Identification of an independent autophagy-gene prognostic index for papillary renal cell carcinoma

A meta-analysis and bioinformatics analysis of P4HB expression levels in the prognosis of cancer patients

BACKGROUND

P4HB (prolyl 4-hydroxylase, beta polypeptide) is a human chromosomal gene that encodes an endoplasmic reticulum (ER) molecular chaperone protein with oxidoreductase, chaperone and isomerase activities. Recent studies indicated that P4HB may have clinical significance, with elevated P4HB expression reported in cancer patients, but its impact on tumor prognosis is not yet clear. To our knowledge, this is the first meta-analysis to show an association between P4HB expression and the prognosis of various cancers.

METHODS

We conducted a systematic literature search in the PubMed, PubMed Central, Web of Science, Embase, CNKI, Wanfang and Weipu databases, followed by a quantitative meta-analysis using Stata SE14.0 and R statistics software 4.2.1. The hazard ratio (HR) and relative risk (RR) were analyzed to evaluate the relationships of P4HB expression levels with overall survival (OS), disease-free survival (DFS), and clinicopathological parameters of cancer patients. Subsequently, P4HB expression in various cancer types was validated using the Gene Expression Profiling Interactive Analysis (GEPIA) online database.

RESULTS

Ten articles containing the data of 4121 cancer patients were included in the analysis, and a significant correlation of high P4HB expression with apparently shorter OS was found (HR, 1.90; 95% CI, 1.50-2.40; P < 0.01), while there was no significant correlation with gender (RR, 1.06; 95% CI, 0.91-1.22; P = 0.084), or age. Additionally, GEPIA online analysis revealed significant upregulation of P4HB in 13 types of cancer. Among them, P4HB overexpression was associated with shorter OS in 9 and worse DFS in 11 cancer types.

CONCLUSIONS

Upregulation of P4HB is correlated with worse prognosis in various cancers, which could provide new ideas for the development of P4HB-related diagnostic biomarkers and new therapeutic targets.

Molecular Characterization, Tumor Microenvironment Association, and Drug Susceptibility of DNA Methylation-Driven Genes in Renal Cell Carcinoma

Accumulating evidence suggests that DNA methylation has essential roles in the development of renal cell carcinoma (RCC). Aberrant DNA methylation acts as a vital role in RCC progression through regulating the gene expression, yet little is known about the role of methylation and its association with prognosis in RCC. The purpose of this study is to explore the DNA methylation-driven genes for establishing prognostic-related molecular clusters and providing a basis for survival prediction. In this study, 5,198 differentially expressed genes (DEGs) and 270 DNA methylation-driven genes were selected to obtain 146 differentially expressed DNA methylation-driven genes (DEMDGs). Two clusters were distinguished by consensus clustering using 146 DEMDGs. We further evaluated the immune status of two clusters and selected 106 DEGs in cluster 1. Cluster-based immune status analysis and functional enrichment analysis of 106 DEGs provide new insights for the development of RCC. To predict the prognosis of patients with RCC, a prognostic model based on eight DEMDGs was constructed. The patients were divided into high-risk groups and low-risk groups based on their risk scores. The predictive nomogram and the web-based survival rate calculator (http://127.0.0.1:3496) were built to validate the predictive accuracy of the prognostic model. Gene set enrichment analysis was performed to annotate the signaling pathways in which the genes are enriched. The correlation of the risk score with clinical features, immune status, and drug susceptibility was also evaluated. These results suggested that the prognostic model might be a promising prognostic tool for RCC and might facilitate the management of patients with RCC.

A novel autophagy-related lncRNA survival model for lung adenocarcinoma

Long non‐coding RNA (lncRNA) is an important regulatory factor in the development of lung adenocarcinoma, which is related to the control of autophagy. LncRNA can also be used as a biomarker of prognosis in patients with lung adenocarcinoma. Therefore, it is important to determine the prognostic value of autophagy‐related lncRNA in lung adenocarcinoma. In this study, autophagy‐related mRNAs‐lncRNAs were screened from lung adenocarcinoma and a co‐expression network of autophagy‐related mRNAs‐lncRNAs was constructed by using The Cancer Genome Atlas (TCGA). The univariate and multivariate Cox proportional hazard analyses were used to evaluate the prognostic value of the autophagy‐related lncRNAs and finally obtained a survival model composed of 11 autophagy‐related lncRNAs. Through Kaplan‐Meier analysis, univariate and multivariate Cox regression analysis and time‐dependent receiver operating characteristic (ROC) curve analysis, it was further verified that the survival model was a new independent prognostic factor for patients with lung adenocarcinoma. In addition, based on the survival model, gene set enrichment analysis (GSEA) was used to illustrate the function of genes in low‐risk and high‐risk groups. These 11 lncRNAs were GAS6‐AS1, AC106047.1, AC010980.2, AL034397.3, NKILA, AL606489.1, HLA‐DQB1‐AS1, LINC01116, LINC01806, FAM83A‐AS1 and AC090559.1. The hazard ratio (HR) of the risk score was 1.256 (1.196‐1.320) (P < .001) in univariate Cox regression analysis and 1.215 (1.149‐1.286) (P < .001) in multivariate Cox regression analysis. And the AUC value of the risk score was 0.809. The 11 autophagy‐related lncRNA survival models had important predictive value for the prognosis of lung adenocarcinoma and may become clinical autophagy‐related therapeutic targets.

Identification of an 11-Autophagy-Related-Gene Signature as Promising Prognostic Biomarker for Bladder Cancer Patients

Simple Summary

Human bladder cancer, one of the most common cancers worldwide, is a molecularly heterogenous and complex disease. Identifying novel prognostic biomarkers and establishing new predictive signatures are important for personalized medicine and effective treatment of bladder cancer patients. Autophagy, a cell self-maintenance process that removes damaged organelles and misfolded proteins, displays both tumor promotion and suppression activities. The aim of our study is to investigate the function of autophagy-related genes in bladder cancer with the main focus on their contribution to prognostic outcome. By analyzing data obtained from The Cancer Genome Atlas (TCGA), we identified 32 autophagy-related genes that were highly associated with overall survival of bladder cancer patients. Further statistical assessment established an 11-autophagy-related-gene signature as an effective prognostic biomarker to predict the survival outcomes of bladder cancer patients.

Abstract

Background: Survival rates for highly invasive bladder cancer (BC) patients have been very low, with a 5-year survival rate of 6%. Accurate prediction of tumor progression and survival is important for diagnosis and therapeutic decisions for BC patients. Our study aims to develop an autophagy-related-gene (ARG) signature that helps to predict the survival of BC patients. Methods: RNA-seq data of 403 BC patients were retrieved from The Cancer Genome Atlas Urothelial Bladder Carcinoma (TCGA-BLCA) database. Univariate Cox regression analysis was performed to identify overall survival (OS)-related ARGs. The Lasso Cox regression model was applied to establish an ARG signature in the TCGA training cohort (N = 203). The performance of the 11-gene ARG signature was further evaluated in a training cohort and an independent validation cohort (N = 200) using Kaplan-Meier OS curve analysis, receiver operating characteristic (ROC) analysis, as well as univariate and multivariate Cox regression analysis. Results: Our study identified an 11-gene ARG signature that is significantly associated with OS, including APOL1, ATG4B, BAG1, CASP3, DRAM1, ITGA3, KLHL24, P4HB, PRKCD, ULK2, and WDR45. The ARGs-derived high-risk bladder cancer patients exhibited significantly poor OS in both training and validation cohorts. The prognostic model showed good predictive efficacy, with the area under the ROC curve (AUCs) for 1-year, 3-year, and 5-year overall survival of 0.702 (0.695), 0.744 (0.640), and 0.794 (0.658) in the training and validation cohorts, respectively. A prognostic nomogram, which included the ARGs-derived risk factor, age and stage for eventual clinical translation, was established. Conclusion: We identified a novel ARG signature for risk-stratification and robust prediction of overall survival for BC patients.