BackgroundIron-sulfur cluster assembly 1 (ISCA1) has a significant effect on respiratory complexes and energy metabolism. Although there is some evidence that ISCA1 gene expression impacts energy metabolism and consequently has a role in tumorigenesis and cancer metastasis in different types of malignancies, no systematic pan-cancer study of the ISCA1 has been conducted. As a result, we sought to investigate ISCA1’s predictive value in 33 cancer types as well as its possible immunological function.MethodsWe included the pan-cancer expression profile dataset and clinical data from the public database. Firstly, the single-sample Gene Set Enrichment Analysis (ssGSEa) approach was employed for analyzing the immune link in pan-cancer, while the limma package was utilized for analyzing the differential expression in cancer species. Subsequently, ciberport, MCP-counter, TIMER2, quanTIseq, and xCELL were employed for analyzing bladder cancer (BLCA)’s immune infiltration. Least absolute shrinkage and selection operator (Lasso) were employed for choosing the best gene to develop the immune risk scoring model.ResultsISCA1 gene expression was positively related to four immune signatures (chemokine, immunostimulator, MHC, and receptor) in BLCA. Samples of BLCA were sorted into two groups by the best cut-off of ISCA1 expression degree. The group with a high level of ISCA1 expression had a higher risk, suggesting that the ISCA1 gene was a risk factor in BLCA, and its high expression resulted in a poorer prognosis. Additionally, it was noted that ISCA1 was positively linked with these immune checkpoints. Moreover, there was a considerable positive link between ISCA1 and different immune properties in subgroups with different immune checkpoint inhibiting responses. Finally, an immune risk scoring model was made and it showed a better score in comparison to that of TIDE.ConclusionISCA1 can be a prognostic marker for a variety of cancers, particularly BLCA. Its high level of expression has a deleterious impact on the prognosis of BLCA patients. This strongly shows that ISCA1 is a significant prognostic factor for BLCA and that it could be used as a new prognostic detection target and treatment approach.
Oxidative stress (OS) response is crucial in oncogenesis and progression of tumor. But the potential prognostic importance of OS-related genes (OSRGs) in stomach adenocarcinoma (STAD) lacked comprehensive study. STAD clinical information and transcriptome data were retrieved from the Gene Expression Omnibus and The Cancer Genome Atlas databases. The prognostic OSRGs were filtered via the univariate Cox analysis and OSRG-based molecular subtypes of STAD were developed using consensus clustering. Weighted gene co-expression network analysis (WGCNA) was subsequently conducted to filter molecular subtype-associated gene modules. The prognosis-related genes were screened via univariate and least absolute shrinkage and selection operator Cox regression analysis were used to construct a prognostic risk signature. Finally, a decision tree model and nomogram were developed by integrating risk signature and clinicopathological characteristics to analyze individual STAD patient’s survival. Four OSRG-based molecular subtypes with significant diversity were developed based on 36 prognostic OSRGs for STAD, and an OSRGs-based subtype-specific risk signature with eight genes for prognostic prediction of STAD was built. Survival analysis revealed a strong prognostic performance of the risk signature exhibited in predicting STAD survival. There were significant differences in mutation patterns, chemotherapy sensitivity, clinicopathological characteristics, response to immunotherapy, biological functions, immune microenvironment, immune cell infiltration among different molecular subtypes and risk groups. The risk score and age were verified as independent risk factors for STAD, and a nomogram integrating risk score and age was established, which showed superior predictive performance for STAD prognosis. We developed an OSRG-based molecular subtype and identified a novel risk signature for prognosis prediction, providing a useful tool to facilitate individual treatment for patients with STAD.
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