Exploring the role of the disulfidptosis-related gene SLC7A11 in adrenocortical carcinoma: implications for prognosis, immune infiltration, and therapeutic strategies

Constructing a Novel Amino Acid Metabolism Signature: A New Perspective on Pheochromocytoma Diagnosis, Immune Landscape, and Immunotherapy

Pheochromocytoma/paraganglioma (PGPG) is a rare neuroendocrine tumor. Amino acid metabolism is crucial for energy production, redox balance, and metabolic pathways in tumor cell proliferation. This study aimed to build a risk model using amino acid metabolism-related genes, enhancing PGPG diagnosis and treatment decisions. We analyzed RNA-sequencing data from the PCPG cohort in the GEO dataset as our training set and validated our findings using the TCGA dataset and an additional clinical cohort. WGCNA and LASSO were utilized to identify hub genes and develop risk prediction models. The single-sample gene set enrichment analysis, MCPCOUNTER, and ESTIMATE algorithm calculated the relationship between amino acid metabolism and immune cell infiltration in PCPG. The TIDE algorithm predicted the immunotherapy efficacy for PCPG patients. The analysis identified 292 genes with differential expression, which are involved in amino acid metabolism and immune pathways. Six genes (DDC, SYT11, GCLM, PSMB7, TYRO3, AGMAT) were identified as crucial for the risk prediction model. Patients with a high-risk profile demonstrated reduced immune infiltration but potentially higher benefits from immunotherapy. Notably, DDC and SYT11 showed strong diagnostic and prognostic potential. Validation through quantitative Real-Time Polymerase Chain Reaction and immunohistochemistry confirmed their differential expression, underscoring their significance in PCPG diagnosis and in predicting immunotherapy response. This study's integration of amino acid metabolism-related genes into a risk prediction model offers critical clinical insights for PCPG risk stratification, potential immunotherapy responses, drug development, and treatment planning, marking a significant step forward in the management of this complex condition.

Construction of a disulfidptosis-associated lncRNA signature to predict prognosis in bladder cancer

Background

Bladder cancer (BCa) is the most common neoplasm of the urinary system, and its high rates of progression and recurrence contribute to a generally poor prognosis, especially in advanced cases. It is reported that disulfidptosis is closely related with tumor proliferation. We aimed to construct a disulfidptosis-associated long non-coding RNA (lncRNA) signature that can predict prognosis and immune microenvironment in BCa.

Methods

We obtained RNA-seq data, clinical information, and mutation data of BCa patients from The Cancer Genome Atlas (TCGA) database. Based on Pearson correlation and uni-Cox regression analysis, we identified disulfidptosis-associated lncRNAs related with overall survival (OS). Then a prognosis signature based on seven disulfidptosis-associated lncRNAs was constructed by least absolute shrinkage and selection operator (LASSO) Cox regression analysis and multi-Cox regression analysis. We performed Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) analyses to examine biological functional of differentially expressed genes related to the risk model. We assessed the immune microenvironment and chemotherapeutic response of several drugs. Finally, the quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) was used to detect the expression level of the disulfidptosis-associated lncRNAs.

Results

We established a prognosis signature based on seven disulfidptosis-associated lncRNAs (AP003419.3, AL161891.1, AC234917.3, LINC00536, AL021707.6, AL445649.1 and AC104785.1). According to the signature, all patients were divided in high- and low-risk group and patients in low-risk group showed a significantly better prognosis. Moreover, the risk model was confirmed to be an independent prognostic factor with high accuracy. Immune cells and several immune checkpoints were more active in high-risk group and patients in this group had a higher tumor mutation burden (TMB) that those in low-risk group. The results of qRT-PCR demonstrated that expression level of the lncRNAs were all significantly different between BCa cell lines and normal urinary epithelial cells.

Conclusions

The disulfidptosis-associated lncRNA signature is a promising biomarker for predicting prognosis and characterizing the immune landscape in BCa, potentially guiding personalized treatment strategies.

Construction of a prognostic model based on disulfidptosis-related genes and identification of CCNA2 as a novel biomarker for hepatocellular carcinoma

BACKGROUND

Disulfidptosis, identified as an innovative form of cellular death subsequent to cuproptosis, is currently under investigation for its mechanisms in oncological contexts. In-depth analyses exploring the relationship between disulfidptosis-related genes (DRGs) and hepatocellular carcinoma (HCC) are currently limited.

METHODS

Transcriptomic data and clinical information were retrieved from the TCGA and GEO databases (GSE76427 and GSE54236), concentrating on the expression levels of 24 DRGs. Subsequently, multifactor and LASSO regression analyses were utilized to construct the 5-DRG prognostic signature. Immunohistochemistry (IHC) was employed to assess Cyclin A2 (CCNA2) protein expression levels. Quantitative real-time PCR (qRT-PCR) and western blot analyses were conducted to detect transcriptomic and protein expression of CCNA2-targeting short interfering RNA (siRNA). The Cell Counting Kit-8 (CCK-8) assay, EdU staining, and scratch experiments were employed to observe the proliferation and migration of hepatoma cell lines subsequent to CCNA2 inhibition.

RESULTS

Three HCC patterns were identified, among which pattern B exhibited the the most unfavorable survival outcomes. Five DRGs (STC2, PBK, CCNA2, SERPINE1, and SLC6A1) were involved to establish the 5-DRG prognostic signature. High-risk groups (HRGs) exhibited prolonged survival durations in comparison to low-risk groups (LRGs). Both bioinformatics analyses and experimental methodologies corroborated the association of CCNA2 with poor prognosis in HCC patients. Functional studies elucidated that interference with CCNA2 significantly inhibited proliferation and migration, while simultaneously promoting apoptosis in hepatoma cells and resulting in the downregulation of epithelial-mesenchymal transition (EMT)-related protein markers.

CONCLUSIONS

The 5-DRG prognostic signature is proficient in predicting clinical outcomes, informing therapeutic strategies, and elucidating the characteristics of the immune microenvironment in HCC patients. Furthermore, this study elucidates the potential of CCNA2 as an innovative biomarker for HCC.

Non-coding RNAs as potential targets in metformin therapy for cancer

Metformin, a widely used oral hypoglycemic drug, has emerged as a potential therapeutic agent for cancer treatment. While initially known for its role in managing diabetes, accumulating evidence suggests that metformin exhibits anticancer properties through various mechanisms. Several cellular or animal experiments have attempted to elucidate the role of non-coding RNA molecules, including microRNAs and long non-coding RNAs, in mediating the anticancer effects of metformin. The present review summarized the current understanding of the mechanisms by which non-coding RNAs modulate the response to metformin in cancer cells. The regulatory roles of non-coding RNAs, particularly miRNAs, in key cellular processes such as cell proliferation, cell death, angiogenesis, metabolism and epigenetics, and how metformin affects these processes are discussed. This review also highlights the role of lncRNAs in cancer types such as lung adenocarcinoma, breast cancer, and renal cancer, and points out the need for further exploration of the mechanisms by which metformin regulates lncRNAs. In addition, the present review explores the potential advantages of metformin-based therapies over direct delivery of ncRNAs, and this review highlights the mechanisms of non-coding RNA regulation when metformin is combined with other therapies. Overall, the present review provides insights into the molecular mechanisms underlying the anticancer effects of metformin mediated by non-coding RNAs, offering novel opportunities for the development of personalized treatment strategies in cancer patients.

Disulfidptosis: A new type of cell death

Disulfidptosis is a novel form of cell death that is distinguishable from established programmed cell death pathways such as apoptosis, pyroptosis, autophagy, ferroptosis, and oxeiptosis. This process is characterized by the rapid depletion of nicotinamide adenine dinucleotide phosphate (NADPH) in cells and high expression of solute carrier family 7 member 11 (SLC7A11) during glucose starvation, resulting in abnormal cystine accumulation, which subsequently induces andabnormal disulfide bond formation in actin cytoskeleton proteins, culminating in actin network collapse and disulfidptosis. This review aimed to summarize the underlying mechanisms, influencing factors, comparisons with traditional cell death pathways, associations with related diseases, application prospects, and future research directions related to disulfidptosis.

Comprehensive identification of a disulfidptosis-associated long non-coding RNA signature to predict the prognosis and treatment options in ovarian cancer

Purpose

Distinguished from cuproptosis and ferroptosis, disulfidptosis has been described as a newly discovered form of non-programmed cell death tightly associated with glucose metabolism. However, the prognostic profile of disulfidptosis-related lncRNAs (DRLRs) in ovarian cancer (OC) and their biological mechanisms need to be further elucidated.

Materials and methods

First, we downloaded the profiles of RNA transcriptome, clinical information for OC patients from the TCGA database. Generated from Cox regression analysis, prognostic lncRNAs were utilized to identify the risk signature by least absolute shrinkage and selection operator analysis. Then, we explored the intimate correlations between disulfidptosis and lncRNAs. What's more, we performed a series of systemic analyses to assess the robustness of the model and unravel its relationship with the immune microenvironment comprehensively.

Results

We identified two DRLR clusters, in which OC patients with low-risk scores exhibited a favorable prognosis, up-regulated immune cell infiltrations and enhanced sensitivity to immunotherapy. Furthermore, validation of the signature by clinical features and Cox analysis demonstrated remarkable consistency, suggesting the universal applicability of our model. It's worth noting that high-risk patients showed more positive responses to immune checkpoint inhibitors and potential chemotherapeutic drugs.

Conclusion

Our findings provided valuable insights into DRLRs in OC for the first time, which indicated an excellent clinical value in the selection of management strategies, spreading brilliant horizons into individualized therapy.

Unraveling pathogenesis, biomarkers and potential therapeutic agents for endometriosis associated with disulfidptosis based on bioinformatics analysis, machine learning and experiment validation

BACKGROUND

Endometriosis (EMs) is an enigmatic disease of yet-unknown pathogenesis. Disulfidptosis, a novel identified form of programmed cell death resulting from disulfide stress, stands a chance of treating diverse ailments. However, the potential roles of disulfidptosis-related genes (DRGs) in EMs remain elusive. This study aims to thoroughly explore the key disulfidptosis genes involved in EMs, and probe novel diagnostic markers and candidate therapeutic compounds from the aspect of disulfidptosis based on bioinformatics analysis, machine learning, and animal experiments.

RESULTS

Enrichment analysis on key module genes and differentially expressed genes (DEGs) of eutopic and ectopic endometrial tissues in EMs suggested that EMs was closely related to disulfidptosis. And then, we obtained 20 and 16 disulfidptosis-related DEGs in eutopic and ectopic endometrial tissue, respectively. The protein-protein interaction (PPI) network revealed complex interactions between genes, and screened nine and ten hub genes in eutopic and ectopic endometrial tissue, respectively. Furthermore, immune infiltration analysis uncovered distinct differences in the immunocyte, human leukocyte antigen (HLA) gene set, and immune checkpoints in the eutopic and ectopic endometrial tissues when compared with health control. Besides, the hub genes mentioned above showed a close correlation with the immune microenvironment of EMs. Furthermore, four machine learning algorithms were applied to screen signature genes in eutopic and ectopic endometrial tissue, including the binary logistic regression (BLR), the least absolute shrinkage and selection operator (LASSO), the support vector machine-recursive feature elimination (SVM-RFE), and the extreme gradient boosting (XGBoost). Model training and hyperparameter tuning were implemented on 80% of the data using a ten-fold cross-validation method, and tested in the testing sets which determined the excellent diagnostic performance of these models by six indicators (Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value, Accuracy, and Area Under Curve). And seven eutopic signature genes (ACTB, GYS1, IQGAP1, MYH10, NUBPL, SLC7A11, TLN1) and five ectopic signature genes (CAPZB, CD2AP, MYH10, OXSM, PDLIM1) were finally identified based on machine learning. The independent validation dataset also showed high accuracy of the signature genes (IQGAP1, SLC7A11, CD2AP, MYH10, PDLIM1) in predicting EMs. Moreover, we screened 12 specific compounds for EMs based on ectopic signature genes and the pharmacological impact of tretinoin on signature genes was further verified in the ectopic lesion in the EMs murine model.

CONCLUSION

This study verified a close association between disulfidptosis and EMs based on bioinformatics analysis, machine learning, and animal experiments. Further investigation on the biological mechanism of disulfidptosis in EMs is anticipated to yield novel advancements for searching for potential diagnostic biomarkers and revolutionary therapeutic approaches in EMs.

Establishment of a diagnostic model of endometriosis based on disulfidptosis-related genes

OBJECTIVES

We aimed to establish a diagnostic model of endometriosis (EM) based on disulfidptosis-related genes (DRGs).

MATERIALS AND METHODS

The mRNA expression data of EM were downloaded from the gene expression omnibus database and subjected to differential analysis, and co-expression analysis was performed based on 10 disulfidptosis genes to acquire DRGs. The differentially expressed DRGs were subjected to biofunctional analysis. Lasso analysis and support vector machine-recursive feature elimination (SVM-RFE) analysis were employed to extract the intersection of feature genes as biomarkers, and the diagnostic values of biomarkers for EM were evaluated based on receiver operating characteristic curves. The correlations between biomarkers and the immune microenvironment were assessed by Pearson analysis of biomarkers and immune cell infiltration levels.

RESULTS

Transforming growth factor β stimulated protein clone 22 domain family member 4 (TSC22D4), and F-box/SPRY domain-containing protein 1 (FBXO45) worked as the diagnostic classifiers in EM, with an obvious decrease in FBXO45 expression and an evident increase in TSC22D4 expression. The areas under the curves of FBXO45 and TSC22D4 were 0.752 and 0.706, respectively, and the area of FBXO45 combined with TSC22D4 reached 0.865, suggesting that TSC22D4 and FBXO45 had high predictive values. The diagnostic markers were closely correlated with immune cell infiltration.

CONCLUSION

The diagnostic markers constructed based on disulfidptosis are good predictors for EM, which have close correlations with EM.

Disulfidptosis, A Novel Cell Death Pathway: Molecular Landscape and Therapeutic Implications

Programmed cell death is pivotal for several physiological processes, including immune defense. Further, it has been implicated in the pathogenesis of developmental disorders and the onset of numerous diseases. Multiple modes of programmed cell death, including apoptosis, pyroptosis, necroptosis, and ferroptosis, have been identified, each with their own unique characteristics and biological implications. In February 2023, Liu Xiaoguang and his team discovered "disulfidptosis," a novel pathway of programmed cell death. Their findings demonstrated that disulfidptosis is triggered in glucose-starved cells exhibiting high expression of a protein called SLC7A11. Furthermore, disulfidptosis is marked by a drastic imbalance in the NADPH/NADP+ ratio and the abnormal accumulation of disulfides like cystine. These changes ultimately lead to the destabilization of the F-actin network, causing cell death. Given that high SLC7A11 expression is a key feature of certain cancers, these findings indicate that disulfidptosis could serve as the basis of innovative anti-cancer therapies. Hence, this review delves into the discovery of disulfidptosis, its underlying molecular mechanisms and metabolic regulation, and its prospective applications in disease treatment.