Combined OLA1 and CLEC3B Gene Is a Prognostic Signature for Hepatocellular Carcinoma and Impact Tumor Progression.

Hepatocellular carcinoma (HCC), partly because of its complexity and high heterogeneity, has a poor prognosis and an extremely high mortality rate. In this study, mRNA sequencing expression profiles and relevant clinical data of HCC patients were gathered from different public databases. Kaplan-Meier survival curves as well as ROC curves validated that OLA1|CLEC3B was an independent predictor with better predictive capability of HCC prognosis compared to OLA1 and CLEC3B separately. Further, the cell transfection experiment verified that knockdown of OLA1 inhibited cell proliferation, facilitated apoptosis, and improved sensitivity of HCC cells to gemcitabine. In this study, the prognostic model of HCC composed of OLA1/CLEC3B genes was constructed and verified, and the prediction ability was favorable. A higher level of OLA1 along with a lower level of CEC3B is a sign of poor prognosis in HCC. We revealed a novel gene pair OLA1|CLEC3B overexpressed in HCC patients, which may serve as a promising independent predictor of HCC survival and an approach for innovative diagnostic and therapeutic strategies.

The involvement of ROS-regulated programmed cell death in hepatocellular carcinoma.

Reactive oxidative species (ROS) is a crucial factor in the regulation of cellular biological activity and function, and aberrant levels of ROS can contribute to the development of a variety of diseases, particularly cancer. Numerous discoveries have affirmed that this process is strongly associated with "programmed cell death (PCD)," which refers to the suicide protection mechanism initiated by cells in response to external stimuli, such as apoptosis, autophagy, ferroptosis, etc. Research has demonstrated that ROS-induced PCD is crucial for the development of hepatocellular carcinoma (HCC). These activities serve a dual function in both facilitating and inhibiting cancer, suggesting the existence of a delicate balance within healthy cells that can be disrupted by the abnormal generation of reactive oxygen species (ROS), thereby influencing the eventual advancement or regression of a tumor. In this review, we summarize how ROS regulates PCD to influence the tumorigenesis and progression of HCC. Studying how ROS-induced PCD affects the progression of HCC at a molecular level can help develop better prevention and treatment methods and facilitate the design of more effective preventative and therapeutic strategies.

Cynarin alleviates acetaminophen-induced acute liver injury through the activation of Keap1/Nrf2-mediated lipid peroxidation defense via the AMPK/SIRT3 signaling pathway.

Overdose of Acetaminophen (APAP) is a major contributor to acute liver injury (ALI), a complex pathological process with limited effective treatments. Emerging evidence links lipid peroxidation to APAP-induced ALI. Cynarin (Cyn), a hydroxycinnamic acid derivative, exhibits liver protective effects, but whether it mitigates APAP-induced ALI is unclear. Our aim was to verify the protective impact of Cyn on APAP-induced ALI and elucidate the molecular mechanisms governing this process. Herein, the regulation of the Kelch-like ECH-associated protein 1 (Keap1)/nuclear factor erythroid 2-related factor 2 (Nrf2) interaction was determined to be a novel mechanism underlying this protective impact of Cyn against APAP-induced ALI. Nrf2 deficiency increased the severity of APAP-induced ALI and lipid peroxidation and counteracted the protective effect of Cyn against this pathology. Additionally, Cyn promoted the dissociation of Nrf2 from Keap1, enhancing the nuclear translocation of Nrf2 and the transcription of downstream antioxidant proteins, thereby inhibiting lipid peroxidation. Molecular docking demonstrated that Cyn bound competitively to Keap1, and overexpression of Keap1 reversed Nrf2-activated anti-lipid peroxidation. Additionally, Cyn activated the adenosine monophosphate-activated protein kinase (AMPK)/sirtuin (SIRT)3 signaling pathway, which exhibits a protective effect on APAP-induced ALI. These findings propose that Cyn alleviates APAP-induced ALI by enhancing the Keap1/Nrf2-mediated lipid peroxidation defense via activation of the AMPK/SIRT3 signaling pathway.

LncRNA SNHG11 reprograms glutaminolysis in hepatic stellate cells via Wnt/ß-catenin/GLS axis.

Long non-coding RNAs (lncRNAs) have been identified as decisive regulators of liver fibrosis. Hepatic stellate cells (HSCs), major hepatic cells contributing to liver fibrosis, undergo metabolic reprogramming for transdifferentiation and activation maintenance. As a crucial part of metabolic reprogramming, glutaminolysis fuels the tricyclic acid (TCA) cycle that renders HSCs addicted to glutamine. However, how lncRNAs reprogram glutamine metabolism in HSCs is unknown. For this research, we characterized the pro-fibrogenic function of small nucleolar host gene 11 (SNHG11). Our data showed that in carbon tetrachloride (CCl4, 7 µL/g, intraperitoneally) treated C57BL/6J mice, SNHG11 expression was highly up-regulated in fibrotic livers and activated primary HSCs. SNHG11 knockdown attenuated the accumulation of fibrotic markers α-SMA and Col1A1 in liver fibrosis tissues and activated HSCs. Western blot and qRT-PCR assays demonstrated that glutaminase (GLS), the rate-limiting enzyme for glutaminolysis, was a downstream target of SNHG11. Furthermore, SNHG11 upregulated glutaminolysis in HSCs through the activation of the Wnt/ß-catenin signaling pathway. The results highlighted that SNHG11 is a glutaminolysis-regulated lncRNA that promotes liver fibrosis. A novel insight into the metabolic mechanism that reprograms glutaminolysis in HSCs could be exploited as anti-fibrotic targets.

A Novel Gene Pair CSTF2/DPE2A Impacts Prognosis and Cell Cycle of Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC), one of the commonest cancers at present, possesses elevated mortality. This study explored the predictive value of CSTF2/PDE2A for HCC prognosis. Methods: In this study, clinical information and RNA sequencing expression profiles of HCC patients were acquired from common databases. Kaplan-Meier curve compound with time-dependent ROC curve, nomogram model, and univariate/multivariate Cox analysis were carried out to access the prediction capacity of CSTF2/PDE2A. The immune status, tumor microenvironment, drug sensitivity, biological function and pathway between HCC and adjacent non-tumorous tissue were analyzed and compared. Finally, RT-qPCR, Western blot, and apoptosis assays were performed to verify the effect on HCC cells of CSTF2/PDE2A. Results: The optimal cut-off value of CSTF2, PDE2A and CSTF2/PDE2A was 6.95, 0.95 and 3.63, respectively. In TCGA and ICGC cohorts, the high group of CSTF2/PDE2A presented higher OS compared to low group. The area under the curve (AUC) for OS at 1-, 2-, and 3-years predicted by CSTF2/PDE2A were 0.731/0.695, 0.713/0.732 and 0.689/0.755, higher than the counterparts of the single gene CSTF2 and PDE2A. Multivariate Cox analysis revealed that CSTF2/PDE2A (HR = 1.860/3.236, 95% CI = 1.265-2.733/1.575-6.645) was an independent prognostic factor for HCC. The OS nomogram model created according to five independent factors including CSTF2/PDE2A showed excellent capacity for HCC prognosis. Furthermore, the immune status of the CSTF2/PDE2A high group was deleted, cell cycle-related genes and chemotherapy resistance were increased. Finally, cell experiments revealed distinct differences in the proliferation, apoptosis, protein and mRNA expression of HCC cells after si-CSTF2 transfection compared with the negative control. Conclusion: Taken together, the gene pair CSTF2/PDE2A is able to forecast the prognosis of HCC and regulates cell cycle, which is promising as a novel prognostic predictor of HCC.

Canagliflozin reduces chemoresistance in hepatocellular carcinoma through PKM2-c-Myc complex-mediated glutamine starvation.

Cisplatin (CPT) is one of the standard treatments for hepatocellular carcinoma (HCC). However, its use is limits as a monotherapy due to drug resistance, and the underlying mechanism remains unclear. To solve this problem, we tried using canagliflozin (CANA), a clinical drug for diabetes, to reduce chemoresistance to CPT, and the result showed that CANA could vigorously inhibit cell proliferation and migration independent of the original target SGLT2. Mechanistically, CANA reduced aerobic glycolysis in HCC by targeting PKM2. The downregulated PKM2 directly bound to the transcription factor c-Myc in the cytoplasm to form a complex, which upregulated the level of phosphorylated c-Myc Thr58 and promoted the ubiquitination and degradation of c-Myc. Decreased c-Myc reduced the expression of GLS1, a key enzyme in glutamine metabolism, leading to impaired glutamine utilization. Finally, intracellular glutamine starvation induced ferroptosis and sensitized HCC to CPT. In conclusion, our study showed that CANA re-sensitized HCC to CPT by inducing ferroptosis through dual effects on glycolysis and glutamine metabolism. This is a novel mechanism to increase chemosensitivity, which may provide compatible chemotherapy drugs for HCC.

TUG1 protects against ferroptosis of hepatic stellate cells by upregulating PDK4-mediated glycolysis.

The induction of ferroptosis in hepatic stellate cells (HSCs) has shown promise in reversing liver fibrosis. And ferroptosis has been confirmed to be associated with glycolysis. The objective of this study is to determine whether ferroptosis inhibition in HSCs, induced by elevation of recombinant pyruvate dehydrogenase kinase isozyme 4 (PDK4)-mediated glycolysis, could mediate the pathogenesis of liver fibrosis. Liver fibrosis was induced using CCl4, the level of which was assessed through histochemical staining. Lentivirus was used to modulate the expression of specific genes. And underlying mechanisms were explored using primary HSCs extracted from normal mice. The results confirmed that Taurine up-regulated gene 1 (TUG1) expression was upregulated in liver fibrotic tissues and HSCs, showing a positive correlation with fibrosis. In addition, TUG1 attenuated ferroptosis in HSCs by promoting PDK4-mediated glycolysis, thereby promoting the progression of liver fibrosis. Moreover, TUG1 was observed to impact HSCs activation, exacerbating liver fibrosis to some extent. In conclusion, our study revealed that TUG1 expression was elevated in mouse models of liver fibrosis and activated HSCs, which inhibited ferroptosis in HSCs through PDK4-mediated glycolysis. This finding may open up a new therapeutic strategy for liver fibrosis.

Salidroside ameliorates acetaminophen-induced acute liver injury through the inhibition of endoplasmic reticulum stress-mediated ferroptosis by activating the AMPK/SIRT1 pathway.

Acetaminophen (APAP) overdose has long been considered a major cause of drug-induced liver injury. Ferroptosis is a type of programmed cell death mediated by iron-dependent lipid peroxidation. Endoplasmic reticulum (ER) stress is a systemic response triggered by the accumulation of unfolded or misfolded proteins in the ER. Ferroptosis and ER stress have been proven to contribute to the progression of APAP-induced acute liver injury (ALI). It was reported that salidroside protects against APAP-induced ALI, but the potential mechanism remain unknown. In this study, male C57BL/6 J mice were intraperitoneally (i.p.) injected APAP (500 mg/kg) to induce an ALI model. Salidroside was i.p. injected at a dose of 100 mg/kg 2 h prior to APAP administration. Mice were sacrificed 12 h after APAP injection and the liver and serum of the mice were obtained for histological and biochemistry analysis. AML12 cells were used in in vitro assays. The results indicated that salidroside mitigated glutathione degradation via inhibiting cation transport regulator homolog 1 (CHAC1) to attenuate ferroptosis, and simultaneously suppressing PERK-eIF2α-ATF4 axis-mediated ER stress, thus alleviating APAP-induced ALI. However, PERK activator CCT020312 and overexpression of ATF4 inhibited the protective function of salidroside on CHAC1-mediated ferroptosis. Besides this, activation of the AMPK/SIRT1 signaling pathway by salidroside was demonstrated to have a protective effect against APAP-induced ALI. Interestingly, selective inhibition of SIRT1 ameliorated the protective effects of salidroside on ER stress and ferroptosis. Overall, salidroside plays a significant part in the mitigation of APAP-induced ALI by activating the AMPK/SIRT1 signaling to inhibit ER stress-mediated ferroptosis in the ATF4-CHAC1 axis.

Characterization of neutrophil extracellular traps related gene pair for predicting prognosis in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a malignant disease with a high incidence rate, high mortality and poor prognosis. Neutrophil extracellular traps (NETs), as an extracellular reticular structure, promote the development and progression of cancer in the tumor microenvironment, and have a promising prospect as a prognostic indicator. In the present study, we elucidated the prognostic value of NET-related genes. METHODS: The NETs gene pair of The Cancer Genome Atlas cohort was constructed by least absolute shrinkage and selection operator analysis. Samples from the International Cancer Genome Consortium were performed to verify its feasibility. Kaplan-Meier analysis was used to compare the overall survival (OS) rate of the two subgroups. The independent predictors of OS were determined by univariate and multivariate Cox analysis. Furthermore, Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes pathway were analyzed by gene set enrichment analysis. The single sample gene set enrichment analysis method was performed to deplore the relationship of risk score with tumor immune microenvironment. The GSE149614 dataset was applied as single cell RNA level validation. PCR was performed to the detect mRNA expression profiles of NETs-related genes. RESULTS: Our analysis of the NETs-related model provides a promising prospect as a prognostic indicator. The OS of high-risk group patients was significantly reduced. The risk score was an important independent predictor of HCC prognosis. The Nomogram model suggested a favorable classification performance. The drug resistance and sensitivity of tumor cells to chemotherapeutics was significantly correlated with the prognostic gene expression. The immune status of the two risk groups showed a marked difference. CONCLUSIONS: The novel prognostic gene pair and immune landscape could predict the prognosis of HCC patients and provide a new understanding of immunotherapy in HCC.

Association between triglyceride-glucose index and all-cause mortality in critically ill patients with ischemic stroke: analysis of the MIMIC-IV database.

BACKGROUND: The triglyceride-glucose (TyG) index was significantly associated with insulin resistance (IR). Several studies have validated the effect of TyG index on cerebrovascular disease. However, the value of TyG index in patients with severe stroke requiring ICU admission remains unclear. The aim of this study was to investigate the association between the TyG index and clinical prognosis of critically ill patients with ischemic stroke (IS). METHODS: This study identified patients with severe IS requiring ICU admission from the Medical Information Mart for Intensive Care (MIMIC-IV) database, and divided them into quartiles based on TyG index level. The outcomes included in-hospital mortality and ICU mortality. The association between the TyG index and clinical outcomes in critically ill patients with IS was elucidated using Cox proportional hazards regression analysis and restricted cubic splines. RESULTS: A total of 733 patients (55.8% male) were enrolled. The hospital mortality and intensive care unit (ICU) mortality reached 19.0% and 14.9%, respectively. Multivariate Cox proportional hazards analysis showed that the elevated TyG index was significantly related to all-cause death. After confounders adjusting, patients with an elevated TyG index had a significant association with hospital mortality (adjusted hazard ratio, 1.371; 95% confidence interval, 1.053-1.784; P = 0.013) and ICU mortality (adjusted hazard ratio, 1.653; 95% confidence interval, 1.244-2.197; P = 0.001). Restricted cubic splines revealed that a progressively increasing risk of all-cause mortality was related to an elevated TyG index. CONCLUSION: The TyG index has a significant association with hospital and ICU all-cause death in critically ill patients with IS. This finding demonstrates that the TyG index might be useful in identifying patients with IS at high risk of all-cause death.

Performance of Machine Learning Algorithms for Predicting Disease Activity in Inflammatory Bowel Disease.

This study aimed to explore the effectiveness of predicting disease activity in patients with inflammatory bowel disease (IBD), using machine learning (ML) models. A retrospective research was undertaken on IBD patients who were admitted into the First Affiliated Hospital of Wenzhou Medical University between September 2011 and September 2019. At first, data were randomly split into a 3:1 ratio of training to test set. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to reduce the dimension of variables. These variables were used to generate seven ML algorithms, namely random forests (RFs), adaptive boosting (AdaBoost), K-nearest neighbors (KNNs), support vector machines (SVMs), naïve Bayes (NB), ridge regression, and eXtreme gradient boosting (XGBoost) to train to predict disease activity in IBD patients. SHapley Additive exPlanation (SHAP) analysis was performed to rank variable importance. A total of 876 participants with IBD, consisting of 275 ulcerative colitis (UC) and 601 Crohn's disease (CD), were retrospectively enrolled in the study. Thirty-three variables were obtained from the clinical characteristics and laboratory tests of the participants. Finally, after LASSO analysis, 11 and 5 variables were screened out to construct ML models for CD and UC, respectively. All seven ML models performed well in predicting disease activity in the CD and UC test sets. Among these ML models, SVM was more effective in predicting disease activity in the CD group, whose AUC reached 0.975, sensitivity 0.947, specificity 0.920, and accuracy 0.933. AdaBoost performed best for the UC group, with an AUC of 0.911, sensitivity 0.844, specificity 0.875, and accuracy 0.855. ML algorithms were available and capable of predicting disease activity in IBD patients. Based on clinical and laboratory variables, ML algorithms demonstrate great promise in guiding physicians' decision-making.

Targeting Hepatic Stellate Cell Death to Reverse Hepatic Fibrosis.

To date, the incidence and mortality of chronic liver diseases such as cirrhosis and hepatocellular carcinoma due to the continued progression of hepatic fibrosis are increasing annually. Unfortunately, although a large number of studies have exhibited that some drugs have great potential for anti-fibrosis in animal and clinical trials, no specific anti-fibrosis drugs have been developed, and there is no better treatment for advanced cirrhosis than liver transplantation. It is a prevailing viewpoint that hepatic stellate cells (HSCs), as the mainstay of extracellular matrix secretion, are of great concern in the development of hepatic fibrosis. Therefore, targeting HSCs becomes extremely important to confront hepatic fibrosis. As previous studies described, inhibition of HSC activation and proliferation, induction of HSC death, and restoration of HSC quiescence are effective in reversing hepatic fibrosis. This review focuses on the current status of research on the treatment of hepatic fibrosis by inducing HSC death and elucidates the HSC death modes in detail and the crosstalk between them.

A pyroptosis-related gene signature provides an alternative for predicting the prognosis of patients with hepatocellular carcinoma.

BACKGROUND: Hepatocellular Carcinoma (HCC) is a common malignant neoplasm with limited treatment options and poor outcomes. Thus, there is an urgent need to find sensitive biomarkers for HCC. METHODS: Gene expression and clinicopathological information were obtained from public databases, based on which a pyroptosis-related gene signature was constructed by the least absolute shrinkage and selection operator Cox regression. The applicability of the signature was evaluated via Kaplan-Meier curve and time-dependent ROC curve. TIMER, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT, ssGSEA, and ESTIMATE were employed to assess the immune status. Comparisons between groups were analyzed with Wilcoxon test. Pearson and Spearman correlation analyses were adopted for linear correlation analysis. Genetic knockdown was conducted using siRNA transfection and the mRNA expression levels of interest genes were measured using quantitative reverse transcription PCR. Finally, protein levels in 10 paired tumor tissues and adjacent non-tumor tissues from HCC patients were measured using immunohistochemistry. RESULTS: A pyroptosis-related gene signature was established successfully to calculate independent prognostic risk scores. It was found that survival outcomes varied significantly between different risk groups. In addition, an attenuated antitumor immune response was found in the high-risk group. Meanwhile, multiple immune checkpoints were up-regulated in high-risk score patients. Cell cycle-related genes, angiogenesis-related genes and tumor drug resistance genes were also markedly elevated. Knockdown of prognostic genes in the signature significantly inhibited the expression of immune checkpoint genes and angiogenesis-related genes. Besides, each prognostic gene was expressed at a higher level in HCC tissues than in adjacent normal tissues. CONCLUSIONS: We successfully established a novel pyroptosis-related gene signature which could help predict the overall survival and assess the immune status of HCC patients.

Establishment and Validation of a Peroxisome-related Gene Signature for Prognostic Prediction and Immune Distinction in Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is a highly heterogeneous disease, which makes the prognostic prediction challenging. Abnormal peroxisomes can promote the development of cancers. This study aimed to construct a prognostic model based on peroxisome-related genes and identify its prognostic prediction and immune distinction abilities in HCC. Methods: The prognostic model was constructed based on The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC). Kaplan-Meier curve, time-dependent receiver operating characteristic curve and Cox analysis were used to evaluate the model. The immune status, tumor microenvironment, drug sensitivity and expression levels of the mRNA and protein between HCC and adjacent non-tumorous tissues were analyzed and compared. Results: A prognostic model of 9 peroxisome-related genes was established and validated. Overall survival was markedly higher in the low-risk group relative to the high-risk group. The risk score was an independent prognostic factor. Tumor-related pathways were enriched in the high-risk group and the HCC patients in high-risk group showed depleted immune status. Furthermore, immune checkpoint-related genes, cell cycle-related genes, and multidrug resistance-related genes were overexpressed in the high-risk group. The expression levels of prognostic genes were negatively related to the anti-tumor drugs sensitivity. In addition, the expression level of each prognostic gene in HCC tissues was higher than that in adjacent non-tumorous tissues in an independent sample cohort and the similar results were found in most cancer types. Conclusion: A signature based on the nine peroxisome-related genes is a promising biomarker of HCC and is beneficial to the realization of individualized treatment.

Glycolysis-Related Gene Signature Can Predict Survival and Immune Status of Hepatocellular Carcinoma.

BACKGROUND: Concise and precise prognostic models are urgently needed due to the intricate genetic variations among hepatocellular carcinoma (HCC) cells. Disorder or change in glycolysis metabolism has been considered one of the "hallmarks" of cancer. However, the prognostic value of glycolysis-related genes in HCC remains elusive. METHODS: A multigene prognostic model was constructed by least absolute shrinkage and selection operator Cox regression analysis in the The Cancer Genome Atlas (TCGA) cohort with 365 HCC patients and validated in the International Cancer Genome Consortium (ICGC) cohort with 231 HCC patients. The Kaplan-Meier methodology and time-dependent receiver operating characteristic curve were employed to confirm its predictive capability. A predictive nomogram was established based on the stepwise multivariate regression model. The differential expression of prognostic genes between HCC tissues and normal tissues was verified by quantitative real-time polymerase chain reaction (PCR) and immunohistochemistry in an independent sample cohort with 30 HCC patients. RESULTS: The glycolysis-related gene signature and the nomogram model exhibited robust validity in predicting prognosis. The risk score was an independent predictor for overall survival (OS). Expression levels of immune checkpoint genes and cell cycle genes were significantly elevated in the high-risk group. The high-risk group presented high levels of immune exclusion. The risk score can distinguish the effect of immunotherapy in the IMvigor210 cohort. The prognostic gene expression showed a significant difference between HCC tissues and adjacent nontumorous tissues in the independent sample cohort. CONCLUSION: The currently established glycolysis-related gene signature can accurately predict prognosis and reflect immune status, which may be a therapeutic alternative.

Comprehensive Analysis Identified Mutation-Gene Signature Impacts the Prognosis Through Immune Function in Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is a life-threatening and refractory malignancy with poor outcome. Genetic mutations are the hallmark of cancer. Thus far, there is no comprehensive prognostic model constructed by mutation-gene transcriptome in HCC. The prognostic value of mutation-gene signature in HCC remains elusive. Methods: RNA expression profiles and the corresponding clinical information were recruited from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was employed to establish gene signature. Kaplan-Meier curve and time-dependent receiver operating characteristic curve were implemented to evaluate the prognostic value. The Wilcoxon test was performed to analyze the expression of immune checkpoint genes, cell cycle genes, and tumor drug resistance genes in different risk groups. Finally, quantitative real-time PCR (qRT-RCR) and immunohistochemistry (IHC) were performed to validate the mRNA and protein expression between HCC and adjacent nontumorous tissues in an independent cohort. Results: A prognostic model consisting of five mutated genes was established by LASSO Cox regression analysis. The prognostic model classified patients into high- and low-risk groups. Compared with the low-risk group, patients in the high-risk group had significantly worse survival results. The prognostic model can accurately predict the overall survival of HCC patients and predict overall survival more accurately when combined with stage. Furthermore, the immune checkpoint genes, cell cycle genes, and tumor drug resistance genes were higher expressed in the high-risk group compared in the low-risk group. In addition, the expression level of prognostic signature genes was validated in an independent sample cohort, which was consistent with RNA sequencing expression in the TCGA database. Conclusion: The prediction model of HCC constructed using mutation-related genes is of great significance for clinical decision making and the personalized treatment of patients with HCC.

A novel focal adhesion related gene signature for prognostic prediction in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a highly heterogeneous disease. Reduced expression of focal adhesion is considered as an important prerequisite for tumor cell invasion and metastasis. However, the prognostic value of focal adhesion related genes in HCC remains to be further determined. In this study, RNA expression profiles were downloaded from public databases. A five focal adhesion related gene signature model was established by the least absolute shrinkage and selection operator Cox regression analysis, which categorized patients into high- and low-risk groups. Multivariate Cox regression analysis showed that the risk score was an independent predictor for overall survival. Single-sample gene set enrichment analysis revealed that immune status was different between the two risk groups, and tumor-related pathways were enriched in high-risk group. The risk score was significantly associated with tumor grade, tumor stage, immune scores, and immune infiltrate types. Pearson correlation showed that the expression level of prognostic genes was associated with anti-tumor drug sensitivity. Besides, the mRNA and protein expression of prognostic genes was significantly different between HCC tissues and adjacent non-tumorous tissues in our separate cohort. Taken together, a novel focal adhesion related gene signature can be used for prognostic prediction in HCC, which may be a therapeutic alternative.

An Inflammatory Response-Related Gene Signature Can Impact the Immune Status and Predict the Prognosis of Hepatocellular Carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a highly heterogeneous disease, which makes the prognostic prediction challenging. As part of the active cross-talk between the tumor and the host, inflammatory response in the tumor or its microenvironment could affect prognosis. However, the prognostic value of inflammatory response-related genes in HCC remains to be further elucidated. METHODS: In this study, the mRNA expression profiles and corresponding clinical data of HCC patients were downloaded from the public database. The least absolute shrinkage and selection operator Cox analysis was utilized to construct a multigene prognostic signature in the TCGA cohort. HCC patients from the ICGC cohort were used for validation. Kaplan Meier analysis was used to compare the overall survival (OS) between high- and low-risk groups. Univariate and multivariate Cox analyses were applied to determine the independent predictors for OS. Single-sample gene set enrichment analysis was utilized to calculate the immune cell infiltration score and immune related pathway activity. Gene set enrichment analysis was implemented to conduct GO terms and KEGG pathways. The qRT-PCR and immunohistochemistry were utilized to perform the mRNA and protein expression of prognostic genes between HCC tissues and normal liver tissues respectively. RESULTS: An inflammatory response-related gene signature model was constructed by LASSO Cox regression analysis. Compared with the low-risk group, patients in the high-risk group showed significantly reduced OS. Receiver operating characteristic curve analysis confirmed the predictive capacity of the prognostic gene signature. Multivariate Cox analysis revealed that the risk score was an independent predictor for OS. Functional analysis indicated that immune status was definitely different between two risk groups, and cancer-related pathways were enriched in high-risk group. The risk score was significantly correlated with tumor grade, tumor stage and immune infiltrate types. The expression levels of prognostic genes were significantly correlated with sensitivity of cancer cells to anti-tumor drugs. Furthermore, the expression of prognostic genes showed significant difference between HCC tissues and adjacent non-tumorous tissues in the separate sample cohort. CONCLUSION: A novel signature constructed with eight inflammatory response-related genes can be used for prognostic prediction and impact the immune status in HCC. Moreover, inhibition of these genes may be a therapeutic alternative.

Erratum: Loss of lncRNA-SNHG7 Promotes the Suppression of Hepatic Stellate Cell Activation via miR-378a-3p and DVL2.

[This corrects the article DOI: 10.1016/j.omtn.2019.05.026.].