Hepatocellular carcinoma (HCC) is a type of liver cancer that originates from liver cells. It is one of the most common types of liver cancer and a leading cause of cancer-related death worldwide. Early detection and treatment can improve the HCC prognosis. Therefore, it is necessary to further improve HCC markers and risk stratification. PANoptosome is a cytoplasmic polymer protein complex that regulates a proinflammatory programmed cell death pathway called “PANoptosis”. The role of PANoptosis in HCC remains unclear. In this study, the molecular changes of PANoptosis related genes (PAN-RGs) in HCC were systematically evaluated. We characterized the heterogeneity of HCC by using consensus clustering to identify two distinct subtypes. The two subtypes showed different survival rate, biological function, chemotherapy drug sensitivity and immune microenvironment. After identification of PAN-RG differential expression genes (DEGs), a prognostic model was established by Cox regression analysis using minimum absolute contraction and selection operator (LASSO), and its prognostic value was verified by Cox regression analysis, Kaplan-Meier curve and receiver operating characteristic (ROC) curve. Our own specimens were also used to further validate the prognostic significance and possible clinical value of the selected targets. Subsequently, we conducted a preliminary discussion on the reasons for the influence of the model on the prognosis through TME analysis, drug resistance analysis, TMB analysis and other studies. This study provides a new idea for individualized and precise treatment of HCC.
Background. Long noncoding RNAs (lncRNAs) are becoming a critical class of metabolic regulate molecule in cancer. Glutamine is a regulator that contributes to each of the core metabolic tasks in proliferating tumor cells. Thus, we aimed to evaluate the association of lncRNAs with glutamine metabolism in lung adenocarcinoma (LUAD). Methods. Using single-sample gene set enrichment analysis (ssGSEA), LUAD specimens were assigned scores based on glutamine metabolism-related genes, and the shared common glutamine metabolism-related lncRNAs in three different LUAD data cohorts were identified. ConsensusClusterPlus was used to perform unsupervised clustering analysis in patients with LUAD. Key glutamine metabolism-related lncRNAs were identified by first-order partial correlation analysis. Results. A total of 11 shared glutamine metabolism-associated lncRNAs were identified in three LUAD data cohorts, and LUAD patients were classified into three glutamine metabolism subtypes based on the expressions of the related genes. C1 exhibited shorter overall survival (OS), poor genomic instability, and inadequate infiltration of immune cell types in the tumor microenvironment (TME) and was representative of the immunodeficiency phenotype. C2 represented the immunosuppressive phenotype while C3 represented the immune activation phenotype, exhibiting the highest sensitivity to immunotherapy. Nine of the 11 lncRNAs were localized to the nucleus. Finally, three key lncRNAs, significantly enriched in multiple metabolic pathways, were screened and found to be remarkably related to the OS of LUAD. Conclusion. We identified three glutamine metabolism subtypes of LUAD, which reflected different OS, genomic, and TME features, and identified three key glutamine metabolism-associated lncRNAs may contribute to further study of lncRNAs in cancer metabolism.
Hepatocellular Carcinoma (HCC) is the leading cause of cancer-related deaths globally. Most HCC patients are already in advanced stages of the disease when a confirmed diagnosis was made with prone to metastasis and a poor prognosis. Anoikis resistance plays a critical role in tumor invasion and metastasis. whereas the role of anoikis in HCC remains unclear. According to univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) analysis, anoikis-related genes (ARGs) associated with the overall rate (OS) were selected. Then, 3 prognostic ARGs (PDK4, STK11 and TFDP1) were identified by multivariate Cox regression, and to establish a risk model. According to the risk score, HCC patients were divided into high- and low-risk group. The OS rate and immune infiltration between two groups were evaluated by Kaplan-Meier, CIBERSORT and ssGSEA analysis. The OS rate of HCC patients in low-risk group was longer than that in the high-risk group. The results of nomogram showed that the ARGs prognostic signature was an independent prognostic predictor. In addition, consensus clustering analysis could cluster the patients into two subgroups with different immune infiltration. Besides, functional enrichment and drug sensitivity were also conducted between high- and low-risk groups. This study was the first to integrate multiple ARGs to establish a risk-predictive model, and might provide a new perspective for individualized and accurate therapy strategies for HCC patients.
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