scholarly journals Comprehensive analyses of glycolysis-related lncRNAs for ovarian cancer patients

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jianfeng Zheng ◽  
Jialu Guo ◽  
Linling Zhu ◽  
Ying Zhou ◽  
Jinyi Tong
Keyword(s):  
Ovarian Cancer ◽  
T Cell ◽  
Immune Checkpoint ◽  
Risk Model ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Model Based ◽  
Potential Biomarker ◽  
Cis And Trans

Abstract Background Not only glycolysis but also lncRNAs play a significant role in the growth, proliferation, invasion and metastasis of of ovarian cancer (OC). However, researches about glycolysis -related lncRNAs (GRLs) remain unclear in OC. Herein, we first constructed a GRL-based risk model for patients with OC. Methods The processed RNA sequencing (RNA-seq) profiles with clinicopathological data were downloaded from TCGA and glycolysis-related genes (GRGs) were obtained from MSigDB. Pearson correlation coefficient between glycolysis-related genes (GRGs) and annotated lncRNAs (|r| > 0.4 and p < 0.05) were calculated to identify GRLs. After screening prognostic GRLs, a risk model based on five GRLs was constructed using Univariate and Cox regression. The identified risk model was validated by two validation sets. Further, the differences in clinicopathology, biological function, hypoxia score, immune microenvironment, immune checkpoint, immune checkpoint blockade, chemotherapy drug sensitivity, N6-methyladenosine (m6A) regulators, and ferroptosis-related genes between risk groups were explored by abundant algorithms. Finally, we established networks based on co-expression, ceRNA, cis and trans interaction. Results A total of 535 GRLs were gained and 35 GRLs with significant prognostic value were identified. The prognostic signature containing five GRLs was constructed and validated and can predict prognosis. The nomogram proved the accuracy of the model for predicting prognosis. After computing hypoxia score of each sample by ssGSEA, we found patients with higher risk scores exhibited higher hypoxia score and high hypoxia score was a risk factor. It was revealed that a total of 21 microenvironment cells (such as Central memory CD4 T cell, Neutrophil, Regulatory T cell and so on) and Stromal score had significant differences between the two groups. Four immune checkpoint genes (CD274, LAG3, VTCN1, and CD47) showed disparate expression levels in the two groups. Besides, 16 m6A regulators and 126 ferroptosis-related genes were expressed higher in the low-risk group. GSEA revealed that the risk groups were associated with tumor-related pathways. The two risk groups were confirmed to be sensitive to several chemotherapeutic agents and patients in the low-risk group were more sensitive to ICB therapy. The networks based on co-expression, ceRNA, cis and trans interaction provided insights into the regulatory mechanisms of GRLs. Conclusions Our identified and validated risk model based on five GRLs is an independent prognostic factor for OC patients. Through comprehensive analyses, findings of our study uncovered potential biomarker and therapeutic target for the risk model based on the GRLs.

scholarly journals N6-methyladenosine Methylation Related Immune Biomarkers Correlates With Clinicopathological Characteristics and Prognosis in Clear Cell Renal Cell Carcinoma

2021 ◽  
Author(s):  
Hui Xiong ◽  
Weiting Kang ◽  
Qi Zhang
Keyword(s):  
High Risk ◽  
Immune Checkpoint ◽  
Risk Model ◽  
Risk Group ◽  
Risk Groups ◽  
Tumor Stage ◽  
Low Risk ◽  
Cell Renal Cell Carcinoma ◽  
Immune Genes ◽  
Immune Biomarkers

Abstract Background: This study aimed to explore N6-methyladenosine (m6A) methylation-related immune biomarkers and their clinical value in clear cell renal cell carcinoma (ccRCC).Methods: The RNA-seq data and clinical phenotype of ccRCC were downloaded from TCGA database. Immune-related genes list was downloaded from InnateDB database. Correlation analysis, survival analysis, univariate and multivariate Cox regression analysis were used to investigate the prognostic independent m6A-related immune genes, followed by prognosis risk model establishment. Patients were divided into high/low risk groups, followed by survival analysis, clinical factors, immune checkpoint genes and gene set variation analysis in high-risk vs. low-risk group. Results: Five prognostic independent m6A-related immune genes (PKHD1, IGF2BP3, RORA, FRK and MZF1) were identified. Low expression of PKHD1, RORA and FRK were associated with poor survival, while high expression of IGF2BP3 and MZF1 were associated with poor survival for ccRCC patients. Their expression showed correlations with multiple m6A genes. The risk model could stratify ccRCC patients into high/low risk group, and patients with high-risk were associated with short survival time. High-risk group had an high proportion of patients in tumor stage Ⅲ-Ⅳ and patients with pathologic T3-T4 tumors, lymph node metastasis (N1) and distant metastasis (M1). Ten immune checkpoint genes were differentially expressed in high/low risk groups, such as PD1 and CTLA-4. The risk group could be an independent prognostic factor (HR=1.69, 95% CI 1.07-2.68, P=0.0246). Conclusion: In this study, we developed a five genes risk model, which had independent prognostic value and associated with tumor stage, pathologic T/N/M and immune checkpoint expression in ccRCC.

Prognosis of mature T cell lymphoma is poorer than that of diffuse large B cell lymphoma in IPI low-risk group, but not in intermediate- and high-risk groups

2012 ◽  
Vol 97 (1) ◽  
pp. 98-102 ◽  
Author(s):  
Rika Kihara ◽  
Tomoyuki Watanabe ◽  
Takahiro Yano ◽  
Naokuni Uike ◽  
Seiichi Okamura ◽  
...  
Keyword(s):  
T Cell ◽  
High Risk ◽  
B Cell ◽  
Cell Lymphoma ◽  
Risk Group ◽  
B Cell Lymphoma ◽  
Risk Groups ◽  
Low Risk ◽  
Large B Cell Lymphoma ◽  
Large B Cell

scholarly journals Hypoxia-Related Long Non-Coding RNA Signature Predicts the Prognosis of Breast Cancer

2021 ◽  
Author(s):  
Jinlong Huo ◽  
Shuang Shen ◽  
Chen Chen ◽  
Rui Qu ◽  
Youming Guo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
T Cell ◽  
High Risk ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Cytolytic Activity ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas

Abstract Background: Breast cancer(BC) is the most common tumour in women. Hypoxia stimulates metastasis in cancer and is linked to poor patient prognosis.Methods: We screened prognostic-related lncRNAs(Long Non-Coding RNAs) from the Cancer Genome Atlas (TCGA) data and constructed a prognostic signature based on hypoxia-related lncRNAs in BC.Results: We identified 21 differentially expressed lncRNAs associated with BC prognosis. Kaplan Meier survival analysis indicated a significantly worse prognosis for the high-risk group(P<0.001). Moreover, the ROC-curve (AUC) of the lncRNAs signature was 0.700, a performance superior to other traditional clinicopathological characteristics. Gene set enrichment analysis (GSEA) showed many immune and cancer-related pathways and in the low-risk group patients. Moreover, TCGA revealed that functions including activated protein C (APC)co-inhibition, Cinnamoyl CoA reductase(CCR),check-point pathways, cytolytic activity, human leukocyte antigen (HLA), inflammation-promotion, major histocompatibility complex(MHC) class1, para-inflammation, T cell co-inhibition, T cell co-stimulation, and Type Ⅰ and Ⅱ Interferons (IFN) responses were significantly different in the low-risk and high-risk groups. Immune checkpoint molecules such as ICOS, IDO1, TIGIT, CD200R1, CD28, PDCD1(PD-1), were also expressed differently between the two risk groups. The expression of m6A-related mRNA indicated that YTHDC1, RBM15, METTL3, and FTO were significantly between the high and low-risk groups.Additionally, immunotherapy in patients with BC from the low-risk group yielded a higher frequency of clinical responses to anti-PD-1/PD-L1 therapy or a combination of anti-PD-1/PD-L1and anti-CTLA4 therapies.Except for lapatinib, the results also show that a high-risk score is related to a higher half-maximal inhibitory concentration (IC50) of chemotherapy drugs.Conclusion: A novel hypoxia-related lncRNAs signature may serve as a prognostic model for BC.

scholarly journals m5C-Related lncRNAs Predict Overall Survival of Patients and Regulate the Tumor Immune Microenvironment in Lung Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Junfan Pan ◽  
Zhidong Huang ◽  
Yiquan Xu
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Immune Cells ◽  
Prognostic Value ◽  
Risk Model ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Rna Methylation

Long non-coding RNAs (lncRNAs), which are involved in the regulation of RNA methylation, can be used to evaluate tumor prognosis. lncRNAs are closely related to the prognosis of patients with lung adenocarcinoma (LUAD); thus, it is crucial to identify RNA methylation-associated lncRNAs with definitive prognostic value. We used Pearson correlation analysis to construct a 5-Methylcytosine (m5C)-related lncRNAs–mRNAs coexpression network. Univariate and multivariate Cox proportional risk analyses were then used to determine a risk model for m5C-associated lncRNAs with prognostic value. The risk model was verified using Kaplan–Meier analysis, univariate and multivariate Cox regression analysis, and receiver operating characteristic curve analysis. We used principal component analysis and gene set enrichment analysis functional annotation to analyze the risk model. We also verified the expression level of m5C-related lncRNAs in vitro. The association between the risk model and tumor-infiltrating immune cells was assessed using the CIBERSORT tool and the TIMER database. Based on these analyses, a total of 14 m5C-related lncRNAs with prognostic value were selected to build the risk model. Patients were divided into high- and low-risk groups according to the median risk score. The prognosis of the high-risk group was worse than that of the low-risk group, suggesting the good sensitivity and specificity of the constructed risk model. In addition, 5 types of immune cells were significantly different in the high-and low-risk groups, and 6 types of immune cells were negatively correlated with the risk score. These results suggested that the risk model based on 14 m5C-related lncRNAs with prognostic value might be a promising prognostic tool for LUAD and might facilitate the management of patients with LUAD.

scholarly journals A Novel Model of Tumor-Infiltrating B Lymphocyte Specific RNA-Binding Protein-Related Genes With Potential Prognostic Value and Therapeutic Targets in Multiple Myeloma

2021 ◽  
Vol 12 ◽  
Author(s):  
JingJing Zhang ◽  
Pengcheng He ◽  
Xiaoning Wang ◽  
Suhua Wei ◽  
Le Ma ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
High Risk ◽  
B Cell ◽  
Risk Score ◽  
Drug Sensitivity ◽  
Rna Binding ◽  
Risk Model ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk

Background: RNA-binding proteins (RBPs) act as important regulators in the progression of tumors. However, their role in the tumorigenesis and prognostic assessment in multiple myeloma (MM), a B-cell hematological cancer, remains elusive. Thus, the current study was designed to explore a novel prognostic B-cell-specific RBP signature and the underlying molecular mechanisms.Methods: Data used in the current study were obtained from the Gene Expression Omnibus (GEO) database. Significantly upregulated RBPs in B cells were defined as B cell-specific RBPs. The biological functions of B-cell-specific RBPs were analyzed by the cluster Profiler package. Univariate and multivariate regressions were performed to identify robust prognostic B-cell specific RBP signatures, followed by the construction of the risk classification model. Gene set enrichment analysis (GSEA)-identified pathways were enriched in stratified groups. The microenvironment of the low- and high-risk groups was analyzed by single-sample GSEA (ssGSEA). Moreover, the correlations among the risk score and differentially expressed immune checkpoints or differentially distributed immune cells were calculated. The drug sensitivity of the low- and high-risk groups was assessed via Genomics of Drug Sensitivity in Cancer by the pRRophetic algorithm. In addition, we utilized a GEO dataset involving patients with MM receiving bortezomib therapy to estimate the treatment response between different groups.Results: A total of 56 B-cell-specific RBPs were identified, which were mainly enriched in ribonucleoprotein complex biogenesis and the ribosome pathway. ADAR, FASTKD1 and SNRPD3 were identified as prognostic B-cell specific RBP signatures in MM. The risk model was constructed based on ADAR, FASTKD1 and SNRPD3. Receiver operating characteristic (ROC) curves revealed the good predictive capacity of the risk model. A nomogram based on the risk score and other independent prognostic factors exhibited excellent performance in predicting the overall survival of MM patients. GSEA showed enrichment of the Notch signaling pathway and mRNA cis-splicing via spliceosomes in the high-risk group. Moreover, we found that the infiltration of diverse immune cell subtypes and the expression of CD274, CD276, CTLA4 and VTCN1 were significantly different between the two groups. In addition, the IC50 values of 11 drugs were higher in the low-risk group. Patients in the low-risk group exhibited a higher complete response rate to bortezomib therapy.Conclusion: Our study identified novel prognostic B-cell-specific RBP biomarkers in MM and constructed a unique risk model for predicting MM outcomes. Moreover, we explored the immune-related mechanisms of B cell-specific RBPs in regulating MM. Our findings could pave the way for developing novel therapeutic strategies to improve the prognosis of MM patients.

Construction of a Prognostic Model Related to Ferroptosis and Iron-Metabolism and the Relationship with the Immune Microenvironment of Gastric Cancer

2021 ◽  
Author(s):  
Fang Wen ◽  
Xiaoxue Chen ◽  
Wenjie Huang ◽  
Shuai Ruan ◽  
Suping Gu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Iron Metabolism ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Immune Microenvironment

Abstract Background: The diagnosis rate and mortality of gastric cancer (GC) are among the highest in the global, so it is of great significance to predict the survival time of GC patients. Ferroptosis and iron-metabolism make a critical impact on tumor development and are closely linked to the treatment of cancer and the prognosis of patients. However, the predictive value of the genes involved in ferroptosis and iron-metabolism in GC and their effects on immune microenvironment remain to be further clarified.Methods: In this study, the RNA sequence information and general clinical indicators of GC patients were acquired from the public databases. We first systematically screen out 134 DEGs and 13 PRGs related to ferroptosis and iron-metabolism. Then, we identified six PRDEGs (GLS2, MTF1, SLC1A5, SP1, NOX4, and ZFP36) based on the LASSO-penalized Cox regression analysis. The 6-gene prognostic risk model was established in the TCGA cohort and the GC patients were separated into the high- and the low-risk groups through the risk score median value. GEO cohort was used for verification. The expression of PRDEGs was verified by quantitative QPCR.Results: Our study demonstrated that patients in the low-risk group had a higher survival probability compared with those in high-risk group. In addition, univariate and multivariate Cox regression analyses confirmed that the risk score was an independent prediction parameter. The ROC curve analysis and nomogram manifested that the risk model had the high predictive ability and was more sensitive than general clinical features. Furthermore, compared with the high-risk group, the low-risk group had higher TMB and a longer 5-year survival period. In the immune microenvironment of GC, there were also differences in immune function and highly infiltrated immune cells between the two risk groups.Conclusions: The prognostic risk model based on the six genes associated with ferroptosis and iron-metabolism has a good performance for predicting the prognosis of patients with GC. The treatment of cancer by inducing tumor ferroptosis or mediating tumor iron-metabolism, especially combined with immunotherapy, provides a new possibility for individualized treatment of GC patients.

scholarly journals A model-based cluster analysis approach to adolescent problem behaviors and young adult outcomes

2008 ◽  
Vol 20 (1) ◽  
pp. 291-318 ◽  
Author(s):  
Eun Young Mun ◽  
Michael Windle ◽  
Lisa M. Schainker
Keyword(s):  
Cluster Analysis ◽  
Substance Use ◽  
Young Adult ◽  
High Risk ◽  
Problem Behaviors ◽  
Young Adulthood ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Model Based

AbstractData from a community-based sample of 1,126 10th- and 11th-grade adolescents were analyzed using a model-based cluster analysis approach to empirically identify heterogeneous adolescent subpopulations from the person-oriented and pattern-oriented perspectives. The model-based cluster analysis is a new clustering procedure to investigate population heterogeneity utilizing finite mixture multivariate normal densities and accordingly to classify subpopulations using more rigorous statistical procedures for the comparison of alternative models. Four cluster groups were identified and labeled multiproblem high-risk, smoking high-risk, normative, and low-risk groups. The multiproblem high risk exhibited a constellation of high levels of problem behaviors, including delinquent and sexual behaviors, multiple illicit substance use, and depressive symptoms at age 16. They had risky temperamental attributes and lower academic functioning and educational expectations at age 15.5 and, subsequently, at age 24 completed fewer years of education, and reported lower levels of physical health and higher levels of continued involvement in substance use and abuse. The smoking high-risk group was also found to be at risk for poorer functioning in young adulthood, compared to the low-risk group. The normative and the low risk groups were, by and large, similar in their adolescent and young adult functioning. The continuity and comorbidity path from middle adolescence to young adulthood may be aided and abetted by chronic as well as episodic substance use by adolescents.

Proliferation-, estrogen-, and T-cell-related metagenes to predict outcome after adjuvant/neoadjuvant chemotherapy for operable breast cancer in the ECTO trial.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 1014-1014 ◽  
Author(s):  
Giampaolo Bianchini ◽  
Vera Cappelletti ◽  
Maurizio Callari ◽  
Maria Luisa Carcangiu ◽  
Wolfgang Eiermann ◽  
...  
Keyword(s):  
Breast Cancer ◽  
T Cell ◽  
High Risk ◽  
Risk Group ◽  
Molecular Subtype ◽  
Expression Profiles ◽  
Risk Groups ◽  
Operable Breast Cancer ◽  
Low Risk ◽  
Phase Iii

1014 Background: Predicting recurrence in operable breast cancer (BC) despite optimal chemotherapy would be relevant to new drug development and tailored treatments. Methods: A large series (n=3,154) of public Affymetrix gene-expression profiles (GEP) was used to define prognostic/predictive metagenes in different BC subtypes. In ER+/HER2- a proliferation and an ER-related metagene were combined to predict low, intermediate and high risk of recurrence. In TN and in HER2+ a T cell metagene was used to predict low, intermediate and high risk (higher expression associated with lower risk). The metagenes were validated in patients enrolled in the phase III ECTO trial (Gianni L. JCO 2009) and treated with the same taxane-anthracycline-CMF regimen as neoadjuvant or adjuvant therapy before endocrine therapy if indicated. The outcome was distant event free survival (DEFS). Results: 283 good quality GEPs were obtained (neoadjuvant n=121; adjuvant n=162) from 464 retrospectively collected samples. Median follow-up was 8.9 years. In ER+/HER2- tumors the 10-yrs DEFS was 92.3, 81.2 and 66.6% in low, intermediate and high risk groups, respectively [high vs low HR 4.38 (1.01-19.1) p=.048] according to proliferation and ER-related metagenes. In HER2+ and TN subgroup the 10-yrs DEFS was 97.2, 75.6 and 78.8% in low, intermediate and high risk groups, respectively [high vs low HR 8.73 (1.09-69.8) p=.041]. In TN tumors, the pCR rate was 20% in the high and 61.5% in the low risk group. By combining the predicted risk group in each molecular subtype the 10-yrs DEFS was 95.3, 79.2 and 71.5% in low (24.2%), intermediate (42.7%) and high (33.1%) risk group, respectively [logrank p=0.003; high vs low HR 6.22 (1.87-20.6) p=.002]. ER, PGR, Ki67 and lymphocyte infiltration (LI) by IHC underperformed compared to genomic predictors. Conclusions: BC patients at higher risk of relapse despite optimal standard treatment can be identified who should be spared ineffective and toxic therapy and considered for investigational new strategies. In TN and HER2+, high T cell metagene and to a lesser extent LI are prognostic/predictive and associated with an extremely low risk of DEFS after chemotherapy.

scholarly journals Glasgow Prognostic Score Predicts Prognosis in Patients with Angioimmunoblastic T-Cell Lymphoma

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 31-32
Author(s):  
Hua Wang
Keyword(s):  
T Cell ◽  
Prognostic Model ◽  
Cell Lymphoma ◽  
Risk Group ◽  
Performance Status ◽  
Risk Groups ◽  
Low Risk ◽  
T Cell Lymphoma ◽  
Newly Diagnosed ◽  
Peripheral T Cell Lymphoma

Introduction Glasgow prognosis score (GPS) is an inflammation-based prognostic scoring system that combines clinical tumor markers including C-reactive protein and albumin. However, its role in predicting the prognosis of angioimmunoblastic T-cell lymphoma (AITL) is unclear. The prognosis index for peripheral T-cell lymphoma (PIT) has been widely used to evaluate the prognosis of various types of T-cell non-Hodgkin's lymphoma. In this study, we conducted a retrospective analysis involving 106 patients newly diagnosed with AITL to determine the prognostic value of GPS and compare with PIT model in patients with AITL. Methods A total of 106 patients newly diagnosed with AITL received standardized chemotherapy at Sun Yat-Sen University Cancer Center and the First Affiliated Hospital of Hainan University between July 2009 and October 2019 were enrolled in our study. The baseline patient characteristics were collected, including Eastern Cooperative Oncology Group performance status, B symptoms, lactate dehydrogenase level, albumin level (ALB), CRP level, extra nodal invasion, bone marrow involvement, and Ann Arbor stage . GPS was calculated according to serum CRP and ALB values. Patients with elevated CRP (&gt;10 mg/L) and hypoalbuminemia (&lt;35 g/L) were rated with a score of 2; patients with CRP &gt;10 mg/L or hypoalbuminemia (&lt;35 g/L) were rated with a score of 1; and patients with neither of these abnormalities were rated with a score of 0. Four variables were used to build up PIT score: age (≤60 versus &gt;60), performance status (ECOG ≤1 versus &gt;2), LDH level (low versus high) and BM involvement (negative versus positive). Depending on the number of adverse prognostic factors (0, 1, 2 or ≥3), patients were classified into low-(0,1) or high-risk (2, ≥3) groups, respectively. Progression-free survival (PFS) and overall survival (OS) rates were the primary endpoints of this study. Results Patients were divided into three groups by GPS: GPS=0, GPS=1 and GPS=2. Among them, 74 (69.8%), 22 (20.8%), and 10 (9.40%) received cyclophosphamide, doxorubicin, vincristine and prednisone (CHOP or similar CHOP regimens); etoposide, doxorubicin, vincristine, cyclo-phosphamide and prednisone (EPOCH); gemcitabine and oxaliplatin (GEMOX) regimens, respectively. The results showed that the survival outcomes among the three groups were significantly different. The 5-year PFS rate (61.0% vs. 38.7% vs.0.00%; P&lt;0.001) and 5-year OS rate (76.0% vs. 43.4% vs. 0.00%; P&lt;0.001) were significantly better in the patients with GPS=0 than in those with GPS=1 and GPS=2. The OS and PFS of the patients with GPS=1 were significantly lower than those of patients with GPS=0 (P=0.003, 0.007). In addition, the OS of patients with GPS=1 was significantly better than that of patients with GPS=2 (P=0.021). However, no significant difference was found in their PFS (P=0.144). Furthermore, the proportion of patients with GPS=2 receiving GEMOX and EPOCH was significantly higher than that of patients with GPS=0 or GPS=1. (P=0.001). GEMOX and EPOCH regimens may produce better outcome than CHOP regimen in aggressive peripheral T-cell lymphoma. However, the long-term survival of patients with GPS=2 was significantly worse than those of patients with GPS=0 and GPS=1(P&lt;0.001, =0.021), further confirming the beneficial effect of the GPS model in distinguishing the prognosis of patients. The GPS prognostic model also could effectively identify patients with poor prognosis in the low-risk group by PIT score. According to the PIT prognostic model, 54.7% of the patients were assigned to the low risk group. In those patients, approximately 28.3%, 44.3%, and 27.4% of patients exhibited GPS=0, 1, 2, respectively. In the low-risk group by PIT score, we get similar results of the GPS model mentioned above to evaluate the prognosis. Univariate and multivariate analysis were conducted to determine the prognostic value of GPS in AITL. The results showed that GPS≥1 (P&lt;0.001) were an independent adverse prognostic factor for predicting PFS (95% CI: 1.668-6.626) and OS (95% CI: 2.214-14.686). Conclusion Our study reveals that GPS is an effective prognostic model for patients with AITL. It tends to balance the distribution of patients in the three risk groups and has better prognostic significance than PIT in low-risk groups. Further studies are needed to explain the underlying mechanism of the relationship between high GPS and low survival outcomes in AITL. Figure Disclosures No relevant conflicts of interest to declare.

scholarly journals Construction of a Prognostic Immune-Related LncRNA Risk Model for Lung Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Yue Li ◽  
Ruoyi Shen ◽  
Anqi Wang ◽  
Jian Zhao ◽  
Jieqi Zhou ◽  
...  
Keyword(s):  
High Risk ◽  
Differentially Expressed Genes ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Low Risk ◽  
Differentially Expressed ◽  
Cox Regression Analysis

BackgroundLung adenocarcinoma (LUAD) originates mainly from the mucous epithelium and glandular epithelium of the bronchi. It is the most common pathologic subtype of non-small cell lung cancer (NSCLC). At present, there is still a lack of clear criteria to predict the efficacy of immunotherapy. The 5-year survival rate for LUAD patients remains low.MethodsAll data were downloaded from The Cancer Genome Atlas (TCGA) database. We used Gene Set Enrichment Analysis (GSEA) database to obtain immune-related mRNAs. Immune-related lncRNAs were acquired by using the correlation test of the immune-related genes with R version 3.6.3 (Pearson correlation coefficient cor = 0.5, P &lt; 0.05). The TCGA-LUAD dataset was divided into the testing set and the training set randomly. Based on the training set to perform univariate and multivariate Cox regression analyses, we screened prognostic immune-related lncRNAs and given a risk score to each sample. Samples were divided into the high-risk group and the low-risk group according to the median risk score. By the combination of Kaplan–Meier (KM) survival curve, the receiver operating characteristic (ROC) (AUC) curve, the independent risk factor analysis, and the clinical data of the samples, we assessed the accuracy of the risk model. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the differentially expressed mRNAs between the high-risk group and the low-risk group. The differentially expressed genes related to immune response between two risk groups were analyzed to evaluate the role of the model in predicting the efficacy and effects of immunotherapy. In order to explain the internal mechanism of the risk model in predicting the efficacy of immunotherapy, we analyzed the differentially expressed genes related to epithelial-mesenchymal transition (EMT) between two risk groups. We extracted RNA from normal bronchial epithelial cell and LUAD cells and verified the expression level of lncRNAs in the risk model by a quantitative real-time polymerase chain reaction (qRT-PCR) test. We compared our risk model with other published prognostic signatures with data from an independent cohort. We transfected LUAD cell with siRNA-LINC0253. Western blot analysis was performed to observed change of EMT-related marker in protein level.ResultsThrough univariate Cox regression analysis, 24 immune-related lncRNAs were found to be strongly associated with the survival of the TCGA-LUAD dataset. Utilizing multivariate Cox regression analysis, 10 lncRNAs were selected to establish the risk model. The K-M survival curves and the ROC (AUC) curves proved that the risk model has a fine predictive effect. The GO enrichment analysis indicated that the effect of the differentially expressed genes between high-risk and low-risk groups is mainly involved in immune response and intercellular interaction. The KEGG enrichment analysis indicated that the differentially expressed genes between high-risk and low-risk groups are mainly involved in endocytosis and the MAPK signaling pathway. The expression of genes related to the efficacy of immunotherapy was significantly different between the two groups. A qRT-PCR test verified the expression level of lncRNAs in LUAD cells in the risk model. The AUC of ROC of 5 years in the independent validation dataset showed that this model had superior accuracy. Western blot analysis verified the change of EMT-related marker in protein level.ConclusionThe immune lncRNA risk model established by us could better predict the prognosis of patients with LUAD.

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