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Construction and validation of a 6-gene nomogram discriminating lung metastasis risk of breast cancer patients.

ABSTRACT: Breast cancer is the most common malignant disease in women. Metastasis is the foremost cause of death. Breast tumor cells have a proclivity to metastasize to specific organs. The lung is one of the most common sites of breast cancer metastasis. Therefore, we aimed to build a useful and convenient prediction tool based on several genes that may affect lung metastasis-free survival (LMFS). We preliminarily identified 319 genes associated with lung metastasis in the training set GSE5327 (n = 58). Enrichment analysis of GO functions and KEGG pathways was conducted based on these genes. The best genes for modeling were selected using a robust likelihood-based survival modeling approach: GOLGB1, TMEM158, CXCL8, MCM5, HIF1AN, and TSPAN31. A prognostic nomogram for predicting lung metastasis in breast cancer was developed based on these six genes. The effectiveness of the nomogram was evaluated in the training set GSE5327 and the validation set GSE2603. Both the internal validation and the external validation manifested the effectiveness of our 6-gene prognostic nomogram in predicting the lung metastasis risk of breast cancer patients. On the other hand, in the validation set GSE2603, we found that neither the six genes in the nomogram nor the risk predicted by the nomogram were associated with bone metastasis of breast cancer, preliminarily suggesting that these genes and nomogram were specifically associated with lung metastasis of breast cancer. What's more, five genes in the nomogram were significantly differentially expressed between breast cancer and normal breast tissues in the TIMER database. In conclusion, we constructed a new and convenient prediction model based on 6 genes that showed practical value in predicting the lung metastasis risk for clinical breast cancer patients. In addition, some of these genes could be treated as potential metastasis biomarkers for antimetastatic therapy in breast cancer. The evolution of this nomogram will provide a good reference for the prediction of tumor metastasis to other specific organs.

SUBMITTER: Wang L

PROVIDER: S-EPMC7773205 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Construction and validation of a 6-gene nomogram discriminating lung metastasis risk of breast cancer patients.

Wang Lingchen L Wang Wenhua W Zeng Shaopeng S Zheng Huilie H Lu Quqin Q

PloS one 20201230 12

Breast cancer is the most common malignant disease in women. Metastasis is the foremost cause of death. Breast tumor cells have a proclivity to metastasize to specific organs. The lung is one of the most common sites of breast cancer metastasis. Therefore, we aimed to build a useful and convenient prediction tool based on several genes that may affect lung metastasis-free survival (LMFS). We preliminarily identified 319 genes associated with lung metastasis in the training set GSE5327 (n = 58). ...[more]

PMID: 33378415

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Project description:The lung is one of the most common sites of distant metastasis in breast cancer (BC). Identifying ideal biomarkers to construct a more accurate prediction model than conventional clinical parameters is crucial. MicroRNAs (miRNAs) data and clinicopathological data were acquired from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database. miR-663, miR-210, miR-17, miR-301a, miR-135b, miR-451, miR-30a, and miR-199a-5p were screened to be highly relevant to lung metastasis (LM) of BC patients. The miRNA-based risk score was developed based on the logistic coefficient of the individual miRNA. Univariate and multivariate logistic regression selected tumor node metastasis (TNM) stage, age at diagnosis, and miRNA-risk score as independent predictive parameters, which were used to construct a nomogram. The Cancer Genome Atlas (TCGA) database was used to validate the signature and nomogram. The predictive performance of the nomogram was compared to that of the TNM stage. The area under the receiver operating characteristics curve (AUC) of the nomogram was higher than that of the TNM stage in all three cohorts (training cohort: 0.774 vs. 0.727; internal validation cohort: 0.763 vs. 0.583; external validation cohort: 0.925 vs. 0.840). The calibration plot of the nomogram showed good agreement between predicted and observed outcomes. The net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision-curve analysis (DCA) of the nomogram showed that its performances were better than that of the TNM classification system. Functional enrichment analyses suggested several terms with a specific focus on LM. Subgroup analysis showed that miR-30a, miR-135b, and miR-17 have unique roles in lung metastasis of BC. Pan-cancer analysis indicated the significant importance of eight predictive miRNAs in lung metastasis. This study is the first to establish and validate a comprehensive lung metastasis predictive nomogram based on the METABRIC and TCGA databases, which provides a reliable assessment tool for clinicians and aids in appropriate treatment selection.

Project description:BackgroundThe N6-methyladenosine (m6 A) can modify long non-coding RNAs (lncRNAs), thereby influencing a wide array of biological functions. However, the prognosis of m6 A-related lncRNAs (m6 ARLncRNAs) in non-small cell lung cancer (NSCLC) remains largely unknown.MethodsPearson correlation analysis was used to identify m6 ARLncRNAs in 1835 NSCLC patients and with the condition (|Pearson R| > 0.4 and p < 0.001). Univariant Cox regression analysis was conducted to explore the prognostic m6 ARLncRNAs. We filtered prognostic m6 ARLncRNAs by LASSO regression and multivariate Cox proportional hazard regression to construct and validate an m6 ARLncRNAs signature (m6 ARLncSig). We analyzed the correlation between the m6 ARLncSig score and clinical features, immune microenvironment, tumor mutation burden, and therapeutic sensitivity and conducted independence and clinical stratification analysis. Finally, we established and validated a nomogram for prognosis prediction in NSCLC patients.ResultsForty-one m6 ARLncRNAs were identified as prognostic lncRNAs, and 12 m6 ARLncRNAs were selected to construct m6 ARLncSig in the TCGA training dataset. The m6 ARLncSig was further validated in the testing dataset, GSE31210, GSE37745, GSE30219, and our NSCLC samples. In terms of m6 ARLncSig, NSCLC patients were divided into high- and low-risk groups, with significantly different overall survival (OS), clinical features (age, sex, and tumor stage), tumor-infiltrating immune cells, chemotherapeutic sensitivity, radiotherapeutic response, and biological pathways. Moreover, m6 ARLncSig independently predicted the OS of NSCLC patients. Finally, the robustness and clinical practicability for predicting NSCLC patient prognosis was improved by constructing a nomogram containing the m6 ARLncSig, age, gender, and tumor stage.ConclusionsOur study demonstrated that m6 ARLncSig could act as a potential biomarker for evaluating the prognosis and therapeutic efficacy in NSCLC patients.

Dataset Information

Construction and validation of a 6-gene nomogram discriminating lung metastasis risk of breast cancer patients.

Publications

Construction and validation of a 6-gene nomogram discriminating lung metastasis risk of breast cancer patients.

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