Construction and Comprehensive Prognostic Analysis of a Novel Immune-Related lncRNA Signature and Immune Landscape in Gastric Cancer

Prognostic analysis and risk assessment based on RNA editing in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, and prognosis assessment is crucial for guiding treatment decisions. In this study, we aimed to develop a personalized prognostic model for HCC based on RNA editing. RNA editing is a post-transcriptional process that can affect gene expression and, in some cases, play a role in cancer development. By analyzing RNA editing sites in HCC, we sought to identify a set of sites associated with patient prognosis and use them to create a prognostic model. We gathered RNA editing data from the Synapse database, comprising 9990 RNA editing sites and 250 HCC samples. Additionally, we collected clinical data for 377 HCC patients from the Cancer Genome Atlas (TCGA) database. We employed a multi-step approach to identify prognosis-related RNA editing sites (PR-RNA-ESs). We assessed how patients in the high-risk and low-risk groups, as defined by the model, fared in terms of survival. A nomogram was developed to predict the precise survival prognosis of HCC patients and assessed the prognostic model's utility through a receiver operating characteristic (ROC) analysis and decision curve analysis (DCA). Our analysis identified 33 prognosis-related RNA editing sites (PR-RNA-ESs) associated with HCC patient prognosis. Using a combination of LASSO regression and cross-validation, we constructed a prognostic model based on 13 PR-RNA-ESs. Survival analysis demonstrated significant differences in the survival outcomes of patients in the high-risk and low-risk groups defined by this model. Additionally, the differential expression of the 13 PR-RNA-ESs played a role in shaping patient survival. Risk-prognostic investigations further distinguished patients based on their risk levels. The nomogram enabled precise survival prognosis prediction. Our study has successfully developed a highly personalized and accurate prognostic model for individuals with HCC, leveraging RNA editing data. This model has the potential to revolutionize clinical evaluation and medical management by providing individualized prognostic information. The identification of specific RNA editing sites associated with HCC prognosis and their incorporation into a predictive model holds promise for improving the precision of treatment strategies and ultimately enhancing patient outcomes in HCC.

Predicting Prognosis and Distinguishing Cold and Hot Tumors in Bladder Urothelial Carcinoma Based on Necroptosis-Associated lncRNAs

Background

In reference to previous studies, necroptosis played an important role in cancer development. Our team decided to explore the potential prognostic values of long non-coding RNAs (lncRNAs) associated with necroptosis in bladder urothelial carcinoma (BLCA) and their relationship with the tumor microenvironment (TME) and the immunotherapeutic response for accurate dose.

Methods

To obtain the required data, bladder urothelial carcinoma transcriptome data were searched from Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/). We used co-expression analysis, differential expression analysis, and univariate Cox regression to screen out prognostic lncRNAs associated with necroptosis in BLCA. Then the least absolute shrinkage and selection operator (LASSO) was conducted to construct the necroptosis-associated lncRNAs model. Based on this model, we also performed the Kaplan–Meier analysis and time-dependent receiver operating characteristics (ROC) to estimate the prognostic power of risk score. Multivariate and univariate Cox regression analysis were performed to build up a nomogram. Calibration curves, and time-dependent ROC were also conducted to evaluate nomogram. Principal component analysis (PCA) revealed a difference between high- and low-risk groups. In addition, we explored immune analysis, gene set enrichment analyses (GSEA), and evaluation of the half-maximal inhibitory concentration (IC50) in constructed model. Finally, the entire samples were divided into three clusters based on model of necroptosis-associated lncRNAs to further compare immunotherapy in cold and hot tumors.

Results

A model was built up based on necroptosis-associated lncRNAs. The model revealed good consistence between calibration plots and prognostic prediction. The area of 1-, 3-, and 5-year OS under the ROC curve (AUC) were 0.707, 0.679, and 0.675. Risk groups could be helpful for systemic therapy due to the markedly diverse IC50 between risk groups. To our delight, clusters could effectively identify cold and hot tumors, which would be beneficial to accurate mediation. Clusters 2 and 3 were considered the hot tumor, which was more sensitive to immunotherapeutic drugs.

Conclusions

The outcomes of our study suggested that necroptosis-associated lncRNAs could effectively predict patients with BLCA prognosis, which may be helpful for distinguishing the cold and hot tumors and improving individual treatment of BLCA.

The Value of m5C-Related lncRNAs in the Prognostic Assessment and Immunotherapy of Stomach Adenocarcinoma

Long noncoding RNAs (lncRNAs) are closely associated with a variety of tumors, including stomach adenocarcinoma (STAD). However, the role of 5-methylcytosine- (m5C-) related lncRNAs in STAD is still uncertain. This study investigated the value of m5C-related lncRNAs in prognostic evaluation and immunotherapy of STAD. STAD transcriptome sequencing data were downloaded from The Cancer Genome Atlas (TCGA) database, and m5C-related lncRNAs were screened by Pearson correlation analysis. A prognostic m5C-related lncRNA signature (m5CRLSig) associated with STAD was established using univariate and multivariate Cox regression analysis. We constructed a prognostic risk model for STAD with six m5C-related lncRNAs. The receiver operating characteristic (ROC) curve was used to examine the predictive efficacy. Univariate and multifactorial Cox regression analysis and principal component analysis (PCA) validated m5CRLSig as an independent factor of STAD prognosis. The clinicopathological characteristics of the model showed higher risk scores for stages II-IV, grade 3, N1-3, and death status. The calibration curve of a nomogram revealed that the nomogram had an excellent predictive function for survival risk. Furthermore, the expression of six m5C-related lncRNAs in STAD and paracancerous tissues was detected by quantitative real-time PCR (qRT-PCR), which verified the feasibility of the m5CRLSig as a prognostic marker for STAD. m5C-related lncRNAs were linked to multiple immune-associated pathways, according to gene set enrichment analysis (GSEA). CIBERSORT analysis indicated that m5CRLSig was involved in immune cell infiltration. Risk score was associated with immune checkpoint gene expression, immune function scores, and chemotherapeutic drug sensitivity. Therefore, m5CRLSig can efficiently assess the prognosis of STAD patients and can be used as a biological marker for immunotherapy.