An Integrated Genomic Strategy to Identify CHRNB4 as a Diagnostic/Prognostic Biomarker for Targeted Therapy in Head and Neck Cancer

Chuang, Yi Hsuan; Lee, Chia Hwa; Lin, Chun Yuan; Liu, Chia Lin; Huang, Sing‐Han; Lee, Jung Yu; Chiu, Yi Yuan; Lee, Jih Chin; Yang, Jinn-Moon

doi:10.3390/cancers12051324

Cited by 3 publications

(1 citation statement)

References 95 publications

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“…9 In recent years, biologically targeted therapy has been proven to be useful for various cancers, significantly improving the prognosis. [10][11][12][13] Therefore, the identification of new biomarkers is essential for the specific treatment of patients.…”

Section: Introductionmentioning

confidence: 99%

Transcriptomics and Prognosis Analysis to Identify Critical Biomarkers in Invasive Breast Carcinoma

Liu

et al. 2020

Technol Cancer Res Treat

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Objective: Invasive breast cancer (BRCA) is one of the prevalent types of invasive tumors with high mortality worldwide. Due to the lack of effective treatment to control the recurrence of distant metastases, the prognosis of BRCA is still very unsatisfactory. We aimed to find some biomarkers by bioinformatics analysis for survival prediction. Methods: Differentially expressed genes (DEGs) were screened out based on tumor group and normal group. Then, the weighted gene correlation network analysis (WGCNA) was employed to identify the clinically associated gene sets. Meanwhile, the enrichment analyses were performed for the functional annotation of the critical genes. The Kaplan Meier analysis calculated the essential genes’ prognostic value. Results: After threshold screening, 1655 DEGs were obtained for subsequent analysis. 51 out of 1655 DEGs were significantly associated with BRCA patients’ estrogen receptor status via WGCNA. Three genes (FABP7, CXCL3, and LOC284578) out of the 51 genes were associated with overall survival, and 3 genes were relapse-free survival associated. Finally, we obtained 5 essential prognostic associated genes (FABP7, CXCL3, LOC284578, CAPN6, and NRG2), which could be used as prognostic factors for BRCA. Conclusion: Our findings obtained a gene module associated with BRCA clinical trait and several key genes that acted as essential components in the prognostic of cancer, which may improve its treatment.

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Section: Introductionmentioning

confidence: 99%

Transcriptomics and Prognosis Analysis to Identify Critical Biomarkers in Invasive Breast Carcinoma

Liu

et al. 2020

Technol Cancer Res Treat

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Gene set correlation enrichment analysis for interpreting and annotating gene expression profiles

Chang,

Lee,

et al. 2023

Nucleic Acids Research

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Pathway analysis, including nontopology-based (non-TB) and topology-based (TB) methods, is widely used to interpret the biological phenomena underlying differences in expression data between two phenotypes. By considering dependencies and interactions between genes, TB methods usually perform better than non-TB methods in identifying pathways that include closely relevant or directly causative genes for a given phenotype. However, most TB methods may be limited by incomplete pathway data used as the reference network or by difficulties in selecting appropriate reference networks for different research topics. Here, we propose a gene set correlation enrichment analysis method, Gscore, based on an expression dataset-derived coexpression network to examine whether a differentially expressed gene (DEG) list (or each of its DEGs) is associated with a known gene set. Gscore is better able to identify target pathways in 89 human disease expression datasets than eight other state-of-the-art methods and offers insight into how disease-wide and pathway-wide associations reflect clinical outcomes. When applied to RNA-seq data from COVID-19-related cells and patient samples, Gscore provided a means for studying how DEGs are implicated in COVID-19-related pathways. In summary, Gscore offers a powerful analytical approach for annotating individual DEGs, DEG lists, and genome-wide expression profiles based on existing biological knowledge.

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Identification of transcription factors associated with the disease-free survival of triple-negative breast cancer through weighted gene co-expression network analysis

Wang,

Hao,

Liu

et al. 2024

Cytojournal

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Objective: Triple-negative breast cancer (TNBC) is a subtype of breast cancer that has a worse prognosis than the other subtypes of breast cancer because of its high recurrence and metastasis rates. The objective of this study is to identify the regulatory factors that are associated with the disease-free survival (DFS) of TNBC and potential biomarkers for TNBC treatment. Material and Methods: We obtained the GSE97342 dataset from the Gene Expression Omnibus website and conducted weighted gene co-expression network analysis (WGCNA) to identify modules associated with the DFS of TNBC. Subsequently, biological functions of the modules were elucidated through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Cross-checking with the Human Transcription Factor Database facilitated the selection of hub transcription factors through univariate Cox regression analysis of overlapping transcription factors. Utilizing bioinformatics analysis, we assessed the prognostic significance of these hub transcription factors, investigated their target genes, and explored their associations with tumor immune cells in TNBC. Finally, the expression levels of the hub transcription factors were validated by immunohistochemical staining, quantitative reverse transcription polymerase chain reaction (qRT-PCR), and Western blotting. Results: Through WGCNA analysis, we identified three modules correlated with DFS in TNBC. GO and KEGG analyses elucidated the biological functions of genes within these modules. Survival analysis pinpointed three hub transcription factors: Forkhead box D1 (FOXD1), aryl hydrocarbon receptor nuclear translocator 2 (ARNT2), and zinc finger protein 132 (ZNF132). The expression level of FOXD1 was negatively associated with the prognoses of patients with TNBC, whereas the other two genes were positively associated with the prognoses of patients with TNBC. Immunohistochemical staining, qRT-PCR, and Western blotting validated the expression levels of the hub transcription factors. Conclusion: We discovered three hub transcription factors (FOXD1, ARNT2, and ZNF132) that were correlated with the DFS of TNBC. These correlations suggested their potential as prognostic predictors for patients with TNBC.

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An Integrated Genomic Strategy to Identify CHRNB4 as a Diagnostic/Prognostic Biomarker for Targeted Therapy in Head and Neck Cancer

Cited by 3 publications

References 95 publications

Transcriptomics and Prognosis Analysis to Identify Critical Biomarkers in Invasive Breast Carcinoma

Transcriptomics and Prognosis Analysis to Identify Critical Biomarkers in Invasive Breast Carcinoma

Gene set correlation enrichment analysis for interpreting and annotating gene expression profiles

Identification of transcription factors associated with the disease-free survival of triple-negative breast cancer through weighted gene co-expression network analysis

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