Identification and Validation of Novel Immunogenic Cell Death- and DNA Damage Response-Related Molecular Patterns Correlated with Immune Status and Prognosis in Hepatocellular Carcinoma

Immunogenic cell death (ICD) and DNA damage response (DDR) are involved in cancer progression and prognosis. Currently, chemotherapy is the first-line treatment for intermediate or advanced hepatocellular carcinoma (HCC), which is mostly based on platinum and anthracyclines that induce DNA damage and ICD. With the treatment of HCC with immune checkpoint inhibitors (ICIs), it is important to understand the molecular characteristics and prognostic values of ICD and DDR-related genes (IDRGs). We aimed to explore the characteristics of ICD and DDR-related molecular patterns, immune status, and the association of immunotherapy and prognosis with IDRGs in HCC. We identified IDRGs in HCC and evaluated their differential expression, biological behaviors, molecular characteristics, immune cell infiltration, and prognostic value. Prognostic IDRGs and subtypes were identified and validated. FFAR3, DDX1, POLR3G, FANCL, ADA, PI3KR1, DHX58, TPT1, MGMT, SLAMF6, and EIF2AK4 were determined as risk factors for HCC, and the biological experiments indicated that high FANCL expression is harmful to the treatment and prognosis. HCC was classified into high- and low-risk groups based on the median values of the risk factors to construct a predictive nomogram. These findings provide novel insights into the treatment and prognosis of HCC and provide a new research direction for HCC.     

Development and validation of LRP1B mutation-associated prognostic model for hepatocellular carcinoma

Purpose: To develop a lipoprotein receptor-related protein 1B (LRP1B) gene mutation-based prognostic model for hepatocellular carcinoma (HCC) patients risk prediction. Methods: The LRP1B gene mutation rate was calculated from HCC patient samples. Meanwhile, differentially expressed genes according to LRP1B mutant were screened out for prognostic model establishment. Based on this innovative model, HCC patients were categorized into high- and low-risk groups. The immune status including immune cell infiltration ratio and checkpoints have been explored in two groups. The functions of LRP1B and risk factors in the model were verified using both in vivo and in vitro experiments. Results: It could be demonstrated that LRP1B was a potential negative predictor for HCC patients prognosis with high mutation frequency. The functions of LRP1B were verified with ELISA and Quantitative Real-time PCR method based on clinic-recruited HCC participants. Eleven genes displayed significant differences according to LRP1B status, which could better predict HCC patient prognosis. The functions of these genes were examined using HCC cell line HCCLM3, suggesting they played a pivotal role in determining HCC cell proliferation and apoptosis. From the immune cell infiltration ratio analysis, there was a significant difference in the infiltration degree of seven types of immune cells and two immune checkpoints between high- and low-risk HCC patients. Conclusion: The present study hypothesized a potential prognostic biomarker and developed a novel LRP1B mutation-associated prognostic model for HCC, which provided a systematic reference for future understanding of clinical research.     

多基因模型在肝细胞癌预后中的应用

利用较少分子信息预测肝细胞癌类型对患者的个性化治疗十分关键。探索已知的与肝细胞癌预后相关的信号通路,共发现41个关键基因。随后,运用机器学习的方法对其构建风险预测模型,并在4个肝细胞癌数据集上进行验证。结果显示,该模型能将肝细胞癌患者分成两个预后差异显著的类型:癌症基因图谱(The cancer genome atlas,TCGA)数据集交叉验证的平均log rank P值为0.03;其他测试数据集的log rank P 值分别为0.000 38、0.002 1和0.01。生物信息学分析显示肝细胞癌的预后与细胞周期等信号通路显著相关,并筛选出12个潜在的肝细胞癌分子标志物。研究结果表明,基于41个基因构建的肝细胞癌预后模型具有较好的稳健性和准确的风险预测能力。     

基于SNP共表达网络肝癌分子分型及预后分析

基于SNP突变数据与mRNA表达谱关联分析,构建一种肝癌分子分型方法并对比不同分型预后的差异,并对不同分型肝癌的发生发展机制进一步研究。首先通过TCGA数据库收集359例肝细胞癌患者的SNP突变数据和mRNA表达数据,采用Wilcoxon秩和检验,筛选突变后差异表达基因,并通过生物信息学工具String和Cytoscape构建差异表达基因的蛋白互作网络,筛选连接度最高的10个Hub基因。利用Consensus Cluster Plus软件包,基于Hub基因mRNA表达水平构建NMF分子分型模型,再结合生存数据评估各分型患者的预后。最后利用加权基因共表达网络分析(WGCNA),识别与肝癌分子分型相关的模块,并针对关键模块的基因进行通路富集,从而对不同分型肝癌的基因表达谱进行比较。结果：NMF模型将肝癌分为高危、低危2个分型,其中CDKN2A和FOXO1基因对分型贡献度高。生存分析显示低危组患者的生存情况显著优于高危组,高危组富集多个与肿瘤细胞侵蚀、转移、复发过程相关的信号通路,低危组则与细胞周期和胰液分泌相关。本研究在无先验性信息的前提下,基于突变后显著差异表达的Hub基因表达水平构建的...     

Identification of a four-gene metabolic signature predicting overall survival for hepatocellular carcinoma

While hundreds of consistently altered metabolic genes had been identified in hepatocellular carcinoma (HCC), the prognostic role of them remains to be further elucidated. Messenger RNA expression profiles and clinicopathological data were downloaded from The Cancer Genome Atlas—Liver Hepatocellular Carcinoma and GSE14520 data set from the Gene Expression Omnibus database. Univariate Cox regression analysis and lasso Cox regression model established a novel four-gene metabolic signature (including acetyl-CoA acetyltransferase 1, glutamic-oxaloacetic transaminase 2, phosphatidylserine synthase 2, and uridine-cytidine kinase 2) for HCC prognosis prediction. Patients in the high-risk group shown significantly poorer survival than patients in the low-risk group. The signature was significantly correlated with other negative prognostic factors such as higher α-fetoprotein. The signature was found to be an independent prognostic factor for HCC survival. Nomogram including the signature shown some clinical net benefit for overall survival prediction. Furthermore, gene set enrichment analyses revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust four-gene metabolic signature for HCC prognosis prediction. The signature might reflect the dysregulated metabolic microenvironment and provided potential biomarkers for metabolic therapy and treatment response prediction in HCC.     

A novel DNA methylation-based model that effectively predicts prognosis in hepatocellular carcinoma

Purpose: To build a novel predictive model for hepatocellular carcinoma (HCC) patients based on DNA methylation data.Methods: Four independent DNA methylation datasets for HCC were used to screen for common differentially methylated genes (CDMGs). Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to explore the biological roles of CDMGs in HCC. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis were performed to identify survival-related CDMGs (SR-CDMGs) and to build a predictive model. The importance of this model was assessed using Cox regression analysis, propensity score-matched (PSM) analysis and stratification analysis. A validation group from the Cancer Genome Atlas (TCGA) was constructed to further validate the model.Results: Four SR-CDMGs were identified and used to build the predictive model. The risk score of this model was calculated as follows: risk score = (0.01489826 × methylation level of WDR69) + (0.15868618 × methylation level of HOXB4) + (0.16674959 × methylation level of CDKL2) + (0.16689301 × methylation level of HOXA10). Kaplan–Meier analysis demonstrated that patients in the low-risk group had a significantly longer overall survival (OS; log-rank P-value =0.00071). The Cox model multivariate analysis and PSM analysis identified the risk score as an independent prognostic factor (P<0.05). Stratified analysis results further confirmed this model performed well. By analyzing the validation group, the results of receiver operating characteristic (ROC) curve analysis and survival analysis further validated this model.Conclusion: Our DNA methylation-based prognosis predictive model is effective and reliable in predicting prognosis for patients with HCC.     

MiR-199a Regulates Cell Proliferation and Survival by Targeting FZD7

A growing amount of evidence indicates that miRNAs are important regulators of multiple cellular processes and, when expressed aberrantly in different types of cancer such as hepatocellular carcinoma (HCC), play significant roles in tumorigenesis and progression. Aberrant expression of miR-199a-5p (also called miR-199a) was found to contribute to carcinogenesis in different types of cancer, including HCC. However, the precise molecular mechanism is not yet fully understood. The present study showed that miR-199a is frequently down-regulated in HCC tissues and cells. Importantly, lower expression of miR-199a was significantly correlated with the malignant potential and poor prognosis of HCC, and restoration of miR-199a in HCC cells led to inhibition of the cell proliferation and cell cycle in vitro and in vivo. Furthermore, Frizzled type 7 receptor (FZD7), the most important Wnt receptor involved in cancer development and progression, was identified as a functional target of miR-199a. In addition, these findings were further strengthened by results showing that expression of FZD7 was inversely correlated with miR-199a in both HCC tissues and cells and that over-expression of miR-199a could significantly down-regulate the expression of genes downstream of FZD7, including β-catenin, Jun, Cyclin D1 and Myc. In conclusion, these findings not only help us to better elucidate the molecular mechanisms of hepatocarcinogenesis from a fresh perspective but also provide a new theoretical basis to further investigate miR-199a as a potential biomarker and a promising approach for HCC treatment.     

Identification of two molecular subtypes of dysregulated immune lncRNAs in ovarian cancer

Long non-coding RNA (lncRNA) has increasingly been identified as a key regulator in pathologies such as cancer. Multiple platforms were used for comprehensive analysis of ovarian cancer to identify molecular subgroups. However, lncRNA and its role in mapping the ovarian cancer subpopulation are still largely unknown. RNA-sequencing and clinical characteristics of ovarian cancer were acquired from The Cancer Genome Atlas database (TCGA). A total of 52 lncRNAs were identified as aberrant immune lncRNAs specific to ovarian cancer. We redefined two different molecular subtypes, C1(188) and C2(184 samples), in “iClusterPlus” R package, among which C2 grouped ovarian cancer samples have higher survival probability and longer median survival time (P <0.05) with activated IFN-gamma response, Wound Healing and Cytotoxic lymphocytes signal; 456 differentially expressed genes were acquired in C1 and C2 subtypes using limma (3.40.6) package, among which 419 were up-regulated and 37 were down-regulated, in TCGA dataset. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis revealed that these genes were actively involved in ECM-receptor interaction, PI3K-Akt signaling pathway interaction KEGG pathway. Compared with the existing immune subtype, the Cluster2 sample showed a substantial increase in the proportion of the existing C2 immune subtype, accounting for 81.37%, which was associated with good prognosis. Our C1 subtype contains only 56.49% of the existing immune C1 and C4, which also explains the poor prognosis of C1. Furthermore, 52 immune-related lncRNAs were used to divide the TCGA-endometrial cancer and cervical cancer samples into two categories, and C2 had a good prognosis. The differentially expressed genes were highly correlated with immune-cell-related pathways. Based on lncRNA, two molecular subtypes of ovarian cancer were identified and had significant prognostic differences and immunological characteristics.     

GABA(A) Receptor Pi (GABRP) Stimulates Basal-like Breast Cancer Cell Migration through Activation of Extracellular-regulated Kinase 1/2 (ERK1/2)

Breast cancer is a heterogeneous disease comprised of distinct subtypes predictive of patient outcome. Tumors of the basal-like subtype have a poor prognosis due to inherent aggressiveness and the lack of targeted therapeutics. Basal-like tumors typically lack estrogen receptor-α, progesterone receptor and HER2/ERBB2, or in other words they are triple negative (TN). Continued evaluation of basal-like breast cancer (BLBC) biology is essential to identify novel therapeutic targets. Expression of the pi subunit of the GABA(A) receptor (GABRP) is associated with the BLBC/TN subtype, and herein, we reveal its expression also correlates with metastases to the brain and poorer patient outcome. GABRP expression in breast cancer cell lines also demonstrates a significant correlation with the basal-like subtype suggesting that GABRP functions in the initiation and/or progression of basal-like tumors. To address this postulate, we stably silenced GABRP in two BLBC cell lines, HCC1187 and HCC70 cells. Decreased GABRP reduces in vitro tumorigenic potential and migration concurrent with alterations in the cytoskeleton, specifically diminished cellular protrusions and expression of the BLBC-associated cytokeratins, KRT5, KRT6B, KRT14, and KRT17. Silencing GABRP also decreases phosphorylation of extracellular regulated kinase 1/2 (ERK1/2) in both cell lines and selective inhibition of ERK1/2 similarly decreases the basal-like cytokeratins as well as migration. Combined, these data reveal a GABRP-ERK1/2-cytokeratin axis that maintains the migratory phenotype of basal-like breast cancer. GABRP is a component of a cell surface receptor, thus, these findings suggest that targeting this new signaling axis may have therapeutic potential in BLBC.     

INK4 cyclin-dependent kinase inhibitors as potential prognostic biomarkers and therapeutic targets in hepatocellular carcinoma

The INK4 family is an important family of cyclin-dependent kinase inhibitors (CDKIs) and consists of CDKN2A, CDKN2B, CDKN2, and CDKN2D. Abnormal expression of CDKN2A has been reported in hepatocellular carcinoma (HCC) and is associated with the prognosis of patients and infiltration of immune cells. However, there is a lack of systematic research on the roles of the other INK4 family members in the diagnosis, prognosis, and immune regulation of HCC. Using online public databases and clinical samples, we comprehensively analyzed the INK4 family in HCC. All four INK4 proteins were overexpressed in HCC and correlated with advanced cancer stage and poor prognosis. INK4 expression accurately distinguished tumor from normal tissue, particularly CDKN2A and CDKN2C. The INK4 family participated in cell-cycle regulation and the DNA damage repair pathway, which inhibited genotoxic-induced apoptosis in tumorigenesis. INK4 proteins were positively correlated with the infiltration of immune cells (B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells) and immune checkpoints (CTLA-4, PD1, and PD-L1). CDKN2D had the highest correlation (correlation coefficient >0.3) with all the above-mentioned infiltrating immune cells and immune checkpoints, indicating that it may be useful as an immunotherapy target. The INK4 family was valuable for diagnosis and predicting the prognosis of HCC and participated in the occurrence, progression, and immune regulation of HCC, demonstrating its potential as a diagnostic and prognostic biomarker and therapeutic target in HCC.     

SIRT1与肝细胞癌

肝细胞癌(hepatocellular carcinoma, HCC)是世界上最常见的癌症之一.然而,就目前现状而言,HCC的治疗效果还很有限.作为一个依赖于烟酰胺腺嘌呤二核苷酸(NAD+)的去乙酰化酶, SIRT1(silent mating type information regulation 2 homolog 1 )参与了代谢、应激反应、衰老以及肿瘤的演进等许多重要的生物学进程.临床研究显示,SIRT1在HCC患者中异常高表达,并可预测其不良预后;进一步的研究表明,SIRT1在HCC演进中发挥了关键作用,且作用范围广泛,分子机制复杂.这提示,SIRT1有望成为新的HCC治疗靶点和诊断、预后标志物.本文拟对SIRT1在HCC的演进和预后中的具体作用及其潜在分子机制作一总结,并就SIRT1作为肝癌治疗靶点和诊断、预后标志物的可行性做出探讨.     

Identification of an m6A-related lncRNA prognostic signature in colorectal cancer

Data sets of colorectal cancer (CRC) were obtained from The Cancer Genome Atlas (TCGA), three N6-methyladenosine (m6A) subtypes were identified using 21 m6A-related long noncoding RNAs (lncRNAs) and differential m6A subtypes of different CRC tumors were determined in this study to evaluate the m6A expression and the prognosis of patients with CRC. Subsequently, eight key lncRNAs were identified based on co-expression with 21 m6A-related genes in CRC tumors using the single-factor Cox and least absolute shrinkage and selection operator. Finally, an m6A-related lncRNA risk score model of CRC tumor was established using multifactor Cox regression based on the eight important lncRNAs and found to have a better performance in evaluating the prognosis of patients in the TCGA-CRC data set. TCGA-CRC tumor samples were divided based on the risk scores: high and low. Then, the clinical characteristics, tumor mutation load, and tumor immune cell infiltration difference between the high- and low-risk-score groups were explored, and the predictive ability of the risk score was assessed for immunotherapeutic benefits. We found that the risk score model can determine the overall survival, be a relatively independent prognostic indicator, and better evaluate the immunotherapeutic benefits for patients with CRC. This study provides data support for accurate immunotherapy in CRC.     

Gene expression profile identifies distinct molecular subtypes and potential therapeutic genes in Merkel cell carcinoma

Merkel cell carcinoma (MCC) is a rare primary cutaneous neoplasm of neuroendocrine carcinoma of the skin. About 80% of the MCC occurs due to Merkel cell polyomavirus (MCPyV) and 20% of the tumors usually occur due to severe UV exposure which is a more aggressive type of MCC. It tends to have an increased incidence rate among elderly and immunosuppressed individuals. On therapeutic level, sub-classification of MCC through molecular subtyping has emerged as a promising technique for MCC prognosis. In current study, two consistent distinct molecular subtypes of MCCs were identified using gene expression profiling data. Subtypes I MCCs were associated with spliceosome, DNA replication and cellular pathways. On the other hand, genes overexpressed in subtype II were found active in TNF signalling pathway and MAPK signalling pathway. We proposed different therapeutic targets based on subtype specificity, such as PTCH1, CDKN2A, AURKA in case of subtype I and MCL1, FGFR2 for subtype II. Such findings may provide fruitful knowledge to understand the intrinsic subtypes of MCCs and the pathways involved in distinct subtype oncogenesis, and will further advance the knowledge in developing a specific therapeutic strategy for these MCC subtypes.     

人肝细胞癌预后不良相关基因的生物信息学分析及其临床意义

目的:筛选肝细胞癌(HCC)预后不良相关基因,并探讨其临床意义。方法:在基因表达综合数据库(GEO)中获取符合分析条件的肝细胞癌全基因组表达谱数据并分析得到差异表达基因(DEGs),再运用生物学信息注释及可视化数据库 (DAVID) 和蛋白相互作用数据库 (String) 分别进行功能富集分析和蛋白质互作用网络的构建。利用癌症基因组图谱数据库(TCGA)和Cox比例风险回归模型对相关差异基因进行预后分析。结果:找到一个符合条件的人类HCC数据库 (GSE84402),共筛选出1141个差异表达基因(DEGs),其中上调基因720个,下调基因421个。基因功能富集分析和蛋白质互作用分析结果显示CDK1、CDC6、CCNA2、CHEK1、CENPE 、PIK3R1、RACGAP1、BIRC5、KIF11和CYP2B6为HCC预后的关键基因。TCGA数据库和Cox回归模型分析显示CDC6、PIK3R1、RACGAP1和KIF11的表达升高,CENPE的表达降低与HCC预后不良密切相关。结论:CDC6、CENPE、PIK3R1、RACGAP1和KIF11可能和HCC的预后不良相关,可作为未来HCC预后研究的参考标志物。