A twelve-gene signature for survival prediction in malignant melanoma patients

Ataxia-Telangiectasia Mutated (ATM) gene signaling pathways in human cancers and their therapeutic implications

Cancer is a multifaceted disease driven by abnormal cell growth and poses a significant global health threat. The multifactorial causes, differences in individual susceptibility to therapeutic drugs, and induced drug resistance pose major challenges in addressing cancers effectively. One of the most important aspects in making cancers highly heterogeneous in their physiology lies in the genes involved and the changes occurring to some of these genes in malignant conditions. The Genetic factors have been implicated in the oncogenesis, progression, responses to treatment, and metastasis. One such gene that plays a key role in human cancers is the mutated form of the Ataxia-telangiectasia gene (ATM). ATM gene located on chromosome 11q23, plays a vital role in maintaining genomic stability. Understanding the genetic basis of A-T is crucial for diagnosis, management, and treatment. Breast cancer, lung cancer, prostate cancer, and gastric cancer exhibit varying relationships with the ATM gene and influence their pathways. Targeting the ATM pathway proves promising for enhancing treatment effectiveness, especially in conjunction with DNA damage response pathways. Analyzing the therapeutic consequences of ATM mutations, especially in these cancer types facilitates the approaches for early detection, intervention, development of personalized treatment approaches, and improved patient outcomes. This review emphasizes the role of the ATM gene in various cancers, highlighting its impact on DNA repair pathways and therapeutic responses.

Factors Affecting Recurrence and Survival for Patients with High-Risk Stage II Melanoma

BACKGROUND

In the current era of effective adjuvant therapies and de-escalation of surgery, distinguishing which patients with high-risk stage II melanoma are at increased risk of recurrence after excision of the primary lesion is essential to determining appropriate treatment and surveillance plans.

METHODS

A single-center retrospective study analyzed patients with stage IIB or IIC melanoma. Demographic and tumor data were collected, and genomic analysis of formalin-fixed, paraffin-embedded tissue samples was performed via an internal next-generation sequencing (NGS) platform (SNaPshot). The end points examined were relapse-free survival (RFS), distant metastasis-free survival (DMFS), overall survival (OS), and melanoma-specific survival (MSS). Uni- and multivariable Cox regressions were performed to calculate the hazard ratios.

RESULTS

The study included 92 patients with a median age of 69 years and a male/female ratio of 2:1. A Breslow depth greater than 4 mm, a higher mitotic rate, an advanced T stage, and a KIT mutation had a negative impact on RFS. A primary lesion in the head and neck, a mitotic rate exceeding 10 mitoses per mm2, a CDH1 mutation, or a KIT mutation was significantly associated with a shorter DMFS. Overall survival was significantly lower with older age at diagnosis and a higher mitotic rate. An older age at diagnosis also had a negative impact on MSS.

CONCLUSION

Traditional histopathologic factors and specific tumor mutations displayed a significant correlation with disease recurrence and survival for patients with high-risk stage II melanoma. This study supported the use of genomic testing of high-risk stage II melanomas for prognostic prediction and risk stratification.

Machine learning-based construction of immunogenic cell death-related score for improving prognosis and response to immunotherapy in melanoma

BACKGROUND

Immunogenic cell death (ICD) is a form of regulated cell death (RCD) which could drive the activation of the innate and adaptive immune responses. In this work, we aimed to develop an ICD-related signature to facilitate the assessment of prognosis and immunotherapy response for melanoma patients.

METHODS

A set of machine learning methods, including consensus clustering, non-negative matrix factorization (NMF) method and least absolute shrinkage and selection operator (LASSO) logistic regression model, and bioinformatics analytic tools were integrated to construct an ICD-related risk score (ICDscore). CIBERSORT and ESTIMATE algorithm were used to evaluate the infiltration of immune cells. The 'pRRophetic' package in R and 6 cohorts of melanoma patients receiving immunotherapy were used for therapy sensitivity analyses. The predictive performance between ICDscore with other mRNA signatures were also compared.

RESULTS

The ICDscore could predict prognosis and immunotherapy response in multiple cohorts, and displayed superior performance than other forms of cell death-related signatures or 52 published signatures. The melanoma patients with low ICDscore were marked with high infiltration of immune cells, high expression of immune checkpoint inhibitor-related genes, and increased tumor mutation burden.

CONCLUSIONS

In conclusion, we constructed a stable and robust ICD-related signature for evaluating the prognosis and benefits of immunotherapy, and it could serve as a promising tool to guide decision-making and surveillance for individual melanoma patients.

UHRF1/UBE2L6/UBR4-mediated ubiquitination regulates EZH2 abundance and thereby melanocytic differentiation phenotypes in melanoma

Cellular heterogeneity in cancer is linked to disease progression and therapy response, although mechanisms regulating distinct cellular states within tumors are not well understood. We identified melanin pigment content as a major source of cellular heterogeneity in melanoma and compared RNAseq data from high-pigmented (HPCs) and low-pigmented melanoma cells (LPCs), suggesting EZH2 as a master regulator of these states. EZH2 protein was found to be upregulated in LPCs and inversely correlated with melanin deposition in pigmented patient melanomas. Surprisingly, conventional EZH2 methyltransferase inhibitors, GSK126 and EPZ6438, had no effect on LPC survival, clonogenicity and pigmentation, despite fully inhibiting methyltransferase activity. In contrast, EZH2 silencing by siRNA or degradation by DZNep or MS1943 inhibited growth of LPCs and induced HPCs. As the proteasomal inhibitor MG132 induced EZH2 protein in HPCs, we evaluated ubiquitin pathway proteins in HPC vs LPCs. Biochemical assays and animal studies demonstrated that in LPCs, the E2-conjugating enzyme UBE2L6 depletes EZH2 protein in cooperation with UBR4, an E3 ligase, via ubiquitination at EZH2's K381 residue, and is downregulated in LPCs by UHRF1-mediated CpG methylation. Targeting UHRF1/UBE2L6/UBR4-mediated regulation of EZH2 offers potential for modulating the activity of this oncoprotein in contexts in which conventional EZH2 methyltransferase inhibitors are ineffective.

A 9‑gene expression signature to predict stage development in resectable stomach adenocarcinoma

BACKGROUND

Stomach adenocarcinoma (STAD) is a highly heterogeneous disease and is among the leading causes of cancer-related death worldwide. At present, TNM stage remains the most effective prognostic factor for STAD. Exploring the changes in gene expression levels associated with TNM stage development may help oncologists to better understand the commonalities in the progression of STAD and may provide a new way of identifying early-stage STAD so that optimal treatment approaches can be provided.

METHODS

The RNA profile retrieving strategy was utilized and RNA expression profiling was performed using two large STAD microarray databases (GSE62254, n = 300; GSE15459, n = 192) from the Gene Expression Omnibus (GEO) and the RNA-seq database within the Cancer Genome Atlas (TCGA, n = 375). All sample expression information was obtained from STAD tissues after radical resection. After excluding data with insufficient staging information and lymph node number, samples were grouped into earlier-stage and later-stage. Samples in GSE62254 were randomly divided into a training group (n = 172) and a validation group (n = 86). Differentially expressed genes (DEGs) were selected based on the expression of mRNAs in the training group and the TCGA group (n = 156), and hub genes were further screened by least absolute shrinkage and selection operator (LASSO) logistic regression. Receiver operating characteristic (ROC) curves were used to evaluate the performance of the hub genes in distinguishing STAD stage in the validation group and the GSE15459 dataset. Univariate and multivariate Cox regressions were performed sequentially.

RESULTS

22 DEGs were commonly upregulated (n = 19) or downregulated (n = 3) in the training and TCGA datasets. Nine genes, including MYOCD, GHRL, SCRG1, TYRP1, LYPD6B, THBS4, TNFRSF17, SERPINB2, and NEBL were identified as hub genes by LASSO-logistic regression. The model achieved discrimination in the validation group (AUC = 0.704), training-validation group (AUC = 0.743), and GSE15459 dataset (AUC = 0.658), respectively. Gene Set Enrichment Analysis (GSEA) was used to identify the potential stage-development pathways, including the PI3K-Akt and Calcium signaling pathways. Univariate Cox regression indicated that the nine-gene score was a significant risk factor for overall survival (HR = 1.28, 95% CI 1.08-1.50, P = 0.003). In the multivariate Cox regression, only SCRG1 was an independent prognostic predictor of overall survival after backward stepwise elimination (HR = 1.21, 95% CI 1.11-1.32, P < 0.001).

CONCLUSION

Through a series of bioinformatics and validation processes, a nine-gene signature that can distinguish STAD stage was identified. This gene signature has potential clinical application and may provide a novel approach to understanding the progression of STAD.

Spectrum of BRAF Aberrations and Its Potential Clinical Implications: Insights From Integrative Pan-Cancer Analysis

B-Raf proto-oncogene serine/threonine-protein kinase (BRAF) is frequently altered in multiple cancer types, and BRAF V600 mutations act as a prime target for precision therapy. Although emerging evidence has investigated the role of BRAF, the comprehensive profiling of BRAF expression, alteration and clinical implications across various cancer types has not been reported. In this study, we used the TCGA dataset, covering 10,967 tumor samples across 32 cancer types, to analyze BRAF abnormal expression, DNA methylation, alterations (mutations and amplification/deletion), and their associations with patient survival. The results showed that BRAF expression, alteration frequency, mutation site distribution, and DNA methylation patterns varied tremendously among different cancer types. The expression of BRAF was found higher in PCPG and CHOL, and lower in TGCT and UCS compared to normal tissues. In terms of pathological stages, BRAF expression was significantly differentially expressed in COAD, KIRC, LUSC, and OV. The methylation levels of BRAF were significantly lower in LUSC, HNSC, and UCEC compared to normal tissue. The expression of BRAF and downstream gene (ETS2) was negatively correlated with methylation levels in various cancers. The overall somatic mutation frequency of BRAF was 7.7% for all cancer samples. Most fusion transcripts were found in THCA and SKCM with distinct fusion patterns. The majority of BRAF mutations were oncogenic and mainly distributed in the Pkinase_Tyr domain of THCA, SKCM, COADREAD, and LUAD. The BRAF mutations were divided into five levels according to the clinical targeted therapy implication. The results showed level 1 was mainly distributed in SKCM, COADREAD, and LUAD, while level 3B in THCA. The overall BRAF CNV frequency was about 42.7%, most of which was gain (75.9%), common in GBM, TGCT, and KIRP. In addition, the forest plot showed that increased BRAF expression was associated with poor patient overall survival in LIHC, OV, and UCEC. Taken together, this study provided a novel insight into the full alteration spectrum of BRAF and its implications for treatment and prognosis.

A B-Raf V600E gene signature for melanoma predicts prognosis and reveals sensitivity to targeted therapies

BACKGROUND

B-Raf V600E mutations account for about half of all skin cutaneous melanoma cases, and patients with this mutation are sensitive to BRAF inhibitors. However, aberrations in other genes in the MAPK/ERK pathway may cascade a similar effect as B-Raf V600E mutations, rendering those patients sensitive to BRAF inhibitors. We rationalized that defining a signature based on B-Raf pathway activity may be more informative for prognosis and drug sensitivity prediction than a binary indicator such as mutation status.

METHODS

In this study, we defined a B-Raf signature score using RNA-seq data from TCGA. A higher score is shown to not only predict B-Raf mutation status, but also predict other aberrations that could similarly activate the MAPK/ERK pathway, such as B-Raf amplification, RAS mutation, and EGFR amplification.

RESULTS

We showed that patients dichotomized by the median B-Raf score is more significantly stratified than by other metrics of measuring B-Raf aberration, such as mutation status, gene expression, and protein expression. We also demonstrated that high B-Raf score predicts higher sensitivity to B-Raf inhibitors SB590885 and PLX4720, as expected, but also correlated with sensitivity to drugs targeting other relevant oncogenic pathways.

CONCLUSION

The BRAF signature may better help guide targeted therapy for melanoma, and such a framework can be applied to other cancers and mutations to provide more information than mutation status alone.

Machine Learning Analysis of Immune Cells for Diagnosis and Prognosis of Cutaneous Melanoma

Tumor infiltration, known to associate with various cancer initiations and progressions, is a promising therapeutic target for aggressive cutaneous melanoma. Then, the relative infiltration of 24 kinds of immune cells in melanoma was assessed by a single sample gene set enrichment analysis (ssGSEA) program from a public database. The multiple machine learning algorithms were applied to evaluate the efficiency of immune cells in diagnosing and predicting the prognosis of melanoma. In comparison with the expression of immune cell in tumor and normal control, we built the immune diagnostic models in training dataset, which can accurately classify melanoma patients from normal (LR AUC = 0.965, RF AUC = 0.99, SVM AUC = 0.963, LASSO AUC = 0.964, and NNET AUC = 0.989). These diagnostic models were also validated in three outside datasets and suggested over 90% AUC to distinguish melanomas from normal patients. Moreover, we also developed a robust immune cell biomarker that could estimate the prognosis of melanoma. This biomarker was also further validated in internal and external datasets. Following that, we created a nomogram with a composition of risk score and clinical parameters, which had high accuracies in predicting survival over three and five years. The nomogram's decision curve revealed a bigger net benefit than the tumor stage. Furthermore, a risk score system was used to categorize melanoma patients into high- and low-risk subgroups. The high-risk group has a significantly lower life expectancy than the low-risk subgroup. Finally, we observed that complement, epithelial-mesenchymal transition, and inflammatory response were significantly activated in the high-risk group. Therefore, the findings provide new insights for understanding the tumor infiltration relevant to clinical applications as a diagnostic or prognostic biomarker for melanoma.

Single-Cell Transcriptomic Analysis Reveals a Tumor-Reactive T Cell Signature Associated With Clinical Outcome and Immunotherapy Response In Melanoma

The infiltration of tumor-reactive T cells in the tumor site is associated with better survival and immunotherapy response. However, tumor-reactive T cells were often represented by the infiltration of total CD8+ T cells, which was confounded by the presence of bystander T cells. To identify tumor-reactive T cells at the cancer lesion, we performed integration analyses of three scRNA-seq data sets of T cells in melanoma. Extensive heterogeneous functional states of T cells were revealed in the tumor microenvironment. Among these states, we identified a subset of tumor-reactive T cells which specifically expressed tumor-reactive markers and T cell activation signature, and were strongly enriched for larger T cell receptor (TCR) clones. We further identified and validated a tumor-reactive T cell signature (TRS) to evaluate the tumor reactivity of T cells in tumor patients. Patients with high TRS scores have strong immune activity and high mutation burden in the TCGA-SKCM cohort. We also demonstrated a significant association of the TRS with the clinical outcomes of melanoma patients, with higher TRS scores representing better survival, which was validated in four external independent cohorts. Furthermore, the TRS scores exhibited greater performance on prognosis prediction than infiltration levels of CD8+ T cells and previously published prognosis-related signatures. Finally, we observed the capability of TRS to predict immunotherapy response in melanoma. Together, based on integrated analysis of single-cell RNA-sequencing, we developed and validated a tumor-reactive-related signature that demonstrated significant association with clinical outcomes and response to immunotherapy.

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

Background

Glycolytic effects and immune microenvironments play important roles in the development of melanoma. However, reliable biomarkers for prognostic prediction of melanoma as based on glycolysis and immune status remain to be identified.

Methods

Glycolysis-related genes (GRGs) were obtained from the Molecular Signatures database and immune-related genes (IRGs) were downloaded from the ImmPort dataset. Prognostic GRGs and IRGs in the TCGA (The Cancer Genome Atlas) and GSE65904 datasets were identified. Least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression were used for model construction. Glycolysis expression profiles and the infiltration of immune cells were analyzed and compared. Finally, in vitro experiments were performed to assess the expression and function of these CIGI genes.

Results

Four prognostic glycolysis- and immune-related signatures (SEMA4D, IFITM1, KIF20A and GPR87) were identified for use in constructing a comprehensive glycolysis and immune (CIGI) model. CIGI proved to be a stable, predictive method as determined from different datasets and subgroups of patients and served as an independent prognostic factor for melanoma patients. In addition, patients in the high-CIGI group showed increased levels of glycolytic gene expressions and exhibited immune-suppressive features. Finally, SEMA4D and IFITM1 may function as tumor suppressor genes, while KIF20A and GPR87 may function as oncogenes in melanoma as revealed from results of in vitro experiments.

Conclusion

In this report we present our findings on the development and validation of a novel prognostic classifier for use in patients with melanoma as based on glycolysis and immune expression profiles.

Interrogating Epigenome toward Personalized Approach in Cutaneous Melanoma

Epigenetic alterations have emerged as essential contributors in the pathogenesis of various human diseases, including cutaneous melanoma (CM). Unlike genetic changes, epigenetic modifications are highly dynamic and reversible and thus easy to regulate. Here, we present a comprehensive review of the latest research findings on the role of genetic and epigenetic alterations in CM initiation and development. We believe that a better understanding of how aberrant DNA methylation and histone modifications, along with other molecular processes, affect the genesis and clinical behavior of CM can provide the clinical management of this disease a wide range of diagnostic and prognostic biomarkers, as well as potential therapeutic targets that can be used to prevent or abrogate drug resistance. We will also approach the modalities by which these epigenetic alterations can be used to customize the therapeutic algorithms in CM, the current status of epi-therapies, and the preliminary results of epigenetic and traditional combinatorial pharmacological approaches in this fatal disease.

Coexpressed Genes That Promote the Infiltration of M2 Macrophages in Melanoma Can Evaluate the Prognosis and Immunotherapy Outcome

Purpose

To improve immunotherapy efficacy for melanoma, a coexpression network and key genes of M2 macrophages in melanoma were explored. A prognostic risk assessment model was established for M2-related coexpressed genes, and the role of M2 macrophages in the immune microenvironment of melanoma was elucidated.

Method

We obtained mRNA data from melanoma and peritumor tissue samples from The Cancer Genome Atlas-skin cutaneous melanoma (TCGA-SKCM). Then, we used CIBERSORT to calculate the proportion of M2 macrophage cells. A coexpression module most related to M2 macrophages in TCGA-SKCM was determined by analyzing the weighted gene coexpression network, and a coexpression network was established. After survival analysis, factors with significant results were incorporated into a Cox regression analysis to establish a model. The model's essential genes were analyzed using functional enrichment, GSEA, and subgroup and total carcinoma. Finally, external datasets GSE65904 and GSE78220 were used to verify the prognostic risk model.

Results

The yellow-green module was the coexpression module most related to M2 macrophages in TCGA-SKCM; NOTCH3, DBN1, KDELC2, and STAB1 were identified as the essential genes that promoted the infiltration of M2 macrophages in melanoma. These genes are concentrated in antigen treatment and presentation, chemokine, cytokine, the T cell receptor pathway, and the IFN-γ pathway. These factors were analyzed for survival, and factors with significant results were included in a Cox regression analysis. According to the methods, a model related to M2-TAM coexpressed gene was established, and the formula was risk score = 0.25^∗NOTCH3 + 0.008^∗ DBN1 − 0.031^∗KDELC2 − 0.032^∗STAB1. The new model was used to perform subgroup evaluation and external queue validation. The results showed good prognostic ability.

Conclusion

We proposed a Cox proportional hazards regression model associated with coexpression genes of melanoma M2 macrophages that may provide a measurement method for generating prognosis scores in patients with melanoma. Four genes coexpressed with M2 macrophages were associated with high levels of infiltration of M2 macrophages. Our findings may provide significant candidate biomarkers for the treatment and monitoring of melanoma.

Modelling Cell Invasion: A Review of What JD Murray and the Embryo Can Teach Us

Cell invasion and cell plasticity are critical to human development but are also striking features of cancer metastasis. By distributing a multipotent cell type from a place of birth to distal locations, the vertebrate embryo builds organs. In comparison, metastatic tumor cells often acquire a de-differentiated phenotype and migrate away from a primary site to inhabit new microenvironments, disrupting normal organ function. Countless observations of both embryonic cell migration and tumor metastasis have demonstrated complex cell signaling and interactive behaviors that have long confounded scientist and clinician alike. James D. Murray realized the important role of mathematics in biology and developed a unique strategy to address complex biological questions such as these. His work offers a practical template for constructing clear, logical, direct and verifiable models that help to explain complex cell behaviors and direct new experiments. His pioneering work at the interface of development and cancer made significant contributions to glioblastoma cancer and embryonic pattern formation using often simple models with tremendous predictive potential. Here, we provide a brief overview of advances in cell invasion and cell plasticity using the embryonic neural crest and its ancestral relationship to aggressive cancers that put into current context the timeless aspects of his work.