Identification of an m6A Regulators-Mediated Prognosis Signature For Survival Prediction and Its Relevance to Immune Infiltration in Melanoma

Network-based analysis of heterogeneous patient-matched brain and extracranial melanoma metastasis pairs reveals three homogeneous subgroups

Melanoma, the deadliest form of skin cancer, can metastasize to different organs. Molecular differences between brain and extracranial melanoma metastases are poorly understood. Here, promoter methylation and gene expression of 11 heterogeneous patient-matched pairs of brain and extracranial metastases were analyzed using melanoma-specific gene regulatory networks learned from public transcriptome and methylome data followed by network-based impact propagation of patient-specific alterations. This innovative data analysis strategy allowed to predict potential impacts of patient-specific driver candidate genes on other genes and pathways. The patient-matched metastasis pairs clustered into three robust subgroups with specific downstream targets with known roles in cancer, including melanoma (SG1: RBM38, BCL11B, SG2: GATA3, FES, SG3: SLAMF6, PYCARD). Patient subgroups and ranking of target gene candidates were confirmed in a validation cohort. Summarizing, computational network-based impact analyses of heterogeneous metastasis pairs predicted individual regulatory differences in melanoma brain metastases, cumulating into three consistent subgroups with specific downstream target genes.

Multiple Myeloma Cells with Increased Proteasomal and ER Stress Are Hypersensitive to ATX-101, an Experimental Peptide Drug Targeting PCNA

Objectives: To examine the regulatory role of PCNA in MM, we have targeted PCNA with the experimental drug ATX-101 in three commercial cell lines (JJN3, RPMI 1660, AMO) and seven in-house patient-derived cell lines with a more primary cell-like phenotype (TK9, 10, 12, 13, 14, 16 and 18) and measured the systemic molecular effects. Methods: We have used a multi-omics untargeted approach, measuring the gene expression (transcriptomics), a subproteomics approach measuring mainly signalling proteins and proteins in complex with these (signallomics) and quantitative metabolomics. These results are supplemented with traditional analysis, e.g., viability, Western and ELISA analysis. Results: The sensitivity of the cell lines to ATX-101 varied, including between three cell lines derived from the same patient at different times of disease. A trend towards increased sensitivity to ATX-101 during disease progression was detected. Although with different sensitivities, ATX-101 treatment resulted in numerous changes in signalling and metabolite pools in all cell lines. Transcriptomics and signallomics analysis of the TK cell lines revealed that elevated endogenous expression of ribosomal genes, elevated proteasomal and endoplasmic reticulum (ER) stress and low endogenous levels of NAD+ and NADH were associated with ATX-101 hypersensitivity. ATX-101 treatment further enhanced the ER stress, reduced primary metabolism and reduced the levels of the redox pair GSH/GSSG in sensitive cells. Signallome analysis suggested that eleven proteins (TPD52, TNFRS17/BCMA, LILRB4/ILT3, TSG101, ZNRF2, UPF3B, FADS2, C11orf38/SMAP, CGREF1, GAA, COG4) were activated only in the sensitive MM cell lines (TK13, 14 and 16 and JJN3), and not in nine other cancer cell lines or in primary monocytes. These proteins may therefore be biomarkers of cells with activated proteasomal and ER stress even though the gene expression levels of these proteins were not elevated. Interestingly, carfilzomib-resistant cells were at least as sensitive to ATX-101 as the wild-type cells, suggesting both low cross-resistance between ATX-101 and proteasome inhibitors and elevated proteasomal stress in carfilzomib-resistant cells. Conclusions: Our multi-omics approach revealed a vital role of PCNA in regulation of proteasomal and ER stress in MM.

Machine learning in the prediction of immunotherapy response and prognosis of melanoma: a systematic review and meta-analysis

Background

The emergence of immunotherapy has changed the treatment modality for melanoma and prolonged the survival of many patients. However, a handful of patients remain unresponsive to immunotherapy and effective tools for early identification of this patient population are still lacking. Researchers have developed machine learning algorithms for predicting immunotherapy response in melanoma, but their predictive accuracy has been inconsistent. Therefore, the present systematic review and meta-analysis was performed to comprehensively evaluate the predictive accuracy of machine learning in melanoma response to immunotherapy.

Methods

Relevant studies were searched in PubMed, Web of Sciences, Cochrane Library, and Embase from their inception to July 30, 2022. The risk of bias and applicability of the included studies were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed on R4.2.0.

Results

A total of 36 studies consisting of 30 cohort studies and 6 case-control studies were included. These studies were mainly published between 2019 and 2022 and encompassed 75 models. The outcome measures of this study were progression-free survival (PFS), overall survival (OS), and treatment response. The pooled c-index was 0.728 (95%CI: 0.629-0.828) for PFS in the training set, 0.760 (95%CI: 0.728-0.792) and 0.819 (95%CI: 0.757-0.880) for treatment response in the training and validation sets, respectively, and 0.746 (95%CI: 0.721-0.771) and 0.700 (95%CI: 0.677-0.724) for OS in the training and validation sets, respectively.

Conclusion

Machine learning has considerable predictive accuracy in melanoma immunotherapy response and prognosis, especially in the former. However, due to the lack of external validation and the scarcity of certain types of models, further studies are warranted.

The potential role of RNA modification in skin diseases, as well as the recent advances in its detection methods and therapeutic agents

RNA modification is considered as an epigenetic modification that plays an indispensable role in biological processes such as gene expression and genome editing without altering nucleotide sequence, but the molecular mechanism of RNA modification has not been discussed systematically in the development of skin diseases. This article mainly presents the whole picture of theoretical achievements on the potential role of RNA modification in dermatology. Furthermore, this article summarizes the latest advances in clinical practice related with RNA modification, including its detection methods and drug development. Based on this comprehensive review, we aim to illustrate the current blind spots and future directions of RNA modification, which may provide new insights for researchers in this field.

The roles and mechanism of m6A RNA methylation regulators in cancer immunity

N⁶-methyladenosine (m⁶A), the most common internal modification in RNA, can be regulated by three types of regulators, including methyltransferases (writers), demethylases (erasers), and m⁶A binding proteins (readers). Recently, immunotherapy represented by immune checkpoint blocking has increasingly become an effective cancer treatment, and increasing shreds of evidence show that m⁶A RNA methylation affects cancer immunity in various cancers. Until now, there have been few reviews about the role and mechanism of m⁶A modification in cancer immunity. Here, we first summarized the regulation of m⁶A regulators on the expression of target messenger RNAs (mRNA) and their corresponding roles in inflammation, immunity response, immune process and immunotherapy in various cancer cells. Meanwhile, we described the roles and mechanisms of m⁶A RNA modification in tumor microenvironment and immune response by affecting the stability of non-coding RNA (ncRNA). Moreover, we also discussed the m⁶A regulators or its target RNAs which might be used as predictor of cancer diagnosis and prognosis, and shed light on the potentiality of m⁶A methylation regulators as therapeutic targets in cancer immunity.

N6-methyladenosine functions and its role in skin cancer

N6-methyladenosine (m6A) methylation is the most abundant mammalian mRNA modification. m6A regulates RNA processing, splicing, nucleation, translation and stability by transferring, removing and recognizing m6A methylation sites, which are critical for cancer initiation, progression, metabolism and metastasis. m6A is involved in pathophysiological tumour development by altering m6A modification and expression levels in tumour oncogenes and suppressor genes. Skin cancers are by far the most common malignancies in humans, with well over a million cases diagnosed each year. Skin cancers are grouped into two main categories: melanoma and non-melanoma skin cancers (NMSC), based on cell origin and clinical behaviour. In this review, we summarize m6A methylation functions in different skin cancers, and discuss how m6A methylation is involved in disease development and progression. Moreover, we review potential prognostic biomarkers and molecular targets for early skin cancer diagnosis and treatment.

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

Background. Melanoma development and progression are significantly influenced by ferroptosis and the immune microenvironment. However, there are no reliable biomarkers for melanoma prognosis prediction based on ferroptosis and immunological response. Methods. Ferroptosis-related genes (FRGs) were retrieved from the FerrDb website. Immune-related genes (IRGs) were collected in the ImmPort dataset. The TCGA (The Cancer Genome Atlas) and GSE65904 datasets both contained prognostic FRGs and IRGs. The model was created using multivariate Cox regression, the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and the analysis and comparison between the expression patterns of ferroptosis and immune cell infiltration were done. Last but not least, research was conducted to assess the expression and involvement of the genes in the comprehensive index of ferroptosis and immune (CIFI). Results. Two prognostic ferroptosis- and immune-related markers (PDGFRB and FOXM1) were utilized to develop a CIFI. In various datasets and patient subgroups, CIFI exhibits consistent predictive performance. The fact that CIFI is an independent prognostic factor for melanoma patients was revealed. Patients in the CIFI-high group further exhibited immune-suppressive characteristics and had elevated ferroptosis gene expression levels. The results of in vitro research point to the possibility that the PDGFRB and FOXM1 genes function as oncogenes in melanoma. Conclusion. In this study, a novel prognostic classifier for melanoma patients was developed and validated using ferroptosis and immune expression profiles.

Prognostic Role of M6A-Associated Immune Genes and Cluster-Related Tumor Microenvironment Analysis: A Multi-Omics Practice in Stomach Adenocarcinoma

N6-methylandrostenedione (m6A) methylation plays a very important role in the development of malignant tumors. The immune system is the key point in the progression of tumors, particularly in terms of tumor treatment and drug resistance. Tumor immunotherapy has now become a hot spot and a new approach for tumor treatment. However, as far as the stomach adenocarcinoma (STAD) is concerned, the in-depth research is still a gap in the m6A-associated immune markers. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases is extremely important for our research, where we obtained gene mutation, gene expression data and relevant clinical information of STAD patients. Firstly, the samples from GEO were used as external validation groups, while the TCGA samples were divided into a training group and an internal validation group randomly. Using the way of Single factor COX-LASSO- and multi-factor Cox to construct the prognostic model. Then, all samples were subjected to cluster analysis to generate high and low expression groups of immune gene. Meanwhile, we also collected the correlation between these types and tumor microenvironment. On this basis, a web version of the dynamic nomogram APP was developed. In addition, we performed microenvironmental correlation, copy number variation and mutation analyses for model genes. The prognostic model for STAD developed here demonstrated a very strong predictive ability. The results of cluster analysis manifested that the immune gene low expression group had lower survival rate and higher degree of immune infiltration. Therefore, the immune gene low expression group was associated with lower survival rates and a higher degree of immune infiltration. Gene set enrichment analysis suggested that the potential mechanism might be related to the activation of immunosuppressive functions and multiple signaling pathways. Correspondingly, the web version of the dynamic nomogram APP produced by the DynNom package has successfully achieved rapid and accurate calculation of patient survival rates. Finally, the multi-omics analysis of model genes further enriched the research content. Interference of RAB19 was confirmed to facilitate migration of STAD cells in vitro, while its overexpression inhibited these features. The prognostic model for STAD constructed in this study is accurate and efficient based on multi-omics analysis and experimental validation. Additionally, the results of the correlation analysis between the tumor microenvironment and m6Ascore are the basics of further exploration of the pathophysiological mechanism in STAD.

ordinalbayes: Fitting Ordinal Bayesian Regression Models to High-Dimensional Data Using R

The stage of cancer is a discrete ordinal response that indicates the aggressiveness of disease and is often used by physicians to determine the type and intensity of treatment to be administered. For example, the FIGO stage in cervical cancer is based on the size and depth of the tumor as well as the level of spread. It may be of clinical relevance to identify molecular features from high-throughput genomic assays that are associated with the stage of cervical cancer to elucidate pathways related to tumor aggressiveness, identify improved molecular features that may be useful for staging, and identify therapeutic targets. High-throughput RNA-Seq data and corresponding clinical data (including stage) for cervical cancer patients have been made available through The Cancer Genome Atlas Project (TCGA). We recently described penalized Bayesian ordinal response models that can be used for variable selection for over-parameterized datasets, such as the TCGA-CESC dataset. Herein, we describe our ordinalbayes R package, available from the Comprehensive R Archive Network (CRAN), which enhances the runjags R package by enabling users to easily fit cumulative logit models when the outcome is ordinal and the number of predictors exceeds the sample size, P > N, such as for TCGA and other high-throughput genomic data. We demonstrate the use of this package by applying it to the TCGA cervical cancer dataset. Our ordinalbayes package can be used to fit models to high-dimensional datasets, and it effectively performs variable selection.