A Prognostic Model of Pancreatic Cancer Based on Ferroptosis-Related Genes to Determine Its Immune Landscape and Underlying Mechanisms

Identification of ferroptosis-related signature predicting prognosis and therapeutic responses in pancreatic cancer

Ferroptosis plays a role in tumorigenesis by affecting lipid peroxidation and metabolic pathways; however, its prognostic or therapeutic relevance in pancreatic adenocarcinoma (PAAD) remains poorly understood. In this study, we developed a prognostic ferroptosis-related gene (FRG)-based risk model using cohorts of The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), proposing plausible therapeutics. Differentially expressed FRGs between tumors from TCGA-PAAD and normal pancreatic tissues from Genotype-Tissue Expression were analyzed to construct a prognostic risk model using univariate and multivariate Cox regression and LASSO analyses. A model incorporating AURKA, CAV1, and PML gene expression effectively distinguished survival differences between high- and low-risk groups among TCGA-PAAD patients, with validation in two ICGC cohorts. The high-risk group was enriched in gene sets involving mTOR, MAPK, and E2F signaling. The immune and stromal cells infiltration score did not differ between the groups. Analysis of PRISM datasets using our risk model to classify pancreatic cell lines suggested the dasatinib's efficacy in the high-risk group, which was experimentally confirmed in four cell lines with a high- or low-risk signature. In conclusion, this study proposed a robust FRG-based prognostic model that may help stratify PAAD patients with poor prognoses and select potential therapeutic avenues.

Targeting Nrf2/PHKG2 axis to enhance radiosensitivity in NSCLC

While ferroptosis shows promise in anti-cancer strategy, the molecular mechanisms behind this process remain poorly understood. Our research aims to highlight the regulation of radiotherapy-induced ferroptosis in non-small cell lung cancer (NSCLC) via the NRF2/PHKG2 axis-mediated mechanism. To identify ferroptosis-associated genes associated with radioresistance in NSCLC, this study employed high-throughput transcriptome sequencing and Lasso risk regression analysis. Clinical samples were analyzed to confirm PHKG2 expression changes before and after radiotherapy. The study further examined ferritinophagy-related factors, intracellular iron levels, mitochondrial function, and ferroptosis in NSCLC cells undergoing radiation exposure to explore the effect of PHKG2 on radiosensitivity or radioresistance. The research also demonstrated the transcriptional inhibition of PHKG2 by NRF2 and created in situ transplantation tumor models of NSCLC to examine the role of NRF2/PHKG2 axis in NSCLC radiosensitivity and resistance in vivo. The Lasso risk regression model that incorporated ferroptosis-associated genes effectively predicted the prognosis of patients with NSCLC. Radiotherapy-sensitive tissues exhibited an increased expression of PHKG2. Overexpression of PHKG2 led to elevated intracellular iron levels by promoting ferritinophagy and increased mitochondrial stress-dependent ferroptosis induced by radiotherapy. PHKG2 transcription repression was achieved through NRF2. The FAGs-Lasso risk regression model can accurately predict the prognosis of NSCLC patients. Targeting Nrf2 upregulates the expression of PHKG2 and reverses radiotherapy resistance in NSCLC by promoting iron autophagy and inducing mitochondrial dysfunction, thereby increasing radiotherapy sensitivity.

A prospective prognostic signature for pancreatic adenocarcinoma based on ubiquitination-related mRNA-lncRNA with experimental validation in vitro and vivo

Ubiquitination-related genes (URGs) exerted a crucial part in a variety of human disease disorders; however, their association with pancreatic adenocarcinoma (PAAD) had yet to be clearly described. We aimed to comprehensively characterize the contributions of URGs in PAAD through in silico analysis and experimental validation, and then identified a robust mRNA-lncRNA-based molecular prognostic panel for patients with PAAD using bulk RNA-sequencing and single-cell RNA-sequencing data. Initially, we collected the multi-omics data from TCGA platform to depict a comprehensive landscape of URGs in pan-cancer. Furthermore, we were accurate to PAAD for in-depth analysis. Significant differences of the activation of ubiquitination pathways and the expression of URGs were detected between normal and malignant cells. Unsupervised hierarchical clustering determined two PAAD subtypes with distinct clinical outcomes, ubiquitination pathway activities, immune microenvironment, and functional annotation characteristics. The expression profiles of ubiquitination-associated mRNAs and lncRNAs in the training and validation datasets were utilized to develop and verify a novel ubiquitination-related mRNA-lncRNA prognostic panel, which had a satisfied prediction efficiency. Our ubiquitination-associated model could function as an effective prognostic index and outperformed four other recognized panels in evaluating PAAD patients' survival status. Tumor immune microenvironment, mutation burden, and chemotherapy response were intensively explored to demonstrate the underlying mechanism of prognostic difference according to our panel. Our findings also revealed that FTI-277, a farnesyltransferase inhibitor, had a better curative effect in high-risk patients, while MK-2206, an Akt allosteric inhibitor, had a superior therapeutic effect in low-risk patients. The real-time PCR results uncovered the RNA expression of AC005062.1 in all the three PAAD cell lines was elevated several thousandfold. In conclusion, our URGs-based classification panel could be triumphantly served as a prediction tool for survival evaluation in patients with PAAD, and the genes in this panel could be developed as a potential target in PAAD therapy.

Establishment of a risk score model for bladder urothelial carcinoma based on energy metabolism-related genes and their relationships with immune infiltration

Bladder urothelial carcinoma (BLCA) is a common malignant tumor of the human urinary system, and a large proportion of BLCA patients have a poor prognosis. Therefore, there is an urgent need to find more efficient and sensitive biomarkers for the prognosis of BLCA patients in clinical practice. RNA sequencing (RNA-seq) data and clinical information were obtained from The Cancer Genome Atlas, and 584 energy metabolism-related genes (EMRGs) were obtained from the Reactome pathway database. Cox regression analysis and least absolute shrinkage and selection operator analysis were applied to assess prognostic genes and build a risk score model. The estimate and cibersort algorithms were used to explore the immune microenvironment, immune infiltration, and checkpoints in BLCA patients. Furthermore, we used the Human Protein Atlas database and our single-cell RNA-seq datasets of BLCA patients to verify the expression of 13 EMRGs at the protein and single-cell levels. We constructed a risk score model; the area under the curve of the model at 5 years was 0.792. The risk score was significantly correlated with the immune markers M0 macrophages, M2 macrophages, CD8 T cells, follicular helper T cells, regulatory T cells, and dendritic activating cells. Furthermore, eight immune checkpoint genes were significantly upregulated in the high-risk group. The risk score model can accurately predict the prognosis of BLCA patients and has clinical application value. In addition, according to the differences in immune infiltration and checkpoints, BLCA patients with the most significant benefit can be selected for immune checkpoint inhibitor therapy.

Construction of a Prognostic Model Based on Cuproptosis-Related lncRNA Signatures in Pancreatic Cancer

Aim

The aim of this study is to identify cuproptosis-related lncRNAs and construct a prognostic model for pancreatic cancer patients for clinical use.

Methods

The expression profile of lncRNAs was downloaded from The Cancer Genome Atlas database, and cuproptosis-related lncRNAs were identified. The prognostic cuproptosis-related lncRNAs were obtained and used to establish and validate a prognostic risk score model in pancreatic cancer.

Results

In total, 181 cuproptosis-related lncRNAs were obtained. The prognostic risk score model was constructed based on five lncRNAs (AC025257.1, TRAM2-AS1, AC091057.1, LINC01963, and MALAT1). Patients were assigned to two groups according to the median risk score. Kaplan-Meier survival curves showed that the difference in the prognosis between the high- and low-risk groups was statistically significant. Multivariate Cox analysis showed that our risk score was an independent risk factor for pancreatic cancer patients. Receiver operator characteristic curves revealed that the cuproptosis-related lncRNA model can effectively predict the prognosis of pancreatic cancer. The principal component analysis showed a difference between the high- and low-risk groups intuitively. Functional enrichment analysis showed that different genes were involved in cancer-related pathways in patients in the high- and low-risk groups.

Conclusion

The risk model based on five prognostic cuproptosis-related lncRNAs can well predict the prognosis of pancreatic cancer patients. Cuproptosis-related lncRNAs could be potential biomarkers for pancreatic cancer diagnosis and treatment.

The mechanism and progress of ferroptosis in pancreatic cancer

Pancreatic cancer is one of the deadliest cancers in the world, causing hundreds of thousands of deaths worldwide annually. Because of late diagnosis, rapid metastasis and drug resistance to chemotherapy, pancreatic cancer has a poor prognosis. Although the treatment of pancreatic cancer has made tremendous progress, the options for effective treatment are still limited, and new treatment methods are in crying needs to improve prognosis in clinic. Ferroptosis is an iron-dependent non-apoptotic cell death mode, which is mediated by lipid peroxidation and iron accumulation. Ferroptosis plays a momentous role in regulating different cancers in recent years, such as breast cancer, hepatocellular carcinoma, lung cancer and pancreatic cancer. In this present review, we elaborate on the regulatory mechanisms and signaling pathways of ferroptosis in pancreatic cancer, with the intention of delivering directions and new ideas for the treatment of pancreatic cancer.

A panel of necroptosis-related genes predicts the prognosis of pancreatic adenocarcinoma

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The 5-NRGs signature can predict the prognosis of pancreatic adenocarcinoma.

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The 5-NRGs signature reflects the immune landscape of pancreatic adenocarcinoma.

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The 5-NRGs can be detected in exosomes of serum using RT-qPCR method.

Pancreatic adenocarcinoma (PAAD) has become one of the deadliest malignancies in the world. Since necroptosis plays a crucial role in regulating the immune system, it is necessary to develop novel prognostic biomarkers associated with necroptosis and explore its potential role in PAAD. The transcriptome RNA-seq data of PAAD were downloaded from the TCGA and GTEx databases. A prognostic signature was constructed by the least absolute shrinkage and selection operator (LASSO) Cox regression, and its prognostic value was evaluated by nomogram and validated in an independent GEO cohort. We identified a total of 24 differentially expressed NRGs in PAAD, and constructed a prognostic signature with 5 NRGs, which showed good performance in predicting the prognosis of PAAD patients. The ROC curves for 1-, 3-, and 5-year survival rate were 0.652, 0.778, and 0.817, respectively. This prognostic signature showed consistent prognosis prediction in an independent patient cohort. Furthermore, the correlations between 5-NRGs signature and TMB, MSI, histopathological classification, immune infiltration, immune types, and immunomodulators were all significant. Notably, the expression profiles of the five NRGs in exosomes of serum were consistent with their expression in tumor tissues. These data suggested that the 5-NRGs signature is a promising biomarker for predicting the prognosis of PAAD.

A Novel Ferroptosis-Related lncRNAs Signature Predicts Clinical Prognosis and Is Associated With Immune Landscape in Pancreatic Cancer

Pancreatic cancer is one of the most lethal malignancies and currently therapies are severely lacking. In this study, we aimed to establish a novel ferroptosis-related lncRNAs signature to predict the prognosis of patients with pancreatic cancer and evaluate the predictive abilities of candidate lncRNAs. According to The Cancer Genome Atlas (TCGA) database, a total of 182 patients with pancreatic cancer were included in our study. Ferroptosis-related lncRNAs were screened by Pearson correlation analysis with 60 reported ferroptosis-related genes. Through univariate, least absolute shrinkage and selection operator (LASSO) regression and multivariate regression analyses, a novel signature based on five ferroptosis-related lncRNAs(ZNF236-DT, CASC8, PAN3-AS1, SH3PXD2A-AS1, LINP1) was constructed. Risk-related differentially expressed genes (DEGs) were subjected to enrichment analyses for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The results revealed that immune cell infiltration, immune-related functions and checkpoints were factors to affect prognoisis of pancreatic cancer. In summary, we identified the prognostic ferroptosis-related lncRNAs(ZNF236-DT, CASC8, PAN3-AS1, SH3PXD2A-AS1, LINP1) in pancreatic cancer and these lncRNAs may serve as therapeutic targets for pancreatic cancer.