Identifying TME signatures for cervical cancer prognosis based on GEO and TCGA databases

Xia, Wen-Tao; Qiu, Wang-Ren; Yu, Wangke; Xu, Zhaochun; Zhang, Shou-Hua

doi:10.1016/j.heliyon.2023.e15096

Cited by 3 publications

(5 citation statements)

References 49 publications

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“…Correspondingly, Xia et al, 2023 generated tumor signatures of cervical cancer by doing the expression analysis using limma on 306 cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) from TCGA and 215 CESC patients from the GEO portal. This was coupled with the application of an estimate algorithm, specialized in estimating the concentration of immune and stromal cells in the samples by using the gene expression data only (targeting the TCGA portal) [99] . Similarly, the gene co-expression network was constructed by importing DEGs to find the 268 co-expressed module genes in Alzheimer’s disease.…”

Section: Dna Methylation Microarray Data Analysismentioning

confidence: 99%

“…This analysis is pursued to investigate the immune infiltration landscape of the sample which can act as a measure for disease progression [100] . The survival analysis validates hub genes by assessing their impact on patient survival and the application of the TIMER database analyzes the role and correlation of immune cells with the progression of the disease [99] . Table 1 encompasses a comprehensive analysis of recent publications in DNA methylation-based studies over the past five years focusing majorly on the identification of DMRs/DMPs, giving a clear takeaway of result analysis done in this section.…”

Section: Dna Methylation Microarray Data Analysismentioning

confidence: 99%

“… (iii) 10 prognostic biomarkers are benchmarked as TME signatures. [99] a. LUAD- Lung squamous cell Adenocarcinoma. ; b. GDM- Gestational Diabetes Mellitus; c. RF-Random Forest; d. OSCC-oral squamous cell carcinoma; e. OPMDs- oral potentially malignant disorders; f. AIN3- Anal intraepithelial neoplasia-3; g. CIN3- cervical intraepithelial neoplasia-3; h. ccRCC- clear cell renal cell carcinoma; i. DS-Down syndrome; j. CHD- congenital heart defect.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Methods in DNA methylation array dataset analysis: A review

Sahoo,

Sundararajan

2024

Computational and Structural Biotechnology Journal

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Section: Dna Methylation Microarray Data Analysismentioning

confidence: 99%

Section: Dna Methylation Microarray Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Methods in DNA methylation array dataset analysis: A review

Sahoo,

Sundararajan

2024

Computational and Structural Biotechnology Journal

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“…In recent years, the field of bioinformatics has been extensively utilized to investigate targets for tumours and other diseases. 20 , 21 , 22 , 23 At the same time, the high accuracy and specificity of single‐cell sequencing technologies have rendered them an ideal tool for single‐cell research. The use of single‐cell sequencing technology facilitates high‐throughput, unbiased analysis of even extremely small sample sizes.…”

Section: Introductionmentioning

confidence: 99%

“…The advancement of genomics and bioinformatics research technology has facilitated the development and enhancement of disease databases, which serve as a theoretical foundation for the identification of novel therapeutic targets and disease mechanisms. In recent years, the field of bioinformatics has been extensively utilized to investigate targets for tumours and other diseases 20–23 . At the same time, the high accuracy and specificity of single‐cell sequencing technologies have rendered them an ideal tool for single‐cell research.…”

Section: Introductionmentioning

confidence: 99%

HMOX1 as a therapeutic target associated with diabetic foot ulcers based on single‐cell analysis and machine learning

Chen,

Zhang,

Jiang

et al. 2024

International Wound Journal

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Diabetic foot ulcers (DFUs) are a serious chronic complication of diabetes mellitus and a leading cause of disability and death in diabetic patients. However, current treatments remain unsatisfactory. Although macrophages are associated with DFU, their exact role in this disease remains uncertain. This study sought to detect macrophage‐related genes in DFU and identify possible therapeutic targets. Single‐cell datasets (GSE223964) and RNA‐seq datasets (GSM68183, GSE80178, GSE134431 and GSE147890) associated with DFU were retrieved from the gene expression omnibus (GEO) database for this study. Analysis of the provided single‐cell data revealed the distribution of macrophage subpopulations in the DFU. Four independent RNA‐seq datasets were merged into a single DFU cohort and further analysed using bioinformatics. This included differential expression (DEG) analysis, multiple machine learning algorithms to identify biomarkers and enrichment analysis. Finally, key results were validated using reverse transcription‐quantitative polymerase chain reaction (RT‐qPCR) and Western bolt. Finally, the findings were validated using RT‐qPCR and western blot. We obtained 802 macrophage‐related genes in single‐cell analysis. Differential expression analysis yielded 743 DEGs. Thirty‐seven macrophage‐associated DEGs were identified by cross‐analysis of marker genes with macrophage‐associated DEGs. Thirty‐seven intersections were screened and cross‐analysed using four machine learning algorithms. Finally, HMOX1 was identified as a potentially valuable biomarker. HMOX1 was significantly associated with biological pathways such as the insulin signalling pathway. The results showed that HMOX1 was significantly overexpressed in DFU samples. In conclusion, the analytical results of this study identified HMOX1 as a potentially valuable biomarker associated with macrophages in DFU. The results of our analysis improve our understanding of the mechanism of macrophage action in this disease and may be useful in developing targeted therapies for DFU.

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Integrating Single-cell RNA-seq to construct a Neutrophil prognostic model for predicting immune responses in cervical carcinoma

Qian,

Xieyidai,

Han

et al. 2023

Preprint

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Objective: The objective of this study is to comprehensively investigate the communication network within the tumor immune microenvironment (Tumor Immune Microenvironment, TIME) of cervical carcinoma (CC). This involves elucidating the intricate relationships among cells to gain a profound understanding of the interactions between immune cells and tumor cells，as well as the assessment of neutrophil differentiation characteristics and the selection of prognostic genes, the primary goal is to establish a risk model with the ability to predict patients' immune responses and prognosis. Additionally, this model seeks to uncover innovative diagnostic and therapeutic targets for cervical carcinoma, thereby furnishing clinicians with dependable strategies for treatment. Methods: Using single-cell RNA sequencing data (scRNA-seq) obtained from CC samples (E-MTAB-11948), this study employed the Seurat(4.3.0) package to integrate data, remove batch effects, and annotate cell types. A cell communication network was constructed using the iTAKL(0.1.0) package for the analysis of intercellular communication. Neutrophil subpopulations were analyzed utilizing the Monocle2(2.26.0) package to discern various cellular states and conduct pathway analysis using KEGG/GO annotations. Results: we successfully distinguished and further categorized 32 cell populations into 9 major cell types, encompassing T cells, B cells, Mast cells, Neutrophils, Epithelial cells, Endothelial cells, Monocytes, Fibroblasts, and Smooth muscle cells. Furthermore, we ascertained that five subgroups of Neutrophils, each representing diverse differentiation states, exhibit close associations with immune regulatory and metabolic pathways.From our analysis of intersecting genes in the TCGA-CESC dataset, we successfully identified four prognostic genes: C5AR1, HSPA5, CXCL2, and OLR1. The stability of our prognostic risk model has been reiterated through internal and external validation, demonstrating its high consistency, differentiation, and clinical applicability. Notably, the CIRBESORT analysis divulged diminished immune cell content within the tumor immune microenvironment of the high-risk group, correlating with an unfavorable prognosis. Low expression of C5AR1 and high expression of HSPA5, CXCL2, and OLR1 were significantly associated with shorter survival and poorer prognosis.Conclusion: This study elucidated the intricate regulatory network governing the immune microenvironment in CC and comprehensively analyzed intercellular interactions.highlighting the significant roles of C5AR1, HSPA5, CXCL2, and OLR1 in predicting patient prognosis and responsiveness to immunotherapy. These findings offer novel insights and potential strategies for identifying fresh treatment targets in CC. Conclusion: This study elucidated the regulatory network of immune microenvironment in CC, and analyzed the interaction between cells. the key roles of C5AR1, HSPA5, CXCL2 and OLR1 in predicting patient prognosis and response to immunotherapy were revealed. This provides new insights and possible strategies for finding new CC treatment targets.

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Identifying TME signatures for cervical cancer prognosis based on GEO and TCGA databases

Cited by 3 publications

References 49 publications

Methods in DNA methylation array dataset analysis: A review

Methods in DNA methylation array dataset analysis: A review

HMOX1 as a therapeutic target associated with diabetic foot ulcers based on single‐cell analysis and machine learning

Integrating Single-cell RNA-seq to construct a Neutrophil prognostic model for predicting immune responses in cervical carcinoma

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