Identification of immune-related genes with prognostic significance in the microenvironment of cutaneous melanoma

The C-type lectin DCIR contributes to the immune response and pathogenesis of colorectal cancer

Development and progression of malignancies are accompanied and influenced by alterations in the surrounding immune microenvironment. Understanding the cellular and molecular interactions between immune cells and cancer cells has not only provided important fundamental insights into the disease, but has also led to the development of new immunotherapies. The C-type lectin Dendritic Cell ImmunoReceptor (DCIR) is primarily expressed by myeloid cells and is an important regulator of immune homeostasis, as demonstrated in various autoimmune, infectious and inflammatory contexts. Yet, the impact of DCIR on cancer development remains largely unknown. Analysis of available transcriptomic data of colorectal cancer (CRC) patients revealed that high DCIR gene expression is associated with improved patients' survival, immunologically "hot" tumors and high immunologic constant of rejection, thus arguing for a protective and immunoregulatory role of DCIR in CRC. In line with these correlative data, we found that deficiency of DCIR1, the murine homologue of human DCIR, leads to the development of significantly larger tumors in an orthotopic murine model of CRC. This phenotype is accompanied by an altered phenotype of tumor-associated macrophages (TAMs) and a reduction in the percentage of activated effector CD4+ and CD8+ T cells in CRC tumors of DCIR1-deficient mice. Overall, our results show that DCIR promotes antitumor immunity in CRC, making it an attractive target for the future development of immunotherapies to fight the second deadliest cancer in the world.

Emerging prognostic biomarkers in advanced cutaneous melanoma: a literature update

INTRODUCTION

Over the past two years, the scientific community has witnessed an exponential growth in research focused on identifying prognostic biomarkers for melanoma, both in pre-clinical and clinical settings. This surge in studies reflects the need of developing effective prognostic indicators in the field of melanoma.

AREAS COVERED

The aim of this work is to review the scientific literature on the most recent findings on the development or validation of prognostic biomarkers in melanoma, in the attempt of providing both clinicians and researchers with an updated broad synopsis of prognostic biomarkers in cutaneous melanoma.

EXPERT OPINION

While the field of prognostic biomarkers in melanoma appears promising, there are several complexities and limitations to address. The interdependence of clinical, histological, and molecular features requires accurate classification of different biomarker families. Correlation does not imply causation, and adjustments for confounding factors are often overlooked. In this scenario, large-scale studies based on high-quality clinical trial data can provide more reliable evidence. It is essential to avoid oversimplification by focusing on a single biomarker, as the interactions among multiple factors contribute to define the disease course and patient's outcome. Furthermore, implementing well-supported evidence in real-life settings can help advance prognostic biomarker research in melanoma.

Construction and verification of a novel prognostic risk model for kidney renal clear cell carcinoma based on immunity-related genes

Background: Currently, there are no useful biomarkers or prognostic risk markers for the diagnosis of kidney renal clear cell carcinoma (KIRC), although recent research has shown that both, the onset and progression of KIRC, are substantially influenced by immune-associated genes (IAGs). Objective: This work aims to create and verify the prognostic value of an immune risk score signature (IRSS) based on IAGs for KIRC using bioinformatics and public databases. Methods: Differentially expressed genes (DEGs) related to the immune systems (IAGs) in KIRC tissues were identified from The Cancer Genome Atlas (TCGA) databases. The DEGs between the tumor and normal tissues were identified using gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analyses. Furthermore, a prognostic IRSS model was constructed and its prognostic and predictive performance was analyzed using survival analyses and nomograms. Kidney renal papillary cell carcinoma (KIRP) sets were utilized to further validate this model. Results: Six independent immunity-related genes (PAEP, PI3, SAA2, SAA1, IL20RB, and IFI30) correlated with prognosis were identified and used to construct an IRSS model. According to the Kaplan-Meier curve, patients in the high-risk group had significantly poorer prognoses than those of patients in the low-risk group in both, the verification set (p <0.049; HR = 1.84; 95% CI = 1.02-3.32) and the training set (p < 0.001; HR = 3.12, 95% CI = 2.23-4.37). The numbers of regulatory T cells (Tregs) were significantly positively correlated with the six immunity-related genes identified, with correlation coefficients were 0.385, 0.415, 0.399, 0.451, 0.485, and 0.333, respectively (p <0.001). Conclusion: This work investigated the association between immune infiltration, immunity-related gene expression, and severity of KIRC to construct and verify a prognostic risk model for KIRC and KIRP.

Identification of immune subtypes of melanoma based on single-cell and bulk RNA sequencing data

The tumor microenvironment plays a crucial role in melanoma. In this study, the abundance of immune cells in melanoma samples was assessed and analyzed using single sample gene set enrichment analysis (ssGSEA), and the predictive value of immune cells was assessed using univariate COX regression analysis. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression analysis was applied to construct an immune cell risk score (ICRS) model with a high predictive value for identifying the immune profile of melanoma patients. The pathway enrichment between the different ICRS groups was also elucidated. Next, five hub genes for diagnosing the prognosis of melanoma were screened by two machine learning algorithms, LASSO and random forest. The distribution of hub genes in immune cells was analyzed on account of Single-cell RNA sequencing (scRNA-seq), and the interaction between genes and immune cells was elucidated by cellular communication. Ultimately, the ICRS model on account of two types of immune cells (Activated CD8 T cell and Immature B cell) was constructed and validated, which can determine melanoma prognosis. In addition, five hub genes were identified as potential therapeutic targets affecting the prognosis of melanoma patients.

Multi-omics analysis of Siglec family genes in cutaneous melanoma

Background

Melanoma is widely recognized as the most aggressive and fatal type of skin cancer; however, effective prognostic markers are lacking. The sialic acid-binding immunoglobulin-type lectin (Siglec) gene family plays an important role in the development of tumors and immune escape, but its prognostic role in melanoma remains unknown.

Results

Siglec genes have a high mutation frequency, with up to 8% in SIGLEC7. High expression levels of Siglecs in tumor bulk suggests a better prognosis. Siglecs also show a high degree of synergistic expression. Immunohistochemistry was used to analyze the expression of SIGLEC9 in tumor tissue microarray. The expression of SIGLEC9 in tumor tissue without metastasis was higher than that in tumor tissue with metastasis. We used unsupervised clustering to create a high expression of Siglec (HES) cluster and a low expression of Siglec (LES) cluster. The HES cluster correlated with high overall survival and increased expression levels of Siglec genes. The HES cluster also showed significant immune cell infiltration and activation of immune signaling pathways. We used least absolute shrinkage and selection operator (LASSO) regression analysis to reduce the dimensionality of Siglec cluster-related genes and constructed a prognostic model composed of SRGN and GBP4, which can risk-stratify patients in both the training and test datasets.

Conclusion

We conducted a multi-omics analysis of the Siglec family genes in melanoma and found that Siglecs play an important role in the occurrence and development of melanoma. Typing constructed using Siglecs can show risk stratification and derived prognostic models can predict a patient's risk score. In summary, Siglec family genes are potential targets for melanoma treatment as well as prognostic markers that can direct individualized treatments and improve overall survival.

Pancreatic adenocarcinoma associated immune-gene signature as a novo risk factor for clinical prognosis prediction in hepatocellular carcinoma

Pancreatic adenocarcinoma (PAAD) has high mortality and a very poor prognosis. Both surgery and chemotherapy have a suboptimal therapeutic effect, and this caused a need to find new approaches such as immunotherapy. Therefore, it is essential to develop a new model to predict patient prognosis and facilitate early intervention. Our study screened out and validated the target molecules based on the TCGA-PAAD dataset. We established the risk signature using univariate and multivariate Cox regression analysis and used GSE62452 and GSE28735 to verify the accuracy and reliability of the model. Expanded application of PAAD-immune-related genes signature (-IRGS) on other datasets was conducted, and the corresponding nomograms were constructed. We also analyzed the correlation between immune-related cells/genes and potential treatments. Our research demonstrated that a high riskscore of PAAD-IRGS in patients with PAAD was correlated with poor overall survival, disease-specific survival and progression free interval. The same results were observed in patients with LIHC. The models constructed were confirmed to be accurate and reliable. We found various correlations between PAAD-IRGS and immune-related cells/genes, and the potential therapeutic agents. These findings indicate that PAAD-IRGS may be a promising indicator for prognosis and of the tumor-immune microenvironment status in PAAD.

Development and Validation of a Novel Survival Model for Cutaneous Melanoma Based on Necroptosis-Related Genes

Background

Necroptosis is crucial for organismal development and pathogenesis. To date, the role of necroptosis in skin cutaneous melanoma (SKCM) is yet unveiled. In addition, the part of melanin pigmentation was largely neglected in the bioinformatic analysis. In this study, we aimed to construct a novel prognostic model based on necroptosis-related genes and analysis the pigmentation phenotype of patients to provide clinically actionable information for SKCM patients.

Methods

We downloaded the SKCM data from the TCGA and GEO databases in this study and identified the differently expressed and prognostic necroptosis-related genes. Patients’ pigmentation phenotype was evaluated by the GSVA method. Then, using Lasso and Cox regression analysis, a novel prognostic model was constructed based on the intersected genes. The risk score was calculated and the patients were divided into two groups. The survival differences between the two groups were compared using Kaplan-Meier analysis. The ROC analysis was performed and the area under curves was calculated to evaluate the prediction performances of the model. Then, the GO, KEGG and GSEA analyses were performed to elucidate the underlying mechanisms. Differences in the tumor microenvironment, patients’ response to immune checkpoint inhibitors (ICIs) and pigmentation phenotype were analyzed. In order to validate the mRNA expression levels of the selected genes, quantitative real-time PCR (qRT-PCR) was performed.

Results

Altogether, a novel prognostic model based on four genes (BOK, CD14, CYLD and FASLG) was constructed, and patients were classified into high and low-risk groups based on the median risk score. Low-risk group patients showed better survival status. The model showed high accuracy in the training and the validation cohort. Pathway and functional enrichment analysis indicated that immune-related pathways were differently activated in the two groups. In addition, immune cells infiltration patterns and sensitivity of ICIs showed a significant difference between patients from two risk groups. The pigmentation score was positively related to the risk score in pigmentation phenotype analysis.

Conclusion

In conclusion, this study established a novel prognostic model based on necroptosis-related genes and revealed the possible connections between necroptosis and melanin pigmentation. It is expected to provide a reference for clinical treatment.

Identification of a novel tumour microenvironment-based prognostic biomarker in skin cutaneous melanoma

Skin cutaneous melanoma (SKCM) is one of the most destructive skin malignancies and has attracted worldwide attention. However, there is a lack of prognostic biomarkers, especially tumour microenvironment (TME)-based prognostic biomarkers. Therefore, there is an urgent need to investigate the TME in SKCM, as well as to identify efficient biomarkers for the diagnosis and treatment of SKCM patients. A comprehensive analysis was performed using SKCM samples from The Cancer Genome Atlas and normal samples from Genotype-Tissue Expression. TME scores were calculated using the ESTIMATE algorithm, and differential TME scores and differentially expressed prognostic genes were successively identified. We further identified more reliable prognostic genes via least absolute shrinkage and selection operator regression analysis and constructed a prognostic prediction model to predict overall survival. Receiver operating characteristic analysis was used to evaluate the diagnostic efficacy, and Cox regression analysis was applied to explore the relationship with clinicopathological characteristics. Finally, we identified a novel prognostic biomarker and conducted a functional enrichment analysis. After considering ESTIMATEScore and tumour purity as differential TME scores, we identified 34 differentially expressed prognostic genes. Using least absolute shrinkage and selection operator regression, we identified seven potential prognostic biomarkers (SLC13A5, RBM24, IGHV3OR16-15, PRSS35, SLC7A10, IGHV1-69D and IGHV2-26). Combined with receiver operating characteristic and regression analyses, we determined PRSS35 as a novel TME-based prognostic biomarker in SKCM, and functional analysis enriched immune-related cells, functions and signalling pathways. Our study indicated that PRSS35 could act as a potential prognostic biomarker in SKCM by investigating the TME, so as to provide new ideas and insights for the clinical diagnosis and treatment of SKCM.

Esophageal Cancer Associated Immune Genes as Biomarkers for Predicting Outcome in Upper Gastrointestinal Tumors

Esophageal cancer (EC) is the seventh most common tumor in the world, ranking the sixth leading cause of cancer death, with a 5-year survival rate of 15-25%. Therefore, reliable prognostic biomarkers are needed to effectively predict the prognosis of EC. In this study, the gene profile information of the EC cohort served as a training set, which was derived from TCGA and Immport databases. GO and KEGG enrichment analysis was performed on the differential genes in normal and tumor groups of EC. The immune genes in differentially expressed genes (DEGs) were further obtained for univariate and multivariate Cox and Lasso regression analysis, and 6 independent immune genes (S100A3, STC2, HSPA6, CCL25, GPER1, and OSM) associated with prognosis were obtained to establish an immune risk score signature (IRSS). The signature was validated using head and neck cancers (HNSC) and gastric cancer (GC)in upper gastrointestinal malignancies as validation sets. The Kaplan-Meier results showed that the prognosis of the high-risk group was significantly favorable than that of the low-risk group in both the training set (P < 0.001; HR = 3.68, 95% CI = 2.14−6.35) and the validation set (P = 0.010; HR = 1.43, 95% CI = 1.09−1.88). A nomogram combining multiple clinical information and IRSS was more effective than a single independent prognostic factor in predicting outcome. This study explored the potential link between immunity and EC, and established and validated prognostic biomarkers that can effectively predict the prognosis of EC, HNSC and GC based on six immune genes.