A novel pyroptosis-related LncRNA signature predicts prognosis and indicates tumor immune microenvironment in skin cutaneous melanoma

Lnc NEAT1 facilitates the progression of melanoma by targeting the miR-152-3p/CDK6 axis: An observational study

Long noncoding (Lnc) RNAs are novel regulators in melanoma. Lnc nuclear enriched autosomal transcript 1 (NEAT1) was reportedly upregulated in melanoma; however, the functional roles and mechanisms of Lnc NEAT1 need further investigation. Therefore, we used quantitative real-time PCR to determine the mRNA levels of Lnc NEAT1, miR-152-3p, and cyclin-dependent protein kinase 6 (CDK6). The protein level of CDK6 was determined by Western blot. Cell counting kit 8 and colony formation assays were used to assess cell proliferation. Cell migration was measured by wound healing and Transwell assays. Direct binding of the indicated molecules was verified by an RNA-binding protein immunoprecipitation assay and a dual luciferase reporter assay. The results revealed that Lnc NEAT1 and CDK6 were elevated, while miR-152-3p was downregulated in melanoma. Furthermore, Lnc NEAT1 was positively correlated with CDK6 expression and negatively correlated with miR-152-3p level. Furthermore, Lnc NEAT1 facilitated proliferation, migration, and invasion of melanoma cells. The underlying mechanism is that Lnc NEAT1 serves as a sponge for miR-152-3p to suppress the inhibitory effect of miR-152-3p on CDK6. Furthermore, the miR-152-3p/ CDK6 axis was implicated in the progression of melanoma accelerated by Lnc NEAT1. Taken together, Lnc NEAT1 may promote melanoma development by serving as an endogenous sponge of miR-152-3p, increasing CDK6 expression, and identifying a new target for the treatment of melanoma.

Exploring a specialized programmed-cell death patterns to predict the prognosis and sensitivity of immunotherapy in cutaneous melanoma via machine learning

The mortality and therapeutic failure in cutaneous melanoma (CM) are mainly caused by wide metastasis and chemotherapy resistance. Meanwhile, immunotherapy is considered a crucial therapy strategy for CM patients. However, the efficiency of currently available methods and biomarkers in predicting the response of immunotherapy and prognosis of CM is limited. Programmed cell death (PCD) plays a significant role in the occurrence, development, and therapy of various malignant tumors. In this research, we integrated fourteen types of PCD, multi-omics data from TCGA-SKCM and other cohorts in GEO, and clinical CM patients to develop our analysis. Based on significant PCD patterns, two PCD-related CM clusters with different prognosis, tumor microenvironment (TME), and response to immunotherapy were identified. Subsequently, seven PCD-related features, especially CD28, CYP1B1, JAK3, LAMP3, SFN, STAT4, and TRAF1, were utilized to establish the prognostic signature, namely cell death index (CDI). CDI accurately predicted the response to immunotherapy in both CM and other cancers. A nomogram with potential superior predictive ability was constructed, and potential drugs targeting CM patients with specific CDI have also been identified. Given all the above, a novel CDI gene signature was indicated to predict the prognosis and exploit precision therapeutic strategies of CM patients, providing unique opportunities for clinical intelligence and new management methods for the therapy of CM.

Characteristics and significance of programmed cell death-related gene expression signature in skin cutaneous melanoma

BACKGROUND

Programmed cell death (PCD) pathways play crucial roles in the pathogenesis of skin cutaneous melanoma (SKCM). Understanding their prognostic significance and clinical implications is imperative for the development of personalized treatment strategies.

METHODS

A total of 1466 PCD-related genes were analyzed using data from The Cancer Genome Atlas (TCGA)-SKCM cohort (n = 353). Prognostic cell death index (CDI) was established and validated through survival analysis and predictive modeling. Functional enrichment, protein-protein interaction (PPI), consensus clustering, and tumor microenvironment assessment and drug sensitivity analysis were performed to elucidate the biological and clinical relevance of CDI.

RESULTS

CDI effectively stratified SKCM patients into high and low-risk groups, demonstrating significant differences in survival outcomes. It exhibited predictive value for survival at 1, 3, and 5 years. The concordance index (C-index) was 0.794 in the training set, and 0.792 and 0.821 in the internal and external validation sets, respectively. The corresponding area under curve (AUC) was all above 0.75 in these data sets. Functional enrichment analysis revealed significant associations with immune response and inflammatory processes. PPI analysis identified key molecular modules associated with apoptosis and chemokine signaling. Consensus clustering unveiled three discernible subtypes demonstrating notable disparities in survival outcomes based on CDI expression profiles. Assessment of the tumor microenvironment highlighted correlations with immune cell infiltration such as M1 macrophages and T cells. Drug sensitivity analysis indicated tight correlations between CDI levels and response to immunotherapy.

CONCLUSION

Our comprehensive analysis establishes the prognostic significance of PCD-related genes in SKCM. CDI emerges as a promising prognostic biomarker, offering insights into tumor biology and potential implications for personalized treatment strategies. Further validation and clinical integration of CDI are warranted to improve SKCM management and patient outcomes.

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.

An ensemble-based deep learning model for detection of mutation causing cutaneous melanoma

When the mutation affects the melanocytes of the body, a condition called melanoma results which is one of the deadliest skin cancers. Early detection of cutaneous melanoma is vital for raising the chances of survival. Melanoma can be due to inherited defective genes or due to environmental factors such as excessive sun exposure. The accuracy of the state-of-the-art computer-aided diagnosis systems is unsatisfactory. Moreover, the major drawback of medical imaging is the shortage of labeled data. Generalized classifiers are required to diagnose melanoma to avoid overfitting the dataset. To address these issues, blending ensemble-based deep learning (BEDLM-CMS) model is proposed to detect mutation of cutaneous melanoma by integrating long short-term memory (LSTM), Bi-directional LSTM (BLSTM) and gated recurrent unit (GRU) architectures. The dataset used in the proposed study contains 2608 human samples and 6778 mutations in total along with 75 types of genes. The most prominent genes that function as biomarkers for early diagnosis and prognosis are utilized. Multiple extraction techniques are used in this study to extract the most-prominent features. Afterwards, we applied different DL models optimized through grid search technique to diagnose melanoma. The validity of the results is confirmed using several techniques, including tenfold cross validation (10-FCVT), independent set (IST), and self-consistency (SCT). For validation of the results multiple metrics are used which include accuracy, specificity, sensitivity, and Matthews's correlation coefficient. BEDLM gives the highest accuracy of 97% in the independent set test whereas in self-consistency test and tenfold cross validation test it gives 94% and 93% accuracy, respectively. Accuracy of in self-consistency test, independent set test, and tenfold cross validation test is LSTM (96%, 94%, 92%), GRU (93%, 94%, 91%), and BLSTM (99%, 98%, 93%), respectively. The findings demonstrate that the proposed BEDLM-CMS can be used effectively applied for early diagnosis and treatment efficacy evaluation of cutaneous melanoma.

Machine learning-derived identification of tumor-infiltrating immune cell-related signature for improving prognosis and immunotherapy responses in patients with skin cutaneous melanoma

BACKGROUND

Immunoblockade therapy based on the PD-1 checkpoint has greatly improved the survival rate of patients with skin cutaneous melanoma (SKCM). However, existing anti-PD-1 therapeutic efficacy prediction markers often exhibit a poor situation of poor reliability in identifying potential beneficiary patients in clinical applications, and an ideal biomarker for precision medicine is urgently needed.

METHODS

10 multicenter cohorts including 4 SKCM cohorts and 6 immunotherapy cohorts were selected. Through the analysis of WGCNA, survival analysis, consensus clustering, we screened 36 prognostic genes. Then, ten machine learning algorithms were used to construct a machine learning-derived immune signature (MLDIS). Finally, the independent data sets (GSE22153, GSE54467, GSE59455, and in-house cohort) were used as the verification set, and the ROC index standard was used to evaluate the model.

RESULTS

Based on computing framework, we found that patients with high MLDIS had poor overall survival and has good prediction performance in all cohorts and in-house cohort. It is worth noting that MLDIS performs better in each data set than almost all models which from 51 prognostic signatures for SKCM. Meanwhile, high MLDIS have a positive prognostic impact on patients treated with anti-PD-1 immunotherapy by driving changes in the level of infiltration of immune cells in the tumor microenvironment. Additionally, patients suffering from SKCM with high MLDIS were more sensitive to immunotherapy.

CONCLUSIONS

Our study identified that MLDIS could provide new insights into the prognosis of SKCM and predict the immunotherapy response in patients with SKCM.

Pyroptosis-related lncRNA prognostic signatures for cutaneous melanoma and tumor microenvironment status

Aims: To explore whether the expression of pyroptosis-related lncRNAs makes a difference in the prognosis and antitumor immunity of cutaneous melanoma (CM) patients. Methods: A series of analyses were conducted to establish a prognostic risk model and validate its accuracy. Immune-related analyses were performed to further assess the associations among immune status, tumor microenvironment and the prognostic risk model. Results: Eight pyroptosis-related lncRNAs relevant to prognosis were ascertained and applied to establish the prognostic risk model. The low-risk group had a higher overall survival rate. Conclusion: The established prognostic risk model presents better prediction ability for the prognosis of CM patients and provides new possible therapeutic targets for CM.

Machine learning-based signature of necrosis-associated lncRNAs for prognostic and immunotherapy response prediction in cutaneous melanoma and tumor immune landscape characterization

Background

Cutaneous melanoma (CM) is one of the malignant tumors with a relative high lethality. Necroptosis is a novel programmed cell death that participates in anti-tumor immunity and tumor prognosis. Necroptosis has been found to play an important role in tumors like CM. However, the necroptosis-associated lncRNAs' potential prognostic value in CM has not been identified.

Methods

The RNA sequencing data collected from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression Project (GTEx) was utilized to identify differentially expressed genes in CM. By using the univariate Cox regression analysis and machine learning LASSO algorithm, a prognostic risk model had been built depending on 5 necroptosis-associated lncRNAs and was verified by internal validation. The performance of this prognostic model was assessed by the receiver operating characteristic curves. A nomogram was constructed and verified by calibration. Furthermore, we also performed sub-group K-M analysis to explore the 5 lncRNAs' expression in different clinical stages. Function enrichment had been analyzed by GSEA and ssGSEA. In addition, qRT-PCR was performed to verify the five lncRNAs' expression level in CM cell line (A2058 and A375) and normal keratinocyte cell line (HaCaT).

Results

We constructed a prognostic model based on five necroptosis-associated lncRNAs (AC245041.1, LINC00665, AC018553.1, LINC01871, and AC107464.3) and divided patients into high-risk group and low-risk group depending on risk scores. A predictive nomogram had been built to be a prognostic indicator to clinical factors. Functional enrichment analysis showed that immune functions had more relationship and immune checkpoints were more activated in low-risk group than that in high-risk group. Thus, the low-risk group would have a more sensitive response to immunotherapy.

Conclusion

This risk score signature could be used to divide CM patients into low- and high-risk groups, and facilitate treatment strategy decision making that immunotherapy is more suitable for those in low-risk group, providing a new sight for CM prognostic evaluation.

The Therapeutic Potential of Pyroptosis in Melanoma

Pyroptosis is a programmed cell death characterized by the rupture of the plasma membranes and release of cellular content leading to inflammatory reaction. Four cellular mechanisms inducing pyroptosis have been reported thus far, including the (i) caspase 1-mediated canonical, (ii) caspase 4/5/11-mediated non-canonical, (iii) caspase 3/8-mediated and (iv) caspase-independent pathways. Although discovered as a defense mechanism protecting cells from infections of intracellular pathogens, pyroptosis plays roles in tumor initiation, progression and metastasis of tumors, as well as in treatment response to antitumor drugs and, consequently, patient outcome. Pyroptosis induction following antitumor therapies has been reported in several tumor types, including lung, colorectal and gastric cancer, hepatocellular carcinoma and melanoma. This review provides an overview of the cellular pathways of pyroptosis and discusses the therapeutic potential of pyroptosis induction in cancer, particularly in melanoma.

Construction of five cuproptosis-related lncRNA signature for predicting prognosis and immune activity in skin cutaneous melanoma

Cuproptosis is a newly discovered new mechanism of programmed cell death, and its unique pathway to regulate cell death is thought to have a unique role in understanding cancer progression and guiding cancer therapy. However, this regulation has not been studied in SKCM at present. In this study, data on Skin Cutaneous Melanoma (SKCM) patients were downloaded from the TCGA database. We screened the genes related to cuproptosis from the published papers and confirmed the lncRNAs related to them. We applied Univariate/multivariate and LASSO Cox regression algorithms, and finally identified 5 cuproptosis-related lncRNAs for constructing prognosis prediction models (VIM-AS1, AC012443.2, MALINC1, AL354696.2, HSD11B1-AS1). The reliability and validity test of the model indicated that the model could well distinguish the prognosis and survival of SKCM patients. Next, immune microenvironment, immunotherapy analysis, and functional enrichment analysis were also performed. In conclusion, this study is the first analysis based on cuproptosis-related lncRNAs in SKCM and aims to open up new directions for SKCM therapy.