scholarly journals An Immune-Related Long Noncoding RNA Signature as a Prognostic Biomarker for Human Endometrial Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ziwei Wang ◽  
Yan Liu ◽  
Jun Zhang ◽  
Rong Zhao ◽  
Xing Zhou ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
High Risk ◽  
Noncoding Rna ◽  
Prognostic Model ◽  
Risk Group ◽  
Risk Groups ◽  
Figo Stage ◽  
Low Risk ◽  
Pathological Grade ◽  
Immune Related Genes

Background. Endometrial cancer is among the most common malignant tumors threatening the health of women. Recently, immunity and long noncoding RNA (lncRNA) have been widely examined in oncology and shown to play important roles in oncology. Here, we searched for immune-related lncRNAs as prognostic biomarkers to predict the outcome of patients with endometrial cancer. Methods. RNA sequencing data for 575 endometrial cancer samples and immune-related genes were downloaded from The Cancer Genome Atlas (TCGA) database and gene set enrichment analysis (GSEA) gene sets, respectively. Immune-related lncRNAs showing a coexpression relationship with immune-related genes were obtained, and Cox regression analysis was performed to construct the prognostic model. Survival, independent prognostic, and clinical correlation analyses were performed to evaluate the prognostic model. Immune infiltration of endometrial cancer samples was also evaluated. Functional annotation of 12 immune-related lncRNAs was performed using GSEA software. Prognostic nomogram and survival analysis for independent prognostic risk factors were performed to evaluate the prognostic model and calculate the survival time based on the prognostic model. Results. Twelve immune-related lncRNAs (ELN-AS1, AC103563.7, PCAT19, AF131215.5, LINC01871, AC084117.1, NRAV, SCARNA9, AL049539.1, POC1B-AS1, AC108134.4, and AC019080.5) were obtained, and a prognostic model was constructed. The survival rate in the high-risk group was significantly lower than that in the low-risk group. Patient age, pathological grade, the International Federation of Gynecology and Obstetrics (FIGO) stage, and risk status were the risk factors. The 12 immune-related lncRNAs correlated with patient age, pathological grade, and FIGO stage. Principal component analysis and functional annotation showed that the high-risk and low-risk groups separated better, and the immune status of the high-risk and low-risk groups differed. Nomogram and receiver operating characteristic (ROC) curves effectively predicted the prognosis of endometrial cancer. Additionally, age, pathological grade, FIGO stage, and risk status were all related to patient survival. Conclusion. We identified 12 immune-related lncRNAs affecting the prognosis of endometrial cancer, which may be useful as therapeutic targets and molecular biomarkers.

scholarly journals An Integrated Autophagy-Related Long Noncoding RNA Signature as a Prognostic Biomarker for Human Endometrial Cancer: A Bioinformatics-Based Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Ziwei Wang ◽  
Jun Zhang ◽  
Yan Liu ◽  
Rong Zhao ◽  
Xing Zhou ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Survival Rate ◽  
High Risk ◽  
Noncoding Rna ◽  
Risk Group ◽  
Figo Stage ◽  
High Risk Group ◽  
Low Risk ◽  
Pathological Grade ◽  
Risk Status

Endometrial cancer is one of the most common malignant tumors, lowering the quality of life among women worldwide. Autophagy plays dual roles in these malignancies. To search for prognostic markers for endometrial cancer, we mined The Cancer Genome Atlas and the Human Autophagy Database for information on endometrial cancer and autophagy-related genes and identified five autophagy-related long noncoding RNAs (lncRNAs) (LINC01871, SCARNA9, SOS1-IT1, AL161618.1, and FIRRE). Based on these autophagy-related lncRNAs, samples were divided into high-risk and low-risk groups. Survival analysis showed that the survival rate of the high-risk group was significantly lower than that of the low-risk group. Univariate and multivariate independent prognostic analyses showed that patients’ age, pathological grade, and FIGO stage were all risk factors for poor prognosis. A clinical correlation analysis of the relationship between the five autophagy-related lncRNAs and patients’ age, pathological grade, and FIGO stage was also per https://orcid.org/0000-0001-7090-1750 formed. Histopathological assessment of the tumor microenvironment showed that the ESTIMATE, immune, and stromal scores in the high-risk group were lower than those in the low-risk group. Principal component analysis and functional annotation were performed to confirm the correlations. To further evaluate the effect of the model constructed on prognosis, samples were divided into training (60%) and validation (40%) groups, regarding the risk status as an independent prognostic risk factor. A prognostic nomogram was constructed using patients’ age, pathological grade, FIGO stage, and risk status to estimate the patients’ survival rate. C-index and multi-index ROC curves were generated to verify the stability and accuracy of the nomogram. From this analysis, we concluded that the five lncRNAs identified in this study could affect the incidence and development of endometrial cancer by regulating the autophagy process. Therefore, these molecules may have the potential to serve as novel therapeutic targets and biomarkers.

scholarly journals A prognostic model based on seven immune-related genes predicts the overall survival of patients with hepatocellular carcinoma

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Qian Yan ◽  
Wenjiang Zheng ◽  
Boqing Wang ◽  
Baoqian Ye ◽  
Huiyan Luo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Immune Cells ◽  
Prognostic Model ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Performance ◽  
Immune Related Genes

Abstract Background Hepatocellular carcinoma (HCC) is a disease with a high incidence and a poor prognosis. Growing amounts of evidence have shown that the immune system plays a critical role in the biological processes of HCC such as progression, recurrence, and metastasis, and some have discussed using it as a weapon against a variety of cancers. However, the impact of immune-related genes (IRGs) on the prognosis of HCC remains unclear. Methods Based on The Cancer Gene Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort) datasets, we integrated the ribonucleic acid (RNA) sequencing profiles of 424 HCC patients with IRGs to calculate immune-related differentially expressed genes (DEGs). Survival analysis was used to establish a prognostic model of survival- and immune-related DEGs. Based on genomic and clinicopathological data, we constructed a nomogram to predict the prognosis of HCC patients. Gene set enrichment analysis further clarified the signalling pathways of the high-risk and low-risk groups constructed based on the IRGs in HCC. Next, we evaluated the correlation between the risk score and the infiltration of immune cells, and finally, we validated the prognostic performance of this model in the GSE14520 dataset. Results A total of 100 immune-related DEGs were significantly associated with the clinical outcomes of patients with HCC. We performed univariate and multivariate least absolute shrinkage and selection operator (Lasso) regression analyses on these genes to construct a prognostic model of seven IRGs (Fatty Acid Binding Protein 6 (FABP6), Microtubule-Associated Protein Tau (MAPT), Baculoviral IAP Repeat Containing 5 (BIRC5), Plexin-A1 (PLXNA1), Secreted Phosphoprotein 1 (SPP1), Stanniocalcin 2 (STC2) and Chondroitin Sulfate Proteoglycan 5 (CSPG5)), which showed better prognostic performance than the tumour/node/metastasis (TNM) staging system. Moreover, we constructed a regulatory network related to transcription factors (TFs) that further unravelled the regulatory mechanisms of these genes. According to the median value of the risk score, the entire TCGA cohort was divided into high-risk and low-risk groups, and the low-risk group had a better overall survival (OS) rate. To predict the OS rate of HCC, we established a gene- and clinical factor-related nomogram. The receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curve showed that this model had moderate accuracy. The correlation analysis between the risk score and the infiltration of six common types of immune cells showed that the model could reflect the state of the immune microenvironment in HCC tumours. Conclusion Our IRG prognostic model was shown to have value in the monitoring, treatment, and prognostic assessment of HCC patients and could be used as a survival prediction tool in the near future.

scholarly journals Development and Validation of a Novel Hypoxia-Related Long Noncoding RNA Model With Regard to Prognosis and Immune Features in Breast Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Peng Gu ◽  
Lei Zhang ◽  
Ruitao Wang ◽  
Wentao Ding ◽  
Wei Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis

Background: Female breast cancer is currently the most frequently diagnosed cancer in the world. This study aimed to develop and validate a novel hypoxia-related long noncoding RNA (HRL) prognostic model for predicting the overall survival (OS) of patients with breast cancer.Methods: The gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 200 hypoxia-related mRNAs were obtained from the Molecular Signatures Database. The co-expression analysis between differentially expressed hypoxia-related mRNAs and lncRNAs based on Spearman’s rank correlation was performed to screen out 166 HRLs. Based on univariate Cox regression and least absolute shrinkage and selection operator Cox regression analysis in the training set, we filtered out 12 optimal prognostic hypoxia-related lncRNAs (PHRLs) to develop a prognostic model. Kaplan–Meier survival analysis, receiver operating characteristic curves, area under the curve, and univariate and multivariate Cox regression analyses were used to test the predictive ability of the risk model in the training, testing, and total sets.Results: A 12-HRL prognostic model was developed to predict the survival outcome of patients with breast cancer. Patients in the high-risk group had significantly shorter median OS, DFS (disease-free survival), and predicted lower chemosensitivity (paclitaxel, docetaxel) compared with those in the low-risk group. Also, the risk score based on the expression of the 12 HRLs acted as an independent prognostic factor. The immune cell infiltration analysis revealed that the immune scores of patients in the high-risk group were lower than those of the patients in the low-risk group. RT-qPCR assays were conducted to verify the expression of the 12 PHRLs in breast cancer tissues and cell lines.Conclusion: Our study uncovered dozens of potential prognostic biomarkers and therapeutic targets related to the hypoxia signaling pathway in breast cancer.

scholarly journals A Prognostic Model Based on Immune-Related Long Non-Coding RNAs for Patients With Cervical Cancer

2020 ◽  
Vol 11 ◽  
Author(s):  
Peijie Chen ◽  
Yuting Gao ◽  
Si Ouyang ◽  
Li Wei ◽  
Min Zhou ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cancer Patients ◽  
Prognostic Model ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Training Set ◽  
Non Coding Rnas ◽  
Validation Set ◽  
Immune Related Genes

Objectives: The study is performed to analyze the relationship between immune-related long non-coding RNAs (lncRNAs) and the prognosis of cervical cancer patients. We constructed a prognostic model and explored the immune characteristics of different risk groups.Methods: We downloaded the gene expression profiles and clinical data of 227 patients from The Cancer Genome Atlas database and extracted immune-related lncRNAs. Cox regression analysis was used to pick out the predictive lncRNAs. The risk score of each patient was calculated based on the expression level of lncRNAs and regression coefficient (β), and a prognostic model was constructed. The overall survival (OS) of different risk groups was analyzed and compared by the Kaplan–Meier method. To analyze the distribution of immune-related genes in each group, principal component analysis and Gene set enrichment analysis were carried out. Estimation of STromal and Immune cells in MAlignant Tumors using Expression data was performed to explore the immune microenvironment.Results: Patients were divided into training set and validation set. Five immune-related lncRNAs (H1FX-AS1, AL441992.1, USP30-AS1, AP001527.2, and AL031123.2) were selected for the construction of the prognostic model. Patients in the training set were divided into high-risk group with longer OS and low-risk group with shorter OS (p = 0.004); meanwhile, similar result were found in validation set (p = 0.013), combination set (p < 0.001) and patients with different tumor stages. This model was further confirmed in 56 cervical cancer tissues by Q-PCR. The distribution of immune-related genes was significantly different in each group. In addition, the immune score and the programmed death-ligand 1 expression of the low-risk group was higher.Conclusions: The prognostic model based on immune-related lncRNAs could predict the prognosis and immune status of cervical cancer patients which is conducive to clinical prognosis judgment and individual treatment.

scholarly journals Prognostic Value of Immune-Related genes in the Tumor Microenvironment of Glioma - Analyzed by Integrated Bioinformatics

2021 ◽  
Author(s):  
Yali Zhong ◽  
Xiaobin Luo ◽  
Fubing Yang ◽  
Xinling Song
Keyword(s):  
T Cells ◽  
High Risk ◽  
Risk Prediction ◽  
Immune Cell ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Lasso Regression ◽  
Immune Related Genes

Abstract Object: Immune related genes play an important role in the process of tumor genesis and development. Therefore, we aim to find the Immune genes which are related to the prognosis of glioma patients, and to explore the infiltration of Immune cells in glioma microenvironment. Methods We downloaded the data of the glioma samples from the CGGA database, and performed batch correction to screen the primary glioma samples for subsequent analysis. Then the ESTIMATE algorithm was used to deal with the Stromal scores and Immune scores of the primary glioma samples, and the difference was analyzed. Then the common Immune related genes (IRGs) were obtained by intersecting with the Immune genes in the ImmPort database. Moreover, we used common IRGs to construct protein-protein interaction (PPI) networks, from which we screened the top 30 genes with high connectivity, and Lasso regression was used to screen the IRGs. Lastly, we obtained the combined genes, which were overlapped both in the top 30 high-connection genes and Lasso regression genes. The final genes were used to construct COX risk prediction models. The accuracy of the model were verified by the TCGA glioma data, and the model genes were analyzed for Immune-related pathways, as well as the Hallmark and KEGG enrichment. Additionally, we used CIBERSOFT algorithm to estimate the Immune cell content of the samples, and analyzed the differences, correlations and survival of the Immune cells in high and low risk groups. Results Firstly, a total of 117 IRGs were obtained from the gene sets, which were overlapped in the data of Stromal score, Immune score and ImmPort database. Secondly, the top 30 genes were selected after the PPI network, and another 26 genes were screened out after the Lasso regression algorithm. And then, six coexist IRGs were obtained from the intersecting sets. Furthermore, the COX risk prediction model was constructed and tested, showing that the overall survival rate of the high-risk group was about 50% of that of the low-risk group. We observed that the high-risk group were enriched in Immune response and Immune process. Most importantly, in KEGG pathways, the high-risk groups were mainly enriched in p53 signaling pathway, JAK-STAT signaling pathway, pathways in cancer and cell cycle. By estimating the Immune cell contents, we also found that the Immune cell Plasma cells, T cells CD8, T cells CD4 naïve, T cells regulatory (Tregs), Macrophages M0 and Neutrophils were higher in high-risk groups, when compared to the low-risk group, with significant difference. Finally, the correlation analysis showed that the degree of Immune infiltration in high-risk groups was related to T cells regulatory (Tregs), Macrophages M0 and Neutrophils. Conclusion A COX risk prediction model of 6 genes was successfully constructed, which was enriched in Immune-related pathways. Meanwhile, survival analysis and TCGA data validation revealed significant differences in the model genes in the overall survival of the glioma patients, and the degree of Immune infiltration in the model was associated with T cells regulatory (Tregs), Macrophages M0 and Neutrophils.

scholarly journals An Immune-Related Long Noncoding RNA Signature as a Prognostic Biomarker for Human Endometrial Cancer

2020 ◽  
Author(s):  
Ziwei Wang ◽  
Yan Liu ◽  
Jun Zhang ◽  
Rong Zhao ◽  
Xing Zhou ◽  
...  
Keyword(s):  
Risk Factors ◽  
Endometrial Cancer ◽  
Survival Analysis ◽  
Survival Rate ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Risk Group ◽  
Figo Stage ◽  
Pathological Grade ◽  
Risk Status

Abstract Endometrial cancer is one of the most common malignant tumors threatening women's health. Recently, immunity and long noncoding RNA (lncRNA) have become hot topics in oncology. Here, to search for prognostic biomarkers, immune-related lncRNAs were identified by collecting endometrial cancer samples’ information from The Cancer Genome Atlas (TCGA) database, and immune-related genes from Gene set enrichment analysis (GSEA) gene sets. These included ELN-AS1, AC103563.7, PCAT19, AF131215.5, LINC01871, AC084127.1, NRAV, SCARNA9. AL049539.1, POC1B-AS1, AC108134.4, and AC019080.5. Models based on these 12 immune-related lncRNAs were constructed. Survival analysis showed that the survival rate in the high-risk group was significantly lower than in the low-risk group. Independent prognostic analysis results showed that the patient's age, pathological grade, FIGO stage, and risk status were risk factors. Clinical correlation analysis showed that the 12 immune-related lncRNAs correlated with patients’ age, pathological grade, and FIGO stage. After principal component analysis and functional annotation, to further determine the effects of these lncRNAs on prognosis, samples were divided into training and validation groups. Multivariate Cox regression analysis of the training group showed that the patient's age, FIGO stage, and risk status were prognostic risk factors. A nomogram constructed with the risk factors was used to estimate the patient's survival rate. C-indexes were calculated and multi-index ROC curves were plotted to evaluate the accuracy and stability of the nomogram. Finally, Kaplan-Meier survival analysis showed that age, pathological grade, FIGO stage, and risk status were all related to patients’ survival. In summary, we identified 12 immune-related lncRNAs that affect the prognosis of endometrial cancer, and that may act as therapeutic targets and molecular biomarkers for the disease.

Nine glycolysis-related gene signature predicting the survival of patients with endometrial adenocarcinoma

2020 ◽  
Author(s):  
liu jinhui ◽  
Li siyue ◽  
Gao feng ◽  
meng huangyang ◽  
Nie sipei ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
High Risk ◽  
Risk Score ◽  
Prognostic Model ◽  
Risk Group ◽  
Gene Signature ◽  
Low Risk ◽  
Cellular Respiration ◽  
Immunohistochemistry Staining ◽  
Glycolysis Pathway

Abstract Background: Endometrial cancer is the fourth most common cancer in women. The death rate for endometrial cancer has increased. Glycolysis of cellular respiration is a complex reaction and is the first step in most carbohydrate catabolism, which was proved to participate in tumors. Methods: We analyzed the sample data of over 500 patients from TCGA database. The bioinformatic analysis included GSEA, cox and lasso regression analysis to select prognostic genes, as well as construction of a prognostic model and a nomogram for OS evaluation. The immunohistochemistry staining, survival analysis and expression level validation were also performed. Maftools package was for mutation analysis. GSEA identified Glycolysis was the most related pathway to EC. Results: According to the prognostic model using the train set, 9 glycolysis-related genes including B3GALT6, PAM, LCT, GMPPB, GLCE, DCN, CAPN5, GYS2 and FBP2 were identified as prognosis-related genes. Based on nine gene signature, the EC patients could be classified into high and low risk subgroups, and patients with high risk score showed shorter survival time. Time-dependent ROC analysis and Cox regression suggested that the risk score predicted EC prognosis accurately and independently. Analysis of test and train sets yielded consistent results A nomogram which incorporated the 9‐mRNA signature and clinical features was also built for prognostic prediction. Immunohistochemistry staining and TCGA validation showed that expression levels of these genes do differ between EC and normal tissue samples. GSEA revealed that the samples of the low-risk group were mainly concentrated on Bile Acid Metabolism. Patients in the low-risk group displayed obvious mutation signatures compared with those in the high-risk group. Conclusion: This study found that the Glycolysis pathway is associated with EC and screened for hub genes on the Glycolysis pathway, which may serve as new target for the treatment of EC.

scholarly journals 55例血管免疫母细胞性T细胞淋巴瘤的临床特征和预后模型

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 37-38
Author(s):  
Xiaohong Tan ◽  
Jie Sun ◽  
Sha He ◽  
Chao Rong ◽  
Hong Cen
Keyword(s):  
High Risk ◽  
Prognostic Model ◽  
Risk Group ◽  
Prognostic Index ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Intermediate Risk ◽  
Ecog Ps ◽  
Medical University

Angioimmunoblastic T-cell lymphoma (AITL) is a distinct subtype of peripheral T-cell lymphoma with unique clinical and pathological features. This study aim to analyze the characteristics of AITL and to design a prognostic model specifically for AITL, providing risk stratification in affected patients. We retrospectively analyzed 55 newly diagnosed AITL patients at the Affiliated Tumor Hospital of Guangxi Medical University from January 2007 to June 2016 and was permitted by the Ethics Committee of the Affiliated Tumor Hospital of Guangxi Medical University. Among these patients, the median age at diagnosis was 61 (27-85) and 54.55% (30/55) of the patients were older than 60 years. 43 patients were male, accounting for 78.18% of the whole. Among these, 92.73% (51/55) of the diagnoses were estimated at advanced stage. A total of 20 (36.36%) patients were scored >1 by the ECOG performance status. Systemic B symptoms were described in 16 (29.09%) patients. In nearly half of the patients (27/55; 49.09%) had extranodal involved sites. The most common extranodal site involved was BM (11/55; 20.00%). 38.18% (21/55) and 27.27% (15/55) patients had fever with body temperature ≥37.4℃ and pneumonia, respectively. 40% (22/55) patients had cavity effusion or edema. Laboratory investigations showed the presence of anemia (hemoglobin <120 g/L) in 60% (33/55), thrombocytopenia (platelet counts <150×109/L) in 29.09% (16/55), and elevated serum LDH level in 85.45% (47/55) of patients. Serum C-reactive protein and β2-microglobulin levels were found to be elevated in 60.98% (25/41) and 75.00% (36/48)of the patients, respectively. All patients had complete information for stratification into 4 risk subgroups by IPI score, in which scores of 0-1 point were low risk (9/55;16.36%), two points were low-intermediate risk (17/55; 30.92%), three points were high-intermediate risk (20/55; 36.36%), and four to five points were high risk (9/55; 16.36%). 55 patients were stratified by PIT score with 7.27% (4/55) of patients classified as low risk, 32.73% (18/55) as low-intermediate risk, 34.55% (19/55) as high-intermediate risk, and 25.45% (14/55) as high risk depending on the numbers of adverse prognostic factors.The estimated two-year and five-year overall survival (OS) rate for all patients were 50.50% and 21.70%. Univariate analysis suggested that ECOG PS (p= 0.000), Systemic B symptoms (p= 0.006), fever with body temperature ≥ 37.4℃ (p= 0.000), pneumonia (p= 0.001), cavity effusion or edema (p= 0.000), anemia (p= 0.013), and serum LDH (p= 0.007) might be prognostic factors (p< 0.05) for OS. Multivariate analysis found prognostic factors for OS were ECOG PS (p= 0.026), pneumonia (p= 0.045), and cavity effusion or edema(p= 0.003). We categorized three risk groups: low-risk group, no adverse factor; intermediate-risk group, one factor; and high-risk group, two or three factors. Five-year OS was 41.8% for low-risk group, 15.2% for intermediate-risk group, and 0.0% for high-risk group (p< 0.000). Patients with AITL had a poor outcome. This novel prognostic model balanced the distribution of patients into different risk groups with better predictive discrimination as compared to the International Prognostic Index and Prognostic Index for PTCL. Disclosures No relevant conflicts of interest to declare.

scholarly journals Model based on five tumour immune microenvironment-related genes for predicting hepatocellular carcinoma immunotherapy outcomes

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Xinyu Gu ◽  
Jun Guan ◽  
Jia Xu ◽  
Qiuxian Zheng ◽  
Chao Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Prognostic Model ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Low Risk ◽  
Immune Microenvironment ◽  
Kaplan Meier ◽  
Immune Related Genes

Abstract Background Although the tumour immune microenvironment is known to significantly influence immunotherapy outcomes, its association with changes in gene expression patterns in hepatocellular carcinoma (HCC) during immunotherapy and its effect on prognosis have not been clarified. Methods A total of 365 HCC samples from The Cancer Genome Atlas liver hepatocellular carcinoma (TCGA-LIHC) dataset were stratified into training datasets and verification datasets. In the training datasets, immune-related genes were analysed through univariate Cox regression analyses and least absolute shrinkage and selection operator (LASSO)-Cox analyses to build a prognostic model. The TCGA-LIHC, GSE14520, and Imvigor210 cohorts were subjected to time-dependent receiver operating characteristic (ROC) and Kaplan–Meier survival curve analyses to verify the reliability of the developed model. Finally, single-sample gene set enrichment analysis (ssGSEA) was used to study the underlying molecular mechanisms. Results Five immune-related genes (LDHA, PPAT, BFSP1, NR0B1, and PFKFB4) were identified and used to establish the prognostic model for patient response to HCC treatment. ROC curve analysis of the TCGA (training and validation sets) and GSE14520 cohorts confirmed the predictive ability of the five-gene-based model (AUC > 0.6). In addition, ROC and Kaplan–Meier analyses indicated that the model could stratify patients into a low-risk and a high-risk group, wherein the high-risk group exhibited worse prognosis and was less sensitive to immunotherapy than the low-risk group. Functional enrichment analysis predicted potential associations of the five genes with several metabolic processes and oncological signatures. Conclusions We established a novel five-gene-based prognostic model based on the tumour immune microenvironment that can predict immunotherapy efficacy in HCC patients.

scholarly journals Construction of a five-gene prognostic model based on immune-related genes for the prediction of survival in pancreatic cancer

2021 ◽  
Author(s):  
Bo Liu ◽  
Tingting Fu ◽  
Ping He ◽  
Chenyou Du ◽  
Ke Xu
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
High Risk ◽  
Prognostic Model ◽  
Risk Group ◽  
Group Versus ◽  
Low Risk ◽  
Risk Patients ◽  
Validation Set ◽  
Immune Related Genes

Purpose: To identify differentially expressed immune-related genes (DEIRGs) and construct a model with survival-related DEIRGs for evaluating the prognosis of patients with pancreatic cancer (PC). Methods: Six microarray gene expression datasets of PC from the Gene Expression Omnibus (GEO) and ImmPort were used to identify DEIRGs. RNA sequencing and clinical data from The Cancer Genome Atlas Program-Pancreatic Adenocarcinoma (TCGA-PAAD) database were used to establish the prognostic model. Univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were applied to determine the final variables of the prognostic model. The median risk score was used as the cut-off value to classify samples into low- and high-risk groups. The prognostic model was further validated using an internal validation set of TCGA and an external validation set of GSE62452. Results: In total, 142 DEIRGs were identified from six GEO datasets, 47 were survival-related DEIRGs. A prognostic model comprising five genes (i.e., ERAP2, CXCL9, AREG, DKK1, and IL20RB) was established. High-risk patients had poor survival compared with low-risk patients. The 1-, 2-, 3-year area under the receiver operating characteristic curve of the model reached 0.85, 0.87, and 0.93, respectively. Additionally, the prognostic model reflected the infiltration of neutrophils and dendritic cells. The expression of most characteristic immune checkpoints was significantly higher in the high-risk group versus the low-risk group.  Conclusions: The five-gene prognostic model showed reliably predictive accuracy. This model may provide useful information for immunotherapy and facilitate personalized monitoring for patients with PC.

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