Identification of the Pyroptosis-Related Prognosis Gene Signature and Immune Infiltration in Hepatocellular Carcinoma

Downregulation of PSAT1 inhibits cell proliferation and migration in uterine corpus endometrial carcinoma

Phosphoserine aminotransferase 1 (PSAT1) has been associated with the occurrence and development of various carcinomas; however, its function in uterine corpus endometrial carcinoma (UCEC) is unknown. We aimed to explore the relationship between PSAT1 and UCEC using The Cancer Genome Atlas database and functional experiments. PSAT1 expression levels in UCEC were employed using the paired sample t-test, Wilcoxon rank-sum test, the Clinical Proteomic Tumor Analysis Consortium database, and the Human Protein Atlas database, while survival curves were constructed using the Kaplan-Meier plotter. We performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to explore the possible functions and related pathways of PSAT1. Furthermore, single-sample gene set enrichment analysis was performed to detect the relationship between PSAT1 and tumor immune infiltration. StarBase and quantitative PCR were used to predict and verify the interactions between miRNAs and PSAT1. The Cell Counting Kit-8, EdU assay, clone formation assay, western blotting and flow cytometry were used to evaluate cell proliferation. Finally, Transwell and Wound healing assays were used to assess cell invasion and migration. Our study found that PSAT1 was significantly overexpressed in UCEC, and this high expression was associated with a worse prognosis. A high level of PSAT1 expression was associated with a late clinical stage and, histological type. In addition, the results of GO and KEGG enrichment analysis showed that PSAT1 was mainly involved in the regulation of cell growth, immune system and cell cycle in UCEC. In addition, PSAT1 expression was positively correlated with Th2 cells and negatively correlated with Th17 cells. Furthermore, we also found that miR-195-5P negatively regulated the expression of PSAT1 in UCEC. Finally, the knockdown of PSAT1 resulted in the inhibition of cell proliferation, migration, and invasion in vitro. Overall, PSAT1 was identified as a potential target for the diagnosis and immunotherapy of UCEC.

Identification and Characterization of an Ageing-Associated 13-lncRNA Signature That Predicts Prognosis and Immunotherapy in Hepatocellular Carcinoma

Background

In cancer pathology, cell senescence not only alters cell function but also reshapes the immune microenvironments in tumours. However, the association between cell senescence, tumour microenvironment, and disease progression of hepatocellular carcinoma (HCC) is yet to be fully understood. Therefore, the role of cell senescence-related genes and long noncoding RNAs (lncRNAs) in evaluating the clinical prognosis and immune cell infiltration (ICI) of HCC patients requires further investigation.

Methods

The limma R package was utilised to investigate differentially expressed genes according to the multiomics data. The CIBERSORT R package was utilised to assess ICI, and unsupervised cluster analysis was conducted using the R software's ConsensusClusterPlus package. A polygenic prognostic model of lncRNAs was constructed by conducting univariate and least absolute shrinkage and selection operator (Lasso) cox proportional-hazards regression analyses. The time-dependent receiver operating characteristic (ROC) curves were used for validation. We utilised the survminer R package to evaluate the tumour mutational burden (TMB). Moreover, the gene set enrichment analysis (GSEA) helped in pathway enrichment analysis, and the immune infiltration level of the model was evaluated using the IMvigor210 cohort.

Results

The identification of 36 prognosis-related genes was achieved based on their differential expression between healthy and liver cancer tissues. Liver cancer individuals were categorised into 3 independent senescence subtypes using the gene list, revealing considerable survival differences (variations). We observed that the prognosis of patients in the ARG-ST2 subtype was substantially better as compared to that in the ARG-ST3 subtype. Differences were observed in gene expression profiles among the three subtypes, with the differentially expressed genes predominantly associated with cell cycle control. The enrichment of upregulated genes in the ARG-ST3 subtype was observed in pathways related to biological processes, for instance, organelle fission, nuclear division, and chromosome recombination. ICI in the ARG-ST1 and ARG-ST2 subtypes, with relatively better prognosis, was substantially higher as compared to the ARG-ST3 subtype. Furthermore, a risk-score model, which can be employed as a reliable prognostic factor in an independent manner for individuals suffering from liver cancer, was constructed based on 13 cell senescence-related lncRNAs (MIR99AHG, LINC01224, LINC01138, SLC25A30AS1, AC006369.2, SOCS2AS1, LINC01063, AC006037.2, USP2AS1, FGF14AS2, LINC01116, KIF25AS1, and AC002511.2). The individuals with higher risk scores had noticeably poor prognoses in contrast with those having low-risk scores. Moreover, increased levels of TMB and ICI were observed in individuals with low-risk scores and gaining more benefit from immune checkpoint therapy.

Conclusion

Cell senescence is an essential factor in HCC onset and progression. We identified 13 senescence-related lncRNAs as HCC prognostic markers, which can help understand their function in the onset and progression of HCC and guide clinical diagnosis and treatment.

A Model of Basement Membrane-Associated Gene Signature Predicts Liver Hepatocellular Carcinoma Response to Immune Checkpoint Inhibitors

Liver hepatocellular carcinoma (LIHC) is a highly lethal malignant tumor originating from the digestive system, which is a serious threat to human health. In recent years, immunotherapy has shown significant therapeutic effects in the treatment of LIHC, but only for a minority of patients. The basement membrane (BM) plays an important role in the occurrence and development of tumors, including LIHC. Therefore, this study is aimed at establishing a risk score model based on basement membrane-related genes (BMRGs) to predict patient prognosis and response to immunotherapy. First, we defined three patterns of BMRG modification (C1, C2, and C3) by consensus clustering of BMRG sets and LIHC transcriptome data obtained from public databases. Survival analysis showed that patients in the C2 group had a better prognosis, and Gene Set Variation Analysis (GSVA) revealed that the statistically significant pathways were mainly enriched in the C2 group. Moreover, we performed Weighted Correlation Network Analysis (WGCNA) on the above three subgroups and obtained 179 intersecting genes. We further applied function enrichment analyses, and the results demonstrated that they were mainly enriched in metabolism-related pathways. Furthermore, we conducted the LASSO regression analysis and obtained 4 BMRGs (MPV17, GNB1, DHX34, and MAFG) that were significantly related to the prognosis of LIHC patients. We further constructed a prognostic risk score model based on the above genes, which was verified to have good predictive performance for LIHC prognosis. In addition, we analyzed the correlation between the risk score and the tumor immune microenvironment (TIM), and the results showed that the high-risk scoring group tended to be in an immunosuppressed status. Finally, we investigated the relationship between the risk score and LIHC immune function. The results demonstrated that the risk score was closely related to the expression levels of multiple immune checkpoints. Patients in the low-risk group had significantly higher IPS scores, and patients in the high-risk group had lower immune escape and TIDE score. In conclusion, we established a novel risk model based on BMRGs, which may serve as a biomarker for prognosis and immunotherapy in LIHC.

Identification and Characterization of Genes Related to the Prognosis of Hepatocellular Carcinoma Based on Single-Cell Sequencing

The heterogeneity of hepatocellular carcinoma (HCC) highlights the importance of precision therapy. In recent years, single-cell RNA sequencing has been used to reveal the expression of genes at the single-cell level and comprehensively study cell heterogeneity. This study combined big data analytics and single-cell data mining to study the influence of genes on HCC prognosis. The cells and genes closely related to the HCC were screened through single-cell RNA sequencing (71,915 cells, including 34,414 tumor cells) and big data analysis. Comprehensive bioinformatics analysis of the key genes of HCC was conducted for molecular classification and multi-dimensional correlation analyses, and a prognostic model for HCC was established. Finally, the correlation between the prognostic model and clinicopathological features was analyzed. 16,880 specific cells, screened from the single-cell expression profile matrix, were divided into 20 sub-clusters. Cell typing revealed that 97% of these cells corresponded to HCC cell lines, demonstrating the high specificity of cells derived from single-cell sequencing. 2,038 genes with high variability were obtained. The 371 HCC samples were divided into two molecular clusters. Cluster 1 (C1) was associated with tumorigenesis, high immune score, immunotherapy targets (PD-L1 and CYLA-4), high pathological stage, and poor prognosis. Cluster 2 (C2) was related to metabolic and immune function, low immune score, low pathological stage, and good prognosis. Seven differentially expressed genes (CYP3A4, NR1I2, CYP2C9, TTR, APOC3, CYP1A2, and AFP) identified between the two molecular clusters were used to construct a prognostic model. We further validated the correlation between the seven key genes and clinical features, and the established prognostic model could effectively predict HCC prognosis. Our study identified seven key genes related to HCC that were used to construct a prognostic model through single-cell sequencing and big data analytics. This study provides new insights for further research on clinical targets of HCC and new biomarkers for clinical application.