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

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.

scholarly journals Immune-Related Four-lncRNA Signature for Patients with Cervical Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Jianfeng Zheng ◽  
Benben Cao ◽  
Xia Zhang ◽  
Zheng Niu ◽  
Jinyi Tong
Keyword(s):  
Cervical Cancer ◽  
High Risk ◽  
Immune Cell ◽  
Risk Group ◽  
Pearson Correlation ◽  
Characteristic Curve ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Gynecological Malignancy

Cervical cancer (CC) is a common gynecological malignancy for which prognostic and therapeutic biomarkers are urgently needed. The signature based on immune-related lncRNAs (IRLs) of CC has never been reported. This study is aimed at establishing an IRL signature for patients with CC. A cohort of 326 CC and 21 normal tissue samples with corresponding clinical information was included in this study. Twenty-eight IRLs were collected according to the Pearson correlation analysis between the immune score and lncRNA expression ( p < 0.01 ). Four IRLs (BZRAP1-AS1, EMX2OS, ZNF667-AS1, and CTC-429P9.1) with the most significant prognostic values ( p < 0.05 ) were identified which demonstrated an ability to stratify patients into the low-risk and high-risk groups by developing a risk score model. It was observed that patients in the low-risk group showed longer overall survival (OS) than those in the high-risk group in the training set, valid set, and total set. The area under the curve (AUC) of the receiver operating characteristic curve (ROC curve) for the four-IRL signature in predicting the one-, two-, and three-year survival rates was larger than 0.65. In addition, the low-risk and high-risk groups displayed different immune statuses in GSEA. These IRLs were also significantly correlated with immune cell infiltration. Our results showed that the IRL signature had a prognostic value for CC. Meanwhile, the specific mechanisms of the four IRLs in the development of CC were ascertained preliminarily.

scholarly journals The Pyroptosis-Related 9 LncRNA Signature and LncRNA-miRNA-mRNA Regulatory Network in Breast Cancer: A Comprehensive Analysis Based On TCGA Database

2021 ◽  
Author(s):  
Ye Tian ◽  
Yanan Zhang ◽  
Jing Dong ◽  
Lin Li
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Regulatory Network ◽  
Prognostic Model ◽  
Immune Cell ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Tumor Immune Microenvironment ◽  
Cox Analysis

Abstract Background: Pytoproptosis has been verified to participate in various malignancies. However, studies on pyroptosis-related lncRNAs in breast cancer and its effects on tumor immune micro-environment are still limited. Consequently, it was aimed in this study to construct a pyroptosis-related lncRNAs signature for prognostic prediction and explore the effect of the pyroptosis-related LncRNAs on tumor immune microenvironment through LncRNA-miRNA-mRNA regulatory network. Methods: The pyroptosis-related differentially expressed genes (DEGs) were discovered using differential expression analysis. The differentially expressed LncRNAs (DELncRNAs) associated with DEGs were discovered using correlation analysis. The function of DEGs was analyed using GO and KEGG analyses. The LncRNAs signature used as the prognostic model of breast cancer was constructed using univariate and multivariate Cox analysis, and the effectiveness was verified by K-M analysis and ROC curve. The risk score calculated using the prognostic model was proved as an independent factor by univariate Cox analysis, multivariate Cox analysis and PCA analysis, and used to predict patient prognosis through nomogram. The pathyways enriched in High risk group and Low risk group were analyzed by GSEA. The differences in immune cell distribution (B cell memory, T cell CD4+, T cell CD8+ among others) were analyzed using ssGSEA. The immune function (type I/II IFN response among others), immune checkpoint (ADORA2A among others) and m6A-related protein expression (FTO among others) of High risk group and Low risk group were compared. The regulatory network of pyroptosis-related LncRNA-miRNA-mRNA was constructed and the core network was extracted. The functions of the target genes of miRNA associated with DELncRNAs were explored using GO and KEGG analysis. Results: A 9 LncRNAs signature (LMNTD2-AS1, AL589765.4, AC079298.3, U62317.3, LINC02446, AL645608.7, HSD11B1-AS1, AC009119.1, AC087239.1) was constructed as the prognostic model of breast cancer. Significant differences were discovered in immune cell distribution, immune function, immune checkpointand m6A-related protein expression between High risk group and Low risk group. The regulatory network of LncRNA-miRNA-mRNA was constructed and found to participate in the crosstalk among apoptosis, pyroptosis and necroptosis of breast cancer. Conclusions: The 9 lncRNAs signature was valuable for predicting breast cancer prognosis, and the pyroptosis-related lncRNAs influenced tumor immune microenvironment of breast cancer through the LncRNA-miRNA-mRNA regulatory network.

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 Development and validation of a ferroptosis-related prognostic model in pancreatic cancer

2021 ◽  
Author(s):  
Chen-jie Qiu ◽  
Xue-bing Wang ◽  
Zi-ruo Zheng ◽  
Chao-zhi Yang ◽  
Kai Lin ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
T Cell ◽  
High Risk ◽  
Risk Score ◽  
Prognostic Model ◽  
Immune Cell ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Immune Cell Infiltration

Abstract Background: The purpose of this study was to identify ferroptosis-related genes (FRGs) associated with the prognosis of pancreatic cancer and to construct a prognostic model based on FRGs. Methods: Based on pancreatic cancer data obtained from The Cancer Genome Atlas database, we established the prognostic model from 232 FRGs. A nomogram was constructed by combining the prognostic model and clinicopathological features. Gene Expression Omnibus datasets and tissue samples obtained from our center were utilized to validate the model. Relationship between risk score and immune cell infiltration was explored by CIBERSORT and TIMER.Results: The prognostic model was established based on four FRGs (ENPP2, ATG4D, SLC2A1 and MAP3K5) and can be an independent risk factor in pancreatic cancer (HR 1.648, 95% CI 1.335-2.035, p < 0.001). Based on the median risk score, patients were divided into a high-risk group and a low-risk group. The prognosis of the low-risk group was significantly better than that of the high-risk group. In the high-risk group, patients treated with chemotherapy had a better prognosis. The nomogram showed that the model was the most important element. Gene set enrichment analysis identified three key pathways, namely, TGFβ signaling, HIF signaling pathway and adherens junction. The prognostic model can also affect the immune cell infiltration, such as macrophages M0, M1, CD4+T cell and CD8+T cell. Conclusion: A ferroptosis-related prognostic model can be employed to predict the prognosis of pancreatic cancer. Ferroptosis can be an important marker and immunotherapy can be a potential therapeutic target for pancreatic cancer.

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 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 Identification of an Exosome-Related Signature Associated with Prognosis and Immune Infiltration in Breast Cancer

2022 ◽  
Author(s):  
Qiaonan Guo ◽  
Pengjun Qiu ◽  
Kelun Pan ◽  
Jianpeng Chen ◽  
Jianqing Lin
Keyword(s):  
Breast Cancer ◽  
T Cells ◽  
High Risk ◽  
Immune Cell ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Immune Cell Infiltration ◽  
Immune Microenvironment ◽  
Expression Levels

Abstract Background: Exosomes are nanosized vesicles, play a vital role in breast cancer (BC) occurrence, development, invasion, metastasis, and drug resistance. Nevertheless, studies about exosome-related genes in breast cancer are limited. Besides, the interaction between the exosomes and tumor immune microenvironment (TIME) in BC are still unclear. Hence, we procced to study the potential prognostic value of exosome-related genes and their relationship to immune microenvironment in BC. Methods: 121 exosome-related genes were provided by ExoBCD database and 7 final genes were selected from the intersection of 33 differential expression genes (DEGs) and 19 prognostic genes in BC. Based on the expression levels of the 7 genes, downloaded from The Cancer Genome Atlas (TCGA) database, as well as the regression coefficients, the exosome-related signature was constructed. As a result, the patients in TCGA and GEO database were separated into low- and high- risk groups, respectively. Subsequently, R clusterProfiler package was applied to identify the distinct enrichment pathways between high-risk group and low-risk group. The ESTIMATE method was used to calculate ESTIMATE Score and CIBERSORT was applied to evaluate the immune cell infiltration. Eventually, the different expression levels of immune checkpoint related genes were analyzed between the two risk groups. Results: Results of BC prognosis vary from different risk groups. The low-risk groups were identified with higher survival rate both in TCGA and GEO cohort. The DEGs between high- and low- risk groups were found to enrich in immunity, biological processes, and inflammation pathways. The BC patients with higher ESTIMATE scores were revealed to have better overall survival (OS). Subsequently, CD8+ T cells, naive B cells, CD4+ resting memory T cells, monocytes, and neutrophils were upregulated, while M0 macrophages and M2 macrophages were downregulated in the low-risk group. At last, 4 genes reported as the targets of immune checkpoint inhibitors were further analyzed. The low-risk groups in TCGA and GEO cohorts were indicated with higher expression levels of LAG-3, CD274, TIGIT and CTLA-4. Conclusion: According to this study, exosomes are closely associated with the prognosis and immune cell infiltration of BC patients. These findings may make contributions to improve immunotherapy and bring a new sight for BC treatment strategies.

scholarly journals Construction of a prognostic model for lung squamous cell carcinoma based on seven N6-methylandenosine-related autophagy genes

2021 ◽  
Vol 18 (5) ◽  
pp. 6709-6723
Author(s):  
Xin Yu ◽  
◽  
Jun Liu ◽  
Ruiwen Xie ◽  
Mengling Chang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Prognostic Model ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Lung Squamous Cell Carcinoma ◽  
Risk Groups ◽  
Low Risk ◽  
Immune Cell Infiltration ◽  
Cox Regression Analysis

<abstract> <sec><title>Objective</title><p>We aimed to construct a novel prognostic model based on N6-methyladenosine (m6A)-related autophagy genes for predicting the prognosis of lung squamous cell carcinoma (LUSC).</p> </sec> <sec><title>Methods</title><p>Gene expression profiles and clinical information of Patients with LUSC were downloaded from The Cancer Genome Atlas (TCGA) database. In addition, m6A- and autophagy-related gene profiles were obtained from TCGA and Human Autophagy Database, respectively. Pearson correlation analysis was performed to identify the m6A-related autophagy genes, and univariate Cox regression analysis was conducted to screen for genes associated with prognosis. Based on these genes, LASSO Cox regression analysis was used to construct a prognostic model. The corresponding prognostic score (PS) was calculated, and patients with LUSC were assigned to low- and high-risk groups according to the median PS value. An independent dataset (GSE37745) was used to validate the prognostic ability of the model. CIBERSORT was used to calculate the differences in immune cell infiltration between the high- and low-risk groups.</p> </sec> <sec><title>Results</title><p>Seven m6A-related autophagy genes were screened to construct a prognostic model: <italic>CASP4</italic>, <italic>CDKN1A</italic>, <italic>DLC1</italic>, <italic>ITGB1</italic>, <italic>PINK1</italic>, <italic>TP63</italic>, and <italic>EIF4EBP1</italic>. In the training and validation sets, patients in the high-risk group had worse survival times than those in the low-risk group; the areas under the receiver operating characteristic curves were 0.958 and 0.759, respectively. There were differences in m6A levels and immune cell infiltration between the high- and low-risk groups.</p> </sec> <sec><title>Conclusions</title><p>Our prognostic model of the seven m6A-related autophagy genes had significant predictive value for LUSC; thus, these genes may serve as autophagy-related therapeutic targets in clinical practice.</p> </sec> </abstract>

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 &gt;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 &lt;120 g/L) in 60% (33/55), thrombocytopenia (platelet counts &lt;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&lt; 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&lt; 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 Identification of a novel autophagy signature for predicting survival in patients with lung adenocarcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11074
Author(s):  
Jin Duan ◽  
Youming Lei ◽  
Guoli Lv ◽  
Yinqiang Liu ◽  
Wei Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Cell Analysis ◽  
Prognostic Signature

Background Lung adenocarcinoma (LUAD) is the most commonhistological lung cancer subtype, with an overall five-year survivalrate of only 17%. In this study, we aimed to identify autophagy-related genes (ARGs) and develop an LUAD prognostic signature. Methods In this study, we obtained ARGs from three databases and downloaded gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used TCGA-LUAD (n = 490) for a training and testing dataset, and GSE50081 (n = 127) as the external validation dataset.The least absolute shrinkage and selection operator (LASSO) Cox and multivariate Cox regression models were used to generate an autophagy-related signature. We performed gene set enrichment analysis (GSEA) and immune cell analysis between the high- and low-risk groups. A nomogram was built to guide the individual treatment for LUAD patients. Results We identified a total of 83 differentially expressed ARGs (DEARGs) from the TCGA-LUAD dataset, including 33 upregulated DEARGs and 50 downregulated DEARGs, both with thresholds of adjusted P < 0.05 and |Fold change| > 1.5. Using LASSO and multivariate Cox regression analyses, we identified 10 ARGs that we used to build a prognostic signature with areas under the curve (AUCs) of 0.705, 0.715, and 0.778 at 1, 3, and 5 years, respectively. Using the risk score formula, the LUAD patients were divided into low- or high-risk groups. Our GSEA results suggested that the low-risk group were enriched in metabolism and immune-related pathways, while the high-risk group was involved in tumorigenesis and tumor progression pathways. Immune cell analysis revealed that, when compared to the high-risk group, the low-risk group had a lower cell fraction of M0- and M1- macrophages, and higher CD4 and PD-L1 expression levels. Conclusion Our identified robust signature may provide novel insight into underlying autophagy mechanisms as well as therapeutic strategies for LUAD treatment.

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