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.

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.

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.

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.

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.

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.