Comprehensive prediction of immune microenvironment and hot and cold tumor differentiation in cutaneous melanoma based on necroptosis-related lncRNA

Survival expectations in melanoma patients: a molecular prognostic model associated with aging

BACKGROUND

Aging and long non-coding RNAs (lncRNAs) are research hotspots in melanoma. However, no study has so far explored the relationship between melanoma prognosis and aging-related lncRNAs (ARLs).

METHODS

The Cancer Genome Atlas database, the GTEx database, and the HAGR database were used in this study in a combined manner. Univariate and multivariate cox regression analyses were used to screen out lncRNA signatures associated with overall survival (OS) in the primary dataset. The risk scoring model was analyzed by risk stratification and tested internally. The protein expression levels of possible target genes of ARLs were verified by immunohistochemistry analysis in HPA database. Finally, gene enrichment analysis was performed.

RESULTS

In the primary dataset, five OS-related lncRNA signatures (AC011481.1, USP30-AS1, EBLN3P, LINC01527, HLA-DQB1-AS1) were screened out. The survival curve showed that the high-risk group had a worse prognosis than the low-risk group. The immunohistochemical analysis revealed that reduced expression of Epidermal Growth Factor Receptor (EGFR), along with increased expression of Activating Transcription Factor 2 (ATF2) and DNA Polymerase Delta 1 (POLD1), was linked to a worse prognosis. Finally, enrichment analysis revealed that OS-related DELs were significantly enriched in the regulation of reactive oxygen metabolism, etc. The ARGs were significantly activated in the SKCM tissues. The regulation of aging in melanoma cells may be realized through ferroptosis, immunity, and autophagy and so on.

CONCLUSION

The ARL signature obtained in this study had better prognostic ability than individual clinical features.

Defining the high-risk category of patients with cutaneous melanoma: a practical tool based on prognostic modeling

Introduction

Although most cutaneous melanoma (CM) in its early stages is treatable, the risk of recurrence remains high and there is a particular ambiguity on patients prognosis. This drives to identification of prognostic biomarkers for predicting CM recurrence to guide appropriate treatment in patients with localized melanoma.

Aim

This study aimed to develop a prognostic model for assessing the risk of recurrence in patients with CM, enabling prompt prognosis-driven further clinical decision-making for high-risk patients.

Materials and methods

This case-control study included 172 patients with CM recurrence (high-risk group) and 30 patients with stable remission (low-risk group) 3 years after primary diagnosis. The impact of sex, age at diagnosis, anatomical site, histological characteristics (the histological type, pathological stage, ulceration; the depth of invasion, mitotic rate, lymphovascular invasion, neurotropism, association with a nevus, tumor-infiltrating lymphocyte density, tumor regression and BRAF codon 600 mutation status) on CM recurrence was evaluated.

Results

Five independent variables, including nodal status, a high mitotic rate, Breslow thickness, lymphovascular invasion, perineural invasion and regression features were identified as the most significant. A 5-factor logistic regression model was developed to assess the risk of melanoma recurrence. The sensitivity and specificity of the model were 86.1% and 72.7%, respectively.

Conclusion

The developed model, which relies on routine histological features, allows the identification of individuals at high risk of CM recurrence to tailor their further management.

The Role of Non-coding RNAs in Tumorigenesis, Diagnosis/Prognosis, and Therapeutic Strategies for Cutaneous Melanoma

RNA is a substance with various biological functions. It serves as blueprint for proteins and shuttles information from the genes to the protein factories of the cells. However, these factories-the ribosomes-are also composed mainly of RNA, whose purpose is not storing information but enzymatic action. In addition, there is a cornucopia of RNA molecules within our cells that form a complex regulatory network, connected with all aspects of cellular development and maintenance. These non-coding RNAs can be used for diagnostics and therapeutic strategies in cancer. In this chapter we give an overview of recent developments in non-coding RNA-based diagnostics and therapies for cutaneous melanoma. It is not meant to be comprehensive; however, it describes examples based on some of the most recent publications in this field.

Construction of a tumor mutational burden-derived LncRNA prognostic computational framework associated with therapy sensitivity in skin cutaneous melanoma

BACKGROUND

Skin cutaneous melanoma (SKCM) poses a significant public health challenge due to its aggressive nature and limited treatment options. To address this, the study introduces the Tumor Mutational Burden-Derived Immune lncRNA Prognostic Index (TILPI) as a potential prognostic tool for SKCM.

METHODS

TILPI was developed using a combination of gene set variation analysis, differential expression analysis, and COX regression analysis. Additionally, functional experiments were conducted to validate the findings, focusing on the effects of STARD4-AS1 knockdown on SKCM tumor cell behavior. These experiments encompassed assessments of tumor cell proliferation, gene and protein expression, migration, invasion, and in vivo tumor growth.

RESULTS

The results demonstrated that knockdown of STARD4-AS1 led to a significant reduction in tumor cell proliferation and impaired migration and invasion abilities. Moreover, it resulted in the downregulation of ADCY4, PRKACA, and SOX10 gene expression, as well as decreased protein expression of ADCY4, PRKACA, and SOX10. In vivo experiments further confirmed the efficacy of STARD4-AS1 knockdown in reducing tumor growth.

CONCLUSIONS

This study elucidates the mechanistic role of STARD4-AS1 and its downstream targets in SKCM progression, highlighting the importance of the ADCY4/PRKACA/SOX10 pathway. The integration of computational analysis with experimental validation enhances the understanding of TILPI and its clinical implications. Overall, the findings underscore the potential of novel computational frameworks like TILPI in predicting and managing SKCM, particularly through targeting the ADCY4/PRKACA/SOX10 pathway.

Anoikis-Related Long Non-Coding RNA Signatures to Predict Prognosis and Immune Infiltration of Gastric Cancer

Anoikis is a distinct type of programmed cell death and a unique mechanism for tumor progress. However, its exact function in gastric cancer (GC) remains unknown. This study aims to investigate the function of anoikis-related lncRNA (ar-lncRNA) in the prognosis of GC and its immunological infiltration. The ar-lncRNAs were derived from RNA sequencing data and associated clinical information obtained from The Cancer Genome Atlas. Pearson correlation analysis, differential screening, LASSO and Cox regression were utilized to identify the typical ar-lncRNAs with prognostic significance, and the corresponding risk model was constructed, respectively. Comprehensive methods were employed to assess the clinical characteristics of the prediction model, ensuring the accuracy of the prediction results. Further analysis was conducted on the relationship between immune microenvironment and risk features, and sensitivity predictions were made about anticancer medicines. A risk model was built according to seven selected ar-lncRNAs. The model was validated and the calibration plots were highly consistent in validating nomogram predictions. Further analyses revealed the great accuracy of the model and its ability to serve as a stand-alone GC prognostic factor. We subsequently disclosed that high-risk groups display significant enrichment in pathways related to tumors and the immune system. Additionally, in tumor immunoassays, notable variations in immune infiltrates and checkpoints were noted between different risk groups. This study proposes, for the first time, that prognostic signatures of ar-lncRNA can be established in GC. These signatures accurately predict the prognosis of GC and offer potential biomarkers, suggesting new avenues for basic research, prognosis prediction and personalized diagnosis and treatment of GC.

Identification of hypoxic-related lncRNAs prognostic model for revealing clinical prognostic and immune infiltration characteristic of cutaneous melanoma

BACKGROUND

Cutaneous melanoma (CM) remains a significant threat to human health. There are clues to the potential role of hypoxia in CM progression. However, the role of hypoxia-related lncRNAs (HRLs) in CM has not been clarified.

METHODS

We obtained hypoxia related genes from MSigDB database and subsequently identified HRLs by applying TCGA database. LASSO-univariate and multivariate Cox analysis were used to comprehensively analyze the survival characteristics and HRLs expressions, and a novel HRLs-related prognostic risk model was subsequently established for comprehensive analysis.

RESULTS

The established risk model could evaluate the clinical outcome of CM accurately. The ability of the model-related risk score was also validated as an independent prognostic indicator of CM. Immune infiltration, TMB analysis, drug sensitivity analysis and immunotherapy evaluation were conducted to comprehensively assess the possible causes of the difference in prognosis. The reliability of bioinformatics results was partially verified by RT-qPCR.

CONCLUSION

We established a new HRLs related risk model and discussed the potential role of hypoxia in the development of CM, which provided a novel basis for CM risk stratification.

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.

lncRNAs-EZH2 interaction as promising therapeutic target in cutaneous melanoma

Melanoma is the most lethal skin cancer with increasing incidence worldwide. Despite a great improvement of diagnostics and treatment of melanoma patients, this disease is still a serious clinical problem. Therefore, novel druggable targets are in focus of research. EZH2 is a component of the PRC2 protein complex that mediates epigenetic silencing of target genes. Several mutations activating EZH2 have been identified in melanoma, which contributes to aberrant gene silencing during tumor progression. Emerging evidence indicates that long non-coding RNAs (lncRNAs) are molecular "address codes" for EZH2 silencing specificity, and targeting lncRNAs-EZH2 interaction may slow down the progression of many solid cancers, including melanoma. This review summarizes current knowledge regarding the involvement of lncRNAs in EZH2-mediated gene silencing in melanoma. The possibility of blocking lncRNAs-EZH2 interaction in melanoma as a novel therapeutic option and plausible controversies and drawbacks of this approach are also briefly discussed.

TCGA RNA-Seq and Tumor-Infiltrating Lymphocyte Imaging Data Reveal Cold Tumor Signatures of Invasive Ductal Carcinomas and Estrogen Receptor-Positive Human Breast Tumors

Comparative studies of immune-active hot and immune-deserted cold tumors are critical for identifying therapeutic targets and strategies to improve immunotherapy outcomes in cancer patients. Tumors with high tumor-infiltrating lymphocytes (TILs) are likely to respond to immunotherapy. We used the human breast cancer RNA-seq data from the cancer genome atlas (TCGA) and classified them into hot and cold tumors based on their lymphocyte infiltration scores. We compared the immune profiles of hot and cold tumors, their corresponding normal tissue adjacent to the tumor (NAT), and normal breast tissues from healthy individuals from the Genotype-Tissue Expression (GTEx) database. Cold tumors showed a significantly lower effector T cells, lower levels of antigen presentation, higher pro-tumorigenic M2 macrophages, and higher expression of extracellular matrix (ECM) stiffness-associated genes. Hot/cold dichotomy was further tested using TIL maps and H&E whole-slide pathology images from the cancer imaging archive (TCIA). Analysis of both datasets revealed that infiltrating ductal carcinoma and estrogen receptor ER-positive tumors were significantly associated with cold features. However, only TIL map analysis indicated lobular carcinomas as cold tumors and triple-negative breast cancers (TNBC) as hot tumors. Thus, RNA-seq data may be clinically relevant to tumor immune signatures when the results are supported by pathological evidence.