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

Drug-Resistant Epithelial Ovarian Cancer: Current and Future Perspectives

Epithelial ovarian cancer (EOC) is a complex disease with diverse histological subtypes, which, based on the aggressiveness and course of disease progression, have recently been broadly grouped into type I (low-grade serous, endometrioid, clear cell, and mucinous) and type II (high-grade serous, high-grade endometrioid, and undifferentiated carcinomas) categories. Despite substantial differences in pathogenesis, genetics, prognosis, and treatment response, clinical diagnosis and management of EOC remain similar across the subtypes. Debulking surgery combined with platinum-taxol-based chemotherapy serves as the initial treatment for High Grade Serous Ovarian Carcinoma (HGSOC), the most prevalent one, and for other subtypes, but most patients exhibit intrinsic or acquired resistance and recur in short duration. Targeted therapies, such as anti-angiogenics (e.g., bevacizumab) and PARP inhibitors (for BRCA-mutated cancers), offer some success, but therapy resistance, through various mechanisms, poses a significant challenge. This comprehensive chapter delves into emerging strategies to address these challenges, highlighting factors like aberrant miRNAs, metabolism, apoptosis evasion, cancer stem cells, and autophagy, which play pivotal roles in mediating resistance and disease relapse in EOC. Beyond standard treatments, the focus of this study extends to alternate targeted agents, including immunotherapies like checkpoint inhibitors, CAR T cells, and vaccines, as well as inhibitors targeting key oncogenic pathways in EOC. Additionally, this chapter covers disease classification, diagnosis, resistance pathways, standard treatments, and clinical data on various emerging approaches, and advocates for a nuanced and personalized approach tailored to individual subtypes and resistance mechanisms, aiming to enhance therapeutic outcomes across the spectrum of EOC subtypes.

Autophagy-related risk signature based on CDNK2A to facilitate survival prediction of patients with endometrial cancer

BACKGROUND

Autophagy plays an important role in immunity and inflammation. The present study aimed to explore the prognostic significance of autophagy-related genes (ARGs) in endometrial cancer (EC) using bioinformatics.

METHODS

The list of ARGs was obtained from the Human Autophagy Database. The differentially expressed ARGs (DEARGs) between the EC and normal endometrial tissue samples were screened from The Cancer Genome Atlas database. Cox regression analysis was performed on the DEARGs to screen the prognostic ARGs and construct risk signatures for overall survival (OS) and progression-free survival (PFS). The hub ARGs were identified from a protein-protein interaction network, and CDKN2A was obtained from the intersection of prognostic ARGs and hub ARGs. The association of CDKN2A expression with clinical characteristics and immune infiltration were analyzed. Finally, the role of CDKN2A in autophagy was confirmed in EC cell lines.

RESULTS

CDKN2A, PTK6 and DLC1 were used to establish risk signatures for predicting the survival of EC patients. Receiver operating characteristic curve analysis indicated that the risk signatures can accurately predict both OS and PFS. CDKN2A was the only hub prognostic ARG, and showed significant association with the age, survival status, grade, histological type, body mass index and FIGO (i.e. International Federation of Gynecology and Obstetrics) stage (p < 0.05). Furthermore, CDKN2A expression was also correlated with the infiltration of immune cells, indicating that CDKN2A might play a critical role in regulating the immune microenvironment and immune responses in EC. In addition, silencing of CDKN2A gene promoted autophagy in the HEC-1A cell line and upregulated the expression levels of autophagy-related proteins.

CONCLUSIONS

CDKN2A is a prognostic factor and therapeutic target in EC, and is likely associated with the tumor immune landscape and autophagy.

A macrophage related signature for predicting prognosis and drug sensitivity in ovarian cancer based on integrative machine learning

BACKGROUND

Ovarian cancer ranks the leading cause of gynecologic cancer-related death in the United States and the fifth most common cause of cancer-related mortality among American women. Increasing evidences have highlighted the vital role of macrophages M2/M1 proportion in tumor progression, prognosis and immunotherapy.

METHODS

Weighted gene co-expression network analysis (WGCNA) was performed to identify macrophages related markers. Integrative procedure including 10 machine learning algorithms were performed to develop a prognostic macrophage related signature (MRS) with TCGA, GSE14764, GSE140082 datasets. The role of MRS in tumor microenvironment (TME) and therapy response was evaluated with the data of CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER and EPIC, GSE91061 and IMvigor210 dataset.

RESULTS

The optimal MRS developed by the combination of CoxBoost and StepCox[forward] algorithm served as an independent risk factor in ovarian cancer. Compared with stage, grade and other established prognostic signatures, the current MRS had a better performance in predicting the overall survival rate of ovarian cancer patients. Low risk score indicated a higher TME score, higher level of immune cells, higher immunophenoscore, higher tumor mutational burden, lower TIDE score and lower IC50 value in ovarian cancer. The survival prediction nomogram had a good potential for clinical application in predicting the 1-, 3-, and 5-year overall survival rate of ovarian cancer patients.

CONCLUSION

All in all, the current study constructed a powerful prognostic MRS for ovarian cancer patients using 10 machine learning algorithms. This MRS could predict the prognosis and drug sensitivity in ovarian cancer.

Role of autophagy and its regulation by noncoding RNAs in ovarian cancer

Autophagy is a self-digestion process by which misfolded proteins and damaged organelles in eukaryotic cells are degraded to maintain cellular homeostasis. This process is involved in the tumorigenesis, metastasis, and chemoresistance of various tumors such as ovarian cancer (OC). Noncoding RNAs (ncRNAs), mainly including microRNAs, long noncoding RNAs, and circular RNAs, have been extensively investigated in cancer research for their roles in the regulation of autophagy. Recent studies have shown that in OC cells, ncRNAs can modulate the formation of autophagosomes, which affect tumor progression and chemoresistance. An understanding of the role of autophagy in OC progression, treatment, and prognosis is important, and the identification of the regulatory roles of ncRNAs in autophagy leads to intervention strategies for OC therapy. This review summarizes the role of autophagy in OC and discusses the role of ncRNA-mediated autophagy in OC, as an understanding of these roles may contribute to the development of potential therapeutic strategies for this disease.

Autophagy-related tumor subtypes associated with significant gene expression profiles and immune cell infiltration signatures to reveal the prognosis of non-small cell lung cancer

Autophagy plays an important role in non-small cell lung cancer (NSCLC). We aimed to establish novel autophagy-related tumor subtypes to distinguish the prognosis of NSCLC. In this study, gene expression profiles, mutation data and clinical information obtained from the Cancer Genome Atlas. Kaplan Meier-plotter could evaluate prognostic value of autophagy-related genes. Consensus clustering revealed autophagy-related tumor subtypes. Gene expression profiles, mutation data and immune infiltration signatures were identified, oncogenic pathways and gene-drug interactions were performed according to the clusters. Finally, a total of 23 prognostic genes were screened and consensus clustering analysis divided the NSCLC into 2 clusters. The mutation signature showed that 6 genes are special. Immune infiltration signatures showed that higher fraction of immune cells was associated with cluster 1. The oncogenic pathways and gene-drug interactions also showed different patterns. In conclusion, autophagy-related tumor subtypes have different prognosis. Understanding the subtypes of NSCLC are helpful to accurately identify the NSCLC and personalized treatment.

Identification of an Individualized Prognostic Biomarker for Serous Ovarian Cancer: A Qualitative Model

Serous ovarian cancer is the most common type of ovarian epithelial cancer and usually has a poor prognosis. The objective of this study was to construct an individualized prognostic model for predicting overall survival in serous ovarian cancer. Based on the relative expression orderings (Ea > Eb/Ea ≤ Eb) of gene pairs closely associated with serous ovarian prognosis, we tried constructing a potential individualized qualitative biomarker by the greedy algorithm and evaluated the performance in independent validation datasets. We constructed a prognostic biomarker consisting of 20 gene pairs (SOV-P20). The overall survival between high- and low-risk groups stratified by SOV-P20 was statistically significantly different in the training and independent validation datasets from other platforms (p < 0.05, Wilcoxon test). The average area under the curve (AUC) values of the training and three validation datasets were 0.756, 0.590, 0.630, and 0.680, respectively. The distribution of most immune cells between high- and low-risk groups was quite different (p < 0.001, Wilcoxon test). The low-risk patients tended to show significantly better tumor response to chemotherapy than the high-risk patients (p < 0.05, Fisher’s exact test). SOV-P20 achieved the highest mean index of concordance (C-index) (0.624) compared with the other seven existing prognostic signatures (ranging from 0.511 to 0.619). SOV-P20 is a promising prognostic biomarker for serous ovarian cancer, which will be applicable for clinical predictive risk assessment.

Identification of an autophagy-related gene signature for predicting prognosis and immune activity in pancreatic adenocarcinoma

Adenocarcinoma of the pancreas (PAAD) is a cancerous growth that deteriorates rapidly and has a poor prognosis. Researchers are investigating autophagy in PAAD to identify a new biomarker and treatment target. An autophagy-related gene (ARG) model for overall survival (OS) was constructed using multivariate Cox regression analyses. A cohort of the Cancer Genome Atlas (TCGA)-PAAD was used as the training group as a basis for model construction. This prediction model was validated with several external datasets. To evaluate model performance, the analysis with receiver operating characteristic curves (ROC) was performed. The Human Protein Atlas (HPA) and Cancer Cell Line Encyclopedia (CCLE) were investigated to validate the effects of ARGs expression on cancer cells. Comparing the levels of immune infiltration between high-risk and low-risk groups was finished through the use of CIBERSORT. The differentially expressed genes (DEGs) between the low-/high-risk groups were analyzed further via Gene Ontology biological process (GO-BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, which were used to identify potential small-molecule compounds in Connectivity Map (CMap), followed by half-maximal inhibitory concentration (IC50) examination with PANC-1 cells. The risk score was finally calculated as follows: BAK1 × 0.34 + ITGA3 × 0.38 + BAG3 × 0.35 + APOL1 × 0.26-RAB24 × 0.67519. ITGA3 and RAB24 both emerged as independent prognostic factors in multivariate Cox regression. Each PAAD cohort had a significantly shorter OS in the high-risk group than in the low-risk group. The high-risk group exhibited infiltration of several immune cell types, including naive B cells (p = 0.003), plasma cells (p = 0.044), and CD8 T cells (nearly significant, p = 0.080). Higher infiltration levels of NK cells (p = 0.025), resting macrophages (p = 0.020), and mast cells (p = 0.007) were found in the high-risk group than the low-risk group. The in vitro and in vivo expression of signature ARGs was consistent in the CCLE and HPA databases. The top 3 enriched Gene Ontology biological processes (GO-BPs) were signal release, regulation of transsynaptic signaling, and modulation of chemical synaptic transmission, and the top 3 enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were MAPK, cAMP, and cell adhesion molecules. Four potential small-molecule compounds (piperacetazine, vinburnine, withaferin A and hecogenin) that target ARGs were also identified. Taking the results together, our research shows that the ARG signature may serve as a useful prognostic indicator and reveal potential therapeutic targets in patients with PAAD.

Identification of epigenetic genes for predicting prognosis and immunotherapy response of ovarian cancer

BACKGROUND

Epigenetic factors play a critical role in tumour development and progression. The aim of this study was to construct and validate a robust epigenetic gene set-based signature for predicting prognosis of ovarian cancer.

METHODS

By using LASSO Cox regression model, we screened out the most useful prognostic epigenetic factors and a prognostic signature was developed based on them. Survival receiver operating characteristic was used to test the prognostic accuracy of signature in training and validation sets. The associations between the risk scores and immune cell infiltration, tumour purity, immune checkpoint inhibitor genes expression were also assessed in ovarian cancer .

RESULTS

A total of 26 epigenetic factors were identified to develop the prognostic signature. In the training set, the prognosis of high-risk patients was strikingly poorer than that of low-risk patients (hazard ratio: 2.11, 95% confidence interval: 1.65-2.72, P < 0.001). Similar results were further observed in the internal validation set (hazard ratio: 1.69, 95% confidence interval: 1.07-2.63, P = 0.020) and external validation set (hazard ratio:1.95, 95% confidence interval: 1.41-2.69; P < 0.001). Survival receiver operating characteristic at 5 year showed the epigenetic signature (area under the curve = 0.700) performed better than other clinical features in predicting prognosis. Distinct difference in immune activation related pathways, immune cells infiltration, tumour purity reflected by immune and stromal score and immune checkpoint inhibitor genes gene expression was observed between high- and low-risk samples.

CONCLUSIONS

This study constructed an epigenetic signature that was capable of predicting postoperative outcomes and may also serve as potential biomarker for immunotherapy responses for ovarian cancer.

Accurate prediction and further dissection of neonicotinoid elimination in the water treatment by CTS@AgBC using multihead attention-based convolutional neural network combined with the time-dependent Cox regression model

Imidacloprid (IMI), as the most widely used neonicotinoid insecticide, poses a serious threat to the water ecosystem due to the inefficient elimination in the traditional water treatment. Chitosan (CTS)-stabilized biochar (BC)-supported Ag nanoparticles (CTS@AgBC) are applied to eliminate the IMI in the water treatment effectively. Batch experiments depict that the modification of BC by CTS and Ag nanoparticles remarkably improve its adsorption performance. The pseudo-second-order and Elovich models have good performance in simulating the adsorption processes of CTS@AgBC and BC. This indicates that the chemical adsorption on real surfaces plays the dominant role in the adsorption of IMI by CTS@AgBC and BC. In addition, the multihead attention (MHA)-based convolutional neural network (CNN) combined with the time-dependent Cox regression model are initially applied to predict and dissect the adsorption elimination processes of IMI by CTS@AgBC. The proposed MHA-CNN model achieves more accurate concentration prediction of IMI than traditional models. According to influence weights by MHA module, biochar category, pH, and treatment temperature are considered the three dominant environmental variables to determine the IMI elimination processes. This study provides insights into roles of environmental variables in the elimination of IMI by CTS@AgBC and the accurate prediction of IMI concentration.

Autophagy awakens-the myriad roles of autophagy in head and neck cancer development and therapeutic response

Autophagy is an evolutionarily conserved cell survival mechanism that degrades damaged proteins and organelles to generate cellular energy during times of stress. Recycling of these cellular components occurs in a series of sequential steps with multiple regulatory points. Mechanistic dysfunction can lead to a variety of human diseases and cancers due to the complexity of autophagy and its ability to regulate vital cellular functions. The role that autophagy plays in both the development and treatment of cancer is highly complex, especially given the fact that most cancer therapies modulate autophagy. This review aims to discuss the balance of autophagy in the development, progression, and treatment of head and neck cancer, as well as highlighting the need for a deeper understanding of what is still unknown about autophagy.

The Role of the Extracellular Matrix and Tumor-Infiltrating Immune Cells in the Prognostication of High-Grade Serous Ovarian Cancer

Ovarian cancer is the eighth global leading cause of cancer-related death among women. The most common form is the high-grade serous ovarian carcinoma (HGSOC). No further improvements in the 5-year overall survival have been seen over the last 40 years since the adoption of platinum- and taxane-based chemotherapy. Hence, a better understanding of the mechanisms governing this aggressive phenotype would help identify better therapeutic strategies. Recent research linked onset, progression, and response to treatment with dysregulated components of the tumor microenvironment (TME) in many types of cancer. In this study, using bioinformatic approaches, we identified a 19-gene TME-related HGSOC prognostic genetic panel (19 prognostic genes (PLXNB2, HMCN2, NDNF, NTN1, TGFBI, CHAD, CLEC5A, PLXNA1, CST9, LOXL4, MMP17, PI3, PRSS1, SERPINA10, TLL1, CBLN2, IL26, NRG4, and WNT9A) by assessing the RNA sequencing data of 342 tumors available in the TCGA database. Using machine learning, we found that specific patterns of infiltrating immune cells characterized each risk group. Furthermore, we demonstrated the predictive potential of our risk score across different platforms and its improved prognostic performance compared with other gene panels.

Identification of a Five Autophagy Subtype-Related Gene Expression Pattern for Improving the Prognosis of Lung Adenocarcinoma

Background: Autophagy plays an important role in lung adenocarcinoma (LUAD). In this study, we aimed to explore the autophagy-related gene (ARG) expression pattern and to identify promising autophagy-related biomarkers to improve the prognosis of LUAD.

Methods: The gene expression profiles and clinical information of LUAD patients were downloaded from the Cancer Genome Atlas (TCGA), and validation cohort information was extracted from the Gene Expression Omnibus database. The Human Autophagy Database (HADb) was used to extract ARGs. Gene expression data were analyzed using the limma package and visualized using the ggplot2 package as well as the pheatmap package in R software. Functional enrichment analysis was also performed for the differentially expressed ARGs (DEARGs). Then, consensus clustering revealed autophagy-related tumor subtypes, and differentially expressed genes (DEGs) were screened according to the subtypes. Next, the univariate Cox and multivariate Cox regression analyses were used to identify independent prognostic ARGs. After overlapping DEGs and the independent prognostic ARGs, the predictive risk model was established and validated. Correlation analyses between ARGs and clinicopathological variables were also explored. Finally, the TIMER and TISIDB databases were used to further explore the correlation analysis between immune cell infiltration levels and the risk score as well as clinicopathological variables in the predictive risk model.

Results: A total of 222 genes from the HADb were identified as ARGs, and 28 of the 222 genes were pooled as DEARGs. The most significant GO term was autophagy (p = 3.05E-07), and KEGG analysis results indicated that 28 DEARGs were significantly enriched in the ErbB signaling pathway (p < 0.001). Then, consensus clustering analysis divided the LUAD into two clusters, and a total of 168 DEGs were identified according to cluster subtypes. Then univariate and multivariate Cox regression analyses were used to identify 12 genes that could serve as independent prognostic indicators. After overlapping 168 DEGs and 12 genes, 10 genes (ATG4A, BAK1, CAPNS1, CCR2, CTSD, EIF2AK3, ITGB1, MBTPS2, SPHK1, ST13) were selected for the further exploration of the prognostic pattern. Survival analysis results indicated that this risk model identified the prognosis (p = 4.379E-10). Combined with the correlation analysis results between ARGs and clinicopathological variables, five ARGs were screened as prognostic genes. Among them, SPHK1 expression levels were positively correlated with CD4⁺ T cells and dendritic cell infiltration levels.

Conclusions: In this study, we constructed a predictive risk model and identified a five autophagy subtype-related gene expression pattern to improve the prognosis of LUAD. Understanding the subtypes of LUAD is helpful to accurately characterize the LUAD and develop personalized treatment.

Development and validation of a survival model for esophageal adenocarcinoma based on autophagy-associated genes

Autophagy is a highly conserved catabolic process which has been implicated in esophageal adenocarcinoma (EAC). We sought to investigate the biological functions and prognostic value of autophagy-related genes (ARGs) in EAC. A total of 21 differentially expressed ARGs were identified between EAC and normal samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were then applied for the differentially expressed ARGs in EAC, and the protein-protein interaction (PPI) network was established. Cox survival analysis and Lasso regression analysis were performed to establish a prognostic prediction model based on nine overall survival (OS)-related ARGs (CAPN1, GOPC, TBK1, SIRT1, ARSA, BNIP1, ERBB2, NRG2, PINK1). The 9-gene prognostic signature significantly stratified patient outcomes in The Cancer Genome of Atlas (TCGA)-EAC cohort and was considered as an independently prognostic predictor for EAC patients. Moreover, Gene set enrichment analysis (GSEA) analyses revealed several important cellular processes and signaling pathways correlated with the high-risk group in EAC. This prognostic prediction model was confirmed in an independent validation cohort (GSE13898) from The Gene Expression Omnibus (GEO) database. We also developed a nomogram with a concordance index of 0.78 to predict the survival possibility of EAC patients by integrating the risk signature and clinicopathological features. The calibration curves substantiated favorable concordance between actual observation and nomogram prediction. Last but not least, Erb-B2 Receptor Tyrosine Kinase 2 (ERBB2), a member of the prognostic gene signature, was identified as a potential therapeutic target for EAC patients. To sum up, we established and verified a novel prognostic prediction model based on ARGs which could optimize the individualized survival prediction in EAC.

Development of an Autophagy-Based and Stemness-Correlated Prognostic Model for Hepatocellular Carcinoma Using Bulk and Single-Cell RNA-Sequencing

Accumulating evidence has proved that autophagy serves as a tumor promoter in formed malignancies, and the autophagy-related prognostic signatures have been constructed as clinical tools to predict prognosis in many high-mortality cancers. Autophagy-related genes have participated in the development and metastasis of hepatocellular carcinoma (HCC), but the understanding of their prognostic value is limited. Thereafter, LIMMA and survival analysis were conducted in both ICGC and TCGA databases and a total of 10 hub autophagy-related genes, namely, NPC1, CDKN2A, RPTOR, SPHK1, HGS, BIRC5, SPNS1, BAK1, ATIC, and MAPK3, were collected. Then, GO, KEGG, correlation, consensus, and PCA analyses were utilized to reveal their potential targeted role in HCC treatment. Single-cell RNA-seq of cancer stem cells also indicated that there was a positive correlation between these genes and stemness. In parallel, we applied univariate, LASSO, and multivariate regression analyses to study the autophagy-related genes and finally proposed that ATIC and BIRC5 were the valuable prognostic indicators of HCC. The signature based on ATIC and BIRC5 exhibited moderate power for predicting the survival of HCC in the ICGC cohort, and its efficacy was further validated in the TCGA cohort. Taken together, we suggested that 10 aforementioned hub genes are promising therapeutic targets of HCC and the ATIC/BIRC5 prognostic signature is a practical prognostic indicator for HCC patients.

Tumor suppressor gene mutations correlate with prognosis and immunotherapy benefit in hepatocellular carcinoma

INTRODUCTION

The tumor microenvironment (TME) has profound impacts on prognosis and immunotherapy. The TME can be altered by the genomic mutations on specific tumor-suppressor genes (TSG), thus, comprehending the association between TME and TSG in hepatocellular carcinoma (HCC) is imperative.

METHODS

With a total of 1699 HCC patients from 6 international multicenter cohorts, we delineated the mutational landscape of TSG and summarized the proportion of TSG mutated HCC in different countries. Using the genomic and transcriptomic data, we comprehensively explored the impacts of TSG mutations on TME and immunity in HCC. A dataset of 31 HCC patients from the cBioPortal database was utilized to evaluate the predictive value of TSG subtypes for immunotherapy response.

RESULTS

Interestingly, TSG non-mutated HCC will have more "immune-hot" tumors, and display the infiltration abundance of immune cells such as B cell, CD4+/CD8+T cell, and neutrophil. Moreover, TSG non-mutated HCC was characterized by the higher expression level of three immune checkpoints, including CD40, CD40LG, and TNFRSF4. In line with the TME characterization and immune checkpoint profiles, TSG non-mutated HCC displayed prolonged overall survival and relapse-free survival, notably, are more likely to respond to immune checkpoint inhibitors.

CONCLUSIONS

Our findings suggested the TSG subtypes could serve as a promising biomarker for guiding surveillance protocol and immunotherapeutic decisions for patients with HCC.

Identification of a glycolysis-related gene signature for survival prediction of ovarian cancer patients

Background

Ovarian cancer (OV) is deemed the most lethal gynecological cancer in women. The aim of this study was to construct an effective gene prognostic model for predicting overall survival (OS) in patients with OV.

Methods

The expression profiles of glycolysis‐related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed using training and test sets.

Results

A gene risk signature based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4) was identified to predict the survival outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high‐grade OV, in the TCGA dataset, with areas under the curve (AUC) of 0.709 and 0.762 for 3‐ and 5‐year survival, respectively. Similar results were found in the test sets, and the AUCs of 3‐, 5‐year OS were 0.714 and 0.772 in the combined test set. And our signature was an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was developed.

Conclusion

Our study established a nine‐GRG risk model and nomogram to better predict OS in patients with OV. The risk model represents a promising and independent prognostic predictor for patients with OV. Moreover, our study on GRGs could offer guidance for the elucidation of underlying mechanisms in future studies.

Targeting BRF2 in Cancer Using Repurposed Drugs

Simple Summary

BRF2, a subunit of the RNA polymerase III transcription complex, is upregulated in a wide variety of cancers and is a potential therapeutic target; however, no effective drugs are available to target BRF2. The upregulation of BRF2 in cancer cells confers survival via the prevention of oxidative stress-induced apoptosis. In this manuscript, we report the identification of potential BRF2 inhibitors through in silico drug repurposing screening. We further characterized bexarotene as a hit compound for the development of selective BRF2 inhibitors and provide experimental validation to support the repurposing of this FDA-approved drug as an agent to reduce the cellular levels of ROS and consequent BRF2 expression in cancers with elevated levels of oxidative stress.

Abstract

The overexpression of BRF2, a selective subunit of RNA polymerase III, has been shown to be crucial in the development of several types of cancers, including breast cancer and lung squamous cell carcinoma. Predominantly, BRF2 acts as a central redox-sensing transcription factor (TF) and is involved in rescuing oxidative stress (OS)-induced apoptosis. Here, we showed a novel link between BRF2 and the DNA damage response. Due to the lack of BRF2-specific inhibitors, through virtual screening and molecular dynamics simulation, we identified potential drug candidates that interfere with BRF2-TATA-binding Protein (TBP)-DNA complex interactions based on binding energy, intermolecular, and torsional energy parameters. We experimentally tested bexarotene as a potential BRF2 inhibitor. We found that bexarotene (Bex) treatment resulted in a dramatic decline in oxidative stress and Tert-butylhydroquinone (tBHQ)-induced levels of BRF2 and consequently led to a decrease in the cellular proliferation of cancer cells which may in part be due to the drug pretreatment-induced reduction of ROS generated by the oxidizing agent. Our data thus provide the first experimental evidence that BRF2 is a novel player in the DNA damage response pathway and that bexarotene can be used as a potential inhibitor to treat cancers with the specific elevation of oxidative stress.

Identification of autophagy-related risk signatures for the prognosis, diagnosis, and targeted therapy in cervical cancer

Background

To rummage autophagy-related prognostic, diagnostic, and therapeutic biomarkers in cervical cancer (CC).

Methods

The RNA-sequence and clinical information were from the TCGA and GTEx databases. We operated Cox regression to determine signatures related to overall survival (OS) and recurrence-free survival (RFS) respectively. The diagnostic and therapeutic effectiveness of prognostic biomarkers were further explored.

Results

We identified nine (VAMP7, MTMR14, ATG4D, KLHL24, TP73, NAMPT, CD46, HGS, ATG4C) and three risk signatures (SERPINA1, HSPB8, SUPT20H) with prognostic values for OS and RFS respectively. Six risk signatures (ATG4C, ATG4D, CD46, TP73, SERPINA1, HSPB8) were selected for qPCR. We screened five prognostic signatures(ATG4C, CD46, HSPB8, MTMR14, NAMPT) with diagnostic function through the GEO database. Correlation between our models and treatment targets certificated the prognostic score provided a reference for precision medicine.

Conclusions

We constructed OS and RFS prognostic models in CC. Autophagy-related risk signatures might serve as diagnostic and therapeutic biomarkers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02073-w.

Identification of a novel autophagy signature for predicting survival in patients with lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most commonhistological lung cancer subtype, with an overall five-year survivalrate of only 17%. In this study, we aimed to identify autophagy-related genes (ARGs) and develop an LUAD prognostic signature.

Methods

In this study, we obtained ARGs from three databases and downloaded gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used TCGA-LUAD (n = 490) for a training and testing dataset, and GSE50081 (n = 127) as the external validation dataset.The least absolute shrinkage and selection operator (LASSO) Cox and multivariate Cox regression models were used to generate an autophagy-related signature. We performed gene set enrichment analysis (GSEA) and immune cell analysis between the high- and low-risk groups. A nomogram was built to guide the individual treatment for LUAD patients.

Results

We identified a total of 83 differentially expressed ARGs (DEARGs) from the TCGA-LUAD dataset, including 33 upregulated DEARGs and 50 downregulated DEARGs, both with thresholds of adjusted P < 0.05 and |Fold change| > 1.5. Using LASSO and multivariate Cox regression analyses, we identified 10 ARGs that we used to build a prognostic signature with areas under the curve (AUCs) of 0.705, 0.715, and 0.778 at 1, 3, and 5 years, respectively. Using the risk score formula, the LUAD patients were divided into low- or high-risk groups. Our GSEA results suggested that the low-risk group were enriched in metabolism and immune-related pathways, while the high-risk group was involved in tumorigenesis and tumor progression pathways. Immune cell analysis revealed that, when compared to the high-risk group, the low-risk group had a lower cell fraction of M0- and M1- macrophages, and higher CD4 and PD-L1 expression levels.

Conclusion

Our identified robust signature may provide novel insight into underlying autophagy mechanisms as well as therapeutic strategies for LUAD treatment.

Long noncoding RNA GAS8-AS1: A novel biomarker in human diseases

Long non-coding RNAs (lncRNAs) represent a group of ncRNAs with more than 200 nucleotides. These RNAs can specifically regulate gene expression at both the transcriptional and the post-transcriptional levels, and increasing evidence indicates that they play vital roles in a variety of disease-related cellular processes. The lncRNA GAS8 antisense RNA 1 (GAS8-AS1, also known as C16orf3) is located in the second intron of GAS8 and has been reported to be both abnormally expressed in several diseases and closely correlated with many clinical characteristics. GAS8-AS1 has been shown to affect many biological functions, including cell proliferation, migration, invasiveness, and autophagy using several signaling pathways. In this review, we have summarized current studies on GAS8-AS1 roles in disease and discuss its potential clinical utility. GAS8-AS1 may be a promising biomarker for both diagnoses and prognoses, and a novel target for many disease therapies.

Development of a novel transcription factors-related prognostic signature for serous ovarian cancer

Growing evidence suggest that transcription factors (TFs) play vital roles in serous ovarian cancer (SOC). In the present study, TFs mRNA expression profiles of 564 SOC subjects in the TCGA database, and 70 SOC subjects in the GEO database were screened. A 17-TFs related prognostic signature was constructed using lasso cox regression and validated in the TCGA and GEO cohorts. Consensus clustering analysis was applied to establish a cluster model. The 17-TFs related prognostic signature, risk score and cluster models were effective at accurately distinguishing the overall survival of SOC. Analysis of genomic alterations were used to elaborate on the association between the 17-TFs related prognostic signature and genomic aberrations. The GSEA assay results suggested that there was a significant difference in the inflammatory and immune response pathways between the high-risk and low-risk score groups. The potential immune infiltration, immunotherapy, and chemotherapy responses were analyzed due to the significant difference in the regulation of lymphocyte migration and T cell-mediated cytotoxicity between the two groups. The results indicated that patients with low-risk score were more likely to respond anti-PD-1, etoposide, paclitaxel, and veliparib but not to gemcitabine, doxorubicin, docetaxel, and cisplatin. Also, the prognostic nomogram model revealed that the risk score was a good prognostic indicator for SOC patients. In conclusion, we explored the prognostic values of TFs in SOC and developed a 17-TFs related prognostic signature to predict the survival of SOC patients.

Development of an Autophagy-Related Gene Prognostic Model and Nomogram for Estimating Renal Clear Cell Carcinoma Survival

Background

Kidney renal clear cell carcinoma (KIRC) is a fatal malignancy of the urinary system. Autophagy is implicated in KIRC occurrence and development. Here, we evaluated the prognostic value of autophagy-related genes (ARGs) in kidney renal clear cell carcinoma.

Materials and Methods

We analyzed RNA sequencing and clinical KIRC patient data obtained from TCGA and ICGC to develop an ARG prognostic signature. Differentially expressed ARGs were further evaluated by functional assessment and bioinformatic analysis. Next, ARG score was determined in 215 KIRC patients using univariable Cox and LASSO regression analyses. An ARG nomogram was built based on multivariable Cox analysis. The prognosis nomogram model based on the ARG signatures and clinicopathological information was evaluated for discrimination, calibration, and clinical usefulness.

Results

A total of 47 differentially expressed ARGs were identified. Of these, 8 candidates that significantly correlated with KIRC overall survival were subjected to LASSO analysis and an ARG score built. Functional enrichment and bioinformatic analysis were used to reveal the differentially expressed ARGs in cancer-related biological processes and pathways. Multivariate Cox analysis was used to integrate the ARG nomogram with the ARG signature and clinicopathological information. The nomogram exhibited proper calibration and discrimination (C-index = 0.75, AUC = >0.7). Decision curve analysis also showed that the nomogram was clinically useful.

Conclusions

KIRC patients and doctors could benefit from ARG nomogram use in clinical practice.

Identification of Three Autophagy-Related Long Non-Coding RNAs as a Novel Head and Neck Squamous Cell Carcinoma Prognostic Signature

Head and neck squamous cell carcinoma (HNSCC) has a poor prognosis. Considerable evidence indicates that autophagy and non-coding RNA play essential roles in the biological processes involved in cancers, but associations between autophagy-related long non-coding RNAs (lncRNAs) and HNSCC remain unclear. In the present study, HNSCC RNA sequences and autophagy-related gene data were extracted from The Cancer Genome Atlas database and the Human Autophagy Database. A total of 1,153 autophagy-related lncRNAs were selected via calculating Pearson’s correlation coefficient. Three prognosis-related autophagy lncRNAs were identified via univariate Cox regression, least absolute shrinkage and selection operator analysis, and multivariate Cox regression analysis. We also constructed a prognostic model based on these autophagy-related lncRNAs and evaluated its ability to accurately and independently predict the prognosis of HNSCC patients. The area under the curve (AUC) was 0.864 (3-year) and 0.836 (5-year), and our model can independently predict the prognosis of HNSCC. The prognostic value of the three autophagy lncRNAs was confirmed via analysis of samples from five databases. To further identify the functions of the three lncRNAs, a co-expression network was constructed and pathway analysis was performed. In that analysis the lncRNAs were correlated with 189 related genes and 20 autophagy-related genes, and these lncRNAs mainly involved homologous recombination, the Fanconi anemia pathway, the autophagy-related pathway, and immune-related pathways. In addition, we validated the expression levels of three lncRNAs and autophagy markers (ATG12, BECN1, and MAP1LC3B) based on TIMER, Oncomine, and HPA database analysis. Our results indicated that TTTY15 was increased in HPV positive and HPV negative HNSCC patients, and three autophagy markers were up-regulated in all HNSCCC patients. Lastly, association between three lncRNAs and autophagy markers was performed, and our results showed that TTTY15 and MIF-AS1 were associated with autophagy markers. Collectively, these results suggested that three autophagy-related lncRNAs have prognostic value in HNSCC patients.

The Clinical Significance of DNA Damage Repair Signatures in Clear Cell Renal Cell Carcinoma

DNA damage repair plays an important role in cancer’s initiation and progression, and in therapeutic resistance. The prognostic potential of damage repair indicators was studied in the case of clear cell renal cell carcinoma (ccRCC). Gene expression profiles of the disease were downloaded from cancer genome databases and gene ontology was applied to the DNA repair-related genes. Twenty-six differentially expressed DNA repair genes were identified, and regression analysis was used to identify those with prognostic potential and to construct a risk model. The model accurately predicted patient outcomes and distinguished among patients with different expression levels of immune evasion genes. The data indicate that DNA repair genes can be valuable for predicting the progression of clear cell renal cell carcinoma and the clinical benefits of immunotherapy.

Identification of an Autophagy-Related Pair Signature for Predicting Prognoses and Immune Activity in Pancreatic Adenocarcinoma

BACKGROUND

Pancreatic adenocarcinoma (PAAD) spreads quickly and has a poor prognosis. Autophagy research on PAAD could reveal new biomarkers and targets for diagnosis and treatment.

METHODS

Autophagy-related genes were translated into autophagy-related gene pairs, and univariate Cox regression was performed to obtain overall survival (OS)-related IRGPs (P<0.001). LASSO Cox regression analyses were performed to construct an autophagy-related gene pair (ARGP) model for predicting OS. The Cancer Genome Atlas (TCGA)-PAAD cohort was set as the training group for model construction. The model predictive value was validated in multiple external datasets. Receiver operating characteristic (ROC) curves were used to evaluate model performance. Tumor microenvironments and immune infiltration were compared between low- and high-risk groups with ESTIMATE and CIBERSORT. Differentially expressed genes (DEGs) between the groups were further analyzed by Gene Ontology biological process (GO-BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and used to identify potential small-molecule compounds in L1000FWD.

RESULTS

Risk scores were calculated as follows: ATG4B|CHMP4C×(-0.31) + CHMP2B|MAP1LC3B×(0.30) + CHMP6|RIPK2 ×(-0.33) + LRSAM1|TRIM5×(-0.26) + MAP1LC3A|PAFAH1B2×(-0.15) + MAP1LC3A|TRIM21×(-0.08) + MET|MFN2×(0.38) + MET|MTDH×(0.47) + RASIP1|TRIM5×(-0.23) + RB1CC1|TPCN1×(0.22). OS was significantly shorter in the high-risk group than the low-risk group in each PAAD cohort. The ESTIMATE analysis showed no difference in stromal scores but a significant difference in immune scores (p=0.0045) and ESTIMATE scores (p=0.014) between the groups. CIBERSORT analysis showed higher naive B cell, Treg cell, CD8 T cell, and plasma cell levels in the low-risk group and higher M1 and M2 macrophage levels in the high-risk group. In addition, the results showed that naive B cells (r=-0.32, p<0.001), Treg cells (r=-0.31, p<0.001), CD8 T cells (r=-0.24, p=0.0092), and plasma cells (r=-0.2, p<0.026) were statistically correlated with the ARGP risk score. The top 3 enriched GO-BPs were signal release, regulation of transsynaptic signaling, and modulation of chemical synaptic transmission, and the top 3 enriched KEGG pathways were the insulin secretion, dopaminergic synapse, and NF-kappa B signaling pathways. Several potential small-molecule compounds targeting ARGs were also identified.

CONCLUSION

Our results demonstrate that the ARGP-based model may be a promising prognostic indicator for identifying drug targets in patients with PAAD.

A broad analysis in clinical and in vitro models on regulator of G-protein signalling 10 regulation that is associated with ovarian cancer progression and chemoresistance

Ovarian cancer is one of the deadliest types of gynaecological cancers and more than half of the patients die within 5 years after diagnosis. Recurrence in advanced staged patients after chemotherapy is associated with increased chemoresistance, which results in poor prognosis. Regulator of G-protein signalling 10 (RGS10) negatively regulates cell proliferation, migration and survival by attenuating G-protein coupled-receptors mediated signalling pathways. Recent studies have shown that loss of RGS10 expression is significantly associated with proliferation and cisplatin resistance in ovarian cancer cells. SIGNIFICANCE OF THE STUDY: In this study, we analysed differential RGS10 expression levels using public microarray datasets from clinical and in vitro ovarian cancer samples. We validated that cancer progression and chemotherapy exposure change RGS10 expression. We enriched our study to evaluate the relationship between chemoresistance and differential RGS10 expression against ovarian cancer potential chemotherapeutic agent, palbociclib. Results showed that palbociclib treatment reduced cell viability, despite significantly decreased RGS10 expression in chemoresistant cells. Overall, the results confirmed that cancer progression and chemoresistance are significantly associated with the down-regulation of RGS10 while some chemotherapeutics seem to be beneficial in decreasing chemoresistance in ovarian cancer.

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.

[Molecular pathways of autophagy regulation by BRCA1: Implications in cancer]

The BRCA1 protein contributes to maintain genomic integrity, through transcriptional regulation of proteins that control the cell cycle and DNA repair or by direct interaction with these proteins. The genetic instability caused by mutations that result in a deficit of BRCA1 activity, confers an increased risk of mainly breast and ovarian cancers. In recent years, it has been shown that autophagy has a dual role in tumor development, and chemical agents such as lucanthone, chloroquine, Z-ligustilide, spautin-1, tunicamycin, T-12, and olaparib, regulate tumor survival/death autophagy-dependent. Here we also review the different molecular pathways by which BRCA1 regulates (mostly negatively) autophagy, mainly in breast and ovarian cancers, and where the cellular redox state (ROS, GSH) and proteins mTOR, p53-Mdm2, STAT3, and Parkin, have been shown to play an essential role.

Development of a Prognostic Signature Based on Autophagy-related Genes for Head and Neck Squamous Cell Carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is a malignant tumor with relative low survival rate. Increasingly evidences have emphasized the importance of autophagy in cancer initiation, progression, and the responses to cancer treatment.

AIM OF THE STUDY

This study aimed to investigate the potential biological and prognostic significance of autophagy-related genes (ARGs) in HNSCC patients.

METHODS

We collected a list of ARGs from Human Autophagy Database and obtained expression profiles and clinical information of HNSCC samples from the Cancer Genome Atlas (TCGA) portal. Differential expression analysis and functional enrichment analysis were performed by R software. The prognostic value of differentially expressed ARGs was detected by Cox regression analysis and prognosis-related ARGs were subjected to LASSO regression analysis. Univariate and multivariate Cox regression analysis were applied to identify promising independent prognosticators for HNSCC.

RESULTS

A total of 35 differentially expressed ARGs were screened out and functional enrichment analysis results indicated these genes were mainly associated with autophagy-related biological processes and pathways. Seven prognosis-related ARGs (ITGA3, CDKN2A, FADD, NKX2-3, BAK1, CXCR4, and HSPB8) were selected to construct a risk signature, which proved to be effective in predicting the survival rate of HNSCC patients. Moreover, univariate analysis showed risk score, tumor stage, T stage, and N stage were negatively correlated with patient overall survival and the multivariate Cox regression analysis results indicated risk score, age, and N stage was significantly associated with patient prognosis.

CONCLUSIONS

Our findings may provide novel evidences for the diagnosis and prognosis evaluation for HNSCC.

Prognostic Implications of Immune-Related Genes' (IRGs) Signature Models in Cervical Cancer and Endometrial Cancer

Cervical cancer and endometrial cancer remain serious threats to women's health. Even though some patients can be treated with surgery plus chemoradiotherapy as a conventional option, the overall efficacy is deemed unsatisfactory. As such, the development for new treatment approaches is truly necessary. In recent years, immunotherapy has been widely used in clinical practice and it is an area of great interest that researchers are keeping attention on. However, a thorough immune-related genes (IRGs) study for cervical cancer and endometrial cancer is still lacking. We therefore aim to make a comprehensive evaluation of IRGs through bioinformatics and large databases, and also investigate the relationship between the two types of cancer. We reviewed the transcriptome RNAs of IRGs and clinical data based on the TCGA database. Survival-associated IRGs in cervical/endometrial cancer were identified using univariable and multivariable Cox proportional-hazard regression analysis for developing an IRG signature model to evaluate the risk of patients. In the end, this model was validated based on the enrichment analyses through GO, KEGG, and GSEA pathways, Kaplan-Meier survival curve, ROC curves, and immune cell infiltration. Our results showed that out of 25/23 survival-associated IRGs for cervical/endometrial cancer, 13/12 warranted further examination by multivariate Cox proportional-hazard regression analysis and were selected to develop an IRGs signature model. As a result, enrichment analyses for high-risk groups indicated main enriched pathways were associated with tumor development and progression, and statistical differences were found between high-risk and low-risk groups as shown by Kaplan-Meier survival curve. This model could be used as an independent measure for risk assessment and was considered relevant to immune cell infiltration, but it had nothing to do with clinicopathological characteristics. In summary, based on comprehensive analysis, we obtained the IRGs signature model in cervical cancer (LTA, TFRC, TYK2, DLL4, CSK, JUND, NFATC4, SBDS, FLT1, IL17RD, IL3RA, SDC1, PLAU) and endometrial cancer (LTA, PSMC4, KAL1, TNF, SBDS, HDGF, LTB, HTR3E, NR2F1, NR3C1, PGR, CBLC), which can effectively evaluate the prognosis and risk of patients and provide justification in immunology for further researches.

Prognostic value of an autophagy-related gene expression signature for endometrial cancer patients

Background

Autophagy is associated with cancer development. Autophagy-related genes play significant roles in endometrial cancer (EC), a major gynecological malignancy worldwide, but little was known about their value as prognostic markers. Here we evaluated the value of a prognostic signature based on autophagy-related genes for EC.

Methods

First, various autophagy-related genes were obtained via the Human Autophagy Database and their expression profiles were downloaded from The Cancer Genome Atlas. Second, key prognostic autophagy-related genes were identified via univariate, LASSO and multivariate Cox regression analyses. Finally, a risk score to predict the prognosis of EC was calculated and validated by using the test and the entire data sets. Besides, the key genes mRNA expression were validated using quantitative real-time PCR in clinical tissue samples.

Results

A total of 40 differentially expressed autophagy-related genes in EC were screened and five of them were prognosis-related (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1). A prognostic signature was constructed based on these five genes using the train set, which stratified EC patients into high-risk and low-risk groups (p < 0.05). In terms of overall survival, the analyses of the test set and the entire set yielded consistent results (test set: p < 0.05; entire set: p < 0.05). Time-dependent ROC analysis suggested that the risk score predicted EC prognosis accurately and independently (0.674 at 1 year, 0.712 at 3 years and 0.659 at 5 years). A nomogram with clinical utility was built. Patients in the high-risk group displayed distinct mutation signatures compared with those in the low-risk group. For clinical sample validation, we found that EIF4EBP1and ERBB2 had higher level in EC than that in normal tissues while CDKN1B, DLC1 and GRID1 had lower level, which was consistent with the results predicted.

Conclusions

Based on five autophagy-related genes (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1), our model can independently predict the OS of EC patients by combining molecular signature and clinical characteristics.

Identification of an energy metabolism‑related gene signature in ovarian cancer prognosis

Changes in energy metabolism may be potential biomarkers and therapeutic targets for cancer as they frequently occur within cancer cells. However, basic cancer research has failed to reach a consistent conclusion on the function(s) of mitochondria in energy metabolism. The significance of energy metabolism in the prognosis of ovarian cancer remains unclear; thus, there remains an urgent need to systematically analyze the characteristics and clinical value of energy metabolism in ovarian cancer. Based on gene expression patterns, the present study aimed to analyze energy metabolism‑associated characteristics to evaluate the prognosis of patients with ovarian cancer. A total of 39 energy metabolism‑related genes significantly associated with prognosis were obtained, and three molecular subtypes were identified by nonnegative matrix factorization clustering, among which the C1 subtype was associated with poor clinical outcomes of ovarian cancer. The immune response was enhanced in the tumor microenvironment. A total of 888 differentially expressed genes were identified in C1 compared with the other subtypes, and the results of the pathway enrichment analysis demonstrated that they were enriched in the 'PI3K‑Akt signaling pathway', 'cAMP signaling pathway', 'ECM‑receptor interaction' and other pathways associated with the development and progression of tumors. Finally, eight characteristic genes (tolloid‑like 1 gene, type XVI collagen, prostaglandin F2α, cartilage intermediate layer protein 2, kinesin family member 26b, interferon inducible protein 27, growth arrest‑specific gene 1 and chemokine receptor 7) were obtained through LASSO feature selection; and a number of them have been demonstrated to be associated with ovarian cancer progression. In addition, Cox regression analysis was performed to establish an 8‑gene signature, which was determined to be an independent prognostic factor for patients with ovarian cancer and could stratify sample risk in the training, test and external validation datasets (P<0.01; AUC >0.8). Gene Set Enrichment Analysis results revealed that the 8‑gene signature was involved in important biological processes and pathways of ovarian cancer. In conclusion, the present study established an 8‑gene signature associated with metabolic genes, which may provide new insights into the effects of energy metabolism on ovarian cancer. The 8‑gene signature may serve as an independent prognostic factor for ovarian cancer patients.

Identification and validation of an individualized autophagy-clinical prognostic index in gastric cancer patients

Background

The purpose of this study is to perform bioinformatics analysis of autophagy-related genes in gastric cancer, and to construct a multi-gene joint signature for predicting the prognosis of gastric cancer.

Methods

GO and KEGG analysis were applied for differentially expressed autophagy-related genes in gastric cancer, and PPI network was constructed in Cytoscape software. In order to optimize the prognosis evaluation system of gastric cancer, we established a prognosis model integrating autophagy-related genes. We used single factor Cox proportional risk regression analysis to screen genes related to prognosis from 204 autophagy-related genes in The Atlas Cancer Genome (TCGA) gastric cancer cohort. Then, the generated genes were applied to the Least Absolute Shrinkage and Selection Operator (LASSO). Finally, the selected genes were further included in the multivariate Cox proportional hazard regression analysis to establish the prognosis model. According to the median risk score, patients were divided into high-risk group and low-risk group, and survival analysis was conducted to evaluate the prognostic value of risk score. Finally, by combining clinic-pathological features and prognostic gene signatures, a nomogram was established to predict individual survival probability.

Results

GO analysis showed that the 28 differently expressed autophagy-related genes was enriched in cell growth, neuron death, and regulation of cell growth. KEGG analysis showed that the 28 differently expressed autophagy-related genes were related to platinum drug resistance, apoptosis and p53 signaling pathway. The risk score was constructed based on 4 genes (GRID2, ATG4D,GABARAPL2, CXCR4), and gastric cancer patients were significantly divided into high-risk and low-risk groups according to overall survival. In multivariate Cox regression analysis, risk score was still an independent prognostic factor (HR = 1.922, 95% CI = 1.573–2.349, P < 0.001). Cumulative curve showed that the survival time of patients with low-risk score was significantly longer than that of patients with high-risk score (P < 0.001). The external data GSE62254 proved that nomograph had a great ability to evaluate the prognosis of individual gastric cancer patients.

Conclusions

This study provides a potential prognostic marker for predicting the prognosis of GC patients and the molecular biology of GC autophagy.

Mechanistically derived patient-level framework for precision medicine identifies a personalized immune prognostic signature in high-grade serous ovarian cancer

An accurate prognosis assessment for cancer patients could aid in guiding clinical decision-making. Reliance on traditional clinical features alone in a complex clinical environment is challenging and unsatisfactory in the era of precision medicine; thus, reliable prognostic biomarkers are urgently required to improve a patient staging system. In this study, we proposed a patient-level computational framework from mechanistic and translational perspectives to establish a personalized prognostic signature (named PLPPS) in high-grade serous ovarian carcinoma (HGSOC). The PLPPS composed of 68 immune genes achieved accurate prognostic risk stratification for 1190 patients in the meta-training cohort and was rigorously validated in multiple cross-platform independent cohorts comprising 792 HGSOC patients. Furthermore, the PLPPS was shown to be the better prognostic factor compared with clinical parameters in the univariate analysis and retained a significant independent association with prognosis after adjusting for clinical parameters in the multivariate analysis. In benchmark comparisons, the performance of PLPPS (hazard ratio (HR), 1.371; concordance index (C-index), 0.604 and area under the curve (AUC), 0.637) is comparable to or better than other published gene signatures (HR, 0.972 to 1.340; C-index, 0.495 to 0.592 and AUC, 0.48-0.624). With further validation in prospective clinical trials, we hope that the PLPPS might become a promising genomic tool to guide personalized management and decision-making of HGSOC in clinical practice.

Effects of autophagy-associated genes on the prognosis for lung adenocarcinoma

Background

Several studies show that autophagy plays an important part in the biological processes of lung adenocarcinoma. Therefore, this work aimed to establish one scoring system on the basis of the expression profiles of differentially expressed autophagy-related genes (DEARGs) in patients with lung adenocarcinoma.

Methods

The Cancer Genome Atlas (TCGA) was applied to retrieve lung adenocarcinoma data. The overall survival (OS)-associated DEARGs were selected for the DEARG scoring scale. Moreover, the online database Kaplan-Meier Plotter (www.Kmplot.com) was employed to verify the accuracy of our results.

Results

The expression patterns of DEARG were detected in lung adenocarcinoma as well as normal lung tissues. A gene set related to autophagy was identified, along with 9 genes that showed marked significance in predicting the lung adenocarcinoma prognosis. According to the cox regression results, DEARGs (including ITGB4, BIRC5, ERO1A, and NLRC4) were applied to calculate the DEARGs risk score. Patients with lower DEARGs risk scores were associated with better OS. Moreover, based on analysis with the receiver operating characteristic (ROC) curve, DEARGs accurately distinguished the healthy tissues from lung adenocarcinoma tissues [area under the curve (AUC) value of >0.6].

Conclusions

A scoring system is constructed based on the primary DEARGs, which accurately predicts the outcomes of lung adenocarcinoma.

Prognostic implications of autophagy-associated gene signatures in non-small cell lung cancer

Autophagy, a highly conserved cellular proteolysis process, has been involved in non-small cell lung cancer (NSCLC). We tried to develop a prognostic prediction model for NSCLC patients based on the expression profiles of autophagy-associated genes. Univariate Cox regression analysis was used to determine autophagy-associated genes significantly correlated with overall survival (OS) of the TCGA lung cancer cohort. LASSO regression was performed to build multiple-gene prognostic signatures. We found that the 22-gene and 11-gene signatures could dichotomize patients with significantly different OS and independently predict the OS in TCGA lung adenocarcinoma (HR=2.801, 95% CI=2.252-3.486, P<0.001) and squamous cell carcinoma (HR=1.105, 95% CI=1.067-1.145, P<0.001), respectively. The prognostic performance of the 22-gene signature was validated in four GEO lung cancer cohorts. Moreover, GO, KEGG, and GSEA analyses unveiled several fundamental signaling pathways and cellular processes associated with the 22-gene signature in lung adenocarcinoma. We also constructed a clinical nomogram with a concordance index of 0.71 to predict the survival possibility of NSCLC patients by integrating clinical characteristics and the autophagy gene signature. The calibration curves substantiated fine concordance between nomogram prediction and actual observation. Overall, we constructed and verified a novel autophagy-associated gene signature that could improve the individualized outcome prediction in NSCLC.

Molecular Mechanisms Underlying Autophagy-Mediated Treatment Resistance in Cancer

Despite advances in diagnostic tools and therapeutic options, treatment resistance remains a challenge for many cancer patients. Recent studies have found evidence that autophagy, a cellular pathway that delivers cytoplasmic components to lysosomes for degradation and recycling, contributes to treatment resistance in different cancer types. A role for autophagy in resistance to chemotherapies and targeted therapies has been described based largely on associations with various signaling pathways, including MAPK and PI3K/AKT signaling. However, our current understanding of the molecular mechanisms underlying the role of autophagy in facilitating treatment resistance remains limited. Here we provide a comprehensive summary of the evidence linking autophagy to major signaling pathways in the context of treatment resistance and tumor progression, and then highlight recently emerged molecular mechanisms underlying autophagy and the p62/KEAP1/NRF2 and FOXO3A/PUMA axes in chemoresistance.

A Novel Scoring System for Pivotal Autophagy-Related Genes Predicts Outcomes after Chemotherapy in Advanced Ovarian Cancer Patients

BACKGROUND

In the clinical practice of ovarian cancer, the application of autophagy, an important regulator of carcinogenesis and chemoresistance, is still limited. This study aimed to establish a scoring system based on expression profiles of pivotal autophagy-related (ATG) genes in patients with stage III/IV ovarian cancer who received chemotherapy.

METHODS

Data of ovarian serous cystadenocarcinoma in The Cancer Genome Atlas (TCGA-OV) were used as training dataset. Two validation datasets comprised patients in a Chinese local database and a dataset from the Gene Expression Omnibus (GEO). ATG genes significantly (P < 0.1) associated with overall survival (OS) were selected and aggregated into an ATG scoring scale, of which the abilities to predict OS and recurrence-free survival (RFS) were examined.

RESULTS

Forty-three ATG genes were selected to develop the ATG score. In TCGA-OV, patients with lower ATG scores had better OS [HR = 0.41; 95% confidence interval (CI), 0.26-0.65; P < 0.001] and RFS [HR = 0.47; 95% CI, 0.27-0.82; P = 0.007]. After complete or partial remission to primary therapy, the rate of recurrence was 47.2% in the low-score group and 68.3% in the high-score group (odds ratio = 0.42; 95% CI, 0.18-0.92; P = 0.03). Such findings were verified in the two validation datasets.

CONCLUSIONS

We established a novel scoring system based on pivotal ATG genes, which accurately predicts the outcomes of patients with advanced ovarian cancer after chemotherapy.

IMPACT

The present ATG scoring system may provide a novel perspective and a promising tool for the development of personalized therapy in the future.