Systemic Analysis of the Prognosis-Related RNA Alternative Splicing Signals in Melanoma

The RAL Enigma: Distinct Roles of RALA and RALB in Cancer

RALA and RALB are highly homologous small G proteins belonging to the RAS superfamily. Like other small GTPases, the RALs are molecular switches that can be toggled between inactive GDP-bound and active GTP-bound states to regulate diverse and critical cellular functions such as vesicle trafficking, filopodia formation, mitochondrial fission, and cytokinesis. The RAL paralogs are activated and inactivated by a shared set of guanine nucleotide exchange factors (GEFs) and GTPase-activating proteins (GAPs) and utilize similar sets of downstream effectors. In addition to their important roles in normal cell biology, the RALs are known to be critical mediators of cancer cell survival, invasion, migration, and metastasis. However, despite their substantial similarities, the RALs often display striking functional disparities in cancer. RALA and RALB can have redundant, unique, or even antagonistic functions depending on cancer type. The molecular basis for these discrepancies remains an important unanswered question in the field of cancer biology. In this review we examine the functions of the RAL paralogs in normal cellular physiology and cancer biology with special consideration provided to situations where the roles of RALA and RALB are non-redundant.

Integrative Predictive Modeling of Metastasis in Melanoma Cancer Based on MicroRNA, mRNA, and DNA Methylation Data

Introduction: Despite the significant progress in understanding cancer biology, the deduction of metastasis is still a challenge in the clinic. Transcriptional regulation is one of the critical mechanisms underlying cancer development. Even though mRNA, microRNA, and DNA methylation mechanisms have a crucial impact on the metastatic outcome, there are no comprehensive data mining models that combine all transcriptional regulation aspects for metastasis prediction. This study focused on identifying the regulatory impact of genetic biomarkers for monitoring metastatic molecular signatures of melanoma by investigating the consolidated effect of miRNA, mRNA, and DNA methylation.

Method: We developed multiple machine learning models to distinguish the metastasis by integrating miRNA, mRNA, and DNA methylation markers. We used the TCGA melanoma dataset to differentiate between metastatic melanoma samples by assessing a set of predictive models. For this purpose, machine learning models using a support vector machine with different kernels, artificial neural networks, random forests, AdaBoost, and Naïve Bayes are compared. An iterative combination of differentially expressed miRNA, mRNA, and methylation signatures is used as a candidate marker to reveal each new biomarker category’s impact. In each iteration, the performances of the combined models are calculated. During all comparisons, the choice of the feature selection method and under and oversampling approaches are analyzed. Selected biomarkers of the highest performing models are further analyzed for the biological interpretation of functional enrichment.

Results: In the initial model, miRNA biomarkers can identify metastatic melanoma with an 81% F-score. The addition of mRNA markers upon miRNA increased the F-score to 92%. In the final integrated model, the addition of the methylation data resulted in a similar F-score of 92% but produced a stable model with low variance across multiple trials.

Conclusion: Our results support the role of miRNA regulation in metastatic melanoma as miRNA markers model metastasis outcomes with high accuracy. Moreover, the integrated evaluation of miRNA with mRNA and methylation biomarkers increases the model’s power. It populates selected biomarkers on the metastasis-associated pathways of melanoma, such as the “osteoclast”, “Rap1 signaling”, and “chemokine signaling” pathways.

Source Code:https://github.com/aysegul-kt/MelonomaMetastasisPrediction/

Systemic characterization of alternative splicing related to prognosis and immune infiltration in malignant mesothelioma

BACKGROUND

Malignant mesothelioma (MM) is a relatively rare and highly lethal tumor with few treatment options. Thus, it is important to identify prognostic markers that can help clinicians diagnose mesothelioma earlier and assess disease activity more accurately. Alternative splicing (AS) events have been recognized as critical signatures for tumor diagnosis and treatment in multiple cancers, including MM.

METHODS

We systematically examined the AS events and clinical information of 83 MM samples from TCGA database. Univariate Cox regression analysis was used to identify AS events associated with overall survival. LASSO analyses followed by multivariate Cox regression analyses were conducted to construct the prognostic signatures and assess the accuracy of these prognostic signatures by receiver operating characteristic (ROC) curve and Kaplan-Meier survival analyses. The ImmuCellAI and ssGSEA algorithms were used to assess the degrees of immune cell infiltration in MM samples. The survival-related splicing regulatory network was established based on the correlation between survival-related AS events and splicing factors (SFs).

RESULTS

A total of 3976 AS events associated with overall survival were identified by univariate Cox regression analysis, and ES events accounted for the greatest proportion. We constructed prognostic signatures based on survival-related AS events. The prognostic signatures proved to be an efficient predictor with an area under the curve (AUC) greater than 0.9. Additionally, the risk score based on 6 key AS events proved to be an independent prognostic factor, and a nomogram composed of 6 key AS events was established. We found that the risk score was significantly decreased in patients with the epithelioid subtype. In addition, unsupervised clustering clearly showed that the risk score was associated with immune cell infiltration. The abundances of cytotoxic T (Tc) cells, natural killer (NK) cells and T-helper 17 (Th17) cells were higher in the high-risk group, whereas the abundances of induced regulatory T (iTreg) cells were lower in the high-risk group. Finally, we identified 3 SFs (HSPB1, INTS1 and LUC7L2) that were significantly associated with MM patient survival and then constructed a regulatory network between the 3 SFs and survival-related AS to reveal potential regulatory mechanisms in MM.

CONCLUSION

Our study provided a prognostic signature based on 6 key events, representing a better effective tumor-specific diagnostic and prognostic marker than the TNM staging system. AS events that are correlated with the immune system may be potential therapeutic targets for MM.

Development and Validation of an Immune-Related Gene Pair Signature in Skin Cutaneous Melanoma

INTRODUCTION

Skin cutaneous melanoma (SKCM) is a common skin malignancy worldwide, and its metastasis and mortality rates are high. The molecular characteristics exhibited by tumor-immune interactions have drawn the attention from researchers. Therefore, increased knowledge and new strategies to identify effective immune-related biomarkers may improve the clinical management of SKCM by providing more accurate prognostic information.

PATIENTS AND METHODS

In this study, we established a prognostic immune-related gene pair (IRGP) signature for predicting the survival of SKCM patients. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases provided gene expression profiles together with clinical information, and the samples were randomly divided into three groups including the training, testing, and validation datasets. The regression model of least absolute shrinkage and selection operator (LASSO) helped to identify a 13-IRGP signature with a significant relation to the survival of SKCM patients.

RESULTS

The training, TCGA, and independent sets have an average value of area under the curve of 0.79, 0.76, and 0.82, respectively. In addition, this 13-IRGP signature can noticeably divide SKCM patients into high-risk group and low-risk group with significantly different prognoses. Many biological activities such as gene family were enriched among the genes in our IRGP signature. While analyzing the risk signature and clinical characteristics, there was a large difference in the risk score between T stage and tumor stage grouping. Finally, we constructed a nomogram and forest plots of the risk score and clinical features.

CONCLUSION

In summary, we developed a robust 13-IRGP prognostic signature in SKCM, which can identify and provide new insights into immunological biomarkers.

Aberrant RNA Splicing Events Driven by Mutations of RNA-Binding Proteins as Indicators for Skin Cutaneous Melanoma Prognosis

The worldwide incidence of skin cutaneous melanoma (SKCM) is increasing at a more rapid rate than other tumors. Aberrant alternative splicing (AS) is found to be common in cancer; however, how this process contributes to cancer prognosis still remains largely unknown. Mutations in RNA-binding proteins (RBPs) may trigger great changes in the splicing process. In this study, we comprehensively analyzed DNA and RNA sequencing data and clinical information of SKCM patients, together with widespread changes in splicing patterns induced by RBP mutations. We screened mRNA expression-related and prognosis-related mutations in RBPs and investigated the potential affections of RBP mutations on splicing patterns. Mutations in 853 RBPs were demonstrated to be correlated with splicing aberrations (p < 0.01). Functional enrichment analysis revealed that these alternative splicing events (ASEs) may participate in tumor progress by regulating the modification process, cell-cycle checkpoint, metabolic pathways, MAPK signaling, PI3K-Akt signaling, and other important pathways in cancer. We also constructed a prediction model based on overall survival-related AS events (OS-ASEs) affected by RBP mutations, which exhibited a good predict efficiency with the area under the curve of 0.989. Our work highlights the importance of RBP mutations in splicing alterations and provides effective biomarkers for prediction of prognosis of SKCM.

Transcriptomic Analysis Revealed an Emerging Role of Alternative Splicing in Embryonal Tumor with Multilayered Rosettes

Embryonal tumor with multilayered rosettes (ETMR) is an aggressive and rare pediatric embryonal brain tumor. Amplification of C19MC microRNA cluster and expression of LIN28 are distinctive features of ETMR. Despite the increasing efforts to decipher ETMR, the biology remains poorly understood. To date, the role of aberrant alternative splicing in ETMR has not been thoroughly investigated. In the current study, a comprehensive analysis was performed on published unprocessed RNA-seq reads of tissue-matched ETMR and fetal controls datasets. Gene expression was quantified in samples using Kallisto/sleuth pipeline. For the alternative splicing analysis, STAR, SplAdder and rMATS were used. Functional enrichment analysis was subsequently performed using Metascape. The expression analysis identified a total of 3622 differentially expressed genes (DEGs) between ETMR and fetal controls while 1627 genes showed differential alternative splicing patterns. Interestingly, genes with significant alternative splicing events in ETMR were identified to be involved in signaling pathways such as ErbB, mTOR and MAPK pathways as well as ubiquitin-mediated proteolysis, cell cycle and autophagy. Moreover, up-regulated DEGs with alternative splicing events were involved in important biological processes including nuclear transport, regulation of cell cycle and regulation of Wnt signaling pathway. These findings highlight the role of aberrant alternative splicing in shaping the ETMR tumor landscape, and the identified pathways constitute potential therapeutic targets.