Molecular characterization of breast cancer: a potential novel immune-related lncRNAs signature

Abstract Background Accumulating evidence has demonstrated that immune-related lncRNAs (IRLs) are commonly aberrantly expressed in breast cancer (BC). Thus, we aimed to establish an IRL-based tool to improve prognosis prediction in BC patients. Methods We obtained IRL expression profiles in large BC cohorts (N = 911) from The Cancer Genome Atlas (TCGA) database. Then, in light of the correlation between each IRL and recurrence-free survival (RFS), we screened prognostic IRL signatures to construct a novel RFS nomogram via a Cox regression model. Subsequently, the performance of the IRL-based model was evaluated through discrimination, calibration ability, risk stratification ability and decision curve analysis (DCA). Results A total of 52 IRLs were obtained from TCGA. Based on multivariate Cox regression analyses, four IRLs (A1BG-AS1, AC004477.3, AC004585.1 and AC004854.2) and two risk parameters (tumor subtype and TNM stage) were utilized as independent indicators to develop a novel prognostic model. In terms of predictive accuracy, the IRL-based model was distinctly superior to the TNM staging system (AUC: 0.728 VS 0.673, P = 0.010). DCA indicated that our nomogram had favorable clinical practicability. In addition, risk stratification analysis showed that the IRL-based tool efficiently divided BC patients into high- and low-risk groups (P < 0.001). Conclusions A novel IRL-based model was constructed to predict the risk of 5-year RFS in BC. Our model can improve the predictive power of the TNM staging system and identify high-risk patients with tumor recurrence to implement more appropriate treatment strategies.

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Development and validation of a prognostic nomogram for predicting overall survival in patients with primary bladder sarcoma: a SEER-based retrospective study

BMC Urology ◽

10.1186/s12894-021-00929-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Shijie Li ◽

Xuefeng Liu ◽

Xiaonan Chen

Keyword(s):

Overall Survival ◽

Risk Stratification ◽

Cox Regression ◽

Staging System ◽

Tnm Staging ◽

Calibration Curves ◽

Tnm Staging System ◽

Primary Bladder ◽

Stratification System ◽

Risk Stratification System

Abstract Background Primary bladder sarcoma (PBS) is a rare malignant tumor of the bladder with a poor prognosis, and its disease course is inadequately understood. Therefore, our study aimed to establish a prognostic model to determine individualized prognosis of patients with PBS. Patients and Methods Data of 866 patients with PBS, registered from 1973 to 2015, were extracted from the surveillance, epidemiology, and end result (SEER) database. The patients included were randomly split into a training (n = 608) and a validation set (n = 258). Univariate and multivariate Cox regression analyses were employed to identify the important independent prognostic factors. A nomogram was then established to predict overall survival (OS). Using calibration curves, receiver operating characteristic curves, concordance index (C-index), decision curve analysis (DCA), net reclassification improvement (NRI) and integrated discrimination improvement (IDI), the performance of the nomogram was internally validated. We compared the nomogram with the TNM staging system. The application of the risk stratification system was tested using Kaplan–Meier survival analysis. Results Age at diagnosis, T-stage, N-stage, M-stage, and tumor size were identified as independent predictors of OS. C-index of the training cohort were 0.675, 0.670, 0.671 for 1-, 3- and 5-year OS, respectively. And that in the validation cohort were 0.701, 0.684, 0.679, respectively. Calibration curves also showed great prediction accuracy. In comparison with TNM staging system, improved net benefits in DCA, evaluated NRI and IDI were obtained. The risk stratification system can significantly distinguish the patients with different survival risk. Conclusion A prognostic nomogram was developed and validated in the present study to predict the prognosis of the PBS patients. It may assist clinicians in evaluating the risk factors of patients and formulating an optimal individualized treatment strategy.

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A Nomogram for Predicting Survival in Patients With Breast Cancer Liver Metastasis: A Population-Based Study

Frontiers in Oncology ◽

10.3389/fonc.2021.600768 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yu Xiong ◽

Xia Shi ◽

Qi Hu ◽

Xingwei Wu ◽

Enwu Long ◽

...

Keyword(s):

Breast Cancer ◽

Prognostic Factors ◽

Liver Metastasis ◽

Risk Stratification ◽

Staging System ◽

Tnm Staging ◽

Tnm Staging System ◽

Stratification System ◽

Breast Cancer Liver Metastasis ◽

Risk Stratification System

ObjectiveThe prognosis of patients with breast cancer liver metastasis (BCLM) was poor. We aimed at constructing a nomogram to predict overall survival (OS) for BCLM patients using the SEER (Surveillance Epidemiology and End Results) database, thus choosing an optimized therapeutic regimen to treat.MethodsWe identified 1173 patients with BCLM from the SEER database and randomly divided them into training (n=824) and testing (n=349) cohorts. The Cox proportional hazards model was applied to identify independent prognostic factors for BCLM, based on which a nomogram was constructed to predict 1-, 2-, and 3-year OS. Its discrimination and calibration were evaluated by the Concordance index (C-index) and calibration plots, while the accuracy and benefits were assessed by comparing it to AJCC-TNM staging system using the decision curve analysis (DCA). Kaplan-Meier survival analyses were applied to test the clinical utility of the risk stratification system.ResultsGrade, marital status, surgery, radiation therapy, chemotherapy, CS tumor size, tumor subtypes, bone metastatic, brain metastatic, and lung metastatic were identified to be independent prognostic factors of OS. In comparison with the AJCC-TNM staging system, an improved C-index was obtained (training group: 0.701 vs. 0.557, validation group: 0.634 vs. 0.557). The calibration curves were consistent between nomogram-predicted survival probability and actual survival probability. Additionally, the DCA curves yielded larger net benefits than the AJCC-TNM staging system. Finally, the risk stratification system can significantly distinguish the ones with different survival risk based on the different molecular subtypes.ConclusionWe have successfully built an effective nomogram and risk stratification system to predict OS in BCLM patients, which can assist clinicians in choosing the appropriate treatment strategies for individual BCLM patients.

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Identification of a prognostic long non-coding RNA signature in breast cancer

10.21203/rs.3.rs-27286/v1 ◽

2020 ◽

Author(s):

Gaochen Lan ◽

Xiaoling Yu ◽

Yanna Zhao ◽

Jinjian Lan ◽

Wan Li ◽

...

Keyword(s):

Breast Cancer ◽

Cox Regression ◽

Prognostic Biomarker ◽

Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Cox Regression Analysis

Abstract Background: Breast cancer is the most common malignant disease among women. At present, more and more attention has been paid to long non-coding RNAs (lncRNAs) in the field of breast cancer research. We aimed to investigate the expression profiles of lncRNAs and construct a prognostic lncRNA for predicting the overall survival (OS) of breast cancer.Methods: The expression profiles of lncRNAs and clinical data with breast cancer were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened out by R package (limma). The survival probability was estimated by the Kaplan‑Meier Test. The Cox Regression Model was performed for univariate and multivariate analysis. The risk score (RS) was established on the basis of the lncRNAs’ expression level (exp) multiplied regression coefficient (β) from the multivariate cox regression analysis with the following formula: RS=exp a1 * β a1 + exp a2 * β a2 +……+ exp an * β an. Functional enrichment analysis was performed by Metascape.Results: A total of 3404 differentially expressed lncRNAs were identified. Among them, CYTOR, MIR4458HG and MAPT-AS1 were significantly associated with the survival of breast cancer. Finally, The RS could predict OS of breast cancer (RS=exp CYTOR * β CYTOR + exp MIR4458HG * β MIR4458HG + exp MAPT-AS1 * β MAPT-AS1). Moreover, it was confirmed that the three-lncRNA signature could be an independent prognostic biomarker for breast cancer (HR=3.040, P=0.000).Conclusions: This study established a three-lncRNA signature, which might be a novel prognostic biomarker for breast cancer.

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New and Important Changes in the TNM Staging System for Breast Cancer

American Society of Clinical Oncology Educational Book ◽

10.1200/edbk_201313 ◽

2018 ◽

pp. 457-467 ◽

Cited By ~ 10

Author(s):

Gabriel N. Hortobagyi ◽

Stephen B. Edge ◽

Armando Giuliano

Keyword(s):

Breast Cancer ◽

Regional Lymph Node ◽

Staging System ◽

Distant Metastases ◽

Tnm Staging ◽

Pathology Report ◽

Imaging Evaluation ◽

Complete Surgical Excision ◽

Tnm Staging System ◽

Eighth Edition

Expanded understanding of biologic factors that modulate the clinical course of malignant disease have led to the gradual integration of biomarkers into staging classifications. The American Joint Committee on Cancer (AJCC) TNM staging system is universally used and has largely displaced other staging classifications for most, although not all, cancers. Many of the chapters of the eighth edition of the AJCC TNM staging system integrated biomarkers with anatomic definitions. The Breast Chapter added estrogen receptor (ER) and progesterone receptor (PR) expression, HER2 expression, and/or amplification and histologic grade to the anatomic assessment of tumor size, regional lymph node involvement, and distant metastases (known as TNM). While preserving an anatomic staging system for continuity and for regions where modern biomarkers are not always available, the eighth edition emphasizes the increased prognostic precision of the clinical prognostic stage groups and the pathologic prognostic stage groups. The clinical prognostic stage groups are applicable to all patients with primary breast cancer before any treatment has been implemented, but require a clinical and imaging evaluation as well as a biopsy with grade and available ER, PR, and HER2 results; the pathologic prognostic stage groups are applicable to all patients treated with complete surgical excision as first treatment and also require a complete pathology report, grade, and ER, PR, and HER2. Applying the pathologic prognostic stage groups to a large database of patients staged by basic TNM groupings changed the stage grouping of almost 40% of patients. Grouping by pathologic prognostic stage groups led to a better prognostic distribution of the group and more precise individual prognostication.

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Inflammatory breast cancer: a proposed conceptual shift in the UICC–AJCC TNM staging system

The Lancet Oncology ◽

10.1016/s1470-2045(17)30192-4 ◽

2017 ◽

Vol 18 (4) ◽

pp. e228-e232 ◽

Cited By ~ 31

Author(s):

Tamer M Fouad ◽

Angelica M Gutierrez Barrera ◽

James M Reuben ◽

Anthony Lucci ◽

Wendy A Woodward ◽

...

Keyword(s):

Breast Cancer ◽

Inflammatory Breast Cancer ◽

Staging System ◽

Tnm Staging ◽

Tnm Staging System ◽

Conceptual Shift

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The 2003 Revised TNM Staging System for Breast Cancer: Results of Stage Re-classification on Survival and Future Comparisons among Stage Groups

Annals of Surgical Oncology ◽

10.1245/s10434-006-9147-0 ◽

2006 ◽

Vol 14 (1) ◽

pp. 143-147 ◽

Cited By ~ 13

Author(s):

Pedro F Escobar ◽

Rebecca J Patrick ◽

Lisa A Rybicki ◽

David E Weng ◽

Joseph P Crowe

Keyword(s):

Breast Cancer ◽

Staging System ◽

Tnm Staging ◽

Tnm Staging System

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A six-gene-based signature for breast cancer radiotherapy sensitivity prediction

10.21203/rs.3.rs-20019/v1 ◽

2020 ◽

Author(s):

Xing Chen ◽

Junjie Zheng ◽

Min ling Zhuo ◽

Ailong Zhang ◽

Zhenhui You

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Regression Analysis ◽

Cox Regression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Breast Cancer Radiotherapy

Abstract Background: Breast cancer (BRCA) represents the most common malignancy among women worldwide that with high mortality. Radiotherapy is a prevalent therapeutic for BRCA that with heterogeneous effectiveness among patients. Methods: we proposed to develop a gene expression-based signature for BRCA radiotherapy sensitivity prediction. Gene expression profiles of BRCA samples from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) were obtained and used as training and independent testing dataset, respectively. Differential expression genes (DEGs) in BRCA tumor samples compared with their paracancerous samples in the training set were identified by using edgeR Bioconductor package followed by dimensionality reduction through autoencoder method and univariate Cox regression analysis to screen genes among DEGs that with significant prognosis significance in patients that were previously treated with radiation. LASSO Cox regression method was applied to screen optimal genes for constructing radiotherapy sensitivity prediction signature. Results: 603 DEGs were obtained in BRCA tumor samples, and seven out of which were retained after univariate cox regression analysis. LASSO Cox regression analysis finally remained six genes based on which the radiotherapy sensitivity prediction model was constructed. The signature was proved to be robust in both training and independent testing sets and an independent marker for BRCA radiotherapy sensitivity prediction. Conclusions: this study should be helpful for BRCA patients’ therapeutics selection and clinical decision.

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A nomogram based on Aspartate Aminotransferase/Alanine Aminotransferase (AST/ALT) ratio after surgery to predict the prognostic value in patients with gastric cancer

10.21203/rs.3.rs-18794/v1 ◽

2020 ◽

Author(s):

Linfang Li ◽

Shan Xing ◽

Ning Xue ◽

Miantao Wu ◽

Yaqing Liang ◽

...

Keyword(s):

Gastric Cancer ◽

Aspartate Aminotransferase ◽

Alanine Aminotransferase ◽

Cox Regression ◽

Staging System ◽

Tnm Staging ◽

Calibration Plot ◽

Study Cohort ◽

Tnm Staging System ◽

Resectable Gastric Cancer

Abstract Background This study aimed to develop an effective nomogram for predicting overall survival (OS) in surgically treated gastric cancer. Methods We retrospectively evaluated 190 gastric cancer in this study. Cox regression analyses were performed to identify significant prognostic factors for OS in patients with resectable gastric cancer. The predictive accuracy of nomogram was assessed by calibration plot, concordance index (C-index) and decision curve, and then were compared with the traditional TNM staging system. Based on the total points (TPS) by nomogram, we further divided patients into different risk groups. Results On multivariate analysis of the 190 cohort, independent factors for survival were age, clinical stage and Aspartate Aminotransferase/Alanine Aminotransferase (SLR), which were entered into the nomogram. The calibration curve for the probability of OS showed that the nomogram-based predictions were in good agreement with actual observations. And the C-index of the established nomogram for predicting OS had a superior discrimination power compared with the TNM staging system [0.768 (95% CI: 0.725-0.810) vs 0.730 (95% CI: 0.688-0.772), p < 0.05]. Decision curve also demonstrated that the nomogram was better than TNM staging system. Based on the TPS of the nomogram, we further subdivided the study cohort into 3 groups: low risk (TPS ≤ 158), middle risk (158 < TPS ≤ 188), high risk (TPS > 188), the differences of OS rate were significant in the groups. Conclusions The established nomogram resulted in more accurate prognostic prediction for individual patient with resectable gastric cancer.

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