Guanylyl cyclase C (GCC) expression in lymph nodes (LNs) as a determinant of recurrence in stage II colon cancer (CC) patients (pts).

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 3639-3639
Author(s):  
Daniel J. Sargent ◽  
Qian Shi ◽  
Sharlene Gill ◽  
Christophe Louvet ◽  
Richard Bernard Everson ◽  
...  
Keyword(s):  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Tumor Grade ◽  
High Risk Group ◽  
Low Risk ◽  
Stage Ii ◽  
Risk Class ◽  
Tissue Procurement ◽  
The Relationship

3639 Background: The first phase of the multi-center prospectively specified retrospective study Validating Indicators To Associate Recurrence (VITAR), assessing the relationship between GCC gene expression in formalin fixed (FFPE) LNs and time to recurrence (TTR) in stage II CC pts not treated with adjuvant chemotherapy (Sargent, Annals Surg Onc 2011), showed promising initial results. Here we report a validation set of 463 new stage II CC pts. Methods: GCC mRNA was quantified by RT-qPCR using FFPE LNs from untreated T3N0 CC pts diagnosed from 1999-2008 with at least 12 LNs examined , blinded to clinical outcomes. Patients were classified by GCC LN ratio (LNR) (high risk: LNR > 0.1; low risk: LNR ≤ 0.1), with LNR defined as ratio of GCC positive to GCC informative LNs. Cox regression models tested the relationship between GCC and the primary endpoint of TTR, adjusted for age, tumor grade, number of LN examined pathologically, and lymphovascular invasion. Mismatch repair (MMR) status was also assessed. All primary analyses and cut-points were pre-specified. Results: 46pts (10%) recurred (rec), median follow-up was 65 months, median LNs examined was 20, and 42% (195/463) were classified high risk. Overall, TTR was not significantly associated with binary GCC LNR risk class (HR=1.47, p=.208) or DFS (HR= 1.39, p=.097). One site’s (n=97) tissue grossing method precluded appropriate LN assessment with existing GCC qualification methods. Excluding this site resulted in a TTR HR=1.91, p=0.051 (multivariate). In a post-hocanalysis excluding this site and using a 3-level GCC risk group of high (LNR > 0.20), intermediate (0.10 < LNR < 0.20) and low (LNR < 0.10), high risk group pts had a 5-yr rec risk of 22% versus 8% in low risk (HR 2.72, p=0.006). MMR status was not significantly associated with TTR (multivariate p=0.30). Conclusions: GCC status is a promising prognostic factor in appropriately staged stage II CC pts not treated with adjuvant therapy independent of traditional histopathology risk factors, but GCC determination must be performed with methodology adapted to the tissue procurement and fixation technique. Outcome associations were strengthened when considering a 3-level GCC categorization.

GCC expression in lymph nodes (LNs) as a significant determinant of recurrence in stage II colon cancer (CC) patients (pts).

2011 ◽  
Vol 29 (4_suppl) ◽  
pp. 369-369 ◽  
Author(s):  
D. J. Sargent ◽  
Q. Shi ◽  
B. M. Bot ◽  
M. B. Resnick ◽  
M. O. Meyers ◽  
...  
Keyword(s):  
High Risk ◽  
Adjuvant Chemotherapy ◽  
Cox Regression ◽  
Risk Group ◽  
Recurrence Risk ◽  
High Risk Group ◽  
Stage Ii ◽  
Significant Determinant ◽  
Recurrence Rates ◽  
The Relationship

369 Background: A multi-center prospectively specified retrospective study Validating Indicators to Associate Recurrence (VITAR) is assessing the relationship between guanylyl cyclase C (GCC) gene expression in formalin fixed LNs and recurrence risk in stage II CC pts not treated with adjuvant chemotherapy. Here we report the preplanned initial analysis performed with 241 pts. Methods: GCC mRNA was quantified by RT-qPCR using FFPE LNs tissues from untreated stage II CC pts diagnosed from 1999-2006 with at least 10 LN examined blinded to clinical outcomes. Cox regression models examined the relationship between GCC nodal status and the prespecified primary endpoint of recurrence risk. Results: Twenty-ninepts (12%) had a disease recurrence or cancer death, median follow-up was 60 months and median LNs examined was 15. The ratio of the number of GCC+ LNs over the total number of informative LNs (LNR) significantly predicted higher recurrence risk for 84 pts classified as high risk (HR, 2.38; p=0.02). The estimated 5-yr recurrence rates were 10% and 27% for the low and high risk group, respectively. After adjusting for age, T stage, number of LNs assessed, and MMR status, the significant association remained (HR, 2.61; 95% CI, 1.17-5.83; p=0.02). In a subset of 181 pts with negative margin, T3 tumor only and ≥12 LN examined, the GCC LNR had a HR for recurrence of 5.06 (95% CI 1.61-15.91, p=0.003), translating into 5-yr recurrence rates of 4% among low risk pts and 27% for the high-risk group. Conclusions: Our results suggest that GCC expression in LNs is a significant determinant of recurrence in appropriately staged CC pts not treated with adjuvant chemotherapy. The validation component of the study is ongoing. [Table: see text] [Table: see text]

scholarly journals A Nomogram for the Determination of the Necessity of Concurrent Chemotherapy in Patients With Stage II–IVa Nasopharyngeal Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Kaixuan Yang ◽  
Qian Zhang ◽  
Mengxi Zhang ◽  
Wenji Xie ◽  
Mei Li ◽  
...  
Keyword(s):  
High Risk ◽  
Nasopharyngeal Carcinoma ◽  
Cox Regression ◽  
Risk Group ◽  
Intensity Modulated Radiotherapy ◽  
Concurrent Chemotherapy ◽  
High Risk Group ◽  
Low Risk ◽  
Stage Ii ◽  
Risk Recurrence

BackgroundThe efficiency of concurrent chemotherapy (CC) remains controversial for stage II–IVa nasopharyngeal carcinoma (NPC) patients treated with induction chemotherapy (IC) followed by intensity-modulated radiotherapy (IMRT). Therefore, we aimed to propose a nomogram to identify patients who would benefit from CC.MethodsA total of 434 NPC patients (stage II–IVa) treated with IC followed by IMRT between January 2010 and December 2015 were included. There were 808 dosimetric parameters extracted by the in-house script for each patient. A dosimetric signature was developed with the least absolute shrinkage and selection operator algorithm. A nomogram was built by incorporating clinical factors and dosimetric signature using Cox regression to predict recurrence-free survival (RFS). The C-index was used to evaluate the performance of the nomogram. The patients were stratified into low- and high-risk recurrence according to the optimal cutoff of risk score.ResultsThe nomogram incorporating age, TNM stage, and dosimetric signature yielded a C-index of 0.719 (95% confidence interval, 0.658–0.78). In the low-risk group, CC was associated with a 9.4% increase of 5-year locoregional RFS and an 8.8% increase of 5-year overall survival (OS), whereas it was not significantly associated with an improvement of locoregional RFS (LRFS) and OS in the high-risk group. However, in the high-risk group, patients could benefit from adjuvant chemotherapy (AC) by improving 33.6% of the 5-year LRFS.ConclusionsThe nomogram performed an individualized risk quantification of RFS in patients with stage II–IVa NPC treated with IC followed by IMRT. Patients with low risk could benefit from CC, whereas patients with high risk may require additional AC.

scholarly journals Ferroptosis regulator genes are a favorable biomarker for hepatocellular carcinoma

2021 ◽  
Author(s):  
Yongfei He ◽  
Shuqi Zhao ◽  
Zhongliu Wei ◽  
Xin Zhou ◽  
Tianyi Liang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Clinical Information ◽  
High Risk Group ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Regulator Genes ◽  
The Relationship

Abstract BackgroundIn this study, we comprehensively analyzed the relationship between ferroptosis regulator genes (FRGs) and prognosis of hepatocellular carcinoma (HCC), determined the prognostics value of FRGs, established a prediction model, and explored the relationship with immunotherapy for HCC.MethodsThe mRNA transcriptional levels and clinical information of HCC were obtained from The Cancer Genome Atlas (TCGA) database. The 24 FRGs were combined with the differential expression genes (DEGs) of HCC for further analysis. The prognostics values of differential FRGs via the construction of model and validation by the Cox regression analysis.ResultThere were three genes (CARS1, FANCD2, and SLC7A11) were identified as independent risk factors for HCC, and a predictive model was constructed based on CARS1, FANCD2, and SLC7A11. The model showed that the low-risk group HCC patients with a more prolonged overall survival (OS) than the high-risk group (P=0.001). The high-risk group with higher expression of FRGs than the low-risk group. Finally, the relations between FGEs and immune infiltration showed that CARS1, FANCD2, and SLC7A11 had a positive relationship with macrophage infiltration. From these, three genes might be the potential therapeutic targets.ConclusionOur study indicated that CARS1, FANCD2, and SLC7A11 might have potential value for therapeutic strategies as practical and reliable prognostic tools for HCC.

scholarly journals A Novel Seventeen-Gene Metabolic Signature for Predicting Prognosis in Colon Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Dakui Luo ◽  
Zezhi Shan ◽  
Qi Liu ◽  
Sanjun Cai ◽  
Qingguo Li ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Differentially Expressed ◽  
Metabolic Signature ◽  
Metabolic Genes ◽  
Prognostic Prediction

A metabolic disorder is considered one of the hallmarks of cancer. Multiple differentially expressed metabolic genes have been identified in colon cancer (CC), and their biological functions and prognostic values have been well explored. The purpose of the present study was to establish a metabolic signature to optimize the prognostic prediction in CC. The related data were downloaded from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) database, and Gene Expression Omnibus (GEO) combined with GSE39582 set, GSE17538 set, GSE33113 set, and GSE37892 set. The differentially expressed metabolic genes were selected for univariate Cox regression and lasso Cox regression analysis using TCGA and GTEx datasets. Finally, a seventeen-gene metabolic signature was developed to divide patients into a high-risk group and a low-risk group. Patients in the high-risk group presented poorer prognosis compared to the low-risk group in both TCGA and GEO datasets. Moreover, gene set enrichment analyses demonstrated multiple significantly enriched metabolism-related pathways. To sum up, our study described a novel seventeen-gene metabolic signature for prognostic prediction of colon cancer.

scholarly journals A novel signature of two long non-coding RNAs in BRCA mutant ovarian cancer to predict prognosis and efficiency of chemotherapy

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Yinglian Pan ◽  
Li Ping Jia ◽  
Yuzhu Liu ◽  
Yiyu Han ◽  
Qian Li ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Gynecologic Cancer ◽  
High Risk Group ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Chemotherapy Treatment ◽  
Prognostic Signature

Abstract Background In this study we aimed to identify a prognostic signature in BRCA1/2 mutations to predict disease progression and the efficiency of chemotherapy ovarian cancer (OV), the second most common cause of death from gynecologic cancer in women worldwide. Methods Univariate Cox proportional-hazards and multivariate Cox regression analyses were used to identifying prognostic factors from data obtained from The Cancer Genome Atlas (TCGA) database. The area under the curve of the receiver operating characteristic curve was assessed, and the sensitivity and specificity of the prediction model were determined. Results A signature consisting of two long noncoding RNAs(lncRNAs), Z98885.2 and AC011601.1, was selected as the basis for classifying patients into high and low-risk groups (median survival: 7.2 years vs. 2.3 years). The three-year overall survival (OS) rates for the high- and low-risk group were approximately 38 and 100%, respectively. Chemotherapy treatment survival rates indicated that the high-risk group had significantly lower OS rates with adjuvant chemotherapy than the low-risk group. The one-, three-, and five-year OS were 100, 40, and 15% respectively in the high-risk group. The survival rate of the high-risk group declined rapidly after 2 years of OV chemotherapy treatment. Multivariate Cox regression associated with other traditional clinical factors showed that the 2-lncRNA model could be used as an independent OV prognostic factor. Analyses of data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) indicated that these signatures are pivotal to cancer development. Conclusion In conclusion, Z98885.2 and AC011601.1 comprise a novel prognostic signature for OV patients with BRCA1/2 mutations, and can be used to predict prognosis and the efficiency of chemotherapy.

scholarly journals Discovery and validation of immune- related long non-coding RNA biomarkers associated with prognosis in hepatocellular carcinoma

2020 ◽  
Author(s):  
Li Liu ◽  
She Tian ◽  
Zhu Li ◽  
Yongjun Gong ◽  
Hao Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Survival Curve ◽  
Principal Component ◽  
High Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Background : Hepatocellular carcinoma (HCC) is one of the most common clinical malignant tumors, resulting in high mortality and poor prognosis. Studies have found that LncRNA plays an important role in the onset, metastasis and recurrence of hepatocellular carcinoma. The immune system plays a vital role in the development, progression, metastasis and recurrence of cancer. Therefore, immune-related lncRNA can be used as a novel biomarker to predict the prognosis of hepatocellular carcinoma. Methods : The transcriptome data and clinical data of HCC patients were obtained by using The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA‑LIHC), and immune-related genes were extracted from the Molecular Signatures Database (IMMUNE RESPONSE M19817 and IMMUNE SYSTEM PROCESS M13664). By constructing the co-expression network and Cox regression analysis, 13 immune-lncRNAs was identified to predict the prognosis of HCC patients. Patients were divided into high risk group and low risk group by using the risk score formula, and the difference in overall survival (OS) between the two groups was reflected by Kaplan-Meier survival curve. The time - dependent receiver operating characteristics (ROC) analysis and principal component analysis (PCA) were used to evaluate 13 immune -lncRNAs signature. Results : Through TCGA - LIHC extracted from 343 cases of patients with hepatocellular carcinoma RNA - Seq data and clinical data, 331 immune-related genes were extracted from the Molecular Signatures Database , co-expression networks and Cox regression analysis were constructed, 13 immune-lncRNAs signature was identified as biomarkers to predict the prognosis of patients. At the same time using the risk score median divided the patients into high risk and low risk groups, and through the Kaplan-Meier survival curve analysis found that high-risk group of patients' overall survival (OS) less low risk group of patients. The AUC value of the ROC curve is 0.828, and principal component analysis (PCA) results showed that patients could be clearly divided into two parts by immune-lncRNAs, which provided evidence for the use of 13 immune-lncRNAs signature as prognostic markers. Conclusion : Our study identified 13 immune-lncRNAs signature that can effectively predict the prognosis of HCC patients, which may be a new prognostic indicator for predicting clinical outcomes.

scholarly journals Identification of an Autophagy-Related Pair Signature for Predicting Prognoses and Immune Activity in Pancreatic Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Qian Zhang ◽  
Liping Lv ◽  
Ping Ma ◽  
Yangyang Zhang ◽  
Jiang Deng ◽  
...  
Keyword(s):  
High Risk ◽  
Pancreatic Adenocarcinoma ◽  
Small Molecule ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
M2 Macrophage ◽  
Related Gene ◽  
Significant Difference

BackgroundPancreatic adenocarcinoma (PAAD) spreads quickly and has a poor prognosis. Autophagy research on PAAD could reveal new biomarkers and targets for diagnosis and treatment.MethodsAutophagy-related genes were translated into autophagy-related gene pairs, and univariate Cox regression was performed to obtain overall survival (OS)-related IRGPs (P&lt;0.001). LASSO Cox regression analyses were performed to construct an autophagy-related gene pair (ARGP) model for predicting OS. The Cancer Genome Atlas (TCGA)-PAAD cohort was set as the training group for model construction. The model predictive value was validated in multiple external datasets. Receiver operating characteristic (ROC) curves were used to evaluate model performance. Tumor microenvironments and immune infiltration were compared between low- and high-risk groups with ESTIMATE and CIBERSORT. Differentially expressed genes (DEGs) between the groups were further analyzed by Gene Ontology biological process (GO-BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and used to identify potential small-molecule compounds in L1000FWD.ResultsRisk scores were calculated as follows: ATG4B|CHMP4C×(-0.31) + CHMP2B|MAP1LC3B×(0.30) + CHMP6|RIPK2 ×(-0.33) + LRSAM1|TRIM5×(-0.26) + MAP1LC3A|PAFAH1B2×(-0.15) + MAP1LC3A|TRIM21×(-0.08) + MET|MFN2×(0.38) + MET|MTDH×(0.47) + RASIP1|TRIM5×(-0.23) + RB1CC1|TPCN1×(0.22). OS was significantly shorter in the high-risk group than the low-risk group in each PAAD cohort. The ESTIMATE analysis showed no difference in stromal scores but a significant difference in immune scores (p=0.0045) and ESTIMATE scores (p=0.014) between the groups. CIBERSORT analysis showed higher naive B cell, Treg cell, CD8 T cell, and plasma cell levels in the low-risk group and higher M1 and M2 macrophage levels in the high-risk group. In addition, the results showed that naive B cells (r=-0.32, p&lt;0.001), Treg cells (r=-0.31, p&lt;0.001), CD8 T cells (r=-0.24, p=0.0092), and plasma cells (r=-0.2, p&lt;0.026) were statistically correlated with the ARGP risk score. The top 3 enriched GO-BPs were signal release, regulation of transsynaptic signaling, and modulation of chemical synaptic transmission, and the top 3 enriched KEGG pathways were the insulin secretion, dopaminergic synapse, and NF-kappa B signaling pathways. Several potential small-molecule compounds targeting ARGs were also identified.ConclusionOur results demonstrate that the ARGP-based model may be a promising prognostic indicator for identifying drug targets in patients with PAAD.

scholarly journals Development and Validation of a Novel Hypoxia-Related Long Noncoding RNA Model With Regard to Prognosis and Immune Features in Breast Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Peng Gu ◽  
Lei Zhang ◽  
Ruitao Wang ◽  
Wentao Ding ◽  
Wei Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis

Background: Female breast cancer is currently the most frequently diagnosed cancer in the world. This study aimed to develop and validate a novel hypoxia-related long noncoding RNA (HRL) prognostic model for predicting the overall survival (OS) of patients with breast cancer.Methods: The gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 200 hypoxia-related mRNAs were obtained from the Molecular Signatures Database. The co-expression analysis between differentially expressed hypoxia-related mRNAs and lncRNAs based on Spearman’s rank correlation was performed to screen out 166 HRLs. Based on univariate Cox regression and least absolute shrinkage and selection operator Cox regression analysis in the training set, we filtered out 12 optimal prognostic hypoxia-related lncRNAs (PHRLs) to develop a prognostic model. Kaplan–Meier survival analysis, receiver operating characteristic curves, area under the curve, and univariate and multivariate Cox regression analyses were used to test the predictive ability of the risk model in the training, testing, and total sets.Results: A 12-HRL prognostic model was developed to predict the survival outcome of patients with breast cancer. Patients in the high-risk group had significantly shorter median OS, DFS (disease-free survival), and predicted lower chemosensitivity (paclitaxel, docetaxel) compared with those in the low-risk group. Also, the risk score based on the expression of the 12 HRLs acted as an independent prognostic factor. The immune cell infiltration analysis revealed that the immune scores of patients in the high-risk group were lower than those of the patients in the low-risk group. RT-qPCR assays were conducted to verify the expression of the 12 PHRLs in breast cancer tissues and cell lines.Conclusion: Our study uncovered dozens of potential prognostic biomarkers and therapeutic targets related to the hypoxia signaling pathway in breast cancer.

scholarly journals A Four-Gene-Based Prognostic Model Predicts Overall Survival in Patients With Cutaneous Melanoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiaoxia Tong ◽  
Xiaofei Qu ◽  
Mengyun Wang
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Risk Score ◽  
Cutaneous Melanoma ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Rank Test ◽  
Score Model

BackgroundCutaneous melanoma (CM) is one of the most aggressive cancers with highly metastatic ability. To make things worse, there are limited effective therapies to treat advanced CM. Our study aimed to investigate new biomarkers for CM prognosis and establish a novel risk score system in CM.MethodsGene expression data of CM from Gene Expression Omnibus (GEO) datasets were downloaded and analyzed to identify differentially expressed genes (DEGs). The overlapped DEGs were then verified for prognosis analysis by univariate and multivariate COX regression in The Cancer Genome Atlas (TCGA) datasets. Based on the gene signature of multiple survival associated DEGs, a risk score model was established, and its prognostic and predictive role was estimated through Kaplan-Meier (K-M) analysis and log-rank test. Furthermore, the correlations between prognosis related genes expression and immune infiltrates were analyzed via Tumor Immune Estimation Resource (TIMER) site.ResultsA total of 103 DEGs were obtained based on GEO cohorts, and four genes were verified in TCGA datasets. Subsequently, four genes (ADAMDEC1, GNLY, HSPA13, and TRIM29) model was developed by univariate and multivariate Cox regression analyses. The K-M plots showed that the high-risk group was associated with shortened survival than that in the low-risk group (P &lt; 0.0001). Multivariate analysis suggested that the model was an independent prognostic factor (high-risk vs. low-risk, HR= 2.06, P &lt; 0.001). Meanwhile, the high-risk group was prone to have larger breslow depth (P&lt; 0.001) and ulceration (P&lt; 0.001).ConclusionsThe four-gene risk score model functions well in predicting the prognosis and treatment response in CM and will be useful for guiding therapeutic strategies for CM patients. Additional clinical trials are needed to verify our findings.

scholarly journals m6A RNA methylation regulators can contribute to malignant progression and impact the prognosis of bladder cancer

2019 ◽  
Vol 39 (12) ◽  
Author(s):  
Mei Chen ◽  
Zhen-yu Nie ◽  
Xiao-hong Wen ◽  
Yuan-hui Gao ◽  
Hui Cao ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Malignant Progression ◽  
Low Risk ◽  
Rna Methylation ◽  
M6a Rna Methylation ◽  
Clinicopathological Variables

Abstract N6-methyladenosine (m6A) is the most common form of messenger RNA (mRNA) modification. An increasing number of studies have proven that m6A RNA methylation regulators are overexpressed in many cancers and participate in the development of cancer through the dynamic regulation of m6A RNA methylation regulators. However, the prognostic role of m6A RNA methylation regulators in bladder cancer (BC) is poorly understood. In the present study, we downloaded the mRNA expression data from The Cancer Genome Atlas (TCGA) database and the corresponding clinical and prognostic information. The relationship between m6A RNA methylation regulators and clinicopathological variables of BC patients was assessed by the Kolmogorov–Smirnov test. The expression of the m6A RNA methylation regulators was differentially associated with different clinicopathological variables of BC patients. The least absolute shrinkage and selection operator (LASSO) Cox regression model was then applied to identify three m6A RNA methylation regulators. The risk signature was constructed as follows: 0.164FTO − (0.081YTHDC1+0.032WTAP). Based on the risk signature, the risk score of each patient was calculated, and the patients were divided into a high-risk group and a low-risk group. The overall survival (OS) rate of the high-risk group was significantly lower than that of the low-risk group. The risk signature was not only an independent prognostic marker for BC patients but also a predictor of clinicopathological variables. In conclusion, m6A RNA methylation regulators can participate in the malignant progression of BC, and a risk signature with three selected m6A RNA methylation regulators may be a promising prognostic biomarker to guide personalized treatment for BC patients.

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