Prognostic significance of ferroptosis pathway gene signature and correlation with macrophage infiltration in cervical squamous cell carcinoma

Li, Chia-Jung; Chang, Chi‐Sen; Tsang, Yi-Ling; Fang, Shao-Hsuan; Chen, San-Nung; Chiang, An‐Jen

doi:10.1016/j.intimp.2022.109273

Cited by 13 publications

(8 citation statements)

References 32 publications

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“…The first two genes identified were Cuproptosis-associated genes. The NFE2L2 gene is now mostly studied in the cancer field; in esophageal squamous cell carcinoma NFE2L2 may confer oncogenic activity [ 39 ]; in cervical squamous carcinoma it is involved in immune prognosis, mainly acting in the tumor microenvironment [ 40 ]; autophagy and the NFE2L2 pathway activate ubiquitin ligases in prostate cancer [ 41 ]. In the non-cancerous cellular domain, NFE2L2 gene variants affect metabolic and renal function parameters in patients with diabetes and hypertension [ 42 ]; are also genetic markers of susceptibility to cirrhosis [ 43 ] ; and evidence has even been found in the effect of obesity on heart rate [ 44 ].…”

Section: Discussionmentioning

confidence: 99%

Identification and validation of cuproptosis related genes and signature markers in bronchopulmonary dysplasia disease using bioinformatics analysis and machine learning

Jia

Zhang

et al. 2023

BMC Med Inform Decis Mak

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Background Bronchopulmonary Dysplasia (BPD) has a high incidence and affects the health of preterm infants. Cuproptosis is a novel form of cell death, but its mechanism of action in the disease is not yet clear. Machine learning, the latest tool for the analysis of biological samples, is still relatively rarely used for in-depth analysis and prediction of diseases. Methods and results First, the differential expression of cuproptosis-related genes (CRGs) in the GSE108754 dataset was extracted and the heat map showed that the expression of NFE2L2 gene was significantly higher in the control group whereas the expression of GLS gene was significantly higher in the treatment group. Chromosome location analysis showed that both the genes were positively correlated and associated with chromosome 2. The results of immune infiltration and immune cell differential analysis showed differences in the four immune cells, significantly in Monocytes cells. Five new pathways were analyzed through two subgroups based on consistent clustering of CRG expression. Weighted correlation network analysis (WGCNA) set the screening condition to the top 25% to obtain the disease signature genes. Four machine learning algorithms: Generalized Linear Models (GLM), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB) were used to screen the disease signature genes, and the final five marker genes for disease prediction. The models constructed by GLM method were proved to be more accurate in the validation of two datasets, GSE190215 and GSE188944. Conclusion We eventually identified two copper death-associated genes, NFE2L2 and GLS. A machine learning model-GLM was constructed to predict the prevalence of BPD disease, and five disease signature genes NFATC3, ERMN, PLA2G4A, MTMR9LP and LOC440700 were identified. These genes that were bioinformatics analyzed could be potential targets for identifying BPD disease and treatment.

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Section: Discussionmentioning

confidence: 99%

Identification and validation of cuproptosis related genes and signature markers in bronchopulmonary dysplasia disease using bioinformatics analysis and machine learning

Jia

Zhang

et al. 2023

BMC Med Inform Decis Mak

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“…We acquired scRNA-seq data from the GEO database (GSE162115 and GSE159929 datasets) and executed quality control (QC) through the R package Seurat, ensuring the inclusion of high-quality cells while reducing batch effects. To detect distinct cell subpopulations, we used uniform manifold approximation and projection (UMAP) clustering with the "BiocManager" and Gene Set Variation Analysis ("GSVA") packages in R. To annotate cell types, we compared the expression profiles of identified cell subpopulations with previously acknowledged cellular marker genes utilizing the "SingleR" package in R. Additionally, we followed the same methodology outlined in a previous study [ 34 , 35 ], utilizing TIMER to investigate the association between different ALKBH1 genes' expression and the abundance of immune infiltrates in STAD. We also assessed the correlation between ALKBH1 expression and genetic markers of tumor-infiltrating immune cells.…”

Section: Methodsmentioning

confidence: 99%

Spatial and single-cell analyses uncover links between ALKBH1 and tumor-associated macrophages in gastric cancer

Chang,

Tsui,

Pan

et al. 2024

Cancer Cell Int

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Background AlkB homolog 1, histone H2A dioxygenase (ALKBH1), a crucial enzyme involved in RNA demethylation in humans, plays a significant role in various cellular processes. While its role in tumor progression is well-established, its specific contribution to stomach adenocarcinoma (STAD) remains elusive. This study seeks to explore the clinical and pathological relevance of ALKBH1, its impact on the tumor immune microenvironment, and its potential for precision oncology in STAD. Methods We adopted a comprehensive multi-omics approach to identify ALKBH1 as an potential diagnostic biomarker for STAD, demonstrating its association with advanced clinical stages and reduced overall survival rates. Our analysis involved the utilization of publicly available datasets from GEO and TCGA. We identified differentially expressed genes in STAD and scrutinized their relationships with immune gene expression, overall survival, tumor stage, gene mutation profiles, and infiltrating immune cells. Moreover, we employed spatial transcriptomics to investigate ALKBH1 expression across distinct regions of STAD. Additionally, we conducted spatial transcriptomic and single-cell RNA-sequencing analyses to elucidate the correlation between ALKBH1 expression and immune cell populations. Our findings were validated through immunohistochemistry and bioinformatics on 60 STAD patient samples. Results Our study unveiled crucial gene regulators in STAD linked with genetic variations, deletions, and the tumor microenvironment. Mutations in these regulators demonstrated a positive association with distinct immune cell populations across six immune datasets, exerting a substantial influence on immune cell infiltration in STAD. Furthermore, we established a connection between elevated ALKBH1 expression and macrophage infiltration in STAD. Pharmacogenomic analysis of gastric cancer cell lines further indicated that ALKBH1 inactivation correlated with heightened sensitivity to specific small-molecule drugs. Conclusion In conclusion, our study highlights the potential role of ALKBH1 alterations in the advancement of STAD, shedding light on novel diagnostic and prognostic applications of ALKBH1 in this context. We underscore the significance of ALKBH1 within the tumor immune microenvironment, suggesting its utility as a precision medicine tool and for drug screening in the management of STAD.

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“…We employed a method that has been described previously, which is summarized as follows [ 18 ]. Immunofluorescence analysis was performed by fixing the tissue with 4% paraformaldehyde at room temperature for 10 minutes, permeabilizing it with 0.2% Triton X-100 for 5 minutes, blocking it with 5% BSA for 30 minutes, and incubating it overnight at 4° C with the specified primary antibodies PGAM1_A4170 and CD163_A22619 purchased from Abclonal (Woburn, MA, USA).…”

Section: Methodsmentioning

confidence: 99%

Single-cell landscape and spatial transcriptomic analysis reveals macrophage infiltration and glycolytic metabolism in kidney renal clear cell carcinoma

Wen,

Hsiao,

Tzeng

et al. 2023

Aging

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The present study investigates the clinical relevance of glycolytic factors, specifically PGAM1, in the tumor microenvironment of kidney renal clear cell carcinoma (KIRC). Despite the established role of glycolytic metabolism in cancer pathophysiology, the prognostic implications and key targets in KIRC remain elusive. We analyzed GEO and TCGA datasets to identify DEGs in KIRC and studied their relationship with immune gene expression, survival, tumor stage, gene mutations, and infiltrating immune cells. We explored Pgam1 gene expression in different kidney regions using spatial transcriptomics after mouse kidney injury analysis. Single-cell RNA-sequencing was used to assess the association of PGAM1 with immune cells. Findings were validated with tumor specimens from 60 KIRC patients, correlating PGAM1 expression with clinicopathological features and prognosis using bioinformatics and immunohistochemistry. We demonstrated the expression of central gene regulators in renal cancer in relation to genetic variants, deletions, and tumor microenvironment. Mutations in these hub genes were positively associated with distinct immune cells in six different immune datasets and played a crucial role in immune cell infiltration in KIRC. Single-cell RNA-sequencing revealed that elevated PGAM1 was associated with immune cell infiltration, specifically macrophages. Furthermore, pharmacogenomic analysis of renal cancer cell lines indicated that inactivation of PGAM1 was associated with increased sensitivity to specific small-molecule drugs. Altered PGAM1 in KIRC is associated with disease progression and immune microenvironment. It has diagnostic and prognostic implications, indicating its potential in precision medicine and drug screening.

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Prognostic significance of ferroptosis pathway gene signature and correlation with macrophage infiltration in cervical squamous cell carcinoma

Cited by 13 publications

References 32 publications

Identification and validation of cuproptosis related genes and signature markers in bronchopulmonary dysplasia disease using bioinformatics analysis and machine learning

Identification and validation of cuproptosis related genes and signature markers in bronchopulmonary dysplasia disease using bioinformatics analysis and machine learning

Spatial and single-cell analyses uncover links between ALKBH1 and tumor-associated macrophages in gastric cancer

Single-cell landscape and spatial transcriptomic analysis reveals macrophage infiltration and glycolytic metabolism in kidney renal clear cell carcinoma

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