scholarly journals The Integrative Analysis Identifies Three Cancer Subtypes and Stemness Features in Cutaneous Melanoma

2021 ◽  
Vol 7 ◽  
Author(s):  
Xiaoran Wang ◽  
Qi Wan ◽  
Lin Jin ◽  
Chengxiu Liu ◽  
Chang Liu ◽  
...  
Keyword(s):  
Survival Time ◽  
Cutaneous Melanoma ◽  
Immune Therapy ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Tumor Purity ◽  
Melanoma Patients ◽  
Immune Score

Background: With the growing uncovering of drug resistance in melanoma treatment, personalized cancer therapy and cancer stem cells are potential therapeutic targets for this aggressive skin cancer.Methods: Multi-omics data of cutaneous melanoma were obtained from The Cancer Genome Atlas (TCGA) database. Then, these melanoma patients were classified into different subgroups by performing "CancerSubtypes" method. The differences of stemness indices (mRNAsi and mDNAsi) and tumor microenvironment indices (immune score, stromal score, and tumor purity) among subtypes were investigated. Moreover, the Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithms were performed to identify a cancer cell stemness feature, and the likelihood of immuno/chemotherapeutic response was further explored.Results: Totally, 3 specific subtypes of melanoma with different survival outcomes were identified from TCGA. We found subtype 2 of melanoma with the higher immune score and stromal score and lower mRNAsi and tumor purity score, which has the best survival time than the other subtypes. By performing Kaplan–Meier survival analysis, we found that mRNAsi was significantly associated with the overall survival time of melanomas in subtype 2. Correlation analysis indicated surprising associations between stemness indices and subsets of tumor-infiltrating immune cells. Besides, we developed and validated a prognostic stemness-related genes feature that can divide melanoma patients into high- and low-risk subgroups by applying risk score system. The high-risk group has a significantly shorter survival time than the low-risk subgroup, which is more sensitive to CTLA-4 immune therapy. Finally, 16 compounds were screened out in the Connectivity Map database which may be potential therapeutic drugs for melanomas.Conclusion: Thus, our finding provides a new framework for classification and finds some potential targets for the treatment of melanoma.

Analysis of Chinese acral and mucosal melanoma patient genomic and neoantigen profiles in cancer vaccine development: A pilot study.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e14300-e14300 ◽  
Author(s):  
Xianling Guo ◽  
Song Gao ◽  
Li Yang ◽  
Juemin Fang ◽  
Guochao Wei ◽  
...  
Keyword(s):  
Pilot Study ◽  
Cutaneous Melanoma ◽  
Vaccine Development ◽  
Mucosal Melanoma ◽  
The Cancer Genome Atlas ◽  
Driver Mutations ◽  
Data Set ◽  
Acral Melanoma ◽  
Tumor Mutational Burden ◽  
Melanoma Patients

e14300 Background: Acral and mucosal melanoma are rare subtypes accounting for about 3% of all melanoma cases. The cutaneous melanoma genomic landscape is well defined; however, little is known about the acral and mucosal melanoma mutational spectrum. In this pilot study, we evaluated the genomic and neo-antigen profiles and tumor mutational burden (TMB) from acral and mucosal melanoma patients with the aim of designing personalized vaccines and longitudinally tracking patients’ clinical courses. Methods: Tumor whole exome sequencing and neo-antigen profiling of 5 acral and 3 mucosal melanoma patients at Shanghai Tenth Peoples Hospital, Tongji University, China between April 2018 and January 2019 was performed using YuceBio’s proprietary analytics platform. Watsonä for Genomics, an artificial intelligence decision-support system, was used for variant interpretation and annotation. A comparative analysis was performed on Chinese acral melanoma data with the published Caucasian acral cohort from the Translational Genomics Research Institute (TGen) and The Cancer Genome Atlas (TCGA) predominantly Caucasian cutaneous melanoma data set. Results: TMB in our acral/mucosal melanoma cohort was 2.26/Megabase (Mb) compared to over 20/Mb in published cutaneous melanoma studies. Tumor neo-antigen burden (TNAB) in our group was 1.03 neo-epitopes/Mb. Low TNAB levels were associated with low TMB levels in all tumors. Incidence of BRAF and NRAS mutant cases in our cohort was 0% (0/8) and 13% (1/8) respectively compared to 19% (5/27) and 7% (2/27) of the Caucasian acral population in the TGen dataset. Incidence of BRAF and NRAS mutations in the TCGA cutaneous melanoma dataset was 54% (237/440) and 28% (125/440), respectively. Conclusions: TMB was significantly lower in acral/mucosal than in cutaneous melanoma and may be a surrogate for TNAB. Detection of BRAF and NRAS mutations, the two most prevalent driver mutations in cutaneous melanoma, were significantly lower frequencies in both Chinese and Caucasian acral melanoma patients in this study, suggesting alternate cancer drivers may exist in this subtype. Strategies to address challenges of low TNAB in vaccine development are being explored.

scholarly journals Identification of a five-miRNA signature predicting survival in cutaneous melanoma cancer patients

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7831 ◽  
Author(s):  
Tao Lu ◽  
Shuang Chen ◽  
Le Qu ◽  
Yunlin Wang ◽  
Hong-duo Chen ◽  
...  
Keyword(s):  
Cutaneous Melanoma ◽  
Target Genes ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Microarray Dataset ◽  
Bioinformatic Analysis ◽  
The Cancer Genome Atlas ◽  
Mirna Signature ◽  
Sequencing Data ◽  
Melanoma Patients

Background Cutaneous melanoma (CM) is the deadliest form of skin cancer. Numerous studies have revealed that microRNAs (miRNAs) are expressed abnormally in melanoma tissues. Our work aimed to assess multiple miRNAs using bioinformatic analysis in order to predict the prognoses of cutaneous melanoma patients. Methods The microarray dataset GSE35579 was downloaded from the Gene Expression Omnibus (GEO) database to detect the differential expression of miRNAs (DEMs), including 41 melanoma (primary and metastatic) tissues and 11 benign nevi. Clinical information and miRNA sequencing data of cutaneous melanoma tissues were downloaded from the Cancer Genome Atlas database (TCGA) to assess the prognostic values of DEMs. Additionally, the target genes of DEMs were anticipated using miRanda, miRmap, TargetScan, and PicTar. Finally, functional analysis was performed using selected target genes on the Annotation, Visualization and Integrated Discovery (DAVID) website. Results After performing bioinformatic analysis, a total of 185 DEMs were identified: 80 upregulated miRNAs and 105 downregulated miRNAs. A five-miRNA (miR-25, miR-204, miR-211, miR-510, miR-513c) signature was discovered to be a potential significant prognostic biomarker of cutaneous melanoma when using the Kaplan–Meier survival method (P = 0.001). Univariate and multivariate Cox regression analyses showed that the five-miRNA signature could be an independent prognostic marker (HR = 0.605, P = 0.006) in cutaneous melanoma patients. Biological pathway analysis indicated that the target genes may be involved in PI3K-Akt pathways, ubiquitin-mediated proteolysis, and focal adhesion. Conclusion The identified five-miRNA signature may serve as a prognostic biomarker, or as a potential therapeutic target, in cutaneous melanoma patients.

scholarly journals Five genes involved in circular RNA-associated competitive endogenous RNA network correlates with metastasis in papillary thyroid carcinoma

2021 ◽  
Vol 18 (6) ◽  
pp. 9016-9032
Author(s):  
Jie Qiu ◽  
◽  
Maolin Sun ◽  
Chuanshan Zang ◽  
Liwei Jiang ◽  
...  
Keyword(s):  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Circular Rna ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Regulatory Mechanisms ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Papillary Thyroid ◽  
Competitive Endogenous Rna

<abstract> <p>&gt;This study aimed to identify potential circular RNA (circRNA), microRNA (miRNA) and mRNA biomarkers as well as their underlying regulatory mechanisms in papillary thyroid carcinoma (PTC). Three microarray datasets from the Gene Expression Omnibus database as well as expression data and clinical phenotype from The Cancer Genome Atlas (TCGA) were downloaded, followed by differential expression, functional enrichment, protein–protein interaction (PPI), and module analyses. The support vector machine (SVM)-recursive feature elimination (RFE) algorithm was used to screen the key circRNAs. Finally, the mRNA-miRNA-circRNA regulatory network and competitive endogenous RNA (ceRNA) network were constructed. The prognostic value and clinical correlations of key mRNAs were investigated using TCGA dataset, and their expression was validated using the UALCAN database. A total of 1039 mRNAs, 18 miRNAs and 137 circRNAs were differentially expressed in patients with PTC. A total of 37 key circRNAs were obtained using the SVM-RFE algorithm, whereas 46 key mRNAs were obtained from significant modules in the PPI network. A total of 11 circRNA-miRNA pairs and 40 miRNA-mRNA pairs were predicted. Based on these interaction pairs, 46 circRNA-miRNA-mRNA regulatory pairs were integrated, of which 8 regulatory pairs in line with the ceRNA hypothesis were obtained, including two circRNAs (circ_0004053 and circ_0028198), three miRNAs (miR-199a-5p, miR-199b-5p, and miR-7-5p), and five mRNAs, namely <italic>APOA2</italic>, <italic>CCL20</italic>, <italic>LPAR5</italic>, <italic>MFGE8</italic>, and <italic>TIMP1</italic>. Survival analysis showed that <italic>LPAR5</italic> expression was associated with patient survival. <italic>APOA2</italic> expression showed significant differences between metastatic and non-metastatic tumors, whereas <italic>CCL20</italic>, <italic>LPAR5</italic>, <italic>MFGE8</italic> and <italic>TIMP1</italic> showed significant differences between metastatic and non-metastatic lymph nodes. Overall, we identified several potential targets and regulatory mechanisms involved in PTC. <italic>APOA2</italic>, <italic>CCL20</italic>, <italic>LPAR5</italic>, <italic>MFGE8</italic>, and <italic>TIMP1</italic> may be correlated with PTC metastasis.</p> </abstract>

scholarly journals Prognostic Value and Immunological Characteristics of a Novel RNA Binding Protein Signature in Cutaneous Melanoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Jun Tian ◽  
Chongzhi Ma ◽  
Li Yang ◽  
Yang Sun ◽  
Yuan Zhang
Keyword(s):  
High Risk ◽  
Cutaneous Melanoma ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Risk Group ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Potential Biomarker ◽  
Melanoma Patients

BackgroundThe existing studies indicate that RNA binding proteins (RBPs) are closely correlated with the genesis and development of cancers. However, the role of RBPs in cutaneous melanoma remains largely unknown. Therefore, the present study aims to establish a reliable prognostic signature based on RBPs to distinguish cutaneous melanoma patients with different prognoses and investigate the immune infiltration of patients.MethodsAfter screening RBPs from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, Cox and least absolute shrinkage and selection operator (LASSO) regression analysis were then used to establish a prediction model. The relationship between the signature and the abundance of immune cell types, the tumor microenvironment (TME), immune-related pathways, and immune checkpoints were also analyzed.ResultsIn total, 7 RBPs were selected to establish the prognostic signature. Patients categorized as a high-risk group demonstrated worse overall survival (OS) rates compared to those of patients categorized as a low-risk group. The signature was validated in an independent external cohort and indicated a promising prognostic ability. Further analysis indicated that the signature wasan independent prognostic indicator in cutaneous melanoma. A nomogram combining risk score and clinicopathological features was then established to evaluate the 3- and 5-year OS in cutaneous melanoma patients. Analyses of immune infiltrating, the TME, immune checkpoint, and drug susceptibility revealed significant differences between the two groups. GSEA analysis revealed that basal cell carcinoma, notch signaling pathway, melanogenesis pathways were enriched in the high-risk group, resulting in poor OS.ConclusionWe established and validated a robust 7-RBP signature that could be a potential biomarker to predict the prognosis and immunotherapy response of cutaneous melanoma patients, which provides new insights into cutaneous melanoma immunotherapeutic strategies.

scholarly journals Pathogenic germline variants are associated with poor survival in stage III/IV melanoma patients

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Lauren G. Aoude ◽  
Vanessa F. Bonazzi ◽  
Sandra Brosda ◽  
Kalpana Patel ◽  
Lambros T. Koufariotis ◽  
...  
Keyword(s):  
Cutaneous Melanoma ◽  
Stage Iv ◽  
Progression Free Survival ◽  
The Cancer Genome Atlas ◽  
Stage Iii ◽  
Poor Overall Survival ◽  
Pathogenic Variants ◽  
Mutation Burden ◽  
Cancer Genome Atlas ◽  
Melanoma Patients

Abstract Patients with late stage resected cutaneous melanoma have poor overall survival (OS) and experience irreversible adverse events from systemic therapy. There is a clinical need to identify biomarkers to predict outcome. Performing germline/tumour whole-exome sequencing of 44 stage III/IV melanoma patients we identified pathogenic germline mutations in CDKN2A, CDK4, ATM, POLH, MRE11A, RECQL4 and XPC, affecting 7/44 patients. These mutations were associated with poor OS (p = 0.0082). We confirmed our findings in The Cancer Genome Atlas (TCGA) human skin cutaneous melanoma cohort where we identified pathogenic variants in 40/455 patients (p = 0.0203). Combining these cohorts (n = 499) further strengthened these findings showing germline carriers had worse OS (p = 0.0009). Additionally, we determined whether tumour mutation burden (TMB) or BRAF status were prognostic markers of survival. Low TMB rate (< 20 Mut/Mb; p = 0.0034) and BRAF p.V600 mutation (p = 0.0355) were associated with worse progression-free survival. Combining these biomarkers indicated that V600 mutant patients had significantly lower TMB (p = 0.0155). This was confirmed in the TCGA (n = 443, p = 0.0007). Integrative analysis showed germline mutation status conferred the highest risk (HR 5.2, 95% CI 1.72–15.7). Stage IV (HR 2.5, 0.74–8.6) and low TMB (HR 2.3, 0.57–9.4) were similar, whereas BRAF V600 status was the weakest prognostic biomarker (HR 1.5, 95% CI 0.44–5.2).

scholarly journals Integrative Analysis of DNA Methylation and Gene Expression in Skin Cutaneous Melanoma by Bioinformatic Approaches

2021 ◽  
Author(s):  
Yan Sun ◽  
Zhilin Wu ◽  
Rui Chen ◽  
Yan Wu ◽  
Yun Lin
Keyword(s):  
Cutaneous Melanoma ◽  
Original Data ◽  
Integrative Analysis ◽  
The Cancer Genome Atlas ◽  
Gene Methylation ◽  
Life Threatening ◽  
Cancer Genome Atlas ◽  
Bioinformatic Approaches ◽  
Melanoma Patients ◽  
Pathological Stages

Abstract Skin cutaneous melanoma is the most life-threatening skin cancer. Finding key methylation genes of prognostic value is an under-explored but intriguing field in the research of skin cutaneous melanoma. This work is aimed to identify survival related methylated genes and their specific methylation sites in skin cutaneous melanoma via an integrative analysis with bioinformatic approaches. The original data, including gene methylation and expression files, were downloaded from the Cancer Genome Atlas database. Statistical analysis revealed that skin cutaneous melanoma patients with highly expressed and hypomethylated HHEX had a better outcome than patients with lowly-expressed and hypermethylated HHEX. In addition, fifteen methylation sites of HHEX were identified to be significantly correlated with HHEX expression changes. In various pathological stages, the expression levels of HHEX were different, and exhibited a downward trend from stage Ⅰ to stage Ⅳ. Therefore, we speculate that the driven gene HHEX may play an important role in the survival of skin cutaneous melanoma. This finding provides novel epigenetic molecular clues and potential detection targets for early prediction of the prognosis of skin cutaneous melanoma.

The effect of glioblastoma heterogeneity on survival stratification: a multimodal MR imaging texture analysis

2018 ◽  
Vol 59 (10) ◽  
pp. 1239-1246 ◽  
Author(s):  
Yang Liu ◽  
Xi Zhang ◽  
Na Feng ◽  
Lulu Yin ◽  
Yalong He ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Texture Analysis ◽  
Characteristic Curve ◽  
The Cancer Genome Atlas ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Survival Group ◽  
Term Survival ◽  
Heterogeneous Distribution ◽  
Optimal Subset

Background Quantitative evaluation of the effect of glioblastoma (GBM) heterogeneity on survival stratification would be critical for the diagnosis, treatment decision, and follow-up management. Purpose To evaluate the effect of GBM heterogeneity on survival stratification, using texture analysis on multimodal magnetic resonance (MR) imaging. Material and Methods A total of 119 GBM patients (65 in long-term and 54 in short-term survival group, separated by overall survival of 12 months) were selected from the Cancer Genome Atlas, who underwent the T1-weighted (T1W) contrast-enhanced (CE), T1W, T2-weighted (T2W), and FLAIR sequences. For each sequence, the co-occurrence matrix, run-length matrix, and histogram features were extracted to reflect GBM heterogeneity on different scale. The recursive feature elimination based support vector machine was adopted to find an optimal subset. Then the stratification performance of four MR sequences was evaluated, both alone and in combination. Results When each sequence used alone, the T1W-CE sequence performed best, with an area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity of 0.7915, 80.67%, 78.45%, and 83.33%, respectively. When the four sequences combined, the stratification performance was basically equal to that of T1W-CE sequence. In the optimal subset of features extracted from multimodality, those from the T2W sequence weighted the most. Conclusion All the four sequences could reflect heterogeneous distribution of GBM and thereby affect the survival stratification, especially T1W-CE and T2W sequences. However, the stratification performance using only the T1W-CE sequence can be preserved with omission of other three sequences, when investigating the effect of GBM heterogeneity on survival stratification.

scholarly journals Use of Machine Learning to Investigate the Quantitative Checklist for Autism in Toddlers (Q-CHAT) towards Early Autism Screening

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 574
Author(s):  
Gennaro Tartarisco ◽  
Giovanni Cicceri ◽  
Davide Di Pietro ◽  
Elisa Leonardi ◽  
Stefania Aiello ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Behavioral Science ◽  
Autistic Traits ◽  
Classification Performance ◽  
Recursive Feature Elimination ◽  
Diagnostic Tools ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Autism Screening

In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM–recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings.

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