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Methodological workflow of the study: The analysis integrates statistical and ML approaches across diverse biological data types (DNA <t>methylation,</t> microarray, and RNA-seq). The statistical approach retrieves differential genes from the GEO database, while the ML approach focuses on the TCGA database. Integrated MeDEGs and significant features identify 27 hub genes, establishing them as potential CRC biomarkers. These genes undergo validation of promoter methylation; stage-based expression profiling and Regulatory network analysis deriving candidate genes showing an elevated expression. Candidate genes are analyzed for prognostic and correlational relationships with immune cells to identify molecular signatures with diagnostic and prognostic potential, establishing them as therapeutic targets related to immune infiltration in CRC
Human Methylation 27 K, supplied by Illumina Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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human methylation 27 k platform  (Illumina Inc)
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Methodological workflow of the study: The analysis integrates statistical and ML approaches across diverse biological data types <t>(DNA</t> <t>methylation,</t> microarray, and RNA-seq). The statistical approach retrieves differential genes from the GEO database, while the ML approach focuses on the TCGA database. Integrated MeDEGs and significant features identify 27 hub genes, establishing them as potential CRC biomarkers. These genes undergo validation of promoter methylation; stage-based expression profiling and Regulatory network analysis deriving candidate genes showing an elevated expression. Candidate genes are analyzed for prognostic and correlational relationships with immune cells to identify molecular signatures with diagnostic and prognostic potential, establishing them as therapeutic targets related to immune infiltration in CRC
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genome-wide methylation chips illumina human methylation beadchips 27 k  (Illumina Inc)
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Methodological workflow of the study: The analysis integrates statistical and ML approaches across diverse biological data types <t>(DNA</t> <t>methylation,</t> microarray, and RNA-seq). The statistical approach retrieves differential genes from the GEO database, while the ML approach focuses on the TCGA database. Integrated MeDEGs and significant features identify 27 hub genes, establishing them as potential CRC biomarkers. These genes undergo validation of promoter methylation; stage-based expression profiling and Regulatory network analysis deriving candidate genes showing an elevated expression. Candidate genes are analyzed for prognostic and correlational relationships with immune cells to identify molecular signatures with diagnostic and prognostic potential, establishing them as therapeutic targets related to immune infiltration in CRC
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genome-wide methylation chips illumina human methylation beadchips 27 k - by Bioz Stars, 2026-07
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Methodological workflow of the study: The analysis integrates statistical and ML approaches across diverse biological data types (DNA methylation, microarray, and RNA-seq). The statistical approach retrieves differential genes from the GEO database, while the ML approach focuses on the TCGA database. Integrated MeDEGs and significant features identify 27 hub genes, establishing them as potential CRC biomarkers. These genes undergo validation of promoter methylation; stage-based expression profiling and Regulatory network analysis deriving candidate genes showing an elevated expression. Candidate genes are analyzed for prognostic and correlational relationships with immune cells to identify molecular signatures with diagnostic and prognostic potential, establishing them as therapeutic targets related to immune infiltration in CRC

Journal: Discover Oncology

Article Title: IL-1β and associated molecules as prognostic biomarkers linked with immune cell infiltration in colorectal cancer: an integrated statistical and machine learning approach

doi: 10.1007/s12672-025-01989-3

Figure Lengend Snippet: Methodological workflow of the study: The analysis integrates statistical and ML approaches across diverse biological data types (DNA methylation, microarray, and RNA-seq). The statistical approach retrieves differential genes from the GEO database, while the ML approach focuses on the TCGA database. Integrated MeDEGs and significant features identify 27 hub genes, establishing them as potential CRC biomarkers. These genes undergo validation of promoter methylation; stage-based expression profiling and Regulatory network analysis deriving candidate genes showing an elevated expression. Candidate genes are analyzed for prognostic and correlational relationships with immune cells to identify molecular signatures with diagnostic and prognostic potential, establishing them as therapeutic targets related to immune infiltration in CRC

Article Snippet: TCGA , Data category: DNA methylation and transcriptome profiling. Data availability: Open Access , Keywords: Colorectal cancer Filters for Data type: Gene Expression Quantification and methylation beta values; Platform: Illumina Human Methylation 27 K and Illumina Human Methylation 450 K , 699 CRC samples (51 normal and 648 tumour) , , 861 CRC samples (119 normal and 742 tumor).

Techniques: DNA Methylation Assay, Microarray, RNA Sequencing, Biomarker Discovery, Methylation, Expressing, Diagnostic Assay

The filters/keyword used for retrieving different type of datasets from the public repositories i.e., TCGA and GEO database

Journal: Discover Oncology

Article Title: IL-1β and associated molecules as prognostic biomarkers linked with immune cell infiltration in colorectal cancer: an integrated statistical and machine learning approach

doi: 10.1007/s12672-025-01989-3

Figure Lengend Snippet: The filters/keyword used for retrieving different type of datasets from the public repositories i.e., TCGA and GEO database

Article Snippet: TCGA , Data category: DNA methylation and transcriptome profiling. Data availability: Open Access , Keywords: Colorectal cancer Filters for Data type: Gene Expression Quantification and methylation beta values; Platform: Illumina Human Methylation 27 K and Illumina Human Methylation 450 K , 699 CRC samples (51 normal and 648 tumour) , , 861 CRC samples (119 normal and 742 tumor).

Techniques: Microarray, DNA Methylation Assay, Selection, Methylation, Expressing, Next-Generation Sequencing, Gene Expression

The differential common genes derived from the analysis of DNA methylation, RNA-seq and microarray gene expression data for colorectal cancer. A combination of the common Hypermethylated-downregulated genes (Module I); B combination of the common Hypomethylated-upregulated genes (Module II); C Calculation of overlapping genes between Module I and Module II

Journal: Discover Oncology

Article Title: IL-1β and associated molecules as prognostic biomarkers linked with immune cell infiltration in colorectal cancer: an integrated statistical and machine learning approach

doi: 10.1007/s12672-025-01989-3

Figure Lengend Snippet: The differential common genes derived from the analysis of DNA methylation, RNA-seq and microarray gene expression data for colorectal cancer. A combination of the common Hypermethylated-downregulated genes (Module I); B combination of the common Hypomethylated-upregulated genes (Module II); C Calculation of overlapping genes between Module I and Module II

Article Snippet: TCGA , Data category: DNA methylation and transcriptome profiling. Data availability: Open Access , Keywords: Colorectal cancer Filters for Data type: Gene Expression Quantification and methylation beta values; Platform: Illumina Human Methylation 27 K and Illumina Human Methylation 450 K , 699 CRC samples (51 normal and 648 tumour) , , 861 CRC samples (119 normal and 742 tumor).

Techniques: Derivative Assay, DNA Methylation Assay, RNA Sequencing, Microarray, Gene Expression

The classification report of the generated random forest and KNN classification model for both transcriptomics and DNA methylation CRC dataset

Journal: Discover Oncology

Article Title: IL-1β and associated molecules as prognostic biomarkers linked with immune cell infiltration in colorectal cancer: an integrated statistical and machine learning approach

doi: 10.1007/s12672-025-01989-3

Figure Lengend Snippet: The classification report of the generated random forest and KNN classification model for both transcriptomics and DNA methylation CRC dataset

Article Snippet: TCGA , Data category: DNA methylation and transcriptome profiling. Data availability: Open Access , Keywords: Colorectal cancer Filters for Data type: Gene Expression Quantification and methylation beta values; Platform: Illumina Human Methylation 27 K and Illumina Human Methylation 450 K , 699 CRC samples (51 normal and 648 tumour) , , 861 CRC samples (119 normal and 742 tumor).

Techniques: Generated, DNA Methylation Assay, Methylation

Methodological workflow of the study: The analysis integrates statistical and ML approaches across diverse biological data types (DNA methylation, microarray, and RNA-seq). The statistical approach retrieves differential genes from the GEO database, while the ML approach focuses on the TCGA database. Integrated MeDEGs and significant features identify 27 hub genes, establishing them as potential CRC biomarkers. These genes undergo validation of promoter methylation; stage-based expression profiling and Regulatory network analysis deriving candidate genes showing an elevated expression. Candidate genes are analyzed for prognostic and correlational relationships with immune cells to identify molecular signatures with diagnostic and prognostic potential, establishing them as therapeutic targets related to immune infiltration in CRC

Journal: Discover Oncology

Article Title: IL-1β and associated molecules as prognostic biomarkers linked with immune cell infiltration in colorectal cancer: an integrated statistical and machine learning approach

doi: 10.1007/s12672-025-01989-3

Figure Lengend Snippet: Methodological workflow of the study: The analysis integrates statistical and ML approaches across diverse biological data types (DNA methylation, microarray, and RNA-seq). The statistical approach retrieves differential genes from the GEO database, while the ML approach focuses on the TCGA database. Integrated MeDEGs and significant features identify 27 hub genes, establishing them as potential CRC biomarkers. These genes undergo validation of promoter methylation; stage-based expression profiling and Regulatory network analysis deriving candidate genes showing an elevated expression. Candidate genes are analyzed for prognostic and correlational relationships with immune cells to identify molecular signatures with diagnostic and prognostic potential, establishing them as therapeutic targets related to immune infiltration in CRC

Article Snippet: , DNA methylation TCGA-CRC datasets analysis (Illumina human methylation 27 k platform) , , , , , , , .

Techniques: DNA Methylation Assay, Microarray, RNA Sequencing, Biomarker Discovery, Methylation, Expressing, Diagnostic Assay

The filters/keyword used for retrieving different type of datasets from the public repositories i.e., TCGA and GEO database

Journal: Discover Oncology

Article Title: IL-1β and associated molecules as prognostic biomarkers linked with immune cell infiltration in colorectal cancer: an integrated statistical and machine learning approach

doi: 10.1007/s12672-025-01989-3

Figure Lengend Snippet: The filters/keyword used for retrieving different type of datasets from the public repositories i.e., TCGA and GEO database

Article Snippet: , DNA methylation TCGA-CRC datasets analysis (Illumina human methylation 27 k platform) , , , , , , , .

Techniques: Microarray, DNA Methylation Assay, Selection, Methylation, Expressing, Next-Generation Sequencing, Gene Expression

The differential common genes derived from the analysis of DNA methylation, RNA-seq and microarray gene expression data for colorectal cancer. A combination of the common Hypermethylated-downregulated genes (Module I); B combination of the common Hypomethylated-upregulated genes (Module II); C Calculation of overlapping genes between Module I and Module II

Journal: Discover Oncology

Article Title: IL-1β and associated molecules as prognostic biomarkers linked with immune cell infiltration in colorectal cancer: an integrated statistical and machine learning approach

doi: 10.1007/s12672-025-01989-3

Figure Lengend Snippet: The differential common genes derived from the analysis of DNA methylation, RNA-seq and microarray gene expression data for colorectal cancer. A combination of the common Hypermethylated-downregulated genes (Module I); B combination of the common Hypomethylated-upregulated genes (Module II); C Calculation of overlapping genes between Module I and Module II

Article Snippet: , DNA methylation TCGA-CRC datasets analysis (Illumina human methylation 27 k platform) , , , , , , , .

Techniques: Derivative Assay, DNA Methylation Assay, RNA Sequencing, Microarray, Gene Expression

The classification report of the generated random forest and KNN classification model for both transcriptomics and DNA methylation CRC dataset

Journal: Discover Oncology

Article Title: IL-1β and associated molecules as prognostic biomarkers linked with immune cell infiltration in colorectal cancer: an integrated statistical and machine learning approach

doi: 10.1007/s12672-025-01989-3

Figure Lengend Snippet: The classification report of the generated random forest and KNN classification model for both transcriptomics and DNA methylation CRC dataset

Article Snippet: , DNA methylation TCGA-CRC datasets analysis (Illumina human methylation 27 k platform) , , , , , , , .

Techniques: Generated, DNA Methylation Assay, Methylation