Dataset Information

Identification of hub gene and lncRNA signature related to entotic cell death in cutaneous melanoma for prognostic and immune prediction.

ABSTRACT: Entotic cell death (ECD), a cell death program observed in cancer cell competition, predominantly occurs in an autophagy protein-dependent, non-apoptotic manner. However, the relationship between cutaneous melanoma (CM) and ECD-associated genes and lncRNAs has remained unclear. This study aimed to elucidate the role and mechanism of ECD-associated genes in CM. To achieve this, 4 mechanism learning algorithms and integrated bioinformatic analyses were employed to identify the core ECD-associated genes and lncRNAs. Subsequently, 2 risk signatures based on ECD-associated genes and hub lncRNAs were constructed for CM patients. As a result, we observed significant differential expression of ECD-associated genes in CM, indicating their potential as valuable predictors for CM patients. Moreover, RHOA was identified as a core ECD-associated gene in CM, and its expression was found to be associated with patients' survival and immune infiltration, suggesting its relevance as a potential therapeutic target. Additionally, this study provided clarification on hub ECD-associated lncRNAs in CM, offering insights into their roles in the disease. Through bioinformatic analyses, we identified 2 risk signatures based on the expression of ECD-associated genes and hub ECD-associated lncRNAs, respectively. Both risk signatures were strongly linked to the prognosis and cancer growth of CM, underscoring their potential as valuable prognostic indicators. Furthermore, mechanistic analyses suggested a significant association between the risk signature and the immune microenvironment in CM, highlighting potential immune-related implications in disease progression. In conclusion, we propose that ECD-associated genes and lncRNAs hold promise as potential targets in CM. Moreover, our findings revealed a significant correlation between ECD and the immune microenvironment, providing crucial insights for guiding individualized treatment strategies in CM.

SUBMITTER: Zhang C

PROVIDER: S-EPMC10637533 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Identification of hub gene and lncRNA signature related to entotic cell death in cutaneous melanoma for prognostic and immune prediction.

Zhang Chen C Shen Chenyang C

Medicine 20231101 45

Entotic cell death (ECD), a cell death program observed in cancer cell competition, predominantly occurs in an autophagy protein-dependent, non-apoptotic manner. However, the relationship between cutaneous melanoma (CM) and ECD-associated genes and lncRNAs has remained unclear. This study aimed to elucidate the role and mechanism of ECD-associated genes in CM. To achieve this, 4 mechanism learning algorithms and integrated bioinformatic analyses were employed to identify the core ECD-associated ...[more]

PMID: 37960805

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Project description:Background Cutaneous melanoma (CM) is one of the malignant tumors with a relative high lethality. Necroptosis is a novel programmed cell death that participates in anti-tumor immunity and tumor prognosis. Necroptosis has been found to play an important role in tumors like CM. However, the necroptosis-associated lncRNAs’ potential prognostic value in CM has not been identified. Methods The RNA sequencing data collected from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression Project (GTEx) was utilized to identify differentially expressed genes in CM. By using the univariate Cox regression analysis and machine learning LASSO algorithm, a prognostic risk model had been built depending on 5 necroptosis-associated lncRNAs and was verified by internal validation. The performance of this prognostic model was assessed by the receiver operating characteristic curves. A nomogram was constructed and verified by calibration. Furthermore, we also performed sub-group K-M analysis to explore the 5 lncRNAs’ expression in different clinical stages. Function enrichment had been analyzed by GSEA and ssGSEA. In addition, qRT-PCR was performed to verify the five lncRNAs’ expression level in CM cell line (A2058 and A375) and normal keratinocyte cell line (HaCaT). Results We constructed a prognostic model based on five necroptosis-associated lncRNAs (AC245041.1, LINC00665, AC018553.1, LINC01871, and AC107464.3) and divided patients into high-risk group and low-risk group depending on risk scores. A predictive nomogram had been built to be a prognostic indicator to clinical factors. Functional enrichment analysis showed that immune functions had more relationship and immune checkpoints were more activated in low-risk group than that in high-risk group. Thus, the low-risk group would have a more sensitive response to immunotherapy. Conclusion This risk score signature could be used to divide CM patients into low- and high-risk groups, and facilitate treatment strategy decision making that immunotherapy is more suitable for those in low-risk group, providing a new sight for CM prognostic evaluation.

Project description:PurposeFerroptosis-related lncRNAs are promising biomarkers for predicting the prognosis of many cancers. However, a ferroptosis-related signature to predict the prognosis of cutaneous melanoma (CM) has not been identified. The purpose of this study was to construct a ferroptosis-related lncRNA signature to predict prognosis and immunotherapy efficacy in CM.MethodsFerroptosis-related differentially expressed genes (FDEGs) and lncRNAs (FDELs) were identified using TCGA, GTEx, and FerrDb datasets. We performed Cox and LASSO regressions to identify key FDELs, and constructed a risk score to stratify patients into high- and low-risk groups. The lncRNA signature was evaluated using the areas under the receiver operating characteristic curves (AUCs) and Kaplan-Meier analyses in the training, testing, and entire cohorts. Multivariate Cox regression analyses including the lncRNA signature and common clinicopathological characteristics were performed to identify independent predictors of overall survival (OS). A nomogram was developed for clinical use. We performed gene set enrichment analyses (GSEA) to identify significantly enriched pathways. Differences in the tumor microenvironment (TME) between the 2 groups were assessed using 7 algorithms. To predict the efficacy of immune checkpoint inhibitors (ICI), we analyzed the association between PD1 and CTLA4 expression and the risk score. Finally, differences in Tumor Mutational Burden (TMB) and molecular drugs Sensitivity between the 2 groups were performed.ResultsWe identified 5 lncRNAs (AATBC, AC145423.2, LINC01871, AC125807.2, and AC245041.1) to construct the risk score. The AUC of the lncRNA signature was 0.743 in the training cohort and was validated in the testing and entire cohorts. Kaplan-Meier analyses revealed that the high-risk group had poorer prognosis. Multivariate Cox regression showed that the lncRNA signature was an independent predictor of OS with higher accuracy than traditional clinicopathological features. The 1-, 3-, and 5-year survival probabilities for CM patients were 92.7%, 57.2%, and 40.2% with an AUC of 0.804, indicating a good accuracy and reliability of the nomogram. GSEA showed that the high-risk group had lower ferroptosis and immune response. TME analyses confirmed that the high-risk group had lower immune cell infiltration (e.g., CD8+ T cells, CD4+ memory-activated T cells, and M1 macrophages) and lower immune functions (e.g., immune checkpoint activation). Low-risk patients whose disease expressed PD1 or CTLA4 were likely to respond better to ICIs. The analysis demonstrated that the TMB had significantly difference between low- and high- risk groups. Chemotherapy drugs, such as sorafenib, Imatinib, ABT.888 (Veliparib), Docetaxel, and Paclitaxel showed Significant differences in the estimated IC50 between the two risk groups.ConclusionOur novel ferroptosis-related lncRNA signature was able to accurately predict the prognosis and ICI outcomes of CM patients. These ferroptosis-related lncRNAs might be potential biomarkers and therapeutic targets for CM.

Dataset Information

Identification of hub gene and lncRNA signature related to entotic cell death in cutaneous melanoma for prognostic and immune prediction.

Publications

Identification of hub gene and lncRNA signature related to entotic cell death in cutaneous melanoma for prognostic and immune prediction.

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