Identification and validation of a novel nine-gene prognostic signature of stem cell characteristic in hepatocellular carcinoma.

Currently, cancer stem cells (CSCs) are regarded as the most promising target for cancer therapy due to their close association with tumor resistance, invasion, and recurrence. Thus, identifying CSCs-related genes and constructing a prognostic risk model associated with CSCs may be crucial for hepatocellular carcinoma (HCC) therapy. Xena Browser was used to download gene expression profiles and clinical data, while MSigDB was used to obtain genes associated with CSCs. Firstly, the non-negative matrix factorization (NMF) algorithm was used to cluster the HCC samples based on CSCs-related genes. To evaluate the predictive performance of the risk model, the receiver operating characteristic curves (ROC) and Kaplan-Meier analysis were used. The R package "rms" was used to construct the final nomogram based on risk scores and clinical characteristics. Based on 449 CSCs-related genes, a total of 588 HCC samples from TCGA-LIHC and ICGC-LIRI_JP were classified into four molecular subtypes with marked differences in survival and mRNA stemness index (mRNAsi) between subtypes. Univariate Cox regression, multivariate Cox regression, and LASSO regression analyses were performed on a total of 1417 differentially expressed genes (DEGs) between subtypes, and a nine-gene prognostic model was constructed with TTK, ST6GALNAC4, SPP1, SGCB, MEP1A, HTRA1, CD79A, C6, and ATP2A3. In both the training and testing sets and the external validation cohort, the risk model performed well in predicting HCC patients' survival. A nomogram was constructed and had high predictive efficacy in short-term survival. In comparison with the other two prognostic models, our nine-gene signature model performed best. We constructed a nine-gene signature model to predict the survival of HCC patients, which has good predictive efficacy and stability. The model may contribute to guiding the prognostic assessment of HCC patients in clinical practice.

An Y ，Liu W ，Yang Y ，Chu Z ，Sun J ... -  《-》

To construct and validate a risk score model of angiogenesis-related genes to predict the prognosis of hepatocellular carcinoma.

Gao D ，Lu Y ，Jiang T ，Duan Q ，Huang Z ... -  《Scientific Reports》

Establishment and Validation of the Novel Necroptosis-related Genes for Predicting Stemness and Immunity of Hepatocellular Carcinoma via Machine-learning Algorithm.

Necroptosis, a recently identified mechanism of programmed cell death, exerts significant influence on various aspects of cancer biology, including tumor cell proliferation, stemness, metastasis, and immunosuppression. However, the role of necroptosis-related genes (NRGs) in Hepatocellular Carcinoma (HCC) remains elusive. In this study, we assessed the mutation signature, copy number variation, and expression of 37 NRGs in HCC using the TCGA-LIHC dataset. We further validated our results using the ICGC-LIRI-JP dataset. To construct our prognostic model, we utilized the least absolute shrinkage and selection operator (LASSO), and evaluated the predictive efficacy of the NRGs-score using various machine learning algorithms, including K-M curves, time-ROC curves, univariate and multivariate Cox regression, and nomogram. In addition, we analyzed immune infiltration using the CIBERSOFT and ssGSEA algorithms, calculated the stemness index through the one-class logistic regression (OCLR) algorithm, and performed anti-cancer stem cells (CSCs) drug sensitivity analysis using oncoPredict. Finally, we validated the expression of the prognostic NRGs through qPCR both in vitro and in vivo. About 18 out of 37 NRGs were found to be differentially expressed in HCC and correlated with clinical outcomes. To construct a prognostic model, six signature genes (ALDH2, EZH2, PGAM5, PLK1, SQSTM1, and TARDBP) were selected using LASSO analysis. These genes were then employed to categorize HCC patients into two subgroups based on NRGs-score (low vs. high). A high NRGs score was associated with a worse prognosis. Furthermore, univariate and multivariate Cox regression analyses were performed to confirm the NRGs-score as an independent risk factor. These analyses revealed strong associations between NRGs-score and critical factors, such as AFP, disease stage, and tumor grade in the HCC cohort. NRGs-score effectively predicted the 1-, 3-, and 5-year survival of HCC patients. Immune infiltration analysis further revealed that the expression of immune checkpoint molecules was significantly enhanced in the high NRGs-score group. Stemness analysis in the HCC cohort showed that NRGs-score was positively correlated with mRNA stemness index, and patients with high NRGs-score were sensitive to CSCs inhibitors. The findings from the external validation cohort provided confirmation that the NRGs-score presented a trait with universal applicability in accurately predicting the survival of HCC. Additionally, the six prognostic genes were consistently differentially expressed in both the HCC cell line and the mouse HCC model. Our study demonstrated the pivotal role of NRGs in promoting stemness and immune suppression in HCC and established a robust model which could successfully predict HCC prognosis.

Li YT ，Zeng XZ 《-》

Analysis and Validation of Tyrosine Metabolism-related Prognostic Features for Liver Hepatocellular Carcinoma Therapy.

To explore tyrosine metabolism-related characteristics in liver hepatocellular carcinoma (LIHC) and to establish a risk signature for the prognostic prediction of LIHC. Novel prognostic signatures contribute to the mining of novel biomarkers, which are essential for the construction of a precision medicine system for LIHC and the improvement of survival. Tyrosine metabolism plays a critical role in the initiation and development of LIHC. Based on the tyrosine metabolism-related characteristics in LIHC, this study developed a risk signature to improve the prognostic prediction of patients with LIHC. To investigate the correlation between tyrosine metabolism and progression of LIHC and to develop a tyrosine metabolism-related prognostic model. Gene expression and clinicopathological information of LIHC were obtained from The Cancer Genome Atlas (TCGA) database. Distinct subtypes of LIHC were classified by performing consensus cluster analysis on the tyrosine metabolism-related genes. Univariate and Lasso Cox regression were used to develop a RiskScore prognosis model. Kaplan-Meier (KM) survival analysis with log-rank test and area under the curve (AUC) of receiver operating characteristic (ROC) were employed in the prognostic evaluation and prediction validation. Immune infiltration, tyrosine metabolism score, and pathway enrichment were evaluated using single-sample gene set enrichment analysis (ssGSEA). Finally, a nomogram model was developed with the RiskScore and other clinicopathological features. Based on the tyrosine metabolism genes in the TCGA cohort, we identified 3 tyrosine metabolism-related subtypes showing significant prognostic differences. Four candidate genes selected from the common differentially expressed genes (DEGs) between the 3 subtypes were used to develop a RiskScore model, which could effectively divide LIHC patients into high- and lowrisk groups. In both the training and validation sets, high-risk patients tended to have worse overall survival, less active immunotherapy response, higher immune infiltration and clinical grade, and higher oxidative, fatty, and xenobiotic metabolism pathways. Multivariate analysis confirmed that the RiskScore was an independent indicator for the prognosis of LIHC. The results from pan-- cancer analysis also supported that the RiskScore had a strong prognostic performance in other cancers. The nomogram demonstrated that the RiskScore contributed the most to the prediction of LIHC prognosis. Our study developed a tyrosine metabolism-related risk model that performed well in survival prediction, showing the potential to serve as an independent prognostic predictor for LIHC treatment.

Cui Z ，Liu C ，Li H ，Wang J ，Li G ... -  《-》

The survival prediction analysis and preliminary study of the biological function of YEATS2 in hepatocellular carcinoma.

Our study aims to develop and validate a novel molecular marker for the prognosis and diagnosis of hepatocellular carcinoma (HCC) MATERIALS & METHODS: We retrospectively analyzed mRNA expression profile and clinicopathological data of HCC patients fetched from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and The International Cancer Genome Consortium (ICGC) datasets. Univariate Cox regression analysis was performed to collect differentially expressed mRNA (DEmRNAs) from HCC and non-tumor tissues, and YEATS2, a prognostic marker, was identified by further analysis. ROC curve, survival analysis and multivariate Cox regression analysis as well as nomograms were used to evaluate the prognosis of this gene. Finally, the biological function of this gene was preliminarily discussed by using single gene Gene Set Enrichment Analysis (GSEA), and the YEATS2 overexpression and knockdown hepatoma cell line was used to verify the results in vitro and in vivo. Based on the clinical information of HCC in TCGA, GEO and ICGC databases, the gene YEATS2 with significant differences from HCC was identified. There was a statistical difference in the survival prognosis between the two databases and the ROC curve showed that the survival of HCC in both TCGA, GSE14520 and ICGC groups had a satisfactory predictive effect. Univariate and multivariate Cox regression analysis showed that YEATS2 was an independent prognostic factor for HCC, and Nomograms, which combined this prognostic feature with significant clinical features, provided an important reference for the clinical prognostic diagnosis of HCC. Next, we constructed overexpression and knockdown YEATS2 cell line in Hep3B and LM3 cells, and further proved that overexpression YEATS2 promote the proliferation and migration of HCC cells by CCK8, colony formation experiment, and transwell assays, and knockdown YEATS2 inhibited the proliferation and migration of HCC cells by CCK8, colony formation experiment, and transwell assays. Finally, the biological function of YEATS2 was preliminarily explored through GSEA analysis of a single gene, and it was found that it was significantly correlated with cell cycle and DNA repair, which provided us with ideas for further analysis. Furthermore, the knockdown of YEATS2 promoted radiation-induced DNA damage, enhanced radiosensitivity, and ultimately inhibited the proliferation of hepatocellular carcinoma cells in vitro and in vivo. Our study identified a promising prognostic marker for hepatocellular carcinoma that is useful for clinical decision-making and individualized treatment.

Long Y ，Wang W ，Liu S ，Wang X ，Tao Y ... -  《-》