Prognostic Nomogram Based on Circular RNA-Associated Competing Endogenous RNA Network for Patients with Lung Adenocarcinoma

Evidence is increasingly indicating that circular RNAs (circRNAs) are closely involved in tumorigenesis and cancer progression. However, the function and application of circRNAs in lung adenocarcinoma (LUAD) are still unknown. In this study, we constructed a circRNA-associated competitive endogenous RNA (ceRNA) network to investigate the regulatory mechanism of LUAD procession and further constructed a prognostic signature to predict overall survival for LUAD patients. Differentially expressed circRNAs (DEcircRNAs), differentially expressed miRNAs (DEmiRNAs), and differentially expressed mRNAs (DEmRNAs) were selected to construct the ceRNA network. Based on the TargetScan prediction tool and Pearson correlation coefficient, we constructed a circRNA-associated ceRNA network including 11 DEcircRNAs, 8 DEmiRNAs, and 49 DEmRNAs. GO and KEGG enrichment indicated that the ceRNA network might be involved in the regulation of GTPase activity and endothelial cell differentiation. After removing the discrete points, a PPI network containing 12 DEmRNAs was constructed. Univariate Cox regression analysis showed that three DEmRNAs were significantly associated with overall survival. Therefore, we constructed a three-gene prognostic signature for LUAD patients using the LASSO method in the TCGA-LUAD training cohort. By applying the signature, patients could be categorized into the high-risk or low-risk subgroups with significant survival differences (HR: 1.62, 95% CI: 1.12-2.35, log-rank p = 0.009 ). The prognostic performance was confirmed in an independent GEO cohort (GSE42127, HR: 2.59, 95% CI: 1.32-5.10, log-rank p = 0.004 ). Multivariate Cox regression analysis proved that the three-gene signature was an independent prognostic factor. Combining the three-gene signature with clinical characters, a nomogram was constructed. The primary and external verification C -indexes were 0.717 and 0.716, respectively. The calibration curves for the probability of 3- and 5-year OS showed significant agreement between nomogram predictions and actual observations. Our findings provided a deeper understanding of the circRNA-associated ceRNA regulatory mechanism in LUAD pathogenesis and further constructed a useful prognostic signature to guide personalized treatment of LUAD patients.

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Identification of a circadian gene signature that predicts overall survival in lung adenocarcinoma

PeerJ ◽

10.7717/peerj.11733 ◽

2021 ◽

Vol 9 ◽

pp. e11733

Author(s):

Xinliang Gao ◽

Mingbo Tang ◽

Suyan Tian ◽

Jialin Li ◽

Wei Liu

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

Lung Adenocarcinoma ◽

Circadian Clock ◽

Cox Regression ◽

Gene Signature ◽

Differentially Expressed ◽

Circadian Gene ◽

Cox Regression Analysis ◽

Circadian Genes

Background Lung adenocarcinoma (LUAD) is one of the most common subtypes of lung cancer which is the leading cause of death in cancer patients. Circadian clock disruption has been listed as a likely carcinogen. However, whether the expression of circadian genes affects overall survival (OS) in LUAD patients remains unknown. In this article, we identified a circadian gene signature to predict overall survival in LUAD. Methods RNA sequencing (HTSeq-FPKM) data and clinical characteristics were obtained for a cohort of LUAD patients from The Cancer Genome Atlas (TCGA). A multigene signature based on differentially expressed circadian clock-related genes was generated for the prediction of OS using Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox regression analysis, and externally validated using the GSE72094 dataset from the GEO database. Results Five differentially expressed genes (DEGs) were identified to be significantly associated with OS using univariate Cox proportional regression analysis (P < 0.05). Patients classified as high risk based on these five DEGs had significantly lower OS than those classified as low risk in both the TGCA cohort and GSE72094 dataset (P < 0.001). Multivariate Cox regression analysis revealed that the five-gene-signature based risk score was an independent predictor of OS (hazard ratio > 1, P < 0.001). Receiver operating characteristic (ROC) curves confirmed its prognostic value. Gene set enrichment analysis (GSEA) showed that Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to cell proliferation, gene damage repair, proteasomes, and immune and autoimmune diseases were significantly enriched. Conclusion A novel circadian gene signature for OS in LUAD was found to be predictive in both the derivation and validation cohorts. Targeting circadian genes is a potential therapeutic option in LUAD.

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Development of a prognostic signature based on six immune related lncRNAs for hepatocellular carcinoma.

10.21203/rs.3.rs-16348/v1 ◽

2020 ◽

Author(s):

Ze-bing Song ◽

Guo-pei Zhang ◽

shaoqiang li

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Cerna Network

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumor in the world which prognosis is poor. Therefore, a precise biomarker is needed to guide treatment and improve prognosis. More and more studies have shown that lncRNAs and immune response are closely related to the prognosis of hepatocellular carcinoma. The aim of this study was to establish a prognostic signature based on immune related lncRNAs for HCC.Methods: Univariate cox regression analysis was performed to identify immune related lncRNAs, which had negative correlation with overall survival (OS) of 370 HCC patients from The Cancer Genome Atlas (TCGA). A prognostic signature based on OS related lncRNAs was identified by using multivariate cox regression analysis. Gene set enrichment analysis (GSEA) and a competing endogenous RNA (ceRNA) network were performed to clarify the potential mechanism of lncRNAs included in prognostic signature. Results: A prognostic signature based on OS related lncRNAs (AC145207.5, AL365203.2, AC009779.2, ZFPM2-AS1, PCAT6, LINC00942) showed moderately in prognosis prediction, and related with pathologic stage (Stage I&II VS Stage III&IV), distant metastasis status (M0 VS M1) and tumor stage (T1-2 VS T3-4). CeRNA network constructed 15 aixs among differentially expressed immune related genes, lncRNAs included in prognostic signature and differentially expressed miRNA. GSEA indicated that these lncRNAs were involved in cancer-related pathways. Conclusion: We constructed a prognostic signature based on immune related lncRNAs which can predict prognosis and guide therapies for HCC.

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Establishment and validation of a prognostic signature for lung adenocarcinoma based on metabolism‐related genes

Cancer Cell International ◽

10.1186/s12935-021-01915-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Zhihao Wang ◽

Kidane Siele Embaye ◽

Qing Yang ◽

Lingzhi Qin ◽

Chao Zhang ◽

...

Keyword(s):

Regression Analysis ◽

Lung Adenocarcinoma ◽

Cancer Biology ◽

Prognostic Value ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Calibration Plot ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background Given that dysregulated metabolism has been recently identified as a hallmark of cancer biology, this study aims to establish and validate a prognostic signature of lung adenocarcinoma (LUAD) based on metabolism-related genes (MRGs). Methods The gene sequencing data of LUAD samples with clinical information and the metabolism-related gene set were obtained from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB), respectively. The differentially expressed MRGs were identified by Wilcoxon rank sum test. Then, univariate cox regression analysis was performed to identify MRGs that related to overall survival (OS). A prognostic signature was developed by multivariate Cox regression analysis. Furthermore, the signature was validated in the GSE31210 dataset. In addition, a nomogram that combined the prognostic signature was created for predicting the 1-, 3- and 5-year OS of LUAD. The accuracy of the nomogram prediction was evaluated using a calibration plot. Finally, cox regression analysis was applied to identify the prognostic value and clinical relationship of the signature in LUAD. Results A total of 116 differentially expressed MRGs were detected in the TCGA dataset. We found that 12 MRGs were most significantly associated with OS by using the univariate regression analysis in LUAD. Then, multivariate Cox regression analyses were applied to construct the prognostic signature, which consisted of six MRGs-aldolase A (ALDOA), catalase (CAT), ectonucleoside triphosphate diphosphohydrolase-2 (ENTPD2), glucosamine-phosphate N-acetyltransferase 1 (GNPNAT1), lactate dehydrogenase A (LDHA), and thymidylate synthetase (TYMS). The prognostic value of this signature was further successfully validated in the GSE31210 dataset. Furthermore, the calibration curve of the prognostic nomogram demonstrated good agreement between the predicted and observed survival rates for each of OS. Further analysis indicated that this signature could be an independent prognostic indicator after adjusting to other clinical factors. The high-risk group patients have higher levels of immune checkpoint molecules and are therefore more sensitive to immunotherapy. Finally, we confirmed six MRGs protein and mRNA expression in six lung cancer cell lines and firstly found that ENTPD2 might played an important role on LUAD cells colon formation and migration. Conclusions We established a prognostic signature based on MRGs for LUAD and validated the performance of the model, which may provide a promising tool for the diagnosis, individualized immuno-/chemotherapeutic strategies and prognosis in patients with LUAD.

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A seven-gene prognostic signature predicts overall survival of patients with lung adenocarcinoma (LUAD)

Cancer Cell International ◽

10.1186/s12935-021-01975-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Aisha Al-Dherasi ◽

Qi-Tian Huang ◽

Yuwei Liao ◽

Sultan Al-Mosaib ◽

Rulin Hua ◽

...

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Training Group ◽

The Cancer Genome Atlas ◽

Individual Treatment ◽

Significant Implication ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background Lung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients. Methods Raw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan–Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature. Results A prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041–39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779–3.276]; P < 0.0001). Cox regression analysis (univariate and multivariate) demonstrated that the seven-gene signature is an independent prognostic biomarker for predicting the survival of LUAD patients. ROC curves revealed that the 7-gene prognostic signature achieved a good performance in training and validation groups (AUC = 0.91, AUC = 0.7 respectively) in predicting OS for LUAD patients. Furthermore, the stratified analysis of the signature showed another classification to predict the prognosis. Conclusion Our study suggested a new and reliable prognostic signature that has a significant implication in predicting overall survival for LUAD patients and may help with early diagnosis and making effective clinical decisions regarding potential individual treatment.

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Development and Validation of a Ferroptosis-Related Gene Signature for Overall Survival Prediction in Lung Adenocarcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.684259 ◽

2021 ◽

Vol 9 ◽

Author(s):

Qi Tian ◽

Yan Zhou ◽

Lizhe Zhu ◽

Huan Gao ◽

Jin Yang

Keyword(s):

Cell Cycle ◽

Overall Survival ◽

Regression Analysis ◽

Lung Adenocarcinoma ◽

Cell Lines ◽

Cox Regression ◽

Risk Group ◽

Risk Groups ◽

Prognostic Signature ◽

Cox Regression Analysis

Background: Ferroptosis is an iron-dependent programmed cell death process. Recent studies have found that ferroptosis inducers hold promising potential in the treatment of lung adenocarcinoma (LUAD). However, the comprehensive analysis about the prognostic value of ferroptosis-related genes in LUAD remains to be elucidated.Methods: The RNA sequencing data and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A total of 259 ferroptosis-related genes were extracted from FerrDb website. The ferroptosis-related prognostic signature was developed by least absolute shrinkage and selection operator (LASSO) Cox regression analysis in TCGA LUAD cohort, and then validated by 5 independent GEO cohorts. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed to identify the difference in biological processes and functions between different risk groups. The expression levels of core prognostic genes were then verified in LUAD samples by immunohistochemistry (IHC) and erastin-treated LUAD cell lines by real-time polymerase chain reaction (PCR). The potential roles of GPX2 and DDIT4 as ferroptosis drivers in LUAD cell line were further confirmed by in vitro experiments.Results: A total of 20 intersecting genes between 70 ferroptosis-related DEGs and 45 potential prognostic genes were obtained for LASSO Cox regression analysis. The ferroptosis-related prognostic signature was developed by 7 core prognostic DEGs, and stratified LUAD patients into two risk groups. Kaplan-Meier analysis showed that the overall survival (OS) of LUAD patients in the high-risk group was significantly worse than that of the low-risk group. External validation of 5 independent GEO cohorts further confirmed that the ferroptosis-related prognostic signature was an ideal biomarker for predicting the survival of LUAD patients. Significant enrichment of fatty acid metabolism and cell cycle-related pathways were found in different risk groups. The expression patterns of 7 core prognostic genes in LUAD and adjacent normal lung tissues were validated by IHC, which was almost consistent with the results from public database. Furthermore, the changes related to cell cycle and ferroptosis after erastin treatment were also validated in LUAD cell lines. In addition, silencing GPX2 or DDIT4 could partially reverse the erastin-induced ferroptosis.Conclusion: In summary, the ferroptosis-related prognostic signature based on 7 core prognostic DEGs indicated superior predictive performance of LUAD patients. Targeting ferroptosis holds potential to be a therapeutic alternative for LUAD.

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A Three-Gene Prognostic Signature Based on Circular RNA-Associated Competing Endogenous RNA Network for Patients with Lung Adenocarcinoma

10.21203/rs.3.rs-84423/v1 ◽

2020 ◽

Author(s):

Xuelong Wang ◽

Bin Zhou ◽

Yuxin Xia ◽

Jianxin Zuo ◽

Yanchao Liu ◽

...

Keyword(s):

Regulatory Mechanism ◽

Cox Regression ◽

Differentially Expressed ◽

Ppi Network ◽

Prognostic Signature ◽

Competing Endogenous Rna ◽

Cox Regression Analysis ◽

Prognostic Performance ◽

Cerna Network ◽

Lasso Method

Abstract Background: Evidence is increasingly indicating that circular RNAs (circRNAs) are closely involved in tumorigenesis and cancer progression. However, functions of circRNAs in lung adenocarcinoma (LUAD) are still unknown. It is necessary to investigate the regulatory mechanism of circRNAs based on competing endogenous RNA (ceRNA) network in LUAD procession and further construct a prognostic signature for predicting overall survival of LUAD patients.Methods: Differentially expressed circRNAs (DEcircRNAs), differentially expressed miRNAs (DEmiRNAs) and differentially expressed mRNAs (DEmRNAs) were selected to construct the ceRNA network based on TargetScan prediction tool and Pearson correlation coefficient. Functional and pathway enrichment analysis were performed using DAVID database. A PPI network was constructed and then visualized by Cytoscape software. Finally, we constructed a prognostic signature for LUAD patients using LASSO method and assessed the prognostic performance in the validation cohort.Results: A total of 38 DEcircRNAs, 56 DEmiRNAs, and 960 DEmRNAs were identifed. Based on the interactions predicted by TargetScan, we constructed a circRNA-associated ceRNA network including 11 DEcircRNAs, 8 DEmiRNAs and 49 DEmRNAs. GO and KEGG pathway analysis indicated that the circRNA-associated ceRNA network might be involved in regulation of GTPase activity and endothelial cell differentiation. After removing the discrete points, a PPI network containing 12 DEmRNAs was constructed. Univariate cox regression analysis showed that three DEmRNAs were significantly associated with overall survival. Therefore, we constructed a three-gene prognostic signature for LUAD patients using LASSO method. By applying the signature, patients in the training cohort could be categorized into high-risk or low-risk subgroup with significant survival difference (HR: 1.62, 95% CI: 1.12-2.35, log-rank p = 0.009). The prognostic performance was confirmed in an independent GEO cohort (HR: 2.59, 95% CI: 1.32-5.10, log-rank p = 0.004). Multivariate cox regression analysis proved that the three-gene signature was an independent prognostic factor for LUAD.Conclusions: Our findings provided a deeper understanding of the circRNA-associated ceRNA regulatory mechanism in LUAD pathogenesis and constructed a prognostic signature that could be a useful guide for personalized treatment of LUAD patients.

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Establishment and Validation of a Prognostic Signature for Lung Adenocarcinoma Based on Metabolism-Related Genes

10.21203/rs.3.rs-62222/v1 ◽

2020 ◽

Author(s):

Zhihao Wang ◽

Kidane Siele Embaye ◽

Qing Yang ◽

Lingzhi Qin ◽

Chao Zhang ◽

...

Keyword(s):

Regression Analysis ◽

Lung Adenocarcinoma ◽

Cancer Biology ◽

Prognostic Value ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Calibration Plot ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background: Given that dysregulated metabolism has been recently identified as a hallmark of cancer biology, this study aims to establish and validate a prognostic signature of lung adenocarcinoma (LUAD) based on metabolism-related genes(MRGs). Methods: The gene sequencing data of LUAD samples with clinical information and the metabolism-related gene set were obtained from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB), respectively. The differentially expressed MRGs were identified by Wilcoxon rank sum test. Then, univariate Cox regression analysis were performed to identify MRGs that related to overall survival(OS). A prognostic signature was developed by multivariate Cox regression analysis. Furthermore, the signature was validated in the GSE31210 dataset. In addition, a nomogram that combined the prognostic signature was created for predicting the 1-, 3- and 5-year OS of LUAD.The accuracy of the nomogram prediction was evaluated using a calibration plot. Finally, cox regression analysis was applied to identify the prognostic value and clinical relationship of the signature in LUAD. Results: A total of 116 differentially expressed MRGs were detected in the TCGA dataset. We found that 12 MRGs were most significantly associated with OS by using the univariate regression analysis in LUAD. Then, multivariate Cox regression analyses were applied to construct the prognostic signature, which consisted of six MRGs(ALDOA, CAT, ENTPD2, GNPNAT1, LDHA, and TYMS). The prognostic value of this signature was further successfully validated in the GSE31210 dataset. Furthermore, the calibration curve of the prognostic nomogram demonstrated good agreement between the predicted and observed survival rates for each of OS. Further analysis indicated that this signature could be an independent prognostic indicator after adjusting to other clinical factors. Finally, the signature was found to be associated with various clinicopathological features. Conclusions: We established a prognostic signature based on MRGs for LUAD and validated the performance of the model, which may provide a promising tool for the diagnosis and prognosis in patients with LUAD.

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A signature related to lung adenocarcinoma prognosis: A study based on TCGA database

10.21203/rs.2.21607/v1 ◽

2020 ◽

Author(s):

Jia Wang ◽

Xiaolu Zhang ◽

Xiaoming Zhang ◽

Yan Yao ◽

Xiaoran Ma ◽

...

Keyword(s):

Regression Analysis ◽

Survival Analysis ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Risk Groups ◽

Clinical Treatment ◽

Low Risk ◽

Differentially Expressed ◽

Cox Regression Analysis ◽

Cerna Network

Abstract Background: The intrinsic molecular subtypes of lung adenocarcinoma (LUAD) impact clinical treatment decision-making, but the molecular mechanisms are still unclear. Therefore, we aimed to identify sensitive biomarkers to evaluate LUAD patient prognosis. Methods: Differentially expressed RNAs from LUAD patients were obtained from The Cancer Genome Atlas (TCGA) database and they were used to construct a competitive endogenous RNA (ceRNA) network. Based on the examination of clinical data, long noncoding RNAs (lncRNAs) and mRNAs in the network were selected by univariate and multivariate Cox regression analysis. Finally, functional enrichment analysis was used to reveal prognostic signatures based on the classification into high and low-risk groups, survival analysis, and an independence test. Results: The ceRNA network consisted of 21 mRNAs, 53 lncRNAs, and 8 miRNAs that were selected from the differentially expressed RNAs identified. Next, based on univariate and multivariate Cox regression analysis, a prognostic signature, including two mRNAs (HOXA10 and CBX2) and four lncRNAs (LINC00460, LINC00330, DGCR5, and C14orf132) was constructed. Eventually, survival analysis showed that significant differences in survival rates between high and low-risk groups and the area under the curve (AUC) for three‐year survival was 0.714. Compared with clinical risk factors, including age, pathological stage, and TNM stage, our risk score had a higher prognostic value. Conclusion: By screening from a ceRNA network, we constructed a signature, including two mRNAs (HOXA10 and CBX2) and four lncRNAs (LINC00460, LINC00330, DGCR5, and C14orf132), that can be utilized as a prognostic biomarker in LUAD. This signature may provide options for clinical treatment.

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A NOD-Like Receptor Signaling-Based Gene Signature Identified as a Novel Prognostic Biomarker for Predicting Overall Survival of Colorectal Cancer Patients

Current Bioinformatics ◽

10.2174/1574893616666211005122422 ◽

2021 ◽

Vol 16 ◽

Author(s):

Xin Qi ◽

Jiachen Zuo ◽

Donghui Yan ◽

Guang Hu ◽

Rui Wang ◽

...

Keyword(s):

Colorectal Cancer ◽

Overall Survival ◽

Regression Analysis ◽

Cox Regression ◽

Predictive Ability ◽

Gene Signature ◽

Survival Prediction ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Chemotherapeutic Response

Background: Colorectal cancer (CRC) is the most frequently diagnosed gastrointestinal tract malignant tumor worldwide, which is closely associated with distant metastasis and poor prognosis. Due to high degree of heterogeneity, reliable prognostic biomarkers are urgently needed to guide the therapeutic intervention of CRC patients. Objective: The present study aimed to develop a NOD-like receptors (NLRs) signaling-based gene signature that can successfully predict the overall survival of CRC patients. Methods: Firstly, differentially expressed NLR signaling-related genes were identified between primary and metastatic human CRC samples. Genes with prognostic value were then screened through univariate Cox regression analysis. Next, the NLR signaling-based prognostic signature was constructed by LASSO-penalized Cox regression analysis, and its predictive ability was further confirmed in an independent cohort. Furthermore, functional studies including GO, GSEA, ssGSEA and chemotherapeutic response analyses were performed to explore the role of the NLR signaling-based signature in CRC pathogenesis and therapy. Results: The established prognostic signature that consisted of 7 NLR signaling-related genes can effectively stratify the high-risk and low-risk CRC patients in both training and validation cohorts. Moreover, the signature proved to be an independent indicator of overall survival in CRC patients. Functional annotation and chemotherapeutic response analyses showed that the signature was closely associated with immune status and chemotherapeutic sensitivity of CRC patients. Conclusion: The novel NLR signaling-based gene signature could serve as a potential tool for survival prediction and therapeutic evaluation, thereby contributing to the personalized prognostic management of CRC patients.

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Identification of a 9-Gene Prognostic signature for breast cancer

10.21203/rs.3.rs-26949/v1 ◽

2020 ◽

Author(s):

Zelin Tian ◽

Jianing Tang ◽

Xing Liao ◽

Qian Yang ◽

Yumin Wu ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Overall Survival ◽

Regression Analysis ◽

Cox Regression ◽

Risk Group ◽

Disease Free Survival ◽

Gene Signature ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background Breast cancer (BRCA) is the most common cancer among women worldwide and results in the second leading cause of woman cancer death.Methods This study sought to develop a prognostic gene signature to predict the prognosis of patients with BRCA. Studies were performed using the genome-wide data of BRCA patients from the Gene Expression Omnibus dataset (GSE20685, GSE42568, GSE20711, GSE88770). Univariate COX regression analysis was used to determine the association between gene expression levels and overall survival(OS) in each dataset. Taking P value < 0.05 as the inclusion criterion, the common genes in all datasets were selected as prognostic genes, and a 9-gene prognostic signature was developed.Results The Kaplan-Meier survival curve was constructed using log-rank test to assess survival differences. The overall survival of patients in the low-risk group was significantly higher than that in the high-risk group. ROC analysis showed that this 9-gene signature showed good diagnostic efficiency both in overall survival(OS) and disease free survival(DFS). The 9-gene signature was further validated using GSE16446 dataset. In addition, multiple Cox regression analysis showed that this 9-gene signature was an independent risk factor. Finally, we established a nomogram that integrates conventional clinicopathological features and 9-gene signature. The analysis of the calibration plots showed that the nomogram has good performance.Conclusions This study has developed a reliable 9-gene prognostic signature, which is of great value in predicting the prognosis of BRCA and will help to make personalized treatment decisions for patients at different risk score.

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