Dataset Information

Immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with skin cutaneous melanoma.

ABSTRACT: Skin cutaneous melanoma (SKCM) is the skin cancer that causes the highest number of deaths worldwide. There is growing evidence that the tumour immune microenvironment is associated with cancer prognosis, however, there is little research on the role of immune status in melanoma prognosis. In this study, data on patients with Skin cutaneous melanoma were downloaded from the GEO, TCGA, and GTEx databases. Genes associated with the immune pathway were screened from published papers and lncRNAs associated with them were identified. We performed immune microenvironment and functional enrichment analyses. The analysis was followed by applying univariate/multivariate Cox regression algorithms to finally identify three lncRNAs associated with the immune pathway for the construction of prognostic prediction models (CXCL10, RXRG, and SCG2). This stepwise downscaling method, which finally screens out prognostic factors and key genes and then uses them to build a risk model, has excellent predictive power. According to analyses of the model's reliability, it was able to differentiate the prognostic value and continued existence of Skin cutaneous melanoma patient populations more effectively. This study is an analysis of the immune pathway that leads lncRNAs in Skin cutaneous melanoma in an effort to open up new treatment avenues for Skin cutaneous melanoma.

SUBMITTER: Shen X

PROVIDER: S-EPMC9845246 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with skin cutaneous melanoma.

Shen Xin X Shang Lifeng L Han Junwei J Zhang Yi Y Niu Wenkai W Liu Haiwang H Shi Hai H

Frontiers in genetics 20230104

Skin cutaneous melanoma (SKCM) is the skin cancer that causes the highest number of deaths worldwide. There is growing evidence that the tumour immune microenvironment is associated with cancer prognosis, however, there is little research on the role of immune status in melanoma prognosis. In this study, data on patients with Skin cutaneous melanoma were downloaded from the GEO, TCGA, and GTEx databases. Genes associated with the immune pathway were screened from published papers and lncRNAs ass ...[more]

PMID: 36685954

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Project description:Wilms tumour (WT) is the most common kidney malignancy in children. Chemoresistance is the leading cause of tumour recurrence and poses a substantial therapeutic challenge. Increasing evidence has underscored the role of the tumour immune microenvironment (TIM) in cancers and the potential for immunotherapy to improve prognosis. There remain no reliable molecular markers for reflecting the immune landscape and predicting patient survival in WT. Here, we examine differences in gene expression by high-throughput RNA sequencing, focused on differentially expressed immune-related genes (IRGs) based on the ImmPort database. Via univariate Cox regression analysis and Lasso-penalized Cox regression analysis, IRGs were screened out to establish an immune signature. Kaplan-Meier curves, time-related ROC analysis, univariate and multivariate Cox regression studies, and nomograms were used to evaluate the accuracy and prognostic significance of this signature. Furthermore, we found that the immune signature could reflect the immune status and the immune cell infiltration character played in the tumour microenvironment (TME) and showed significant association with immune checkpoint molecules, suggesting that the poor outcome may be partially explained by its immunosuppressive TME. Remarkably, TIDE, a computational method to model tumour immune evasion mechanisms, showed that this signature holds great potential for predicting immunotherapy responses in the TARGET-wt cohort. To decipher the underlying mechanism, GSEA was applied to explore enriched pathways and biological processes associated with immunophenotyping and Connectivity map (CMap) along with DeSigN analysis for drug exploration. Finally, four candidate immune genes were selected, and their expression levels in WT cell lines were monitored via qRT-PCR. Meanwhile, we validated the function of a critical gene, NRP2. Taken together, we established a novel immune signature that may serve as an effective prognostic signature and predictive biomarker for immunotherapy response in WT patients. This study may give light on therapeutic strategies for WT patients from an immunological viewpoint.

Project description:BackgroundDNA damage repair plays an important role in the onset and progression of cancers and its resistance to treatment therapy. This study aims to assess the prognostic potential of DNA damage repair markers in skin cutaneous melanoma (SKCM).MethodIn this study, we have analyzed the gene expression profiles being downloaded from TCGA, GTEx, and GEO databases. We sequentially used univariate and LASSO Cox regression analyses to screen DNA repair genes associated with prognosis. Then, we have conducted a multivariate regression analysis to construct the prognostic profile of DNA repair-related genes (DRRGs). The risk coefficient is used to calculate the risk scores and divide the patients into two cohorts. Additionally, we validated our prognosis model on an external cohort as well as evaluated the link between immune response and the DRRGs prognostic profiles. The risk signature is compared to immune cell infiltration, chemotherapy, and immune checkpoint inhibitors (ICIs) treatment.ResultsAn analysis using LASSO-Cox stepwise regression established a prognostic signature consisting of twelve DRRGs with strong predictive ability. Disease-specific survival (DSS) is found to be lower among high-risk patients group as compared to low-risk patients. The signature may be employed as an independent prognostic predictor after controlling for clinicopathological factors, as demonstrated by validation on one external GSE65904 cohort. A strong correlation is also found between the risk score and the immune microenvironment, along with the infiltrating immune cells, and ICIs key molecules. The gene enrichment analysis results indicate a wide range of biological activities and pathways to be exhibited by high-risk groups. Furthermore, Cisplatin exhibited a considerable response sensitivity in low-risk groups as opposed to the high-risk incidents, while docetaxel exhibited a considerable response sensitivity in high-risk groups.ConclusionsOur findings provide a thorough investigation of DRRGs to develop an DSS-related prognostic indicator which may be useful in forecasting SKCM progression and enabling more enhanced clinical benefits from immunotherapy.

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Project description:Osteosarcoma has been the most common malignant bone tumor in children and adolescents, while the 5-y survival of osteosarcoma patients gained no significant improvement over the past decades. This study aimed to explore the role of ferroptosis-related genes (FRGs) in the development and prognosis of osteosarcoma. The datasets of osteosarcoma patients including RNA sequencing data and clinical information were acquired from the TRGET and Gene Expression Omnibus (GEO) databases. The identification of molecular subgroups with different FRG expression patterns was achieved through nonnegative matrix factorization (NMF) clustering. The prognostic model was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analysis. The ESTIMATE algorithm was applied for determining the stromal score, immune score, ESTIMA score, and tumor purity of osteosarcoma patients. Functional analyses including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) were conducted to explore the underlying mechanisms in the development and prognosis of osteosarcoma. Two molecular subgroups with different FRGs expression patterns were identified. The molecular subgroups with higher immune score and more active immune status showed better prognostic survival. On the basis of FRGs, a prognostic model and a nomogram integrating clinical characteristics were constructed and their prediction efficiency for osteosarcoma prognosis were well validated. Gene functional enrichment analysis showed that these differentially expressed FRGs were mainly enriched in immunity-related signaling pathways, indicating that FRGs may affect the development and prognosis of osteosarcoma by regulating the immune microenvironment. The expression profiles of FRGs were closely related to the immunity status and prognostic survival of osteosarcoma patients. The interaction between ferroptosis and immunity in the development of osteosarcoma could provide a new insight into the exploration of molecular mechanisms and targeted therapies of osteosarcoma patients.

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

Immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with skin cutaneous melanoma.

Publications

Immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with skin cutaneous melanoma.

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