scholarly journals Glycolysis-Related Gene Expression Profiling Screen for Prognostic Risk Signature of Pancreatic Ductal Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Wenjing Song ◽  
Xin He ◽  
Pengju Gong ◽  
Yan Yang ◽  
Sirui Huang ◽  
...  
Keyword(s):  
High Risk ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Risk Group ◽  
Mrna Level ◽  
High Risk Group ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Ductal Adenocarcinoma ◽  
Related Gene

Objective: Pancreatic ductal adenocarcinoma (PDAC) is highly lethal. Although progress has been made in the treatment of PDAC, its prognosis remains unsatisfactory. This study aimed to develop novel prognostic genes related to glycolysis in PDAC and to apply these genes to new risk stratification.Methods: In this study, based on the Cancer Genome Atlas (TCGA) PAAD cohort, the expression level of glycolysis-related gene at mRNA level in PAAD and its relationship with prognosis were analyzed. Non-negative matrix decomposition (NMF) clustering was used to cluster PDAC patients according to glycolytic genes. Prognostic glycolytic genes, screened by univariate Cox analysis and LASSO regression analysis were established to calculate risk scores. The differentially expressed genes (DEGs) in the high-risk group and the low-risk group were analyzed, and the signal pathway was further enriched to analyze the correlation between glycolysis genes. In addition, based on RNA-seq data, CIBERSORT was used to evaluate the infiltration degree of immune cells in PDAC samples, and ESTIMATE was used to calculate the immune score of the samples.Results: A total of 319 glycolysis-related genes were retrieved, and all PDAC samples were divided into two clusters by NMF cluster analysis. Survival analysis showed that PDAC patients in cluster 1 had shorter survival time and worse prognosis compared with cluster 2 samples (P < 0.001). A risk prediction model based on 11 glycolysis genes was constructed, according to which patients were divided into two groups, with significantly poorer prognosis in high-risk group than in low-risk group (P < 0.001). Both internal validation and external dataset validation demonstrate good predictive ability of the model (AUC = 0.805, P < 0.001; AUC = 0.763, P < 0.001). Gene aggregation analysis showed that DEGs highly expressed in high-risk group were mainly concentrated in the glycolysis level, immune status, and tumor cell proliferation, etc. In addition, the samples in high-risk group showed immunosuppressed status and infiltrated by relatively more macrophages and less CD8+T cell.Conclusions: These findings suggested that the gene signature based on glycolysis-related genes had potential diagnostic, therapeutic, and prognostic value for PDAC.

Evaluation of Early Prognostic Factors in Patients With Pancreatic Ductal Adenocarcinoma Receiving Gemcitabine Together With Nab-paclitaxel

2021 ◽  
Vol 1 (5) ◽  
pp. 399-409
Author(s):  
WATARU IZUMO ◽  
RYOTA HIGUCHI ◽  
TORU FURUKAWA ◽  
TAKEHISA YAZAWA ◽  
SHUICHIRO UEMURA ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prognostic Factors ◽  
High Risk ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Nutrition Status ◽  
Ductal Adenocarcinoma ◽  
Changing Rate

Background: Gemcitabine together with nab-paclitaxel (GnP) has been shown to improve outcomes in patients with pancreatic ductal adenocarcinoma (PDAC). However, the predictive markers for treatment effects remain unclear. This study aimed to identify early prognostic factors in patients with PDAC receiving GnP. Patients and Methods: We analyzed 113 patients who received GnP for PDAC and evaluated the relationship between clinical factors and outcomes. Results: The median survival time (MST) was 1.2 years. In multivariate analysis, baseline carbohydrate antigen 19-9 (CA19-9) ≥747 U/ml [hazard ratio (HR)=1.9], baseline controlling nutrition status (CONUT) score ≥5 (HR=3.7) and changing rate of CA19-9 after two GnP cycles ≥0.69 (HR=3.7) were independent risk factors for poor prognosis. When examining outcomes according to pre-chemotherapeutic measurable factors (baseline CA19-9 and CONUT), the MSTs of patients with pre-chemotherapeutic zero risk factors (pre-low-risk group, n=63) and one or more risk factors (pre-high-risk group, n=50) were 1.7 and 0.65 years (p<0.001), respectively. The MST for those with a changing rate of CA19-9 after two GnP cycles <0.69 and ≥0.69 was significantly different in both groups (2.0 and 1.2 years in the pre-low-risk group, p<0.001; 1.0 and 0.52 years in the pre-high-risk group, p<0.001). Conclusion: These results may be useful for decision-making regarding treatment strategies in patients with PDAC receiving GnP.

scholarly journals New Onset of DiabetEs in aSsociation with pancreatic ductal adenocarcinoma (NODES Trial): protocol of a prospective, multicentre observational trial

BMJ Open ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. e037267
Author(s):  
Dóra Illés ◽  
Emese Ivány ◽  
Gábor Holzinger ◽  
Klára Kosár ◽  
M Gordian Adam ◽  
...  
Keyword(s):  
High Risk ◽  
Early Detection ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Clinical Symptoms ◽  
Risk Group ◽  
High Risk Group ◽  
Ductal Adenocarcinoma ◽  
Onset Diabetes ◽  
Dismal Prognosis ◽  
New Onset

IntroductionPancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis with an overall 5-year survival of approximately 8%. The success in reducing the mortality rate of PDAC is related to the discovery of new therapeutic agents, and to a significant extent to the development of early detection and prevention programmes. Patients with new-onset diabetes mellitus (DM) represent a high-risk group for PDAC as they have an eightfold higher risk of PDAC than the general population. The proposed screening programme may allow the detection of PDAC in the early, operable stage. Diagnosing more patients in the curable stage might decrease the morbidity and mortality rates of PDAC and additionally reduce the burden of the healthcare.Methods and analysisThis is a prospective, multicentre observational cohort study. Patients ≥60 years old diagnosed with new-onset (≤6 months) diabetes will be included. Exclusion criteria are (1) Continuous alcohol abuse; (2) Chronic pancreatitis; (3) Previous pancreas operation/pancreatectomy; (4) Pregnancy; (5) Present malignant disease and (6) Type 1 DM. Follow-up visits are scheduled every 6 months for up to 36 months. Data collection is based on questionnaires. Clinical symptoms, body weight and fasting blood will be collected at each, carbohydrate antigen 19–9 and blood to biobank at every second visit. The blood samples will be processed to plasma and analysed with mass spectrometry (MS)-based metabolomics. The metabolomic data will be used for biomarker validation for early detection of PDAC in the high-risk group patients with new-onset diabetes. Patients with worrisome features will undergo MRI or endoscopic ultrasound investigation, and surgical referral depending on the radiological findings. One of the secondary end points is the incidence of PDAC in patients with newly diagnosed DM.Ethics and disseminationThe study has been approved by the Scientific and Research Ethics Committee of the Hungarian Medical Research Council (41085-6/2019). We plan to disseminate the results to several members of the healthcare system includining medical doctors, dietitians, nurses, patients and so on. We plan to publish the results in a peer-reviewed high-quality journal for professionals. In addition, we also plan to publish it for lay readers in order to maximalise the dissemination and benefits of this trial.Trial registration numberClinicalTrials.gov NCT04164602

scholarly journals A Novel Ferroptosis-Related Gene Signature Robustly Predicts Clinical Prognosis And Tumor Immunity in Patients With Kidney Renal Clear Cell Carcinoma

2021 ◽  
Author(s):  
Wei Song ◽  
Weiting Kang ◽  
Qi Zhang
Keyword(s):  
High Risk ◽  
Clear Cell ◽  
Risk Group ◽  
Gene Signature ◽  
Renal Clear Cell Carcinoma ◽  
High Risk Group ◽  
Risk Scores ◽  
Related Gene ◽  
Clinical Prognosis ◽  
Validation Set

Abstract Objective: This study aimed to construct a ferroptosis-related gene signature to predict clinical prognosis and tumor immunity in patients with kidney renal clear cell carcinoma (KIRC).Methods: The mRNA expression profiles and corresponding clinical data of KIRC patients were downloaded from The Cancer Genome Atlas (TCGA), which were randomly divided into training (398 patients) and validation set (132 patients). The iron death related (IDR) prediction model was constructed based on training set and 60 ferroptosis-related genes from previous literatures, followed by prognostic performance evaluation and verification using the validation set. Moreover, functional enrichment, immune cell infiltration, metagene clusters correlation, and TIDE scoring analyses were performed. Results: In total, 23 ferroptosis-related genes were significantly associated with overall survival (OS). The IDR prediction model (a 10-gene signature) was then constructed to stratify patients into two risk groups. The OS of KIRC patients with high-risk scores was significantly shorter than those with low-risk scores. Moreover, the risk score was confirmed as an independent prognostic predictor for OS. The positive and negative correlated genes with this model were significantly enriched in p53 signaling pathway, and cGMP-PKG signaling pathway. The patients in the high-risk group had higher ratios of plasma cells, T cells CD8, and T cells regulatory Tregs. Furthermore, IgG, HCK, LCK, and Interferson metagenes were significantly correlated with risk score. By TIDE score analysis, patients in the high-risk group could benefit from immunotherapy.Conclusions: The identified ferroptosis-related gene signature is significantly correlated with clinical prognosis and immune immunity in KIRC patients.

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 Ferroptosis-related gene signature predicts the prognosis of papillary thyroid carcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jinyuan Shi ◽  
Pu Wu ◽  
Lei Sheng ◽  
Wei Sun ◽  
Hao Zhang
Keyword(s):  
High Risk ◽  
Immune Cells ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Lasso Regression ◽  
Papillary Thyroid ◽  
Related Gene ◽  
Normal Thyroid

Abstract Background Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer (TC), accounting for more than 80% of all cases. Ferroptosis is a novel iron-dependent and Reactive oxygen species (ROS) reliant type of cell death which is distinct from the apoptosis, necroptosis and pyroptosis. Considerable studies have demonstrated that ferroptosis is involved in the biological process of various cancers. However, the role of ferroptosis in PTC remains unclear. This study aims at exploring the expression of ferroptosis-related genes (FRG) and their prognostic values in PTC. Methods A ferroptosis-related gene signature was constructed using lasso regression analysis through the PTC datasets of the Cancer Genome Atlas (TCGA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to investigate the bioinformatics functions of significantly different genes (SDG) of ferroptosis. Additionally, the correlations of ferroptosis and immune cells were assessed through the single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT database. Finally, SDG were test in clinical PTC specimens and normal thyroid tissues. Results LASSO regression model was utilized to establish a novel FRG signature with 10 genes (ANGPTL7, CDKN2A, DPP4, DRD4, ISCU, PGD, SRXN1, TF, TFRC, TXNRD1) to predicts the prognosis of PTC, and the patients were separated into high-risk and low-risk groups by the risk score. The high-risk group had poorer survival than the low-risk group (p < 0.001). Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Multivariate regression analysis identified the prognostic signature-based risk score was an independent prognostic indicator for PTC. The functional roles of the DEGs in the TGCA PTC cohort were explored using GO enrichment and KEGG pathway analyses. Immune related analysis demonstrated that the most types of immune cells and immunological function in the high-risk group were significant different with those in the low-risk group. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) verified the SDG have differences in expression between tumor tissue and normal thyroid tissue. In addition, cell experiments were conducted to observe the changes in cell morphology and expression of signature’s genes with the influence of ferroptosis induced by sorafenib. Conclusions We identified differently expressed FRG that may involve in PTC. A ferroptosis-related gene signature has significant values in predicting the patients’ prognoses and targeting ferroptosis may be an alternative for PTC’s therapy.

scholarly journals Identification of N6-Methyladenosine-Associated Long Non-coding RNAs for Immunotherapeutic Response and Prognosis in Patients With Pancreatic Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Xinshuang Yu ◽  
Peng Dong ◽  
Yu Yan ◽  
Fengjun Liu ◽  
Hui Wang ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
T Cell ◽  
High Risk ◽  
Messenger Rna ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Risk Scores ◽  
Significant Difference ◽  
Selection Operator

Pancreatic cancer is a highly aggressive disease with poor prognosis. N6-methyladenosine (m6A) is critical for post-transcriptional modification of messenger RNA (mRNA) and long non-coding RNA (lncRNA). However, the m6A-associated lncRNAs (m6A-lncRNA) and their values in predicting clinical outcomes and immune microenvironmental status in pancreatic cancer patients remain largely unexplored. This study aimed to evaluate the importance of m6A-lncRNA and established a m6A-lncRNA signature for predicting immunotherapeutic response and prognosis of pancreatic cancer. The m6A-lncRNA co-expression networks were constructed using data from the TCGA and GTEx database. Based on the least absolute shrinkage and selection operator (LASSO) analysis, we constructed an 8 m6A-lncRNA signature risk model, and selection operator (LASSO) analysis, and stratified patients into the high- and low-risk groups with significant difference in overall survival (OS) (HR = 2.68, 95% CI = 1.74–4.14, P &lt; 0.0001). Patients in the high-risk group showed significantly reduced OS compared to patients in the low-risk group (P &lt; 0.001). The clinical characteristics and m6A-lncRNA risk scores were used to construct a nomogram which accurately predicted the OS in pancreatic cancer. TIMER 2.0 were used to investigate tumor immune infiltrating cells and its relationship with pancreatic cancer. CIBERSORT analysis revealed increased higher infiltration proportions of M0 and M2 macrophages, and lower infiltration of naive B cell, CD8+ T cell and Treg cells in the high-risk group. Compared to the low-risk group, functional annotation using ssGSEA showed that T cell infiltration and the differential immune-related check-point genes are expressed at low level in the high-risk group (P &lt; 0.05). In summary, our study constructed a novel m6A-associated lncRNAs signature to predict immunotherapeutic responses and provided a novel nomogram for the prognosis prediction of pancreatic cancer.

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 Validation of an Immune-related Gene Pair Index as a Prognostic Marker of Early-Stage Lung Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Zihao Wang ◽  
Xuan Xiang ◽  
Xiaoshan Wei ◽  
Linlin Ye ◽  
Yiran Niu ◽  
...  
Keyword(s):  
High Risk ◽  
Validation Cohort ◽  
Risk Group ◽  
Early Stage ◽  
Lung Squamous Cell Carcinoma ◽  
High Risk Group ◽  
Low Risk ◽  
Data Sets ◽  
Related Gene ◽  
Immune Related Gene

Abstract Background. Lung squamous cell carcinoma (LUSC) is one of the subtypes of non-small-cell lung cancer (NSCLC) and accounts for approximately 20 to 30% of all lung cancers.Methods. In this study, we developed an immune-related gene pair index (IRGPI) for early-stage LUSC from 3 public LUSC data sets, including The Cancer Genome Atlas LUSC cohort and Gene Expression Omnibus data sets, and explored whether IRGPI could act as a prognostic marker to identify patients with early-stage LUSC at high risk.Results. IRGPI was constructed by 68 gene pairs consisting of 123 unique immune-related genes from TCGA LUSC cohort. In the derivation cohort, the hazard of death among high-risk group was 10.51 times that of the low-risk group (HR, 10.51; 95%CI, 6.96-15.86; p<0.001). The hazard of death among the high-risk group was 2.26 times that of the low-risk group (HR, 2.26; 95%CI, 1.2-4.25; p=0.009) in the GSE37745 validation cohort and was 3.2 times that of low-risk group (HR, 3.2; 95%CI, 0.98-10.4; p=0.042) in the GSE41271 validation cohort. The infiltrations of CD8+ T cells and T follicular helper cells were lower in the high-risk group, as compared with the low-risk group in the TCGA cohort (6.94% vs 9.63%, p=0.004; 2.15% vs 3%, p=0.002, respectively). The infiltrations of neutrophils, activated mast cells and monocytes were higher in the high-risk group, as compared with the low-risk group in the TCGA cohort (1.63% vs 0.72%, p=0.001; 1.64% vs 1.02%, p=0.007; 0.57% vs 0.35%, p=0.041, respectively).Conclusions. IRGPI is a significant prognostic biomarker for predicting overall survival in early-stage LUSC patients.

scholarly journals Hypoxia-Related Long Non-Coding RNA Signature Predicts the Prognosis of Breast Cancer

2021 ◽  
Author(s):  
Jinlong Huo ◽  
Shuang Shen ◽  
Chen Chen ◽  
Rui Qu ◽  
Youming Guo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
T Cell ◽  
High Risk ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Cytolytic Activity ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas

Abstract Background: Breast cancer(BC) is the most common tumour in women. Hypoxia stimulates metastasis in cancer and is linked to poor patient prognosis.Methods: We screened prognostic-related lncRNAs(Long Non-Coding RNAs) from the Cancer Genome Atlas (TCGA) data and constructed a prognostic signature based on hypoxia-related lncRNAs in BC.Results: We identified 21 differentially expressed lncRNAs associated with BC prognosis. Kaplan Meier survival analysis indicated a significantly worse prognosis for the high-risk group(P<0.001). Moreover, the ROC-curve (AUC) of the lncRNAs signature was 0.700, a performance superior to other traditional clinicopathological characteristics. Gene set enrichment analysis (GSEA) showed many immune and cancer-related pathways and in the low-risk group patients. Moreover, TCGA revealed that functions including activated protein C (APC)co-inhibition, Cinnamoyl CoA reductase(CCR),check-point pathways, cytolytic activity, human leukocyte antigen (HLA), inflammation-promotion, major histocompatibility complex(MHC) class1, para-inflammation, T cell co-inhibition, T cell co-stimulation, and Type Ⅰ and Ⅱ Interferons (IFN) responses were significantly different in the low-risk and high-risk groups. Immune checkpoint molecules such as ICOS, IDO1, TIGIT, CD200R1, CD28, PDCD1(PD-1), were also expressed differently between the two risk groups. The expression of m6A-related mRNA indicated that YTHDC1, RBM15, METTL3, and FTO were significantly between the high and low-risk groups.Additionally, immunotherapy in patients with BC from the low-risk group yielded a higher frequency of clinical responses to anti-PD-1/PD-L1 therapy or a combination of anti-PD-1/PD-L1and anti-CTLA4 therapies.Except for lapatinib, the results also show that a high-risk score is related to a higher half-maximal inhibitory concentration (IC50) of chemotherapy drugs.Conclusion: A novel hypoxia-related lncRNAs signature may serve as a prognostic model for BC.

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