Identification of senescence-associated long non-coding RNAs to predict prognosis and immune microenvironment in patients with hepatocellular carcinoma

Prognostic Value of an Immune Long Non-Coding RNA Signature in Liver Hepatocellular Carcinoma

In recent years, there has been a growing recognition of the important role that long non-coding RNAs (lncRNAs) play in the immunological process of hepatocellular carcinoma (LIHC). An increasing number of studies have shown that certain lncRNAs hold great potential as viable options for diagnosis and treatment in clinical practice. The primary objective of our investigation was to devise an immune lncRNA profile to explore the significance of immune-associated lncRNAs in the accurate diagnosis and prognosis of LIHC. Gene expression profiles of LIHC samples obtained from TCGA database were screened for immune-related genes. The optimal immune-related lncRNA signature was built via correlational analysis, univariate and multivariate Cox analysis. Then, the Kaplan-Meier plot, ROC curve, clinical analysis, gene set enrichment analysis, and principal component analysis were performed to evaluate the capability of the immune lncRNA signature as a prognostic indicator. Six long non-coding RNAs were identified via correlation analysis and Cox regression analysis considering their interactions with immune genes. Subsequently, tumor samples were categorized into two distinct risk groups based on different clinical outcomes. Stratification analysis indicated that the prognostic ability of this signature acted as an independent factor. The Kaplan-Meier method was employed to conduct survival analysis, results showed a significant difference between the two risk groups. The predictive performance of this signature was validated by principal component analysis (PCA). Additionally, data obtained from gene set enrichment analysis (GSEA) revealed several potential biological processes in which these biomarkers may be involved. To summarize, this study demonstrated that this six-lncRNA signature could be identified as a potential factor that can independently predict the prognosis of LIHC patients.

A refined therapeutic plan based on the machine-learning prognostic model of liver hepatocellular carcinoma

To deeply explore new strategy of the individual therapy for the patients with liver hepatocellular carcinoma (LIHC), we observed gene expression profile in patients with LIHC and made a comprehensive analysis of the inflammation-related phenotypes, we detected a set of characteristic genes associated with the biological activities of tumor cells, among which 3 genes and 2 lncRNAs are tagged on the LIHC prognosis. Then we constructed a novel prognostic model by machine learning, called Inf-PR model, and evaluated the drug sensitivity and immune targets by a series of bioinformatics tools. Ten-fold cross-validation testified that the model achieved excellent performance on prediction and classification of prognostic risks, which was not only able to get more reliable prognosis information than the age, gender, grade and stage, but also exceeded those previously reported similar models. Accordingly, drug sensitivity was detected in different prognostic risk groups, the result displayed that 10 FDA-approved small molecular drugs including lovastatin and sorafenib had higher sensitivities and perturbativities in the high-risk group, and other 15 drugs including doxorubicin and lenvatinib had better sensitivities and perturbativities in the low-risk group. Moreover, it suggested the patients with high risk would better combine with immunotherapy than those with low risk. Taken together, this study presents a new individual precision strategy about drug and target selection to treat LIHC based on this evaluation model, which is a powerful supplement for current anti-tumor therapy.

Establishment of a 6-signature risk model associated with cellular senescence for predicting the prognosis of breast cancer

This study focused on screening novel markers associated with cellular senescence for predicting the prognosis of breast cancer. The RNA-seq expression profile of BRCA and clinical data were obtained from TCGA. The pam algorithm was used to cluster patients based on senescence-related genes. The weighted gene co-expression network analysis was used to identify co-expressed genes, and LASSO-Cox analysis was performed to build a risk prognosis model. The performance of the model was also evaluated. We additionally explored the role of senescence in cancer development and possible regulatory mechanism. The patients were clustered into 2 subtypes. A total of 5259 genes significantly related to senescence were identified by weighted gene co-expression network analysis. LASSO-Cox finally established a 6-signature risk model (ADAMTS8, DCAF12L2, PCDHA10, PGK1, SLC16A2, and TMEM233) that exhibited favorable and stable performance in our training, validation, and whole BRCA datasets. Furthermore, the superiority of our model was also observed after comparing it to other published models. The 6-signature was proved to be an independent risk factor for prognosis. In addition, mechanism prediction implied the activation of glycometabolism processes such as glycolysis and TCA cycle under the condition of senescence. Glycometabolism pathways were further found to negatively correlate with the infiltration level of CD8 T-cells and natural killer cells but positively correlate with M2 macrophage infiltration and expressions of tissue degeneration biomarkers, which suggested the deficit immune surveillance and risk of tumor migration. The constructed 6-gene model based on cellular senescence could be an effective indicator for predicting the prognosis of BRCA.