Comprehensive Analysis of Disulfidptosis-Related LncRNAs in Molecular Classification, Immune Microenvironment Characterization and Prognosis of Gastric Cancer

Disulfidptosis in tumor progression

Disulfidptosis, a regulated cell death modality driven by the cystine transporter solute carrier family 7 member 11 (SLC7A11), is characterized by actin cytoskeleton collapse under glucose starvation. This review systematically elucidates the pivotal role of disulfidptosis in tumor metabolic reprogramming, with a focus on its molecular mechanisms and distinctions from other cell death pathways. The core mechanisms include SLC7A11-mediated cystine overload and NRF2/c-Myc-regulated pentose phosphate pathway activation. By integrating multiomics data and single-cell transcriptomics, we comprehensively decipher the heterogeneous expression patterns of disulfidptosis-related genes (DRGs) and their dynamic interplay with immune microenvironment remodeling. Furthermore, the coexpression networks of DRGs and disulfidptosis-related long noncoding RNAs (DRLs) offer novel insights into tumor diagnosis, prognosis, and targeted therapy. Therapeutically, SLC7A11 inhibitors (e.g., HG106) and glucose transporter inhibitors (e.g., BAY-876) demonstrate efficacy by exploiting metabolic vulnerabilities, whereas natural compounds synergizing with immune checkpoint blockade provide strategies to counteract immunosuppressive microenvironments. Through interdisciplinary collaboration and clinical translation, disulfidptosis research holds transformative potential in redefining precision oncology.

A novel prognostic model based on migrasome-related LncRNAs for gastric cancer

Gastric cancer (GC) represents a substantial public health challenge, characterized by elevated morbidity and mortality rates. Migrasomes, a newly discovered type of extracellular vesicle, have been highlighted as important contributors to cancer progression, though their specific role in GC remains unclear. To address this issue, we developed the first prognostic model utilizing migrasome-related long non-coding RNAs (MRLs). This model aims to deepen the understanding of GC pathogenesis and improve patient outcomes. Clinical and transcriptional data for 407 GC patients from TCGA were classified as training and testing sets. Through Pearson correlation analysis, 537 MRLs were recognized, and LASSO and Cox regression analyses further refined the list to four key lncRNAs (AC012055.1, LINC01150, AC053503.4, AC107021.2) for constructing the prognostic model. Kaplan-Meier survival analysis indicated a significantly poorer prognosis for the high-risk group. PCA confirmed the model's robustness, and univariate and multivariate analyses validated it as an independent predictor of clinical outcomes. The ROC curve and C-index evaluations further affirmed the model's predictive power. We developed a nomogram combining the MRLs signature with clinical parameters to enhance prognostic accuracy. GO, KEGG and GSEA were performed on migrasome-related genes associated with GC. Furthermore, high-risk patients exhibited increased immune cell infiltration and reduced tumor mutation burden, both associated with poorer outcomes. Additionally, twenty-nine potential therapeutic agents were identified. This novel MRLs-based model provides crucial insights into GC biology and represents a valuable tool for improving patient management and therapeutic strategies.

Integrative analysis of anti-breast CancerPotential of metabolites from Pseudomonas frederiksbergensis isolated from Taoerqi

Introduction

The Tibetan medicinal botanical drug Taoerqi has long been recognized for its anti-inflammatory, antibacterial, and tumor-inhibitory properties.

Methods

Botanical drug focuses on the isolation and characterization of secondary metabolites from Pseudomonas frederiksbergensis, an endophytic bacterium isolated from Taoerqi roots. The metabolites were obtained through fermentation and purification processes and were evaluated for their anti-breast cancer activities using cellular assays and transcriptomic analysis. Key regulatory targets, including SARM1, RGS5, PROM2, and BAG1, were identified through bioinformatics analysis and validated using qPCR and Western blotting. Furthermore, a clinical risk assessment model was constructed using breast cancer transcriptome databases to explore the potential prognostic value of these targets.

Results

The secondary metabolites from Pseudomonas frederiksbergensis exhibit significant anti-tumor effects and highlight their potential molecular mechanisms in breast cancer regulation.

Discussion

This study provides insights into the therapeutic potential of these metabolites and lays the groundwork for future preclinical and in vivo investigations.

Unraveling disulfidptosis for prognostic modeling and personalized treatment strategies in lung adenocarcinoma

AIM

To construct and identify a prognostic and therapeutic signature based on disulfidptosis-related genes in lung adenocarcinoma.

METHODS

Bioinformatic analysis was performed to assess the differential expression of disulfidptosis-related genes between cancerous and control samples from The Cancer Genome Atlas-Lung Adenocarcinoma (TCGA-LUAD) database. Survival analysis, immune cell infiltration assessment, and examination of oncogenic pathways were performed to uncover potential clinical implications of disulfidptosis gene expression. Differential gene expression analysis between subtypes facilitated the development of a prognostic model using a combination of genes associated with survival. A nomogram was further created using independent clinical and molecular factors.

RESULTS

We identified the significant upregulation of ten disulfidptosis-related genes and delineated two distinct subtypes, C1 and C2. Subtype C2 was associated with prolonged survival. Then, prognostic modeling utilizing six genes (TXNRD1, CPS1, S100P, SCGB3A1, CYP24A1, NAPSA) demonstrated predictive power in both training and validation datasets. The nomogram, incorporating the risk model with clinical features, provided a reliable tool for predicting one-year (AUC 0.77), three-year (AUC 0.75), and five-year (AUC 0.78) survival rates. Additionally, chemotherapy sensitivity analysis highlighted significant resistance in the high-risk group, primarily associated with subtype C1.

CONCLUSION

Our study reveals distinct LUAD subtypes, offers a robust prognostic model, and underscores clinical implications for personalized therapy based on disulfidptosis-related genes expression profiles.

Cuproptosis-related lncRNAs emerge as a novel signature for predicting prognosis in prostate carcinoma and functional experimental validation

Background

Prostate cancer (PCa) is one of the most common malignancies of the urinary system. Cuproptosis, a newly discovered form of cell death. The relationship between cuproptosis-related long non-coding RNAs (ClncRNAs) related to PCa and prognosis remains unclear. This study aimed to explore the clinical significance of novel ClncRNAs in the prognostic assessment of PCa.

Methods

ClncRNAs and differentially expressed mRNAs linked to these ClncRNAs were identified using Pearson's correlation and differential expression analyses. A prognostic signature (risk score) comprising three ClncRNAs was established based on multivariable Cox regression analysis. The predictive performance of this ClncRNAs signature was validated using receiver operating characteristic curves and nomograms. Finally, further in vitro cell experiments were conducted for validation, including quantitative polymerase chain reaction (qPCR), western blot (WB), cell proliferation assays, cell migration assays, cell invasion assays, apoptosis, and cell cycle analysis.

Results

We constructed a prognostic signature of ClncRNAs for PCa comprising three key differentially expressed ClncRNAs(AC010896-1, AC016394-2, and SNHG9). Multivariable Cox regression analysis indicated that clinical staging and risk scores of the ClncRNAs signature were independent prognostic factors for PCa. Compared to other clinical features, the ClncRNAs signature exhibited higher diagnostic efficiency and performed well in predicting the 1-, 3-, and 5-year progression-free intervals (PFIs) for PCa. Notably, in terms of immune activity, PCa patients with high-risk scores exhibited higher tumor mutational burden (TMB) levels, while their Tumor Immune Dysfunction and Exclusion (TIDE) scores were lower than those of PCa patients with low-risk scores. Additionally, in vitro cellular functional experiments, we knocked down SNHG9 that is the most significantly differentially expressed ClncRNA among the three key ClncRNAs. SNHG9 knockdown resulted in a significant increase in G1 phase cells and a decrease in S and G2 phases, indicating inhibition of DNA synthesis and cell cycle progression. Colony formation assays showed reduced clonogenic ability, with fewer and smaller colonies. Western blot analysis revealed the upregulation of the key cuproptosis-related mRNAs FDX1 and DLST. These findings suggested that SNHG9 promotes PCa cell proliferation, migration, and invasion.

Conclusion

Building on the three ClncRNAs, we identified a novel prognostic signature of PCa. The ClncRNA SNHG9 can promote PCa cell proliferation, migration, and invasion.

Identification and Verification of a Novel Disulfidptosis-Related lncRNAs Prognostic Signature to Predict the Prognosis and Immune Activity of Head and Neck Squamous Carcinoma

Background

We aimed to explore the prediction value of disulfidptosis-related long noncoding RNAs (lncRNAs) on the prognosis and immunotherapy efficiency of patients with head and neck squamous carcinoma (HNSCC).

Methods

Clinical and RNA-seq information were collected from The Cancer Genome Atlas (TCGA) and Genome Data Sharing (GDC) portal. The Pearson correlation analysis, univariate COX regression analysis, the least absolute shrinkage and selection operator (LASSO) COX regression were employed to construct the disulfidptosis-related lncRNAs (DRLs) prognostic model. The Kaplan-Meier survival curve, principal component analysis (PCA), receiver operating characteristic (ROC) curves and areas under the curves (AUCs) were used to examine the accuracy of the prognostic model. ssGSEA, mutation and functional and gene set enrichment analysis was performed to quantify the immune cell infiltration, immune function and functional enrichments. Finally, the mRNA expression of the DRLs was verified by real-time PCR (RT-PCR) in HNSCC cells.

Results

A new DRLs prognostic model (AC083967.1, AC106820.5, AC245041.2, AL590617.2, AP002478.1, and VPS9D1-AS1) with an independent prognostic value of HNSCC patients was successfully identified. In addition, the DRLs prognostic model was related with immune signature and drug therapy response. Meanwhile, the mRNA expression level of the 6 DRLs detected by RT-PCR was consistent with the results of bioinformatic analysis.

Conclusion

We developed a new DRLs prognostic model of HNSCC, which could effectively predicate the prognosis and therapy response of HNSCC patients and provide insights into personalized therapeutics.

Identification and validation of immunity- and disulfidptosis-related genes signature for predicting prognosis in ovarian cancer

Background

Ovarian cancer (OC) ranks as the fifth most prevalent neoplasm in women and exhibits an unfavorable prognosis. To improve the OC patient's prognosis, a pioneering risk signature was formulated by amalgamating disulfidptosis-related genes.

Methods

A comparative analysis of OC tissues and normal tissues was carried out, and differentially expressed disulfidptosis-related genes (DRGs) were found using the criteria of |log2 (fold change) | > 0.585 and adjusted P-value <0.05. Subsequently, the TCGA training set was utilized to create a prognostic risk signature, which was validated by employing both the TCGA testing set and the GEO dataset. Moreover, the immune cell infiltration, mutational load, response to chemotherapy, and response to immunotherapy were analyzed. To further validate these findings, QRT-PCR analysis was conducted on ovarian tumor cell lines.

Results

A risk signature was created using fourteen differentially expressed genes (DEGs) associated with disulfidptosis, enabling the classification of ovarian cancer (OC) patients into high-risk group (HRG) and low-risk group (LRG). The HRG exhibited a lower overall survival (OS) compared to the LRG. In addition, the risk score remained an independent predictor even after incorporating clinical factors. Furthermore, the LRG displayed lower stromal, immune, and estimated scores compared to the HRG, suggesting a possible connection between the risk signature, immune cell infiltration, and mutational load. Finally, the QRT-PCR experiments revealed that eight genes were upregulated in the human OC cell line SKOV3 compared with the human normal OC line IOSE80, while six genes were down-regulated.

Conclusions

A fourteen-biomarker signature composed of disulfidptosis-related genes could serve as a valuable risk stratification tool in OC, facilitating the identification of patients who may benefit from individualized treatment and follow-up management.

Analysis of m6A-related lncRNAs for prognostic and immunotherapeutic response in hepatocellular carcinoma

Background: RNA methylation modifications are important post-translational modifications that are regulated in an epigenetic manner. Recently, N6-methyladenosine (m6A) RNA modifications have emerged as potential epigenetic markers in tumor biology. Methods: Gene expression and clinicopathological data of LIHC were obtained from the cancer genome atlas (TCGA) database. The relationship between long non-coding RNAs (lncRNAs) and m6A-related genes was determined by gene expression analysis using Perl and R software. Co-expression network of m6A-lncRNA was constructed, and the relevant lncRNAs associated with prognosis were identified using univariate Cox regression analysis. These lncRNAs were then divided into two clusters (cluster 1 and cluster 2) to determine the differences in survival, pathoclinical parameters, and immune cell infiltration between the different lncRNA subtypes. The least absolute shrinkage and selection operator (LASSO) was carried out for regression analysis and prognostic model. The HCC patients were randomly divided into a train group and a test group. According to the median risk score of the model, HCC patients were divided into high-risk and low-risk groups. We built models using the train group and confirmed them through the test group. The m6A-lncRNAs derived from the models were analyzed for the tumor mutational burden (TMB), immune evasion and immune function using R software. AL355574.1 was identified as an important m6A-associated lncRNA and selected for further investigation. Finally, in vitro experiments were conducted to confirm the effect of AL355574.1 on the biological function of HCC and the possible biological mechanisms. Huh7 and HepG2 cells were transfected with AL355574.1 siRNA and cell proliferation ability was measured by CCK-8, EdU and colony formation assays. Wound healing and transwell assays were used to determine the cell migration capacity. The expression levels of MMP-2, MMP-9, E-cadherin, N-cadherin and Akt/mTOR phosphorylation were all determined by Western blotting. Results: The lncRNAs with significant prognostic value were classified into two subtypes by a consistent clustering analysis. We found that the clinical features, immune cell infiltration and tumor microenvironment (TME) were significantly different between the lncRNA subtypes. Our analysis revealed significant correlations between these different lncRNA subtypes and immune infiltrating and stromal cells. We created the final risk profile using LASSO regression, which notably included three lncRNAs (AL355574.1, AL158166.1, TMCC1-AS1). A prognostic signature consisting of the three lncRNAs was constructed, and the model showed excellent prognostic predictive ability. The overall survival (OS) of the low-risk cohort was significantly higher than that of the high-risk cohort in both the train and test group. Both risk score [hazard ratio (HR)=1.062; P<0.001] and stage (HR=1.647; P< 0.001) were considered independent indicators of HCC prognosis by univariate and multivariate Cox regression analysis. In Huh7 and HepG2 cells, AL355574.1 knockdown inhibited cell proliferation and migration, suppressed the protein expression levels of MMP-2, MMP-9, N-cadherin and Akt/mTOR phosphorylation, but promoted the protein expression levels of E-cadherin. Conclusions: This study established a predictive model for the OS of HCC patients, and these OS-related m6A-lncRNAs, especially AL355574.1 may play a potential role in the progression of HCC. In vitro experiments also showed that AL355574.1 could enhance the expression of MMPs and EMT through the Akt/mTOR signaling pathway, thereby affected the proliferation and migration of HCC. This provides a new perspective on the anticancer molecular mechanism of AL355574.1 in HCC.