Identification of an Endoplasmic Reticulum Stress-Related Gene Signature to Evaluate the Immune Status and Predict the Prognosis of Hepatocellular Carcinoma

Long non-coding RNAs in ferroptosis and cuproptosis impact on prognosis and treatment in hepatocellular carcinoma

Ferroptosis and cuproptosis are recently discovered forms of cell death that have gained interest as potential cancer treatments, particularly for hepatocellular carcinoma. Long non-coding RNAs (lncRNAs) influence cancer cell activity by interacting with various nucleic acids and proteins. However, the role of ferroptosis and cuproptosis-related lncRNAs (FCRLs) in cancer remains underexplored. Ferroptosis and cuproptosis scores for each sample were assessed using Gene Set Variation Analysis (GSVA). Weighted correlation network analysis identified the FCRLs most relevant to our study. A risk model based on FCRLs was developed to categorize patients into high-risk and low-risk groups. We then compared overall survival (OS), tumor immune microenvironment, and clinical characteristics between these groups. The IPS score and ImmuCellAI webpage were used to predict the association between FCRL-related signatures and immunotherapy response. Finally, we validated the accuracy of FCRLs in hepatocellular carcinoma cell lines using induction agents (elesclomol and erastin). Patients in different risk subgroups showed significant differences in OS, immune cell infiltration, pathway activity, and clinical characteristics. Cellular assays revealed significant changes in the expression of AC019080.5, AC145207.5, MIR210HG, and LINC01063 in HCC cell lines following the addition of ferroptosis and cuproptosis inducers. We created a signature of four FCRLs that accurately predicted survival in HCC patients, laid the foundation for basic research related to ferroptosis and cuproptosis in hepatocellular carcinoma, and provided therapeutic recommendations for HCC patients.

Construction and verification of an endoplasmic reticulum stress-related prognostic model for endometrial cancer based on WGCNA and machine learning algorithms

Background

Endoplasmic reticulum (ER) stress arises from the accumulation of misfolded or unfolded proteins within the cell and is intricately linked to the initiation and progression of various tumors and their therapeutic strategies. However, the precise role of ER stress in uterine corpus endometrial cancer (UCEC) remains unclear.

Methods

Data on patients with UCEC and control subjects were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Using differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA), we identified pivotal differentially expressed ER stress-related genes (DEERGs). Further validation of the significance of these genes in UCEC was achieved through consensus clustering and bioinformatic analyses. Using Cox regression analysis and several machine learning algorithms (least absolute shrinkage and selection operator [LASSO], eXtreme Gradient Boosting [XGBoost], support vector machine recursive feature elimination [SVM-RFE], and Random Forest), hub DEERGs associated with patient prognosis were effectively identified. Based on the four identified hub genes, a prognostic model and nomogram were constructed. Additionally, a drug sensitivity analysis and in vitro validation experiments were performed.

Results

A total of 94 DEERGs were identified in patients with UCEC and healthy controls. Consensus clustering analysis revealed significant differences in prognosis, typical immune checkpoints, and tumor microenvironments between the subtypes. Using Cox regression analysis and machine learning, four hub DEERGs, MYBL2, RADX, RUSC2, and CYP46A1, were identified to construct a prognostic model. The reliability of the model was validated using receiver operating characteristic (ROC) curves. Decision curve analysis (DCA) demonstrated the superior predictive ability of the nomogram in terms of 3- and 5-year survival, compared with that of other clinical indicators. Drug sensitivity analysis revealed increased sensitivity to dactinomycin, docetaxel, selumetinib, and trametinib in the low-risk group. The expressions of RADX, RUSC2, and CYP46A1 were downregulated, whereas that of MYBL2 was upregulated in UCEC tissues, as demonstrated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and immunofluorescence assays.

Conclusion

This study developed a stable and accurate prognostic model based on multiple bioinformatics analyses, which can be used to assess the prognosis of UCEC. This model may contribute to future research on the risk stratification of patients with UCEC and the formulation of novel treatment strategies.

Development of a promising PPAR signaling pathway-related prognostic prediction model for hepatocellular carcinoma

The peroxisome proliferator-activated receptor (PPAR) signaling pathway plays a crucial role in systemic cell metabolism, energy homeostasis and immune response inhibition. However, its significance in hepatocellular carcinoma (HCC) has not been well documented. In our study, based on the RNA sequencing data of HCC, consensus clustering analyses were performed to identify PPAR signaling pathway-related molecular subtypes, each of which displaying varying survival probabilities and immune infiltration status. Following, a prognostic prediction model of HCC was developed by using the random survival forest method and Cox regression analysis. Significant difference in survival outcome, immune landscape, drug sensitivity and pathological features were observed between patients with different prognosis. Additionally, decision tree and nomogram models were adopted to optimize the prognostic prediction model. Furthermore, the robustness of the model was verified through single-cell RNA-sequencing data. Collectively, this study systematically elucidated that the PPAR signaling pathway-related prognostic model has good predictive efficacy for patients with HCC. These findings provide valuable insights for further research on personalized treatment approaches for HCC.

Immunologic Crosstalk of Endoplasmic Reticulum Stress Signaling in Bladder Cancer

Bladder cancer (BC) is a common malignant tumor of the urinary system. While current approaches involving adjuvant chemotherapy, radiotherapy, and immunotherapy have shown significant progress in BC treatment, challenges, such as recurrence and drug resistance, persist, especially in the case of muscle-invasive bladder cancer (MIBC). It is mainly due to the lack of pre-existing immune response cells in the tumor immune microenvironment. Micro-environmental changes (such as hypoxia and under-nutrition) can cause the aggregation of unfolded and misfolded proteins in the lumen, which induces endoplasmic reticulum (ER) stress. ER stress and its downstream signaling pathways are closely related to immunogenicity and tumor drug resistance. ER stress plays a pivotal role in a spectrum of processes within immune cells and the progression of BC cells, encompassing cell proliferation, autophagy, apoptosis, and resistance to therapies. Recent studies have increasingly recognized the potential of natural compounds to exhibit anti-BC properties through ER stress induction. Still, the efficacy of these natural compounds remains less than that of immune checkpoint inhibitors (ICIs). Currently, the ER stress-mediated immunogenic cell death (ICD) pathway is more encouraging, which can enhance ICI responses by mediating immune stemness. This article provides an overview of the recent developments in understanding how ER stress influences tumor immunity and its implications for BC. Targeting this pathway may soon emerge as a compelling therapeutic strategy for BC.

Activation of endoplasmic reticulum stress by harmine suppresses the growth of esophageal squamous cell carcinoma

The worldwide overall 5-year survival rate of esophageal squamous cell carcinoma (ESCC) patients is less than 20%, and novel therapeutic strategies for these patients are urgently needed. Harmine is a natural β-carboline alkaloid, which received great interest in cancer research because of its biological and anti-tumor activities. The aim of this study is to examine the effects of harmine on ESCC and its mechanism. We investigated the effects of harmine on proliferation, cell cycle, apoptosis, and tumor growth in vivo. RNA sequencing (RNA-seq), real-time PCR, and western blotting were used to detect the mechanism. Harmine inhibited ESCC cell growth in vitro and tumor growth in vivo. Differentially expressed genes in harmine-treated ESCC cells were mainly involved in protein processing in the endoplasmic reticulum (ER). Real-time PCR and western blotting confirmed harmine-induced cellular ER stress. CRISPR-Cas9 knockout of C/EBP homologous protein (CHOP) abolished harmine-induced expression of death receptor 5 and apoptosis. Harmine also induced the expression of CHOP-mediated sestrin-2, which in turn contributes to autophagosome formation via suppressing the AMP-activated protein kinase-protein kinase B-mammalian target of rapamycin signaling pathway. In conclusion, our results demonstrate that harmine inhibits the growth of ESCC through its regulation of ER stress, suggesting that it is a promising candidate for ESCC treatment.

Endoplasmic reticulum stress related IncRNA signature predicts the prognosis and immune response evaluation of uterine corpus endometrial carcinoma

Background

Endoplasmic reticulum (ER) stress is closely related to the occurrence, development and treatment of tumors. Recent studies suggest ER stress as a therapeutic strategy of choice for cancer. However, ER stress-related long non-coding RNA (lncRNA) predictive value in endometrial carcinoma (UCEC) remains to be further evaluated. The purpose of this study was to establish relies on the signature of ER stress-related lncRNA forecast to predict the prognosis of patients with UCEC.

Methods

We downloaded the RNA expression profile dataset and matched clinical data from the Cancer Genome Atlas (TCGA) database, and applied univariate and multivariate Cox regression analysis to build predictive signature. Kaplan-meier method was used to evaluate overall survival (OS) and disease-free survival (DFS). Gene set enrichment analysis (GSEA) was used to study the functional characteristics. Single sample Gene set enrichment analysis (ssGSEA) was used to analyze the relationship between immune status and predicted signature. Correlations between the potential usefulness of treatment for UCEC patients and predictive signature were also analyzed.

Results

We established a signature composed of eight ER stress-related lncRNAs (MIR34AHG, AC073842.2, PINK1AS, AC024909.2, MIR31HG, AC007422.2, AC061992.1, AC003102.1). The signature of ER stress-related lncRNA provided better diagnostic value compared with age and tumor grade, and the area under the receiver operating curve was 0.788. The overall and disease-free survival probability of patients in the high-risk group is lower than that in the low-risk group. GSEA indicated that the pathways were mainly enriched for cancer, immunity and reproduction related pathways. ss-GSEA shows that prediction signature and activation of dendritic cells, immature dendritic cells, T helper cells and immune status of the Treg are significantly related. High-risk groups may against PD - 1/L1 immunotherapy and JNK inhibitors VIII, Z.LLNle.CHO, DMOG and JNK. 9 l more sensitive.

Conclusion

The ER stress signature can independently predict the prognosis of UCEC patients, and provide guidance for conventional chemotherapy and immunotherapy of UCEC patients.

Identification of a novel histone phosphorylation prognostic signature in hepatocellular carcinoma based on bulk and single-cell RNA sequencing

BACKGROUND

Hepatocellular carcinoma (HCC) is the third leading cause of death in the world, characterized by high morbidity, poor prognosis and high mortality. Histone modifications regulate intracellular gene expression at the post-transcriptional level, and disturbances in the regulatory pattern of histone modifications at individual locus or across the genome can lead to tumorigenesis of HCC. In this study, we constructed a prognosis-related histone phosphorylation regulated (HPR) genes signature and elucidated whether HPR genes can predict overall survival in HCC patients.

METHODS

Differentially expressed genes were screened using TCGA, ICGC and GEO databases, and a new risk signature was constructed by univariate Cox regression and Lasso regression analysis. Predictive nomograms were established by multivariate Cox regression of risk scores and clinical parameters, calibration curve and decision curve analysis were used to evaluate the models. The ssGSEA methods were used to determine the effect of risk scores on the tumor immune microenvironment. Data for HCC single-cell RNA sequencing (scRNA-seq) have been downloaded from Gene Expression Omnibus (GEO) to understand the role of HPR genes in tumorigenesis.

RESULTS

Our analyses of nine HPR genes provided prognostic insights. Overall survival in the low-risk and high-risk groups was statistically higher, respectively (P<0.001). Cox regression analysis revealed that the risk score is a significant predictor of HCC outcomes (HR=2. 2.62, 95%CI: 1.248-5.514, P=0.011). In addition, a nomogram combining risk scores with TNM stages was constructed and tested from calibration curves and decision curves (AUC=0.780). MHC-class-I genes, iDCs, Macrophages, Tfh, Treg, Th2 were overexpressed in the high-risk group.

CONCLUSION

HPR genes risk score is closely related to the prognosis of HCC, tumor immune process and tumor cell progression.