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Identification of Genes Related to Immune Infiltration in the Tumor Microenvironment of Cutaneous Melanoma.

ABSTRACT: Cutaneous melanoma (CM) is the leading cause of skin cancer deaths and is typically diagnosed at an advanced stage, resulting in a poor prognosis. The tumor microenvironment (TME) plays a significant role in tumorigenesis and CM progression, but the dynamic regulation of immune and stromal components is not yet fully understood. In the present study, we quantified the ratio between immune and stromal components and the proportion of tumor-infiltrating immune cells (TICs), based on the ESTIMATE and CIBERSORT computational methods, in 471 cases of skin CM (SKCM) obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were analyzed by univariate Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis to identify prognosis-related genes. The developed prognosis model contains ten genes, which are all vital for patient prognosis. The areas under the curve (AUC) values for the developed prognostic model at 1, 3, 5, and 10 years were 0.832, 0.831, 0.880, and 0.857 in the training dataset, respectively. The GSE54467 dataset was used as a validation set to determine the predictive ability of the prognostic signature. Protein-protein interaction (PPI) analysis and weighted gene co-expression network analysis (WGCNA) were used to verify "real" hub genes closely related to the TME. These hub genes were verified for differential expression by immunohistochemistry (IHC) analyses. In conclusion, this study might provide potential diagnostic and prognostic biomarkers for CM.

SUBMITTER: Qin R

PROVIDER: S-EPMC8202075 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectivesMalignant melanoma is a highly malignant and heterogeneous skin cancer. Although immunotherapy has improved survival rates, the inhibitory effect of tumor microenvironment has weakened its efficacy. To improve survival and treatment strategies, we need to develop immune-related prognostic models. Based on the analysis of the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Sequence Read Archive (SRA) database, this study aims to establish an immune-related prognosis prediction model, and to evaluate the tumor immune microenvironment by risk score to guide immunotherapy.MethodsSkin cutaneous melanoma (SKCM) transcriptome sequencing data and corresponding clinical information were obtained from the TCGA database, differentially expressed genes were analyzed, and prognostic models were developed using univariate Cox regression, the LASSO method, and stepwise regression. Differentially expressed genes in prognostic models confirmed by real-time reverse transcription PCR (real-time RT-PCR) and Western blotting. Survival analysis was performed by using the Kaplan-Meier method, and the effect of the model was evaluated by time-dependent receiver operating characteristic curve as well as multivariate Cox regression, and the prognostic model was validated by 2 GEO melanoma datasets. Furthermore, correlations between risk score and immune cell infiltration, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) score, immune checkpoint mRNA expression levels, tumor immune cycle, or tumor immune micro-environmental pathways were analyzed. Finally, we performed association analysis for risk score and the efficacy of immunotherapy.ResultsWe identified 4 genes that were differentially expressed in TCGA-SKCM datasets, which were mainly associated with the tumor immune microenvironment. A prognostic model was also established based on 4 genes. Among 4 genes, the mRNA and protein levels of killer cell lectin like receptor D1 (KLRD1), leukemia inhibitory factor (LIF), and cellular retinoic acid binding protein 2 (CRABP2) genes in melanoma tissues differed significantly from those in normal skin (all P<0.01). The prognostic model was a good predictor of prognosis for patients with SKCM. The patients with high-risk scores had significantly shorter overall survival than those with low-risk scores, and consistent results were achieved in the training cohort and multiple validation cohorts (P<0.001). The risk score was strongly associated with immune cell infiltration, ESTIMATE score, immune checkpoint mRNA expression levels, tumor immune cycle, and tumor immune microenvironmental pathways (P<0.001). The correlation analysis showed that patients with the high-risk scores were in an inhibitory immune microenvironment based on the prognostic model (P<0.01).ConclusionsThe immune-related SKCM prognostic model constructed in this study can effectively predict the prognosis of SKCM patients. Considering its close correlation to the tumor immune microenvironment, the model has some reference value for clinical immunotherapy of SKCM.

Project description:The "writers" of four types of adenosine (A)-related RNA modifications (N6-methyladenosine, N1-methyladenosine, alternative polyadenylation, as well as A-to-inosine RNA editing) are closely related to the tumorigenesis and progression of many cancer types, including skin cutaneous melanoma (SKCM). However, the potential roles of the crosstalk between these RNA modification "writers" in the tumor microenvironment (TME) remain unclear. The RNA modification patterns were identified using an unsupervised clustering method. Subsequently, based on differentially expressed genes responsible for the aforementioned RNA modification patterns, an RNA modification "writer" scoring model (W_Score) was constructed to quantify the RNA modification-associated subtypes in individual patients. Moreover, a correlation analysis for W_Score and the TME characteristics, clinical features, molecular subtypes, drug sensitivities, immune responses, and prognosis was performed. We identified three RNA modification patterns, corresponding to distinct tumor immune microenvironment characteristics and survival outcomes. Based on the W_Score score, which was extracted from the RNA modification-related signature genes, patients with SKCM were divided into high- and low-W_Score groups. The low-W_Score group was characterized by better survival outcomes and strengthened immunocyte infiltration. Further analysis showed that the low-W_Score group was positively associated with higher tumor mutation burden and PD-L1 expression. Of note, two immunotherapy cohorts demonstrated that patients with low W_Score exhibited long-term clinical benefits and an enhanced immune response. This study is the first to systematically analyze four types of A-related RNA modifications in SKCM, revealing that these "writers" essentially contribute to TME complexity and diversity. We quantitatively evaluated the RNA modification patterns in individual tumors, which could aid in developing personalized immunotherapy strategies for patients.

Project description:BackgroundsOsteosarcomas are one of the most common primary malignant tumors of bone. It primarily occurs in children and adolescents, with the second highest incidence among people over 50 years old. Although there were immense improvements in the survival of patients with osteosarcoma in the past 30 years, targetable mutations and agents of osteosarcomas still have been generally not satisfactory. Therefore, it is of great importance to further explore the highly specialized immune environment of bone, genes related to macrophage infiltration and potential therapeutic biomarkers and targets.MethodsThe 11 expression data sets of OS tissues and the 11 data sets of adjacent non-tumorous tissues available in the GEO database GSE126209 were used to conduct immune infiltration analysis. Then, through WGCNA analysis, we acquired the co-expression modules related to Mast cells activated and performed the GO and KEGG enrichment analysis. Next, we did the survival prognosis analysis and plotted a survival curve. Finally, we analyzed the COX multivariate regression of gene expression on clinical parameters and drew forest maps for visualization by the forest plot package.ResultsOS disease-related immune cell populations, mainly Mast cells activated, have higher cell content (p = 0.006) than the normal group. Then, we identified co-expression modules related to Mast cells activated. In sum, a total of 822 genes from the top three strongest positive correlation module MEbrown4, MEdarkslateblue and MEnavajowhite2 and the strongest negative correlation module MEdarkturquoise. From that, we identified nine genes with different levels in immune cell infiltration related to osteosarcoma, eight of which including SORBS2, BAIAP2L2, ATAD2, CYGB, PAMR1, PSIP1, SNAPC3 and ZDHHC21 in their low abundance have higher disease-free survival probability than the group in their high abundances.ConclusionThese results could assist clinicians to select targets for immunotherapies and individualize treatment strategies for patients with OS.

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Identification of Genes Related to Immune Infiltration in the Tumor Microenvironment of Cutaneous Melanoma.

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