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 《-》

Construction of a Cancer Stem Cell related Histone Acetylation Regulatory Genes Prognostic Model for Hepatocellular Carcinoma via Bioinformatics Analysis: Implications for Tumor Chemotherapy and Immunity.

Cancer stem cells (CSC) play an important role in the development of Liver Hepatocellular Carcinoma (LIHC). However, the regulatory mechanisms between acetylation- associated genes (HAGs) and liver cancer stem cells remain unclear. To identify a set of histone acetylation genes (HAGs) with close associations to liver cancer stem cells (LCSCs), and to construct a prognostic model that facilitates more accurate prognosis assessments for LIHC patients. LIHC expression data were downloaded from the public databases. Using mRNA expression- based stemness indices (mRNAsi) inferred by One-Class Logistic Regression (OCLR), Differentially Expressed Genes (DEGs) (mRNAsi-High VS. mRNAsi-Low groups) were intersected with DEGs (LIHC VS. normal samples), as well as histone acetylation-associated genes (HAGs), to obtain mRNAsi-HAGs. A risk model was constructed employing the prognostic genes, which were acquired through univariate Cox and Least Shrinkage and Selection Operator (LASSO) regression analyses. Subsequently, independent prognostic factors were identified via univariate and multivariate Cox regression analyses and then a nomogram for prediction of LIHC survival was developed. Additionally, immune infiltration and drug sensitivity analysis were performed to explore the relationships between prognostic genes and immune cells. Finally, the expressions of selected mRNAsi-HAGs were validated in the LIHC tumor sphere by quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) assay and western blot analysis. Among 13 identified mRNAsi-HAGs, 3 prognostic genes (HDAC1, HDAC11, and HAT1) were selected to construct a risk model (mRNAsi-HAGs risk score = 0.02 * HDAC1 + 0.09 * HAT1 + 0.05 * HDAC11). T-stage, mRNAsi, and mRNAsi-HAGs risk scores were identified as independent prognostic factors to construct the nomogram, which was proved to predict the survival probability of LIHC patients effectively. We subsequently observed strongly positive correlations between mRNAsi-HAGs risk score and tumor-infiltrating T cells, B cells and macrophages/monocytes. Moreover, we found 8 drugs (Mitomycin C, IPA 3, FTI 277, Bleomycin, Tipifarnib, GSK 650394, AICAR and EHT 1864) had significant correlations with mRNAsi-HAGs risk scores. The expression of HDAC1 and HDAC11 was higher in CSC-like cells in the tumor sphere. This study constructed a mRNAsi and HAGs-related prognostic model, which has implications for potential immunotherapy and drug treatment of LIHC.

Dai Q ，Zhu J ，Yang J ，Zhang CY ，Yang WJ ，Pan BS ，Yang XR ，Guo W ，Wang BL ... -  《-》

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》

Construction of a random survival forest model based on a machine learning algorithm to predict early recurrence after hepatectomy for adult hepatocellular carcinoma.

Zhang J ，Chen Q ，Zhang Y ，Zhou J ... -  《BMC CANCER》