ATF4 regulated by MYC has an important function in anoikis resistance in human osteosarcoma cells

Clinical and therapeutical significances of the cluster and signature based on oxidative stress for osteosarcoma

BACKGROUND

It is of great clinical significance to find out the ideal tumor biomarkers and therapeutic targets to improve the prognosis of patients with osteosarcoma (OS). Oxidative stress (OXS) can directly target intracellular macromolecules and exhibit dual effects of tumor promotion and suppression.

METHODS

OXS-related genes (OXRGs) were extracted from public databases, including TARGET and GEO. Univariate Cox regression analysis, Random Survival Forest algorithm, and LASSO regression were performed to identify prognostic genes and establish the OXS-signature. The efficacy of the OXS-signature was further evaluated by Kaplan-Meier curves and timeROC package. Evaluation of immunological characteristics was achieved based on ESTIMATE algorithm and ssGSEA. Submap algorithm was used to explore the response to anti-PD1 and anti-CTLA4 therapy for OS. Drug response prediction was conducted by using pRRophetic package. The expression values of related genes in the OXS-signature were detected with PCR assays.

RESULTS

Two OXS-clusters were identified for OS, with remarkable differences of clusters presented in prognosis. Kyoto Encyclopedia of Genes Genomes (KEGG) analysis showed that differentially expressed genes (DEGs) between the OXS-clusters were significantly enriched in several immune-related pathways. Patients with lower OS-scores attained better clinical outcomes, and presented more sensitivity to ICB therapy. By contrast, OS patients with higher OS-scores revealed more sensitivity to certain drugs. Furthermore, critical genes, RHBDL2 and CGREF1 from the model, were significantly higher expressed in OS cell lines.

CONCLUSIONS

Our study identified the clusters and signature based on OXS, which would lay the foundation for molecular experimental research, disease prevention and treatment of OS.

Identification and verification of a novel anoikis-related gene signature with prognostic significance in clear cell renal cell carcinoma

PURPOSE

Clear cell renal cell carcinomas (ccRCCs) are the most common form of renal cancer in the world. The loss of extracellular matrix (ECM) stimulates cell apoptosis, known as anoikis. A resistance to anoikis in cancer cells is believed to contribute to tumor malignancy, particularly metastasis; however, the potential influence of anoikis on the prognosis of ccRCC patients is not fully understood.

METHODS

In this study, anoikis-related genes (ARGs) with discrepant expression were selected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The anoikis-related gene signature (ARS) was built using a combination of the univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses. ARS was also evaluated for their prognostic value. We explored the tumor microenvironment and enrichment pathways between different clusters of ccRCC. We also examined differences in clinical characteristics, immune cell infiltration and drug sensitivity between the high- and low-risk sets. In addition, we utilized three external databases and quantitative real-time polymerase chain reaction (qRT-PCR) to validate the expression and prognosis of ARGs.

RESULTS

Eight ARGs (PLAUR, HMCN1, CDKN2A, BID, GLI2, PLG, PRKCQ and IRF6) were identified as anoikis-related prognostic factors. According to Kaplan-Meier (KM) analysis, ccRCC patients with high-risk ARGs have a worse prognosis. The risk score was found to be a significant independent prognostic indicator. According to tumor microenvironment (TME) scores, stromal score, immune score, and estimated score of the high-risk group were superior to those of the low-risk group. There were significant differences between the two groups regarding the amount of infiltrated immune cells, immune checkpoint expression as well as drug sensitivity. A nomogram was constructed using ccRCC clinical features and risk scores. The signature and the nomogram both performed well in predicting overall survival (OS) for ccRCC patients. According to a decision curve analysis (DCA), clinical treatment options for patients with ccRCC could be improved using this model.

CONCLUSION

The results of validation from external databases and qRT-PCR were basically agreement with findings in TCGA and GEO databases. The ARS serving as biomarkers may provide an important reference for individual therapy of ccRCC patients.

Identification of a new anoikis-related gene signature for prognostic significance in head and neck squamous carcinomas

Anoikis, a mode of programmed cell death, is essential for normal development and homeostasis in the organism and plays an important role in the onset and progression of cancers. The authors of this research sought to establish a gene signature associated with anoikis to predict therapy outcomes and patient prognosis for individuals with head and neck squamous cell carcinoma (HNSCC). Transcriptome data of anoikis-related genes (ARGs) in individuals with HNSCC were retrieved from public databases to aid in the formulation of the gene signature. A novel ARG signature was then created using a combination of the Least Absolute Shrinkage and Selection Operator regression and Cox regression analysis. The relationship between ARGs and tumor immune microenvironment in HNSCC was explored using single-cell analysis. HNSCC individuals were classified into high-risk and low-risk groups as per the median value of risk score. The study also investigated the variations in the infiltration status of immune cells, tumor microenvironment, sensitivity to immunotherapy and chemotherapeutics, as well as functional enrichment between the low-risk and high-risk categories. A total of 18 ARGs were incorporated in the formulation of the signature. Our signature's validity as a standalone predictive predictor was validated by multivariate Cox regression analysis and Kaplan-Meier survival analysis. Generally, the prognosis was worse for high-risk individuals. Subjects in the low-risk groups had a better prognosis and responded in a better way to combination immunotherapy, had higher immunological ratings and activity levels, and had more immune cell infiltration. In addition, gene set enrichment analysis findings showed that the low-risk subjects exhibited heightened activity in several immune-related pathways. However, the high-risk patients responded better to chemotherapy. The aim of this research was to develop a new ARG signature to predict the prognosis and sensitivity to immunotherapeutic and chemotherapeutic schemes for HNSCC patient. As a result, this could help spur the creation of new chemotherapeutics and immunotherapeutic approaches for patients with HNSCC.

An anoikis-related gene signature for prediction of the prognosis in prostate cancer

Purpose

This study presents a novel approach to predict postoperative biochemical recurrence (BCR) in prostate cancer (PCa) patients which involves constructing a signature based on anoikis-related genes (ARGs).

Methods

In this study, we utilised data from TCGA-PARD and GEO databases to identify specific ARGs in prostate cancer. We established a signature of these ARGs using Cox regression analysis and evaluated their clinical predictive efficacy and immune-related status through various methods such as Kaplan-Meier survival analysis, subject work characteristics analysis, and CIBERSORT method. Our findings suggest that these ARGs may have potential as biomarkers for prostate cancer prognosis and treatment. To investigate the biological pathways of genes associated with anoikis, we utilised GSVA, GO, and KEGG. The expression of ARGs was confirmed by the HPA database. Furthermore, we conducted PPI analysis to identify the core network of ARGs in PCa.

Results

Based on analysis of the TCGA database, a set of eight ARGs were identified as prognostic signature genes for prostate cancer. The reliability and validity of this signature were well verified in both the TCGA and GEO codifications. Using this signature, patients were classified into two groups based on their risk for developing BCR. There was a significant difference in BCR-free time between the high and low risk groups (P < 0.05).This signature serves as a dependable and unbiased prognostic factor for predicting biochemical recurrence (BCR) in prostate cancer (PCa) patients. It outperforms clinicopathological characteristics in terms of accuracy and reliability. PLK1 may play a potential regulatory role as a core gene in the development of prostate cancer.

Conclusion

This signature suggests the potential role of ARGs in the development and progression of PCa and can effectively predict the risk of BCR in PCa patients after surgery. It also provides a basis for further research into the mechanism of ARGs in PCa and for the clinical management of patients with PCa.

Inhibition of protein translation under matrix-deprivation stress in breast cancer cells

Matrix-deprivation stress leads to cell-death by anoikis, whereas overcoming anoikis is critical for cancer metastasis. Work from our lab and others has identified a crucial role for the cellular energy sensor AMPK in anoikis-resistance, highlighting a key role for metabolic reprogramming in stress survival. Protein synthesis is a major energy-consuming process that is tightly regulated under stress. Although an increase in protein synthesis in AMPK-depleted experimentally-transformed MEFs has been associated with anoikis, the status and regulation of protein translation in epithelial-origin cancer cells facing matrix-detachment remains largely unknown. Our study shows that protein translation is mechanistically abrogated at both initiation and elongation stages by the activation of the unfolded protein response (UPR) pathway and inactivation of elongation factor eEF2, respectively. Additionally, we show inhibition of the mTORC1 pathway known for regulation of canonical protein synthesis. We further functionally assay this inhibition using SUnSET assay, which demonstrates repression of global protein synthesis in MDA-MB-231 and MCF7 breast cancer cells when subjected to matrix-deprivation. In order to gauge the translational status of matrix-deprived cancer cells, we undertook polysome profiling. Our data revealed reduced but continuous mRNA translation under matrix-deprivation stress. An integrated analysis of transcriptomic and proteomic data further identifies novel targets that may aid cellular adaptations to matrix-deprivation stress and can be explored for therapeutic intervention.

Near-Death Cells Cause Chemotherapy-Induced Metastasis via ATF4-Mediated NF-κB Signaling Activation

Cytotoxic chemotherapy is a primary treatment modality for many patients with advanced cancer. Increasing preclinical and clinical observations indicate that chemotherapy can exacerbate tumor metastasis. However, the underlying mechanism remains unclear. Here, it is attempted to identify the mechanisms underlying chemotherapy-induced cancer recurrence and metastasis. It is revealed that a small subpopulation of "near-death cells" (NDCs) with compromised plasma membranes can reverse the death process to enhance survival and repopulation after exposure to lethal doses of cytotoxins. Moreover, these NDCs acquire enhanced tumorigenic and metastatic capabilities, but maintain chemosensitivity in multiple models. Mechanistically, cytotoxin exposure induces activating transcription factor 4 (ATF4)-dependent nonclassical NF-κB signaling activation; ultimately, this results in nuclear translocation of p52 and RelB in NDCs. Deletion of ATF4 in parental cancer cells significantly reduces colony formation and metastasis of NDCs, whereas overexpression of ATF4 activates the nonclassical NF-κB signaling pathway to promote chemotherapy-induced metastasis of NDCs. Overall, these results provide novel mechanistic insights into the chemotherapy-induced metastasis and indicate the pivotal role of NDCs in mediating tumor relapse after cytotoxic therapy. This study also suggests that targeting ATF4 may be an effective approach in improving the efficacy of chemotherapy.

Employing machine learning using ferroptosis-related genes to construct a prognosis model for patients with osteosarcoma

Identifying effective biomarkers in osteosarcoma (OS) is important for predicting prognosis. We investigated the prognostic value of ferroptosis-related genes (FRGs) in OS. Transcriptome and clinical data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus. FRGs were obtained from the ferroptosis database. Univariate COX regression and LASSO regression screening were performed and an FRG-based prognostic model was constructed, which was validated using the Gene Expression Omnibus cohort. The predictive power of the model was assessed via a subgroup analysis. A nomogram was constructed using clinical markers with independent prognostic significance and risk score results. The CIBERSORT algorithm was used to detect the correlation between prognostic genes and 22 tumor-infiltrating lymphocytes. The expression of prognostic genes in erastin-treated OS cell lines was verified via real-time PCR. Six prognostic FRGs (ACSL5, ATF4, CBS, CDO1, SCD, and SLC3A2) were obtained and used to construct the risk prognosis model. Subjects were divided into high- and low-risk groups. Prognosis was worse in the high-risk group, and the model had satisfactory prediction performance for patients younger than 18 years, males, females, and those with non-metastatic disease. Univariate COX regression analysis showed that metastasis and risk score were independent risk factors for patients with OS. Nomogram was built on independent prognostic factors with superior predictive power and patient benefit. There was a significant correlation between prognostic genes and tumor immunity. Six prognostic genes were differentially expressed in ferroptosis inducer-treated OS cell lines. The identified prognostic genes can regulate tumor growth and progression by affecting the tumor microenvironment.

ATF4/CEMIP/PKCα promotes anoikis resistance by enhancing protective autophagy in prostate cancer cells

The survival of cancer cells after detaching from the extracellular matrix (ECM) is essential for the metastatic cascade. The programmed cell death after detachment is known as anoikis, acting as a metastasis barrier. However, the most aggressive cancer cells escape anoikis and other cell death patterns to initiate the metastatic cascade. This study revealed the role of cell migration-inducing protein (CEMIP) in autophagy modulation and anoikis resistance during ECM detachment. CEMIP amplification during ECM detachment resulted in protective autophagy induction via a mechanism dependent on the dissociation of the B-cell lymphoma-2 (Bcl-2)/Beclin1 complex. Additional investigation revealed that acting transcription factor 4 (ATF4) triggered CEMIP transcription and enhanced protein kinase C alpha (PKCα) membrane translocation, which regulated the serine70 phosphorylation of Bcl-2, while the subsequent dissociation of the Bcl-2/Beclin1 complex led to autophagy. Therefore, CEMIP antagonization attenuated metastasis formation in vivo. In conclusion, inhibiting CEMIP-mediated protective autophagy may provide a therapeutic strategy for metastatic prostate cancer (PCa). This study delineates a novel role of CEMIP in anoikis resistance and provides new insight into seeking therapeutic strategies for metastatic PCa.

MYC and the unfolded protein response in cancer: synthetic lethal partners in crime?

The transcription factors of the MYC family play pivotal roles in the initiation and progression of human cancers. High oncogenic level of MYC invades low‐affinity sites and enhancer sequences, which subsequently alters the transcriptome, causes metabolic imbalance, and induces stress response. The endoplasmic reticulum (ER) not only plays a central role in maintaining proteostasis, but also contributes to other key biological processes, including Ca²⁺ metabolism and the synthesis of lipids and glucose. Stress conditions, such as shortage in glucose or oxygen and disruption of Ca²⁺ homeostasis, may perturb proteostasis and induce the unfolded protein response (UPR), which either restores homeostasis or triggers cell death. Crucial roles of ER stress and UPR signaling have been implicated in various cancers, from oncogenesis to treatment response. Here, we summarize the current knowledge on the interaction between MYC and UPR signaling, and its contribution to cancer development. We also discuss the potential of targeting key UPR signaling nodes as novel synthetic lethal strategies in MYC‐driven cancers.

High expression of ID1 facilitates metastasis in human osteosarcoma by regulating the sensitivity of anoikis via PI3K/AKT depended suppression of the intrinsic apoptotic signaling pathway

A lack of understanding of the molecular basis underlying the regulation of metastatic disease and its effective therapy are the primary causes of high mortality in osteosarcoma. Thus, new insights into metastases and novel effective targets for metastatic osteosarcoma are urgently required. Anoikis resistance is considered a hallmark of cancer cells with metastatic ability. However, the molecular mechanism of anoikis is poorly understood in osteosarcoma. We applied immunohistochemistry to investigate the correlation between inhibitor of differentiation or DNA binding 1 (ID1) and clinicopathological features, and investigated the correlation between ID1 and the metastatic behavior of osteosarcoma cells, in vitro and in vivo. The results revealed that ID1 is overexpressed in human osteosarcoma tissues, is positively associated with lung metastases, and is a potential biomarker of poor prognosis. Overexpression of ID1 could increase anoikis insensitivity of osteosarcoma cells to facilitate metastasis through the PI3K/AKT-dependent mitochondrial apoptosis pathway. Knockdown of ID1 partly reversed the high potential of metastasis in anoikis-resistant osteosarcoma cells. Our findings revealed, that ID1 is a candidate molecular target for metastatic potential osteosarcoma by highlighting the role of anoikis resistance. In addition ID1 might be a potential predictor of poor prognosis in patients with osteosarcoma.