Development and Validation of a Pyroptosis-Related Signature for Predicting Prognosis in Hepatocellular Carcinoma

Pyroptosis-related genes features on prediction of the prognosis in liver cancer: An integrated analysis of bulk and single-cell RNA sequencing

Objective

This study explores the impact of pyroptosis-related genes (PRG) on the prognosis of liver cancer (LC).

Methods

421 samples (371 tumor samples and 50 normal samples) from the Cancer Genome Atlas (TCGA) were included in this study. GSE14520 dataset (data of RNA expression and relevant clinicopathological features), GSE125449 dataset (single-cell data in LC) and HCCDB18 dataset (validation on the reliability of the model) were downloaded as appropriate. Download the PRG and its corresponding pathway information from the gene set enrichment analysis (GSEA) website. The consensus clustering was performed by ConsensusClusterPlus package. Differentially expressed genes (DEGs) were identified using limma package, and prognostic features were constructed using un/multivariate and Lasso Cox regression. Pathway enrichment analysis was conducted by ssGSEA method. Receiver Operating Characteristic and the survival analysis were conducted by timeROC and Survminer packages. The Seurat package was used for single-cell RNA sequencing (scRNA-seq) analysis. For cellular validation, following the quantification on the key genes via reverse-transcription quantitative PCR, the Transwell and scratch assays were applied to evaluate the in-vitro invasion and migration of LC cells Huh-7.

Results

12 prognosis-related genes were identified to be related to the progression of LC. Three subtypes including C1, C2 and C3 were categorized using the 12 prognosis-related genes and PRGs significantly related to the prognosis of LC patients. The worst and best prognosis was seen in C3 subtype and C2 subtype, respectively. Hallmark pathway enrichment analysis has shown the concurrent immunoactivation and immune escape in C3 subtype. A RiskScore model was constructed using 8 key genes (KPNA2, UCK2, FTCD, CBX2, RAB32, HMMR, S100A9 and ANXA10) from the DEGs of three subtypes. The RiskScore system as an independent prognostic factor dividing the patients into high and low risk groups, and patients of the high-risk group had poor prognosis in both test set and validation set. A nomogram model combining the risk score had the extreme higher benefit. Further, 6 subclusters were identified from scRNA-seq analysis, where the highest PYROPTOSIS score was seen in Monocytic-Macrophages. The quantification on the key genes has suggested the high expressions of KPNA2, UCK2, CBX2, RAB32, HMMR and S100A9 and the low expressions of FTCD and ANXA10 in LC cells Huh-7. Particularly, UCK2 knockdown evidently diminished the number of invaded and migrated LC cells in vitro.

Conclusion

The risk model associated with pyproptosis is crucial for the tumor immunity of LC and may serve as a prognostic indicator for patients suffering from LC. Our findings will offer new perspectives for immunotherapies targeting LC.

Identification of two heterogeneous subtypes of hepatocellular carcinoma with distinct pathway activities and clinical outcomes based on gene set variation analysis

Background

High heterogeneity is an essential feature of malignant tumors. This study aims to reveal the drivers of hepatocellular carcinoma heterogeneity for prognostic stratification and to guide individualized treatment.

Methods

Omics data and clinical data for two HCC cohorts were derived from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Atlas (ICGC), respectively. CNV data and methylation data were downloaded from the GSCA database. GSVA was used to estimate the transcriptional activity of KEGG pathways, and consensus clustering was used to categorize the HCC samples. The pRRophetic package was used to predict the sensitivity of samples to anticancer drugs. TIMER, MCPcounter, quanTIseq, and TIDE algorithms were used to assess the components of TME. LASSO and COX analyses were used to establish a prognostic gene signature. The biological role played by genes in HCC cells was confirmed by in vitro experiments.

Results

We classified HCC tissues into two categories based on the activity of prognostic pathways. Among them, the transcriptional profile of cluster A HCC is similar to that of normal tissue, dominated by cancer-suppressive metabolic pathways, and has a better prognosis. In contrast, cluster B HCC is dominated by high proliferative activity and has significant genetic heterogeneity. Meanwhile, cluster B HCC is often poorly differentiated, has a high rate of serum AFP positivity, is prone to microvascular invasion, and has shorter overall survival. In addition, we found that mutations, copy number variations, and aberrant methylation were also crucial drivers of the differences in heterogeneity between the two HCC subtypes. Meanwhile, the TME of the two HCC subtypes is also significantly different, which offers the possibility of precision immunotherapy for HCC patients. Finally, based on the prognostic value of molecular subtypes, we developed a gene signature that could accurately predict patients' OS. The riskscore quantified by the signature could evaluate the heterogeneity of HCC and guide clinical treatment. Finally, we confirmed through in vitro experiments that RFPL4B could promote the progression of Huh7 cells.

Conclusion

The molecular subtypes we identified effectively exposed the heterogeneity of HCC, which is important for discovering new effective therapeutic targets.

A 15-Gene-Based Risk Signature for Predicting Overall Survival in SCLC Patients Who Have Undergone Surgical Resection

Small cell lung cancer (SCLC) is a malignancy with a poor prognosis whose treatment has not progressed for decades. The survival benefit of surgery and the selection of surgical candidates are still controversial in SCLC. This study is the first report to identify transcriptomic alterations associated with prognosis and propose a gene expression-based risk signature that can be used to predict overall survival (OS) in SCLC patients who have undergone potentially curative surgery. An integrative transcriptome analysis of three gene expression datasets (GSE30219, GSE43346, and GSE149507) revealed 1734 up-regulated and 2907 down-regulated genes. Cox-Mantel test, Cox regression, and Lasso regression analyses were used to identify genes to be included in the risk signature. EGAD00001001244 and GSE60052-cohorts were used for internal and external validation, respectively. Overall survival was significantly poorer in patients with high-risk scores compared to the low-risk group. The discriminatory performance of the risk signature was superior to other parameters. Multivariate analysis showed that the risk signature has the potential to be an independent predictor of prognosis. The prognostic genes were enriched in pathways including regulation of transcription, cell cycle, cell metabolism, and angiogenesis. Determining the roles of the identified prognostic genes in the pathogenesis of SCLC may contribute to the development of new treatment strategies. The risk signature needs to be validated in a larger cohort of patients to test its usefulness in clinical decision-making.

Identification of molecular subtypes based on PANoptosis-related genes and construction of a signature for predicting the prognosis and response to immunotherapy response in hepatocellular carcinoma

Background

Previous studies have demonstrated that PANoptosis is strongly correlated with cancer immunity and progression. This study aimed to develop a PANoptosis-related signature (PANRS) to explore its potential value in predicting the prognosis and immunotherapy response of hepatocellular carcinoma (HCC).

Methods

Based on the expression of PANoptosis-related genes, three molecular subtypes were identified. To construct a signature, the differentially expressed genes between different molecular subtypes were subjected to multivariate least absolute shrinkage and selection operator Cox regression analyses. The risk scores of patients in the training set were calculated using the signature. The patients were classified into high-risk and low-risk groups based on the median risk scores. The predictive performance of the signature was evaluated using Kaplan-Meier plotter, receiving operating characteristic curves, nomogram, and calibration curve. The results were validated using external datasets. Additionally, the correlation of the signature with the immune landscape and drug sensitivity was examined. Furthermore, the effect of LPCAT1 knockdown on HCC cell behavior was verified using in vitro experiments.

Results

This study developed a PANRS. The risk score obtained by using the PANRS was an independent risk factor for the prognosis of patients with HCC and exhibited good prognostic predictive performance. The nomogram constructed based on the risk score and clinical information can accurately predicted the survival probability of patients with HCC. Patients with HCC in the high-risk groups have high immune scores and tend to generate an immunosuppressive microenvironment. They also exhibited a favorable response to immunotherapy, as evidenced by high tumor mutational burden, high immune checkpoint gene expression, high human leukocyte antigen gene expression, low tumor immune dysfunction and low exclusion scores. Additionally, the PANRS enabled the identification of 15 chemotherapeutic agents, including sorafenib, for patients with HCC with different risk levels, guiding clinical treatment. The signature gene LPCAT1 was upregulated in HCC cell lines. LPCAT1 knockdown markedly decreased HCC cell proliferation and migration.

Conclusion

PANRS can accurately predict the prognosis and immunotherapy response of patients with HCC and consequently guide individualized treatment.

Novel roles of karyopherin subunit alpha 2 in hepatocellular carcinoma

Hepatocellular carcinoma is the most common type of liver cancer and associated with a high fatality rate. This disease poses a major threat to human health worldwide. A considerable number of genetic and epigenetic factors are involved in the development of hepatocellular carcinoma. However, the molecular mechanism underlying the progression of hepatocellular carcinoma remains unclear. Karyopherin subunit alpha 2 (KPNA2), also termed importin α1, is a member of the nuclear transporter family. In recent years, KPNA2 has been gradually linked to the nuclear transport pathway for a variety of tumor-associated proteins. Furthermore, it promotes tumor development by participating in various pathophysiological processes such as cell proliferation, apoptosis, immune response, and viral infection. In hepatocellular carcinoma, it has been found that KPNA2 expression is significantly higher in liver cancer tissues versus paracancerous tissues. Moreover, it has been identified as a marker of poor prognosis and early recurrence in patients with hepatocellular carcinoma. Nevertheless, the role of KPNA2 in the development of hepatocellular carcinoma remains to be determined. This review summarizes the current knowledge on the pathogenesis and role of KPNA2 in hepatocellular carcinoma, and provides new directions and strategies for the diagnosis, treatment, and prediction of prognosis of this disease.

Significance of pyroptosis-related gene in the diagnosis and classification of rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA), a chronic autoimmune inflammatory disease, is often characterized by persistent morning stiffness, joint pain, and swelling. Early diagnosis and timely treatment of RA can effectively delay the progression of the condition and significantly reduce the incidence of disability. In the study, we explored the function of pyroptosis-related genes (PRGs) in the diagnosis and classification of rheumatoid arthritis based on Gene Expression Omnibus (GEO) datasets.

METHOD

We downloaded the GSE93272 dataset from the GEO database, which contains 35 healthy controls and 67 RA patients. Firstly, the GSE93272 was normalized by the R software "limma" package. Then, we screened PRGs by SVM-RFE, LASSO, and RF algorithms. To further investigate the prevalence of RA, we established a nomogram model. Besides, we grouped gene expression profiles into two clusters and explored their relationship with infiltrating immune cells. Finally, we analyzed the relationship between the two clusters and the cytokines.

RESULT

CHMP3, TP53, AIM2, NLRP1, and PLCG1 were identified as PRGs. The nomogram model revealed that decision-making based on established model might be beneficial for RA patients, and the predictive power of the nomogram model was significant. In addition, we identified two different pyroptosis patterns (pyroptosis clusters A and B) based on the 5 PRGs. We found that eosinophil, gamma delta T cell, macrophage, natural killer cell, regulatory T cell, type 17 T helper cell, and type 2 T helper cell were significant high expressed in cluster B. And, we identified gene clusters A and B based on 56 differentially expressed genes (DEGs) between pyroptosis cluster A and B. And we calculated the pyroptosis score for each sample to quantify the different patterns. The patients in pyroptosis cluster B or gene cluster B had higher pyroptosis scores than those in pyroptosis cluster A or gene cluster A.

CONCLUSION

In summary, PRGs play vital roles in the development and occurrence of RA. Our findings might provide novel views for the immunotherapy strategies with RA.

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.

Bioinformatics analysis and experimental verification of the prognostic and biological significance mediated by fatty acid metabolism related genes for hepatocellular carcinoma

Background

Liver cancer is among the leading causes of death related to cancer around the world. The most frequent type of human liver cancer is hepatocellular carcinoma (HCC). Fatty acid (FA) metabolism is an emerging hallmark that plays a promoting role in numerous malignancies. This study aimed to discover a FA metabolism-related risk signature and formulate a better model for HCC patients’ prognosis prediction.

Methods

We collected mRNA expression data and clinical parameters of patients with HCC using the TCGA databases, and the differential FA metabolism-related genes were explored. To create a risk prognostic model, we carried out the consensus clustering as well as univariate and multivariate Cox regression analyses. 16 genes were used to establish a prognostic model, which was then validated in the ICGC dataset. The accuracy of the model was performed using receiver operating characteristic (ROC) analyses, decision curve analysis (DCA) and nomogram. The immune cell infiltration level of risk genes was evaluated with single-sample GSEA (ssGSEA) algorithm. To reflect the response to immunotherapy, immunophenoscore (IPS) was obtained from TCGA-LIHC. Then, the expression of the candidate risk genes (p < 0.05) was validated by qRT-PCR, Western blotting and single-cell transcriptomics. Cellular function assays were performed to revealed the biological function of HAVCR1.

Results

According to the TCGA-LIHC cohort analysis, the majority of the FA metabolism-related genes were expressed differentially in the HCC and normal tissues. The prognosis of patients with high-risk scores was observed to be worse. Multivariate COX regression analysis confirmed that the model can be employed as an independent prognosis factor for HCC patients. Furthermore, ssGSEA analysis revealed a link between the model and the levels of immune cell infiltration. Our model scoring mechanism also provides a high predictive value in HCC patients receiving anti-PDL1 immunotherapy. One of the FA metabolism-related genes, HAVCR1, displays a significant differential expression between normal and HCC cell lines. Hepatocellular carcinoma cells (Huh7, and HepG2) proliferation, motility, and invasion were all remarkably inhibited by HAVCR1 siRNA.

Conclusion

Our study identified a novel FA metabolism-related prognostic model, revealing a better potential treatment and prevention strategy for HCC.

A Comprehensive Analysis of Pyroptosis-Related lncRNAs Signature Associated With Prognosis and Tumor Immune Microenvironment of Pancreatic Adenocarcinoma

Background: Globally, pancreatic adenocarcinoma (PAAD) is a common and highly devastating gastrointestinal malignancy that seriously threatens human health. Pyroptosis refers to an emerging form of programmed cell death that has been discovered in recent years, and studies have demonstrated that long non-coding RNA (lncRNA) may act as a moderator in the pyroptosis process of cancer cells. However, relevant explorations about lncRNAs and pyroptosis are still insufficient in PAAD. Therefore, our research is designed to make a comprehensive analysis of the potential values of pyroptosis-related lncRNAs in PAAD.

Methods: By integrating the RNA-sequencing, somatic mutation, and copy number variation (CNV) datasets, as well as the clinicopathological features, we established and validated a risk signature based on pyroptosis-related lncRNAs, and comprehensively analyzed its clinical significance and the potential connection with the tumor immune microenvironment (TIME).

Consequences: The genetic variation landscape displayed that the somatic mutations were rare while CNV changes were general and mainly concentrated on copy number amplification of these 52 pyroptosis-related genes. Subsequently, a risk signature consisting of 10 lncRNAs (TRAF3IP2-AS1, LINC00519, LINC01133, LINC02251, AC005332.6, AL590787.1, AC090114.2, TRPC7-AS1, MIR223HG, and MIR3142HG) was constructed and patients were divided into different subgroups according to the median risk score; patients with high-risk scores presented worse outcomes compared to those with low-risk scores in the training, testing, and entire cohorts. Furthermore, patients at low-risk scores possessed a higher infiltration abundance of immune cells compared with high-risk patients, which was consistent with the expression levels of lncRNAs between the high/low-risk groups. Drug sensitivity analysis showed that low-risk scores were related to anti-cancer agents like AICAR and Axitinib, whereas high-risk scores were connected with certain drugs such as AUY922. These results demonstrated that our risk signature could be used for prognosis prediction; additionally, it was also related to the TIME that might act as a potential indicator to instruct immunotherapeutic strategies.

Conclusion: This work explored the significance of the risk model constructed by pyroptosis-related lncRNAs in prognosis prediction and its internal link with the immune microenvironment of PAAD. The results are expected to assist in the diagnosis, prognostic assessment, and management of patients with PAAD.