m(6)A-Related Angiogenic Genes to Construct Prognostic Signature, Reveal Immune and Oxidative Stress Landscape, and Screen Drugs in Hepatocellular Carcinoma.

m6A modification plays a key role in the development of hepatocellular carcinoma (HCC). Angiogenesis-related genes (ARGs) are increasingly being used to define signatures predicting patient prognosis. The correlations between m6A-related ARGs (mARGs), clinical outcomes, and the immune and oxidative stress landscape are unclear. Univariate Cox regression analysis of 24 mARGs yielded 13 prognostic genes, which were then analyzed for their enriched functions and pathways. After LASSO regression analysis, a prognostic signature was constructed and its reliability validated. Patients were grouped by risk using the signature score, and then the clinical prognosis, the immune landscape, and the oxidative stress landscape between the two groups were analyzed. Drug sensitivity analysis was performed to identify potentially efficient therapeutic agents. Thirteen prognosis-related mARGs consistently clustered patients with HCC into four groups with significantly different prognosis. Four mARGs (EGF, ITGA5, ITGAV, and PLG) were used to construct a prognostic signature and define risk groups. Among them, EGF, ITGA5, and ITGAV, were defined as prognostic risk factors, while PLG was defined as a prognostic protective factor. Compared to low-risk patients, HCC patients in the high-risk group had a poorer prognosis and showed significant differences in clinical characteristics, enriched pathways, tumor stemness, and tumor microenvironment. The drug sensitivity of oxaliplatin and LDK-378 negatively correlated with ITGAV expression. Ten drugs had lower IC50s in the high-risk group, indicating better antitumor efficacy than in the low-risk group, with epothilone B having the lowest IC50 value. A prognostic model consisting of mARGs can be used to predict the prognosis of HCC patients. The risk grouping of our model can be used to reveal differences in the tumor immune microenvironment of patients with HCC. Further in-depth study may provide new targets for future treatment.

Qu X ，Zhang L ，Li S ，Li T ，Zhao X ，Wang N ，Shi Y ... -  《-》

M2-like tumor-associated macrophage-related biomarkers to construct a novel prognostic signature, reveal the immune landscape, and screen drugs in hepatocellular carcinoma.

M2-like tumor-associated macrophages (M2-like TAMs) have important roles in the progression and therapeutics of cancers. We aimed to detect novel M2-like TAM-related biomarkers in hepatocellular carcinoma (HCC) via integrative analysis of single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data to construct a novel prognostic signature, reveal the "immune landscape", and screen drugs in HCC. M2-like TAM-related genes were obtained by overlapping the marker genes of TAM identified from scRNA-seq data and M2 macrophage modular genes identified by weighted gene co-expression network analysis (WGCNA) using bulk RNA-seq data. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were carried out to screen prognostic genes from M2-like TAM-related genes, followed by a construction of a prognostic signature, delineation of risk groups, and external validation of the prognostic signature. Analyses of immune cells, immune function, immune evasion scores, and immune-checkpoint genes between high- and low-risk groups were done to further reveal the immune landscape of HCC patients. To screen potential HCC therapeutic agents, analyses of gene-drug correlation and sensitivity to anti-cancer drugs were conducted. A total of 127 M2-like TAM-related genes were identified by integrative analysis of scRNA-seq and bulk-seq data. PDLIM3, PAM, PDLIM7, FSCN1, DPYSL2, ARID5B, LGALS3, and KLF2 were screened as prognostic genes in HCC by univariate Cox regression and LASSO regression analyses. Then, a prognostic signature was constructed and validated based on those genes for predicting the survival of HCC patients. In terms of drug screening, expression of PAM and LGALS3 was correlated positively with sensitivity to simvastatin and ARRY-162, respectively. Based on risk grouping, we predicted 10 anticancer drugs with high sensitivity in the high-risk group, with epothilone B having the lowest half-maximal inhibitory concentration among all drugs tested. Our findings enhance understanding of the M2-like TAM-related molecular mechanisms involved in HCC, reveal the immune landscape of HCC, and provide potential targets for HCC treatment.

Qu X ，Zhao X ，Lin K ，Wang N ，Li X ，Li S ，Zhang L ，Shi Y ... -  《Frontiers in Immunology》

Signature construction and molecular subtype identification based on cuproptosis-related genes to predict the prognosis and immune activity of patients with hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignancies in the world, with high incidence, high malignancy, and low survival rate. Cuproptosis is a novel form of cell death mediated by lipoylated TCA cycle proteins-mediated novel cell death pathway and is highly associated with mitochondrial metabolism. However, the relationship between the expression level of cuproptosis-related genes (CRGs) and the prognosis of HCC is still unclear. Combining the HCC transcriptomic data from The Cancer Genome Atlas(TCGA) and Gene Expression Omnibus (GEO) databases, we identified the differentially expressed cuproptosis-related genes (DECRGs) and obtained the prognosis-related DECRGs through univariate regression analysis.LASSO and multivariate COX regression analyses of these DECRGs yielded four genes that were used to construct the signature. Next, we use ROC curves to evaluate the performance of signatures. The tumor microenvironment, immune infiltration, tumor mutation load, half-maximum suppression concentration, and immunotherapy effects were also compared between the low-risk and high-risk groups. Finally, we analyzed the expression level, prognosis, and immune infiltration correlation on the four genes that constructed the model. Four DECRGs s were used to construct the signature. The ROC curves indicated that signature can better assess the prognosis of HCC patients. Patients were grouped according to the signature risk score. Patients in the low-risk group had a significantly longer survival time than those in the high-risk group. Furthermore, the tumor mutation burden (TMB) values were associated with the risk score and the higher-risk group had a higher proportion of TP53 mutations than the low-risk group.ESTIMATE analysis showed significant differences in stromal scores between the two groups.N6-methyladenosine (m6A) and multiple immune checkpoints were expressed at higher levels in the high-risk group. Then, we found that signature score correlated with chemotherapeutic drug sensitivity and immunotherapy efficacy in HCC patients. Finally, we further confirmed that the four DECRGs genes were associated with the prognosis of HCC through external validation. We studied from the cuproptosis perspective and developed a new prognostic feature to predict the prognosis of HCC patients. This signature with good performance will help physicians to evaluate the overall prognosis of patients and may provide new ideas for clinical decision-making and treatment strategies.

Peng X ，Zhu J ，Liu S ，Luo C ，Wu X ，Liu Z ，Li Y ，Yuan R ... -  《Frontiers in Immunology》

Construction of a risk score prognosis model based on hepatocellular carcinoma microenvironment.

Hepatocellular carcinoma (HCC) is a common cancer with a poor prognosis. Previous studies revealed that the tumor microenvironment (TME) plays an important role in HCC progression, recurrence, and metastasis, leading to poor prognosis. However, the effects of genes involved in TME on the prognosis of HCC patients remain unclear. Here, we investigated the HCC microenvironment to identify prognostic genes for HCC. To identify a robust gene signature associated with the HCC microenvironment to improve prognosis prediction of HCC. We computed the immune/stromal scores of HCC patients obtained from The Cancer Genome Atlas based on the ESTIMATE algorithm. Additionally, a risk score model was established based on Differentially Expressed Genes (DEGs) between high- and low-immune/stromal score patients. The risk score model consisting of eight genes was constructed and validated in the HCC patients. The patients were divided into high- or low-risk groups. The genes (Disabled homolog 2, Musculin, C-X-C motif chemokine ligand 8, Galectin 3, B-cell-activating transcription factor, Killer cell lectin like receptor B1, Endoglin and adenomatosis polyposis coli tumor suppressor) involved in our risk score model were considered to be potential immunotherapy targets, and they may provide better performance in combination. Functional enrichment analysis showed that the immune response and T cell receptor signaling pathway represented the major function and pathway, respectively, related to the immune-related genes in the DEGs between high- and low-risk groups. The receiver operating characteristic (ROC) curve analysis confirmed the good potency of the risk score prognostic model. Moreover, we validated the risk score model using the International Cancer Genome Consortium and the Gene Expression Omnibus database. A nomogram was established to predict the overall survival of HCC patients. The risk score model and the nomogram will benefit HCC patients through personalized immunotherapy.

Zhang FP ，Huang YP ，Luo WX ，Deng WY ，Liu CQ ，Xu LB ，Liu C ... -  《-》

A novel stemness-hypoxia-related signature for prognostic stratification and immunotherapy response in hepatocellular carcinoma.

The specific differentiation potential, unlimited proliferation, and self-renewal capacity of cancer stem cells (CSCs) are closely related to the occurrence, recurrence, and drug resistance of hepatocellular carcinoma (HCC), as well as hypoxia. Therefore, an in-depth analysis of the relationship between HCC stemness, oxygenation status, and the effectiveness of immunotherapy is necessary to improve the poor prognosis of HCC patients. The weighted gene co-expression network analysis (WGCNA) was utilized to find hypoxia-related genes, and the stemness index (mRNAsi) was evaluated using the one-class logistic regression (OCLR) technique. Based on stemness-hypoxia-related genes (SHRGs), population subgroup categorization using NMF cluster analysis was carried out. The relationship between SHRGs and survival outcomes was determined using univariate Cox regression. The LASSO-Cox regression strategy was performed to investigate the quality and establish the classifier associated with prognosis. The main effect of risk scores on the tumor microenvironment (TME) and its response to immune checkpoint drugs was also examined. Finally, qRT-PCR was performed to explore the expression and prognostic value of the signature in clinical samples. After identifying tumor stemness- and hypoxia-related genes through a series of bioinformatics analyses, we constructed a prognostic stratification model based on these SHRGs, which can be effectively applied to the prognostic classification of HCC patients and the prediction of immune checkpoint inhibitors (ICIs) efficacy. Independent validation of the model in the ICGC cohort yielded good results. In addition, we also constructed hypoxic cell models in Herp3B and Huh7 cells to verify the expression of genes in the prognostic model and found that C7, CLEC1B, and CXCL6 were not only related to the tumor stemness but also related to hypoxia. Finally, we found that the constructed signature had a good prognostic value in the clinical sample. We constructed and validated a stemness-hypoxia-related prognostic signature that can be used to predict the efficacy of ICIs therapy. We also verified that C7, CLEC1B, and CXCL6 are indeed associated with stemness and hypoxia through a hypoxic cell model, which may provide new ideas for individualized immunotherapy.

Zhang G ，Zhang K ，Zhao Y ，Yang Q ，Lv X ... -  《BMC CANCER》