Leveraging a disulfidptosis-related signature to predict the prognosis and immunotherapy effectiveness of cutaneous melanoma based on machine learning

Clarifying new molecular subtyping and precise treatment of melanoma based on disulfidptosis-related lncRNA signature

Disulfidptosis, the newest form of programmed cell death, is closely associated with the immune microenvironment of cancer cells. Long non-coding RNA (lncRNA) has also been found to play a crucial role in melanoma. However, the role of disulfidptosis-related lncRNA in melanoma remains unclear. Through bioinformatic analysis of the transcriptional, clinical, and pathological data from the TCGA-SKCM (The Cancer Genome Atlas-Skin cutaneous melanoma) database, we established a 2-Disulfidptosis-related lncRNA (DRL) prognostic model and a novel molecular subtype for melanoma. The survival and ROC curves of the 2-DRL prognostic model demonstrated its strong efficacy in predicting the prognosis of melanoma. The high-risk group of melanoma exhibited a significant decrease in ESTIMATEScore, ImmuneScore, and StromalScore, indicative of pronounced immune suppression and exhaustion. Subgroup C2 of melanoma displayed an immune-activated state, while subgroups C1 and C3 showed immune suppression and exhaustion, potentially leading to poorer prognosis. Subgroup C1 demonstrated better sensitivity to Zoledronate, UMI-77, Nilotinib, and Cytarabine. Subgroup C2 exhibited greater sensitivity to Ribociclib, XAV939, Topotecan, and Ruxolitinib. Subgroup C3 showed higher sensitivity to VX-11e, Ulixertinib, Trametinib, and Afatinib. This study revealed the immune microenvironment status and targeted drug sensitivity in melanoma patients with different risk scores and molecular subtypes, offering valuable guidance for clinical treatment and identifying significant DRL targets for future in-depth research.

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.

Prognosis and immunotherapy in melanoma based on selenoprotein k-related signature

Selenium and selenoproteins are closely related to melanoma progression. However, it is unclear how SELENOK affects lipid metabolism, endoplasmic reticulum stress (ERS), immune cell infiltration, survival, and prognosis in melanoma patients. Transcriptome data from melanoma patients was used to investigate SELENOK levels and their effect on prognosis, followed by an investigation of SELENOK's effects on immune cell infiltration. Furthermore, a risk model based on ERS, lipid metabolism, and immune-related genes was constructed, and its utility in melanoma prognosis was evaluated. Finally, the drug sensitivity of the risk model was analyzed to provide a reference for melanoma therapy. The results showed that melanoma with a high SELENOK level had a greater degree of immune cell infiltration and a better prognosis. Additionally, SELENOK was found to regulate ERS, lipid metabolism, and immune cell infiltration in melanoma. The risk model based on SELENOK signature genes successfully predicted the prognosis of melanoma, and the low-risk group exhibited a favorable immunological microenvironment. Furthermore, high-risk patients with melanoma were candidates for chemotherapy with RAS pathway inhibitors, whereas low-risk patients were more susceptible to routinely used chemotherapy medicines. In summary, SELENOK was shown to regulate ERS, lipid metabolism, and immune cell infiltration in melanoma, and SELENOK was positively associated with the prognosis of melanoma. The risk model based on SELENOK signature genes was valuable for melanoma prognosis and therapy.

Disulfidptosis-related lncRNA signatures assess immune microenvironment and drug sensitivity in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is associated with a high mortality rate, where resistance to immunotherapy and chemotherapy plays a crucial role. A newly identified form of cell death called disulfidptosis shows promise, but its biological mechanism in HCC remains uncertain. In this study, a prognostic model was developed for Disulfidptosis-related long non-coding RNAs (DRLs) from 370 HCC patients sourced from TCGA-LIHC, utilizing five key features: AC026356.1, AC073254.1, PXN-AS1 expression, AC026412.3, and AC099066.2. High-risk HCC patients had lower survival, CD4+ T cell infiltration, and elevated immune checkpoint gene expression. Furthermore, based on the features of DRLs, HCC was classified into three subtypes. Notably, patients belonging to different subtypes demonstrated varying overall survival rates, immune cell infiltration patterns, and sensitivity to immune therapy. Moreover, the novel DRL AC026412.3 (HR = 40.207) emerged as the most significant prognostic factor, exhibiting high expression across all HCC cells. Elevated expression of AC026412.3 promoted HCC cell proliferation and induced resistance to gefitinib. In conclusion, we have discovered five DRLs and constructed a prognostic risk model. Our findings validate the correlation between DRL-related prognostic models, tumor subtypes, and the HCC immune microenvironment along with its implications for immunotherapy. Moreover, further investigation into the molecular mechanisms of key biomarkers like AC026412.3 in the future will contribute significantly to advancing our comprehension of HCC's pathogenesis and drug resistance mechanisms.