Identification and validation of immune-related lncRNA prognostic signatures for melanoma

Immune-Related Long Non-Coding RNA Signature Determines Prognosis and Immunotherapeutic Coherence in Esophageal Cancer

Objectives

Aim of this study was to explore the immune-related lncRNAs having prognostic role and establishing risk score model for better prognosis and immunotherapeutic coherence for esophageal cancer (EC) patients.

Methods

To determine the role of immune-related lncRNAs in EC, we analyzed the RNA-seq expression data of 162 EC patients and 11 non-cancerous individuals and their clinically relevant information from the cancer genome atlas (TCGA) database. Bioinformatic and statistical analysis such as Differential expression analysis, co-expression analysis, Kaplan Meier survival analysis, Cox proportional hazards model, ROC analysis of risk model was employed.

Results

Utilizing a cutoff criterion (log2FC > 1 + log2FC < -1 and FDR < 0.01), we identified 3737 RNAs were significantly differentially expressed in EC patients. Among these, 2222 genes were classified as significantly differentially expressed mRNAs (demRNAs), and 966 were significantly differentially expressed lncRNAs (delncRNA). Through Pearson correlation analysis between differentially expressed lncRNAs and immune related-mRNAs, we identified 12 immune-related lncRNAs as prognostic signatures for EC. Notably, through Kaplan-Meier analysis on these lncRNAs, we found the low-risk group patients showed significantly improved survival compared to the high-risk group. Moreover, this prognostic signature has consistent performance across training, testing and entire validation cohort sets. Using ESTIMATE and CIBERSORT algorithm we further observed significant enriched infiltration of naive B cells, regulatory T cells resting CD4+ memory T cells, and, plasma cells in the low-risk group compared to high-risk EC patients group. On the contrary, tumor-associated M2 macrophages were highly enriched in high-risk patients. Additionally, we confirmed immune-related biological functions and pathways such as inflammatory, cytokines, chemokines response and natural killer cell-mediated cytotoxicity, toll-like receptor signaling pathways, JAK-STAT signaling pathways, chemokine signaling pathways significantly associated with identified IRlncRNA signature and their co-expressed immune genes. Furthermore, we assessed the predictive potential of the lncRNA signature in immune checkpoint inhibitors; we found that programed cell death ligand 1 (PD-L1; P-value = .048), programed cell death ligand 2 (PD-L2; P-value = .002), and T cell immunoglobulin and mucin-domain containing-3 (TIM-3; P-value = .045) expression levels were significantly higher in low-risk patients compared to high-risk patients.

Conclusion

We believe this study will contribute to better prognosis prediction and targeted treatment of EC in the future.

Long non-coding RNA as a potential diagnostic and prognostic biomarker in melanoma: A systematic review and meta-analysis

Recently, long noncoding RNAs (lncRNAs) have been applied as biomarkers for melanoma patients. In this systematic review and meta-analysis, we investigated the diagnostic and prognostic value of lncRNAs. We used the keywords 'lncRNA' and 'melanoma' to search databases for studies published before June 14th, 2023. The specificity, sensitivity and AUC were utilized to assess diagnostic accuracy and the prognostic value was assessed using overall survival, progression-free survival and disease-free survival hazard ratios. After screening 1191 articles, we included seven studies in the diagnostic evaluation section and 17 studies in the prognosis evaluation section. The Reitsma bivariate model estimated a cumulative sensitivity of 0.724 (95% CI: 0.659-0.781, p < 0.001) and specificity of 0.812 (95% CI: 0.752-0.859, p < 0.001). The pooled AUC was 0.780 (95% CI: 0.749-0.811, p < 0.0001). The HR for overall survival was 2.723 (95% CI: 2.259-3.283, p < 0.0001). Two studies reported an HR for overall survival less than one, with an HR of 0.348 (95% CI: 0.200-0.607, p < 0.0002). The HR for progression-free survival was 2.913 (95% CI: 2.050-4.138, p < 0.0001). Four studies reported an HR less than one, with an HR of 0.457 (95% CI: 0.256-0.817). The HR for disease-free survival was 2.760 (95% CI: 2.009-3.792, p < 0.0001). In conclusion, the expression of lncRNAs in melanoma patients affects survival and prognosis. LncRNAs can also be employed as diagnostic biomarkers.

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.

Regulatory miRNAs and lncRNAs in Skin Cancer: A Narrative Review

Non-coding RNAs (ncRNAs) have a significant regulatory role in the pathogenesis of skin cancer, despite the fact that protein-coding genes have generally been the focus of research efforts in the field. We comment on the actions of long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) in the current review with an eye toward potential therapeutic treatments. LncRNAs are remarkably adaptable, acting as scaffolding, guides, or decoys to modify key signaling pathways (i.e., the Wnt/β-catenin pathway) and gene expression. As post-transcriptional gatekeepers, miRNAs control gene expression by attaching to messenger RNAs and causing their degradation or suppression during translation. Cell cycle regulation, cellular differentiation, and immunological responses are all affected by the dysregulation of miRNAs observed in skin cancer. NcRNAs also show promise as diagnostic biomarkers and prognostic indicators. Unraveling the complexity of the regulatory networks governed by ncRNAs in skin cancer offers unprecedented opportunities for groundbreaking targeted therapies, revolutionizing the landscape of dermatologic care.

A model based on immune-related lncRNA pairs and its potential prognostic value in immunotherapy for melanoma

A model based on long non-coding RNA (lncRNA) pairs independent of expression quantification was constructed to evaluate prognosis melanoma and response to immunotherapy in melanoma. RNA sequencing data and clinical information were retrieved and downloaded from The Cancer Genome Atlas and the Genotype-Tissue Expression databases. We identified differentially expressed immune-related lncRNAs (DEirlncRNAs), matched them, and used least absolute shrinkage and selection operator and Cox regression to construct predictive models. The optimal cutoff value of the model was determined using a receiver operating characteristic curve and used to categorize melanoma cases into high-risk and low-risk groups. The predictive efficacy of the model with respect to prognosis was compared with that of clinical data and ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data). Then, we analyzed the correlations of risk score with clinical characteristics, immune cell invasion, anti-tumor, and tumor-promoting activities. Differences in survival, degree of immune cell infiltration, and intensity of anti-tumor and tumor-promoting activities were also evaluated in the high- and low-risk groups. A model based on 21 DEirlncRNA pairs was established. Compared with ESTIMATE score and clinical data, this model could better predict outcomes of melanoma patients. Follow-up analysis of the model's effectiveness showed that patients in the high-risk group had poorer prognosis and were less likely to benefit from immunotherapy compared with those in the low-risk group. Moreover, there were differences in tumor-infiltrating immune cells between the high-risk and low-risk groups. By pairing the DEirlncRNA, we constructed a model to evaluate the prognosis of cutaneous melanoma independent of a specific level of lncRNA expression.

A novel endoplasmic reticulum stress-related lncRNA prognostic risk model for cutaneous melanoma

OBJECTIVE

Endoplasmic reticulum stress (ERS) and long non-coding RNAs (lncRNAs) are important in melanoma development and progression. This study aimed to explore the prognostic value of ERS-associated lncRNA profiles in cutaneous melanoma (CM).

METHODS

The Cancer Genome Atlas (TCGA) provides the raw data of CM. GSEA website was used to obtain ERS-related genes, and mRNA and LncRNA co-expression network were used to obtain ERS-related lncRNAs. A Lasso regression analysis was used to identify a prognostic risk model for the composition of ERS-related lncRNAs. Patients were divided into high- and low-risk groups based on the model's risk score. The researchers then compared the two groups' survival rates, immune infiltration, chemotherapeutic drug sensitivity, and immune checkpoint gene expression.

RESULTS

Thirty-nine ERS-related lncRNAs were discovered to be prognostic. A prognostic risk model made up of ten ERS-related lncRNAs was discovered. Patients in the low-risk group had a better prognosis than those in the high-risk group. An examination of tumor microenvironment revealed that risk scores correlated with immune cell infiltration in eight cases. Dacarbazine, paclitaxel, and cisplatin, three chemotherapy drugs, were more sensitive in the low-risk group than in the high-risk group.

CONCLUSION

This study identified a risk model of ten ERS-related lncRNAs that have significant prognostic value in CM and could help guide clinical treatment.

Construction of a tumor immune infiltration macrophage signature for predicting prognosis and immunotherapy response in liver cancer

Liver cancer is an extraordinarily heterogeneous malignant disease. The tumor microenvironment (TME) and tumor-associated macrophages (TAMs) are the major drivers of liver cancer initiation and progression. It is critical to have a better understanding of the complicated interactions between liver cancer and the immune system for the development of cancer immunotherapy. Based on the gene expression profiles of tumor immune infiltration cells (TIICs), upregulated genes in TAMs and downregulated genes in other types of immune cells were identified as macrophage-specific genes (MSG). In this study, we combined MSG, immune subtypes, and clinical information on liver cancer to develop a tumor immune infiltration macrophage signature (TIMSig). A four-gene signature (S100A9, SLC22A15, TRIM54, and PPARGC1A) was identified as the TAM-related prognostic genes for liver cancer, independent of multiple clinicopathological parameters. Survival analyses showed that patients with low TIMSig had a superior survival rate than those with high TIMSig. Additionally, clinical immunotherapy response and TIMSig was observed as highly relevant. In addition, TIMSig could predict the response to chemotherapy. Collectively, the TIMSig could be a potential tool for risk-stratification, clinical decision making, treatment planning, and oncology immunotherapeutic drug development.

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.

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 and Construction of a Predictive Immune-Related lncRNA Signature Model for Melanoma

Objective

The occurrence and development mechanisms of melanoma are related to immunity and lncRNAs. Therefore, it is necessary to systematically explore immune-related lncRNA profiles to help improve the prognosis of melanoma.

Methods

We integrated immune-related lncRNAs and the basic clinical information of melanoma patients in the TCGA dataset. Immune-associated lncRNAs were selected by differential expression screening and enriched for analysis. After univariate and multivariate Cox regression analyses, a new prognostic indicator based on immune-associated lncRNAs was established.

Results

Overall, differentially expressed immune-related lncRNAs were significantly associated with clinical outcomes in patients with melanoma. A prognostic model was then established based on 14 immune-associated lncRNAs (LRRC8C-DT, AC021188.1, MALINC1, CCR5AS, EIF2AK3-DT, AC022306.2, AC242842.1, AL034376.1, AL662844.4, AC009065.3, AC099811.3, AC125807.2, SPINT1-AS1 and AC009495.2). Melanoma patients in the high-risk group had worse overall survival than those in the low-risk group. The AUC of the risk score was 0.786.

Conclusion

This study identified several clinically significant immune-related lncRNAs and established a relevant prognostic model, which provided a molecular analysis of immunity in melanoma and potential prognostic lncRNAs for melanoma.

LncRNA LINC00184 promotes docetaxel resistance and immune escape via miR-105-5p/PD-L1 axis in prostate cancer

BACKGROUND

Docetaxel (DTX) resistance is a common factor in metastatic prostate cancer (PC) chemotherapy that leads to treatment failure. Because lncRNA is involved in a variety of regulatory processes in tumor progression, this study aimed to explore the function and mechanism of LINC00184 in docetaxel resistance of PC.

METHODS

Two PC cell lines and their docetaxel resistant cell lines (DU145/DTX and PC3/DTX) were used. The expression of LINC00184 in both cell lines and PC patient samples were evaluated. SiRNA knocking down was used to test the function of LINC00184 in proliferation and colony formation. Interaction between LINC00184 and its target miR-105-5p, as well as miR-105-5p and PD-L1 was checked by luciferase reporter assay and RNA pull-down assay. PC cell line and CD8 + T cell co-culture system was established, miR-105-5p inhibitor was co-transfected with LINC00184 siRNA to investigate the underline mechanism.

RESULTS

LINC00184 was found to be associated with docetaxel resistance and adverse prognosis of prostate cancer. It regulated docetaxel resistance and T-cell-mediated immune response in prostate cancer cells. LINC00184 was induced by adsorption of miR-105-5p and negatively regulated it, subsequently inhibited the expression level of PD-L1.

CONCLUSIONS

LINC00184 promoted docetaxel resistance and immune escape in prostate cancer cells by adsorption of miR-105-5p, resulted in upregulation of the expression of PD-L1. LINC00184 could possibly be considered as a potential target for treatment in prostate cancer patients with docetaxel-resistance.

Identification and validation of immune-related lncRNA prognostic signatures for melanoma

Introduction

Melanoma is a highly aggressive malignant skin tumor as well as the primary reason for skin cancer‐specific deaths. We first identified immune‐related long noncoding RNA (lncRNA) prognostic signature and found potential immunotherapeutic targets for melanoma cancer.

Methods

RNA‐seq data and clinical features of melanoma samples were obtained from The Cancer Genome Atlas. Samples of melanoma were randomly assigned to the training and testing cohort. The immune‐related lncRNA signature was then obtained via using univariate, LASSO, and multivariate Cox analysis of patients in the training cohort. Eight significant immune‐related lncRNA signature was then subsequently obtained through correlation analysis between immune‐related genes and lncRNAs. The association between risk score and immune cell infiltration was finally assessed using TIMER and CIBERSORT.

Results

Three hundred and fifty‐six immune‐related lncRNAs were obtained. Among them, eight immune‐related lncRNAs were identified to build a prognostic risk signature model. The model's performance was then confirmed using the Kaplan–Meier curves, risk plots, and time‐dependent receiver‐operating characteristic curves in the training cohort. The risk score was identified and confirmed as an independent prognostic factor through univariate and multivariate Cox regression analyses. These results were further verified in the testing and whole cohorts. CIBERSORT algorithm showed that the infiltration levels of T cells CD8, M1 macrophages, plasma cells, T cells CD4 memory activated, T cells gamma delta, and mast cells activated were significantly lower in the high‐risk group while the infiltration level of macrophages M0 was significantly lower in the low‐risk group.

Conclusion

The immune‐related lncRNA signature offers prognostic markers and potential immunotherapeutic targets for melanoma.