A Novel Risk Scoring System to Predict Survival in Soft Tissue-Related Extramedullary Multiple Myeloma

Abstract Objective: Extramedullary multiple myeloma (EMM) is an aggressive subtype of multiple myeloma, especially those soft tissue-related extramedullary multiple myeloma (EM-S). Plasma cells often infiltrate in extramedullary tissues, but the proportion of plamas cells in bone marrow is not high, so FISH test often produces false negative results. Therefore, the R-ISS staging system cannot predict the really prognosis of this type of patients. A novel risk score is needed for these patients. Methos: A cohort of 73 patients with EM-S in four centers in China were included, excluding those only with bone related extramedullary multiple myeloma, primary or secondary plasma leukemia. We performed univariate and multivariate Cox regression analysis, generated risk scores to predict outcomes in EM-S MM and compared with R-ISS staging. Results: In the Cox multivariate model, high LDH, low count of platelet、central nervous system involved, abdominal viscera involved (including liver, spleen, pancreas, kidney) and Ki67≥70% were found to significantly and independently predict outcomes for EM-S. According to these parameters, the patients were divided into three groups: the low-risk group with a median TTP of 14.13 months, and the medium-risk group with a median TTP of 8.53 months. In the high-risk group, the median TTP was 2.73 months (p<0.05 between any two groups). This novel risk is better to predict prognosis of this group of patients than R-ISS stage, in which the TTP in R-ISS stage I was 20.33 months, R-ISS stage II 9.83 months, and R-ISS stage III 4.83 months (p=0.065). Conclusion: The novel risk stratification for EM-S may be may be superior to R-ISS for EM-S MM patients. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

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Prognostic model of head and neck squamous cell carcinoma based on 12 ferroptosis-related genes

10.21203/rs.3.rs-349666/v1 ◽

2021 ◽

Author(s):

Sijia Li ◽

Hongyang Zhang ◽

Wei Li

Keyword(s):

Regression Analysis ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Risk Scores ◽

Cox Regression Analysis ◽

Normal Tissues ◽

Model Based ◽

Cox Analysis

Abstract Background: The purpose of our study is establishing a model based on ferroptosis-related genes predicting the prognosis of patients with head and neck squamous cell carcinoma (HNSCC).Methods: In our study, transcriptome and clinical data of HNSCC patients were from The Cancer Genome Atlas, ferroptosis-related genes and pathways were from Ferroptosis Signatures Database. Differentially expressed genes (DEGs) were screened by comparing tumor and adjacent normal tissues. Functional enrichment analysis of DEGs, protein-protein interaction network and gene mutation examination were applied. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression were used to identified DEGs. The model was constructed by multivariate Cox regression analysis and verified by Kaplan-Meier analysis. The relationship between risk scores and other clinical features was also analyzed. Univariate and multivariate Cox analysis was used to verified the independence of our model. The model was evaluated by receiver operating characteristic analysis and calculation of the area under the curve (AUC). A nomogram model based on risk score, age, gender and TNM stages was constructed.Results: We analyzed data including 500 tumor tissues and 44 adjacent normal tissues and 259 ferroptosis-related genes, then obtained 73 DEGs. Univariate Cox regression analysis screened out 16 genes related to overall survival, and LASSO analysis fingered out 12 of them with prognostic value. A risk score model based on these 12 genes was constructed by multivariate Cox regression analysis. According to the median risk score, patients were divided into high-risk group and low-risk group. The survival rate of high-risk group was significantly lower than that of low-risk group in Kaplan-Meier curve. Risk scores were related to T and grade. Univariate and multivariate Cox analysis showed our model was an independent prognostic factor. The AUC was 0.669. The nomogram showed high accuracy predicting the prognosis of HNSCC patients.Conclusion: Our model based on 12 ferroptosis-related genes performed excellently in predicting the prognosis of HNSCC patients. Ferroptosis-related genes may be promising biomarkers for HNSCC treatment and prognosis.

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Super-Enhancer Associated Five-Gene Risk Score Model Predicts Overall Survival in Multiple Myeloma Patients

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2020.596777 ◽

2020 ◽

Vol 8 ◽

Author(s):

Tingting Qi ◽

Jian Qu ◽

Chao Tu ◽

Qiong Lu ◽

Guohua Li ◽

...

Keyword(s):

Multiple Myeloma ◽

Prognostic Factors ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Bone Lesions ◽

Cox Regression Analysis ◽

Super Enhancer ◽

Score Model

Multiple myeloma (MM) is a malignant plasma cell tumor with high heterogeneity, characterized by anemia, hypercalcemia, renal failure, and lytic bone lesions. Although various powerful prognostic factors and models have been exploited, the development of more accurate prognosis and treatment for MM patients is still facing many challenges. Given the essential roles of super-enhancer (SE) associated genes in the tumorigenesis of MM, we tried to initially screen and identify the significant prognostic factors from SE associated genes in MM by the least absolute shrinkage and selection operator (Lasso) penalized Cox regression, univariate and multivariate Cox regression analysis using GSE24080 and GSE9782 datasets. Risk score model of five genes including CSGALNACT1, FAM53B, TAPBPL, REPIN1, and DDX11, was further constructed and the Kaplan-Meier (K-M) curves showed that the low-risk group seems to have better clinical outcome of survival compared to the high-risk group. Time-dependent receiver operating characteristic (ROC) curves presented the favorable performance of the model. An interactive nomogram consisting of the five-gene risk group and eleven clinical traits was established and identified by calibration curves. Therefore, the risk score model of SE associated five genes developed here could be used to predict the prognosis of MM patients, which may assist the clinical treatment of MM patients in the future.

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Prognostic Model of Head and Neck Squamous Cell Carcinoma Based on 12 Ferroptosis-related Genes

10.21203/rs.3.rs-349666/v2 ◽

2021 ◽

Author(s):

Sijia Li ◽

Hongyang Zhang ◽

Wei Li

Keyword(s):

Regression Analysis ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Risk Scores ◽

Cox Regression Analysis ◽

Normal Tissues ◽

Model Based ◽

Cox Analysis

Abstract Background: The purpose of our study is establishing a model based on ferroptosis-related genes predicting the prognosis of patients with head and neck squamous cell carcinoma (HNSCC).Methods: In our study, transcriptome and clinical data of HNSCC patients were from The Cancer Genome Atlas, ferroptosis-related genes and pathways were from Ferroptosis Signatures Database. Differentially expressed genes (DEGs) were screened by comparing tumor and adjacent normal tissues. Functional enrichment analysis of DEGs, protein-protein interaction network and gene mutation examination were applied. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression were used to identified DEGs. The model was constructed by multivariate Cox regression analysis and verified by Kaplan-Meier analysis. The relationship between risk scores and other clinical features was also analyzed. Univariate and multivariate Cox analysis was used to verified the independence of our model. The model was evaluated by receiver operating characteristic analysis and calculation of the area under the curve (AUC). A nomogram model based on risk score, age, gender and TNM stages was constructed. Our model was also validated in the Gene Expression Omnibus (GEO) verification set. Results: We analyzed data including 500 tumor tissues and 44 adjacent normal tissues and 259 ferroptosis-related genes, then obtained 73 DEGs. Univariate Cox regression analysis screened out 16 genes related to overall survival, and LASSO analysis fingered out 12 of them with prognostic value. A risk score model based on these 12 genes was constructed by multivariate Cox regression analysis. According to the median risk score, patients were divided into high-risk group and low-risk group. The survival rate of high-risk group was significantly lower than that of low-risk group in Kaplan-Meier curve. Risk scores were related to T and grade. Univariate and multivariate Cox analysis showed our model was an independent prognostic factor. The AUC was 0.669. Those all could prove our model had great predictive ability of HNSCC prognosis and it could be validated in GEO dataset. The nomogram showed high accuracy predicting the prognosis of HNSCC patients.Conclusion: Our model based on 12 ferroptosis-related genes performed excellently in predicting the prognosis of HNSCC patients. Ferroptosis-related genes may be promising biomarkers for HNSCC treatment and prognosis.

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Hematopoietic Cell Transplantation - Comorbidity Index (HCT-CI) as a Surrgoate Marker for Predition of Transplant Outcome for Pateints (pts) with Multiple Myeloma (MM) Undergoing Autologous Stem Cell Transplantation (ASCT): High HCT-CI Score Predicts for Poor Outcome.

Blood ◽

10.1182/blood.v110.11.5104.5104 ◽

2007 ◽

Vol 110 (11) ◽

pp. 5104-5104

Author(s):

Mohamed I. Farhat ◽

Ronald Myint ◽

Stephanie A. Gregory ◽

Parameswaran Venugopal ◽

Mohamad Kassar ◽

...

Keyword(s):

Multiple Myeloma ◽

High Risk ◽

Cell Transplantation ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Heart Valve Disease ◽

Risk Scores ◽

Transplant Outcome

Abstract Background: For all “transplant eligible” pts with MM, established practice guidelines recommend ASCT as part of the front line treatment. However, the definition of “transplant eligible” remains undefined. The HCT-CI is a new tool that encapsulates pre-transplant comorbidities used in predicting transplant outcomes in pts undergoing allogeneic SCT. This scoring system has been shown to be a good predictor for non-relapse mortality (NRM) & survival in pts undergoing alloSCT. In this study, we hypothesize that HCT-CI could predict the transplant outcome on pts with MM undergoing ASCT and could potentially be utilized as a tool for selecting pts with MM for transplant. Methods: A retrospective analysis of 75 pts with multiple myeloma whom underwent ASCT in our institution between 02/99 and 12/03 with a median follow up of 30 months. Pts were assigned scores based on the HCT-CI. Definitions of comorbidities were as previously reported by Sorror et. al. (Blood2005; 106:2912). Results: Median age was 56 years (38 – 73); M:F 1:1. 51 pts received a single & 24 had tandem ASCT. The majority of pt. had IgG myeloma (IgG kappa: 45; IgG Lambda 17). Comorbidities (points, number of pts): mild hepatic (1,16), renal (2,6), cardiac (1,8), arrhythmia (1,1), heart valve disease (3,4), cerebrovascular (1,8), DM (1,11), PUD (2,2), inflammatory bowel disease (1,0), Tumor (3,6), pulmonary (2,5), psychiatric (1,8), rheumatologic (2,3), infection (1,6), and obesity (1,10). HCT-CI score of 0 seen in 32%, 1 in 28%, 2–8 in 40% of the pts, with a median score of 1.65. 20 patients died with only one due to NRM. Pts were categorized into 2 groups: low-risk (scores of 0–1) – 46 pts and high-risk (scores 2–8) – 29. Using a cox regression model, the low risk group had a survival advantage (HR = 2.55, P = 0.04). Using Kaplan Meier survival estimate comparing the low risk and high risk group (figure1), the 5 yrs overall survival were 77% & 22% respectively (P = 0.04). While the median survival for the high risk group was 3.52 years, it has not been reached for the low risk group. Conclusion: Here, we have demonstrated a survival benefit for pts with low (0–1) compared with high (≥ 2) HCT-CI score. In addition, the outcome of pts with high HCT-CI score was also similar to non-transplant pts as published in the literature. This raises the question of “benefit” of ASCT for pts with high HCT-CI score. Thus, HCT-CI may serve as a useful tool to select pts whom would benefit most from ASCT. Figure 1 Figure 1

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Five-mRNA Signature for the Prognosis of Breast Cancer Based on the ceRNA Network

BioMed Research International ◽

10.1155/2020/9081852 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17

Author(s):

Wenjie Shi ◽

Daojun Hu ◽

Sen Lin ◽

Rui Zhuo

Keyword(s):

Breast Cancer ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Differentially Expressed ◽

Risk Scores ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Protein Levels ◽

Cerna Network

Background. The purpose of this study was to investigate the regulatory mechanisms of ceRNAs in breast cancer (BC) and construct a new five-mRNA prognostic signature. Methods. The ceRNA network was constructed by different RNAs screened by the edgeR package. The BC prognostic signature was built based on the Cox regression analysis. The log-rank method was used to analyse the survival rate of BC patients with different risk scores. The expression of the 5 genes was verified by the GSE81540 dataset and CPTAC database. Results. A total of 41 BC-adjacent tissues and 473 BC tissues were included in this study. A total of 2,966 differentially expressed lncRNAs, 5,370 differentially expressed mRNAs, and 359 differentially expressed miRNAs were screened. The ceRNA network was constructed using 13 lncRNAs, 267 mRNAs, and 35 miRNAs. Kaplan-Meier (K-M) methods showed that two lncRNAs (AC037487.1 and MIR22HG) are related to prognosis. Five mRNAs (VPS28, COL17A1, HSF1, PUF60, and SMOC1) in the ceRNA network were used to establish a prognostic signature. Survival analysis showed that the prognosis of patients in the low-risk group was significantly better than that in the high-risk group (p=0.0022). ROC analysis showed that this signature has a good diagnostic ability (AUC=0.77). Compared with clinical features, this signature was also an independent prognostic factor (HR: 1.206, 95% CI 1.108−1.311; p<0.001). External verification results showed that the expression of the 5 mRNAs differed between the normal and tumour groups at the chip and protein levels (p<0.001). Conclusions. These ceRNAs may play a key role in the development of BC, and the new 5-mRNA prognostic signature can improve the prediction of survival for BC patients.

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A Risk Signature of Nine Autophagy-Related Genes Associated With Immune Microenvironment and Clinical Prognosis in Wilms Tumor

10.21203/rs.3.rs-892534/v1 ◽

2021 ◽

Author(s):

Shaopei Ye ◽

Wenbin Tang ◽

Ke Huang

Keyword(s):

Regression Analysis ◽

High Risk ◽

Risk Score ◽

Cox Regression ◽

Wilms Tumor ◽

Risk Group ◽

High Risk Group ◽

Differentially Expressed ◽

Clinical Prognosis ◽

Cox Regression Analysis

Abstract Background: Autophagy is a biological process to eliminate dysfunctional organelles, aggregates or even long-lived proteins. . Nevertheless, the potential function and prognostic values of autophagy in Wilms Tumor (WT) are complex and remain to be clarifed. Therefore, we proposed to systematically examine the roles of autophagy-associated genes (ARGs) in WT.Methods: Here, we obtained differentially expressed autophagy-related genes (ARGs) between healthy and Wilms tumor from Therapeutically Applicable Research To Generate Effective Treatments(TARGET) and The Cancer Genome Atlas (TCGA) database. The functionalities of the differentially expressed ARGs were analyzed using Gene Ontology. Then univariate COX regression analysis and multivariate COX regression analysis were performed to acquire nine autophagy genes related to WT patients’ survival. According to the risk score, the patients were divided into high-risk and low-risk groups. The Kaplan-Meier curve demonstrated that patients with a high-risk score tend to have a poor prognosis.Results: Eighteen DEARGs were identifed, and nine ARGs were fnally utilized to establish the FAGs based signature in the TCGA cohort. we found that patients in the high-risk group were associated with mutations in TP53. We further conducted CIBERSORT analysis, and found that the infiltration of Macrophage M1 was increased in the high-risk group. Finally, the expression levels of crucial ARGs were verifed by the experiment, which were consistent with our bioinformatics analysis.Conclusions: we emphasized the clinical significance of autophagy in WT, established a prediction system based on autophagy, and identified a promising therapeutic target of autophagy for WT.

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The Frequency of Genetic Anomalies According to Clonal Plasma Cells' Phenotype in Patients with Newly Diagnosed (ND) Multiple Myeloma (MM)

Blood ◽

10.1182/blood.v126.23.5316.5316 ◽

2015 ◽

Vol 126 (23) ◽

pp. 5316-5316

Author(s):

Andrei Garifullin ◽

Irina Martynkevich ◽

Sergei Voloshin ◽

Alexei Kuvshinov ◽

Ludmila Martynenko ◽

...

Keyword(s):

Multiple Myeloma ◽

Bone Marrow ◽

Plasma Cells ◽

Conflicts Of Interest ◽

Risk Group ◽

Risk Groups ◽

Fish Analysis ◽

Newly Diagnosed ◽

Definition Of ◽

Standard Risk

Abstract Background. Genetic anomalies (GA) are primary link of pathogenesis in MM. GA lead to formation of clonal plasma cells, which has different phenotype. Aim. To estimate the incidence of GA and their correlation with clonal plasma cells' phenotype in patients with ND MM. Methods. We analysed 22 patients with ND MM (median age 57 years, range 38-80; male/female - 1:1.75). Cytogenetic analysis was performed on bone marrow samples using standard GTG-method. Metaphase FISH analysis was performed according to the manufacturer's protocol using DNA probes: LSI 13(RB1)13q14, IGH/CCND1, IGH/FGFR3, LSI TP53 (17q13.1). 8-color immunophenotypic by flow cytometry using antibody to CD45, CD38, CD138, CD56, CD19, CD20, CD27 and CD117 antigenes. Results. Translocation t(11;14) was detected in 3/14 (21.4%) patients, del(13q) - 2/14 (14.3%), t(11;14) - 3/14 (21.4%), hypodyploidy - 1/20 (5%), del(17р) - 0% patients. Clonal plasma cells' phenotype CD38+CD138+CD45- was detected in 100%. Expression CD56+ was revealed in 11/22 (50%) patients, CD19+ in 9/22 (40.9%), CD117+ in 5/22 (22.7%), CD20+ in 1/22 (4.5%), CD27+ in 1/22 (4.5%). The frequency of GA didn't depend on clonal plasma cells' phenotype and was 27.3%(3/11) in CD56+ phenotype, 23.8%(5/21) - CD20-, 23.8%(5/21) - CD27-, 23.5%(4/17) - CD117-, 23%(3/13) - CD19-, 22.2%(2/9) - CD19+, 20%(1/5) - CD117+, 18.2%(2/11) - CD56-, 0%(0/1) - CD20+, 0%(0/1) - in CD27+ phenotype. Patients of standard risk group according to mSMART 2.0 with GA had CD19-negative plasma cells' phenotype vs. CD19-positive phenotype in patients of intermediate and high-risk groups (p<0.05). 3-years overall survival in standard risk group with CD19- phenotype was 92,3%, CD19+ - 77,7% (p>0.05). Conclusion . Identification of GA, which has adverse forecast, correlates with CD19+ plasma cells phenotype. The combined definition of plasma cells phenotype and GA can improve the system of risk stratification in MM. Disclosures No relevant conflicts of interest to declare.

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Identification of a Novel Glycolysis-Related Signature to Predict the Prognosis of Patients with Breast Cancer

10.21203/rs.3.rs-720991/v1 ◽

2021 ◽

Author(s):

Menglin He ◽

Cheng Hu ◽

Jian Deng ◽

Hui Ji ◽

Weiqian Tian

Keyword(s):

Breast Cancer ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

High Risk Group ◽

Low Risk ◽

Cox Regression Analysis ◽

Expression Levels ◽

Incidence And Mortality

Abstract Background: Breast cancer (BC) is a kind of cancer with high incidence and mortality in female. Conventional clinical characteristics are far from accurate to predict individual outcomes. Therefore, we aimed to develop a novel signature to predict the survival of patients with BC. Methods: We analyzed the data of a training cohort from the TCGA database and a validation cohort from GEO database. After the applications of GSEA and Cox regression analyses, a glycolysis-related signature for predicting the survival of patients with BC was developed. The signature contains AK3, CACNA1H, IL13RA1, NUP43, PGK1, and SDC1. Then, we constructed a risk score formula to classify the patients into high and low-risk groups based on the expression levels of six-gene in patients. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to assess the predicted capacity of the model. Next, a nomogram was developed to predict the outcomes of patients with risk score and clinical features in 1, 3, and 5 years. We further used Human Protein Atlas (HPA) database to validate the expressions of the six biomarkers in tumor and sample tissues.Results: We constructed a six-gene signature to predict the outcomes of patients with BC. The patients in high-risk group showed poor prognosis than that in low-risk group. The AUC values were 0.719 and 0.702, showing that the prediction performance of the signature is acceptable. Additionally, Cox regression analysis revealed that these biomarkers could independently predict the prognosis of BC patients without being affected by clinical factors. The expression levels of all six biomarkers in BC tissues were higher than that in normal tissues except AK3. Conclusion: We developed a six-gene signature to predict the prognosis of patients with BC. Our signature has been proved to have the ability to make an accurate and obvious prediction and might be used to expand the prediction methods in clinical.

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Discovery and validation of immune- related long non-coding RNA biomarkers associated with prognosis in hepatocellular carcinoma

10.21203/rs.3.rs-17851/v1 ◽

2020 ◽

Author(s):

Li Liu ◽

She Tian ◽

Zhu Li ◽

Yongjun Gong ◽

Hao Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Survival Curve ◽

Principal Component ◽

High Risk Group ◽

Low Risk ◽

Cox Regression Analysis ◽

Immune Related Genes

Abstract Background : Hepatocellular carcinoma (HCC) is one of the most common clinical malignant tumors, resulting in high mortality and poor prognosis. Studies have found that LncRNA plays an important role in the onset, metastasis and recurrence of hepatocellular carcinoma. The immune system plays a vital role in the development, progression, metastasis and recurrence of cancer. Therefore, immune-related lncRNA can be used as a novel biomarker to predict the prognosis of hepatocellular carcinoma. Methods : The transcriptome data and clinical data of HCC patients were obtained by using The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA‑LIHC), and immune-related genes were extracted from the Molecular Signatures Database (IMMUNE RESPONSE M19817 and IMMUNE SYSTEM PROCESS M13664). By constructing the co-expression network and Cox regression analysis, 13 immune-lncRNAs was identified to predict the prognosis of HCC patients. Patients were divided into high risk group and low risk group by using the risk score formula, and the difference in overall survival (OS) between the two groups was reflected by Kaplan-Meier survival curve. The time - dependent receiver operating characteristics (ROC) analysis and principal component analysis (PCA) were used to evaluate 13 immune -lncRNAs signature. Results : Through TCGA - LIHC extracted from 343 cases of patients with hepatocellular carcinoma RNA - Seq data and clinical data, 331 immune-related genes were extracted from the Molecular Signatures Database , co-expression networks and Cox regression analysis were constructed, 13 immune-lncRNAs signature was identified as biomarkers to predict the prognosis of patients. At the same time using the risk score median divided the patients into high risk and low risk groups, and through the Kaplan-Meier survival curve analysis found that high-risk group of patients' overall survival (OS) less low risk group of patients. The AUC value of the ROC curve is 0.828, and principal component analysis (PCA) results showed that patients could be clearly divided into two parts by immune-lncRNAs, which provided evidence for the use of 13 immune-lncRNAs signature as prognostic markers. Conclusion : Our study identified 13 immune-lncRNAs signature that can effectively predict the prognosis of HCC patients, which may be a new prognostic indicator for predicting clinical outcomes.

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Identification of a Five-Autophagy-Related-lncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2020.611626 ◽

2021 ◽

Vol 7 ◽

Author(s):

Xiaoyu Deng ◽

Qinghua Bi ◽

Shihan Chen ◽

Xianhua Chen ◽

Shuhui Li ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Signature ◽

Prediction Ability ◽

Cox Regression Analysis

Although great progresses have been made in the diagnosis and treatment of hepatocellular carcinoma (HCC), its prognostic marker remains controversial. In this current study, weighted correlation network analysis and Cox regression analysis showed significant prognostic value of five autophagy-related long non-coding RNAs (AR-lncRNAs) (including TMCC1-AS1, PLBD1-AS1, MKLN1-AS, LINC01063, and CYTOR) for HCC patients from data in The Cancer Genome Atlas. By using them, we constructed a five-AR-lncRNA prognostic signature, which accurately distinguished the high- and low-risk groups of HCC patients. All of the five AR lncRNAs were highly expressed in the high-risk group of HCC patients. This five-AR-lncRNA prognostic signature showed good area under the curve (AUC) value (AUC = 0.751) for the overall survival (OS) prediction in either all HCC patients or HCC patients stratified according to several clinical traits. A prognostic nomogram with this five-AR-lncRNA signature predicted the 3- and 5-year OS outcomes of HCC patients intuitively and accurately (concordance index = 0.745). By parallel comparison, this five-AR-lncRNA signature has better prognosis accuracy than the other three recently published signatures. Furthermore, we discovered the prediction ability of the signature on therapeutic outcomes of HCC patients, including chemotherapy and immunotherapeutic responses. Gene set enrichment analysis and gene mutation analysis revealed that dysregulated cell cycle pathway, purine metabolism, and TP53 mutation may play an important role in determining the OS outcomes of HCC patients in the high-risk group. Collectively, our study suggests a new five-AR-lncRNA prognostic signature for HCC patients.

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