An 8-lncRNA signature predicts the survival of triple-negative breast cancer patients without germline BRCA1/2 mutation

Purpose: TNBC with germline BRCA1/2 mutation (gBRCAm) have higher sensitivity to DNA damaging agents including platinum-based chemotherapy and PARP inhibitors. But the treatment of TNBC without gBRCAm remains challenging. This study aimed to develop a long non-coding RNA (lncRNA) signature of TNBC patients without gBRCAm to improve risk stratification and optimize individualized treatment. Methods: 98 TNBC patients without gBRCAm were acquired from The Cancer Genome Atlas database. The univariable Cox regression analysis and LASSO Cox regression model were applied to establish an lncRNA signature in the training cohort. Then Kaplan–Meier survival curve and time-dependent ROC curve were used to validate the prognostic ability of the signature. The qPCR assay was performed to confirm the expressions and clinicopathological correlations of two potential lncRNAs HAGLROS and TONSL-AS1 in 30 paired clinical triple-negative breast cancer samples without gBRCAm. Results: We developed an 8-lncRNA signature in the training cohort including HAGLROS, AL139002.1, AL391244.2, AP000696.1, AL391056.1, AL513304.1, TONSL-AS1 and AL031008.1. Patients with higher risk scores showed significantly worse overall survival compared to those with lower risk scores (P=0.00018 and P =0.0068 respectively). 30 paired specimens of TNBC without gBRCAm in our center showed that two potential lncRNAs HAGLROS and TONSL-AS1 were found frequently overexpressed, and significantly associated with tumor grade and invasion. Conclusion: We constructed a novel 8-lncRNA signature which significantly associated with the overall survival of TNBC patients without gBRCAm. Among those 8 lncRNAs, HAGLROS and TONSL-AS1 may be potential therapeutic targets which function needed further exploration.

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An 8-lncRNA Signature Predicts Survival of Triple-Negative Breast Cancer Patients Without Germline BRCA1/2 Mutation

10.21203/rs.3.rs-60544/v1 ◽

2020 ◽

Author(s):

Minling Liu ◽

Wei Dai ◽

Mengyuan Zhu ◽

Xueying Li ◽

Shan Huang ◽

...

Keyword(s):

Breast Cancer ◽

Overall Survival ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Cox Regression ◽

Validation Cohort ◽

Functional Enrichment ◽

Biological Behavior ◽

Risk Scores ◽

Training Cohort

Abstract Background: Triple-negative breast cancer (TNBC) is a particular breast cancer subtype with poor prognosis due to its aggressive biological behavior and strong heterogeneity. TNBC with germline BRCA1/2 mutation (gBRCAm) have higher sensitivity to DNA damaging agents including platinum-based chemotherapy and PARP inhibitors. But the treatment of TNBC without gBRCAm remains challenging. This study aimed to develop a long non-coding RNA (lncRNA) signature of TNBC patients without gBRCAm to improve risk stratification and optimize individualized treatment.Methods: 98 TNBC patients without gBRCAm were acquired from The Cancer Genome Atlas (TCGA) database. The univariable Cox regression analysis and LASSO Cox regression model were applied to establish an lncRNA signature in the training cohort (N = 59). Then Kaplan–Meier survival curve and time-dependent ROC curve were used to validate the prognostic ability of the signature. The signature related mRNAs were identified using the Pearson correlation. Functional enrichment analysis of related mRNA was performed using the Metascape. The qPCR assay was performed to confirm the expressions and clinicopathological correlationsof two potential lncRNAs HAGLROS and TONSL-AS1 in 30 paired clinical triple-negative breast cancer samples without gBRCAm.Results:We developed an 8-lncRNA signature in the training cohort including HAGLROS, AL139002.1, AL391244.2, AP000696.1, AL391056.1, AL513304.1, TONSL-AS1 and AL031008.1. In both the training and validation cohort, patients with higher risk scores showed significantly worse overall survival compared to those with lower risk scores(P=0.00018 and P =0.0068 respectively). 1, 5, 8-year AUC in the training cohort were 1.000, 1.000 and 0.908 respectively, in the validation cohort were 0.785, 0.790 and 0.892 respectively indicating that our signature has a good prognostic capacity. Signature related mRNA mainly enriched in terms include RNA metabolic process, DNA repair pathways, and so on. Two potential lncRNAs HAGLROS and TONSL-AS1 were found frequently overexpressed in TNBC without gBRCAm, and significantly associated with tumor grade and invasion.Conclusions: We constructed a novel 8-lncRNA signaturewhich significantly associated with the overall survival of TNBC patients without gBRCAm. Among those 8lncRNAs, HAGLROS and TONSL-AS1 may be potential therapeutic targetswhich function needed further exploration.

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An 8-lncRNA signature predicts survival of triple-negative breast cancer patients without germline BRCA1/2 mutation

10.21203/rs.3.rs-66893/v1 ◽

2020 ◽

Author(s):

Minling Liu ◽

Wei Dai ◽

Mengyuan Zhu ◽

Xueying Li ◽

Shan Huang ◽

...

Keyword(s):

Breast Cancer ◽

Overall Survival ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Cox Regression ◽

Validation Cohort ◽

Functional Enrichment ◽

Biological Behavior ◽

Risk Scores ◽

Training Cohort

Abstract Background Triple-negative breast cancer (TNBC) is a particular breast cancer subtype with poor prognosis due to its aggressive biological behavior and strong heterogeneity. TNBC with germline BRCA1/2 mutation (gBRCAm) have higher sensitivity to DNA damaging agents including platinum-based chemotherapy and PARP inhibitors. But the treatment of TNBC without gBRCAm remains challenging. This study aimed to develop a long non-coding RNA (lncRNA) signature of TNBC patients without gBRCAm to improve risk stratification and optimize individualized treatment. Methods 98 TNBC patients without gBRCAm were acquired from The Cancer Genome Atlas (TCGA) database. The univariable Cox regression analysis and LASSO Cox regression model were applied to establish an lncRNA signature in the training cohort (N = 59). Then Kaplan–Meier survival curve and time-dependent ROC curve were used to validate the prognostic ability of the signature. The signature related mRNAs were identified using the Pearson correlation. Functional enrichment analysis of related mRNA was performed using the Metascape. The qPCR assay was performed to confirm the expressions and clinicopathological correlationsof two potential lncRNAs HAGLROS and TONSL-AS1 in 30 paired clinical triple-negative breast cancer samples without gBRCAm. Results We developed an 8-lncRNA signature in the training cohort including HAGLROS, AL139002.1, AL391244.2, AP000696.1, AL391056.1, AL513304.1, TONSL-AS1 and AL031008.1. In both the training and validation cohort, patients with higher risk scores showed significantly worse overall survival compared to those with lower risk scores(P = 0.00018 and P = 0.0068 respectively). 1, 5, 8-year AUC in the training cohort were 1.000, 1.000 and 0.908 respectively, in the validation cohort were 0.785, 0.790 and 0.892 respectively indicating that our signature has a good prognostic capacity. Signature related mRNA mainly enriched in terms include RNA metabolic process, DNA repair pathways, and so on. Two potential lncRNAs HAGLROS and TONSL-AS1 were found frequently overexpressed in TNBC without gBRCAm, and significantly associated with tumor grade and invasion. Conclusions We constructed a novel 8-lncRNA signaturewhich significantly associated with the overall survival of TNBC patients without gBRCAm. Among those 8lncRNAs, HAGLROS and TONSL-AS1 may be potential therapeutic targetswhich function needed further exploration.

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Construction and Validation of a Prognostic Risk Model for Triple Negative Breast Cancer Based on Autophagy-Related Genes

10.21203/rs.3.rs-1141994/v1 ◽

2021 ◽

Author(s):

Cheng Yan ◽

Qingling Liu ◽

Ruoling Jia

Keyword(s):

Breast Cancer ◽

Overall Survival ◽

Regression Analysis ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Risk Model ◽

Cox Regression ◽

Risk Group ◽

Lasso Regression ◽

Cox Regression Analysis

Abstract Background: Autophagy plays an important role in triple negative breast cancer (TNBC). However, the prognostic value of autophagy-related genes (ARGs) in TNBC remains unknown. In this study, we established a survival model to evaluate the prognosis of TNBC patients using ARGs signature.Methods: A total of 222 autophagy-related genes were downloaded from The Human Autophagy Database. The RNA-sequencing data and corresponding clinical data of TNBC were obtained from the TCGA database. Differential gene expression of ARGs (DE-ARGs) between normal samples and TNBC samples was determined by the EdgeR software package. Then, univariate Cox, Lasso, and multivariate Cox regression analyses were performed. According to the Lasso regression results based on univariate Cox, we identified a prognostic signature for overall-survival (OS), which was further validated by using GEO cohort. We also found an independent prognostic marker that can predict the clinicopathological features of TNBC. Furthermore, a nomogram was drawn to predict the survival probability of TNBC patients, which could help in clinical decision for TNBC treatment. Finally, we validated the requirement of a ARG in our model for TNBC cell survival and metastasis.Results: There are 43 differentially expressed ARGs (DE-ARGs) were identified between normal and tumor samples. A risk model for OS using CDKN1A, CTSD, CTSL, EIF4EBP1, TMEM74 and VAMP3 by Lasso regression analysis was established based on univariate Cox regression analysis. Overall survival of TNBC patients was significantly shorter in the high-risk group than in the low-risk group for both the training and validation cohorts. Using the Kaplan-Meier curves and ROC curves, we demonstrated the accuracy of the prognostic model. Multivariate Cox regression analysis was used to verify risk score as independent predictor. Then a nomogram was proposed to predict 1-, 3-, and 5-year survival for TNBC patients. The calibration curves showed great accuracy of the model for survival prediction. Finally, we found that depletion of EIF4EBP1, one of ARGs in our model, significantly reduced cell proliferation and metastasis of TNBC cells. Conclusion: An autophagy-related prognosis model in TNBCs was constructed using ARGs signature containing CDKN1A, CTSD, CTSL, EIF4EBP1, TMEM74 and VAMP3. It could serve as an independent prognostic biomarker in TNBC.

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Prognostic Biomarkers on a Competitive Endogenous RNA Network Reveals Overall Survival in Triple-Negative Breast Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.681946 ◽

2021 ◽

Vol 11 ◽

Author(s):

Wenxing Qin ◽

Feng Qi ◽

Jia Li ◽

Ping Li ◽

Yuan-Sheng Zang

Keyword(s):

Breast Cancer ◽

Overall Survival ◽

Triple Negative Breast Cancer ◽

Prognostic Model ◽

Triple Negative ◽

Cox Regression ◽

Critical Role ◽

Differentially Expressed ◽

Cerna Network ◽

Competitive Endogenous Rna

The objective of this study was to construct a competitive endogenous RNA (ceRNA) regulatory network using differentially expressed long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs in patients with triple-negative breast cancer (TNBC) and to construct a prognostic model for predicting overall survival (OS) in patients with TNBC. Differentially expressed lncRNAs, miRNAs, and mRNAs in TNBC patients from the TCGA and Metabric databases were examined. A prognostic model based on prognostic scores (PSs) was established for predicting OS in TNBC patients, and the performance of the model was assessed by a recipient that operated on a distinctive curve. A total of 874 differentially expressed RNAs (DERs) were screened, among which 6 lncRNAs, 295 miRNAs and 573 mRNAs were utilized to construct targeted and coexpression ceRNA regulatory networks. Eight differentially expressed genes (DEGs) associated with survival prognosis, DBX2, MYH7, TARDBP, POU4F1, ABCB11, LHFPL5, TRHDE and TIMP4, were identified by multivariate Cox regression and then used to establish a prognostic model. Our study shows that the ceRNA network has a critical role in maintaining the aggressiveness of TNBC and provides comprehensive molecular-level insight for predicting individual mortality hazards for TNBC patients. Our data suggest that these prognostic mRNAs from the ceRNA network are promising therapeutic targets for clinical intervention.

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Analysis of CK5/6 and EGFR and Its Effect on Prognosis of Triple Negative Breast Cancer

Frontiers in Oncology ◽

10.3389/fonc.2020.575317 ◽

2021 ◽

Vol 10 ◽

Author(s):

Zhen Wang ◽

Lei Liu ◽

Ying Li ◽

Zi’an Song ◽

Yi Jing ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Neoadjuvant Chemotherapy ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Cox Regression ◽

Multivariate Logistic Regression Analysis ◽

Pathological Examination ◽

Cox Regression Analysis ◽

Tumor Stages

BackgroundTriple-negative breast cancer (TNBC) is considered to be higher grade, more aggressive and have a poorer prognosis than other types of breast cancer. Discover biomarkers in TNBC for risk stratification and treatments that improve prognosis are in dire need.MethodsClinical data of 195 patients with triple negative breast cancer confirmed by pathological examination and received neoadjuvant chemotherapy (NAC) were collected. The expression levels of EGFR and CK5/6 were measured before and after NAC, and the relationship between EGFR and CK5/6 expression and its effect on prognosis of chemotherapy was analyzed.ResultsThe overall response rate (ORR) was 86.2% and the pathological complete remission rate (pCR) was 29.2%. Univariate and multivariate logistic regression analysis showed that cT (clinical Tumor stages) stage was an independent factor affecting chemotherapy outcome. Multivariate Cox regression analysis showed pCR, chemotherapy effect, ypT, ypN, histological grades, and post- NAC expression of CK5/6 significantly affected prognosis. The prognosis of CK5/6-positive patients after NAC was worse than that of CK5/6-negative patients (p=0.036). Changes in CK5/6 and EGFR expression did not significantly affect the effect of chemotherapy, but changes from positive to negative expression of these two markers are associated with a tendency to improve prognosis.ConclusionFor late-stage triple negative breast cancer patients receiving NAC, patients who achieved pCR had a better prognosis than those with non- pCR. Patients with the change in expression of EGFR and CK5/6 from positive to negative after neoadjuvant chemotherapy predicted a better prognosis than the change from negative to positive group.

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A Novel Platelet-Related Gene Signature for Predicting the Prognosis of Triple-Negative Breast Cancer

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.795600 ◽

2022 ◽

Vol 9 ◽

Author(s):

Jindong Xie ◽

Yutian Zou ◽

Feng Ye ◽

Wanzhen Zhao ◽

Xinhua Xie ◽

...

Keyword(s):

Breast Cancer ◽

High Risk ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Clinical Prognosis

Regarded as the most invasive subtype, triple-negative breast cancer (TNBC) lacks the expression of estrogen receptors (ERs), progesterone receptors (PRs), and human epidermal growth factor receptor 2 (HER2) proteins. Platelets have recently been shown to be associated with metastasis of malignant tumors. Nevertheless, the status of platelet-related genes in TNBC and their correlation with patient prognosis remain unknown. In this study, the expression and variation levels of platelet-related genes were identified and patients with TNBC were divided into three subtypes. We collected cohorts from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. By applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, we constructed a seven-gene signature which classified the two cohorts of patients with TNBC into low- or high-risk groups. Patients in the high-risk group were more likely to have lower survival rates than those in the low-risk group. The risk score, incorporated with the clinical features, was confirmed as an independent factor for predicting the overall survival (OS) time. Functional enrichment analyses revealed the involvement of a variety of vital biological processes and classical cancer-related pathways that could be important to the ultimate prognosis of TNBC. We then built a nomogram that performed well. Moreover, we tested the model in other cohorts and obtained positive outcomes. In conclusion, platelet-related genes were closely related to TNBC, and this novel signature could serve as a tool for the assessment of clinical prognosis.

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High Expression of microRNA-223 Indicates a Good Prognosis in Triple-Negative Breast Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.630432 ◽

2021 ◽

Vol 11 ◽

Author(s):

Li Chen ◽

Xiuzhi Zhu ◽

Boyue Han ◽

Lei Ji ◽

Ling Yao ◽

...

Keyword(s):

Breast Cancer ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Cox Regression ◽

Molecular Subtype ◽

External Validation ◽

Disease Free Survival ◽

The Cancer Genome Atlas ◽

Locked Nucleic Acid ◽

Tnbc Subtype

PurposeMicroRNAs can influence many biological processes and have shown promise as cancer biomarkers. Few studies have focused on the expression of microRNA-223 (miR-223) and its precise role in breast cancer (BC). We aimed to examine the expression level of miR-223 and its prognostic value in BC.MethodsTissue microarray (TMA)-based miRNA detection in situ hybridization (ISH) with a locked nucleic acid (LNA) probe was used to detect miR-223 expression in 450 BC tissue samples. Overall survival (OS) and disease-free survival (DFS) were compared between two groups using the Kaplan-Meier method and Cox regression model.ResultsOS and DFS were prolonged in the high miR-223 expression group compared to the low miR-223 expression group (p < 0.0001 and p = 0.017, respectively), especially in patients with the triple-negative breast cancer (TNBC) subtype (p = 0.046 and p < 0.001, respectively). Univariate and multivariate Cox regression analyses revealed that TNM stage (p = 0.008), the molecular subtype (p = 0.049), and miR-223 (p < 0.001) were independently associated with OS and DFS. External validation was performed with the METABRIC and The Cancer Genome Atlas (TCGA) databases via online webtools and was consistent with the data described above.ConclusionsThis study provides evidence that high miR-223 expression at diagnosis is associated with improved DFS and OS for BC patients, especially those with the TNBC subtype. miR-223 is a valid and independent prognostic biomarker in BC.

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Ovarian Cancer Risk Scores Based on Immune-Related Pseudogenes to Predict Overall Survival and Guide Immunotherapy and Chemotherapy

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/1586312 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Jie Zhao ◽

Rixiang Zhao ◽

Xiaocen Wei ◽

Xiaojing Jiang ◽

Fan Su

Keyword(s):

Ovarian Cancer ◽

Overall Survival ◽

High Risk ◽

Cox Regression ◽

High Risk Group ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Ovarian Cancer Risk ◽

Cox Regression Analysis ◽

The Impact

Background. Ovarian cancer (OC) is the top of the aggressive malignancies in females with a poor survival rate. However, the roles of immune-related pseudogenes (irPseus) in the immune infiltration of OC and the impact on overall survival (OS) have not been adequately studied. Therefore, this study aims to identify a novel model constructed by irPseus to predict OS in OC and to determine its significance in immunotherapy and chemotherapy. Methods. In this study, with the use of The Cancer Genome Atlas (TCGA) combined with Genotype-Tissue Expression (GTEx), 55 differentially expressed irPseus (DEirPseus) were identified. Then, we constructed 10 irPseus pairs with the help of univariate, Lasso, and multivariate Cox regression analysis. The prognostic performance of the model was determined and measured by the Kaplan–Meier curve, a time-dependent receiver operating characteristic (ROC) curve. Results. After dividing OC subjects into high- and low-risk subgroups via the cut-off point, it was revealed that subjects in the high-risk group had a shorter OS. The multivariate Cox regression performed between the model and multiple clinicopathological variables revealed that the model could effectively and independently predict the prognosis of OC. The prognostic model characterized infiltration by various kinds of immune cells and demonstrated the immunotherapy response of subjects with cytotoxic lymphocyte antigen 4 (CTLA4), anti-programmed death-1 (PD-1), and anti-PD-ligand 1 (PD-L1) therapy. A high risk score was related to a higher inhibitory concentration (IC50) for etoposide ( P = 0.0099 ) and mitomycin C ( P = 0.0013 ). Conclusion. It was the first study to identify a novel signature developed by DEirPseus pairs and verify the role in predicting OS, immune infiltrates, immunotherapy, and chemosensitivity. The irPseus are vital factors predicting the prognosis of OC and could act as a novel potential treatment target.

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Establishment and Validation of Prognostic Nomograms Based on Serum Copper Level for Patients With Early-Stage Triple-Negative Breast Cancer

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.770115 ◽

2021 ◽

Vol 9 ◽

Author(s):

Fangfang Duan ◽

Jianpei Li ◽

Jiajia Huang ◽

Xin Hua ◽

Chenge Song ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Triple Negative ◽

Cox Regression ◽

Early Stage ◽

Serum Copper ◽

Copper Level ◽

Serum Copper Level ◽

Training Cohort ◽

Cox Regression Analysis

Background: Altered copper levels have been observed in several cancers, but studies on the relationship between serum copper and early-stage triple-negative breast cancer (TNBC) remain scare. We sought to establish a predictive model incorporating serum copper levels for individualized survival predictions.Methods: We retrospectively analyzed clinicopathological information and baseline peripheric blood samples of patients diagnosed with early-stage TNBC between September 2005 and October 2016 at Sun Yat-sen University Cancer Center. The optimal cut-off point of serum copper level was determined using maximally selected log-rank statistics. Kaplan-Meier curves were used to estimate survival probabilities. Independent prognostic indicators associated with survival were identified using multivariate Cox regression analysis, and subsequently, prognostic nomograms were established to predict individualized disease-free survival (DFS) and overall survival (OS). The nomograms were validated in a separate cohort of 86 patients from the original randomized clinical trial SYSUCC-001 (SYSUCC-001 cohort).Results: 350 patients were eligible in this study, including 264 in the training cohort and 86 in the SYSUCC-001 cohort. An optimal cut-off value of 21.3 μmol/L of serum copper was determined to maximally divide patients into low- and high-copper groups. After a median follow-up of 87.1 months, patients with high copper levels had significantly worse DFS (p = 0.002) and OS (p < 0.001) than those with low copper levels in the training cohort. Multivariate Cox regression analysis revealed that serum copper level was an independent factor for DFS and OS. Further, prognostic models based on serum copper were established for individualized predictions. These models showed excellent discrimination [C-index for DFS: 0.689, 95% confidence interval (CI): 0.621–0.757; C-index for OS: 0.728, 95% CI: 0.654–0.802] and predictive calibration, and were validated in the SYSUCC-001 cohort.Conclusion: Serum copper level is a potential predictive biomarker for patients with early-stage TNBC. Predictive nomograms based on serum copper might be served as a practical tool for individualized prognostication.

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A Novel Immune Checkpoint-Related Gene Signature to Predict Overall Survival and Immunotherapy Response in Triple-Negative Breast Cancer

10.21203/rs.3.rs-528492/v1 ◽

2021 ◽

Author(s):

Jingyi Liu ◽

Siyuan Tian ◽

Yuwei Ling ◽

Xinyi Zhang ◽

Yan Li ◽

...

Keyword(s):

Breast Cancer ◽

Triple Negative Breast Cancer ◽

Immune Checkpoint ◽

Triple Negative ◽

Cox Regression ◽

Expression Profiles ◽

Expression Patterns ◽

Molecular Taxonomy ◽

Gene Signature ◽

Cox Regression Analysis

Abstract Triple-negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer that lacks effective therapeutic targets. Immunotherapy is considered as a novel treatment strategy for TNBC. However, only some patients could benefit from the treatment. Limited studies have comprehensively explored expression patterns and prognostic value of immune checkpoint genes (ICGs) in TNBC. In this study, we downloaded relevant ICGs expression profiles and clinical TNBC data from the Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was employed to develop a multi-gene signature for predicting the prognostic outcome. PDCD1, PDCD1LG2 and KIR3DL2 were identified as hub genes and incorporated into the model. This gene signature could stratify patients into two prognostic subgroups, and unfavorable clinical outcomes were observed in high-risk patients. The predictive performance was assessed by the receiver operating characteristic curves. Moreover, we also analyzed differences in immune status and therapeutic response between both groups. This novel gene signature may be served as a robust prognostic marker, but also an indicator reflecting immunotherapy response.

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