A prognostic model for melanoma patients on the basis of immune-related lncRNAs

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.

Prognostic nomogram for external ear melanoma patients in the elderly: a SEER-based study

AIM

The aim of this study was to construct and validate a nomogram to predict the 1-, 3- and 5-year overall survival (OS) in external ear melanoma (EEM) patients in the elderly based on the Surveillance, Epidemiology, and End Results (SEER) database.

METHODS

The information of patients diagnosed with EEM in the elderly between 2010 and 2014 was downloaded from the SEER database. Univariable and multivariable Cox analyses were carried out to identify the independent characteristics, and the independent factors were further included to construct a nomogram. The discriminative ability and calibration of the nomogram to predict OS were tested using C-index value, and calibration plots. Based on the risk score of the nomogram, the patients were divided into high- and low-risk subgroup. Finally, the survival differences of different subgroups were explored by Kaplan-Meier curves. All statistical analyses were performed by R 4.2.0.

RESULTS

A total of 710 elderly EMM patients were included and randomly divided into training cohort and validation cohort. Univariable Cox regression were used to identify age, race, sex, American Joint Committee on Cancer (AJCC), T, surgery, radiation, chemotherapy, and tumor size as independent risk factors. Then, multivariable Cox model to determine significant risk factors was used to establish the selected factors. A nomogram for predicting the 1-, 3- and 5-year OS was constructed using the independent variables including age, AJCC, T, surgery and chemotherapy. The C-index values were 0.78 (95% CI 0.75-0.81) in training set and 0.72 (95% CI 0.66-0.78) in validation set. The calibration curves were closer to ideal curves indicated the accurate predictive ability of this nomogram. The elderly patients with EEM in the low-risk group showed a longer OS than patients in the high-risk group in both training and validation cohorts.

CONCLUSIONS

Our study established and validated a novel model to predict 1-, 3- and 5-year OS for EEM. The individualized nomogram has a good prognostic ability and can be used as a new survival prediction tool for the elderly patients with EMM.

Long non-coding RNAs and melanoma: From diagnosis to therapy

Although extremely rare, malignant melanoma is the deadliest type of skin malignancy with the inherent capability to invade other organs and metastasize to distant tissues. In 2021, it was estimated that approximately 106,110 patients may have received the diagnosis of melanoma, with a mortality rate of 7180. Surgery remains the common choice for treatment in patients with melanoma. Despite many advances in the treatment of melanoma, some patients, such as those who have received cytotoxic chemotherapeutic and immunotherapic agents, a significant number of patients may show inadequate treatment response following initiating these treatments. Non-coding RNAs, including lncRNAs, have become recently popular and attracted the attention of many researchers to make new insights into the pathogenesis of many diseases, particularly malignancies. LncRNAs have been thoroughly investigated in multiple cancers such as melanoma and have been shown to play a major role in regulating various physiological and pathological cellular processes. Considering their core regulatory function, these non-coding RNAs may be appropriate candidates for melanoma patients' diagnosis, prognosis, and treatment. In this review, we will cover all the current literature available for lncRNAs in melanoma and will discuss their potential benefits as diagnostic and/or prognostic markers or potent therapeutic targets in the treatment of melanoma patients.

The role of lncRNAs in the tumor microenvironment and immunotherapy of melanoma

Melanoma is one of the most lethal tumors with highly aggressive and metastatic properties. Although immunotherapy and targeted therapy have certain therapeutic effects in melanoma, a significant proportion of patients still have drug resistance after treatment. Recent studies have shown that long noncoding RNAs (lncRNAs) are widely recognized as regulatory factors in cancer. They can regulate numerous cellular processes, including cell proliferation, metastasis, epithelial-mesenchymal transition (EMT) progression and the immune microenvironment. The role of lncRNAs in malignant tumors has received much attention, whereas the relationship between lncRNAs and melanoma requires further investigation. Our review summarizes tumor suppressive and oncogenic lncRNAs closely related to the occurrence and development of melanoma. We summarize the role of lncRNAs in the immune microenvironment, immunotherapy and targeted therapy to provide new targets and therapeutic methods for clinical treatment.

Whole Transcriptome Sequencing Reveals Cancer-Related, Prognostically Significant Transcripts and Tumor-Infiltrating Immunocytes in Mantle Cell Lymphoma

Mantle cell lymphoma (MCL) is an aggressive B-cell non-Hodgkin lymphoma (NHL) subtype characterized by overexpression of CCND1 and SOX11 genes. It is generally associated with clinically poor outcomes despite recent improvements in therapeutic approaches. The genes associated with the development and prognosis of MCL are still largely unknown. Through whole transcriptome sequencing (WTS), we identified mRNAs, lncRNAs, and alternative transcripts differentially expressed in MCL cases compared with reactive tonsil B-cell subsets. CCND1, VCAM1, and VWF mRNAs, as well as MIR100HG and ROR1-AS1 lncRNAs, were among the top 10 most significantly overexpressed, oncogenesis-related transcripts. Survival analyses with each of the top upregulated transcripts showed that MCL cases with high expression of VWF mRNA and low expression of FTX lncRNA were associated with poor overall survival. Similarly, high expression of MSTRG.153013.3, an overexpressed alternative transcript, was associated with shortened MCL survival. Known tumor suppressor candidates (e.g., PI3KIP1, UBXN) were significantly downregulated in MCL cases. Top differentially expressed protein-coding genes were enriched in signaling pathways related to invasion and metastasis. Survival analyses based on the abundance of tumor-infiltrating immunocytes estimated with CIBERSORTx showed that high ratios of CD8⁺ T-cells or resting NK cells and low ratios of eosinophils are associated with poor overall survival in diagnostic MCL cases. Integrative analysis of tumor-infiltrating CD8⁺ T-cell abundance and overexpressed oncogene candidates showed that MCL cases with high ratio CD8⁺ T-cells and low expression of FTX or PCA3 can potentially predict high-risk MCL patients. WTS results were cross-validated with qRT-PCR of selected transcripts as well as linear correlation analyses. In conclusion, expression levels of oncogenesis-associated transcripts and/or the ratios of microenvironmental immunocytes in MCL tumors may be used to improve prognostication, thereby leading to better patient management and outcomes.

Ferroptosis-related lncRNA signature predicts prognosis and immunotherapy efficacy in cutaneous melanoma

Purpose

Ferroptosis-related lncRNAs are promising biomarkers for predicting the prognosis of many cancers. However, a ferroptosis-related signature to predict the prognosis of cutaneous melanoma (CM) has not been identified. The purpose of this study was to construct a ferroptosis-related lncRNA signature to predict prognosis and immunotherapy efficacy in CM.

Methods

Ferroptosis-related differentially expressed genes (FDEGs) and lncRNAs (FDELs) were identified using TCGA, GTEx, and FerrDb datasets. We performed Cox and LASSO regressions to identify key FDELs, and constructed a risk score to stratify patients into high- and low-risk groups. The lncRNA signature was evaluated using the areas under the receiver operating characteristic curves (AUCs) and Kaplan-Meier analyses in the training, testing, and entire cohorts. Multivariate Cox regression analyses including the lncRNA signature and common clinicopathological characteristics were performed to identify independent predictors of overall survival (OS). A nomogram was developed for clinical use. We performed gene set enrichment analyses (GSEA) to identify significantly enriched pathways. Differences in the tumor microenvironment (TME) between the 2 groups were assessed using 7 algorithms. To predict the efficacy of immune checkpoint inhibitors (ICI), we analyzed the association between PD1 and CTLA4 expression and the risk score. Finally, differences in Tumor Mutational Burden (TMB) and molecular drugs Sensitivity between the 2 groups were performed.

Results

We identified 5 lncRNAs (AATBC, AC145423.2, LINC01871, AC125807.2, and AC245041.1) to construct the risk score. The AUC of the lncRNA signature was 0.743 in the training cohort and was validated in the testing and entire cohorts. Kaplan-Meier analyses revealed that the high-risk group had poorer prognosis. Multivariate Cox regression showed that the lncRNA signature was an independent predictor of OS with higher accuracy than traditional clinicopathological features. The 1-, 3-, and 5-year survival probabilities for CM patients were 92.7%, 57.2%, and 40.2% with an AUC of 0.804, indicating a good accuracy and reliability of the nomogram. GSEA showed that the high-risk group had lower ferroptosis and immune response. TME analyses confirmed that the high-risk group had lower immune cell infiltration (e.g., CD8+ T cells, CD4+ memory-activated T cells, and M1 macrophages) and lower immune functions (e.g., immune checkpoint activation). Low-risk patients whose disease expressed PD1 or CTLA4 were likely to respond better to ICIs. The analysis demonstrated that the TMB had significantly difference between low- and high- risk groups. Chemotherapy drugs, such as sorafenib, Imatinib, ABT.888 (Veliparib), Docetaxel, and Paclitaxel showed Significant differences in the estimated IC50 between the two risk groups.

Conclusion

Our novel ferroptosis-related lncRNA signature was able to accurately predict the prognosis and ICI outcomes of CM patients. These ferroptosis-related lncRNAs might be potential biomarkers and therapeutic targets for CM.

Value of a Signature of Immune-Related Genes in Predicting the Prognosis of Melanoma and Its Responses to Immune Checkpoint Blocker Therapies

Melanoma is becoming increasingly common worldwide, with high rates of transformation into malignancy compared to other skin lesions. The prognosis of patients with melanoma at an advanced stage is highly unsatisfying despite the development of immunotherapy, target therapy, or combinative therapy. The major barrier to exploiting immune checkpoint therapies and achieving the best benefits clinically is resistance that can easily develop if regimens are not selected appropriately. In this study, we investigated the possibility of using immune-related genes to predict patient survival and their responses to immune checkpoint blocker therapies with the expression profiles available at The Cancer Genome Atlas (TCGA) Program plus expression data from the Gene Expression Omnibus (GEO) for validation. A five gene signature that is highly correlated with the local infiltration of cytotoxic lymphocytes in the tumor microenvironment was identified, and a scoring model was developed with stepwise regression after multivariate Cox analyses. The score calculated strongly correlates with Breslow depth, and this model effectively predicts the prognosis of patients with melanoma, whether primary or metastasized. It also depicts the heterogenous immune-related nature of melanoma by revealing different predicted responses to immune checkpoint blocker therapies through its correlation to tumor immune dysfunction and exclusion (TIDE) score.

Regulation of LncRNAs in Melanoma and Their Functional Roles in the Metastatic Process

Long non-coding RNAs (lncRNAs) are key regulators of numerous intracellular processes leading to tumorigenesis. They are frequently deregulated in cancer, functioning as oncogenes or tumor suppressors. As they act through multiple mechanisms, it is not surprising that they may exert dual functions in the same tumor. In melanoma, a highly invasive and metastatic tumor with the propensity to rapidly develop drug resistance, lncRNAs play different roles in: (i) guiding the phenotype switch and leading to metastasis formation; (ii) predicting the response of melanoma patients to immunotherapy; (iii) triggering adaptive responses to therapy and acquisition of drug resistance phenotypes. In this review we summarize the most recent findings on the lncRNAs involved in melanoma growth and spreading to distant sites, focusing on their role as biomarkers for disease diagnosis and patient prognosis, or targets for novel therapeutic approaches.

Identification of a chromatin regulator signature and potential candidate drugs for bladder cancer

Background

Bladder cancer (BLCA) is a malignant tumor with a dismay outcome. Increasing evidence has confirmed that chromatin regulators (CRs) are involved in cancer progression. Therefore, we aimed to explore the function and prognostic value of CRs in BLCA patients.

Methods

Chromatin regulators (CRs) were acquired from the previous top research. The mRNA expression and clinical information were downloaded from TCGA and GEO datasets. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed to select the prognostic gene and construct the risk model for predicting outcome in BLCA. The Kaplan-Meier analysis was used to assess the prognosis between high- and low-risk groups. We also investigated the drug sensitivity difference between high- and low-risk groups. CMAP dataset was performed to screen the small molecule drugs for treatment.

Results

We successfully constructed and validated an 11 CRs-based model for predicting the prognosis of patients with BLCA. Moreover, we also found 11 CRs-based model was an independent prognostic factor. Functional analysis suggested that CRs were mainly enriched in cancer-related signaling pathways. The CR-based model was also correlated with immune cells infiltration and immune checkpoint. Patients in the high-risk group were more sensitive to several drugs, such as mitomycin C, gemcitabine, cisplatin. Eight small molecule drugs could be beneficial to treatment for BLCA patients.

Conclusion:

In conclusion, our study provided novel insights into the function of CRs in BLCA. We identified a reliable prognostic biomarker for the survival of patients with BLCA.

The Functional Role of Long Non-Coding RNAs in Melanoma

Melanoma is the most lethal skin cancer, with increasing incidence worldwide. The molecular events that drive melanoma development and progression have been extensively studied, resulting in significant improvements in diagnostics and therapeutic approaches. However, a high drug resistance to targeted therapies and adverse effects of immunotherapies are still a major challenge in melanoma treatment. Therefore, the elucidation of molecular mechanisms of melanomagenesis and cancer response to treatment is of great importance. Recently, many studies have revealed the close association of long noncoding RNAs (lncRNAs) with the development of many cancers, including melanoma. These RNA molecules are able to regulate a plethora of crucial cellular processes including proliferation, differentiation, migration, invasion and apoptosis through diverse mechanisms, and even slight dysregulation of their expression may lead to tumorigenesis. lncRNAs are able to bind to protein complexes, DNA and RNAs, affecting their stability, activity, and localization. They can also regulate gene expression in the nucleus. Several functions of lncRNAs are context-dependent. This review summarizes current knowledge regarding the involvement of lncRNAs in melanoma. Their possible role as prognostic markers of melanoma response to treatment and in resistance to therapy is also discussed.

Pathogenic roles of long noncoding RNAs in melanoma: Implications in diagnosis and therapies

Melanoma is one of the most dangerous types of cutaneous neoplasms, which are pigment-producing cells of neuroectodermal origin found all over the body. A great deal of research is focused on the mechanisms of melanoma to promote better diagnostic and treatment options for melanoma in its advanced stages. The progression of melanoma involves alteration in different levels of gene expression. With the successful implementation of next-generation sequencing technology, an increasing number of long noncoding RNAs (lncRNAs) sequences have been discovered, and a significant number of them have phenotypic effects in both in vitro and in vivo studies, implying that they play an important role in the occurrence and progression of human cancers, particularly melanoma. A number of evidence indicated that lncRNAs are important regulators in tumor cell proliferation, invasion, apoptosis, immune escape, energy metabolism, drug resistance, epigenetic regulation. To better understand the role of lncRNAs in melanoma tumorigenesis, we categorize melanoma-associated lncRNAs according to their cellular functions and associations with gene expression and signaling pathways in this review. Based on the mechanisms of lncRNA, we discuss the possibility of lncRNA-target treatments, and the application of liquid biopsies to detect lncRNAs in melanoma diagnosis and prognosis.

Construction of a novel risk model based on the random forest algorithm to distinguish pancreatic cancers with different prognoses and immune microenvironment features

Immune-related long noncoding RNAs (irlncRNAs) are actively involved in regulating the immune status. This study aimed to establish a risk model of irlncRNAs and further investigate the roles of irlncRNAs in predicting prognosis and the immune landscape in pancreatic cancer. The transcriptome profiles and clinical information of 176 pancreatic cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Immune-related genes (irgenes) downloaded from ImmPort were used to screen 1903 immune-related lncRNAs (irlncRNAs) using Pearson’s correlation analysis (R > 0.5; p < 0.001). Random survival forest (RSF) and survival tree analysis showed that 9 irlncRNAs were highly correlated with overall survival (OS) according to the variable importance (VIMP) and minimal depth. Next, Cox regression analysis was used to establish a risk model with 3 irlncRNAs (LINC00462, LINC01887, RP11-706C16.8) that was evaluated by Kaplan-Meier analysis, the areas under the curve (AUCs) of the receiver operating characteristics and the C-index. Additionally, we performed Cox regression analysis to establish the clinical prognostic model, which showed that the risk score was an independent prognostic factor (p < 0.001). A nomogram and calibration plots were drawn to visualize the clinical features. The Wilcoxon signed-rank test and Pearson’s correlation analysis further explored the irlncRNA signatures and immune cell infiltration, as well as the immunotherapy response.

Tumor microenvironment immune-related lncRNA signature for patients with melanoma

Background

The incidence of malignant melanoma accounts for only approximately 5% of skin malignant tumors, however, it accounts for 75% of its mortality. Long-chain non-coding RNA (lncRNA) has a wide range of functional activities. Disorders of lncRNAs may lead to the occurrence and development of melanoma, and may also be related to immunotherapy.

Methods

The transcriptomic data of primary and metastatic melanoma patients and 331 immune-related genes were downloaded from skin cutaneous melanoma (SKCM) in the The Cancer Genome Atlas (TCGA) database. On this basis, 460 immunologically relevant lncRNAs were identified by constructing a co-expression network of immunogenic genes and lncRNAs in primary and metastatic melanoma patients. Prognostic genes were screened using univariate Cox regression analysis. ROC analysis was performed to evaluate the robustness of the prognostic signature.

Results

Univariate correlation analysis showed that only 3 of the 23 immune-related lncRNAs were at high risk and the rest were at low risk. Signatures of 7 immune-related lncRNAs were identified by multivariate correlation analysis. The clinical correlation analysis showed that the 7 immune-related lncRNAs were associated with the clinical stage of primary and metastatic melanoma. Principal component analysis (PCA) showed that only 7 immune-related lncRNA signals divided tumor patients into high-risk and low-risk groups, while the low-risk group was enriched in the immune system process M13664 and immune response M19817 sets. PPI interaction network analysis showed that 11 G protein-coupled receptors and 6 corresponding ligands in the 2 gene sets affected the tumor microenvironment and were negatively related to the risk of the 7 immune-related lncRNAs. The tumor microenvironment immune cell infiltration analysis also supported the finding that anti-tumor immunity in the low-risk group was stronger than in the high-risk group.

Conclusions

These results indicate that characteristics of the 7 immune-related lncRNAs have prognostic value for melanoma patients and can be used as potential immunotherapy targets.