Characterization of Aging-Related Genes to Predict Prognosis and Evaluate the Tumor Immune Microenvironment in Malignant Melanoma

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.

A seven-immune-genes risk model predicts the survival and suitable treatments for patients with skin cutaneous melanoma

Background

Skin cutaneous melanoma is characterized by high malignancy and prognostic heterogeneity. Immune cell networks are critical to the biological progression of melanoma through the tumor microenvironment. Thus, identifying effective biomarkers for skin cutaneous melanoma from the perspective of the tumor microenvironment may offer strategies for precise prognosis prediction and treatment selection.

Methods

A total of 470 cases from The Cancer Genome Atlas and 214 from the Gene Expression Omnibus were systematically evaluated to construct an optimal independent immune cell risk model with predictive value using weighted gene co-expression network analysis, Cox regression, and least absolute shrinkage and selection operator assay. The predictive power of the developed model was estimated through receiver operating characteristic curves and Kaplan-Meier analysis. The association of the model with tumor microenvironment status, immune checkpoints, and mutation burden was assessed using multiple algorithms. Additionally, the sensitivity of immune and chemotherapeutics was evaluated using the ImmunophenScore and pRRophetic algorithm. Furthermore, the expression profiles of risk genes were validated using gene expression profiling interactive analysis and Human Protein Atlas resources.

Results

The risk model integrated seven immune-related genes: ARNTL, N4BP2L1, PARP11, NUB1, GSDMD, HAPLN3, and IRX3. The model demonstrated considerable predictive ability and was positively associated with clinical and molecular characteristics. It can be utilized as a prognostic factor for skin cutaneous melanoma, where a high-risk score was linked to a poor prognosis and indicated an immunosuppressive microenvironment. Furthermore, the model revealed several potential target checkpoints and predicted the therapeutic benefits of multiple clinically used drugs.

Conclusion

Our findings provide a comprehensive landscape of the tumor immune microenvironment in skin cutaneous melanoma and identify prognostic markers that may serve as efficient clinical diagnosis and treatment selection tools.

Identification and Validation of an Aging-Associated circRNA-miRNA-mRNA Network in Neovascular Age-Related Macular Degeneration

INTRODUCTION

Neovascular age-related macular degeneration (NVAMD) is a leading cause of severe vision impairment in the elderly. Aging is one of the most pivotal underlying molecular mechanisms of NVAMD.

METHODS

In this study, we identified the potential aging-related genes involved in NVAMD. Considering that noncoding RNAs are vital regulators of NVAMD progression, we further explored and constructed an aging-originated circRNA-miRNA-mRNA network of NVAMD. Differential expression of 23 aging-associated genes was identified based on sequencing data and the Human Aging Genomic Resources tool at a threshold of p < 0.05, and log2|fold change| > 1.

RESULTS

We screened 12 microRNAs (miRNAs) using public datasets and miRNet database. A total of 13 circRNAs were subsequently mined using the starBase tool. Merging these 13 circRNAs, 12 miRNAs, and 15 genes together, we obtained 281 pairs of circRNA-miRNA and 30 pairs of miRNA-mRNA.

CONCLUSION

We created an aging-related circRNA-miRNA-mRNA network, which could be a promising target for future AMD treatments.

Analysis and validation of aging-related genes in prognosis and immune function of glioblastoma

BACKGROUND

Glioblastoma (GBM) is a common malignant brain tumor with poor prognosis and high mortality. Numerous reports have identified the correlation between aging and the prognosis of patients with GBM. The purpose of this study was to establish a prognostic model for GBM patients based on aging-related gene (ARG) to help determine the prognosis of GBM patients.

METHODS

143 patients with GBM from The Cancer Genomic Atlas (TCGA), 218 patients with GBM from the Chinese Glioma Genomic Atlas (CGGA) of China and 50 patients from Gene Expression Omnibus (GEO) were included in the study. R software (V4.2.1) and bioinformatics statistical methods were used to develop prognostic models and study immune infiltration and mutation characteristics.

RESULTS

Thirteen genes were screened out and used to establish the prognostic model finally, and the risk scores of the prognostic model was an independent factor (P < 0.001), which indicated a good prediction ability. In addition, there are significant differences in immune infiltration and mutation characteristics between the two groups with high and low risk scores.

CONCLUSION

The prognostic model of GBM patients based on ARGs can predict the prognosis of GBM patients. However, this signature requires further investigation and validation in larger cohort studies.

Bioinformatics identification and validation of aging‑related molecular subtype and prognostic signature in breast cancer

Patients with metastatic breast cancer have a poor clinical outcome, accounting for more than 90 percent of breast cancer-related deaths. Aging could regulate many biological processes in malignancies by regulating cell senescence. The role of aging has not been fully clarified. Consensus cluster analysis was performed to differentiate The Cancer Genome Atlas (TCGA) breast cancer cases. Least absolute shrinkage and selection operator (LASSO) cox regression analysis was performed to construct an aging-related prognostic signature. A total of 118 differentially expressed aging-related genes (ARGs) was obtained in breast cancer. Consensus clustering analysis identified 3 categories of TCGA-breast cancer with significant difference in prognosis and immune infiltration. We also constructed an aging-related prognostic signature for breast cancer, which had a good performance in predicting the 1-year, 3-year and 5-year OS and disease specific survival (DSS) of breast cancer patients. Further single gene analysis revealed that the expression of PIK3R1 was significantly different in different pT and pN stages of breast cancer. Moreover, low expression of PIK3R1 showed resistance to many drugs based on the data of Genomics of Drug Sensitivity in Cancer (GDSC) and Genomics of Therapeutics Response Portal (CTRP). PIK3R1 played a vital role in many well-known cancer-related pathways. The current study identified 3 clusters of TCGA-breast cancer cases with significant differences in prognosis and immune infiltration. We also constructed an aging-related prognostic signature for breast cancer. However, further in vivo and in vitro studies should be conducted to verify these results.

A new ferroptosis-related genetic mutation risk model predicts the prognosis of skin cutaneous melanoma

Background: Ferroptosis is an iron-dependent cell death mode and closely linked to various cancers, including skin cutaneous melanoma (SKCM). Although attempts have been made to construct ferroptosis-related gene (FRG) signatures for predicting the prognosis of SKCM, the prognostic impact of ferroptosis-related genetic mutations in SKCM remains lacking. This study aims to develop a prediction model to explain the relationship between ferroptosis-related genetic mutations and clinical outcomes of SKCM patients and to explore the potential value of ferroptosis in SKCM treatment. Methods: FRGs which significantly correlated with the prognosis of SKCM were firstly screened based on their single-nucleotide variant (SNV) status by univariate Cox regression analysis. Subsequently, the least absolute shrinkage and selection operator (LASSO) and Cox regressions were performed to construct a new ferroptosis-related genetic mutation risk (FerrGR) model for predicting the prognosis of SKCM. We then illustrate the survival and receiver operating characteristic (ROC) curves to evaluate the predictive power of the FerrGR model. Moreover, independent prognostic factors, genomic and clinical characteristics, immunotherapy, immune infiltration, and sensitive drugs were compared between high-and low-FerrGR groups. Results: The FerrGR model was developed with a good performance on survival and ROC analysis. It was a robust independent prognostic indicator and followed a nomogram constructed to predict prognostic outcomes for SKCM patients. Besides, FerrGR combined with tumor mutational burden (TMB) or MSI (microsatellite instability) was considered as a combined biomarker for immunotherapy response. The high FerrGR group patients were associated with an inhibitory immune microenvironment. Furthermore, potential drugs target to high FerrGR samples were predicted. Conclusion: The FerrGR model is valuable to predict prognosis and immunotherapy in SKCM patients. It offers a novel therapeutic option for SKCM.

Construction and evaluation of an aging-associated genes-based model for pancreatic adenocarcinoma prognosis and therapies

Objectives: Pancreatic adenocarcinoma (PAAD) is a highly malignant tumor. Despite extensive research, the precise role of aging-related genes in the initiation, microenvironment regulation, and progression of PAAD remains unclear.Methods: Patients with PAAD were selected from the International Cancer Genome Consortium (ICGC), and The Cancer Genome Atlas (TCGA) cohorts and the cell senescence-associated genes were obtained from CellAge. ConsensusClusterPlus was utilized for cluster identification. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct a prognosis prediction model.Results: We identified three clusters (C1, C2, and C3) based on aging-associated gene profiles. The C1 cluster had a shorter overall survival time, advanced clinical grades, lower immune ESTIMATE score, and tumor immune dysfunction and exclusion (TIDE) score than the C3 subgroup. Moreover, signaling pathways for cell cycle activation were enriched in the C1 cluster. We also identified eight hub genes and constructed a risk model. The high cellular senescence-related signature (CSRS) score subtype exhibited poor prognosis, advanced clinical grades, M2 macrophage infiltration, higher immune checkpoint gene expression, and lower immunotherapeutic benefits.Conclusion: Our risk score model shows high prediction accuracy and survival prediction ability in individual clinical prognosis and pre-immunotherapy evaluation.

Crosstalk of four kinds of cell deaths defines subtypes of cutaneous melanoma for precise immunotherapy and chemotherapy

Background

Cell death patterns can give therapeutic and biological clues that facilitate the development of individualized treatments for this lethal form of skin cancer.

Methods

We employed unsupervised clustering to establish robust classifications based on the four kinds of cell death-associated gene expression of 462 melanoma patients in the Cancer Genome Atlas (TCGA) and tested their reproducibility in two independent melanoma cohorts of 558 patients. We then used dimensionality reduction of graph learning to display the different characteristics of cell death patterns and immune microenvironments.

Results

We examined 570 cell death-associated gene expression data of melanoma patients for exploration, independent verification, and comprehensive classification of five reproducible melanoma subtypes (CS1 to CS5) with different genomic and clinical features. Patients in death-inactive subtypes (CS1, CS2, and CS5) had the least immune and stromal cell infiltration, and their prognosis was the poorest. A death-active subtype (CS4), on the other hand, had the highest infiltrated immune and stromal cells and elevated immune-checkpoints. As a result, these patients had the highest response to immunotherapy and the best prognosis. An additional subtype (CS3) had more diversified cell death and immune characteristics with moderate prognoses. Based on graph learning, we successfully divided the CS3 subtype into two subgroups (group A and group B) with distinct survival outcomes and immune features. Finally, we identified eight potential chemical drugs that were specifically targeted for the therapy of melanoma subtypes.

Conclusions

This research defines the intrinsic subtypes of melanoma based on the crosstalk of four kinds of cell deaths, which affords a blueprint for clinical strategies and guiding precise immunotherapy and chemotherapy for melanoma patients.

Identification and Development of an Age-Related Classification and Signature to Predict Prognosis and Immune Landscape in Osteosarcoma

Background. In childhood and adolescence, the prevailing bone tumor is osteosarcoma associated with frequent recurrence and lung metastasis. This research focused on predicting the survival and immune landscape of osteosarcoma by developing a prognostic signature and establishing aging-related genes (ARGs) subtypes. Methods. The training group comprised of the transcriptomic and associated clinical data of 84 patients with osteosarcoma accessed at the TARGET database and the validation group consisted of 53 patients from GSE21257. The aging-related subtypes were identified using unsupervised consensus clustering analysis. The ARG signature was developed utilizing multivariate Cox analysis and LASSO regression. The prognostic value was assessed using the univariate and multivariate Cox analyses, Kaplan-Meier plotter, time-dependent ROC curve, and nomogram. The functional enrichment analyses were performed by GSEA, GO, and KEGG analysis, while the ssGSEA, ESTIMATE, and CIBERSORT analyses were conducted to reveal the immune landscape in osteosarcoma. Results. The two clusters of osteosarcoma patients formed based on 543 ARGs, depicted a considerable difference in the tumor microenvironment, and the overall survival and immune cell infiltration rate varied as well. Among these, the selected 23 ARGs were utilized for the construction of an efficient predictive prognostic signature for the overall survival prediction. The testing in the validation group of osteosarcoma patients confirmed the status of the high-risk score as an independent indicator for poor prognosis, which was already identified as such using the univariate and multivariate Cox analyses. Furthermore, the ARG signature could distinguish different immune-related functions, infiltration status of immune cells, and tumor microenvironment, as well as predict the immunotherapy response of osteosarcoma patients. Conclusion. The aging-related subtypes were identified and a prognostic signature was developed in this research, which determined different prognoses and allowed for treatment of osteosarcoma patients to be tailored. Additionally, the immunotherapeutic response of individuals with osteosarcoma could also be predicted by the ARG signature.

Immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with Wilms tumour

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.

A novel risk model based on cuproptosis-related lncRNAs predicted prognosis and indicated immune microenvironment landscape of patients with cutaneous melanoma

Cutaneous melanoma (CM) is an aggressive form of malignancy with poor prognostic value. Cuproptosis is a novel type of cell death regulatory mechanism in tumors. However, the role of cuproptosis-related long noncoding RNAs (lncRNAs) in CM remains elusive. The cuproptosis-related lncRNAs were identified using the Pearson correlation algorithm. Through the univariate and multivariate Cox regression analysis, the prognosis of seven lncRNAs associated with cuproptosis was established and a new risk model was constructed. ESTIMATE, CIBERSORT, and single sample gene set enrichment analyses (ssGSEA) were applied to evaluate the immune microenvironment landscape. The Kaplan–Meier survival analysis revealed that the overall survival (OS) of CM patients in the high-risk group was remarkably lower than that of the low-risk group. The result of the validated cohort and the training cohort indicated that the risk model could produce an accurate prediction of the prognosis of CM. The nomogram result demonstrated that the risk score based on the seven prognostic cuproptosis-related lncRNAs was an independent prognostic indicator feature that distinguished it from other clinical features. The result of the immune microenvironment landscape indicated that the low-risk group showed better immunity than high-risk group. The immunophenoscore (IPS) and immune checkpoints results conveyed a better benefit potential for immunotherapy clinical application in the low-risk groups. The enrichment analysis and the gene set variation analysis (GSVA) were adopted to reveal the role of cuproptosis-related lncRNAs mediated by the immune-related signaling pathways in the development of CM. Altogether, the construction of the risk model based on cuproptosis-related lncRNAs can accurately predict the prognosis of CM and indicate the immune microenvironment of CM, providing a new perspective for the future clinical treatment of CM.

Identification of several senescence-associated genes signature in head and neck squamous cell carcinoma

Background

As one of the core aging processes, cellular senescence is associated with tumorigenesis, growth, and immune modulation in cancers. Nevertheless, the prognosis of senescence‐associated genes (SAGs) signature in head and neck squamous cell carcinoma (HNSCC) remains to be further evaluated.

Methods

The transcriptome and corresponding clinical datasets of SAGs in patients with HNSCC were downloaded from public databases. A new prognostic SAGs signature was established with least absolute shrinkage and selection operator discussion. Patients with HNSCC were fallen into two risk groups based on each sample's risk mark and the cutoff point. The survival analysis was extended to determine the predictive accuracy of the SAGs signature. Furthermore, the evaluation of SAGs signature was made according to clinicopathological characteristics, survival state, the infiltration of inflammatory cells, and efficacy of immunotherapy.

Results

41 SAGs were recognized and adopted to establish the forecast signature. The survival analysis indicated that patients with HNSCC in the high‐senescent score group had significantly reduced overall survival compared with those in the low‐senescent score group. It was certified that the risk score of SAGs signature was a separate predicting agent for HNSCC applying Cox regression analysis. According to functional analysis, some immune‐associated pathways were increased in the low‐senescent score group significantly. High‐senescent score group was correlated with poor clinicopathological characteristics, given less the infiltration of inflammatory cells state and worse immunotherapeutic effect.

Conclusion

A new SAG signature predicting result and response to immunotherapy of HNSCC was identified. Cellular senescence may be a hidden target for HNSCC.