An energy metabolism-based eight-gene signature correlates with the clinical outcome of esophagus carcinoma

Zheng, Wei; Chen, Chaoying; Yu, Jianghao; Jin, Chengfeng; Han, Tie-Mei

doi:10.1186/s12885-021-08030-0

Cited by 13 publications

(12 citation statements)

References 29 publications

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“…This process was performed based on R package “survcomp”. In addition, we used restricted mean survival time (RMST) curves to evaluate the performance of each model and compare their differences ( Schröder et al, 2011 ; Zheng et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Identification of immune-related ferroptosis prognostic marker and in-depth bioinformatics exploration of multi-omics mechanisms in thyroid cancer

Fan

Xie

Zhang

et al. 2022

Front. Mol. Biosci.

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Background: Factors such as variations in thyroid carcinoma (THCA) gene characteristics could influence the clinical outcome. Ferroptosis and immunity have been verified to play an essential role in various cancers, and could affect the cancer patients’ prognosis. However, their relationship to the progression and prognosis of many types of THCA remains unclear.Methods: First, we extracted prognosis-related immune-related genes and ferroptosis-related genes from 2 databases for co-expression analysis to obtain prognosis-related differentially expressed immune-related ferroptosis genes (PR-DE-IRFeGs), and screened BID and CDKN2A for building a prognostic model. Subsequently, multiple validation methods were used to test the model’s performance and compare its performance with other 4 external models. Then, we explored the mechanism of immunity and ferroptosis in the occurrence, development and prognosis of THCA from the perspectives of anti-tumor immunity, CDKN2A-related competitive endogenous RNA regulatory, copy number variations and high frequency gene mutation. Finally, we evaluated this model’s clinical practice value.Results: BID and CDKN2A were identified as prognostic risk and protective factors, respectively. External data and qRT-PCR experiment also validated their differential expression. The model’s excellent performance has been repeatedly verified and outperformed other models. Risk scores were significantly associated with most immune cells/functions. Risk score/2 PR-DE-IRFeGs expression was strongly associated with BRAF/NRAS/HRAS mutation. Single copy number deletion of CDKN2A is associated with upregulation of CDKN2A expression and worse prognosis. The predicted regulatory network consisting of CYTOR, hsa-miRNA-873-5p and CDKN2A was shown to significantly affect prognosis. The model and corresponding nomogram have been shown to have excellent clinical practice value.Conclusion: The model can effectively predict the THCA patients’ prognosis and guide clinical treatment. Ferroptosis and immunity may be involved in the THCA’s progression through antitumor immunity and BRAF/NRAS/HRAS mutation. CYTOR-hsa-miRNA-873-5p-CDKN2A regulatory networks and single copy number deletion of CDKN2A may also affect THCA′ progression and prognosis.

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Section: Methodsmentioning

confidence: 99%

Identification of immune-related ferroptosis prognostic marker and in-depth bioinformatics exploration of multi-omics mechanisms in thyroid cancer

Fan

Xie

Zhang

et al. 2022

Front. Mol. Biosci.

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“…In this study, we found that the amplification of ZBTB6 was related to a high risk of LNM. There have been few studies of ZBTB6, a gene related to energy metabolism, and it has only been reported to be a prognostic indicator in esophageal cancer (27). Here, we found that the amplification of ZBTB6 was related to a high risk of LNM in TNBC, but the mechanism is still unknown.…”

Section: Discussionmentioning

confidence: 64%

Prediction of axillary lymph node metastasis in triple-negative breast cancer by multi-omics analysis and an integrated model

Li¹,

Li²,

Ma³

et al. 2022

Ann Transl Med

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Background: To avoid unnecessary postoperative complications, it is essential to select breast cancer patients without axillary lymph node (LN) metastasis who might be eligible for exemption from sentinel lymph node biopsy (SLNB). However, the lymph node metastasis (LNM) of triple-negative breast cancer (TNBC) is difficult to predict if only considering clinical parameters. Hence, by investigating the difference between LN positive and LN negative patients, we aimed to build a multi-omics model able to better predict LNM in TNBC.Methods: A total of 445 TNBC patients with lymph node status and multi-omics data were enrolled and divided into training and validation sets. We analyzed both clinicopathological characteristics and multiomics data to search for robust biomarkers, which were used to establish a multi-omics model.Results: Compared with LN negative patients, LN positive patients had an increasing number of mutational events, while the frequencies of both amplification and deletion in somatic copy number alterations (SCNAs) were lower in LN positive cases. After analyzing upregulated gene-related pathways, neutrophil-related pathways were found to be enriched in LN positive patients. Based on these omics analyses, 5 predictors were utilized to build a multi-omics model, and the area under the receiver operating characteristic curve was 0.790 in the training set and 0.807 in the validation set, showing a better performance than models using individual omics data.Conclusions: After analyzing the largest TNBC multi-omics cohorts, we identified the potential clinical and molecular characteristics that are related to LNM. A multi-omics model was developed and performed robustly in predicting LNM, with the potential assistance of tailoring unnecessary axillary LN management among TNBC patients.

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“…Consequently, exploiting novel prognostic indicators and therapeutic approaches is particularly crucial. Numerous researchers have made efforts on biomarker-based classifier and obtained encouraging achievements in assessing the survival outcome of EC [ 22 , 23 ]. Nevertheless, these prognostic models are more or less flawed and we need to discover more powerful signature for prognosis prediction.…”

Section: Discussionmentioning

confidence: 99%

Endoplasmic Reticulum Stress-Related Four-Biomarker Risk Classifier for Survival Evaluation in Esophageal Cancer

Tang

Zhu

Luo

2022

Journal of Oncology

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Purpose. Esophageal cancer (EC) is a lethal digestive tumor worldwide with a dismal clinical outcome. Endoplasmic reticulum (ER) stress poses essential implications for a variety of tumor malignant behaviors. Here, we set up an ER stress-based risk classifier for assessing patient outcome and exploiting robust targets for medical decision-making of EC cases. Methods. 340 EC cases with transcriptome and survival data from two independent public datasets (TCGA and GEO) were recruited for this project. Cox regression analyses were employed to create a risk classifier based on ER stress-related genes (ERGs) which were strongly linked to EC cases’ outcomes. Then, we detected and confirmed the predictive ability of our proposed classifier via a host of statistical methods, including survival analysis and ROC method. In addition, immune-associated algorithm was implemented to analyze the immune activity of EC samples. Results. Four EGRs (BCAP31, HSPD1, PDHA1, and UBE2D1) were selected to build an EGR-related classifier (ERC). This classifier could distinguish the patients into different risky subgroups. The remarkable differences in patient outcome between the two groups were observed, and similar results were also confirmed in GEO cohort. In terms of the immune analysis, the ERC could forecast the infiltration level of immunocytes, such as Tregs and NK cells. Conclusion. We created a four-ERG risk classifier which displays the powerful capability of survival evaluation for EC cases.

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An energy metabolism-based eight-gene signature correlates with the clinical outcome of esophagus carcinoma

Cited by 13 publications

References 29 publications

Identification of immune-related ferroptosis prognostic marker and in-depth bioinformatics exploration of multi-omics mechanisms in thyroid cancer

Identification of immune-related ferroptosis prognostic marker and in-depth bioinformatics exploration of multi-omics mechanisms in thyroid cancer

Prediction of axillary lymph node metastasis in triple-negative breast cancer by multi-omics analysis and an integrated model

Endoplasmic Reticulum Stress-Related Four-Biomarker Risk Classifier for Survival Evaluation in Esophageal Cancer

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