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 Identification of four genes associated with cutaneous metastatic melanoma

Open Medicine ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. 531-539
Author(s):  
Chen Ji ◽  
Yuming Li ◽  
Kai Yang ◽  
Yanwei Gao ◽  
Yan Sha ◽  
...  
Keyword(s):  
Survival Rate ◽  
Metastatic Melanoma ◽  
Cutaneous Melanoma ◽  
Target Genes ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Regression Analyses ◽  
Incidence And Mortality ◽  
Cancer Genome Atlas ◽  
Novel Target

AbstractBackgroundCutaneous melanoma is an aggressive cancer with increasing incidence and mortality rates worldwide. Metastasis is one of the primary elements that influence the prognosis of patients with cutaneous melanoma. This study aims to clarify the potential mechanism underlying the low survival rate of metastatic melanoma and to search for novel target genes to improve the survival rate of patients with metastatic tumors.MethodsGene expression dataset and clinical data were downloaded from The Cancer Genome Atlas portal. Differentially expressed genes (DEGs) were identified, and their functions were studied through gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. Survival and multivariate Cox regression analyses were used to screen out candidate genes that could affect the prognosis of patients with metastatic melanoma.ResultsAfter a series of comprehensive statistical analysis, 464 DEGs were identified between primary tumor tissues and metastatic tissues. Survival and multivariate Cox regression analyses revealed four vital genes, namely, POU2AF1, ITGAL, CXCR2P1, and MZB1, that affect the prognosis of patients with metastatic melanoma.ConclusionThis study provides a new direction for studying the pathogenesis of metastatic melanoma. The genes related to cutaneous metastatic melanoma that affect the overall survival time of patients were identified.

scholarly journals Prognostic value of intratumoral lymphocyte-to-monocyte ratio and M0 macrophage enrichment in tumor immune microenvironment of melanoma

2020 ◽  
Vol 7 (4) ◽  
pp. MMT51
Author(s):  
Neil K Jairath ◽  
Mark W Farha ◽  
Ruple Jairath ◽  
Paul W Harms ◽  
Lam C Tsoi ◽  
...  
Keyword(s):  
Cutaneous Melanoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Primary Melanoma ◽  
The Cancer Genome Atlas ◽  
Significant Heterogeneity ◽  
Sequencing Data ◽  
Melanoma Tumor ◽  
Poor Prognostic Factor ◽  
Lymphocyte To Monocyte Ratio

Skin cutaneous melanoma is characterized by significant heterogeneity in its molecular, genomic and immunologic features. Whole transcriptome RNA sequencing data from The Cancer Genome Atlas of skin cutaneous melanoma (n = 328) was utilized. CIBERSORT was used to identify immune cell type composition, on which unsupervised hierarchical clustering was performed. Analysis of overall survival was performed using Kaplan–Meier estimates and multivariate Cox regression analyses. Membership in the lymphocyte:monocytelow, monocytehigh and M0high cluster was an independently poor prognostic factor for survival (HR: 3.03; 95% CI: 1.12–8.20; p = 0.029) and correlated with decreased predicted response to immune checkpoint blockade. In conclusion, an M0-macrophage-enriched, lymphocyte-to-monocyte-ratio-low phenotype in the primary melanoma tumor site independently characterizes an aggressive phenotype that may differentially respond to treatment.

scholarly journals Identification of the angiogenesis related genes for predicting prognosis of patients with gastric cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sheng Zheng ◽  
Zizhen Zhang ◽  
Ning Ding ◽  
Jiawei Sun ◽  
Yifeng Lin ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
High Efficiency ◽  
Cox Regression ◽  
Risk Group ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Lasso Regression ◽  
Sequencing Data ◽  
Cox Regression Analysis

Abstract Introduction Angiogenesis is a key factor in promoting tumor growth, invasion and metastasis. In this study we aimed to investigate the prognostic value of angiogenesis-related genes (ARGs) in gastric cancer (GC). Methods mRNA sequencing data with clinical information of GC were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The differentially expressed ARGs between normal and tumor tissues were analyzed by limma package, and then prognosis‑associated genes were screened using Cox regression analysis. Nine angiogenesis genes were identified as crucially related to the overall survival (OS) of patients through least absolute shrinkage and selection operator (LASSO) regression. The prognostic model and corresponding nomograms were establish based on 9 ARGs and verified in in both TCGA and GEO GC cohorts respectively. Results Eighty-five differentially expressed ARGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that ARGs-related signaling pathway genes were highly related to tumor angiogenesis development. Kaplan–Meier analysis revealed that patients in the high-risk group had worse OS rates compared with the low-risk group in training cohort and validation cohort. In addition, RS had a good prognostic effect on GC patients with different clinical features, especially those with advanced GC. Besides, the calibration curves verified fine concordance between the nomogram prediction model and actual observation. Conclusions We developed a nine gene signature related to the angiogenesis that can predict overall survival for GC. It’s assumed to be a valuable prognosis model with high efficiency, providing new perspectives in targeted therapy.

scholarly journals Effect of Different Expression of Immune-Related lncRNA on Colon Adenocarcinoma and Its Relation to Prognosis

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Meiwei Mu ◽  
Yi Tang ◽  
Zheng Yang ◽  
Yuling Qiu ◽  
Xiaohong Li ◽  
...  
Keyword(s):  
Cox Regression ◽  
Colon Adenocarcinoma ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Future Research ◽  
Sequencing Data ◽  
Tissue Samples ◽  
Gene Sets ◽  
Cancer Genome Atlas ◽  
Immune Related Genes

Objective. To explore the expression of immune-related lncRNAs in colon adenocarcinoma and find out the effect on how these lncRNAs influence the development and prognosis of colon adenocarcinoma. Method. Transcriptome data of colon adenocarcinoma from The Cancer Genome Atlas (TCGA) were downloaded, and gene sets “IMMUNE RESPONSE” and “IMMUNE SYSTEM PROCESS” were sought from the Molecular Signatures Database (MSigDB). The expression of immune-related genes was extracted that were immune-related mRNAs. Then, the immune-related lncRNAs were sought out by utilizing of the above data. Clinical traits were combined with immune-related lncRNAs, so that prognostic-related lncRNAs were identified by Cox regression. Multivariate Cox regression was built to calculate risk scores. Relationships between clinical traits and immune-related lncRNAs were also calculated. Result. A total of 480 colorectal adenocarcinoma patients and 41 normal control patients’ transcriptome sequencing data of tissue samples were obtained from TCGA database. 918 immune-related lncRNAs were screened. Cox regression showed that 34 immune-related lncRNAs were associated with colon adenocarcinoma prognosis. Seven lncRNAs were independent risk factors. Conclusion. This study revealed that some lncRNAs can affect the development and prognosis of colon adenocarcinoma. It may provide new theory evidence of molecular mechanism for the future research and molecular targeted therapy of colon adenocarcinoma.

scholarly journals Ferroptosis-Related Long Non-Coding RNAs and the Roles of LASTR in Stomach Adenocarcinoma

2021 ◽  
Author(s):  
Gongjun Wang ◽  
Libin Sun ◽  
Shasha Wang ◽  
Jing Guo ◽  
Hui Li ◽  
...  
Keyword(s):  
Cox Regression ◽  
Comprehensive Evaluation ◽  
Progression Free Survival ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Sequencing Data ◽  
Regulatory Relationship ◽  
Data Resource ◽  
Stomach Adenocarcinoma

Abstract Background: Ferroptosis is a form of cell death involved in diverse physiological context. Increasing evidence suggests that there is a closely regulatory relationship between ferroptosis and long noncoding RNAs (lncRNAs).Method: RNA-sequencing data from The Cancer Genome Atlas (TCGA) data resource and ferroptosis-related genes from FerrDb (http://www.zhounan.org/ferrdb/) data resource were employed to select differentially expressed lncRNAs. We performed Univariate Cox regression and multivariate Cox analyses analysis on these differentially expressed lncRNAs to screen independent predictive factors. Subsequently, we established two signatures for predicting overall survival (OS) and progression-free survival (PFS). Finally, experiments were conducted to verify the roles of LASTR in gastric cancer (GC).Results: We identified 12 differentially expressed lncRNAs linked with OS and 13 associated with PFS. Kaplan-Meier(K-M) analyses exhibited that the high-risk group was related to a poor prognosis of stomach adenocarcinoma (STAD). The AUCs of the OS, as well as PFS signatures of lncRNAs were 0.734 and 0.771, respectively, indicating their excellent efficacy in predicting STAD prognosis. Our experimental results illustrated that the inhibition of LASTR inhibited tumor proliferation and migration in GC.Conclusion: This comprehensive evaluation of the ferroptosis-related lncRNA landscape in STAD unearthed novel lncRNAs related to carcinogenesis. In addition, we also experimentally confirmed the effects of LASTR on proliferation, migration and ferroptosis. These results provide potential novel targets for tumor treatment and promote personalized medicine.

scholarly journals Identification of a prognostic long non-coding RNA signature in breast cancer

2020 ◽  
Author(s):  
Gaochen Lan ◽  
Xiaoling Yu ◽  
Yanna Zhao ◽  
Jinjian Lan ◽  
Wan Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Cox Regression Analysis

Abstract Background: Breast cancer is the most common malignant disease among women. At present, more and more attention has been paid to long non-coding RNAs (lncRNAs) in the field of breast cancer research. We aimed to investigate the expression profiles of lncRNAs and construct a prognostic lncRNA for predicting the overall survival (OS) of breast cancer.Methods: The expression profiles of lncRNAs and clinical data with breast cancer were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened out by R package (limma). The survival probability was estimated by the Kaplan‑Meier Test. The Cox Regression Model was performed for univariate and multivariate analysis. The risk score (RS) was established on the basis of the lncRNAs’ expression level (exp) multiplied regression coefficient (β) from the multivariate cox regression analysis with the following formula: RS=exp a1 * β a1 + exp a2 * β a2 +……+ exp an * β an. Functional enrichment analysis was performed by Metascape.Results: A total of 3404 differentially expressed lncRNAs were identified. Among them, CYTOR, MIR4458HG and MAPT-AS1 were significantly associated with the survival of breast cancer. Finally, The RS could predict OS of breast cancer (RS=exp CYTOR * β CYTOR + exp MIR4458HG * β MIR4458HG + exp MAPT-AS1 * β MAPT-AS1). Moreover, it was confirmed that the three-lncRNA signature could be an independent prognostic biomarker for breast cancer (HR=3.040, P=0.000).Conclusions: This study established a three-lncRNA signature, which might be a novel prognostic biomarker for breast cancer.

Synthesize Analysis of the IFN-γ and Immune Infiltrates of m6A RNA Methylation Regulators in Human Skin Cutaneous Melanoma

2021 ◽  
Author(s):  
Yuqi Wang ◽  
Le Huang ◽  
Mingxin Li ◽  
Yunfeng Qi
Keyword(s):  
Cutaneous Melanoma ◽  
Cox Regression ◽  
Tissue Expression ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Mortality And Morbidity ◽  
Tumour Immunity ◽  
Ifn Γ ◽  
Expression Pathways ◽  
Immune Pathway

Abstract Background: SKCM is a major common cancer with highly mortality and morbidity, causing about 72% of deaths in skin carcinoma. In recent years, more scholars have selected one or two m6A regulatory factors to explore the abnormal expression and potential mechanism of m6A in tumorigenesis and development. So as to find the relevant biomarkers in the whole tumorigenesis process.Methods: In current study, we used public transcriptome datasets from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Genotype-Tissue Expression (GTEx) to investigate the relationships between N6-methyl adenosine(m6A) regulators genes and Skin cutaneous melanoma (SKCM). Then, SKCM patients were grouped based on the cluster analysis of m6A regulators expression. Compared the relationship between Interferon (IFN)-γ and immune infiltrates. Results: Based on the survival curves of subgroups, we selected 20 potential predictive m6A-related genes were overexpressed in SKCM. And based on the typing results, we got the 15 differential expression genes between the three groups. Four of them (CYP2U1, SEMA6A, GRIA2, and TSPAN13) were selected in Cox regression, which were significant expression in overall survival(p<0.05). Interferon (IFN)-γ is a central cytokine, which effecting immune-provoking and recognizing transformed cells in anti-tumour immunity. Conclusions: To investigate the correlation between IFN-γ and SKCM. We have identified that IFN-γ is a potential factor for cancer therapy, and affecting expression pathways of SKCM. Among them, IFN-γ and WTAP were the most positive correlation. These sights suggest that IFN-γ can potentially be utilized for immune pathway such as melanoma skin cancer.

scholarly journals Opposite Roles of BAP1 in Overall Survival of Uveal Melanoma and Cutaneous Melanoma

2020 ◽  
Vol 9 (2) ◽  
pp. 411 ◽  
Author(s):  
Feng Liu-Smith ◽  
Yunxia Lu
Keyword(s):  
Overall Survival ◽  
Uveal Melanoma ◽  
Cutaneous Melanoma ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Copy Number Variations ◽  
The Cancer Genome Atlas ◽  
Small Subset ◽  
Mrna Levels ◽  
Cancer Genome Atlas

Background: BRCA1-Associated Protein 1 (BAP1) germline mutations predispose individuals to cancers, including uveal melanoma (UM) and cutaneous melanoma (CM). BAP1 loss is common in UM and is associated with a worse prognosis. BAP1 loss is rare in CM and the outcome is unclear. Methods: UM and CM data was retrieved from The Cancer Genome Atlas (TCGA) database. Cox regression model was performed to examine whether BAP1 mRNA levels or copy number variations were associated with overall survival (OS). Results: BAP1-low mRNA predicted a poor OS in UM (HR = 9.57, 95% CI: 2.82, 32.5) but a contrasting better OS in CM (HR = 0.73, 95% CI: 0.56, 0.95). These results remained unchanged after adjusting for sex, age, and stage in UM and CM, or after adjusting for ulceration or Breslow depth in CM. Additionally, low BAP1 mRNA predicted a better OS in CM patients older than 50 years but not in younger patients. Co-expression and enrichment analysis revealed differential genes and mutations that were correlated with BAP1 expression levels in UM and CM tumors. Conclusions: Low BAP1 mRNA was significantly associated with a better OS in CM patients, in sharp contrast to UM. High BAP1 expression in CM was significantly associated with over-expressed CDK1, BCL2, and KIT at the protein level which may explain the poor OS in this sub-group of patients. Function of BAP1 was largely different in CM and UM despite of a small subset of shared co-expressed genes.

scholarly journals Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Qidong Cai ◽  
Boxue He ◽  
Pengfei Zhang ◽  
Zhenyu Zhao ◽  
Xiong Peng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alternative Splicing ◽  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Machine Learning Algorithms ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract Background Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms. Method RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs). Results Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient = − 1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them. Conclusion In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.

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.

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