The comparison of cancer gene mutation frequencies in Chinese and U.S. patient populations

Ma, Fayang; Laster, Kyle Vaughn; Dong, Zigang

doi:10.1038/s41467-022-33351-4

Cited by 9 publications

(5 citation statements)

References 80 publications

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“…Because of the association between gene mutations and the occurrence and progression of cancer, 13 we conducted an analysis of gene mutations in the two groups of patients. We utilized COAD SNV mutation data to calculate gene mutations in two groups, and selected top 30 tumor mutation burden (TMB) genes to draw a waterfall plot.…”

Section: Resultsmentioning

confidence: 99%

Construction of a prognostic model based on genes associated with mitochondrial energy metabolic pathway in colon adenocarcinoma and its clinical significance

Zhang

Liang

Zhou

et al. 2023

J of Molecular Recognition

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Mitochondria are the main sites of oxidative metabolism and energy release of sugars, fats and amino acids in the body. According to studies, malignant tumor occurrence and development have been linked to abnormal mitochondrial energy metabolism (MEM). However, the feasible role of abnormal MEM in colon adenocarcinoma (COAD) is poorly understood. In this work, we obtained COAD patient data from The Cancer Genome Atlas (TCGA) as the training set, and GSE103479 from Gene Expression Omnibus (GEO) as the validation set. Combined with the mitochondrial energy metabolic pathway (MEMP)-related genes in Kyoto Encyclopedia of Genes and Genomes (KEGG) database, a risk prognostic model was constructed by utilizing Cox regression analysis to identify 6 feature genes (CYP4A11, PGM2, PKLR, PPARGC1A, CPT2 and ACAT2) that were significantly associated with MEMP in COAD. By stratifying the samples based on riskscore, two distinct groups, namely the high-and low-risk groups, were identified. The model demonstrated accurate assessment of the prognosis risk in COAD patients and exhibited independent prognostic capability, as evidenced by the survival curve and receiver operating characteristic (ROC) curve analysis. A nomogram was plotted based on clinical information and riskscore. We proved it could predict the survival time of COAD patients effectively combined with the calibration curve of risk prediction. Subsequently, based on the immune evaluation and mutation frequency analysis performed on COAD patients, patients in high-risk group had observably higher immune scores, immune activity and PDCD1 expression level than low-risk group. In general, the prognostic model developed using MEMP-related genes served as a valuable biomarker for forecasting the prognosis of COAD patients, which offered a reference for the prognosis evaluation and clinical cure of COAD patients.

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Section: Resultsmentioning

confidence: 99%

Construction of a prognostic model based on genes associated with mitochondrial energy metabolic pathway in colon adenocarcinoma and its clinical significance

Zhang

Liang

Zhou

et al. 2023

J of Molecular Recognition

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“…Ethnicity is an important factor affecting tumor gene expression profiles; herein, we found no significant effect of ethnicity on IGFBP1 expression in our study. 30 …”

Section: Discussionmentioning

confidence: 99%

“…Ethnicity is an important factor affecting tumor gene expression profiles; herein, we found no significant effect of ethnicity on IGFBP1 expression in our study. 30 During tumor development, some tumor cells may escape antitumor immunity. 31 Changes in the characteristic tumor microenvironment, such as tumor necrosis and hypoxia, promote the accumulation of neutrophils in the adjacent tissues, which profoundly affects tumor progression.…”

Section: Discussionmentioning

confidence: 99%

Clinical Characterization of the Expression of Insulin-Like Growth Factor Binding Protein 1 and Tumor Immunosuppression Caused by Ferroptosis of Neutrophils in Non-Small Cell Lung Cancer

Wang¹,

Xing²,

Deng³

et al. 2023

IJGM

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Purpose The efficacy of immunotherapy for non-small cell lung cancer (NSCLC) is limited owing to cold tumors and drug resistance. Therefore, it is important to identify the molecular mechanisms underlying immune evasion in NSCLC. Spontaneous ferroptosis of neutrophils has been suggested as a key mechanism of immunosuppression in cancer. Insulin-like growth factor binding protein 1 (IGFBP1) plays an important role in immune infiltration in several cancers. However, the role of IGFBP1 in NSCLC is unknown. Therefore, in this study, we aimed to investigate the association of IGFBP1 mRNA expression with infiltration of myeloid-derived suppressor cells and prognosis in NSCLC. Patients and Methods Retrospective RNA-seq data from 990 patients in the Cancer Genome Atlas (TCGA) database were analyzed in relation to patient clinical characteristics. The Timer2 database was used to assess immune infiltration, and the FerrDb V2 database was used to obtain ferroptosis-related genes. Finally, the results were validated by the proteomic analysis of serum samples collected from six patients with NSCLC and six healthy individuals. Results IGFBP1 expression was enriched in lung adenocarcinoma samples and positively correlated with the pathological grade of NSCLC. IGFBP1 expression was an independent prognostic factor for the overall survival of patients with NSCLC. In addition, IGFBP1 expression correlated with myeloid-derived suppressor cell infiltration. Notably, Gene Ontology analysis of IGFBP1 -related genes revealed that the major molecular functions of their protein products were related to NADP + 1-oxidoreductase activity. Furthermore, expression levels of multiple ferroptosis suppressor genes positively correlated with IGFBP1 expression. Conclusion High IGFBP1 expression indicates a poor prognosis in patients with NSCLC, which may be related to tumor immunosuppression caused by neutrophil ferroptosis.

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“…6). 32 Taken together, the processed data extracted from TCGA represents a broad spectrum of cancer patients, and the diverse distribution of each omics data type helps capture the varying landscapes of multi-omics in cancer, allowing GNN models to generalize better to different scenarios and make more accurate predictions or classifications.…”

Section: Multi-omics Features Of Driver Genes In Cancermentioning

confidence: 99%

DriverOmicsNet: An Integrated Graph Convolutional Network for Multi-Omics Exploration of Cancer Driver Genes

Dai,

Chang,

Shen

et al. 2024

Preprint

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Background: Cancer is a complex and heterogeneous group of diseases driven by genetic mutations and molecular changes. Identifying and characterizing cancer driver genes (CDgs) is crucial for understanding cancer biology and guiding precision oncology. Integrating multi-omics data can reveal the intricate molecular interactions underlying cancer progression and treatment responses. Methods: We developed a graph convolutional network (GCN) framework, DriverOmicsNet, that integrates multi-omics data using STRING protein-protein interaction (PPI) networks and correlation-based weighted correlation network analysis (WGCNA). We applied this framework to 15 cancer types, analyzing 5555 tumor samples to predict cancer-related features such as homologous recombination deficiency (HRD), cancer stemness, immune clusters, tumor stage, and survival outcomes. Findings: DriverOmicsNet demonstrated superior predictive accuracy and model performance metrics across all target labels when compared with GCN models based on STRING network alone. Gene expression emerged as the most significant feature, reflecting the dynamic and functional state of cancer cells. The combined use of STRING PPI and WGCNA networks enhanced the identification of key driver genes and their interactions. Interpretation: Our study highlights the effectiveness of using GCNs to integrate multi-omics data for precision oncology. The integration of STRING PPI and WGCNA networks provides a comprehensive framework that improves predictive power and facilitates the understanding of cancer biology, paving the way for more tailored treatments.

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The comparison of cancer gene mutation frequencies in Chinese and U.S. patient populations

Cited by 9 publications

References 80 publications

Construction of a prognostic model based on genes associated with mitochondrial energy metabolic pathway in colon adenocarcinoma and its clinical significance

Construction of a prognostic model based on genes associated with mitochondrial energy metabolic pathway in colon adenocarcinoma and its clinical significance

Clinical Characterization of the Expression of Insulin-Like Growth Factor Binding Protein 1 and Tumor Immunosuppression Caused by Ferroptosis of Neutrophils in Non-Small Cell Lung Cancer

DriverOmicsNet: An Integrated Graph Convolutional Network for Multi-Omics Exploration of Cancer Driver Genes

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