A Novel lncRNA Panel Related to Ferroptosis, Tumor Progression, and Microenvironment is a Robust Prognostic Indicator for Glioma Patients

Objective: To establish a lncRNA panel related to ferroptosis, tumor progression, and microenvironment for prognostic estimation in patients with glioma.Methods: LncRNAs associated with tumor progression and microenvironment were screened via the weighted gene co-expression network analysis (WGCNA). Overlapped lncRNAs highlighted in WGCNA, related to ferroptosis, and incorporated in Chinese Glioma Genome Atlas (CGGA) were identified as hub lncRNAs. With expression profiles of the hub lncRNA, we conducted the least absolute shrinkage and selection operator (LASSO) regression and built a ferroptosis-related lncRNA signature to separate glioma patients with distinct survival outcomes. The lncRNA signature was validated in TCGA, the CGGA_693, and CGGA_325 cohorts using Kaplan-Meier survival analysis and ROC curves. The ferroptosis-related lncRNA panel was validated with 15 glioma samples using quantitative real-time PCR (qRT-PCR). Multivariate Cox regression was performed, and a nomogram was mapped and validated. Immune infiltration correlated to the signature was explored using TIMER and CIBERSORT algorithms.Results: The present study identified 30 hub lncRNAs related to ferroptosis, tumor progression, and microenvironment. With the 30 hub lncRNAs, we developed a lncRNA signature with distinct stratification of survival chance in patients with glioma in two independent cohorts (HRs>1, p < 0.05). The lncRNA signature revealed a panel of 14 lncRNAs, i.e., APCDD1L-AS1, H19, LINC00205, LINC00346, LINC00475, LINC00484, LINC00601, LINC00664, LINC00886, LUCAT1, MIR155HG, NEAT1, PVT1, and SNHG18. These lncRNA expressions were validated in clinical specimens using qRT-PCR. Robust predictive accuracies of the signature were present across different datasets at multiple timepoints. With univariate and multivariate regressions, we demonstrated that the risk score based on the lncRNA signature is an independent prognostic indicator after clinical factors were adjusted. A nomogram was constructed with these prognostic factors, and it has demonstrated decent classification and accuracy. Additionally, the signature-based classification was observed to be correlated with multiple clinical characteristics and molecular subtypes. Further, extensive immune cells were upregulated in the high-risk group, such as CD8+ T cell, neutrophil, macrophage, and myeloid dendritic cell, indicating increased immune infiltrations.Conclusion: We established a novel ferroptosis-related lncRNA signature that could effectively stratify the prognosis of glioma patients with adequate predictive performance.

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Development of Prognostic Indicator Based on Autophagy-Related lncRNA Analysis in Colon Adenocarcinoma

BioMed Research International ◽

10.1155/2020/9807918 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Weige Zhou ◽

Shijing Zhang ◽

Hui-biao Li ◽

Zheyou Cai ◽

Shuting Tang ◽

...

Keyword(s):

Risk Score ◽

Noncoding Rna ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

Colon Adenocarcinoma ◽

High Risk Group ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Lasso Regression

There were no systematic researches about autophagy-related long noncoding RNA (lncRNA) signatures to predict the survival of patients with colon adenocarcinoma. It was necessary to set up corresponding autophagy-related lncRNA signatures. The expression profiles of lncRNAs which contained 480 colon adenocarcinoma samples were obtained from The Cancer Genome Atlas (TCGA) database. The coexpression network of lncRNAs and autophagy-related genes was utilized to select autophagy-related lncRNAs. The lncRNAs were further screened using univariate Cox regression. In addition, Lasso regression and multivariate Cox regression were used to develop an autophagy-related lncRNA signature. A risk score based on the signature was established, and Cox regression was used to test whether it was an independent prognostic factor. The functional enrichment of autophagy-related lncRNAs was visualized using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Ten prognostic autophagy-related lncRNAs (AC027307.2, AC068580.3, AL138756.1, CD27-AS1, EIF3J-DT, LINC01011, LINC01063, LINC02381, AC073896.3, and SNHG16) were identified to be significantly different, which made up an autophagy-related lncRNA signature. The signature divided patients with colon adenocarcinoma into the low-risk group and the high-risk group. A risk score based on the signature was a significantly independent factor for the patients with colon adenocarcinoma (HR=1.088, 95%CI=1.057−1.120; P<0.001). Additionally, the ten lncRNAs were significantly enriched in autophagy process, metabolism, and tumor classical pathways. In conclusion, the ten autophagy-related lncRNAs and their signature might be molecular biomarkers and therapeutic targets for the patients with colon adenocarcinoma.

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An energy metabolism-based eight-gene signature correlates with the clinical outcome of esophagus carcinoma

BMC Cancer ◽

10.1186/s12885-021-08030-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Weifeng Zheng ◽

Chaoying Chen ◽

Jianghao Yu ◽

Chengfeng Jin ◽

Tiemei Han

Keyword(s):

Energy Metabolism ◽

Cox Regression ◽

Expression Profiles ◽

Predictive Performance ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Prognostic Gene ◽

Kaplan Meier ◽

Esophagus Carcinoma

Abstract Background The essence of energy metabolism has spread to the field of esophageal cancer (ESC) cells. Herein, we tried to develop a prognostic prediction model for patients with ESC based on the expression profiles of energy metabolism associated genes. Materials and methods The overall survival (OS) predictive gene signature was developed, internally and externally validated based on ESC datasets including The Cancer Genome Atlas (TCGA), GSE54993 and GSE19417 datasets. Hub genes were identified in each energy metabolism related molecular subtypes by weighted gene correlation network analysis, and then enrolled for determination of prognostic genes. Univariate, LASSO and multivariate Cox regression analysis were applied to assess prognostic genes and build the prognostic gene signature. Kaplan-Meier curve, time-dependent receiver operating characteristic (ROC) curve, nomogram, decision curve analysis (DCA), and restricted mean survival time (EMST) were used to assess the performance of the gene signature. Results A novel energy metabolism based eight-gene signature (including UBE2Z, AMTN, AK1, CDCA4, TLE1, FXN, ZBTB6 and APLN) was established, which could dichotomize patients with significantly different OS in ESC. The eight-gene signature demonstrated independent prognostication potential in patient with ESC. The prognostic nomogram constructed based on the gene signature showed excellent predictive performance, whose robustness and clinical usability were higher than three previous reported prognostic gene signatures. Conclusions Our study established a novel energy metabolism based eight-gene signature and nomogram to predict the OS of ESC, which may help in precise clinical management.

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Identification of a ferroptosis-related gene signature predictive model in colon cancer

World Journal of Surgical Oncology ◽

10.1186/s12957-021-02244-z ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Ye Wang ◽

Heng-bo Xia ◽

Zhang-ming Chen ◽

Lei Meng ◽

A-man Xu

Keyword(s):

Colon Cancer ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

Risk Groups ◽

High Risk Group ◽

Low Risk ◽

The Cancer Genome Atlas ◽

Lasso Regression ◽

Cox Regression Analysis

Abstract Background The prognosis of colon cancer (CC) is challenging to predict due to its highly heterogeneous nature. Ferroptosis, an iron-dependent form of cell death, has roles in various cancers; however, the correlation between ferroptosis-related genes (FRGs) and prognosis in CC remains unclear. Methods The expression profiles of FRGs and relevant clinical information were retrieved from the Cancer Genome Atlas (TCGA) database. Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression model were performed to build a prognostic model in TCGA cohort. Results Ten FRGs, five of which had mutation rates ≥ 3%, were found to be related to the overall survival (OS) of patients with CC. Patients were divided into high- and low-risk groups based on the results of Cox regression and LASSO analysis. Patients in the low-risk group had a significantly longer survival time than patients in the high-risk group (P < 0.001). Enrichment analyses in different risk groups showed that the altered genes were associated with the extracellular matrix, fatty acid metabolism, and peroxisome. Age, risk score, T stage, N stage, and M stage were independent predictors of patient OS based on the results of Cox analysis. Finally, a nomogram was constructed to predict 1-, 3-, and 5-year OS of patients with CC based on the above five independent factors. Conclusion A novel FRG model can be used for prognostic prediction in CC and may be helpful for individualized treatment.

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NDRG3 overexpression is associated with a poor prognosis in patients with hepatocellular carcinoma

Bioscience Reports ◽

10.1042/bsr20180907 ◽

2018 ◽

Vol 38 (6) ◽

Cited By ~ 1

Author(s):

Ji-sheng Jing ◽

Hongbo Li ◽

Shun-cai Wang ◽

Jiu-ming Ma ◽

La-qing Yu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Cox Regression ◽

Survival Curve ◽

Disease Free Survival ◽

Prognostic Indicator ◽

Clinicopathological Characteristics ◽

Pcr Analysis ◽

Kaplan Meier ◽

Tumor Tissues ◽

The Relationship

N-myc downstream-regulated gene 3 (NDRG3), an important member of the NDRG family, is involved in cell proliferation, differentiation, and other biological processes. The present study analyzed NDRG3 expression in hepatocellular carcinoma (HCC) and explored the relationship between expression of NDRG3 in HCC patients and their clinicopathological characteristics. We performed quantitative real-time reverse-transcription polymerase chain reaction (qRT-PCR) analysis and immunohistochemistry (IHC) analyses on HCC tissues to elucidate NDRG3 expression characteristics in HCC patients. Kaplan–Meier survival curve and Cox regression analyses were used to evaluate the prognoses of 102 patients with HCC. The results revealed that compared with non-tumor tissues, HCC tissues showed significantly higher NDRG3 expression. In addition, our analyses showed that NDRG3 expression was statistically associated with tumor size (P=0.048) and pathological grade (P=0.001). Survival analysis and Kaplan–Meier curves revealed that NDRG3 expression is an independent prognostic indicator for disease-free survival (P=0.002) and overall survival (P=0.005) in HCC patients. The data indicate that NDRG3 expression may be considered as a oncogenic biomarker and a novel predictor for HCC prognosis.

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Circular RNA Hsa_Circ_0091579 Serves as a Diagnostic and Prognostic Marker for Hepatocellular Carcinoma

Cellular Physiology and Biochemistry ◽

10.1159/000495230 ◽

2018 ◽

Vol 51 (1) ◽

pp. 290-300 ◽

Cited By ~ 18

Author(s):

Chenxing Zhang ◽

Chenyue Zhang ◽

Jiamao Lin ◽

Haiyong Wang

Keyword(s):

Hepatocellular Carcinoma ◽

Liver Tumors ◽

Expression Profiles ◽

Critical Role ◽

Roc Curves ◽

Altered Expression ◽

Qrt Pcr ◽

Specificity And Sensitivity ◽

Tumor Tissues ◽

Potential Biomarker

Background/Aims: An increasing number of studies have suggested that circular RNAs (circRNAs) have vital roles in carcinogenesis and tumor progression. However, the function of circRNAs in hepatocellular carcinoma (HCC) remains poorly characterized. Methods: We investigated the levels of circRNAs in patients with HCC to identify potential diagnostic biomarkers. We examined circRNA expression profiles in liver tumors and paired non-cancerous liver tissues from three HCC patients with cancer thrombus using a circRNA microarray. Bioinformatics analysis was performed to find circRNAs with significantly altered expression levels between tumors and their paired non-tumor tissues. We confirmed our initial findings by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Receiver operating characteristic (ROC) curves were also applied to identify a candidate circRNA with the optimal specificity and sensitivity. Finally, X-tile software was adopted to calculate the most efficient cut-off value for hsa_circ_0091579 expression. Results: Microarray analysis identified 20 unique circRNAs that were differentially expressed between tumor and non-tumor tissues (P < 0.05). The expression of these 20 circRNAs was verified by qRT-PCR. The expression of hsa_circ_16245-1 and hsa_circ_0091579 mRNA was consistent with their levels as tested by the microarray. The ROC curves showed that both hsa_circ_16245-1 and hsa_circ_0091579 had favorable specificity and sensitivity. We further confirmed that hsa_circ_0091579 was significantly upregulated in HCC and its high expression was intimately associated with a worse overall survival in patients with HCC. Conclusion: Hsa_circ_0091579 may play a critical role in HCC progression and serve as a potential biomarker for the prognosis of patients with HCC.

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Development and Validation of a Prognostic Nomogram Based on DNA Methylation-Driven Genes for Patients With Ovarian Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.675197 ◽

2021 ◽

Vol 12 ◽

Author(s):

Min Zhou ◽

Shasha Hong ◽

Bingshu Li ◽

Cheng Liu ◽

Ming Hu ◽

...

Keyword(s):

Ovarian Cancer ◽

Dna Methylation ◽

Mrna Expression ◽

Risk Score ◽

Cox Regression ◽

Expression Profiles ◽

Figo Stage ◽

Prognostic Indicator ◽

Tissue Expression ◽

The Cancer Genome Atlas

Background: DNA methylation affects the development, progression, and prognosis of various cancers. This study aimed to identify DNA methylated-differentially expressed genes (DEGs) and develop a methylation-driven gene model to evaluate the prognosis of ovarian cancer (OC).Methods: DNA methylation and mRNA expression profiles of OC patients were downloaded from The Cancer Genome Atlas, Genotype-Tissue Expression, and Gene Expression Omnibus databases. We used the R package MethylMix to identify DNA methylation-regulated DEGs and built a prognostic signature using LASSO Cox regression. A quantitative nomogram was then drawn based on the risk score and clinicopathological features.Results: We identified 56 methylation-related DEGs and constructed a prognostic risk signature with four genes according to the LASSO Cox regression algorithm. A higher risk score not only predicted poor prognosis, but also was an independent poor prognostic indicator, which was validated by receiver operating characteristic (ROC) curves and the validation cohort. A nomogram consisting of the risk score, age, FIGO stage, and tumor status was generated to predict 3- and 5-year overall survival (OS) in the training cohort. The joint survival analysis of DNA methylation and mRNA expression demonstrated that the two genes may serve as independent prognostic biomarkers for OS in OC.Conclusion: The established qualitative risk score model was found to be robust for evaluating individualized prognosis of OC and in guiding therapy.

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Identification of Epithelial–Mesenchymal Transition-Related Prognostic lncRNAs Biomarkers Associated With Melanoma Microenvironment

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.679133 ◽

2021 ◽

Vol 9 ◽

Author(s):

Bo Xiao ◽

Liyan Liu ◽

Zhuoyuan Chen ◽

Aoyu Li ◽

Pingxiao Wang ◽

...

Keyword(s):

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Enrichment Analysis ◽

Roc Curves ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Mesenchymal Transition ◽

Cox Regression Analysis ◽

Kaplan Meier

Melanoma is the most common cancer of the skin, associated with a worse prognosis and distant metastasis. Epithelial–mesenchymal transition (EMT) is a reversible cellular biological process that plays significant roles in diverse tumor functions, and it is modulated by specific genes and transcription factors. The relevance of EMT-related lncRNAs in melanoma has not been determined. Therefore, RNA expression data and clinical features were collected from the TCGA database (N = 447). Melanoma samples were randomly assigned into the training (315) and testing sets (132). An EMT-related lncRNA signature was constructed via comprehensive analyses of lncRNA expression level and corresponding clinical data. The Kaplan-Meier analysis showed significant differences in overall survival in patients with melanoma in the low and high-risk groups in two sets. Receiver operating characteristic (ROC) curves were used to measure the performance of the model. Cox regression analysis indicated that the risk score was an independent prognostic factor in two sets. Besides, a nomogram was constructed based on the independent variables. Gene Set Enrichment Analysis (GSEA) was applied to evaluate the potential biological functions in the two risk groups. Furthermore, the melanoma microenvironment was evaluated using ESTIMATE and CIBERSORT algorithms in the risk groups. This study indicates that EMT-related lncRNAs can function as potential independent prognostic biomarkers for melanoma survival.

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Identification and Validation of an Immune-Related eRNA Prognostic Signature for Hepatocellular Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.657051 ◽

2021 ◽

Vol 12 ◽

Author(s):

Shenglan Cai ◽

Xingwang Hu ◽

Ruochan Chen ◽

Yiya Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

Immune Cells ◽

Cox Regression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Roc Curves ◽

The Cancer Genome Atlas ◽

Immune Checkpoints ◽

Immune Genes ◽

Normal Tissues

BackgroundEnhancer RNAs (eRNAs) are intergenic long non-coding RNAs (lncRNAs) that participate in the progression of malignancies by targeting tumor-related genes and immune checkpoints. However, the potential role of eRNAs in hepatocellular carcinoma (HCC) is unclear. In this study, we aimed to construct an immune-related eRNA prognostic model that could be used to prospectively assess the prognosis of patients with HCC.MethodsGene expression profiles of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA). The eRNAs co-expressed from immune genes were identified as immune-related eRNAs. Cox regression analyses were applied in a training cohort to construct an immune-related eRNA signature (IReRS), that was subsequently used to analyze a testing cohort and combination of the two cohorts. Kaplan-Meier and receiver operating characteristic (ROC) curves were used to validate the predictive effect in the three cohorts. Gene Set Enrishment Analysis (GSEA) computation was used to identify an IReRS-related signaling pathway. A web-based cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) computation was used to evaluate the relationship between the IReRS and infiltrating immune cells.ResultsA total of sixty-four immune-related eRNAs (IReRNAs) was identified in HCC, and 14 IReRNAs were associated with overall survival (OS). Five IReRNAs were used for constructing an immune-related eRNA signature (IReRS), which was shown to correlate with poor survival and to be an independent prognostic biomarker for HCC. The GSEA results showed that the IReRS was correlated to cancer-related and immune-related pathways. Moreover, we found that IReRS was correlated to infiltrating immune cells, including CD8+ T cells and M0 macrophages. Finally, differential expressions of the five risk IReRNAs in tumor tissues vs. adjacent normal tissues and their prognostic values were verified, in which the AL445524.1 may function as an oncogene that affects prognosis partly by regulating CD4-CLTA4 related genes.ConclusionOur results suggest that the IReRS could serve as a biomarker for predicting prognosis in patients with HCC. Additionally, it may be correlated to the tumor immune microenvironment and could also be used as a biomarker in immunotherapy for HCC.

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Identification of prognostic gene signature associated with microenvironment of lung adenocarcinoma

PeerJ ◽

10.7717/peerj.8128 ◽

2019 ◽

Vol 7 ◽

pp. e8128 ◽

Cited By ~ 12

Author(s):

Cheng Yue ◽

Hongtao Ma ◽

Yubai Zhou

Keyword(s):

High Risk ◽

Lung Adenocarcinoma ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

Gene Signature ◽

Low Risk ◽

Differentially Expressed ◽

Lasso Regression

Background Lung cancer has the highest morbidity and mortality worldwide, and lung adenocarcinoma (LADC) is the most common pathological subtype. Accumulating evidence suggests the tumor microenvironment (TME) is correlated with the tumor progress and the patient’s outcome. As the major components of TME, the tumor-infiltrated immune cells and stromal cells have attracted more and more attention. In this study, differentially expressed immune and stromal signature genes were used to construct a TME-related prognostic model for predicting the outcomes of LADC patients. Methods The expression profiles of LADC samples with clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) related to the TME of LADC were identified using TCGA dataset by Wilcoxon rank sum test. The prognostic effects of TME-related DEGs were analyzed using univariate Cox regression. Then, the least absolute shrinkage and selection operator (LASSO) regression was performed to reduce the overfit and the number of genes for further analysis. Next, the prognostic model was constructed by step multivariate Cox regression and risk score of each sample was calculated. Then, survival and Receiver Operating Characteristic (ROC) analyses were conducted to validate the model using TCGA and GEO datasets, respectively. The Kyoto Encyclopedia of Genes and Genomes analysis of gene signature was performed using Gene Set Enrichment Analysis (GSEA). Finally, the overall immune status, tumor purity and the expression profiles of HLA genes of high- and low-risk samples was further analyzed to reveal the potential mechanisms of prognostic effects of the model. Results A total of 93 TME-related DEGs were identified, of which 23 DEGs were up-regulated and 70 DEGs were down-regulated. The univariate cox analysis indicated that 23 DEGs has the prognostic effects, the hazard ratio ranged from 0.65 to 1.25 (p < 0.05). Then, seven genes were screened out from the 23 DEGs by LASSO regression method and were further analyzed by step multivariate Cox regression. Finally, a three-gene (ADAM12, Bruton Tyrosine Kinase (BTK), ERG) signature was constructed, and ADAM12, BTK can be used as independent prognostic factors. The three-gene signature well stratified the LADC patients in both training (TCGA) and testing (GEO) datasets as high-risk and low-risk groups, the 3-year area under curve (AUC) of ROC curves of three GEO sets were 0.718 (GSE3141), 0.646 (GSE30219) and 0.643 (GSE50081). The GSEA analysis indicated that highly expressed ADAM12, BTK, ERG mainly correlated with the activation of pathways involving in focal adhesion, immune regulation. The immune analysis indicated that the low-risk group has more immune activities and higher expression of HLA genes than that of the high-risk group. In sum, we identified and constructed a three TME-related DEGs signature, which could be used to predict the prognosis of LADC patients.

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Identification of a Novel Gene Model-Based Homologous Recombination Deficiency Score to Improve Survival Prediction of TNBC.

10.21203/rs.3.rs-472194/v1 ◽

2021 ◽

Author(s):

Wenxiang Zhang ◽

Bolun Ai ◽

Xiangyi Kong ◽

Xiangyu Wang ◽

Jie Zhai ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Homologous Recombination ◽

Risk Score ◽

Cox Regression ◽

Roc Curves ◽

Cox Regression Analysis ◽

Homologous Recombination Deficiency ◽

Ajcc Stage ◽

Kaplan Meier

Abstract Background Triple-negative breast cancer (TNBC) is a specific histological type of breast cancer with a poor prognosis, early recurrence, which lacks durable chemotherapy responses and effective targeted therapies. We aimed to construct an accurate prognostic risk model based on homologous recombination deficiency (HRD) - gene expression profiles for improving prognosis prediction of TNBC. Methods Triple-negative breast cancer RNA sequencing data and sample clinical information were downloaded from the breast invasive carcinoma (BRCA) cohort in the Cancer Genome Atlas (TCGA) database. Combined with the HRD database, tumor samples were divided into two sets. We screened differentially expressed genes (DEGs) and then identified HRD-related prognostic genes using weighted gene co-expression network analysis (WGCNA) and Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis were used to identifying key prognostic genes. Risk scores were calculated and compared with HRD score, Kaplan–Meier (KM) survival analysis were used to assess its prognostic power. GSE103091 dataset from GEO (Gene Expression Omnibus) database was used to validate the signature. Univariate and multivariate Cox regression were performed to independently verify the prognosis of the risk score. A nomogram was constructed and revealed by time-dependent ROC curves to guide clinical practice. Results We found that HRD tumor samples (HRD score > = 42) in TNBC patients were associated with poor overall survival (p = 0.027). We identified a total of 147 differential genes including 203 up-regulated and 213 down-regulated genes, among which 29 were prognosis-related genes. Through the LASSO method, 6 key prognostic genes ((MUCL1, IVL, FAM46C, CHI3L1, PRR15L, and CLEC3A) were selected and a 6-gene risk score was constructed. We found risk score was negatively associated with homologous recombination deficiency (HRD) scores (r = -0.22, p = 0.019). Compared with the low-risk group, Kaplan-Meier survival analysis shows that the high-risk group has an obvious poorer prognosis (P < 0.0001). Finally, we integrated the risk score model and clinical factors of TNBC (AJCC-stage, HRD score, T stage, and N stage) to construct a compound nomogram. Time-dependent ROC curves showed the risk score performed better in 1-, 3- and 5-year survival predictions compared with AJCC-stage. Conclusions Based on HRD gene expression data, our six HRD-related gene signature and nomogram could be practical and reliable tools for predicting OS in patients with TNBC.

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