Comprehensive analysis of oxidative stress-related lncRNA signatures in glioma reveals the discrepancy of prognostic and immune infiltration.

Oxidative stress refers to the process of reactive oxide species (ROS) increase in human body due to various factors, which leads to oxidative damage in human tissues. Current studies have confirmed that sustained oxidative stress is one of the distinctive features throughout the development of tumors. Numerous reports have shown that lncRNAs can regulate the process of oxidative stress through multiple pathways. However, the relationship between glioma-associated oxidative stress and lncRNAs is not clearly investigated. RNA sequencing data of GBM (glioblastoma) and LGG (low grade glioma) and corresponding clinical data were retrieved from the TCGA database. Oxidative stress related lncRNAs (ORLs) were identified by Pearson correlation analysis. Prognostic models for 6-ORLs were structured in the training cohort by univariate Cox regression analysis, multivariate Cox regression analysis and LASSO regression analysis. We constructed the nomogram and verified its predictive efficacy by Calibration curves and DCA decision curves. The biological functions and pathways of 6-ORLs-related mRNAs were inferred by Gene Set Enrichment Analysis. Immune cell abundance and immune function associated with risk score (RS) were estimated by ssGSEA, CIBERSORT and MCPcounter synthetically. External validation of the signature was completed using the CGGA-325 and CGGA-693 datasets. 6-ORLs signature-AC083864.2, AC107294.1, AL035446.1, CRNDE, LINC02600, and SNAI3-AS1-were identified through our analysis as being predictive of glioma prognosis. Kaplan-Meier and ROC curves indicated that the signature has a dependable predictive efficacy in the TCGA training cohort, validation cohort and CGGA-325/CGGA-693 test cohort. The 6-ORLs signature were verified to be independent prognostic predictors by multivariate cox regression and stratified survival analysis. Nomogram built with risk scores had strong predictive efficacy for patients' overall survival (OS). The outcomes of the functional enrichment analysis revealing potential molecular regulatory mechanisms for the 6-ORLs. Patients in the high-risk subgroup presented a significant immune microenvironment of macrophage M0 and cancer-associated fibroblast infiltration which was associated with a poorer prognosis. Finally, the expression levels of 6-ORLs in U87/U251/T98/U138 and HA1800 cell lines were verified by RT-qPCR. The nomogram in this study has been made available as a web version for clinicians. This 6-ORLs risk signature has the capabilities to predict the prognosis of glioma patients, assist in evaluating immune infiltration, and assess the efficacy of various anti-tumor systemic therapy regimens.

The Construction of a Prognostic Model Based on a Peptidyl Prolyl Cis-Trans Isomerase Gene Signature in Hepatocellular Carcinoma.

Objective: The aim of the present study was to construct a prognostic model based on the peptidyl prolyl cis-trans isomerase gene signature and explore the prognostic value of this model in patients with hepatocellular carcinoma. Methods: The transcriptome and clinical data of hepatocellular carcinoma patients were downloaded from The Cancer Genome Atlas and the International Cancer Genome Consortium database as the training set and validation set, respectively. Peptidyl prolyl cis-trans isomerase gene sets were obtained from the Molecular Signatures Database. The differential expression of peptidyl prolyl cis-trans isomerase genes was analyzed by R software. A prognostic model based on the peptidyl prolyl cis-trans isomerase signature was established by Cox, Lasso, and stepwise regression methods. Kaplan-Meier survival analysis was used to evaluate the prognostic value of the model and validate it with an independent external data. Finally, nomogram and calibration curves were developed in combination with clinical staging and risk score. Results: Differential gene expression analysis of hepatocellular carcinoma and adjacent tissues showed that there were 16 upregulated genes. A prognostic model of hepatocellular carcinoma was constructed based on three gene signatures by Cox, Lasso, and stepwise regression analysis. The Kaplan-Meier curve showed that hepatocellular carcinoma patients in high-risk score group had a worse prognosis (p < 0.05). The receiver operating characteristic curve revealed that the area under curve values of predicting the survival rate at 1, 2, 3, 4, and 5 years were 0.725, 0.680, 0.644, 0.630, and 0.639, respectively. In addition, the evaluation results of the model by the validation set were basically consistent with those of the training set. A nomogram incorporating clinical stage and risk score was established, and the calibration curve matched well with the diagonal. Conclusion: A prognostic model based on 3 peptidyl prolyl cis-trans isomerase gene signatures is expected to provide reference for prognostic risk stratification in patients with hepatocellular carcinoma.

CKMT1B is a potential prognostic biomarker and associated with immune infiltration in Lower-grade glioma.

BACKGROUND: Lower-grade glioma (LGG) is the most common histology identified in glioma. CKMT1B has not been investigated in glioma. The purpose of this research was to investigate the prognostic value of CKMT1B and its correlation with immune infiltration in LGG. METHODS: We used Gene Expression Profiling Interactive Analysis (GEPIA) to analyze the expression of CKMT1B in LGG. Univariate and multivariate Cox regression analyses were used to assess the effect of CKMT1B expression and screened variables on survival. The correlation between CKMT1B and immune infiltration was evaluated by TIMER and CIBERSORT. Moreover, the possible biological functions of CKMT1B were studied by GSEA. The statistical analysis was conducted by R software. RESULTS: The expression of CKMT1B was significantly lower than the normal samples in LGG. Low expression of CKMT1B predicts a worse prognosis. Multivariate Cox analyses revealed that CKMT1B might be an independent favorable prognostic indicator. TIMER analysis revealed that CKMT1B expression level was related to immune infiltration. CIBERSORT analysis showed that CKMT1B expression was positively related to the infiltration level of activated mast cells and negatively related to macrophage M2 in LGG. Moreover, GESA showed that multiple cancer-related and immune-related gene sets were enriched in the low-CKMT1B group in the top 5 of the most significant differences. CONCLUSION: CKMT1B is a prognostic biomarker with potential applications and associated with immune infiltration in Lower-grade glioma.

Application of an Autophagy-Related Gene Prognostic Risk Model Based on TCGA Database in Cervical Cancer.

Background: Autophagy plays an important role in the development of cancer. However, the prognostic value of autophagy-related genes (ARGs) in cervical cancer (CC) is unclear. The purpose of this study is to construct a survival model for predicting the prognosis of CC patients based on ARG signature. Methods: ARGs were obtained from the Human Autophagy Database and Molecular Signatures Database. The expression profiles of ARGs and clinical data were downloaded from the TCGA database. Differential expression analysis of CC tissues and normal tissues was performed using R software to screen out ARGs with an aberrant expression. Univariate Cox, Lasso, and multivariate Cox regression analyses were used to construct a prognostic model which was validated by using the test set and the entire set. We also performed an independent prognostic analysis of risk score and some clinicopathological factors of CC. Finally, a clinical practical nomogram was established to predict individual survival probability. Results: Compared with normal tissues, there were 63 ARGs with an aberrant expression in CC tissues. A risk model based on 3 ARGs was finally obtained by Lasso and Cox regression analysis. Patients with high risk had significantly shorter overall survival (OS) than low-risk patients in both train set and validation set. The ROC curve validated its good performance in survival prediction, suggesting that this model has a certain extent sensitivity and specificity. Multivariate Cox analysis showed that the risk score was an independent prognostic factor. Finally, we mapped a nomogram to predict 1-, 3-, and 5-year survival for CC patients. The calibration curves indicated that the model was reliable. Conclusion: A risk prediction model based on CHMP4C, FOXO1, and RRAGB was successfully constructed, which could effectively predict the prognosis of CC patients. This model can provide a reference for CC patients to make precise treatment strategy.