Five crucial prognostic-related autophagy genes stratified female breast cancer patients aged 40–60 years

Abstract Background Autophagy is closely related to the progression of breast cancer. The aim at this study is to establish a prognostic-related model comprised of hub autophagy genes (AGs) to assess patient prognosis. Simultaneously, the model can guide clinicians to make up individualized strategies and stratify patients aged 40–60 years based on risk level. Methods The hub AGs were identified with univariate COX regression and LASSO regression. The functions and alterations of these selected AGs were analyzed as well. Moreover, the multivariate COX regression and correlation analysis between hub AGs and clinicopathological parameters were done. Results Totally, 33 prognostic-related AGs were obtained from the univariate COX regression (P < 0.05). SERPINA1, HSPA8, HSPB8, MAP1LC3A, and DIRAS3 were identified to constitute the prognostic model by the LASSO regression. The survival curve of patients in the high-risk and low-risk groups was statistically significant (P < 0.05). The 3-year and 5-year ROC displayed that their AUC value reached 0.762 and 0.825, respectively. Stage and risk scores were independent risk factors relevant to prognosis. RB1CC1, RPS6KB1, and BIRC6 were identified as the most predominant mutant genes. It was found that AGs were mainly involved in regulating the endopeptidases synthesis and played important roles in the ErbB signal pathway. SERPIN1, risk score was closely related to the stage (P < 0.05); HSPA8, risk score were closely related to T stag (P < 0.05); HSPB8 was closely related to N stag (P < 0.05). Conclusions Our prognostic model had the relatively robust predictive ability on prognosis for patients aged 40–60 years. If the stage was added into the prognostic model, the predictive ability would be more powerful.

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Five Crucial Prognostic-Related Autophagy Genes Stratified Female Breast Cancer Patients Aged 40-60 Years

10.21203/rs.3.rs-107943/v1 ◽

2020 ◽

Author(s):

Xiaolong Li ◽

Hengchao Zhang ◽

Jingjing Liu ◽

Ping Li ◽

Yi Sun

Keyword(s):

Breast Cancer ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

Survival Curve ◽

Predictive Ability ◽

Clinicopathological Parameters ◽

Risk Level ◽

Lasso Regression ◽

Breast Cancer Patients

Abstract Background : Autophagy is closely related to the progression of breast cancer. The aim at this study is to establish a prognostic-related model comprised of hub autophagy-genes (AGs ) to assess patient prognosis. Simultaneously, the model can guide clinicians to make up individualized strategies and stratify patients aged 40-60 years based on risk level. Methods : The hub AGs were identified with univariate COX regression and LASSO regression. The functions and alterations of these selected AGs were analyzed as well. Moreover, the multivariate COX regression and correlation analysis between hub AGs and clinicopathological parameters were done. Results : Totally, 33 prognostic-related AGs were obtained from the univariate COX regression (P<0.05 ) . SERPINA1 , HSPA8 , HSPB8 , MAP1LC3A , and DIRAS3 were identified to constitute the prognostic model by the LASSO regression . The survival curve of patients in high-risk and in low-risk group was statistically significant (P<0.05 ) . The 3-year and 5-year ROC displayed that their AUC value reached 0.762 and 0.825 , respectively . Stage and risk score were independent risk factors relevant about prognosis . RB1CC1 , RPS6KB1 , and BIRC6 were identified as the most predominant mutant genes . It was found that AGs were mainly involved in regulating the endopeptidases synthesis and played important roles in ErbB signal pathway . SERPIN1 , risk score were closely related to stage (P<0.05 ) ; HSPA8, risk score were closely related to T stag (P<0.05 ) ; HSPB8 was closely related to N stag (P<0.05 ). Conclusions : Our prognostic model had relatively robust predictive ability on prognosis for patients aged 40-60 years. If stage was added into 3 the prognostic model, the predictive ability would be more powerful.

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Five crucial prognostic-related autophagy genes stratified female breast cancer patients aged 40-60 years

10.21203/rs.3.rs-68453/v1 ◽

2020 ◽

Author(s):

Xiaolong Li ◽

Hengchao Zhang ◽

Jingjing Liu ◽

Ping Li ◽

Yi Sun

Keyword(s):

Prognostic Model ◽

Cox Regression ◽

Survival Curve ◽

Predictive Ability ◽

Female Breast Cancer ◽

Clinicopathological Parameters ◽

Risk Level ◽

Lasso Regression ◽

Breast Cancer Patients ◽

Auc Value

Abstract Background: Autophagy is closely related to the progression of breast cancer.The aim of this study is to establish a prognostic-related model comprised of hub autophagy-genes(AGs) to assess patitents prognosis. Simultaneously, the model can guide clinicians to make up individualized strategies and stratify patients aged 40-60 years based on risk level.Methods: The hub AGs were identified through univariate COX regression and LASSO regression. The functions and alterations of these selected AGs were analyzed as well.Moreover,the multivariate COX regression and correlation analysis between hub AGs and clinicopathological parameters were done. Results: Totally,33 prognostic-related AGs were obtained from the univariate COX regression(P<0.05).SERPINA1, HSPA8, HSPB8, MAP1LC3A, and DIRAS3 were identified to constitute the prognostic model by the LASSO regression. The survival curve of patients in high-risk and in low-risk group was statistically significant(P<0.05).The 3-year and 5-year ROC displayed that their AUC value reached 0.762 and 0.825,respectively. Stage and riskscore were independent risk factors relevant about prognosis.RB1CC1, RPS6KB1, and BIRC6 were identified as the most predominant mutant genes. It was found that AGs were mainly involved in regulating the endopeptidases synthesis and played important roles in ErbB signal pathway. SERPIN1, riskscore were closely related to stage(P<0.05); HSPA8, riskscore were closely related to T staging(P<0.05); HSPB8 was closely related to N staging(P<0.05). Conclusions: Our prognostic model had relatively robust predictive ability on prognosis for patients aged 40-60 years.If stage was added into the prognostic model, the predictive ability would be more powerful.

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A Five Autophagy-Related Long Non-Coding RNA Prognostic Model for Patients with Lung Adenocarcinoma

10.21203/rs.3.rs-543033/v1 ◽

2021 ◽

Author(s):

Boxuan Liu ◽

Yun Zhao ◽

Shuanying Yang

Keyword(s):

Lung Adenocarcinoma ◽

Risk Score ◽

Prognostic Model ◽

Risk Model ◽

Cox Regression ◽

Predictive Ability ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Lasso Regression ◽

Cox Regression Analysis

Abstract Background: Lung adenocarcinoma is the most occurred pathological type among non-small cell lung cancer. Although huge progress has been made in terms of early diagnosis, precision treatment in recent years, the overall 5-year survival rate of a patient remains low. In our study, we try to construct an autophagy-related lncRNA prognostic signature that may guide clinical practice.Methods: The mRNA and lncRNA expression matrix of lung adenocarcinoma patients were retrieved from TCGA database. Next, we constructed a co-expression network of lncRNAs and autophagy-related genes. Lasso regression and multivariate Cox regression were then applied to establish a prognostic risk model. Subsequently, a risk score was generated to differentiate high and low risk group and a ROC curve and Nomogram to visualize the predictive ability of current signature. Finally, gene ontology and pathway enrichment analysis were executed via GSEA.Results: A total of 1,703 autophagy-related lncRNAs were screened and five autophagy-related lncRNAs (LINC01137, AL691432.2, LINC01116, AL606489.1 and HLA-DQB1-AS1) were finally included in our signature. Judging from univariate(HR=1.075, 95% CI: 1.046–1.104) and multivariate(HR =1.088, 95%CI = 1.057 − 1.120) Cox regression analysis, the risk score is an independent factor for LUAD patients. Further, the AUC value based on the risk score for 1-year, 3-year, 5-year, was 0.735, 0.672 and 0.662 respectively. Finally, the lncRNAs included in our signature were primarily enriched in autophagy process, metabolism, p53 pathway and JAK/STAT pathway. Conclusions: Overall, our study indicated that the prognostic model we generated had certain predictability for LUAD patients’ prognosis.

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A Six CT83-Related Genes Based Prognostic Signature for Lung Adenocarcinoma

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1871520621666210713112630 ◽

2021 ◽

Vol 24 ◽

Author(s):

Yongmei Wang ◽

Guimin Zhang ◽

Ruixian Wang

Keyword(s):

Regression Analysis ◽

Lung Adenocarcinoma ◽

Differential Expression ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

Differential Expression Analysis ◽

Functional Enrichment ◽

Risk Scores ◽

Cox Regression Analysis

Background: This study aims to explore the prognostic values of CT83 and CT83-related genes in lung adenocarcinoma (LUAD). Methods: We downloaded the mRNA profiles of 513 LUAD patients (RNA sequencing data) and 246 NSCLC patients (Affymetrix Human Genome U133 Plus 2.0 Array) from TCGA and GEO databases. According to the median expression of CT83, the TCGA samples were divided into high and low expression groups, and differential expression analysis between them was performed. Functional enrichment analysis of differential expression genes (DEGs) was conducted. Univariate Cox regression analysis and LASSO Cox regression analysis were performed to screen the optimal prognostic DEGs. Then we established the prognostic model. A Nomogram model was constructed to predict the overall survival (OS) probability of LUAD patients. Results: CT83 expression was significantly correlated to the prognosis of LUAD patients. A total of 59 DEGs were identified, and a predictive model was constructed based on six optimal CT83-related DEGs, including CPS1, RHOV, TNNT1, FAM83A, IGF2BP1, and GRIN2A, could effectively predict the prognosis of LUAD patients. The nomogram could reliably predict the OS of LUAD patients. Moreover, the six important immune checkpoints (CTLA4, PD1, IDO1, TDO2, LAG3, and TIGIT) were closely correlated with the Risk Score, which was also differentially expressed between the LUAD samples with high and low-Risk Scores, suggesting that the poor prognosis of LUAD patients with high-Risk Score might be due to the immunosuppressive microenvironments. Conclusion: A prognostic model based on six optimal CT83 related genes could effectively predict the prognosis of LUAD patients.

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Integrated analysis of the functions and prognostic values of RNA-binding proteins in neuroblastoma

PLoS ONE ◽

10.1371/journal.pone.0260876 ◽

2021 ◽

Vol 16 (12) ◽

pp. e0260876

Author(s):

Jun Yang ◽

Jiaying Zhou ◽

Cuili Li ◽

Shaohua Wang

Keyword(s):

Regression Analysis ◽

Risk Score ◽

Prognostic Model ◽

Binding Proteins ◽

Rna Binding ◽

Risk Model ◽

Cox Regression ◽

Rna Binding Proteins ◽

Differentially Expressed ◽

Lasso Regression

Background Neuroblastoma (NB) is the most common solid tumor in children. NB treatment has made significant progress; however, given the high degree of heterogeneity, basic research findings and their clinical application to NB still face challenges. Herein, we identify novel prognostic models for NB. Methods We obtained RNA expression data of NB and normal nervous tissue from TARGET and GTEx databases and determined the differential expression patterns of RNA binding protein (RBP) genes between normal and cancerous tissues. Lasso regression and Cox regression analyses identified the five most important differentially expressed genes and were used to construct a new prognostic model. The function and prognostic value of these RBPs were systematically studied and the predictive accuracy verified in an independent dataset. Results In total, 348 differentially expressed RBPs were identified. Of these, 166 were up-regulated and 182 down-regulated RBPs. Two hubs RBPs (CPEB3 and CTU1) were identified as prognostic-related genes and were chosen to build the prognostic risk score models. Multivariate Cox analysis was performed on genes from univariate Cox regression and Lasso regression analysis using proportional hazards regression model. A five gene prognostic model: Risk score = (-0.60901*expCPEB3)+(0.851637*expCTU1) was built. Based on this model, the overall survival of patients in the high-risk subgroup was lower (P = 2.152e-04). The area under the curve (AUC) of the receiver-operator characteristic curve of the prognostic model was 0.720 in the TARGET cohort. There were significant differences in the survival rate of patients in the high and low-risk subgroups in the validation data set GSE85047 (P = 0.1237e-08), with the AUC 0.730. The risk model was also regarded as an independent predictor of prognosis (HR = 1.535, 95% CI = 1.368–1.722, P = 2.69E-13). Conclusions This study identified a potential risk model for prognosis in NB using Cox regression analysis. RNA binding proteins (CPEB3 and CTU1) can be used as molecular markers of NB.

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The Construction and Analysis of a Novel Four-Long Non-Coding RNA Signature Predicting Survival of Breast Cancer Patients

10.21203/rs.3.rs-196427/v1 ◽

2021 ◽

Author(s):

Chengdong Qin ◽

Weiliang Feng ◽

Xingfei Yu ◽

Chenlu Liang ◽

Yuqin Ding ◽

...

Keyword(s):

Breast Cancer ◽

Breast Carcinoma ◽

Cox Regression ◽

Expression Profiles ◽

Functional Enrichment ◽

Risk Scores ◽

Clinicopathological Parameters ◽

Differential Analysis ◽

Breast Cancer Patients ◽

Cox Regression Analysis

Abstract Background As the critical regulators for tumorigenesis and progression, long-noncoding RNAs (lncRNAs) are becoming novel prognostic biomarkers for tumor patients. By the levels of lncRNAs expression, the patients with breast carcinoma may be divided into subgroups with different risk scores. Nevertheless, there is limited evidence to evaluate the role of lncRNAs in the prognosis of breast carcinoma. The present study aimed to construct lncRNA signatures for prognostic analysis and assist clinicians in choosing optimal therapies. Methods Abnormal expression profiles of breast cancer-associated lncRNAs were analyzed based on the TCGA datasets. Univariate and multivariate Cox regression analysis was used to build a prognostic risk signature according to the lncRNAs expression. The prognostic ability of this signature was verified in various subgroups. Functional enrichment analysis was employed to reveal the potential roles of these predictive lncRNAs in cancer-related biological processes and pathways. Results Compared with normal breast tissues, the differential analysis demonstrated that 286 lncRNAs were abnormally expressed in breast carcinoma. A four-lncRNA signature (RP1-193H18.2, AL022341.3, WDR86-AS1, LINC00511) was found to be closely related to the prognosis of breast carcinoma. The four-lncRNA signature could also qualify the magnitude of treatment benefits for different breast cancer subtypes. Additionally, it was an independent risk factor out of other clinicopathological parameters based on the multivariate Cox analysis. We also uncovered that the four predictive lncRNAs are involved in multiple cellular progression and pathways of breast cancer. Conclusions The four-lncRNA signature could be an essential reference for prognostic prediction and making therapeutic strategies.

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Ferroptosis regulators, especially SQLE, play an important role in prognosis, progression and immune environment of breast cancer

BMC Cancer ◽

10.1186/s12885-021-08892-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Wenqing Tang ◽

Fangshi Xu ◽

Meng Zhao ◽

Shuqun Zhang

Keyword(s):

Breast Cancer ◽

Risk Score ◽

Prognostic Value ◽

Risk Group ◽

Immune Checkpoints ◽

Risk Level ◽

Lasso Regression ◽

Luminal A ◽

Prognostic Assessment

Abstract Background Ferroptosis, a new form of programmed cell death, has great potential for cancer treatment. However, the roles of ferroptosis-related (FR) genes in breast cancer (BC) remain elusive. Materials and methods Using TCGA database, a novel FR risk signature was constructed through the Lasso regression analysis. Meanwhile, its prognostic value was assessed by a series of survival analyses. Besides, a nomogram was constructed to predict the overall survival rate (OSR) of individual at 1,3,5 year. Four validation cohorts (n = 2248), including METABRIC, GSE58812, GSE20685 and ICGC-KR datasets, were employed to test the prognostic value of FR risk signature. The effects of FR risk signature on BC immune microenvironment were explored by CIBERSORT algorithm and ssGSEA method. The histological expressions of FR risk genes were presented by HPA database. The biofunctions of SQLE were determined by qPCR, MTT, wound-healing and Transwell assays. Results We constructed a novel FR risk signature consisting of eight genes. High FR risk led a poor prognosis and was identified as an independent prognostic factor. Besides, A higher proportion of patients with luminal A type was observed in low-risk group (53%), while a higher proportion of patients with basal type in high-risk group (24%). FR risk score could discriminate the prognostic difference of most clinical subgroups, except for M1 stage, HER2 and basal types. Moreover, its prognostic value was successfully validated in other four cohorts. Through immune analyses, we found that the reduced infiltration levels of CD8+ and NK cells, whereas the enhanced activity of antigen presentation process appeared in high FR risk. Then, FR risk score was found to weakly correlate with the expressions of six immune checkpoints. Through the experiments in vitro, we confirmed that overexpression of SQLE could promote, whereas blocking SQLE could inhibit the proliferative, migrative and invasive abilities of BC cells. Conclusions FR risk signature was conducive to BC prognostic assessment. High FR risk level was closely associated with BC immunosuppression, but may not predict ICIs efficacy. Moreover, SQLE was identified as a crucial cancer-promoting gene in BC. Our findings provide new insights into prognostic assessment and molecular mechanism of BC.

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Exploring an RNA binding proteins-associated prognostic model for Gastric Cancer

10.21203/rs.3.rs-33114/v1 ◽

2020 ◽

Author(s):

Ming Liu ◽

Jiayi Xie ◽

Xiaobei Luo ◽

Yaxin Luo ◽

Side Liu ◽

...

Keyword(s):

Gastric Cancer ◽

High Risk ◽

Risk Score ◽

Prognostic Model ◽

Rna Binding ◽

Cox Regression ◽

Rna Binding Proteins ◽

Roc Curves ◽

Low Risk ◽

Lasso Regression

Abstract Background: Gastric cancer (GC) is one of the most prevalent malignant cancers around the world. Given that abnormal RNA binding proteins (RBPs) are involved in the tumorigenesis, we aimed to explore the potential value of RBPs-associated genes in gastric cancer.Methods: RNA-seq and clinical data were retrieved from The Cancer Genome Atlas (TCGA) database and differentially expressed RBPs genes were screened. GO and KEGG pathway enrichment analyses were implemented to elucidate the roles of RBPs in GC. The protein-protein interaction (PPI) networks of RBPs were carried out, and the hub genes were determined by MCODE built in Cytoscape. The TCGA-STAD dataset was randomly divided into training and testing groups. A prognostic signature including five RBPs was developed within the training cohort after Cox regression and Lasso regression analyses. We used Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves to evaluate the capacity of the model in both groups. Then, a nomogram based on hub RBPs expression was established. Gene Set Enrichment Analysis was performed between the high-risk and low-risk group.Results: A total of 166 up-regulated RBPs and 130 down-regulated RBPs were identified. Via Cox regression and Lasso regression analysis within the training group, five hub RBPs (RNASE1, SETD7, BOLL, PPARGC1B, MSI2) were screened and the prognostic model was constructed. The risk score was calculated and gastric cancer patients were divided into high-risk and low-risk groups. In multivariate analysis, risk score was still an independent prognostic indicator (HR = 1.80, 95% CI = 1.45-2.22, P < 0.01). Patients with low risk had favorable survival rate in both training and testing group compared to those at high risk (P < 0.001). The areas under the ROC curves (AUC) of the prognostic model are 0.718 in the training cohort and 0.651 in the testing cohort. The hub RBPs-based nomogram model exhibited excellent ability to predict the OS of GC. GSEA illustrated that several cancer-related signaling pathways were enriched in patients with a high-risk score.Conclusions: This study discovered a five RBPs signature which might provide a potential prognostic value to GC patients.

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Development of a Ten-lncRNA Signature Prognostic Model for Breast Cancer Survival: A Study with the TCGA Database

Analytical Cellular Pathology ◽

10.1155/2020/6827057 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Wenqing Zhou ◽

Yongkui Pang ◽

Yunmin Yao ◽

Huiying Qiao

Keyword(s):

Breast Cancer ◽

Gene Ontology ◽

Regression Analysis ◽

Pathway Analysis ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

Patient Survival ◽

Receptor Interaction ◽

Cox Regression Analysis

Long noncoding RNA (lncRNA) plays a critical role in the development of tumors. The aim of our study was construction of a lncRNA signature model to predict breast cancer (BRCA) patient survival. We downloaded RNA-seq data and relevant clinical information from the Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNA were computed using the “edgeR” package and subjected to the univariate and multivariate Cox regression analysis. Corresponding protein-coding genes were used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis. Finally, 521 differentially expression lncRNA were obtained. We constructed a ten-lncRNA signature model (LINC01208, RP5-1011O1.3, LINC01234, LINC00989, RP11-696F12.1, RP11-909N17.2, CTC-297N7.9, CTA-384D8.34, CTC-276P9.4, and MAPT-IT1) to predict BRCA patient survival using the multivariate Cox proportional hazard regression model. The C-index was 0.712, and AUC scores of training, test, and entire sets were 0.746, 0.717, and 0.732, respectively. Univariate Cox regression analysis indicated that age, tumor status, N status, M status, and risk score were significantly related to overall survival in patients with BRCA. Further, the multivariate analysis showed that risk score and M status had outstanding independent prognostic values, both with p<0.001. The Gene Ontology (GO) function and KEEG pathway analysis was primarily enriched in immune response, receptor binding, external surface of plasma membrane, signal transduction, cytokine-cytokine receptor interaction, and cell adhesion molecules (CAMs). Finally, we constructed a ten-lncRNA signature model that can serve as an independent prognostic model to predict BRCA patient survival.

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Comprehensive Analysis of the Immune-Oncology Targets and Immune Infiltrates of N6-Methyladenosine-Related Long Noncoding RNA Regulators in Breast Cancer

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.686675 ◽

2021 ◽

Vol 9 ◽

Author(s):

Xiaoqiang Zhang ◽

Li Shen ◽

Ruyu Cai ◽

Xiafei Yu ◽

Junzhe Yang ◽

...

Keyword(s):

Breast Cancer ◽

High Risk ◽

Risk Score ◽

Noncoding Rna ◽

Immune Cell ◽

Cox Regression ◽

High Risk Group ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Immune Oncology

Breast cancer (BRCA) has become the highest incidence of cancer due to its heterogeneity. To predict the prognosis of BRCA patients, sensitive biomarkers deserve intensive investigation. Herein, we explored the role of N6-methyladenosine-related long non-coding RNAs (m6A-related lncRNAs) as prognostic biomarkers in BRCA patients acquired from The Cancer Genome Atlas (TCGA; n = 1,089) dataset and RNA sequencing (RNA-seq) data (n = 196). Pearson’s correlation analysis, and univariate and multivariate Cox regression were performed to select m6A-related lncRNAs associated with prognosis. Twelve lncRNAs were identified to construct an m6A-related lncRNA prognostic signature (m6A-LPS) in TCGA training (n = 545) and validation (n = 544) cohorts. Based on the 12 lncRNAs, risk scores were calculated. Then, patients were classified into low- and high-risk groups according to the median value of risk scores. Distinct immune cell infiltration was observed between the two groups. Patients with low-risk score had higher immune score and upregulated expressions of four immune-oncology targets (CTLA4, PDCD1, CD274, and CD19) than patients with high-risk score. On the contrary, the high-risk group was more correlated with overall gene mutations, Wnt/β-catenin signaling, and JAK-STAT signaling pathways. In addition, the stratification analysis verified the ability of m6A-LPS to predict prognosis. Moreover, a nomogram (based on risk score, age, gender, stage, PAM50, T, M, and N stage) was established to evaluate the overall survival (OS) of BRCA patients. Thus, m6A-LPS could serve as a sensitive biomarker in predicting the prognosis of BRCA patients and could exert positive influence in personalized immunotherapy.

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