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

Wu, Liuxing; Hu, Xin; Dai, Hongji; Chen, Kexin; Liu, Ben

doi:10.3389/fcell.2021.718912

Cited by 8 publications

(7 citation statements)

References 86 publications

(70 reference statements)

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“…IL22RA2 is highly expressed in various tissues, including those in the female reproductive system [ 38 ]. With respect to genes associated with other cancers, NXT2 was among 12 genes used to define prognostic risk groups in melanoma [ 39 ]. A review article described that the aberrant expression of HS3ST3B1 is observed in many cancers, and the authors posited that HS3ST3B1 may act as a tumor-promoting enzyme [ 40 ].…”

Section: Resultsmentioning

confidence: 99%

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

Archer

Seffernick

Sun

et al. 2022

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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.

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Section: Resultsmentioning

confidence: 99%

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

Archer

Seffernick

Sun

et al. 2022

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show abstract

“…IL22RA2 is highly expressed in various tissues including those in the female reproductive system [37]. With respect to genes associated with other cancers, NXT2 was among 12 genes used to define prognostic risk groups in melanoma [38]. A review article described that aberrant expression of HS3ST3B1 is observed in many cancers and the authors posited that HS3ST3B1 may act as a tumor-promoting enzyme [39].…”

Section: Resultsmentioning

confidence: 99%

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

Archer¹,

Seffernick²,

Sun³

et al. 2022

Preprint

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Stage of cancer is a discrete ordinal response that indicates 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 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 has 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 is capable of fitting cumulative logit models when the outcome is ordinal and the number of predictors exceeds the sample size, P>N, such as for TCGA data. We demonstrate use of this package through application to TCGA cervical cancer dataset. Our ordinalbayes package can be used to fit models to high-dimensional dataset and effectively performs variable selection.

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“…PAEP, a glycodelin gene, is connected with immune infiltration. It was identified in one study as an independent significant risk factor for Clear Cell Renal Cell Carcinoma (ccRCC) and as one of the survival predictive prognostic markers for melanoma ( Sawyer, 2021 ; Wu et al, 2021 ; Zeng et al, 2022 ). IDE is a multifunctional protease whose main therapeutic areas are neuronal diseases and metabolism.…”

Section: Discussionmentioning

confidence: 99%

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

Luo

Zhang

et al. 2022

Front. Cell Dev. Biol.

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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.

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

Cited by 8 publications

References 86 publications

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

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

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

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

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