Identification of a Gene-Related Risk Signature in Melanoma Patients Using Bioinformatic Profiling

The path of GPR87: from a P2Y-like receptor to its role in cancer progression

GPR87 is a G protein-coupled seven-transmembrane receptor first described as an orphan receptor in 2001. Despite its high structural homology to several extracellular nucleotide-activated P2Y receptors and sharing conserved sequence motifs in transmembrane regions, identification of endogenous ligands from the class of nucleotides and their analogues has failed for GPR87. Although lysophosphatidic acid was proposed to be a natural ligand for this cell surface receptor, these data are preliminary and inconsistent, and IUPHAR is currently considering GPR87 as an orphan receptor. Thus, the endogenous ligands and physiological functions of GPR87 are still required to be determined and/or confirmed. The remarkably higher expression of GPR87 in human malignant tissues compared to the normal healthy ones clearly suggests that this receptor may be involved in the development and progression of cancerous neoplasms. Therefore, in this review article, the main focus is placed on the oncogenic role of GPR87 in various human malignancies, presenting it as a potential novel target site for therapeutic interventions using both humanized monoclonal antibodies and gene therapy but also selective antagonists which are still waiting for their identification. Furthermore, the importance of the expression of GPR87 as a predictive biomarker for evaluating the prognosis and overall survival of cancer patients is also highlighted.

Molecular Susceptibility and Treatment Challenges in Melanoma

Melanoma is the most aggressive subtype of cancer, with a higher propensity to spread compared to most solid tumors. The application of OMICS approaches has revolutionized the field of melanoma research by providing comprehensive insights into the molecular alterations and biological processes underlying melanoma development and progression. This review aims to offer an overview of melanoma biology, covering its transition from primary to malignant melanoma, as well as the key genes and pathways involved in the initiation and progression of this disease. Utilizing online databases, we extensively explored the general expression profile of genes, identified the most frequently altered genes and gene mutations, and examined genetic alterations responsible for drug resistance. Additionally, we studied the mechanisms responsible for immune checkpoint inhibitor resistance in melanoma.

A Novel Glycolysis-Related Gene Signature Predicts Prognosis For Cutaneous Melanoma

BACKGROUND

There exists a lack of effective tools predicting prognosis for cutaneous melanoma patients. Glycolysis plays an essential role in the carcinogenesis process.

OBJECTIVE

We intended to construct a new prognosis model for cutaneous melanoma.

METHODS

Based on the data from the TCGA database, we conducted a univariate Cox regression analysis and identified prognostic glycolysis-related genes (GRGs). Meanwhile, the GSE15605 dataset was used to identify differentially expressed genes (DEGs). The intersection of prognostic GRGs and DEGs was extracted for the subsequent multivariate Cox regression analysis.

RESULTS

A prognostic signature containing ten GRGs was built, and the TCGA cohort was classified into high and low risk subgroups based on the risk score of each patient. K-M analysis manifested that the overall survival of the high-risk group was statistically worse than that of the lowrisk group. Further study indicated that the risk-score could be used as an independent prognostic factor that effectively predicted the clinical prognosis in patients of different ages, genders, and stages. GO and KEGG enrichment analysis showed DEGs between high and low risk groups were enriched in immune-related functions and pathways. In addition, a significant difference existed between high and low risk groups in infiltration pattern of immune cells and expression levels of inhibitory immune checkpoint genes.

CONCLUSION

A new glycolysis-related gene signature was established for identifying cutaneous melanoma patients with poor prognoses and formulating individualized treatment.

Comprehensive characterization of TNFSF14/LIGHT with implications in prognosis and immunotherapy of human gliomas

Glioblastoma multiforme (GBM) is a common central neural system malignant tumor among adults. Alongside its microscopic spread, immunosuppression in the tumor microenvironment also induces its refractoriness, which makes immunotherapy for GBM particularly important. Unfortunately, traditional immune checkpoint inhibitors (ICIs) often show limited therapeutic effects in GBM clinical trials, and new therapeutic strategies or targets are urgently needed. TNFSF14/LIGHT is a novel immune checkpoint molecule that plays essential roles in both innate and acquired immunity. Despite recent advances in our understanding of the function of TNFSF14/LIGHT in a variety of cancer types, the clinical and immunological importance of TNFSF14/LIGHT in human gliomas has not been fully explained. Here, we employed a comprehensive in silico analysis with publicly available data to analyze the molecular and immune characteristics of TNFSF14/LIGHT to explore its feasibility as an immunotherapy target. Totally, 2215 glioma cases were enrolled in the current study. Immunohistochemistry staining based on patient tissues (n = 34) was performed for the validation. TNFSF14/LIGHT was expressed higher in higher-WHO-grade gliomas and mesenchymal subtypes, and it was sensitive as a prognostic marker in GBM and low-grade glioma (LGG). A nomogram prognostic model was established based on TNFSF14/LIGHT expression together with other risk factors. Additionally, Gene Ontology and pathway analysis revealed that TNFSF14/LIGHT participated in T-cell activities and inflammatory processes. Moreover, analysis based on the structure and interactions of TNFSF14/LIGHT revealed its mutation sites in tumors as well as crucial interacting proteins. Analysis of IMvigor210 indicated the role of TNFSF14/LIGHT in immunotherapy. Altogether, our results reveal an underlying role of TNFSF14/LIGHT as an immunotherapy target in GBM.

Protein expression of prognostic genes in primary melanoma and benign nevi

PURPOSE

To evaluate the protein expression characteristics of genes employed in a recently introduced prognostic gene expression assay for patients with cutaneous melanoma (CM).

METHODS

We studied 37 patients with CM and 10 with benign (melanocytic) nevi (BN). Immunohistochemistry of primary tumor tissue was performed for eight proteins: COL6A6, DCD, GBP4, KLHL41, KRT9, PIP, SCGB1D2, SCGB2A2.

RESULTS

The protein expression of most markers investigated was relatively low (e.g., DCD, KRT9, SCGB1D2) and predominantly cytoplasmatic in melanocytes and keratinocytes. COL6A6, GBP4, and KLHL41 expression was significantly enhanced in CM when compared to BN. DCD protein expression was significantly correlated with COL6A6, GBP4, and KLHL41. GBP4 was positively correlated with KLHL41 and inversely correlated with SCGB2B2. The latter was also inversely correlated with serum S100B levels at time of initial diagnosis. The presence of SCGB1D2 expression was significantly associated with ulceration of the primary tumor. KRT9 protein expression was significantly more likely found in acral lentiginous melanoma. The presence of DCD expression was less likely associated with superficial spreading melanoma subtype but significantly associated with non-progressive disease. The absence of SCGB2A2 expression was significantly more often observed in patients who did not progress to stage III or IV.

CONCLUSIONS

The expression levels observed were relatively low but differed in part with those found in BN. Even though we detected some significant correlations between the protein expression levels and clinical parameters (e.g., CM subtype, course of disease), there was no major concordance with the protective or risk-associated functions of the corresponding genes included in a recently introduced prognostic gene expression assay.

Single-Cell Transcriptomic Analysis Reveals a Tumor-Reactive T Cell Signature Associated With Clinical Outcome and Immunotherapy Response In Melanoma

The infiltration of tumor-reactive T cells in the tumor site is associated with better survival and immunotherapy response. However, tumor-reactive T cells were often represented by the infiltration of total CD8+ T cells, which was confounded by the presence of bystander T cells. To identify tumor-reactive T cells at the cancer lesion, we performed integration analyses of three scRNA-seq data sets of T cells in melanoma. Extensive heterogeneous functional states of T cells were revealed in the tumor microenvironment. Among these states, we identified a subset of tumor-reactive T cells which specifically expressed tumor-reactive markers and T cell activation signature, and were strongly enriched for larger T cell receptor (TCR) clones. We further identified and validated a tumor-reactive T cell signature (TRS) to evaluate the tumor reactivity of T cells in tumor patients. Patients with high TRS scores have strong immune activity and high mutation burden in the TCGA-SKCM cohort. We also demonstrated a significant association of the TRS with the clinical outcomes of melanoma patients, with higher TRS scores representing better survival, which was validated in four external independent cohorts. Furthermore, the TRS scores exhibited greater performance on prognosis prediction than infiltration levels of CD8+ T cells and previously published prognosis-related signatures. Finally, we observed the capability of TRS to predict immunotherapy response in melanoma. Together, based on integrated analysis of single-cell RNA-sequencing, we developed and validated a tumor-reactive-related signature that demonstrated significant association with clinical outcomes and response to immunotherapy.

Development and Validation of a Combined Glycolysis and Immune Prognostic Model for Melanoma

Background

Glycolytic effects and immune microenvironments play important roles in the development of melanoma. However, reliable biomarkers for prognostic prediction of melanoma as based on glycolysis and immune status remain to be identified.

Methods

Glycolysis-related genes (GRGs) were obtained from the Molecular Signatures database and immune-related genes (IRGs) were downloaded from the ImmPort dataset. Prognostic GRGs and IRGs in the TCGA (The Cancer Genome Atlas) and GSE65904 datasets were identified. Least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression were used for model construction. Glycolysis expression profiles and the infiltration of immune cells were analyzed and compared. Finally, in vitro experiments were performed to assess the expression and function of these CIGI genes.

Results

Four prognostic glycolysis- and immune-related signatures (SEMA4D, IFITM1, KIF20A and GPR87) were identified for use in constructing a comprehensive glycolysis and immune (CIGI) model. CIGI proved to be a stable, predictive method as determined from different datasets and subgroups of patients and served as an independent prognostic factor for melanoma patients. In addition, patients in the high-CIGI group showed increased levels of glycolytic gene expressions and exhibited immune-suppressive features. Finally, SEMA4D and IFITM1 may function as tumor suppressor genes, while KIF20A and GPR87 may function as oncogenes in melanoma as revealed from results of in vitro experiments.

Conclusion

In this report we present our findings on the development and validation of a novel prognostic classifier for use in patients with melanoma as based on glycolysis and immune expression profiles.

Identification and validation of a novel redox-related lncRNA prognostic signature in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the main causes of cancer deaths globally. Redox is emerging as a crucial contributor to the pathophysiology of LUAD, which can be regulated by long non-coding RNAs (lncRNAs). The aim of our research is to identify a novel redox-related lncRNA prognostic signature (redox-LPS) for better prediction of LUAD prognosis. 535 LUAD samples from The Cancer Genome Atlas (TCGA) database and 226 LUAD samples from the Gene Expression Omnibus (GEO) database were included in our study. 67 redox genes and 313 redox-related lncRNAs were identified. After performing LASSO-Cox regression analysis, a redox-LPS consisting of four lncRNAs (i.e., CRNDE, CASC15, LINC01137, and CYP1B1-AS1) was developed and validated. Our redox-LPS was superior to another three established models in predicting survival probability of LUAD patients. Univariate and multivariate Cox regression analysis revealed that risk score and stage were independent prognostic indicators. A nomogram plot including risk score and stage was constructed to predict survival probability of LUAD patients; this was further verified by calibration curves. Functional enrichment analysis and gene set enrichment analysis, were performed to determine the differences in cellular processes and signaling pathways between the high – and low-risk subgroups. A variety of algorithms (such as single-sample gene set enrichment analysis and CIBERSOFT) were conducted to uncover the landscape of tumor immune microenvironment in the high- and low-risk subgroups. In conclusion, a novel independent redox-LPS was constructed and validated for LUAD patients, which might provide new insights for clinical decision-making and precision medicine.

Development of a Four-mRNA Expression-Based Prognostic Signature for Cutaneous Melanoma

We aim to find a biomarker that can effectively predict the prognosis of patients with cutaneous melanoma (CM). The RNA sequencing data of CM was downloaded from The Cancer Genome Atlas (TCGA) database and randomly divided into training group and test group. Survival statistical analysis and machine-learning approaches were performed on the RNA sequencing data of CM to develop a prognostic signature. Using univariable Cox proportional hazards regression, random survival forest algorithm, and receiver operating characteristic (ROC) in the training group, the four-mRNA signature including CD276, UQCRFS1, HAPLN3, and PIP4P1 was screened out. The four-mRNA signature could divide patients into low-risk and high-risk groups with different survival outcomes (log-rank p < 0.001). The predictive efficacy of the four-mRNA signature was confirmed in the test group, the whole TCGA group, and the independent GSE65904 (log-rank p < 0.05). The independence of the four-mRNA signature in prognostic prediction was demonstrated by multivariate Cox analysis. ROC and timeROC analyses showed that the efficiency of the signature in survival prediction was better than other clinical variables such as melanoma Clark level and tumor stage. This study highlights that the four-mRNA model could be used as a prognostic signature for CM patients with potential clinical application value.