Prognostic Correlation of Autophagy-Related Gene Expression-Based Risk Signature in Patients with Glioblastoma

Autophagy in brain tumors: molecular mechanisms, challenges, and therapeutic opportunities

Autophagy is responsible for maintaining cellular balance and ensuring survival. Autophagy plays a crucial role in the development of diseases, particularly human cancers, with actions that can either promote survival or induce cell death. However, brain tumors contribute to high levels of both mortality and morbidity globally, with resistance to treatments being acquired due to genetic mutations and dysregulation of molecular mechanisms, among other factors. Hence, having knowledge of the role of molecular processes in the advancement of brain tumors is enlightening, and the current review specifically examines the role of autophagy. The discussion would focus on the molecular pathways that control autophagy in brain tumors, and its dual role as a tumor suppressor and a supporter of tumor survival. Autophagy can control the advancement of different types of brain tumors like glioblastoma, glioma, and ependymoma, demonstrating its potential for treatment. Autophagy mechanisms can influence metastasis and drug resistance in glioblastoma, and there is a complex interplay between autophagy and cellular responses to stress like hypoxia and starvation. Autophagy can inhibit the growth of brain tumors by promoting apoptosis. Hence, focusing on autophagy could offer fresh perspectives on creating successful treatments.

Distinguish active tuberculosis with an immune-related signature and molecule subtypes: a multi-cohort analysis

BACKGROUND

Distinguishing latent tuberculosis infection (LTBI) from active tuberculosis (ATB) is very important. This study aims to analyze cases from multiple cohorts and get the signature that can distinguish LTBI from ATB.

METHODS

Thirteen datasets were downloaded from the gene expression omnibus (GEO) database. Three datasets were selected as discovery datasets, and the hub genes were discovered through WGCNA. In the training cohort, we use machine learning to establish the signature, verify the authentication ability of the signature in the remaining datasets, and compare it with other signatures. Cluster analysis was carried out on ATB cases, immune cell infiltration analysis, GSVA analysis, and drug sensitivity analysis were carried out on different clusters.

RESULTS

In the discovery datasets, we discovered five hub genes. A signature (SLC26A8, ANKRD22, and FCGR1B) is obtained in the training cohort. In the total cohort, the three-gene signature can separate LTBI from ATB (the total area under ROC curve (AUC) is 0.801, 95% CI 0.771-0.830). Compared with other author's signatures, our signature shows good identification ability. Immunological analysis showed that SLC26A8, ANKRD22, and FCGR1B were closely related to the infiltration of immune cells. According to the expression of the three genes, ATB can be divided into two clusters, which are different in immune cell infiltration analysis, gene set variation, and drug sensitivity.

CONCLUSION

Our study produced an immune-related three-gene signature to distinguish LTBI from ATB, which may help us to manage and treat tuberculosis patients.

Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model

Glioblastoma multiforme (GBM) patients show a variety of signs and symptoms that affect their quality of life (QOL) and self-dependence. Since most existing studies have examined prognostic factors based only on clinical factors, there is a need to consider the value of integrating multi-omics data including gene expression and proteomics with clinical data in identifying significant biomarkers for GBM prognosis. Our research aimed to isolate significant features that differentiate between short-term (≤ 6 months) and long-term (≥ 2 years) GBM survival, and between high Karnofsky performance scores (KPS ≥ 80) and low (KPS ≤ 60), using the iterative random forest (iRF) algorithm. Using the Cancer Genomic Atlas (TCGA) database, we identified 35 molecular features composed of 19 genes and 16 proteins. Our findings propose molecular signatures for predicting GBM prognosis and will improve clinical decisions, GBM management, and drug development.

Autophagy Modulation and Its Implications on Glioblastoma Treatment

Autophagy is a vital cellular process that functions to degrade and recycle damaged organelles into basic metabolites. This allows a cell to adapt to a diverse range of challenging conditions. Autophagy assists in maintaining homeostasis, and it is tightly regulated by the cell. The disruption of autophagy has been associated with many diseases, such as neurodegenerative disorders and cancer. This review will center its discussion on providing an in-depth analysis of the current molecular understanding of autophagy and its relevance to brain tumors. We will delve into the current literature regarding the role of autophagy in glioma pathogenesis by exploring the major pathways of JAK2/STAT3 and PI3K/AKT/mTOR and summarizing the current therapeutic interventions and strategies for glioma treatment. These treatments will be evaluated on their potential for autophagy induction and the challenges associated with their utilization. By understanding the mechanism of autophagy, clinical applications for future therapeutics in treating gliomas can be better targeted.

Pyroptosis, ferroptosis, and autophagy cross-talk in glioblastoma opens up new avenues for glioblastoma treatment

Glioma is a common primary tumor of the central nervous system (CNS), with glioblastoma multiforme (GBM) being the most malignant, aggressive, and drug resistant. Most drugs are designed to induce cancer cell death, either directly or indirectly, but malignant tumor cells can always evade death and continue to proliferate, resulting in a poor prognosis for patients. This reflects our limited understanding of the complex regulatory network that cancer cells utilize to avoid death. In addition to classical apoptosis, pyroptosis, ferroptosis, and autophagy are recognized as key cell death modalities that play significant roles in tumor progression. Various inducers or inhibitors have been discovered to target the related molecules in these pathways, and some of them have already been translated into clinical treatment. In this review, we summarized recent advances in the molecular mechanisms of inducing or inhibiting pyroptosis, ferroptosis, or autophagy in GBM, which are important for treatment or drug tolerance. We also discussed their links with apoptosis to better understand the mutual regulatory network among different cell death processes. Video Abstract.

Protein disulfide isomerase blocks the interaction of LC3II-PHB2 and promotes mTOR signaling to regulate autophagy and radio/chemo-sensitivity

Protein disulfide isomerase (PDI) is an endoplasmic reticulum (ER) enzyme that mediates the formation of disulfide bonds, and is also a therapeutic target for cancer treatment. Our previous studies found that PDI mediates apoptotic signaling by inducing mitochondrial dysfunction. Considering that mitochondrial dysfunction is a major contributor to autophagy, how PDI regulates autophagy remains unclear. Here, we provide evidence that high expression of PDI in colorectal cancer tumors significantly increases the risk of metastasis and poor prognosis of cancer patients. PDI inhibits radio/chemo-induced cell death by regulating autophagy signaling. Mechanistically, the combination of PDI and GRP78 was enhanced after ER stress, which inhibits the degradation of AKT by GRP78, and eventually activates the mTOR pathway to inhibit autophagy initiation. In parallel, PDI can directly interact with the mitophagy receptor PHB2 in mitochondrial, then competitively blocks the binding of LC3II and PHB2 and inhibits the mitophagy signaling. Collectively, our results identify that PDI can reduce radio/chemo-sensitivity by regulating autophagy, which could be served as a potential target for radio/chemo-therapy.

Immunoexpression of p62/SQSTM1/Sequestosome‑1 in human primary and recurrent IDH1/2 wild‑type glioblastoma: A pilot study

p62/SQSTM1/Sequestosome-1 is an autophagic protein that serves a crucial role in cellular metabolism, proliferation and malignant growth. Notably, autophagy may influence the development and resistance to therapy of numerous types of human cancer. In the present pilot study, the immunohistochemical pattern of p62 was analyzed in a cohort of patients with isocitrate dehydrogenase (IDH)1/2 wild-type glioblastoma (GBM), in primary and recurrent samples, in order to verify the concordance or discordance between the primary and recurrent tumors. In addition, the association between p62, and patient outcome and O⁶-methylguanine-DNA methyltransferase (MGMT) status was assessed. The results revealed p62 immunoexpression in the nucleus and cytoplasm of neoplastic elements in 45% of primary and 55% of recurrent cases of GBM. A discordant p62 immunoreactivity was detected in 35% of cases, with a variation either with positive or negative conversion of p62 status. Statistically, p62 expression and MGMT status exhibited a significant prognostic value by univariate analysis, whereas only MGMT promoter methylation status emerged as an independent prognostic factor by multivariate analysis. Finally, the most favorable prognosis was documented when the same GBM case was positively concordant for both p62 expression and MGMT methylated status. Since little data are available regarding the association between p62 expression and MGMT in GBM, further investigations may be required to determine if new targeted therapies may be addressed against autophagy-related proteins, such as p62.

Epigenetic Underpinnings of Inflammation: A Key to Unlock the Tumor Microenvironment in Glioblastoma

Glioblastoma (GBM) is the most common malignant brain tumor in adults, and immunotherapies and genetic therapies for GBM have evolved dramatically over the past decade, but GBM therapy is still facing a dilemma due to the high recurrence rate. The inflammatory microenvironment is a general signature of tumors that accelerates epigenetic changes in GBM and helps tumors avoid immunological surveillance. GBM tumor cells and glioma-associated microglia/macrophages are the primary contributors to the inflammatory condition, meanwhile the modification of epigenetic events including DNA methylation, non-coding RNAs, and histone methylation and deacetylases involved in this pathological process of GBM, finally result in exacerbating the proliferation, invasion, and migration of GBM. On the other hand, histone deacetylase inhibitors, DNA methyltransferases inhibitors, and RNA interference could reverse the inflammatory landscapes and inhibit GBM growth and invasion. Here, we systematically review the inflammatory-associated epigenetic changes and regulations in the microenvironment of GBM, aiming to provide a comprehensive epigenetic profile underlying the recognition of inflammation in GBM.

Prognostic value and immune relevancy of a combined autophagy-, apoptosis- and necrosis-related gene signature in glioblastoma

Background

Glioblastoma (GBM) is considered the most malignant and devastating intracranial tumor without effective treatment. Autophagy, apoptosis, and necrosis, three classically known cell death pathways, can provide novel clinical and immunological insights, which may assist in designing personalized therapeutics. In this study, we developed and validated an effective signature based on autophagy-, apoptosis- and necrosis-related genes for prognostic implications in GBM patients.

Methods

Variations in the expression of genes involved in autophagy, apoptosis and necrosis were explored in 518 GBM patients from The Cancer Genome Atlas (TCGA) database. Univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox analysis were performed to construct a combined prognostic signature. Kaplan–Meier survival, receiver-operating characteristic (ROC) curves and Cox regression analyses based on overall survival (OS) and progression-free survival (PFS) were conducted to estimate the independent prognostic performance of the gene signature. The Chinese Glioma Genome Atlas (CGGA) dataset was used for external validation. Finally, we investigated the differences in the immune microenvironment between different prognostic groups and predicted potential compounds targeting each group.

Results

A 16-gene cell death index (CDI) was established. Patients were clustered into either the high risk or the low risk groups according to the CDI score, and those in the low risk group presented significantly longer OS and PFS than the high CDI group. ROC curves demonstrated outstanding performance of the gene signature in both the training and validation groups. Furthermore, immune cell analysis identified higher infiltration of neutrophils, macrophages, Treg, T helper cells, and aDCs, and lower infiltration of B cells in the high CDI group. Interestingly, this group also showed lower expression levels of immune checkpoint molecules PDCD1 and CD200, and higher expression levels of PDCD1LG2, CD86, CD48 and IDO1.

Conclusion

Our study proposes that the CDI signature can be utilized as a prognostic predictor and may guide patients’ selection for preferential use of immunotherapy in GBM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09328-3.

Natural killer cell-related gene signature predicts malignancy of glioma and the survival of patients

Background

Natural killer (NK) cells-based therapies are one of the most promising strategies against cancer. The aim of this study is to investigate the natural killer cell related genes and its prognostic value in glioma.

Methods

The Chinese Glioma Genome Atlas (CGGA) was used to develop the natural killer cell-related signature. Risk score was built by multivariate Cox proportional hazards model. A cohort of 326 glioma samples with whole transcriptome expression data from the CGGA database was included for discovery. The Cancer Genome Atlas (TCGA) datasets was used for validation. GO and KEGG were used to reveal the biological process and function associated with the natural killer cell-related signature. We also collected the clinical pathological features of patients with gliomas to analyze the association with tumor malignancy and patients’ survival.

Results

We screened for NK-related genes to build a prognostic signature, and identified the risk score based on the signature. We found that NK-related risk score was independent of various clinical factors. Nature-killer cell gene expression is correlated with clinicopathological features of gliomas. Innovatively, we demonstrated the tight relation between the risk score and immune checkpoints, and found NK-related risk score combined with PD1/PDL1 patients could predict the patient outcome.

Conclusion

Natural killer cell-related gene signature can predict malignancy of glioma and the survival of patients, these results might provide new view for the research of glioma malignancy and individual immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09230-y.

Polymorphisms in autophagy genes are genetic susceptibility factors in glioblastoma development

Background

Glioblastoma is the most aggressive and common malignant primary brain tumor in adults. Many genetic, epigenetic and genomic mutations have been identified in this tumor, but no driving cause has been identified yet for glioblastoma pathogenesis. Autophagy has proved to be deregulated in different diseases such as cancer where it has a dual role, acting as a tumor suppression mechanism during the first steps of tumor development and promoting cancer cells survival in stablished tumors.

Methods

Here, we aimed to assess the potential association between several candidate polymorphisms in autophagy genes (ATG2B rs3759601, ATG16L1 rs2241880, ATG10 rs1864183, ATG5 rs2245214, NOD2 rs2066844 and rs2066845) and glioblastoma susceptibility.

Results

Our results showed a significant correlation between ATG2B rs3759601, ATG10 rs1864183 and NOD2 rs2066844 variants and higher risk to suffer glioblastoma. In addition, the relationship between the different clinical features listed in glioblastoma patients and candidate gene polymorphisms was also investigated, finding that ATG10 rs1864183 might be a promising prognosis factor for this tumor.

Conclusions

This is the first report evaluating the role of different variants in autophagy genes in modulating glioblastoma risk and our results emphasize the importance of autophagy in glioblastoma development.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09214-y.

A Novel 16-Genes Signature Scoring System as Prognostic Model to Evaluate Survival Risk in Patients with Glioblastoma

Previous studies have found that gene expression levels are associated with prognosis and some genes can be used to predict the survival risk of glioblastoma (GBM) patients. However, most of them just built the survival-related gene signature, and personal survival risk can be evaluated only in group. This study aimed to find the prognostic survival related genes of GBM, and construct survival risk prediction model, which can be used to evaluate survival risk by individual. We collected gene expression data and clinical information from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Cox regression analysis and LASSO-cox regression analysis were performed to get survival-related genes and establish the overall survival prediction model. The ROC curve and Kaplan Meier analysis were used to evaluate the prediction ability of the model in training set and two independent cohorts. We also analyzed the biological functions of survival-related genes by GO and KEGG enrichment analysis. We identified 99 genes associated with overall survival and selected 16 genes (IGFBP2, GPRASP1, C1R, CHRM3, CLSTN2, NELL1, SEZ6L2, NMB, ICAM5, HPCAL4, SNAP91, PCSK1N, PGBD5, INA, UCHL1 and LHX6) to establish the survival risk prediction model. Multivariate Cox regression analysis indicted that the risk score could predict overall survival independent of age and gender. ROC analyses showed that our model was more robust than four existing signatures. The sixteen genes can also be potential transcriptional biomarkers and the model can assist doctors on clinical decision-making and personalized treatment of GBM patients.

Comprehensive analysis of a TNF family based-signature in diffuse gliomas with regard to prognosis and immune significance

BACKGROUND

Several studies have shown that members of the tumor necrosis factor (TNF) family play an important role in cancer immunoregulation, and trials targeting these molecules are already underway. Our study aimed to integrate and analyze the expression patterns and clinical significance of TNF family-related genes in gliomas.

METHODS

A total of 1749 gliomas from 4 datasets were enrolled in our study, including the Cancer Genome Atlas (TCGA) dataset as the training cohort and the other three datasets (CGGA, GSE16011, and Rembrandt) as validation cohorts. Clinical information, RNA expression data, and genomic profile were collected for analysis. We screened the signature gene set by Cox proportional hazards modelling. We evaluated the prognostic value of the signature by Kaplan-Meier analysis and timeROC curve. Gene Ontology (GO) and Gene set enrichment analysis (GSEA) analysis were performed for functional annotation. CIBERSORT algorithm and inflammatory metagenes were used to reveal immune characteristics.

RESULTS

In gliomas, the expression of most TNF family members was positively correlated. Univariate analysis showed that most TNF family members were related to the overall survival of patients. Then through the LASSO regression model, we developed a TNF family-based signature, which was related to clinical, molecular, and genetic characteristics of patients with glioma. Moreover, the signature was found to be an independent prognostic marker through survival curve analysis and Cox regression analysis. Furthermore, a nomogram prognostic model was constructed to predict individual survival rates at 1, 3 and 5 years. Functional annotation analysis revealed that the immune and inflammatory response pathways were enriched in the high-risk group. Immunological analysis showed the immunosuppressive status in the high-risk group.

CONCLUSIONS

We developed a TNF family-based signature to predict the prognosis of patients with glioma. Video abstract.

Breaking Bad: Autophagy Tweaks the Interplay Between Glioma and the Tumor Immune Microenvironment

Though significant strides in tumorigenic comprehension and therapy modality have been witnessed over the past decades, glioma remains one of the most common and malignant brain tumors characterized by recurrence, dismal prognosis, and therapy resistance. Immunotherapy advance holds promise in glioma recently. However, the efficacy of immunotherapy varies among individuals with glioma, which drives researchers to consider the modest levels of immunity in the central nervous system, as well as the immunosuppressive tumor immune microenvironment (TIME). Considering the highly conserved property for sustaining energy homeostasis in mammalian cells and repeatedly reported links in malignancy and drug resistance, autophagy is determined as a cutting angle to elucidate the relations between glioma and the TIME. In this review, heterogeneity of TIME in glioma is outlined along with the reciprocal impacts between them. In addition, controversies on whether autophagy behaves cytoprotectively or cytotoxically in cancers are covered. How autophagy collapses from its homeostasis and aids glioma malignancy, which may depend on the cell type and the cellular context such as reactive oxygen species (ROS) and adenosine triphosphate (ATP) level, are briefly discussed. The consecutive application of autophagy inducers and inhibitors may improve the drug resistance in glioma after overtreatments. It also highlights that autophagy plays a pivotal part in modulating glioma and the TIME, respectively, and the intricate interactions among them. Specifically, autophagy is manipulated by either glioma or tumor-associated macrophages to conform one side to the other through exosomal microRNAs and thereby adjust the interactions. Given that some of the crosstalk between glioma and the TIME highly depend on the autophagy process or autophagic components, there are interconnections influenced by the status and well-being of cells presumably associated with autophagic flux. By updating the most recent knowledge concerning glioma and the TIME from an autophagic perspective enhances comprehension and inspires more applicable and effective strategies targeting TIME while harnessing autophagy collaboratively against cancer.

MET overexpression contributes to STAT4-PD-L1 signaling activation associated with tumor-associated, macrophages-mediated immunosuppression in primary glioblastomas

Background

Dysregulated receptor tyrosine kinases, such as the mesenchymal-epidermal transition factor (MET), have pivotal role in gliomas. MET and its interaction with the tumor microenvironment have been previously implicated in secondary gliomas. However, the contribution of MET gene to tumor cells’ ability to escape immunosurveillance checkpoints in primary gliomas, especially in glioblastoma (GBM), which is a WHO grade 4 glioma with the worst overall survival, is still poorly understood.

Methods

We investigated the relationship between MET expression and glioma microenvironment by using multiomics data and aimed to understand the potential implications of MET in clinical practice through survival analysis. RNA expression data from a total of 1243 primary glioma samples (WHO grades 2–4) were assembled, incorporating The Cancer Genome Atlas, Chinese Glioma Genome Atlas, and GSE16011 data sets.

Results

Pearson’s correlation test from the three data sets indicated that MET showed a robust correlation with programmed death-ligand 1 (PD-L1) and STAT pathways. Western blot analysis revealed that in GBM cell lines (N33 and LN229), PD-L1 and phosphorylated STAT4 were upregulated by MET activation treatment with hepatocyte growth factor and were downregulated on MET suppression by PLB-1001. Tumor tissue microarray analysis indicated a positive correlation between MET and PD-L1 and macrophage-associated markers. Chromatin immunoprecipitation-PCR assay showed enrichment of STAT4 in the PD-L1 DNA. Transwell co-culture and chemotaxis assays revealed that knockdown of MET in GBM cells inhibited macrophage chemotaxis. Moreover, we performed CIBERSORTx and single-cell RNA sequencing data analysis which revealed an elevated number of macrophages in glioma samples with MET overexpression. Kaplan-Meier survival analysis indicated that activation of the MET/STAT4/PD-L1 pathway and upregulation of macrophages were associated with shorter survival time in patients with primary GBM.

Conclusions

These data indicated that the MET-STAT4-PD-L1 axis and tumor-associated macrophages might enforce glioma immune evasion and were associated with poor prognosis in GBM samples, suggesting potential clinical strategies for targeted therapy combined with immunotherapy in patients with primary GBM.

Tumor microenvironment is associated with clinical and genetic properties of diffuse gliomas and predicts overall survival

Tumor microenvironment (TME) is a complex and dynamic evolving environment which facilitates tumor proliferation and progression. We aimed at investigating the characteristics of tumor microenvironment and its prognostic value in gliomas. Transcriptome data of 702 glioma samples from The Cancer Genome Atlas were included as training dataset, while 325 samples from Chinese Glioma Genome Atlas database and 268 samples from GSE16011 database were used to validate. We found that the infiltration of stromal and immune cell varied in gliomas of different grades and pathological types, and was associated with poor prognosis. Based on the gene expression profile, we constructed a TME-related signature (TMERS), which was closely related to clinical features and genomic variation of gliomas. In TMERS-high group, specific gene mutations and increased copy number alternations were observed. Kaplan-Meier survival and Cox regression analysis showed that TMERS was an independent prognostic indicator. Then we developed a nomogram prognostic model to predict 1-year, 3-year and 5-year survival of patients. Functional analysis confirmed that TMERS could reflect the status of glioma microenvironment, and immunological analysis showed that macrophages were significantly enriched in the TMERS-high group. We established a novel TME-related signature for predicting prognosis and provided new insights into immunotherapy.

Impairment of Autophagic Flux Participates in the Antitumor Effects of TAT-Cx43266-283 in Glioblastoma Stem Cells

Simple Summary

Autophagy is a process in which the cell recycles components that are not needed at that moment and uses the resulting elements to satisfy more urgent needs. Depending on the specific context, this can be beneficial or detrimental for tumor development. We found that in glioblastoma, the most lethal brain tumor, autophagy is upregulated and contributes to glioblastoma stem cell survival under starvation. Importantly, the antitumor peptide TAT-Cx43_266-283 blocks autophagy flux, contributing to the death of glioblastoma stem cells. This peptide induces glioblastoma stem cell death in nutrient-deprived and complete environments, while the effect of other unsuccessful drugs for glioblastoma depends on nutrient context, supporting the potential of TAT-Cx43_266-283 as a treatment to improve the lives of glioblastoma patients.

Abstract

Autophagy is a physiological process by which various damaged or non-essential cytosolic components are recycled, contributing to cell survival under stress conditions. In cancer, autophagy can have antitumor or protumor effects depending on the developmental stage. Here, we use Western blotting, immunochemistry, and transmission electron microscopy to demonstrate that the antitumor peptide TAT-Cx43_266-283, a c-Src inhibitor, blocks autophagic flux in glioblastoma stem cells (GSCs) under basal and nutrient-deprived conditions. Upon nutrient deprivation, GSCs acquired a dormant-like phenotype that was disrupted by inhibition of autophagy with TAT-Cx43_266-283 or chloroquine (a classic autophagy inhibitor), leading to GSC death. Remarkably, dasatinib, a clinically available c-Src inhibitor, could not replicate TAT-Cx43_266-283 effect on dormant GSCs, revealing for the first time the possible involvement of pathways other than c-Src in TAT-Cx43_266-283 effect. TAT-Cx43_266-283 exerts an antitumor effect both in nutrient-complete and nutrient-deprived environments, which constitutes an advantage over chloroquine and dasatinib, whose effects depend on nutrient environment. Finally, our analysis of the levels of autophagy-related proteins in healthy and glioma donors suggests that autophagy is upregulated in glioblastoma, further supporting the interest in inhibiting this process in the most aggressive brain tumor and the potential use of TAT-Cx43_266-283 as a therapy for this type of cancer.

A Key Pathway to Cancer Resilience: The Role of Autophagy in Glioblastomas

There are no effective strategies for the successful treatment of glioblastomas (GBM). Current therapeutic modalities effectively target bulk tumor cells but leave behind marginal GBM cells that escape from the surgical margins and radiotherapy field, exhibiting high migratory phenotype and resistance to all available anti-glioma therapies. Drug resistance is mostly driven by tumor cell plasticity: a concept associated with reactivating transcriptional programs in response to adverse and dynamic conditions from the tumor microenvironment. Autophagy, or "self-eating", pathway is an emerging target for cancer therapy and has been regarded as one of the key drivers of cell plasticity in response to energy demanding stress conditions. Many studies shed light on the importance of autophagy as an adaptive mechanism, protecting GBM cells from unfavorable conditions, while others recognize that autophagy can kill those cells by triggering a non-apoptotic cell death program, called 'autophagy cell death' (ACD). In this review, we carefully analyzed literature data and conclude that there is no clear evidence indicating the presence of ACD under pathophysiological settings in GBM disease. It seems to be exclusively induced by excessive (supra-physiological) stress signals, mostly from in vitro cell culture studies. Instead, pre-clinical and clinical data indicate that autophagy is an emblematic example of the 'dark-side' of a rescue pathway that contributes profoundly to a pro-tumoral adaptive response. From a standpoint of treating the real human disease, only combinatorial therapy targeting autophagy with cytotoxic drugs in the adjuvant setting for GBM patients, associated with the development of less toxic and more specific autophagy inhibitors, may inhibit adaptive response and enhance the sensibility of glioma cells to conventional therapies.

Comprehensive analysis of multi-omics data of recurrent gliomas identifies a recurrence-related signature as a novel prognostic marker

Tumor recurrence is a common clinical dilemma in diffuse gliomas. We aimed to identify a recurrence-related signature to predict the prognosis for glioma patients. In the public Chinese Glioma Genome Atlas dataset, we enrolled multi-omics data including genome, epigenome and transcriptome across primary and recurrent gliomas. We included RNA sequencing data from the batch 1 patients (325 patients) as the training set, while RNA sequencing data from the batch 2 patients (693 patients) were selected as the validation set. The R language was used for subsequent analysis. Compared with primary gliomas, more somatic mutations and copy number alterations were revealed in recurrent gliomas. In recurrent gliomas, we identified 113 genes whose methylation levels were significantly different from those of the primary glioma. Through differential expression analysis between primary and recurrent gliomas, we screened 121 recurrence-related genes. Based on these 121 gene expression profiles, consensus clustering of 325 patients yielded two robust groups with different molecular and prognostic features. We developed a recurrence-related risk signature with the lasso regression algorithm. High-risk group had shorter survival and earlier tumor recurrence than the low-risk group. Compared with traditional indicators, the signature showed better prognostic value. In addition, we constructed a nomogram model to predict glioma survival. Functional characteristics analysis found that the signature was associated with cell division and cell cycle. Immune analysis suggested that immunosuppressive status and macrophages might promote glioma recurrence. We demonstrated a novel 18-gene signature that could effectively predict recurrence and prognosis for glioma patients.

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 a 5-Gene Autophagy-Based Prognostic Index in Endometrial Carcinoma

BACKGROUND Endometrial carcinoma (EC) is the most common gynecological malignancy worldwide, and 15-20% of patients with EC have a rapid relapse within 3 years. This study aims to develop an autophagy-related genes (ARGs) signature to predict the prognosis of EC. MATERIAL AND METHODS In our study, differentially expressed ARGs were identified by "edgeR" package in R and pathway enrichment analysis was performed to explore biological functions. Univariate and multivariate Cox regression analyses were employed to build autophagy signature. Gene set enrichment analysis (GSEA), Kaplan-Meier curve analysis, and ROC curve analysis were conducted to compare the differences between the high- and low-risk groups. RESULTS A total of 60 differentially expressed ARGs (DEARGs) including 34 upregulated and 26 downregulated DEARGs were identified from the TCGAUCEC dataset, with the adjusted P<0.05 and |Fold Change| >1.5. By using univariate and multivariate Cox regression analyses, ERBB2, PRKAB2, GRID2, NRG3, CDKN2A were identified to construct a prognostic signature with AUC 0.673, 0.719, and 0.791, at 1-, 3- and 5- years, respectively. Patients with EC were divided into low- or high-risk group by median risk score, and GSEA showed that low-risk group was enriched in adjacent cells communication pathways while high-risk group was involved in metabolism and immune pathways. The nomograms could also help to guide personal prognostic prediction and therapeutic strategies in EC. CONCLUSIONS Our study not only determine 5 ARGs signature that could predict the prognosis of EC but also provide novel insights into the underlying mechanisms of autophagy.

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.

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

Background

Autophagy 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.

Methods

Using 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”.

Results

A 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.

Conclusions

The 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.

Systematic profiling of diagnostic and prognostic value of autophagy-related genes for sarcoma patients

BACKGROUND

Autophagy-related genes (ARGs) have been confirmed to have an important role in tumorigenesis and tumor microenvironment formation. Nevertheless, a systematic analysis of ARGs and their clinical significance in sarcoma patients is lacking.

METHODS

Gene expression files from The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx) were used to select differentially expressed genes (DEGs). Differentially expressed ARGs (DEARGs) were determined by matching the DEG and HADb gene sets, which were evaluated by functional enrichment analysis. Unsupervised clustering of the identified DEARGs was conducted, and associations with tumor microenvironment (TME), immune checkpoints, and immune cells were analyzed simultaneously. Two prognostic signatures, one for overall survival (OS) and one for disease-free survival (DFS), were established and validated in an independent set.

RESULTS

In total, 84 DEARGs and two clusters were identified. TME scores, five immune checkpoints, and several types of immune cells were found to be significantly different between two clusters. Two prognostic signatures incorporating DEARGs showed favorable discrimination and were successfully validated. Two nomograms combining signature and clinical variables were generated. The C-indexes were 0.818 and 0.747 for the OS and DFS nomograms, respectively.

CONCLUSION

This comprehensive analyses of the ARG landscape in sarcoma showed novel ARGs related to carcinogenesis and the immune microenvironment. These findings have implications for prognosis and therapeutic responses, which reveal novel potential prognostic biomarkers, promote precision medicine, and provide potential novel targets for immunotherapy.

Identification of an autophagy-related gene signature for survival prediction in patients with cervical cancer

Cervical cancer is one of the most common female malignancy that occurs worldwide and is reported to cause over 300,000 deaths in 2018. Autophagy controls the survival and death of cancerous cells by regulating the degradation process of cytoplasm and cellular organelle. In the present study, the differentially expressed autophagy-related genes (ARGs) between healthy and cancerous cervical tissues (squamous cell neoplasms) were obtained using data from GTEx and The Cancer Genome Atlas (TCGA) database. The functionalities of the differentially expressed ARGs were analyzed using Gene Ontology (GO) as well as the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Next, we conducted univariate Cox regression assay and obtained 12 ARGs that were associated with the prognosis of cervical cancer patients. We carried out a multivariate Cox regression analysis and developed six ARG-related prognostic signature for the survival prediction of patients with squamous cell cervical cancer (Risk score = − 0.63*ATG3–0.42*BCL2 + 0.85*CD46–0.38*IFNG+ 0.23*NAMPT+ 0.82*TM9SF1). Following the calculation of risk score using the signature, the patients were divided into high and low-risk groups according to the median value. Kaplan-Meier curve demonstrated that patients with a high-risk score tend to have a poor prognosis (P < 0.001). The value for area under the curves corresponding to the receiver operating characteristic (ROC) was 0.740. As observed, the expression of IFNG was negatively associated with lymph node metastasis (P = 0.026), while a high-risk score was significantly associated with increased age (P = 0.008). To further validate the prognostic signature, we carried out a permutation test and confirmed the performance of the risk score. In conclusion, our study developed six ARG-related prognostic signature for patients with squamous cell cervical cancer, which might help in improving the prognostic predictions of such patients.

Clinical and Molecular Characterization of Incidentally Discovered Lower-Grade Gliomas with Enrichment of Aerobic Respiration

Purpose

Incidentally discovered diffusely infiltrating lower-grade gliomas (incidental LGGs, iLGGs) are defined as gliomas occasionally found in patients without tumor-related symptoms. At present, very few in-depth research studies on incidental LGGs were reported. We aimed to find out the inherent difference between iLGGs and LGGs with tumor-related symptoms.

Patients and Methods

We enrolled 2486 all-grade gliomas and screened 1594 lower-grade gliomas for further analysis. Medical records were retrospectively reviewed for iLGGs. Clinical and mRNA sequencing data were collected for in-depth analysis.

Results

We found that with increasing grade, the proportion of incidental glioma patients decreased obviously. In 1594 patients who underwent craniotomy for LGG, 80 (5%) patients were discovered incidentally. Grade II patients (88%) and patients bearing 1p/19q co-deletion in their tumors (23%) were more likely to be diagnosed as iLGGs. Regular radiological screening (48%) and trauma (24%) were the main complaint for brain imaging for iLGGs. Kaplan–Meier survival analysis indicated that iLGGs patients lived a significantly longer survival and Cox regression analysis revealed that iLGGs were an independent indicator of better prognosis. Subsequent gene set enrichment analysis and differential expression analysis based on the gene expression profile revealed that mitochondrial aerobic respiration process was enriched in iLGGs. Moreover, we found that iLGGs tended to generate energy by unique mitochondrial aerobic respiration.

Conclusion

These results provided a primitive exploration of iLGGs, which may potentially assist clinical neurosurgeons with personalized management of iLGGs.

Development of Autophagy Signature-Based Prognostic Nomogram for Refined Glioma Survival Prognostication

The current glioma classification could be optimized to cover such a separate and individualized prognosis ranging from a few months to over ten years. Considering its highly conserved role and potential in therapies, autophagy might be a promising element to be incorporated as a refinement for improved survival prognostication. The expression and RNA-seq data of 881 glioma patients from the Gene Expression Omnibus and The Cancer Genome Atlas were included, mapped with autophagy-related genes. Weighted gene coexpression network analysis and Cox regression analysis were used for the autophagy signature establishment, which composed of MUL1, NPC1, and TRIM13. Validations were represented by Kaplan-Meier plots and receiver operating curves (ROC). Cluster analysis suggested the IDH1 mutant involved in the favorable prognosis of the signature clusters. The signature was also immune-related shown by the Gene Ontology analysis and the Gene Set Enrichment Analysis. The high signature risk group held a higher ESTIMATE score (p = 2.6e - 11) and stromal score (p = 1.8e - 10). CD276 significantly correlated with the signature (r = 0.51, p < 0.05). The final nomogram integrated with the autophagy signature, IDH1 mutation, and pathological grade was built with accuracy and discrimination (1-year survival AUC = 0.812, 5-year survival AUC = 0.822, and 10-year survival AUC = 0.834). Its prognostic value and clinical utility were well-defined by the superiority in the comparisons with the current World Health Organization glioma classification in ROC (p < 0.05) and decision curve analysis. The autophagy signature-based IDH1 mutation and grade nomogram refined glioma classification for a more individualized and clinically applicable survival estimation and inspired potential autophagy-related therapies.

The impact of autophagy during the development and survival of glioblastoma

Glioblastoma is the most common and aggressive adult brain tumour, with poor median survival and limited treatment options. Following surgical resection and chemotherapy, recurrence of the disease is inevitable. Genomic studies have identified key drivers of glioblastoma development, including amplifications of receptor tyrosine kinases, which drive tumour growth. To improve treatment, it is crucial to understand survival response processes in glioblastoma that fuel cell proliferation and promote resistance to treatment. One such process is autophagy, a catabolic pathway that delivers cellular components sequestered into vesicles for lysosomal degradation. Autophagy plays an important role in maintaining cellular homeostasis and is upregulated during stress conditions, such as limited nutrient and oxygen availability, and in response to anti-cancer therapy. Autophagy can also regulate pro-growth signalling and metabolic rewiring of cancer cells in order to support tumour growth. In this review, we will discuss our current understanding of how autophagy is implicated in glioblastoma development and survival. When appropriate, we will refer to findings derived from the role of autophagy in other cancer models and predict the outcome of manipulating autophagy during glioblastoma treatment.