scholarly journals Development of an Oncogenic Driver Alteration Associated Immune-Related Prognostic Model for Stage I-II Lung Adenocarcinoma

2021 ◽  
Vol 10 ◽  
Author(s):  
Jian-Zhao Xu ◽  
Chen Gong ◽  
Zheng-Fu Xie ◽  
Hua Zhao
Keyword(s):  
High Risk ◽  
Mast Cells ◽  
Tumor Microenvironment ◽  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Stage I ◽  
Oncogenic Driver

Lung adenocarcinoma (LUAD) needs to be stratified for its heterogeneity. Oncogenic driver alterations such as EGFR mutation, ALK translocation, ROS1 translocation, and BRAF mutation predict response to treatment for LUAD. Since oncogenic driver alterations may modulate immune response in tumor microenvironment that may influence prognosis in LUAD, the effects of EGFR, ALK, ROS1, and BRAF alterations on tumor microenvironment remain unclear. Immune-related prognostic model associated with oncogenic driver alterations is needed. In this study, we performed the Cox-proportional Hazards Analysis based on the L1-penalized (LASSO) Analysis to establish an immune-related prognostic model (IPM) in stage I-II LUAD patients, which was based on 3 immune-related genes (PDE4B, RIPK2, and IFITM1) significantly enriched in patients without EGFR, ALK, ROS1, and BRAF alterations in The Cancer Genome Atlas (TCGA) database. Then, patients were categorized into high-risk and low-risk groups individually according to the IPM defined risk score. The predicting ability of the IPM was validated in GSE31210 and GSE26939 downloaded from the Gene Expression Omnibus (GEO) database. High-risk was significantly associated with lower overall survival (OS) rates in 3 independent stage I-II LUAD cohorts (all P < 0.05). Moreover, the IPM defined risk independently predicted OS for patients in TCGA stage I-II LUAD cohort (P = 0.011). High-risk group had significantly higher proportions of macrophages M1 and activated mast cells but lower proportions of memory B cells, resting CD4 memory T cells and resting mast cells than low-risk group (all P < 0.05). In addition, the high-risk group had a significantly lower expression of CTLA-4, PDCD1, HAVCR2, and TIGIT than the low-risk group (all P < 0.05). In summary, we established a novel IPM that could provide new biomarkers for risk stratification of stage I-II LUAD patients.

scholarly journals Prognostic model of AU-rich genes predicting the prognosis of lung adenocarcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12275
Author(s):  
Yong Liu ◽  
Zhaofei Pang ◽  
Xiaogang Zhao ◽  
Yukai Zeng ◽  
Hongchang Shen ◽  
...  
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Wilcoxon Test ◽  
Genetic Signature ◽  
Calibration Curves

Background AU-rich elements (ARE) are vital cis-acting short sequences in the 3’UTR affecting mRNA stability and translation. The deregulation of ARE-mediated pathways can contribute to tumorigenesis and development. Consequently, ARE-genes are promising to predict prognosis of lung adenocarcinoma (LUAD) patients. Methods Differentially expressed ARE-genes between LUAD and adjacent tissues in TCGA were investigated by Wilcoxon test. LASSO and Cox regression analyses were performed to identify a prognostic genetic signature. The genetic signature was combined with clinicopathological features to establish a prognostic model. LUAD patients were divided into high- and low-risk groups by the model. Kaplan–Meier curve, Harrell’s concordance index (C-index), calibration curves and decision curve analyses (DCA) were used to assess the model. Function enrichment analysis, immunity and tumor mutation analyses were performed to further explore the underlying molecular mechanisms. GEO data were used for external validation. Results Twelve prognostic genes were identified. The gene riskScore, age and stage were independent prognostic factors. The high-risk group had worse overall survival and was less sensitive to chemotherapy and radiotherapy (P < 0.01). C-index and calibration curves showed good performance on survival prediction in both TCGA (1, 3, 5-year ROC: 0.788, 0.776, 0.766) and the GSE13213 validation cohort (1, 3, 5-year ROC: 0.781, 0.811, 0.734). DCA showed the model had notable clinical net benefit. Furthermore, the high-risk group were enriched in cell cycle, DNA damage response, multiple oncological pathways and associated with higher PD-L1 expression, M1 macrophage infiltration. There was no significant difference in tumor mutation burden (TMB) between high- and low-risk groups. Conclusion ARE-genes can reliably predict prognosis of LUAD and may become new therapeutic targets for LUAD.

scholarly journals Identification of prognostic gene signature associated with microenvironment of lung adenocarcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8128 ◽  
Author(s):  
Cheng Yue ◽  
Hongtao Ma ◽  
Yubai Zhou
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Gene Signature ◽  
Low Risk ◽  
Differentially Expressed ◽  
Lasso Regression

Background Lung cancer has the highest morbidity and mortality worldwide, and lung adenocarcinoma (LADC) is the most common pathological subtype. Accumulating evidence suggests the tumor microenvironment (TME) is correlated with the tumor progress and the patient’s outcome. As the major components of TME, the tumor-infiltrated immune cells and stromal cells have attracted more and more attention. In this study, differentially expressed immune and stromal signature genes were used to construct a TME-related prognostic model for predicting the outcomes of LADC patients. Methods The expression profiles of LADC samples with clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) related to the TME of LADC were identified using TCGA dataset by Wilcoxon rank sum test. The prognostic effects of TME-related DEGs were analyzed using univariate Cox regression. Then, the least absolute shrinkage and selection operator (LASSO) regression was performed to reduce the overfit and the number of genes for further analysis. Next, the prognostic model was constructed by step multivariate Cox regression and risk score of each sample was calculated. Then, survival and Receiver Operating Characteristic (ROC) analyses were conducted to validate the model using TCGA and GEO datasets, respectively. The Kyoto Encyclopedia of Genes and Genomes analysis of gene signature was performed using Gene Set Enrichment Analysis (GSEA). Finally, the overall immune status, tumor purity and the expression profiles of HLA genes of high- and low-risk samples was further analyzed to reveal the potential mechanisms of prognostic effects of the model. Results A total of 93 TME-related DEGs were identified, of which 23 DEGs were up-regulated and 70 DEGs were down-regulated. The univariate cox analysis indicated that 23 DEGs has the prognostic effects, the hazard ratio ranged from 0.65 to 1.25 (p < 0.05). Then, seven genes were screened out from the 23 DEGs by LASSO regression method and were further analyzed by step multivariate Cox regression. Finally, a three-gene (ADAM12, Bruton Tyrosine Kinase (BTK), ERG) signature was constructed, and ADAM12, BTK can be used as independent prognostic factors. The three-gene signature well stratified the LADC patients in both training (TCGA) and testing (GEO) datasets as high-risk and low-risk groups, the 3-year area under curve (AUC) of ROC curves of three GEO sets were 0.718 (GSE3141), 0.646 (GSE30219) and 0.643 (GSE50081). The GSEA analysis indicated that highly expressed ADAM12, BTK, ERG mainly correlated with the activation of pathways involving in focal adhesion, immune regulation. The immune analysis indicated that the low-risk group has more immune activities and higher expression of HLA genes than that of the high-risk group. In sum, we identified and constructed a three TME-related DEGs signature, which could be used to predict the prognosis of LADC patients.

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

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11911
Author(s):  
Lei Liu ◽  
Huayu He ◽  
Yue Peng ◽  
Zhenlin Yang ◽  
Shugeng Gao
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Risk Group ◽  
Gene Signature ◽  
Tnm Staging ◽  
High Risk Group ◽  
Prognostic Signature ◽  
Risk Patients

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

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.

Identification of Novel Tumor Microenvironment-Related Long Non-coding RNAs to Predict the Prognosis for Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Shenglan Huang ◽  
Jian Zhang ◽  
Dan Li ◽  
Xiaolan Lai ◽  
Lingling Zhuang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Clinicopathological Parameters ◽  
Kaplan Meier ◽  
The Impact

Abstract Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. Tumor microenvironment (TME) plays a vital role in the tumor progression of HCC. Thus, we aimed to analyze the association of TME with HCC prognosis, and construct an TME-related lncRNAs signature for predicting the prognosis of HCC patients.Methods: We firstly assessed the stromal/immune /Estimate scores within the HCC microenvironment using the ESTIMATE algorithm based on TCGA database, and its associations with survival and clinicopathological parameters were also analyzed. Then, different expression lncRNAs were filtered out according to immune/stromal scores. Cox regression was performed to built an TME-related lncRNAs risk signature. Kaplan–Meier analysis was carried out to explored the prognostic values of the risk signature. Furthermore, we explored the biological functions and immune microenvironment feathers in high- and low risk groups. Lastly, we probed the association of the risk signature with the treatment responses to immune checkpoint inhibitors (ICIs) in HCC by comparing the immunophenoscore (IPS).Results: Stromal/immune /Estimate scores of HCC patients were obtained based on the ESTIMATE algorithm. The Kaplan-Meier curve analysis showed the high stromal/immune/ Estimate scores were significantly associated with better prognosis of the HCC patients. Then, six TME-related lncRNAs were screened for constructing the prognosis model. Kaplan-Meier survival curves suggested that HCC patients in high-risk group had worse prognosis than those with low-risk. ROC curve and Cox regression analyses demonstrated the signature could predict HCC survival exactly and independently. Function enrichment analysis revealed that some tumor- and immune-related pathways associated with HCC tumorigenesis and progression might be activated in high-risk group. We also discovered that some immune cells, which were beneficial to enhance immune responses towards cancer, were remarkably upregulated in low-risk group. Besides, there was closely correlation of immune checkmate inhibitors (ICIs) with the risk signature and the signature can be used to predict treatment response of ICIs.Conclusions: We analyzed the impact of the tumor microenvironment scores on the prognosis of patients with HCC. A novel TME-related prognostic risk signature was established, which may improve prognostic predictive accuracy and guide individualized immunotherapy for HCC patients.

scholarly journals A Robust Signature Based on Autophagy-Associated LncRNAs for Predicting Prognosis in Lung Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Mi Zhou ◽  
Weihua Shao ◽  
Haiyun Dai ◽  
Xin Zhu
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Risk Group ◽  
Surgical Margin ◽  
Training Group ◽  
High Risk Group ◽  
Low Risk ◽  
Validation Group ◽  
Cox Regression Analysis

Objective. To construct a predictive signature based on autophagy-associated lncRNAs for predicting prognosis in lung adenocarcinoma (LUAD). Materials and Methods. Differentially expressed autophagy genes (DEAGs) and differentially expressed lncRNAs (DElncRNAs) were screened between normal and LUAD samples at thresholds of ∣log2Fold Change∣>1 and P value < 0.05. Univariate Cox regression analysis was conducted to identify overall survival- (OS-) associated DElncRNAs. The total cohort was randomly divided into a training group (n=229) and a validation group (n=228) at a ratio of 1 : 1. Multivariate Cox regression analysis was used to build prognostic models in the training group that were further validated by the area under curve (AUC) values of the receiver operating characteristic (ROC) curves in both the validation and total cohorts. Results. A total of 30 DEAGs and 2997 DElncRNAs were identified between 497 LUAD tissues and 54 normal tissues; however, only 1183 DElncRNAs were related to the 30 DEAGs. A signature consisting of 13 DElncRNAs was built to predict OS in lung adenocarcinoma, and the survival analysis indicated a significant OS advantage of the low-risk group over the high-risk group in the training group, with a 5-year OS AUC of 0.854. In the validation group, survival analysis also indicated a significantly favorable OS for the low-risk group over the high-risk group, with a 5-year OS AUC of 0.737. Univariate and multivariate Cox regression analyses indicated that only positive surgical margin (vs negative surgical margin) and high-risk group (vs low-risk group) based on the predictive signature were independent risk factors predictive of overall mortality in LUAD. Conclusions. This study investigated the association between autophagy-associated lncRNAs and prognosis in LUAD and built a robust predictive signature of 13 lncRNAs to predict OS.

scholarly journals Prognostic Model to Predict Overall Survival for Metastatic Non-Small Cell Lung Cancer Patients Treated With Chemotherapy Combined With Concurrent Radiation Therapy to the Primary Tumor: Analysis From Two Prospective Studies

2021 ◽  
Vol 11 ◽  
Author(s):  
Ling-Feng Liu ◽  
Qing-Song Li ◽  
Yin-Xiang Hu ◽  
Wen-Gang Yang ◽  
Xia-Xia Chen ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Prognostic Model ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Moderate Risk ◽  
Metastatic Nsclc ◽  
Nsclc Patients ◽  
Group 1

PurposeThe role of radiotherapy, in addition to chemotherapy, has not been thoroughly determined in metastatic non-small cell lung cancer (NSCLC). The purpose of the study was to investigate the prognostic factors and to establish a model for the prediction of overall survival (OS) in metastatic NSCLC patients who received chemotherapy combined with the radiation therapy to the primary tumor.MethodsThe study retrospectively reviewed 243 patients with metastatic NSCLC in two prospective studies. A prognostic model was established based on the results of the Cox regression analysis.ResultsMultivariate analysis showed that being male, Karnofsky Performance Status score &lt; 80, the number of chemotherapy cycles &lt;4, hemoglobin level ≤120 g/L, the count of neutrophils greater than 5.8 ×109/L, and the count of platelets greater than 220 ×109/L independently predicted worse OS. According to the number of risk factors, patients were further divided into one of three risk groups: those having ≤ 2 risk factors were scored as the low-risk group, those having 3 risk factors were scored as the moderate-risk group, and those having ≥ 4 risk factors were scored as the high-risk group. In the low-risk group, 1-year OS is 67.7%, 2-year OS is 32.1%, and 3-year OS is 19.3%; in the moderate-risk group, 1-year OS is 59.6%, 2-year OS is 18.0%, and 3-year OS is 7.9%; the corresponding OS rates for the high-risk group were 26.2%, 7.9%, and 0% (P&lt;0.001) respectively.ConclusionMetastatic NSCLC patients treated with chemotherapy in combination with thoracic radiation may be classified as low-risk, moderate-risk, or high-risk group using six independent prognostic factors. This prognostic model may help design the study and develop the plans of individualized treatment.

scholarly journals Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Hui Li ◽  
Linyan Chen ◽  
Hao Zeng ◽  
Qimeng Liao ◽  
Jianrui Ji ◽  
...  
Keyword(s):  
High Risk ◽  
Prognostic Model ◽  
Risk Group ◽  
Colon Adenocarcinoma ◽  
Image Features ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Module ◽  
Histopathological Image ◽  
Histopathological Images

BackgroundColon adenocarcinoma (COAD) is one of the most common malignant tumors in the world. The histopathological features are crucial for the diagnosis, prognosis, and therapy of COAD.MethodsWe downloaded 719 whole-slide histopathological images from TCIA, and 459 corresponding HTSeq-counts mRNA expression and clinical data were obtained from TCGA. Histopathological image features were extracted by CellProfiler. Prognostic image features were selected by the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms. The co-expression gene module correlated with prognostic image features was identified by weighted gene co-expression network analysis (WGCNA). Random forest was employed to construct an integrative prognostic model and calculate the histopathological-genomic prognosis factor (HGPF).ResultsThere were five prognostic image features and one co-expression gene module involved in the model construction. The time-dependent receiver operating curve showed that the prognostic model had a significant prognostic value. Patients were divided into high-risk group and low-risk group based on the HGPF. Kaplan-Meier analysis indicated that the overall survival of the low-risk group was significantly better than the high-risk group.ConclusionsThese results suggested that the histopathological image features had a certain ability to predict the survival of COAD patients. The integrative prognostic model based on the histopathological images and genomic features could further improve the prognosis prediction in COAD, which may assist the clinical decision in the future.

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.

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