Prognostic signature for hepatocellular carcinoma based on 4 pyroptosis-related genes

Duan, Sainan; Gao, Jianying; Lou, Weiming; Zhang, Yize; Deng, Ying; Huang, Haiyue; Xu, Hui; Guo, Sixuan; Lai, Shuhui; Xi, Feiyang; Li, Zhangwang; Deng, Libin; Ye, Zhong

doi:10.1186/s12920-022-01322-9

Cited by 7 publications

(3 citation statements)

References 39 publications

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“… 35 Moreover, PRF1 is an independent factor affecting the prognosis of HCC, and has good predictive performance in terms of prognosis and recurrence. 36 Consistently, in our study, PRF1 was upregulated in relapse patients, and the occurrence of Arm level Deletion in PRF1 resulted in a significant increase in the number of Neutrophil cells, these results indicate that PRF1 can also be used for predicting HCC relapse after LT.…”

Section: Discussionsupporting

confidence: 89%

RNA-Seq and Immune Repertoire Analysis of Normal and Hepatocellular Carcinoma Relapse After Liver Transplantation

Guo,

Yuan,

Cao

et al. 2023

IJGM

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Background: Hepatocellular carcinoma (HCC) relapse is the main reason for the poor prognosis of HCC after Liver transplantation (LT). This study aimed to explore the molecular mechanisms and immune repertoire profiles of HCC relapse. Material and Methods: RNA-seq of blood samples from patients with normal (n=12) and HCC relapse (n=6) after LT was performed to identify differentially expressed genes (DEGs) and key signalling pathways. The DEGs and immune genes were further analyzed by bioinformatics. TRUST4 was used to analyze the differences in the immune repertoire between the two groups. Another 11 blood samples from patients with HCC who had received LT were collected for RT-qPCR verification of key genes. Results: A total of 131 upregulated and 157 downregulated genes were identified using RNA-seq, and GO enrichment analysis revealed that the top 15 pathways were immune-related. The PPI network identified 10 key genes. Immune infiltration analysis revealed a significant difference in the five immune cell types between the two groups. A total of 83 intersecting genes were obtained by intersecting DEGs and immune genes. 6 key genes, including MX1, ISG15, OAS1, PRF1, SPP1, and THBS1 were obtained according to the intersection of DEGs, PPI network top 10 genes and immune intersecting genes. Immune repertoire analysis showed that the usage frequency of variable (V) and joining (J) genes in the normal group was higher than that in the relapse group. RT-qPCR validation showed that the expression levels of key genes were consistent with the RNA-seq results. Conclusion: Our study identified key pathways and genes that could help determine whether transplant recipients are more prone to HCC relapse. Immune repertoire analysis revealed a difference in the usage frequency of VJ genes between the normal and relapse groups, providing a research direction for immunotherapy in patients with HCC relapse after liver transplantation.

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Section: Discussionsupporting

confidence: 89%

RNA-Seq and Immune Repertoire Analysis of Normal and Hepatocellular Carcinoma Relapse After Liver Transplantation

Guo,

Yuan,

Cao

et al. 2023

IJGM

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“…Similarly to our findings, several academics have linked the Tregs to the development of IDD (Wang et al, 2021). Moreover, several other immune cell types, such as neutrophils and T cells, have drawn a lot of interest (Duan et al, 2022). Eventually, it is anticipated that the development of immune cell responses will lead to a breakthrough in the treatment of IDD.…”

Section: Figure 12supporting

confidence: 82%

Identification of novel gene signatures and immune cell infiltration in intervertebral disc degeneration using bioinformatics analysis

Tang

Zhu

et al. 2023

Front. Mol. Biosci.

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Background: Intervertebral disc degeneration (IDD) is the leading cause of lower back pain, and an overall understanding of the molecular mechanisms related to IDD is still lacking. The purpose of this study was to explore gene signatures and immune cell infiltration related to IDD via bioinformatics analysis.Methods: A total of five expression profiles of mRNA and non-coding RNA were downloaded from the Gene Expression Omnibus (GEO) database. The potentially involved lncRNA/circRNA–miRNA–mRNA networks and protein-protein interaction networks were constructed by miRNet, circBank, STRING, and the Cytoscape database. Gene ontology, Kyoto Encyclopaedia of Genes and Genomes Analysis, Gene Set Enrichment Analysis, Gene Set Variation Analysis, Immune Infiltration Analysis, and Drug-Gene Interaction were used to analyse the top 20 hub genes. RT-qPCR was conducted to confirm the 12 differential expressions of genes both in the nucleus pulposus and annulus fibrosus tissuesResults: There were 346 differentially expressed mRNAs, 12 differentially expressed miRNAs, 883 differentially expressed lncRNAs, and 916 differentially expressed circRNAs in the GEO database. Functional and enrichment analyses revealed hub genes associated with platelet activation, immune responses, focal adhesion, and PI3K-Akt signalling. The apoptotic pathway, the reactive oxygen species pathway, and oxidative phosphorylation play an essential role in IDD. Immune infiltration analysis demonstrated that the Treg cells had significant infiltration, and three levels of immune cells, including dendritic cells, Th2 cells, and tumour-infiltrating lymphocytes, were inhibited in IDD. Drug-gene interaction analysis showed that COL1A1 and COL1A2 were targeted by collagenase clostridium histolyticum, ocriplasmin, and PDGFRA was targeted by 66 drugs or molecular compounds. Finally, 24 cases of IDD tissues and 12 cases of normal disc tissues were collected, and the results of RT-qPCR were consistent with the bioinformatics results.Conclusion: Our data indicated that the 20 hub genes and immune cell infiltration were involved in the pathological process of IDD. In addition, the PDGFRA and two potential drugs were found to be significant in IDD development.

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“…Our findings align with existing research showing that TP53 mutation, the most prevalent mutation in cancer, is associated with increased tumor aggressiveness and poorer prognosis, particularly in HCC patients [ 45 ]. Compared to existing studies on HCC prognostic models utilizing bulk RNA-seq technology, such as those based on pyroptosis-related gene models [ 46 ] and immune infiltration-related gene models [ 47 ], there is a notable gap in research at the single-cell level. In contrast to the two current prognostic models incorporating HCC scRNA-seq data [ 48 – 49 ], our developed predictive model focuses on identifying hepatocyte-specific genes with significant expression changes from a cellular evolution perspective.…”

Section: Discussionmentioning

confidence: 99%

Identification and validation of a novel predictive signature based on hepatocyte-specific genes in hepatocellular carcinoma by integrated analysis of single-cell and bulk RNA sequencing

He,

Qi,

Xie

et al. 2024

BMC Med Genomics

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Background Hepatocellular carcinoma represents a significant global burden in terms of cancer-related mortality, posing a substantial risk to human health. Despite the availability of various treatment modalities, the overall survival rates for patients with hepatocellular carcinoma remain suboptimal. The objective of this study was to explore the potential of novel biomarkers and to establish a novel predictive signature utilizing multiple transcriptome profiles. Methods The GSE115469 and CNP0000650 cohorts were utilized for single cell analysis and gene identification. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets were utilized in the development and evaluation of a predictive signature. The expressions of hepatocyte-specific genes were further validated using the GSE135631 cohort. Furthermore, immune infiltration results, immunotherapy response prediction, somatic mutation frequency, tumor mutation burden, and anticancer drug sensitivity were analyzed based on various risk scores. Subsequently, functional enrichment analysis was performed on the differential genes identified in the risk model. Moreover, we investigated the expression of particular genes in chronic liver diseases utilizing datasets GSE135251 and GSE142530. Results Our findings revealed hepatocyte-specific genes (ADH4, LCAT) with notable alterations during cell maturation and differentiation, leading to the development of a novel predictive signature. The analysis demonstrated the efficacy of the model in predicting outcomes, as evidenced by higher risk scores and poorer prognoses in the high-risk group. Additionally, a nomogram was devised to forecast the survival rates of patients at 1, 3, and 5 years. Our study demonstrated that the predictive model may play a role in modulating the immune microenvironment and impacting the anti-tumor immune response in hepatocellular carcinoma. The high-risk group exhibited a higher frequency of mutations and was more likely to benefit from immunotherapy as a treatment option. Additionally, we confirmed that the downregulation of hepatocyte-specific genes may indicate the progression of hepatocellular carcinoma and aid in the early diagnosis of the disease. Conclusion Our research findings indicate that ADH4 and LCAT are genes that undergo significant changes during the differentiation of hepatocytes into cancer cells. Additionally, we have created a unique predictive signature based on genes specific to hepatocytes.

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Prognostic signature for hepatocellular carcinoma based on 4 pyroptosis-related genes

Cited by 7 publications

References 39 publications

RNA-Seq and Immune Repertoire Analysis of Normal and Hepatocellular Carcinoma Relapse After Liver Transplantation

RNA-Seq and Immune Repertoire Analysis of Normal and Hepatocellular Carcinoma Relapse After Liver Transplantation

Identification of novel gene signatures and immune cell infiltration in intervertebral disc degeneration using bioinformatics analysis

Identification and validation of a novel predictive signature based on hepatocyte-specific genes in hepatocellular carcinoma by integrated analysis of single-cell and bulk RNA sequencing

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