Bioinformatics analysis to screen key prognostic genes in the breast cancer tumor microenvironment

Ye, Qian; Han, Xiaofei; Wu, Zhengsheng

doi:10.1080/21655979.2020.1840731

Cited by 12 publications

(14 citation statements)

References 41 publications

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“…However, the role and mechanism of GAD1 in CCA remains poorly defined. Killer cell lectin-like receptor B1 (KLRB1), also named as CD161, has been reported as a prognostic gene in many cancers [25][26][27]. For example, Qin et al showed that high expression of KLRB1 was associated with good overall survival in early cervical squamous cell carcinoma patients [25].…”

Section: Discussionmentioning

confidence: 99%

Identification of Prognostic Gene Signature Associated with Tumor Microenvironment of Cholangiocarcinoma

Chen¹,

Chen²,

Lu³

et al. 2021

Preprint

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Background: Cholangiocarcinoma (CCA) is the most common malignancy of the biliary tract with a dismal prognosis. Increasing evidence suggests that tumor microenvironment (TME) is closely associated with cancer prognosis. However, the prognostic signature for CCA based on TME has not yet been reported. This study aimed to develop a TME-related prognostic signature for accurately predicting the prognosis of patients with CCA. Methods: Based on the TCGA database, we calculated the stromal and immune scores using the ESTIMATE algorithm to assess TME in stromal and immune cells derived from CCA. TME-related differentially expressed genes were identified, followed by functional enrichment analysis and PPI network analysis. Univariate Cox regression analysis, Lasso Cox regression model and multivariable Cox regression analysis were performed to identify and construct the TME-related prognostic gene signature. Gene Set Enrichment Analyses (GSEA) was performed to further investigate the potential molecular mechanisms. The correlations between the risk scores and tumor infiltration immune cells were analyzed using Tumor Immune Estimation Resource (TIMER) database. Results: A total of 784 TME-related differentially expressed genes (DEGs) were identified, which were mainly enriched in immune-related processes and pathways. Among these TME-related DEGs, A novel two‑gene signature (including GAD1 and KLRB1) was constructed for CCA prognosis prediction. The AUC of the prognostic model for predicting the survival of patients at 1-, 2-, and 3- years was 0.811, 0.772, and 0.844, respectively. Cox regression analysis showed that the two‑gene signature was an independent prognostic factor. Based on the risk scores of the prognostic model, CCA patients were divided into high- and low-risk groups, and patients with high-risk score had shorter survival time than those with low-risk score. Furthermore, we found that the risk scores were negatively correlated with TME-scores and the number of several tumor infiltration immune cells, including B cells and CD4+ T cells. Conclusion: Our study established a novel TME-related gene signature to predict the prognosis of patients with CCA. This might provide a new understanding of the potential relationship between TME and CCA prognosis, and serve as a prognosis stratification tool for guiding personalized treatment of CCA patients.

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Section: Discussionmentioning

confidence: 99%

Identification of Prognostic Gene Signature Associated with Tumor Microenvironment of Cholangiocarcinoma

Chen¹,

Chen²,

Lu³

et al. 2021

Preprint

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“…A recent study based on public databases established a tumor microenvironment-related signature model to predict the prognostic features of BC [ 26 ]. However, metabolism-associated lncRNA signatures are lacking.…”

Section: Discussionmentioning

confidence: 99%

Metabolism-related long non-coding RNAs (lncRNAs) as potential biomarkers for predicting risk of recurrence in breast cancer patients

Liu

Chen

et al. 2021

Bioengineered

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Metabolism affects the development, progression, and prognosis of various cancers, including breast cancer (BC). Our aim was to develop a metabolism-related long non-coding RNA (lncRNA) signature to assess the prognosis of BC patients in order to optimize treatment. Metabolism-related genes between breast tumors and normal tissues were screened out, and Pearson correlation analysis was used to investigate metabolism-related lncRNAs. In total, five metabolism-related lncRNAs were enrolled to establish prognostic signatures. Kaplan-Meier plots and the receiver operating characteristic (ROC) curves demonstrated good performance in both training and validation groups. Further analysis demonstrated that the signature was an independent prognostic factor for BC. A nomogram incorporating risk score and tumor stage was then constructed to evaluate the 3 -and 5-year recurrence-free survival (RFS) in patients with BC. In conclusion, this study identified a metabolism-related lncRNA signature that can predict RFS of BC patients and established a prognostic nomogram that helps guide the individualized treatment of patients at different risks.

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“…Previous studies have highlighted the value of analyzing the immune cell infiltration of the tumor microenvironment when conducting prognostic assessments of breast cancer patients. Ye et al screened key prognostic genes in the breast cancer tumor microenvironment by analyzing the immune and stromal scores of tumor samples and also constructed a tumor microenvironment-related prognostic model [ 43 ]. Chemoresistance is a major barrier to breast cancer patient treatment that is often associated with disease recurrence and death [ 7 , 8 , 44 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of a prognostic chemoresistance-related gene signature associated with immune microenvironment in breast cancer

Liu

Zhao

2021

Bioengineered

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Breast cancer is the most common form of cancer among women globally, and chemoresistance is a major challenge to disease treatment that is associated with a poor prognosis. This study was formulated to identify a reliable prognostic biosignature capable of predicting the survival of patients with chemoresistant breast cancer (CRBC) and evaluating the associated tumor immune microenvironment. Through a series of protein-protein interaction and weighted correlation network analyses, genes that were significantly associated with breast cancer chemoresistance were identified. Moreover, univariate Cox regression and lasso-penalized Cox regression analyses were employed to generate a prognostic model, and the prognostic utility of this model was then assessed using time-dependent receiver operating characteristic (ROC) and Kaplan-Meier survival curves. Finally, The CIBERSORT and ESTIMATE algorithms were additionally leveraged to assess relationships between the tumor immune microenvironment and patient prognostic signatures. Overall, a multigenic prognostic biosignature capable of predicting CRBC patient risk was successfully developed based on bioinformatics analysis and in vitro experiments. This biosignature was able to stratify CRBC patients into high-and low-risk subgroups. ROC curves also revealed that this biosignature achieved high diagnostic efficiency, and multivariate regression analyses indicated that this risk signature was an independent risk factor linked to CRBC patient outcomes. In addition, this signature was associated with the infiltration of the tumor microenvironment by multiple immune cell types. In conclusion, the chemoresistance-associated prognostic gene signature developed herein was able to effectively evaluate the prognosis of CRBC patients and to reflect the overall composition of the tumor immune microenvironment.

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Bioinformatics analysis to screen key prognostic genes in the breast cancer tumor microenvironment

Cited by 12 publications

References 41 publications

Identification of Prognostic Gene Signature Associated with Tumor Microenvironment of Cholangiocarcinoma

Identification of Prognostic Gene Signature Associated with Tumor Microenvironment of Cholangiocarcinoma

Metabolism-related long non-coding RNAs (lncRNAs) as potential biomarkers for predicting risk of recurrence in breast cancer patients

Identification of a prognostic chemoresistance-related gene signature associated with immune microenvironment in breast cancer

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