A Novel Pyroptosis-related Prognostic Model for Hepatocellular Carcinoma

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

PURPOSE

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.

RESULTS

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.

CONCLUSIONS

Our study identified a promising prognostic marker for hepatocellular carcinoma that is useful for clinical decision-making and individualized treatment.

A dynamic model to predict early occurrence of acute kidney injury in ICU hospitalized cirrhotic patients: a MIMIC database analysis

BACKGROUND

This study aimed to develop a tool for predicting the early occurrence of acute kidney injury (AKI) in ICU hospitalized cirrhotic patients.

METHODS

Eligible patients with cirrhosis were identified from the Medical Information Mart for Intensive Care database. Demographic data, laboratory examinations, and interventions were obtained. After splitting the population into training and validation cohorts, the least absolute shrinkage and selection operator regression model was used to select factors and construct the dynamic online nomogram. Calibration and discrimination were used to assess nomogram performance, and clinical utility was evaluated by decision curve analysis (DCA).

RESULTS

A total of 1254 patients were included in the analysis, and 745 developed AKI. The mean arterial pressure, white blood cell count, total bilirubin level, Glasgow Coma Score, creatinine, heart rate, platelet count and albumin level were identified as predictors of AKI. The developed model had a good ability to differentiate AKI from non-AKI, with AUCs of 0.797 and 0.750 in the training and validation cohorts, respectively. Moreover, the nomogram model showed good calibration. DCA showed that the nomogram had a superior overall net benefit within wide and practical ranges of threshold probabilities.

CONCLUSIONS

The dynamic online nomogram can be an easy-to-use tool for predicting the early occurrence of AKI in critically ill patients with cirrhosis.

Identification of pyroptosis subtypes and prognosis model of hepatocellular carcinoma based on pyroptosis-related genes

BACKGROUND

Hepatocellular carcinoma (HCC) is a common malignant tumor with poor prognosis. Pyroptosis, a type of programmed cell death, regulates tumor cell development. However, the role of pyroptosis-related genes (PRGs) in HCC and their association with prognosis are unclear.

METHODS

We conducted bioinformatics analysis to identify PRGs in The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) patients. Consensus clustering classified patients into different subtypes. We used LASSO regression to established a pyroptosis subtype-related score (PSRS) related to prognosis. OncoPredict identified potential pharmaceuticals based on PSRS.

RESULTS

We found 20 HCC-related PRGs in 335 TCGA-LIHC patients. Consensus clustering classified patients into two subtypes. Subtype I had better overall survival and higher response to anti-PD1 treatment. The prognostic model involving 20 genes predicted poorer prognosis for high-PSRS group. The model was validated in two external cohorts. OncoPredict identified 65 potential pharmaceuticals based on PSRS.

CONCLUSION

Our investigation revealed a correlation between pyroptosis and HCC. We established PSRS as independent risk factors for predicting prognosis. The study paves the way for using PRGs as prognostic biomarkers and exploring personalized therapy for HCC.

Unveiling the best predictive models for early‑onset metastatic cancer: Insights and innovations (Review)

Cancer metastasis is the primary cause of cancer deaths. Metastasis involves the spread of cancer cells from the primary tumors to other body parts, commonly through lymphatic and vascular pathways. Key aspects include the high mutation rate and the capability of metastatic cells to form invasive tumors even without a large initial tumor mass. Particular emphasis is given to early metastasis, occurring in initial cancer stages and often leading to misdiagnosis, which adversely affects survival and prognosis. The present review highlighted the need for improved understanding and detection methods for early metastasis, which has not been effectively identified clinically. The present review demonstrated the clinicopathological and molecular characteristics of early‑onset metastatic types of cancer, noting factors such as age, race, tumor size and location as well as the histological and pathological grade as significant predictors. In conclusion, the present review underscored the importance of early detection and management of metastatic types of cancer and called for improved predictive models, including advanced techniques such as nomograms and machine learning, so as to enhance patient outcomes, acknowledging the challenges and limitations of the current research as well as the necessity for further studies.

Identification of IRF1 as a Novel Pyroptosis-Related Prognostic Biomarker of Atopic Dermatitis

Purpose: The aim of this study was to characterize key biomarkers associated with pyroptosis in atopic dermatitis (AD). Materials and methods: To identify the differentially expressed pyroptosis-related genes (DEPRGs), the gene expression profiles GSE16161 and GSE32924 from the Gene Expression Omnibus (GEO) database were utilized. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to determine the potential biological functions and involved pathways. Furthermore, protein-protein interaction network analyses were performed to identify hub genes. The types and proportions of infiltrating immune cells were detected by immune filtration analysis using CIBERSORT. A 12-axis competing endogenous RNA (ceRNA) network was constructed utilizing the miRNet database. Immunohistochemistry (IHC) further validated the differential expression of a key gene IRF1 in the skin tissues collected from AD patients. The collection of skin tissue from human subjects in this study were reviewed and approved by the IRB of Yueyang Integrated Chinese and Western Medicine Hospital (KYSKSB2020-125). Results: The study identified a total of 76 DEPRGs, which were enriched in genes associated with the inflammatory response and immune regulation. There was a higher percentage of activated dendritic cells and a lower percentage of resting mast cells in AD samples. PVT1 expression was associated with upregulation of hub genes including CXCL8, IRF1, MKI67, and TP53 in the ceRNA network and was correlated with activated dendritic cells in AD. As a transcription factor, IRF1 could regulate the production of downstream inflammatory factors. The IHC study revealed that IRF1 was overexpressed in the skin tissues of AD patients, which were consistent with the results of the bioinformatic study. Conclusions: IRF1 and its related genes were identified as key pyroptosis-related biomarkers in AD, which is a crucial pathway in the pathogenesis of AD.

Dissection of pyroptosis-related prognostic signature and CASP6-mediated regulation in pancreatic adenocarcinoma: new sights to clinical decision-making

Recent studies have indicated that pyroptosis may participate in the regulation of tumorigenesis and immune microenvironment. However, the role of pyroptosis-related genes (PRGs) in pancreatic adenocarcinoma (PAAD) remains unclear. Through multiple bioinformatics analysis, we constructed a prognostic gene model and competing endogenous RNA network. The correlation between PRGs and prognosis, immune infiltration, immune checkpoints, and tumor mutational burden was analyzed by Kaplan-Meier curve, univariate Cox, multivariate regression, and Spearman's analysis in PAAD patients. The qRT-PCR, Western blotting, CCK-8, Wound healing, and Transwell assay were applied to examine the role of CASP6 in PANC-1 cell. Thirty-one PRGs were upregulated in PAAD. Functional enrichment analysis revealed that the PRGs were mainly involved in pyroptosis, NOD-like receptor signaling pathway, and response to bacteria. We established a novel 4-gene signature related to PRGs for evaluating the prognosis of PAAD patients. Patients with PAAD in the low-risk group had a better prognosis than those in the high-risk group. The nomogram suggested that the 1-, 3-, and 5-years survival probability exhibited robust predictive performance. Significant correlation was observed between prognostic PRGs and immune infiltration, immune checkpoints, and tumor mutational burden. We first identified the potential competing endogenous RNA regulatory axis in PAAD: lncRNA PVT1/hsa-miR-16-5p/CASP6/CASP8. Moreover, knockdown of CASP6 dramatically inhibited the proliferation, migration, and invasion ability of PANC-1 cell in vitro. In conclusion, CASP6 could be a potential biomarker, promoting the occurrence and progression in PAAD. The lncRNA PVT1/hsa-miR-16-5p/CASP6/CASP8 regulatory axis plays an vital role in regulating the anti-tumor immune responses for PAAD.

Comprehensive genomic signature of pyroptosis-related genes and relevant characterization in hepatocellular carcinoma

Background

Currently, the most predominant type of liver cancer is hepatocellular carcinoma (HCC), which is also the fourth leading cause of cancer-related death in the global population. Pyroptosis is an emerging form of cell death that affects the prognosis of cancer patients by modulating tumor cell migration, proliferation and invasion. However, the evaluation of pyroptosis in the prognosis of HCC is still insufficient.

Methods

A total of 365 HCC patients from the TCGA-LIHC cohort were classified into two distinct subtypes using consensus clustering of pyroptosis-related genes (PRGs). Following univariate Cox analysis of differentially expressed genes between subtypes, we established a prognostic model (PRGs-score, PRGS) by LASSO Cox analysis. We further tested the predictive power of the prognostic model in the ICGC (LIRI-JP) and GEO (GSE14520) cohorts. The tumor microenvironment (TME) was studied using the CIBERSORT. The enrichment scores for immune cells and immune functions in low- and high-PRGS groups were assessed using ssGSEA. The IMvigor210 cohort was used to investigate the immunotherapy efficacy. Furthermore, we validated the expression of prognostic genes in PRGS by RT-qPCR in vitro.

Results

The subtyping of HCC based on PRGs exhibited distinct clinical characteristics. We developed a prognostic model PRGS by differentially expressed genes between different subtypes. The results showed that PRGS could well forecast the survival of HCC patients in different cohorts and was associated with the immune microenvironment. Moreover, PRGS was considered to be an independent prognostic risk factor and superior to other pyroptosis-related signatures. Low-PRGS implied greater immune cell infiltration and better overall survival with immunotherapy. The results of RT-qPCR also showed that prognostic genes were significantly dysregulated in HCC.

Conclusions

PRGS has promising application in forecasting the prognosis of HCC patients, and its relationship with the immune microenvironment provides a basis for the subsequent treatment and research of HCC.

Multi-omics analysis of pyroptosis regulation patterns and characterization of tumor microenvironment in patients with hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a primary malignant tumor of the liver, and pyroptosis has been identified as a novel cellular program that plays a role in numerous diseases including cancer. However, the functional role of pyroptosis in HCC remains unclear. The purpose of this study is to explore the relationship between the two found hub genes and provide targets for clinical treatment.

Methods

The Cancer Genome Atlas (TCGA) database was used to collect the gene data and clinically-related information of patients with HCC. After the differentially expressed genes (DEGs) were identified, they were intersected with the genes related to pyroptosis, and a risk prediction model was established to predict the overall survival (OS). Subsequently, drug sensitivity analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) was used to analyze the biological characteristics of the DEGs. Different immune cell infiltration and related pathways were analyzed, and hub genes were identified by protein-protein interaction (PPI). Finally, the expression of hub genes was verified by real-time quantitative PCR (qRT-PCR) and immunohistochemistry.

Results

We conducted a comprehensive bioinformatics analysis to investigate the molecular mechanisms of pyroptosis in hepatocellular carcinoma (HCC). A total of 8,958 differentially expressed genes were identified, and 37 differentially expressed genes were associated with pyroptosis through intersection. Moreover, we developed an OS model with excellent predictive ability and discovered the differences in biological function, drug sensitivity, and immune microenvironment between high-risk and low-risk groups. Through enrichment analysis, we found that the differentially expressed genes are related to various biological processes. Then, 10 hub genes were identified from protein-protein interaction networks. Finally, midkine (MDK) was screened from the 10 hub genes and further verified by PCR and immunohistochemistry, which revealed its high expression in HCC.

Conclusion

We have developed a reliable and consistent predictive model based on the identification of potential hub genes, which can be used to accurately forecast the prognosis of patients, thus providing direction for further clinical research and treatment.

Identification and Validation of Two Heterogeneous Molecular Subtypes and a Prognosis Predictive Model for Hepatocellular Carcinoma Based on Pyroptosis

Hepatocellular carcinoma (HCC) is a worldwide malignant cancer with high incidence and mortality. Considering the high heterogeneity of HCC, clarifying molecular characteristics associated with HCC development could help improve patients' outcomes. Pyroptosis is a novel form of cell death and is noted to be implicated in HCC pathogenesis whereas its molecular feature in HCC is unclear. Thus, we intended to clarify the molecular characteristic as well as the clinical significance of pyroptosis for HCC. A systematic bioinformatics analysis was conducted among 40 pyroptosis-related genes based on The Cancer Genome Atlas, the International Cancer Genome Consortium, and the Gene Expression Omnibus databases. A total of 12 HCC-associated pyroptosis-related genes (HPRGs) were identified to be overexpressed in HCC tissues and significantly connected to patients' poor survival. Through consensus clustering based on the HPRGs' expression, we found patients could be stratified into two distinctive pyroptosis subtypes, PyLow and PyHigh. The PyHigh group owned a notable lower survival rate and a higher high-grade proportion compared with the PyLow subtype. Besides, patients' sensitivities to chemotherapeutic drugs also presented distinctive differences between the two subtypes. Indicated by pathway enrichment analysis and immune characteristic difference analysis, the distinctions between the pyroptosis subtypes may be related to tumor immunity. Further, a five-gene risk model composed of BAK1, CHMP4A, CHMP4B, DHX9, and GSDME was established. Subsequent analyses demonstrated that the model could credibly classify patients as low or high risk and was an independent prognostic indicator for HCC. Abnormal expressions of the five genes were validated by biological experiments and new bioinformatics analysis. In conclusion, this study recognized and verified two heterogeneous pyroptosis subtypes and a predictable prognosis model for HCC. Our work may help facilitate the clinical management and treatment of HCC and understand the functions of pyroptosis in oncology.

N7-Methylguanosine Regulatory Genes Profoundly Affect the Prognosis, Progression, and Antitumor Immune Response of Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is a common abdominal cancer with poor survival outcomes. Although there is growing evidence that N7-methylguanosine (m7G) is closely associated with tumor prognosis, development, and immune response, few studies focus on this topic.

Methods

The novel m7G risk signature was constructed through the Lasso regression analysis. Its prognostic value was evaluated through a series of survival analyses and was tested in ICGC-LIRI, GSE14520, and GSE116174 cohorts. CIBERSORT, ssGSEA, and ESTIMATE methods were applied to explore the effects of the m7G risk score on tumor immune microenvironment (TIM). The GSEA method was used to evaluate the impacts of the m7G risk score on glycolysis, ferroptosis, and pyroptosis. The human protein atlas (HPA) database was used to clarify the histological expression levels of five m7G signature genes. The biofunctions of NCBP2 in hepatocellular cancer (HC) cells were confirmed through qPCR, CCK8, and transwell assays.

Results

Five m7G regulatory genes comprised the novel risk signature. The m7G risk score was identified as an independent prognostic factor of HCC and could increase the decision-making benefit of traditional prognostic models. Besides, we established a nomogram containing the clinical stage and m7G risk score to predict the survival rates of HCC patients. The prognostic value of the m7G model was successfully validated in ICGC and GSE116174 cohorts. Moreover, high m7G risk led to a decreased infiltration level of CD8+ T cells, whereas it increased the infiltration levels of Tregs and macrophages. The glycolysis and pyroptosis processes were found to be enriched in the HCC patients with high m7G risk. Finally, overexpression of NCBP2 could promote the proliferation, migration, and invasion of HC cells.

Conclusions

The m7G risk score was closely related to the prognosis, antitumor immune process, glycolysis, and malignant progression of HCC. NCBP2 has pro-oncogenic abilities, showing promise as a novel treatment target.

Construction of a pyroptosis-related classifier for risk prediction of acute myocardial infarction

BACKGROUND

Acute myocardial infarction (AMI) is a common cardiovascular disease that has a high mortality. Pyroptosis is a programmed cell death mediated by inflammasome. It remains to be clarified on the expression pattern and risk predictive role of pyroptosis-related genes in AMI.

METHODS

The gene expression data were extracted from the Gene Expression Omnibus (GEO), and pyroptosis-related genes were obtained from published articles. Pyroptosis-related differential expressed genes were selected between normal and AMI samples and then we explored their immune infiltration level using CIBERSORT. Univariate Cox and LASSO regression were applied to establish a classifier based on pyroptosis-related genes. ROC analysis was utilized to evaluate the classifier.

RESULTS

In this study, we obtained 20 pyroptosis-related genes which showed differential expression in AMI and normal samples. Among the differential expressed genes, GZMB was significantly positively associated with activated NK cells (R = 0.71, p < 0.01), while NLRP3 exhibited a negative correlation with resting NK cells (R = -0.66, p < 0.01). 9 genes (NLRP9, GSDMD, CASP8, AIM2, GPX4, NOD1, NOD2, SCAF11, GSDME) were eventually identified as a predictive risk classifier for AMI patients. With the classifier, patients at high and low risk could be discriminated. Further external validation showed the high accuracy of the classifier (AUC = 0.75).

CONCLUSIONS

Pyroptosis-related genes are closely related to immune infiltration in AMI, and a 9-gene classifier has good performance in predicting the risk of AMI with high accuracy, which could provide a new way for targeted treatment in AMI.