Weighted Gene Co-Expression Network Analysis Reveals a New Survival Model for Prognostic Prediction in Ewing Sarcoma

Abstract Background: Ewing sarcoma (ES) is a malignant bone or soft-tissue cancer that mainly arises in children and young adults. However, the prognosis of Ewing sarcoma remains very poor, and there is no effective prediction method. The aim of our study was to identify a prognostic model for ES patients based on prognosis-associated mRNA expression profiles. Methods: The GSE17679 dataset was downloaded from the Gene Expression Omnibus (GEO) database. Differently expressed genes (DEGs) between ES and normal control were identified using R package “limma”. A weighted gene co-expression network analysis (WGCNA) was used to screen gene modules associated with recurrence/metastasis and survival status based on DEGs. Results: The prognostic model was constructed based on genes in MEbrown module, which was most associated with recurrence/metastasis and survival status, using Kaplan-Meier survival and lasso regression analysis. Sixteen genes were screened to construct the prognostic model. ES patients were grouped into high- and low-risk groups based on the median of risk score calculated for each of them. ES patients in high-risk group have worse survival than patients in low-risk group. The AUCs (Area under the ROC curve) for 1-year, 3-year, and 6-year overall survival were 0.903, 0.995, 0.953. Conclusions: Taken together, our research constructed a prognostic model which has excellent prediction performance for overall survival of ES patients.

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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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An autophagy-related long non-coding RNA signature for patients with colorectal cancer

Physiology International ◽

10.1556/2060.2021.00125 ◽

2021 ◽

Author(s):

Dongyan Zhao ◽

Xizhen Sun ◽

Sidan Long ◽

Shukun Yao

Keyword(s):

Colorectal Cancer ◽

Overall Survival ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

High Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Cox Regression Analysis

AbstractAimLong non-coding RNAs (lncRNAs) have been identified to regulate cancers by controlling the process of autophagy and by mediating the post-transcriptional and transcriptional regulation of autophagy-related genes. This study aimed to investigate the potential prognostic role of autophagy-associated lncRNAs in colorectal cancer (CRC) patients.MethodsLncRNA expression profiles and the corresponding clinical information of CRC patients were collected from The Cancer Genome Atlas (TCGA) database. Based on the TCGA dataset, autophagy-related lncRNAs were identified by Pearson correlation test. Univariate Cox regression analysis and the least absolute shrinkage and selection operator analysis (LASSO) Cox regression model were performed to construct the prognostic gene signature. Gene set enrichment analysis (GSEA) was used to further clarify the underlying molecular mechanisms.ResultsWe obtained 210 autophagy-related genes from the whole dataset and found 1187 lncRNAs that were correlated with the autophagy-related genes. Using Univariate and LASSO Cox regression analyses, eight lncRNAs were screened to establish an eight-lncRNA signature, based on which patients were divided into the low-risk and high-risk group. Patients’ overall survival was found to be significantly worse in the high-risk group compared to that in the low-risk group (log-rank p = 2.731E-06). ROC analysis showed that this signature had better prognostic accuracy than TNM stage, as indicated by the area under the curve. Furthermore, GSEA demonstrated that this signature was involved in many cancer-related pathways, including TGF-β, p53, mTOR and WNT signaling pathway.ConclusionsOur study constructed a novel signature from eight autophagy-related lncRNAs to predict the overall survival of CRC, which could assistant clinicians in making individualized treatment.

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A Nine-Immune-Related Gene Prognostic Signature for Predicting Outcomes in High-Grade Serous Ovarian Cancer

10.21203/rs.3.rs-801023/v1 ◽

2021 ◽

Author(s):

Yahui Jiang ◽

Tianjiao Lyu ◽

Tianyu Zhou ◽

Yiwen Shi ◽

Weiwei Feng

Keyword(s):

Ovarian Cancer ◽

Overall Survival ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Risk Scores ◽

High Grade ◽

Lasso Regression ◽

Prognostic Signature

Abstract Background: Recently, immune system has been shown to be indispensable for ovarian cancer progression. The key immune-related genes (IRGs) related to the overall survival of ovarian cancer patients should be taken seriously. Here, we screened 9 survival-related IRGs in high-grade serous ovarian cancer (HGSOC) and build a prognostic signature to predict the outcome of HGSOC patients.Methods: We downloaded RNA-sequence profiles from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) databases to identify differentially expressed genes between normal fallopian tube and HGSOC. Among these genes, IRGs were filtered based on the Immunology Database and Analysis Portal (ImmPort). Using univariate Cox regression, Lasso regression and multivariate Cox regression, we selected 9 survival-related IRGs and established a prognostic signature to compute the risk score. Patients were divided into a low-risk group and a high-risk group, and the immunological feature differences between them were analysed with the ESTIMATE R package, TIMER and GSEA software. Moreover, the prognostic signature was validated by data from Gene Expression Omnibus (GEO) datasets.Results: We obtained 1544 differentially expressed genes in HGSOC compared with normal fallopian tube, among which 99 genes were related to immunology. After univariate Cox regression, Lasso regression and multivariate Cox regression, nine IRGs (HLA-F, PSMC1, PI3, CXCL10, CXCL9, CXCL11, LRP1, STAT1 and OGN) were identified as optimal survival-related IRGs and used to establish a prognostic signature for calculating the risk scores of HGSOC patients. The prognostic signature showed its efficiency in predicting the overall survival of HGSOC patients in TCGA training cohort (p=1.018e-8) and GEO test cohort (p=2.632e-2). Age and risk scores were independent risk factors for overall survival. As the risk scores increased, the proportions of neutrophil, dendritic cells, CD8+ T cells, CD4+ T cells and B cells decreased (p values were 0.026, 1.909e-4, 9.165e-10, 0.003 and 2.658e-4, respectively). In addition, 21 out of 24 HLA-related genes were highly expressed in the low-risk group than in the high-risk group. The above might prompt a stronger immune response in the low-risk group.Conclusions: Our study constructed a nine-IRG-based prognostic signature that could effectively predict the overall survival of HGSOC patients and become a promising therapeutic target for HGSOC treatments.

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Risk Stratification Using a New Prognostic Model for Patients with Secondary Acute Myeloid Leukemia - Results of the DSIL-AML96 Trial.

Blood ◽

10.1182/blood.v114.22.2652.2652 ◽

2009 ◽

Vol 114 (22) ◽

pp. 2652-2652

Author(s):

Friedrich Stölzel ◽

Walter E. Aulitzky ◽

Heinrich Bodenstein ◽

Martin Bornhäuser ◽

Michael Kramer ◽

...

Keyword(s):

Overall Survival ◽

High Risk ◽

Myeloid Leukemia ◽

Prognostic Model ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Allogeneic Hsct ◽

Secondary Acute Myeloid Leukemia ◽

Acute Myeloid

Abstract Abstract 2652 Poster Board II-628 Background: Secondary acute myeloid leukemia (sAML) following a myelodysplastic syndrome (mdsAML) or deriving as therapy-related AML (tAML) is regarded as an entity with a poor prognosis and patients are normally treated as high risk AML. However due to progress in elucidating the impact of molecular and cytogenetic markers and therefore combining biological and clinical data for prognosis and treatment outcome the aim of this analysis was to provide a prognostic scoring system for this entity by including clinical and laboratory data from patients being treated in the prospective AML96 trial of the DSIL study group. Patients and methods: A total of 318 patients with sAML (mdsAML = 239 and tAML = 79) were treated within the AML96 trial with a median follow-up for patients alive of 5.66 years (95% CI 4.426 – 6.895). All patients received double induction chemotherapy. Consolidation therapy contained high-dose cytosine arabinoside and for patients ' 60 years of age the option of autologous or allogeneic hematopoietic stem cell transplantation (HSCT) according to donor availability. Prognostic factors for survival were analyzed in the whole group of sAML patients in a multivariate Cox regression model for overall survival (OS) stratified by treatment groups (chemo-consolidation vs. allogeneic HSCT). Model selection was performed by backward selection applying the Likelihood-Ratio-Test. Results: Complete remission (CR) rate for all patients was 30.8% (n = 96). CR rate was lower in patients with mdsAML compared to patients with tAML (25.9% vs. 44.3%, p=.003). Patients with mdsAML were older and had a higher percentage of CD34+ blasts at diagnosis but to a lower extend aberrant karyotypes than patients with tAML. OS and disease free survival (DFS) at three years for all patients was 15.8% and 20.6%, respectively. While disease status (mdsAML vs. tAML) had no independent influence on survival, the dichotomized prognostic factors platelet count in the peripheral blood at diagnosis [HR = 0.535 (95% CI .415 – .689), p=<.000] as well as the NPM1 mutational status in the bone marrow at diagnosis [HR = 0.572 (95% CI .351 – .933), p=.025] were detected as independent predictors for overall survival. By combining these two variables, a prognostic model for OS with two risk groups for patients with sAML could be established with the low risk group being NPM1 positive or having platelets of >50 Gpt/l at diagnosis and the high risk group being NPM1 negative and having platelets of '50 Gpt/l at diagnosis. Three year OS for patients who received chemo-consolidation in the low risk group was 19.9% [95% CI = .128 - .270] and for patients in the high risk group 5.1% [95% CI = .014 - .088], p<.001. For patients who underwent allogeneic HSCT in first CR belonging to the low risk group the three year OS was 53.8% [95% CI = .346 - .730] and for patients in the high risk group 15.4% [95% CI = .000 - .35], p<.001. Conclusions: For patients with sAML we provide a new prognostic model for risk stratification: 1) NPM1+ or Platelets >50 Gpt/l defining a low risk group and 2) NPM1- and Platelets ' 50 Gpt/l defining a high risk group. Disclosures: No relevant conflicts of interest to declare.

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Integrated Analysis Identifies an Immune-Based Prognostic Signature for the Mesenchymal Identity in Gastric Cancer

BioMed Research International ◽

10.1155/2020/9780981 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Dongzhu Peng ◽

Bin Gu ◽

Liming Ruan ◽

Xingguo Zhang ◽

Peng Shu

Keyword(s):

Gastric Cancer ◽

Network Analysis ◽

Plasma Cells ◽

Risk Group ◽

Expression Profiles ◽

Gene Expression Profiles ◽

High Risk Group ◽

Low Risk ◽

Integrated Analysis ◽

Prognostic Signature

Background. Gastric cancer (GC) has been divided into four molecular subtypes, of which the mesenchymal subtype has the poorest survival. Our goal is to develop a prognostic signature by integrating the immune system and molecular modalities involved in the mesenchymal subtype. Methods. The gene expression profiles collected from 6 public datasets were applied to this study, including 1,221 samples totally. Network analysis was applied to integrate the mesenchymal modalities and immune signature to establish an immune-based prognostic signature for GC (IPSGC). Results. We identified six immune genes as key factors of the mesenchymal subtype and established the IPSGC. The IPSGC can significantly divide patients into high- and low-risk groups in terms of overall survival (OS) and relapse-free survival (RFS) in discovery (OS: P<0.001) and 5 independent validation sets (OS range: P=0.05 to P<0.001; RFS range: P=0.03 to P<0.001). Further, in multivariate analysis, the IPSGC remained an independent predictor of prognosis and performed better efficiency compared to clinical characteristics. Moreover, macrophage M2 was significantly enriched in the high-risk group, while plasma cells were enriched in the low-risk group. Conclusions. We propose an immune-based signature identified by network analysis, which is a promising prognostic biomarker and help for the selection of GC patients who might benefit from more rigorous therapies. Further prospective studies are warranted to test and validate its efficiency for clinical application.

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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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The predictive role of Ki-67 protein in determining the aggressiveness of primary malignant bone tumors. A retrospective study

Romanian Journal of Orthopaedic Surgery and Traumatology ◽

10.2478/rojost-2019-0018 ◽

2019 ◽

Vol 2 (2) ◽

pp. 91-95

Author(s):

Ioan-Mihai Japie ◽

Dragoș Rădulescu ◽

Adrian Bădilă ◽

Alexandru Papuc ◽

Traian Ciobanu ◽

...

Keyword(s):

High Risk ◽

Ewing Sarcoma ◽

Bone Tumors ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

The Other ◽

Malignant Bone Tumors ◽

Ki 67

AbstractIntroduction: In order to diagnose and stage malignant bone tumors, the pathologic examination of harvested pieces with immunohistochemistry test is necessary; they also provide information regarding the prognosis on a medium to long term. Among tissular biomarkers with potential predictive value, a raised Ki-67 protein level is used to determine the risk of local recurrence or metastasis.Material and method: This study was performed on 50 patients with primary malignant bone tumors admitted in the Traumatology and Orthopedy Department of University Emergency Hospital, Bucharest. Patients repartition according to diagnosis was the following: 21 patients with osteosarcoma, 18 patients with chondrosarcoma, 6 patients with Ewing sarcoma, 3 patients with malignant fibrous histiocytoma, and 2 with fibrosarcoma. The follow-up period was between 12 and 72 months with a mean of 26 months.Results: Patients were aged between 18 and 77 years old, with a mean age of 41,36. There were 22 women and 28 men. No sex or age difference was notable for the tumor outcome. After calculating the Ki-67 LI, 36 patients were included in the high-risk group (Ki-67 LI > 25%), while 14 had a low risk for metastasis and local relapse (Ki-67 < 25%). The low-risk patients had chondrosarcoma (8 patients), osteosarcoma (5 patients), and fibrosarcoma (1 patient). During the follow-up, 8 patients, all belonging to the high risk group, developed metastasis, while 5 patients developed local recurrences; 4 patients who relapsed belonged to the high risk group and 1 to the low risk group. Metastases developed in 3 patients with osteosarcoma, 2 with Ewing sarcoma, 2 with chondrosarcoma and 1 patient with fibrosarcoma. Most metastases occurred within one year after surgery. The other fibrosarcoma patient developed local recurrence after 6 months, while the other local recurrences occurred in osteosarcoma patients (2 cases) and 1 in a Ewing sarcoma patient and chondrosarcoma patient.Conclusions: Our study concluded that while Ki-67 LI values are useful in determining the aggressivity of primary malignant bone tumors, it should always be used in conjunction with the clinical, imaging and anatomopathological diagnosis methods in order to accurately predict the patients’ outcome.

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

Cancer Cell International ◽

10.1186/s12935-021-02389-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jinyuan Shi ◽

Pu Wu ◽

Lei Sheng ◽

Wei Sun ◽

Hao Zhang

Keyword(s):

High Risk ◽

Immune Cells ◽

Risk Group ◽

Gene Signature ◽

High Risk Group ◽

Low Risk ◽

Lasso Regression ◽

Papillary Thyroid ◽

Related Gene ◽

Normal Thyroid

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

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A four-gene prognostic signature for predicting the overall survival of patients with lung adenocarcinoma

PeerJ ◽

10.7717/peerj.11911 ◽

2021 ◽

Vol 9 ◽

pp. e11911

Author(s):

Lei Liu ◽

Huayu He ◽

Yue Peng ◽

Zhenlin Yang ◽

Shugeng Gao

Keyword(s):

Overall Survival ◽

High Risk ◽

Lung Adenocarcinoma ◽

Prognostic Model ◽

Risk Group ◽

Gene Signature ◽

Tnm Staging ◽

High Risk Group ◽

Prognostic Signature ◽

Risk Patients

Background The prognosis of patients for lung adenocarcinoma (LUAD) is known to vary widely; the 5-year overall survival rate is just 63% even for the pathological IA stage. Thus, in order to identify high-risk patients and facilitate clinical decision making, it is vital that we identify new prognostic markers that can be used alongside TNM staging to facilitate risk stratification. Methods We used mRNA expression from The Cancer Genome Atlas (TCGA) cohort to identify a prognostic gene signature and combined this with clinical data to develop a predictive model for the prognosis of patients for lung adenocarcinoma. Kaplan-Meier curves, Lasso regression, and Cox regression, were used to identify specific prognostic genes. The model was assessed via the area under the receiver operating characteristic curve (AUC-ROC) and validated in an independent dataset (GSE50081) from the Gene Expression Omnibus (GEO). Results Our analyses identified a four-gene prognostic signature (CENPH, MYLIP, PITX3, and TRAF3IP3) that was associated with the overall survival of patients with T1-4N0-2M0 in the TCGA dataset. Multivariate regression suggested that the total risk score for the four genes represented an independent prognostic factor for the TCGA and GEO cohorts; the hazard ratio (HR) (high risk group vs low risk group) were 2.34 (p < 0.001) and 2.10 (p = 0.017). Immune infiltration estimations, as determined by an online tool (TIMER2.0) showed that CD4+ T cells were in relative abundance in the high risk group compared to the low risk group in both of the two cohorts (both p < 0.001). We established a composite prognostic model for predicting OS, combined with risk-grouping and clinical factors. The AUCs for 1-, 3-, 5- year OS in the training set were 0.750, 0.737, and 0.719; and were 0.645, 0.766, and 0.725 in the validation set. The calibration curves showed a good match between the predicted probabilities and the actual probabilities. Conclusions We identified a four-gene predictive signature which represents an independent prognostic factor and can be used to identify high-risk patients from different TNM stages of LUAD. A new prognostic model that combines a prognostic gene signature with clinical features exhibited better discriminatory ability for OS than traditional TNM staging.

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Complete Identification of Autophagy-Related lncRNA Prognostic Signature for Breast Cancer

10.21203/rs.3.rs-347298/v1 ◽

2021 ◽

Author(s):

Wenxi Wang ◽

Na Li ◽

Lin Shen ◽

Qin Zhou ◽

Zhanzhan Li ◽

...

Keyword(s):

Breast Cancer ◽

Prognostic Model ◽

Immune Cell ◽

Risk Group ◽

Pearson Correlation ◽

Risk Groups ◽

Cell Types ◽

High Risk Group ◽

Low Risk ◽

High Morbidity

Abstract Purpose: Breast cancer (BC) has a relatively high morbidity and mortality for women. The research about BC prognosis is significant. Autophagy is an essential process for tumor progression, which could play its role with lncRNA, a kind of ncRNA that have regulatory roles in multiple tumors. Therefore, constructing an autophagy-related prognostic model for breast cancer is meaningful.Methods: We download data from the TCGA and HADb. Pearson correlation analysis was performed to excavate autophagy-related lncRNA. Then with gene expression difference analysis, etc. we explored the relationship between clinical features and the signature. We applied Cytoscape as well as KEGG, etc. to explore expression condition. And the autophagy status of our signature was investigated by GSEA, etc. We also searched the immune distinction by CIBERSORTx to extend our study and preliminarily verified our study in the end.Results: Firstly, we got an independent autophagy-related lncRNA prognostic model, by which BC patients were divided into high- and low-risk groups. We found that the OS of high-risk group is significantly lower than that of low-risk group, which was identical to those within various clinical subgroups. Then, the KEGG and GO analysis enriched several pathways including autophagy. PCA and GSEA analysis demonstrated the autophagy status. Several distinguishing immune cell types in separated groups were revealed by immunity analysis. Then the verification in the end proved the feasibility of our signature.Conclusion: In this study, we acquired an independent autophagy-related lncRNA signature involving 12 lncRNAs, which contributes to the prediction of prognosis of BC patients.

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