Autophagy-related Prognostic Signature Research Articles

Machine learning-based autophagy-related prognostic signature for personalized risk stratification and therapeutic approaches in bladder cancer

ObjectiveBladder cancer (BCa) is a highly lethal urological malignancy characterized by its notable histological heterogeneity. Autophagy has swiftly emerged as a diagnostic and prognostic biomarker in diverse cancer types. Nonetheless, the currently accessible autophagy-related signature specific to BCa remains limited. MethodsA refined autophagy-related signature was developed through a 10-fold cross-validation framework, incorporating 101 combinations of machine learning algorithms. The performance of this signature in predicting prognosis and response to immunotherapy was thoroughly evaluated, along with an exploration of potential drug targets and compounds. In vitro and in vivo experiments were conducted to verify the regulatory mechanism of hub gene. ResultsThe autophagy-related prognostic signature (ARPS) has exhibited superior performance in predicting the prognosis of BCa compared to the majority of clinical features and other developed markers. Higher ARPS is associated with poorer prognosis and reduced sensitivity to immunotherapy. Four potential targets and five therapeutic agents were screened for patients in the high-ARPS group. In vitro and vivo experiments have confirmed that FKBP9 promotes the proliferation, invasion, and metastasis of BCa. ConclusionsOverall, our study developed a valuable tool to optimize risk stratification and decision-making for BCa patients.

Autophagy-related prognostic signature characterizes tumor microenvironment and predicts response to ferroptosis in gastric cancer.

BackgroundGastric cancer (GC) is an important disease and the fifth most common malignancy worldwide. Autophagy is an important process for the turnover of intracellular substances. Autophagy-related genes (ARGs) are crucial in cancer. Accumulating evidence indicates the clinicopathological significance of the tumor microenvironment (TME) in predicting prognosis and treatment efficacy.MethodsClinical and gene expression data of GC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. A total of 22 genes with differences in expression and prognosis were screened from 232 ARGs. Three autophagy patterns were identified using an unsupervised clustering algorithm and scored using principal component analysis to predict the value of autophagy in the prognosis of GC patients. Finally, the relationship between autophagy and ferroptosis was validated in gastric cancer cells.ResultsThe expression of ARGs showed obvious heterogeneity in GC patients. Three autophagy patterns were identified and used to predict the overall survival of GC patients. These three patterns were well-matched with the immunophenotype. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analyses showed that the biological functions of the three autophagy patterns were different. A scoring system was then set up to quantify the autophagy model and further evaluate the response of the patients to the immunotherapy. Patients with high autophagy scores had a more severe tumor mutation burden and better prognosis. High autophagy scores were accompanied by high microsatellite instability. Patients with high autophagy scores had significantly higher PD-L1 expression and increased survival. The experimental results confirmed that the expression of ferroptosis genes was positively correlated with the expression of autophagy genes in different autophagy clusters, and inhibition of autophagy dramatically reversed the decrease in ferroptotic cell death and lipid accumulation.ConclusionsAutophagy patterns are involved in TME diversity and complexity. Autophagy score can be used as an independent prognostic biomarker in GC patients and to predict the effect of immunotherapy and ferroptosis-based therapy. This might benefit individualized treatment for GC.

Identification of a novel autophagy-related prognostic signature and small molecule drugs for glioblastoma by bioinformatics

ObjectiveTo explore the autophagy-related prognostic signature (ARPs) via data mining in gene expression profiles for glioblastoma (GBM).MethodsUsing the Cancer Genome Atlas (TCGA) database, we obtained 156 GBM samples and 5 adjacent normal samples, and denoted them as discovery cohort. Univariate Cox regression analysis was used to screen autophagy genes that related to GBM prognosis. Then, the least absolute shrinkage and selection operator Cox regression model was used to construct an autophagy-based ARPs, which was validated in an external cohort containing 80 GBM samples. The patients in the above-mentioned cohorts were divided into low-risk group and high-risk group according to the median prognostic risk score, and the diagnostic performance of the model was assessed by receiver operating characteristic curve analyses. The gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analyses were performed between the high-risk and low-risk patients. Additionally, the genetic features of ARPs, such as genetic variation profiles, correlations with tumor-infiltrating lymphocytes (TILs), and potential drug sensitivity, were further assessed in the TCGA-GBM data set.ResultsA signature of ARPs including NDUFB9, BAK1, SUPT3H, GAPDH, CDKN1B, CHMP6, and EGFR were detected and validated. We identified a autophagy-related prognosis 7-gene signature correlated survival prognosis, immune infiltration, level of cytokines, and cytokine receptor in tumor microenvironment. Furthermore, the signature was tested in several pathways related to disorders of tumor microenvironment, as well as cancer-related pathways. Additionally, a range of small molecular drugs, shown to have a potential therapeutic effect on GBM.ConclusionsWe constructed an autophagy-based 7-gene signature, which could serve as an independent prognostic indicator for cases of GBM and sheds light on the role of autophagy as a potential therapeutic target in GBM.

Autophagy-related long non-coding RNA prognostic model predicts prognosis and survival of melanoma patients.

BACKGROUNDMelanomas are malignant tumors that can occur in different body parts or tissues such as the skin, mucous membrane, uvea, and pia mater. Long non-coding RNAs (lncRNAs) are key factors in the occurrence and development of many malignant tumors, and are involved in the prognosis of some patients. AIMTo identify autophagy-related lncRNAs in melanoma that are crucial for the diagnosis, treatment, and prognosis of melanoma patients.METHODSWe retrieved transcriptome expression profiles and clinical information of 470 melanoma patients from The Cancer Genome Atlas (TCGA) database. Then, we identified autophagy-related genes in the Human Autophagy Database. Using R, coexpression analysis of lncRNAs and autophagy-related genes was conducted to obtain autophagy-related lncRNAs and their expression levels. We also performed univariate and multivariate Cox proportional risk analyses on the obtained datasets, to systematically evaluate the prognostic value of autophagy-related lncRNAs in melanoma. Fifteen autophagy-related lncRNAs were identified and an autophagy-related prognostic signature for melanoma was established. The Kaplan-Meier and univariate and multivariate Cox regression analyses were used to calculate risk scores. Based on the risk scores, melanoma patients were randomly divided into high- and low-risk groups. Receiver operating characteristic curve analysis, dependent on time, was performed to assess the accuracy of the prognostic model. At the same time, we also downloaded the melanoma data sets GSE65904, GSE19234, and GSE78220 from the GENE EXPRESSION OMNIBUS database for model verification. Finally, we performed Gene Set Enrichment Analysis functional annotation, which showed that the low and the high-risk groups had different enriched pathways.RESULTSThe co-expression network for autophagy-related genes was constructed using R, and 936 lncRNAs related to autophagy were identified. Then, 52 autophagy-related lncRNAs were significantly associated with TCGA melanoma patients’ survival by univariate Cox proportional risk analysis (P < 0.01). Further, the 52 autophagy-related lncRNAs mentioned above were analyzed by multivariate Cox analysis with R. Fifteen lncRNAs were selected: LINC01943, AC090948.3, USP30-AS1, AC068282.1, AC004687.1, AL133371.2, AC242842.1, PCED1B-AS1, HLA-DQB1-AS1, AC011374.2, LINC00324, AC018553.1, LINC00520, DBH-AS1, and ITGB2-AS1. The P values in all survival analyses using these 15 lncRNAs were < 0.05. These lncRNAs were used to build a risk model based on the risk score. Negative correlations were observed between risk scores and overall survival rate in melanoma patients over time. Additionally, the melanoma risk curve and scatter plot analyses showed that the death number increased along with the increase in the risk score. Overall, we identified and established a new prognostic risk model for melanoma using 15 autophagy-related lncRNAs. The risk model constructed with these lncRNAs can help and guide melanoma patient prognosis predictions and individualized treatments in the future.CONCLUSIONOverall, the risk model developed based on the 15 autophagy-related lncRNAs can have important prognostic value and may provide autophagy-related clinical targets for melanoma treatment.

Autophagy-related prognostic signature for survival prediction of triple negative breast cancer.

BackgroundTriple-negative breast cancer (TNBC) is a highly aggressive type of cancer with few available treatment methods. The aim of the current study was to provide a prognostic autophagy-related gene (ARG) model to predict the outcomes for TNBC patients using bioinformatic analysis.MethodsmRNA expression data and its clinical information for TNBC samples obtained from The Cancer Genome Atlas (TCGA) and Metabric databases were extracted for bioinformatic analysis. Differentially expressed autophagy genes were identified using the Wilcoxon rank sum test in R software. ARGs were downloaded from the Human Autophagy Database. The Kaplan–Meier plotter was employed to determine the prognostic significance of the ARGs. The sample splitting method and Cox regression analysis were employed to establish the risk model and to demonstrate the association between the ARGs and the survival duration. The corresponding ARG-transcription factor interaction network was visualized using the Cytoscape software.ResultsA signature-based risk score model was established for eight genes (ITGA3, HSPA8, CTSD, ATG12, CLN3, ATG7, MAP1LC3C, and WIPI1) using the TCGA data and the model was validated with the GSE38959 and Metabric datasets, respectively. Patients with high risk scores had worse survival outcomes than those with low risk scores. Of note, amplification of ATG12 and reduction of WIPI were confirmed to be significantly correlated with the clinical stage of TNBC.ConclusionAn eight-gene autophagic signature model was developed in this study to predict the survival risk for TNBC. The genes identified in the study may favor the design of target agents for autophagy control in advanced TNBC.

Identification and validation of an autophagy-related signature for predicting survival in lower-grade glioma

ABSTRACT Abnormal levels of autophagy have been implicated in the pathogenesis of multiple diseases, including cancer. However, little is known about the role of autophagy-related genes (ARGs) in low-grade gliomas (LGG). Accordingly, the aims of this study were to assess the prognostic values of ARGs and to establish a genetic signature for LGG prognosis. Expression profile data from patients with and without primary LGG were obtained from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression databases, respectively, and consensus clustering was used to identify clusters of patients with distinct prognoses. Nineteen differentially expressed ARGs were selected with threshold values of FDR < 0.05 and |log2 fold change (FC)| ≥ 2, and functional analysis revealed that these genes were associated with autophagy processes as expected. An autophagy-related signature was established using a Cox regression model of six ARGs that separated patients from TCGA training cohort into high- and low-risk groups. Univariate and multivariate Cox regression analysis indicated that the signature-based risk score was an independent prognostic factor. The signature was successfully validated using the TCGA testing, TCGA entire, and Chinese Glioma Genome Atlas cohorts. Stratified analyses demonstrated that the signature was associated with clinical features and prognosis, and gene set enrichment analysis revealed that autophagy- and cancer-related pathways were more enriched in high-risk patients than in low-risk patients. The prognostic value and expression of the six signature-related genes were also investigated. Thus, the present study constructed and validated an autophagy-related prognostic signature that could optimize individualized survival prediction in LGG patients.

Screening of a novel autophagy-related prognostic signature and therapeutic targets in hepatocellular carcinoma.

Many studies have indicated that autophagy plays an important role in multiple cancers, including hepatocellular carcinoma (HCC). This study aimed to establish a prognostic signature for HCC based on autophagy-related genes (ARGs) to predict the prognosis of patients. The list of ARGs was derived from screening National Center for Biotechnology Information (NCBI)-Gene and Molecular Signatures Database (MSigDB) datasets. Differential analysis was conducted via the R limma package in HCC patients based on The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analysis were conducted to identify key prognostic ARGs via the survival package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed by clusterProfiler package. The Estimation of Stromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm was used to conduct immune analysis. Finally, the correlation between the prognostic model and clinical characteristics was also assessed, including age, tumor-node-metastasis (TNM) stages, and tumor grades. Firstly, 106 differential ARGs were identified and 10 candidates were further confirmed via Cox regression analysis, including BAMBI, HIF1A, SERPINE1, EZH2, SLC9A3R1, IGFBP3, HSPB8, DAB2, CXCL1 and PRNP. The receiver operating characteristic (ROC) curve analysis revealed that the ARGs risk model had a well diagnostic positive rate with 1-year area under the curve (AUC)=0.688 and 3-year AUC=0.674. Correlation analysis indicated that only advanced tumor stages were positively associated with high ARGs scores with P=0.0227. There were also significant differences in tumor purity (P=6.71e-05), infiltrating cell analysis (P=7.77e-05), immune analysis (P=7.9e-05), and stromal cells analysis (P=0.0015) in high- and low-risk ARGs samples. The genes HIF1A, IGFBP3, and DAB2 were found to have high frequent missense mutations in samples with high-risk ARGs scores. Lastly, we also established a nomogram to predict overall survival (OS) of HCC by integrating ARGs scores and other clinical parameters. Our study established an autophagy-related signature for predicting the prognosis of HCC patients, providing a thorough understanding of the underlying mechanisms of autophagy in HCC.

Identification of Autophagy-Related Prognostic Signature and Analysis of Immune Cell Infiltration in Low-Grade Gliomas.

Autophagy plays an important role in cancer. Many studies have demonstrated that autophagy-related genes (ARGs) can act as a prognostic signature for some cancers, but little has been known in low-grade gliomas (LGG). In our study, we aimed to establish a prognostical model based on ARGs and find prognostic risk-related key genes in LGG. In the present study, a prognostic signature was constructed based on a total of 8 ARGs (MAPK8IP1, EEF2, GRID2, BIRC5, DLC1, NAMPT, GRID1, and TP73). It was revealed that the higher the risk score, the worse was the prognosis. Time-dependent ROC analysis showed that the risk score could precisely predict the prognosis of LGG patients. Additionally, four key genes (TGFβ2, SERPING1, SERPINE1, and TIMP1) were identified and found significantly associated with OS of LGG patients. Besides, they were also discovered to be strongly related to six types of immune cells which infiltrated in LGG tumor. Taken together, the present study demonstrated the promising potential of the ARG risk score formula as an independent factor for LGG prediction. It also provided the autophagy-related signature of prognosis and potential therapeutic targets for the treatment of LGG.

Immune implication of an autophagy-related prognostic signature in uveal melanoma.

Uveal Melanoma (UM) is a rare cancer deriving from melanocytes within the uvea. It has a high rate of metastasis, especially to the liver, and a poor prognosis thereafter. Autophagy, an intracellular programmed digestive process, has been associated with the development and progression of cancers, with controversial pro- and anti-tumour roles. Although previous studies have been conducted on autophagy-related genes (ARGs) in various cancer types, its role in UM requires a deeper understanding for improved diagnosis and development of novel therapeutics. In the present study, Zheng et al. used univariate Cox regression followed by least absolute shrinkage and selection operator (Lasso) regression to identify a robust 9-ARG signature prognostic of survival in a total of 230 patients with UM. The authors used the Cancer Genome Atlas (TCGA) UM cohort as a training cohort (n=80) to identify the signature and validated it in another four independent cohorts of 150 UM patients from the Gene Expression Omnibus (GEO) repository (GSE22138, GSE27831, GSE44295 and GSE84976). This 9-ARG signature was also significantly associated with the enrichment of cancer hallmarks, including angiogenesis, IL6-KJAK-STAT3 signalling, reactive oxygen species pathway and oxidative phosphorylation. More importantly, this signature is associated with immune-related functional pathways and immune cell infiltration. Thus, this 9-ARG signature predicts prognosis and provides deeper insights into the immune mechanisms in UM, with potential implications for future immunotherapy.

Construction and Validation of an Autophagy-Related Prognostic Signature and a Nomogram for Bladder Cancer.

ObjectiveBladder cancer (BC) is one of the top ten cancers endangering human health but we still lack accurate tools for BC patients’ risk stratification. This study aimed to develop an autophagy-related signature that could predict the prognosis of BC. In order to provide clinical doctors with a visual tool that could precisely predict the survival probability of BC patients, we also attempted to establish a nomogram based on the risk signature.MethodsWe screened out autophagy-related genes (ARGs) combining weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) in BC. Based on the screened ARGs, we performed survival analysis and Cox regression analysis to identify potential prognostic biomarkers. A risk signature based on the prognostic ARGs by multivariate Cox regression analysis was established, which was validated by using seven datasets. To provide clinical doctors with a useful tool for survival possibility prediction, a nomogram assessed by the ARG-based signature and clinicopathological features was constructed, verified using four independent datasets.ResultsThree prognostic biomarkers including BOC (P = 0.008, HR = 1.104), FGF7(P = 0.030, HR = 1.066), and MAP1A (P = 0.001, HR = 1.173) were identified and validated. An autophagy-related risk signature was established and validated. This signature could act as an independent prognostic feature in patients with BC (P = 0.047, HR = 1.419). We then constructed two nomograms with and without ARG-based signature and subsequent analysis indicated that the nomogram with ARG signature showed high accuracy for overall survival probability prediction of patients with BC (C-index = 0.732, AUC = 0.816). These results proved that the ARG signature improved the clinical net benefit of the standard model based on clinicopathological features (age, pathologic stage).ConclusionsThree ARGs were identified as prognosis biomarkers in BC. An ARG-based signature was established for the first time, showing strong potential for prognosis prediction in BC. This signature was proven to improve the clinical net benefit of the standard model. A nomogram was established using this signature, which could lead to more effective prognosis prediction for BC patients.

Identification and validation of autophagy-related prognostic signature for head and neck squamous cell carcinoma

BackgroundMany studies have demonstrated that autophagy plays a significant role in regulating tumor growth and progression. However, the effect of autophagy-related genes (ARGs) on the prognosis have rarely been analyzed in head and neck squamous cell carcinoma (HNSCC). MethodsWe obtained differentially expressed ARGs from HNSCC mRNA data in The Cancer Genome Atlas (TCGA) database. And then we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to explore the autophagy-related biological functions. The overall survival (OS)-related and disease specific survival (DSS)-related ARGs were identified by univariate Cox regression analyses. With these genes, we established OS-related and DSS-related risk signature by LASSO regression method, respectively. We validated the reliability of the risk signature with receiver operating characteristic (ROC) analysis, Kaplan-Meier survival curves, clinical correlation analysis, and nomogram. Then we analyzed relationships between risk signature and immune cell infiltration. ResultsWe established the prognostic signatures based on 14 ARGs for OS and 12 ARGs for DSS. The ROC curves, survival analysis, and nomogram validated the predictive accuracy of the models. Clinic correlation analysis showed that the risk group was closely related to Stage, pathological T stage, pathological N stage and human papilloma virus (HPV) subtype. Cox regression demonstrated that the risk score was an independent predictor for the prognosis of HNSCC patients. Furthermore, patients in low-risk score group exhibited higher immunescore and distinct immune cell infiltration than high-risk score group. And we further analysis revealed that the copy number alterations (CNAs) of ARGs-based signature affected the abundance of tumor-infiltrating immune cells. ConclusionIn this study, we identified novel autophagy-related signature for the prediction of OS and DSS in patients with HNSCC. Meanwhile, our study provides a novel sight to understand the role of autophagy and elucidate the important role of autophagy in tumor immune microenvironment (TIME) of HNSCC.

An autophagy-related prognostic signature associated with immune microenvironment features of uveal melanoma.

Autophagy is involved in cancer initiation and progression but its role in uveal melanoma (UM) was rarely investigated. Herein, we built an autophagy-related gene (ARG) risk model of UM patients by univariate Cox regression and least absolute shrinkage and selection operator (Lasso) regression model and filtrated out nine prognostic ARGs in The Cancer Genome Atlas (TCGA) cohort. Survival and Receiver Operating Characteristic (ROC) Curve analysis in the TCGA and other four independent UM cohorts (GSE22138, GSE27831, GSE44295 and GSE84976) proved that the ARG-signature possessed robust and steady prognosis predictive ability. We calculated risk scores for patients included in our study and patients with higher risk scores showed worse clinical outcomes. We found the expressions of the nine ARGs were significantly associated with clinical and molecular features (including risk score) and overall survival (OS) of UM patients. Furthermore, we utilized univariate and multivariate Cox regression analyses to determine the independent prognostic ability of the ARG-signature. Functional enrichment analysis showed the ARG-signature was correlated with several immune-related processes and pathways like T-cell activation and T-cell receptor signaling pathway. Gene set enrichment analysis (GSEA) found tumor hallmarks including angiogenesis, IL6-JAK-STAT3-signaling, reactive oxygen species pathway and oxidative phosphorylation were enriched in high-risk UM patients. Finally, infiltrations of several immune cells and immune-related scores were found significantly associated with the ARG-signature. In conclusion, the ARG-signature might be a strong predictor for evaluating the prognosis and immune infiltration of UM patients.

Development and validation of an autophagy-related prognostic signature in esophageal cancer.

BackgroundAutophagy has a dual function in cancer, and its role in carcinogenesis of the esophagus remains poorly understood. In the present study, we explored the prognostic value of autophagy in esophageal cancer (ESCA), one of the leading causes of cancer-related deaths worldwide.MethodsUsing ESCA RNA-sequencing (RNA-Seq) data from 158 primary patients with ESCA, including esophageal adenocarcinoma and esophageal squamous cell carcinoma, were downloaded from The Cancer Genome Atlas (TCGA) for this study. We obtained differentially expressed autophagy-related genes (ARGs) by the “limma” package of R. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses unveiled several fundamental signaling pathways associated with the differentially expressed ARGs in ESCA. Univariate Cox regression analyses were used to estimate associations between ARGs and overall survival (OS) in the TCGA ESCA cohort. A Cox proportional hazards model (iteration =1,000) with a lasso penalty was used to create the optimal multiple-gene prognostic signature utilizing an R package called “glmnet”.ResultsA prognostic signature was constructed with four ARGs (DNAJB1, BNIP1, VAMP7 and TBK1) in the training set, which significantly divided ESCA patients into high- and low-risk groups in terms of OS [hazard ratio (HR) =1.508, 95% confidence interval (CI): 1.201–1.894, P<0.001]. In the testing set, the risk score remained an independent prognostic factor in the multivariate analyses (HR =1.572, 95% CI: 1.096–2.257, P=0.014). The area under the curve (AUC) of the receiver operating characteristic (ROC) predicting 1-year survival showed a better predictive power for the prediction model. The AUC in training and testing cohorts were 0.746 and 0.691, respectively. Therefore, the prognostic signature of the four ARGs was successfully validated in the independent cohort.ConclusionsThe prognostic signature may be an independent predictor of survival for ESCA patients. The prognostic nomogram may improve the prediction of individualized outcome. This study also highlights the importance of autophagy in the outcomes of patients with ESCA.

Identification of a novel six autophagy-related genes signature for the prognostic and a miRNA-related autophagy predictor for anti-PD-1 therapy responses in prostate cancer

BackgroundAutophagy is a highly conserved homeostatic process in the human body that is responsible for the elimination of aggregated proteins and damaged organelles. Several autophagy-related genes (ARGs) contribute to the process of tumorigenesis and metastasis of prostate cancer (PCa). Also, miRNAs have been proven to modulate autophagy by targeting some ARGs. However, their potential role in PCa still remains unclear.MethodsAn univariate Cox proportional regression model was used to identify 17 ARGs associated with the overall survival (OS) of PCa. Then, a multivariate Cox proportional regression model was used to construct a 6 autophagy-related prognostic genes signature. Patients were divided into low-risk group and high-risk group using the median risk score as a cutoff value. High-risk patients had shorter OS than low-risk patients. Furthermore, the signature was validated by ROC curves. Regarding mRNA and miRNA, 12 differentially expressed miRNAs (DEMs) and 1073 differentially expressed genes (DEGs) were detected via the GEO database. We found that miR-205, one of the DEMs, was negatively regulated the expression of ARG (NKX2–3). Based on STRING analysis results, we found that the NKX2–3 was moderately related to the part of genes among the 6 autophagy-related genes prognostic signature. Further, NKX 2–3 was significantly correlated with OS and some clinical parameters of PCa by cBioProtal. By gene set enrichment analysis (GSEA). Lastly, we demonstrated that the association between NKX2–3 and tumor mutation burden (TMB) and PDCD1 (programmed cell death 1) of PCa.ResultsWe identified that the six ARGs expression patterns are independent predictors of OS in PCa patients. Furthermore, our results suggest that ARGs and miRNAs are inter-related. MiR-205 was negatively regulated the expression of ARG (NKX2–3). Further analysis demonstrated that NKX2–3 may be a potential biomarker for predicting the efficacy of anti-PD-1 therapy in PCa.ConclusionsThe current study may offer a novel autophagy-related prognostic signature and may identify a promising miRNA-ARG pathway for predicting the efficacy of anti-PD-1 therapy in PCa.

Systematic Analysis of Autophagy-Related Signature Uncovers Prognostic Predictor for Acute Myeloid Leukemia.

Autophagy, a highly conserved cellular protein degradation process, has been involved in acute myeloid leukemia (AML). The present study aims to establish a novel, autophagy-related prognostic signature for prediction of AML prognosis. Differentially expressed autophagy-related genes in AML and healthy samples were screened using GSE1159. Univariate Cox regression analysis was applied to determine survival-associated autophagy-related genes in The Cancer Genome Atlas (TCGA) AML cohort. Lasso regression was performed to develop multiple-gene prognostic signatures. A novel six-gene signature (including CASP3, CHAF1B, KLHL24, OPTN, VEGFA, and VPS37C) DC was established for AML prognosis prediction. The Kaplan-Meier survival analysis revealed that patients in the high-risk score group had poorer overall survival (OS). The receiver operating characteristic (ROC) curve validated its good performance in survival prediction in TCGA AML cohort, and the area under the curve value was 0.817. Moreover, our signature could independently predict OS. A nomogram was constructed, including the six-gene signature and other clinical parameters, and predictive efficiency was confirmed using the ROC curve and calibration curve. Furthermore, gene set enrichment analyses identified several tumor-associated pathways that may contribute to explain the potential molecular mechanisms of our signature. Overall, we developed a new autophagy-associated gene signature and nomogram to predict OS of AML patients, which may help in clinical decision-making for AML treatment.

Identification of Autophagy-Associated Biomarkers and Corresponding Regulatory Factors in the Progression of Colorectal Cancer.

Autophagy is a self-degradation process that maintains homeostasis against stress in cells. Autophagy dysfunction plays a central role in the development of tumors, such as colorectal cancer (CRC). In this study, autophagy-related differentially expressed genes, their downstream functions, and upstream regulatory factors including RNA-binding proteins (RBP) involved in programmed cell death in the CRC were investigated. Transcription factors (TFs) and miRNAs have been shown to mainly regulate autophagy genes. Interestingly, we found that some of the RBP in the CRC, such as DDX17, SETDB1, and POLR3A, play an important regulatory role in maintaining autophagy at a basal level during growth by acting as TFs that regulate autophagy. Promoter methylations showed negative regulations on differentially expressed autophagy gene (DEAG), while copy number variations revealed a positive role in them. A proportional hazards regression analysis indicated that using autophagy-related prognostic signature can divide patients into high-risk and low-risk groups. Autophagy associated FDA-approved drugs were studied by a prognostic network. This would contribute to the identifications of new potential molecular therapeutic targets for CRC.

Development of a Novel Autophagy-related Prognostic Signature for Serous Ovarian Cancer.

Purpose: Considerable evidence suggests that autophagy plays a crucial role in the biological processes of ovarian cancer. The aim of this study was to develop a novel autophagy-related prognostic signature for serous ovarian cancer.Methods: A univariate Cox proportional regression model was used to analyze mRNA microarray and clinical data in The Cancer Genome Atlas (TCGA) for the purpose of selecting autophagy-related prognostic genes. A multivariate Cox proportional regression model and the survival analysis were used to develop an eight-gene prognostic signature. The multivariate Cox and stratification analysis suggested that this signature was an independent prognostic factor for serous ovarian cancer patients. Bioinformatics functions were investigated by a principal components analysis and gene set enrichment analysis (GSEA). Finally, the correlation between the prognostic signature and gene mutation status was further analyzed in serous ovarian cancer, and especially with regard to the mutation status of BRCA1 and BRCA2 (BRCA1/2) genes.Results: Distinctly different autophagy-related gene expression profiles were identified in normal ovarian tissues and serous ovarian cancer tissues. We profiled an autophagy-related gene set and identified eight genes with significant prognostic values for serous ovarian cancer. Subsequently, an autophagy-related ovarian cancer risk signature was constructed, and patients at a high-risk or low-risk for poor prognosis were identified based on their signature. High-risk patients had significantly shorter overall survival (OS) and disease-free survival (DFS) times than low-risk patients. GSEA results suggested an enhanced intensity of autophagy regulation in high-risk patients when compared with low-risk patients. When studied as an independent prognostic factor for serous ovarian cancer, the significant prognostic value of this signature could be seen in the stratified cohorts. For clinical use, we developed a nomogram that included the prognostic classifier and seven clinical risk factors. Additionally, we identified the 10 most frequently mutated genes found in serous ovarian cancer patients, and analyzed them for their differences in high-risk and low-risk patients. Among 293 patients, 62 had BRCA1/2 gene mutations, and this result was significantly correlated with the autophagy-related prognostic signature.Conclusions: Our findings suggest that the eight-gene autophagy-related signature could serve as an independent prognostic indicator for cases of serous ovarian cancer.

Autophagy‐related prognostic signature for breast cancer

Autophagy is a process that degrades intracellular constituents, such as long-lived or damaged proteins and organelles, to buffer metabolic stress under starvation conditions. Deregulation of autophagy is involved in the progression of cancer. However, the predictive value of autophagy for breast cancer prognosis remains unclear. First, based on gene expression profiling, we found that autophagy genes were implicated in breast cancer. Then, using the Cox proportional hazard regression model, we detected autophagy prognostic signature for breast cancer in a training dataset. We identified a set of eight autophagy genes (BCL2, BIRC5, EIF4EBP1, ERO1L, FOS, GAPDH, ITPR1 and VEGFA) that were significantly associated with overall survival in breast cancer. The eight autophagy genes were assigned as a autophagy-related prognostic signature for breast cancer. Based on the autophagy-related signature, the training dataset GSE21653 could be classified into high-risk and low-risk subgroups with significantly different survival times (HR = 2.72, 95% CI = (1.91, 3.87); P = 1.37 × 10(-5)). Inactivation of autophagy was associated with shortened survival of breast cancer patients. The prognostic value of the autophagy-related signature was confirmed in the testing dataset GSE3494 (HR = 2.12, 95% CI = (1.48, 3.03); P = 1.65 × 10(-3)) and GSE7390 (HR = 1.76, 95% CI = (1.22, 2.54); P = 9.95 × 10(-4)). Further analysis revealed that the prognostic value of the autophagy signature was independent of known clinical prognostic factors, including age, tumor size, grade, estrogen receptor status, progesterone receptor status, ERBB2 status, lymph node status and TP53 mutation status. Finally, we demonstrated that the autophagy signature could also predict distant metastasis-free survival for breast cancer.