A novel disulfidptosis and glycolysis related risk score signature for prediction of prognosis and ICI therapeutic responsiveness in colorectal cancer

A disulfidptosis-related glucose metabolism and immune response prognostic model revealing the immune microenvironment in lung adenocarcinoma

Background

Lung adenocarcinoma accounts for the majority of lung cancer cases and impact survival rate of patients severely. Immunotherapy is an effective treatment for lung adenocarcinoma but is restricted by many factors including immune checkpoint expression and the inhibitory immune microenvironment. This study aimed to explore the immune microenvironment in lung adenocarcinoma via disulfidptosis.

Methods

Public datasets of lung adenocarcinoma from the TCGA and GEO was adopted as the training and validation cohort. Based on the differences in the expression of disulfidptosis -related genes, a glucose metabolism and immune response prognostic model was constructed. The prognostic value and clinical relationship of the model were further explored. Immune-related analyses were performed according to CIBERSORT, ssGSEA, TIDE, IPS.

Results

We verified that the model could accurately predict the survival expectancy of lung adenocarcinoma patients. Patients with lung adenocarcinoma and a low-risk score had better survival outcomes according to the model. Moreover, the high-risk group tended to have an immunosuppressive effect, as reflected by the immune cell components, phenotypes and functions. We also found that the clinically relevant immune checkpoint CTLA-4 was significantly higher in low-risk group (P<0.05), indicating that the high-risk group may suffer worse tumor immunotherapy efficacy. Finally, we found that this model has accurate predictive value for the efficacy of immune checkpoint blockade in non-small cell lung cancer (P<0.05).

Conclusion

The prognostic model demonstrated the feasibility of predicting survival and immunotherapy efficacy via disulfidptosis-related genes and will facilitate the development of personalized anticancer therapy.

Disulfidptosis decoded: a journey through cell death mysteries, regulatory networks, disease paradigms and future directions

Cell death is an important part of the life cycle, serving as a foundation for both the orderly development and the maintenance of physiological equilibrium within organisms. This process is fundamental, as it eliminates senescent, impaired, or aberrant cells while also promoting tissue regeneration and immunological responses. A novel paradigm of programmed cell death, known as disulfidptosis, has recently emerged in the scientific circle. Disulfidptosis is defined as the accumulation of cystine by cancer cells with high expression of the solute carrier family 7 member 11 (SLC7A11) during glucose starvation. This accumulation causes extensive disulfide linkages between F-actins, resulting in their contraction and subsequent detachment from the cellular membrane, triggering cellular death. The RAC1-WRC axis is involved in this phenomenon. Disulfidptosis sparked growing interest due to its potential applications in a variety of pathologies, particularly oncology, neurodegenerative disorders, and metabolic anomalies. Nonetheless, the complexities of its regulatory pathways remain elusive, and its precise molecular targets have yet to be definitively identified. This manuscript aims to meticulously dissect the historical evolution, molecular underpinnings, regulatory frameworks, and potential implications of disulfidptosis in various disease contexts, illuminating its promise as a groundbreaking therapeutic pathway and target.

A disulfidptosis-related lncRNA index predicting prognosis and the tumor microenvironment in colorectal cancer

Colorectal cancer (CRC) is a common and deadly cancer worldwide with a high lethality rate. Disulfidptosis has been found to be an emerging mode of death in cancer, and the purpose of this study was to explore the relationship between disulfidptosis-related lncRNAs (DRLs) and CRC and to develop a prognostic model for CRC and DRLs. The gene expression data and clinicopathologic information of colorectal cancer patients were obtained from The Cancer Genome Atlas (TCGA) and screened for DRLs based on correlation analysis. The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to construct the prognostic model, and its validation was carried out by PCA and receiver operating characteristic (ROC) curves. We constructed nomograms combined with the model. Finally, the possible mechanisms by which lncRNAs affect CRC were explored by functional enrichment analysis, immune infiltration and immune escape analysis. In summary, we developed a prognostic marker consisting of lncRNAs associated with disulfidptosis to help clinicians predict the survival of different CRC patients and use different targeted therapies and immunotherapies depending on the condition.

Disulfidptosis-related classification patterns and tumor microenvironment characterization in skin cutaneous melanoma

Aim

To identify distinct disulfidptosis-molecular subtypes and develop a novel prognostic signature.

Methods/materials

We integrated into this study multiple SKCM transcriptomic datasets from the Cancer Genome Atlas database and Gene Expression Omnibus dataset. The consensus clustering algorithm was applied to categorize SKCM patients into different DRG subtypes.

Results

Three distinct DRG subtypes were identified, which were correlated to different clinical outcomes and signaling pathways. Then, a disulfidptosis-relaed signature and nomogram were constructed, which could accurately predict the individual OS of patients with SKCM. The high-risk group was less sensitive to immunotherapy than the low-risk group.

Conclusion

The signature can assist healthcare professionals in making more accurate and individualized treatment choices for patients with SKCM.