Predicting the clinical outcome of melanoma using an immune-related gene pairs signature

Determinants of resistance and response to melanoma therapy

Metastatic melanoma is among the most enigmatic advanced cancers to clinically manage despite immense progress in the way of available therapeutic options and historic decreases in the melanoma mortality rate. Most patients with metastatic melanoma treated with modern targeted therapies (for example, BRAFV600E/K inhibitors) and/or immune checkpoint blockade (for example, anti-programmed death 1 therapy) will progress, owing to profound tumor cell plasticity fueled by genetic and nongenetic mechanisms and dichotomous host microenvironmental influences. Here we discuss the determinants of tumor heterogeneity, mechanisms of therapy resistance and effective therapy regimens that hold curative promise.

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.

Construction and Validation of a Novel Immune-Related Gene Pairs-Based Prognostic Model in Lung Adenocarcinoma

OBJECT

Focus on immune-related gene pairs (IRGPs) and develop a prognostic model to predict the prognosis of patients with lung adenocarcinoma (LUAD).

METHODS

First, the LUAD patient dataset was downloaded from The Cancer Genome Atlas database, and paired analysis of immune-related genes was subsequently conducted. Then, LASSO regression was used to screen prognostic IRGPs for building a risk prediction model. Meanwhile, the Gene Expression Omnibus database was used for external validation of the model. Next, the clinical predictive power of IRGPs features was assessed by uni-multivariate Cox regression analysis, the infiltration of key immune cells in high and low IRGPs risk groups was analyzed with CIBERSORT, quanTIseq, and Timer, and the key pathways enriched for IRGPs were assessed using the Kyoto Encyclopedia of Genes and Genomes. Finally, the expression and related functions of key immune cells and genes were verified by immunofluorescence and cell experiments of tissue samples.

RESULTS

It was revealed that the risk score of 19 IRGPs could be used as accurate indicators to evaluate the prognosis of LUAD patients, and the risk score was mainly related to T cell infiltration based on CIBERSORT analysis. Two genes of IRGPs, IL6, and CCL2, were found to be closely associated with the expression of PD-1/PD-L1 and the function of T-cells. Depending on the results of tissue immunofluorescence, IL6, CCL2, and T cells were highly expressed in the LUAD tissues of patients. Furthermore, IL6 and CCL2 were positively correlated with the expression of T cells. Besides, qRT-PCR assay in four different LUAD cells proved that IL6 and CCL2 were positively correlated with the expression of PD-L1 (P < .001).

CONCLUSIONS

Based on 19 IRGPs, an effective prognosis model was established to predict the prognosis of LUAD patients. In addition, IL6 and CCL2 are closely related to the function of T-cells.

Unravelling the landscape of skin cancer through single-cell transcriptomics

The human skin is a complex organ that forms the first line of defense against pathogens and external injury. It is composed of a wide variety of cells that work together to maintain homeostasis and prevent disease, such as skin cancer. The exponentially rising incidence of skin malignancies poses a growing public health challenge, particularly when the disease course is complicated by metastasis and therapeutic resistance. Recent advances in single-cell transcriptomics have provided a high-resolution view of gene expression heterogeneity that can be applied to skin cancers to define cell types and states, understand disease evolution, and develop new therapeutic concepts. This approach has been particularly valuable in characterizing the contribution of immune cells in skin cancer, an area of great clinical importance given the increasing use of immunotherapy in this setting. In this review, we highlight recent skin cancer studies utilizing bulk RNA sequencing, introduce various single-cell transcriptomics approaches, and summarize key findings obtained by applying single-cell transcriptomics to skin cancer.

Revisiting miRNA Association with Melanoma Recurrence and Metastasis from a Machine Learning Point of View

The diagnostic and prognostic value of miRNAs in cutaneous melanoma (CM) has been broadly studied and supported by advanced bioinformatics tools. From early studies using miRNA arrays with several limitations, to the recent NGS-derived miRNA expression profiles, an accurate diagnostic panel of a comprehensive pre-specified set of miRNAs that could aid timely identification of specific cancer stages is still elusive, mainly because of the heterogeneity of the approaches and the samples. Herein, we summarize the existing studies that report several miRNAs as important diagnostic and prognostic biomarkers in CM. Using publicly available NGS data, we analyzed the correlation of specific miRNA expression profiles with the expression signatures of known gene targets. Combining network analytics with machine learning, we developed specific non-linear classification models that could successfully predict CM recurrence and metastasis, based on two newly identified miRNA signatures. Subsequent unbiased analyses and independent test sets (i.e., a dataset not used for training, as a validation cohort) using our prediction models resulted in 73.85% and 82.09% accuracy in predicting CM recurrence and metastasis, respectively. Overall, our approach combines detailed analysis of miRNA profiles with heuristic optimization and machine learning, which facilitates dimensionality reduction and optimization of the prediction models. Our approach provides an improved prediction strategy that could serve as an auxiliary tool towards precision treatment.

Predicting the Survival and Immune Landscape of Colorectal Cancer Patients Using an Immune-Related lncRNA Pair Model

Background

Accumulating evidence has demonstrated that immune-related long non-coding ribonucleic acids (irlncRNAs) can be used as prognostic indicators of overall survival (OS) in patients with colorectal cancer (CRC). Our aim in this research, therefore, was to construct a risk model using irlncRNA pairs with no requirement for a specific expression level, in hope of reliably predicting the prognosis and immune landscape of CRC patients.

Methods

Clinical and transcriptome profiling data of CRC patients downloaded from the Cancer Genome Atlas (TCGA) database were analyzed to identify differentially expressed (DE) irlncRNAs. The irlncRNA pairs significantly correlated with the prognosis of patients were screened out by univariable Cox regression analysis and a prognostic model was constructed by Lasso and multivariate Cox regression analyses. A receiver operating characteristic (ROC) curve was then plotted, with the area under the curve calculated to confirm the reliability of the model. Based on the optimal cutoff value, CRC patients in the high- or low-risk groups were distinguished, laying the ground for evaluating the risk model from the following perspectives: survival, clinicopathological traits, tumor-infiltrating immune cells (TIICs), antitumor drug efficacy, kinase inhibitor efficacy, and molecules related to immune checkpoints.

Results

A prognostic model consisting of 15 irlncRNA pairs was constructed, which was found to have a high correlation with patient prognosis in a cohort from the TCGA (p < 0.001, HR = 1.089, 95% CI [1.067-1.112]). According to both univariate and multivariate Cox analyses, this model could be used as an independent prognostic indicator in the TCGA cohort (p < 0.001). Effective differentiation between high- and low-risk patients was also accomplished, on the basis of aggressive clinicopathological characteristics, sensitivity to antitumor drugs, and kinase inhibitors, the tumor immune infiltration status, and the expression levels of specific molecules related to immune checkpoints.

Conclusion

The prognostic model established with irlncRNA pairs is a promising indicator for prognosis prediction in CRC patients.

CCR4, CCR8, and P2RY14 as Prognostic Factors in Head and Neck Squamous Cell Carcinoma Are Involved in the Remodeling of the Tumor Microenvironment

The tumor microenvironment (TME) plays a critical role in the initiation and progression of cancer. However, the specific mechanism of its regulation in head and neck squamous cell carcinoma (HNSCC) remains unclear. In this study, we first applied the ESTIMATE method to calculate the immune and stromal scores in patients' tumor tissues from The Cancer Genome Atlas (TCGA) database. GSE41613, GSE30784, and GSE37991 data sets from the Gene Expression Omnibus (GEO) database were recruited for further validation. Differentially expressed genes (DEGs) were identified and then analyzed by Cox regression analysis and protein-protein interaction (PPI) network construction. DEGs significantly associated with prognosis and TME will be identified as hub genes. These genes were also validated at the protein level by immunohistochemical analysis of 10 pairs of primary tumor tissues and the adjacent normal tissues from our institution. The relationship between hub genes expression and immune cell fraction estimated by CIBERSORT software was also examined. 275 DEGs were significantly associated with TME. CCR4, CCR8, and P2RY14 have then identified as hub genes by intersection Cox and PPI analysis. Further investigation revealed that the expression of CCR4, CCR8, and P2RY14 was negatively correlated with clinicopathological characteristics (clinical stage, T stage) and positively associated with survival in HNSCC patients, especially in male patients. The expression of CCR8 and P2RY14 was lower in males than in females. CCR8 and P2RY14 were differentially expressed in tumor tissues than normal tissues, and the results were validated at the protein level by immunohistochemistry experiments. Gene set enrichment analysis (GSEA) showed that the high expression groups' hub genes were mainly enriched for immune-related activities. In the low-expression groups, genes were primarily enriched in metabolic pathways. CIBERSORT results showed that the expression of these genes was all negatively correlated with the fraction of memory B cells and positively correlated with the fraction of the other four cells, including naive B cells, resting T cells CD4 memory, T cells follicular helper, and T cells regulatory (Tregs). The results suggest that CCR4, CCR8, and P2RY14 may be responsible for maintaining the immune dominance of TME, thus leading to a better prognosis.

Development of an Immune-Related Gene Signature for Prognosis in Melanoma

Melanoma remains a potentially deadly malignant tumor. The incidence of melanoma continues to rise. Immunotherapy has become a new treatment method and is widely used in a variety of tumors. Original melanoma data were downloaded from TCGA. ssGSEA was performed to classify them. GSVA software and the "hclust" package were used to analyze the data. The ESTIMATE algorithm screened DEGs. The edgeR package and Venn diagram identified valid immune-related genes. Univariate, LASSO and multivariate analyses were used to explore the hub genes. The "rms" package established the nomogram and calibrated the curve. Immune infiltration data were obtained from the TIMER database. Compared with that of samples in the high immune cell infiltration cluster, we found that the tumor purity of samples in the low immune cell infiltration cluster was higher. The immune score, ESTIMATE score and stromal score in the low immune cell infiltration cluster were lower. In the high immune cell infiltration cluster, the immune components were more abundant, while the tumor purity was lower. The expression levels of TIGIT, PDCD1, LAG3, HAVCR2, CTLA4 and the HLA family were also higher in the high immune cell infiltration cluster. Survival analysis showed that patients in the high immune cell infiltration cluster had shorter OS than patients in the low immune cell infiltration cluster. IGHV1-18, CXCL11, LTF, and HLA-DQB1 were identified as immune cell infiltration-related DEGs. The prognosis of melanoma was significantly negatively correlated with the infiltration of CD4+ T cells, CD8+ T cells, dendritic cells, neutrophils and macrophages. In this study, we identified immune-related melanoma core genes and relevant immune cell subtypes, which may be used in targeted therapy and immunotherapy of melanoma.

Identification of a Set of Genes Improving Survival Prediction in Kidney Renal Clear Cell Carcinoma through Integrative Reanalysis of Transcriptomic Data

BACKGROUND

With an enormous amount of research concerning kidney cancer being conducted, various treatments have been applied to its cure. However, high recurrence and metastasis rates continue to pose a threat to the survival of patients with kidney renal clear cell carcinoma (KIRC).

METHODS

Data from The Cancer Genome Atlas were downloaded, and a series of analyses were performed, including differential analysis, Cox analysis, weighted gene coexpression network analysis, least absolute shrinkage and selection operator analysis, multivariate Cox analysis, survival analysis, and receiver operating characteristic curve and functional enrichment analysis.

RESULTS

A total of 5,777 differentially expressed genes were identified from the differential analysis. The Cox analysis showed 1,853 significant genes (P < 0.01). Weighted gene coexpression network analysis revealed that 226 genes in the module were related to clinical parameters, including Tumor-Node-Metastasis (TNM) staging. Least absolute shrinkage and selection operator and multivariate Cox analyses suggested that four genes (CDKL2, LRFN1, STAT2, and SOWAHB) had a potential function in predicting the survival time of patients with KIRC. Survival analysis uncovered that a high risk of these four genes was associated with an unfavorable prognosis. Receiver operating characteristic curve analysis further confirmed the accuracy of the risk score model. The analysis of clinicopathological parameters of the four identified genes revealed that they were associated with the progression of KIRC.

CONCLUSION

The gene expression model consisting of CDKL2, LRFN1, STAT2, and SOWAHB is a promising tool for predicting the prognosis of patients with KIRC. The results of this study may provide insights into the diagnosis and treatment of KIRC.