Identification of an Autophagy-Related Signature for Prognosis and Immunotherapy Response Prediction in Ovarian Cancer

Single-cell sequencing uncovers the mechanistic role of DAPK1 in glioma and its diagnostic and prognostic implications

Background

We conducted an investigation into the characteristics of single-cell differentiation data in gliomas, with a focus on developing DAPK1-based prognostic markers to predict patient outcomes. Dysregulated expression of DAPK1 has been associated with the invasive behavior of various malignancies, including gliomas. However, the precise role and underlying mechanisms of DAPK1 in gliomas remain inadequately understood.

Methods

We performed analyses on RNA-seq and microarray datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), in addition to single-cell RNA sequencing (scRNA-seq) data from glioma patients available in GEO. Utilizing the Seurat R package, we identified gene clusters associated with survival from the scRNA-seq data. Prognostic models were developed using LASSO and stepwise regression algorithms. Furthermore, we assessed the predictive potential of these genes within the immune microenvironment and their relevance in immunotherapy contexts.

Results

Our scRNA-seq data analysis revealed 32 distinct cell clusters corresponding to 10 cell types. Through dimensionality reduction and clustering, we identified three glial cell subpopulations based on their differentiation trajectories. DAPK1, serving as a marker gene for the terminal subpopulation, exhibited an association with poor prognosis.

Conclusions

DAPK1-based prognostic models show promise for accurately predicting outcomes in glioblastoma and glioma. An in-depth examination of DAPK1's specific mechanisms in glioblastoma could elucidate its role in immunotherapy response. Targeting the DAPK1 gene may offer therapeutic benefits for glioma patients.

Col1A1 as a new decoder of clinical features and immune microenvironment in ovarian cancer

Backgrounds

Collagen type I alpha 1 chain (COL1A1) is a key protein encoding fibrillar collagen, playing a crucial role in the tumor microenvironment (TME) due to its complex functions and close association with tumor invasiveness. This has made COL1A1 a focal point in cancer biology research. However, studies investigating the relationship between COL1A1 expression levels and clinical characteristics of ovarian cancer (OC) remain limited.

Methods

This study integrated resources from publicly available online databases and immunohistochemistry (IHC) techniques to analyze and validate COL1A1 expression in OC tissues, and evaluated its potential association with clinical features in OC patients. The prognostic value of COL1A1 was assessed using Kaplan-Meier (KM) survival curve analysis. The TIMER and TISIDB databases to explore the potential relationship between COL1A1 expression and immune microenvironment in OC tissues. The LinkedOmics and INPUT2 databases were used to analyze differential gene expression in OC, This was followed by enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) annotations to identify and predict potential signaling pathways associated with COL1A1.

Results

Our study demonstrated that COL1A1 expression was significantly elevated in OC tissues compared to normal ovarian tissues. This elevated expression was closely associated with tumor metastasis, poor prognosis, and advanced pathological stages in OC patients. Moreover, COL1A1 expression showed a significant correlation with immune cell infiltration and the expression of immune-related genes within the TME.Further analyses revealed that COL1A1 and its co-expressed genes were primarily enriched in key signaling pathways involved in OC invasion, metastasis, and angiogenesis, indicating its potential role in driving OC progression.

Conclusions

Our study found that upregulation of COL1A1 expression is significantly associated with lymph node metastasis of OC and can affect the immune microenvironment. Based on this, COL1A1 could serve as a promising biomarker for OC prognosis and provide a new perspective for the development of potential immunotherapies for patients with OC.

Single-cell RNA sequencing in ovarian cancer: Current progress and future prospects

Ovarian cancer is one of the most prevalent gynaecological malignancies. The rapid development of single-cell RNA sequencing (scRNA-seq) has allowed scientists to use this technique to study ovarian cancer development, heterogeneity, and tumour environment. Although multiple original research articles have reported the use of scRNA-seq in understanding ovarian cancer and how therapy resistance occurs, there is a lack of a comprehensive review that could summarize the findings from multiple studies. Therefore, this review aimed to fill this gap by comparing and summarizing the results from different studies that have used scRNA-seq in understanding ovarian cancer development, heterogeneity, tumour microenvironment, and treatment resistance. This review will begin with an overview of scRNA-seq workflow, followed by a discussion of various applications of scRNA-seq in studying ovarian cancer. Next, the limitations and future directions of scRNA-seq in ovarian cancer research will be presented.

MUL1 identified as mitochondria-linked biomarker promoting cisplatin resistance in OC cells

Ovarian cancer (OC) ranks among the prevalent tumors affecting the female reproductive system. The aim of this study was to evaluate mitochondria-associated platinum resistance genes using organoid models. Univariate Cox regression, LASSO and multivariate Cox regression analyses were performed on The Cancer Genome Atlas (TCGA) database to construct 2-gene prognostic signature (MUL1 and SSBP1), and GSE26712 dataset was used for external validation. In addition, the relationship between MUL1 and platinum resistance was examined by organoid culture, lentiviral transduction, CCK8 assay, and Western blot. The results showed that patients in the high-risk group exhibited significantly worse OS (P = 0.002, P = 0.017). Drug sensitivity analysis revealed that platinum resistance increased with the upregulation of MUL1 expression (Cor = 0.5154, P = 0.02). Our experimental findings demonstrated that knockout of the MUL1 gene significantly increased apoptosis and enhanced the sensitivity of the OC cell line A2780 to cisplatin. Through this study, we have provided strong evidence for further research on prognostic risk factors and individualized treatment in OC patients, and provided new insights into addressing platinum resistance in OC.

Prediction of acute myeloid leukemia prognosis based on autophagy features and characterization of its immune microenvironment

Background

Autophagy promotes the survival of acute myeloid leukemia (AML) cells by removing damaged organelles and proteins and protecting them from stress-induced apoptosis. Although many studies have identified candidate autophagy genes associated with AML prognosis, there are still great challenges in predicting the survival prognosis of AML patients. Therefore, it is necessary to identify more novel autophagy gene markers to improve the prognosis of AML by utilizing information at the molecular level.

Methods

In this study, the Random Forest, SVM and XGBoost algorithms were utilized to identify autophagy genes linked to prognosis, respectively. Subsequently, six autophagy genes (TSC2, CALCOCO2, BAG3, UBQLN4, ULK1 and DAPK1) that were significantly associated with patients' overall survival (OS) were identified using Lasso-Cox regression analysis. A prediction model incorporating these autophagy genes was then developed. In addition, the immunological microenvironment analysis of autophagy genes was performed in this study.

Results

The experimental results showed that the predictive model had good predictive ability. After adjusting for clinicopathologic parameters, this feature proved an independent prognostic predictor and was validated in an external AML sample set. Analysis of differentially expressed genes in patients in the high-risk and low-risk groups showed that these genes were enriched in immune-related pathways such as humoral immune response, T cell differentiation in thymus and lymphocyte differentiation. Then immune infiltration analysis of autophagy genes in patients showed that the cellular abundance of T cells CD4+ memory activated, NK cells activated and T cells CD4+ in the high-risk group was significantly lower than that in the low-risk group.

Conclusion

This study systematically analyzed autophagy-related genes (ARGs) and developed prognostic predictors related to OS for patients with AML, thus more accurately assessing the prognosis of AML patients. This not only helps to improve the prognostic assessment and therapeutic outcome of patients, but may also provide new help for future research and clinical applications.

Novel protein-based prognostic signature linked to immunotherapeutic efficiency in ovarian cancer

BACKGROUND

Personalized medicine remains an unmet need in ovarian cancer due to its heterogeneous nature and complex immune microenvironments, which has gained increasing attention in the era of immunotherapy. A key obstacle is the lack of reliable biomarkers to identify patients who would benefit significantly from the therapy. While conventional clinicopathological factors have exhibited limited efficacy as prognostic indicators in ovarian cancer, multi-omics profiling presents a promising avenue for comprehending the interplay between the tumor and immune components. Here we aimed to leverage the individual proteomic and transcriptomic profiles of ovarian cancer patients to develop an effective protein-based signature capable of prognostication and distinguishing responses to immunotherapy.

METHODS

The workflow was demonstrated based on the Reverse Phase Protein Array (RPPA) and RNA-sequencing profiles of ovarian cancer patients from The Cancer Genome Atlas (TCGA). The algorithm began by clustering patients using immune-related gene sets, which allowed us to identify immune-related proteins of interest. Next, a multi-stage process involving LASSO and Cox regression was employed to distill a prognostic signature encompassing five immune-related proteins. Based on the signature, we subsequently calculated the risk score for each patient and evaluated its prognostic performance by comparing this model with conventional clinicopathological characteristics.

RESULTS

We developed and validated a protein-based prognostic signature in a cohort of 377 ovarian cancer patients. The risk signature outperformed conventional clinicopathological factors, such as age, grade, stage, microsatellite instability (MSI), and homologous recombination deficiency (HRD) status, in terms of prognoses. Patients in the high-risk group had significantly unfavorable overall survival (p < 0.001). Moreover, our signature effectively stratified patients into subgroups with distinct immune landscapes. The high-risk group exhibited higher levels of CD8 T-cell infiltration and a potentially greater proportion of immunotherapy responders. The co-activation of the TGF-β pathway and cancer-associated fibroblasts could impair the ability of cytotoxic T cells to eliminate cancer cells, leading to poor outcomes in the high-risk group.

CONCLUSIONS

The protein-based signature not only aids in evaluating the prognosis but also provides valuable insights into the tumor immune microenvironments in ovarian cancer. Together our findings highlight the importance of a thorough understanding of the immunosuppressive tumor microenvironment in ovarian cancer to guide the development of more effective immunotherapies.

Autophagy and machine learning: Unanswered questions

Autophagy is a critical conserved cellular process in maintaining cellular homeostasis by clearing and recycling damaged organelles and intracellular components in lysosomes and vacuoles. Autophagy plays a vital role in cell survival, bioenergetic homeostasis, organism development, and cell death regulation. Malfunctions in autophagy are associated with various human diseases and health disorders, such as cancers and neurodegenerative diseases. Significant effort has been devoted to autophagy-related research in the context of genes, proteins, diagnosis, etc. In recent years, there has been a surge of studies utilizing state of the art machine learning (ML) tools to analyze and understand the roles of autophagy in various biological processes. We taxonomize ML techniques that are applicable in an autophagy context, comprehensively review existing efforts being taken in this direction, and outline principles to consider in a biomedical context. In recognition of recent groundbreaking advances in the deep-learning community, we discuss new opportunities in interdisciplinary collaborations and seek to engage autophagy and computer science researchers to promote autophagy research with joint efforts.

Single-cell RNA sequencing in ovarian cancer: revealing new perspectives in the tumor microenvironment

Single-cell sequencing technology has emerged as a pivotal tool for unraveling the complexities of the ovarian tumor microenvironment (TME), which is characterized by its cellular heterogeneity and intricate cell-to-cell interactions. Ovarian cancer (OC), known for its high lethality among gynecologic malignancies, presents significant challenges in treatment and diagnosis, partly due to the complexity of its TME. The application of single-cell sequencing in ovarian cancer research has enabled the detailed characterization of gene expression profiles at the single-cell level, shedding light on the diverse cell populations within the TME, including cancer cells, stromal cells, and immune cells. This high-resolution mapping has been instrumental in understanding the roles of these cells in tumor progression, invasion, metastasis, and drug resistance. By providing insight into the signaling pathways and cell-to-cell communication mechanisms, single-cell sequencing facilitates the identification of novel therapeutic targets and the development of personalized medicine approaches. This review summarizes the advancement and application of single-cell sequencing in studying the stromal components and the broader TME in OC, highlighting its implications for improving diagnosis, treatment strategies, and understanding of the disease's underlying biology.

Prognostic biomarker NRG2 correlates with autophagy and epithelial‑mesenchymal transition in breast cancer

Breast cancer (BRCA) is a leading cause of death in women worldwide, accounting for 31% of female cancer. Autophagy plays a crucial role in cancer progression, however, the function of autophagy-related gene neuroregulatory protein 2 (NRG2) in BRCA and its underlying molecular mechanisms remain unclear. In the present study, the expression of the NRG2 gene in BRCA was significantly down-regulated compared with the normal controls. The low expression level of NRG2 was related to poor survival rate of BRCA. The receiver operating characteristic curve of NRG2 showed a good diagnostic value for distinguishing BRCA from normal tissues (AUC=0.932). GO-KEGG analysis and GSEA enrichment analysis showed that NRG2 and its regulated genes were enriched in autophagy-related and immune-related pathways, and NRG2 was positively correlated with a number of immune cells and immune checkpoint genes. In addition, knockdown of NRG2 significantly promoted the proliferation, invasion and migration of BRCA cells. The autophagy marker, LC3-II and epithelial-mesenchymal transition (EMT) marker, vimentin were increased, while P62 and E-cadherin were decreased in response to NRG2 depletion. The findings of the present study demonstrated that NRG2 acts as a tumor suppressor factor that contributes to the immune escape and anti-tumor immunity inhibition by regulating the pathological process of autophagy and EMT, suggesting that NRG2 could be used as a prognostic biomarker and clinical target for BRCA therapy.

Ovarian Cancer Cell-Conditioning Medium Induces Cancer-Associated Fibroblast Phenoconversion through Glucose-Dependent Inhibition of Autophagy

One aspect of ovarian tumorigenesis which is still poorly understood is the tumor-stroma interaction, which plays a major role in chemoresistance and tumor progression. Cancer-associated fibroblasts (CAFs), the most abundant stromal cell type in the tumor microenvironment, influence tumor growth, metabolism, metastasis, and response to therapy, making them attractive targets for anti-cancer treatment. Unraveling the mechanisms involved in CAFs activation and maintenance is therefore crucial for the improvement of therapy efficacy. Here, we report that CAFs phenoconversion relies on the glucose-dependent inhibition of autophagy. We show that ovarian cancer cell-conditioning medium induces a metabolic reprogramming towards the CAF-phenotype that requires the autophagy-dependent glycolytic shift. In fact, 2-deoxy-D-glucose (2DG) strongly hampers such phenoconversion and, most importantly, induces the phenoreversion of CAFs into quiescent fibroblasts. Moreover, pharmacological inhibition (by proline) or autophagy gene knockdown (by siBECN1 or siATG7) promotes, while autophagy induction (by either 2DG or rapamycin) counteracts, the metabolic rewiring induced by the ovarian cancer cell secretome. Notably, the nutraceutical resveratrol (RV), known to inhibit glucose metabolism and to induce autophagy, promotes the phenoreversion of CAFs into normal fibroblasts even in the presence of ovarian cancer cell-conditioning medium. Overall, our data support the view of testing autophagy inducers for targeting the tumor-promoting stroma as an adjuvant strategy to improve therapy success rates, especially for tumors with a highly desmoplastic stroma, like ovarian cancer.

Autophagy flux in bladder cancer: Cell death crosstalk, drug and nanotherapeutics

Autophagy, a self-digestion mechanism, has emerged as a promising target in the realm of cancer therapy, particularly in bladder cancer (BCa), a urological malignancy characterized by dysregulated biological processes contributing to its progression. This highly conserved catabolic mechanism exhibits aberrant activation in pathological events, prominently featured in human cancers. The nuanced role of autophagy in cancer has been unveiled as a double-edged sword, capable of functioning as both a pro-survival and pro-death mechanism in a context-dependent manner. In BCa, dysregulation of autophagy intertwines with cell death mechanisms, wherein pro-survival autophagy impedes apoptosis and ferroptosis, while pro-death autophagy diminishes tumor cell survival. The impact of autophagy on BCa progression is multifaceted, influencing metastasis rates and engaging with the epithelial-mesenchymal transition (EMT) mechanism. Pharmacological modulation of autophagy emerges as a viable strategy to impede BCa progression and augment cell death. Notably, the introduction of nanoparticles for targeted autophagy regulation holds promise as an innovative approach in BCa suppression. This review underscores the intricate interplay of autophagy with cell death pathways and its therapeutic implications in the nuanced landscape of bladder cancer.

A Stem-like Patient-Derived Ovarian Cancer Model of Platinum Resistance Reveals Dissociation of Stemness and Resistance

To understand chemoresistance in the context of cancer stem cells (CSC), a cisplatin resistance model was developed using a high-grade serous ovarian cancer patient-derived, cisplatin-sensitive sample, PDX4. As a molecular subtype-specific stem-like cell line, PDX4 was selected for its representative features, including its histopathological and BRCA2 mutation status, and exposed to cisplatin in vitro. In the cisplatin-resistant cells, transcriptomics were carried out, and cell morphology, protein expression, and functional status were characterized. Additionally, potential signaling pathways involved in cisplatin resistance were explored. Our findings reveal the presence of distinct molecular signatures and phenotypic changes in cisplatin-resistant PDX4 compared to their sensitive counterparts. Surprisingly, we observed that chemoresistance was not inherently linked with increased stemness. In fact, although resistant cells expressed a combination of EMT and stemness markers, functional assays revealed that they were less proliferative, migratory, and clonogenic-features indicative of an underlying complex mechanism for cell survival. Furthermore, DNA damage tolerance and cellular stress management pathways were enriched. This novel, syngeneic model provides a valuable platform for investigating the underlying mechanisms of cisplatin resistance in a clinically relevant context, contributing to the development of targeted therapies tailored to combat resistance in stem-like ovarian cancer.

Dual inhibitors of DNMT and HDAC induce viral mimicry to induce antitumour immunity in breast cancer

The existing conventional treatments for breast cancer, including immune checkpoint blockade, exhibit limited effects in some cancers, particularly triple-negative breast cancer. Epigenetic alterations, specifically DNMT and HDAC alterations, are implicated in breast cancer pathogenesis. We demonstrated that DNMTs and HDACs are overexpressed and positively correlated in breast cancer. The combination of DNMT and HDAC inhibitors has shown synergistic antitumour effects, and our previously designed dual DNMT and HDAC inhibitor (termed DNMT/HDACi) 15a potently inhibits breast cancer cell proliferation, migration, and invasion and induces apoptosis in vitro and in vivo. Mechanistically, 15a induces a viral mimicry response by promoting the expression of endogenous retroviral elements in breast cancer cells, thus increasing the intracellular level of double-stranded RNA to activate the RIG-I-MAVS pathway. This in turn promotes the production of interferons and chemokines and augments the expression of interferon-stimulated genes and PD-L1. The combination of 15a and an anti-PD-L1 antibody had an additive effect in vivo. These findings indicate that this DNMT/HDACi has immunomodulatory functions and enhances the effectiveness of immune checkpoint blockade therapy. A novel dual DNMT and HDAC inhibitor induces viral mimicry, which induces the accumulation of dsRNA to activate tumoral IFN signalling and cytokine production to enhance the immune response in breast cancer.

Exploiting Nanotechnology for Drug Delivery: Advancing the Anti-Cancer Effects of Autophagy-Modulating Compounds in Traditional Chinese Medicine

Background

Cancer continues to be a prominent issue in the field of medicine, as demonstrated by recent studies emphasizing the significant role of autophagy in the development of cancer. Traditional Chinese Medicine (TCM) provides a variety of anti-tumor agents capable of regulating autophagy. However, the clinical application of autophagy-modulating compounds derived from TCM is impeded by their restricted water solubility and bioavailability. To overcome this challenge, the utilization of nanotechnology has been suggested as a potential solution. Nonetheless, the current body of literature on nanoparticles delivering TCM-derived autophagy-modulating anti-tumor compounds for cancer treatment is limited, lacking comprehensive summaries and detailed descriptions.

Methods

Up to November 2023, a comprehensive research study was conducted to gather relevant data using a variety of databases, including PubMed, ScienceDirect, Springer Link, Web of Science, and CNKI. The keywords utilized in this investigation included "autophagy", "nanoparticles", "traditional Chinese medicine" and "anticancer".

Results

This review provides a comprehensive analysis of the potential of nanotechnology in overcoming delivery challenges and enhancing the anti-cancer properties of autophagy-modulating compounds in TCM. The evaluation is based on a synthesis of different classes of autophagy-modulating compounds in TCM, their mechanisms of action in cancer treatment, and their potential benefits as reported in various scholarly sources. The findings indicate that nanotechnology shows potential in enhancing the availability of autophagy-modulating agents in TCM, thereby opening up a plethora of potential therapeutic avenues.

Conclusion

Nanotechnology has the potential to enhance the anti-tumor efficacy of autophagy-modulating compounds in traditional TCM, through regulation of autophagy.

Comprehensive machine learning-based preoperative blood features predict the prognosis for ovarian cancer

PURPOSE

Significant advancements in improving ovarian cancer (OC) outcomes have been limited over the past decade. To predict prognosis and improve outcomes of OC, we plan to develop and validate a robust prognosis signature based on blood features.

METHODS

We screened age and 33 blood features from 331 OC patients. Using ten machine learning algorithms, 88 combinations were generated, from which one was selected to construct a blood risk score (BRS) according to the highest C-index in the test dataset.

RESULTS

Stepcox (both) and Enet (alpha = 0.7) performed the best in the test dataset with a C-index of 0.711. Meanwhile, the low RBS group possessed observably prolonged survival in this model. Compared to traditional prognostic-related features such as age, stage, grade, and CA125, our combined model had the highest AUC values at 3, 5, and 7 years. According to the results of the model, BRS can provide accurate predictions of OC prognosis. BRS was also capable of identifying various prognostic stratifications in different stages and grades. Importantly, developing the nomogram may improve performance by combining BRS and stage.

CONCLUSION

This study provides a valuable combined machine-learning model that can be used for predicting the individualized prognosis of OC patients.

Identifying Explainable Machine Learning Models and a Novel SFRP2+ Fibroblast Signature as Predictors for Precision Medicine in Ovarian Cancer

Ovarian cancer (OC) is a type of malignant tumor with a consistently high mortality rate. The diagnosis of early-stage OC and identification of functional subsets in the tumor microenvironment are essential to the development of patient management strategies. However, the development of robust models remains unsatisfactory. We aimed to utilize artificial intelligence and single-cell analysis to address this issue. Two independent datasets were screened from the Gene Expression Omnibus (GEO) database and processed to obtain overlapping differentially expressed genes (DEGs) in stage II-IV vs. stage I diseases. Three explainable machine learning algorithms were integrated to construct models that could determine the tumor stage and extract important characteristic genes as diagnostic biomarkers. Correlations between cancer-associated fibroblast (CAF) infiltration and characteristic gene expression were analyzed using TIMER2.0 and their relationship with survival rates was comprehensively explored via the Kaplan-Meier plotter (KM-plotter) online database. The specific expression of characteristic genes in fibroblast subsets was investigated through single-cell analysis. A novel fibroblast subset signature was explored to predict immune checkpoint inhibitor (ICI) response and oncogene mutation through Tumor Immune Dysfunction and Exclusion (TIDE) and artificial neural network algorithms, respectively. We found that Support Vector Machine-Shapley Additive Explanations (SVM-SHAP), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) successfully diagnosed early-stage OC (stage I). The area under the receiver operating characteristic curves (AUCs) of these models exceeded 0.990. Their overlapping characteristic gene, secreted frizzled-related protein 2 (SFRP2), was a risk factor that affected the overall survival of OC patients with stage II-IV disease (log-rank test: p < 0.01) and was specifically expressed in a fibroblast subset. Finally, the SFRP2+ fibroblast signature served as a novel predictor in evaluating ICI response and exploring pan-cancer tumor protein P53 (TP53) mutation (AUC = 0.853, 95% confidence interval [CI]: 0.829-0.877). In conclusion, the models based on SVM-SHAP, XGBoost, and RF enabled the early detection of OC for clinical decision making, and SFRP2+ fibroblast signature used in diagnostic models can inform OC treatment selection and offer pan-cancer TP53 mutation detection.