0.020). Moreover, this signature was an independent prognostic factor. Next, the nomogram system containing risk score, sex and TNM stage indicated better predictive performance than independent prognostic factors alone. Moreover, this signature was significantly associated with immune cells, such as regulatory T cells, resting NK cells and M2 macrophages. Finally, RT‒PCR confirmed that the mRNA expressions of four genes were upregulated in most HCC cell lines.

Conclusion

We developed and validated a nomogram system containing the four-gene risk score, sex, and TNM stage to predict prognosis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09934-1.