scholarly journals Identification of Ferroptosis-Related Gene Prognostic Signature and HSF1 for Reversing Doxorubicin and Gemcitabine Resistance in Uterine Carcinosarcoma

2022 ◽  
Vol 2022 ◽  
pp. 1-16
Author(s):  
Shuxia Han ◽  
Qing Liu ◽  
ZhiJuan Yang ◽  
JingWen Ma ◽  
Dan Liu ◽  
...  
Keyword(s):  
High Risk ◽  
Prognostic Model ◽  
Cox Regression ◽  
External Validation ◽  
Integrated Analysis ◽  
Iron Level ◽  
Uterine Carcinosarcoma ◽  
Intracellular Iron ◽  
Cox Regression Analysis ◽  
Potential Marker

Purpose. Iron metabolism and ferroptosis play crucial roles in the pathogenesis of cancer. In this study, we aim to study the role of ferroptosis-related genes (FRGs) in uterine carcinosarcoma (UCS) and identify potential target for UCS. Methods. Prognostic differentially expressed FRGs were identified of in the TCGA cohort. Integrated analysis, cox regression, and the least absolute shrinkage and selection operator (LASSO) methods of FRGs were performed to construct a multigene signature prognostic model. Moreover, a dataset from Gene Expression Omnibus (GEO) served as an external validation. HSF1 was knockdown in MES-SA and FU-MMT-1 cells, and cell viability, lipid ROS, and intracellular iron level were detected when combined with doxorubicin or gemcitabine. Result. Five FRGs were selected to construct a prognostic model of UCS. The group with high-risk signature score exhibited obviously lower overall survival (OS) than the group with low risk signature score in both TCGA and validated GEO cohorts. Multivariate Cox regression analysis further indicated that the risk score was an independent factor for the prognosis of UCS patients. The high-risk group of UCS has a higher sensitivity in the treatment of doxorubicin and gemcitabine. Knocking down of HSF1 in MES-SA and FU-MMT-1 cells was more sensitive to doxorubicin and gemcitabine via increasing ferroptosis. Conclusions. The five FRGs risk signature prognostic model having a superior and drug sensitivity predictive performance for OS in UCS, and HSF1 is a potential marker sensitive to doxorubicin and gemcitabine in UCS patients.

scholarly journals Identification of a five-miRNA signature as a novel potential prognostic biomarker in patients with nasopharyngeal carcinoma

Hereditas ◽  
2022 ◽  
Vol 159 (1) ◽  
Author(s):  
Bo Tu ◽  
Ling Ye ◽  
Qingsong Cao ◽  
Sisi Gong ◽  
Miaohua Jiang ◽  
...  
Keyword(s):  
High Risk ◽  
Nasopharyngeal Carcinoma ◽  
Prognostic Model ◽  
Target Genes ◽  
Cox Regression ◽  
External Validation ◽  
Risk Groups ◽  
Low Risk ◽  
Mirna Signature ◽  
Cox Regression Analysis

Abstract Background MicroRNAs (miRNAs) are involved in the prognosis of nasopharyngeal carcinoma (NPC). This study used clinical data and expression data of miRNAs to develop a prognostic survival signature for NPC patients to detect high-risk subject. Results We identified 160 differentially expressed miRNAs using RNA-Seq data from the GEO database. Cox regression model consisting of hsa-miR-26a, hsa-let-7e, hsa-miR-647, hsa-miR-30e, and hsa-miR-93 was constructed by the least absolute contraction and selection operator (LASSO) in the training set. All the patients were classified into high-risk or low-risk groups by the optimal cutoff value of the 5-miRNA signature risk score, and the two risk groups demonstrated significant different survival. The 5-miRNA signature showed high predictive and prognostic accuracies. The results were further confirmed in validation and external validation set. Results from multivariate Cox regression analysis validated 5-miRNA signature as an independent prognostic factor. A total of 13 target genes were predicted to be the target genes of miRNA target genes. Both PPI analysis and KEGG analysis networks were closely related to tumor signaling pathways. The prognostic model of mRNAs constructed using data from the dataset GSE102349 had higher AUCs of the target genes and higher immune infiltration scores of the low-risk groups. The mRNA prognostic model also performed well on the independent immunotherapy dataset Imvigor210. Conclusions This study constructed a novel 5-miRNA signature for prognostic prediction of the survival of NPC patients and may be useful for individualized treatment of NPC patients.

scholarly journals Exploration of Various Roles of Hypoxia Genes in Osteosarcoma

2021 ◽  
Author(s):  
Jimin Ma ◽  
Yakun Zhu ◽  
Ziming Guo ◽  
Xuefei Yang ◽  
Haitao Fan
Keyword(s):  
High Risk ◽  
Immune Cells ◽  
Prognostic Model ◽  
Cox Regression ◽  
High Expression ◽  
Cox Regression Analysis ◽  
Model Based ◽  
Survival Difference ◽  
High Expression Group ◽  
Low Expression

Abstract Background: Osteosarcoma is a primary malignant tumor that often metastasizes in orthopedic diseases. Although multi-drug chemotherapy and surgical treatment have significantly improved the survival and prognosis of patients with osteosarcoma, the survival rate is still very low due to frequent metastases in patients with osteosarcoma. In-depth exploration of the relationship between various influencing factors of osteosarcoma is very important for screening promising therapeutic targets. Methods: This study used multivariate COX regression analysis to select the hypoxia genes SLC2A1 and FBP1 in patients with osteosarcoma, and used the expression of these two genes to divide the patients with osteosarcoma into high-risk and low-risk groups. Then, we first constructed a prognostic model based on the patient's risk value, and compared the survival difference between the high expression group and the low expression group. Second, in the high expression group and the low expression group, compare the differences in tumor invasion and inflammatory gene expression between the two groups of immune cells. Finally, the ferroptosis-related genes with differences between the high expression group and the low expression group were screened, and the correlation between these genes was analyzed. Results: In the high-risk group, immune cells with higher tumor invasiveness, macrophages M0 and immune cells with lower invasiveness included: mast cell resting, regulatory T cells (Tregs) and monocytes. Finally, among genes related to ferroptosis, we found AKR1C2, AKR1C1 and ALOX15 that may be related to hypoxia. These ferroptosis-related genes were discovered for the first time in osteosarcoma. Among them, the hypoxia gene FBP1 is positively correlated with the ferroptosis genes AKR1C1 and ALOX15, and the hypoxia gene SLC2A1 is negatively correlated with the ferroptosis genes AKR1C2, AKR1C1 and ALOX15. Conclusion: This study constructed a prognostic model based on hypoxia-related genes SLC2A1 and FBP1 in patients with osteosarcoma, and explored their correlation with immune cells, inflammatory markers and ferroptosis-related genes. This indicates that SLC2A1 and FBP1 are promising targets for osteosarcoma research.

scholarly journals Integrated analysis of the functions and prognostic values of RNA binding proteins in colon adenocarcinoma

2020 ◽  
Author(s):  
Xinhong Liu ◽  
Fang Tan ◽  
Xingyao Long ◽  
Ruokun Yi ◽  
Dingyi Yang ◽  
...  
Keyword(s):  
Prognostic Model ◽  
Binding Proteins ◽  
Rna Binding ◽  
Prediction Models ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Cox Regression Analysis

Abstract Background RNA binding proteins (RBPs) play an important role in a variety of cancers. However, the role of RBPs in colorectal adenocarcinoma (COAD) has not been studied. Integrated analysis of RBPs will provide a better understanding of disease genesis and new insights into COAD treatment. Methods The gene expression data and corresponding clinical information for COAD were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression analysis was used to screen for RBPs associated with COAD recurrence, and multivariate Cox proportional hazards regression analyses were used to identify genes that were associated with COAD recurrence. A nomogram was constructed to predict the recurrence of COAD, and a receiver operating characteristic (ROC) curve analysis was performed to determine the accuracy of the prediction models. The Human Protein Atlas database was used in prediction models to confirm the expression of key genes in COAD patients. Result A total of 177 differentially expressed RBPs was obtained, comprising 123 upregulated and 54 downregulated. GO and KEGG enrichment analysis showed that the differentially expressed RBPs were mainly related to mRNA metabolism, RNA processing and translation regulation. Seven RBP genes (TDRD6, POP1, TDRD7, PPARGC1A, LIN28B, LRRFIP2 and PNLDC1) were identified as prognosis-associated genes and were used to construct the prognostic model. Conclusion We constructed a COAD prognostic model through bioinformatics analysis, which indicated that prognostic model RBPs have a potential role in the diagnosis and prognosis of COAD. Moreover, the nomogram can effectively predict the 1-year, 3-year, and 5-year survival rate for COAD patients.

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 an Mesenchymal-Related Long Non-Coding RNA Prognostic Model in Glioma

2021 ◽  
Vol 11 ◽  
Author(s):  
Kebing Huang ◽  
Xiaoyu Yue ◽  
Yinfei Zheng ◽  
Zhengwei Zhang ◽  
Meng Cheng ◽  
...  
Keyword(s):  
High Risk ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Migration And Invasion ◽  
Aberrant Expression ◽  
Cox Regression Analysis ◽  
Genome Atlas

Glioma is well known as the most aggressive and prevalent primary malignant tumor in the central nervous system. Molecular subtypes and prognosis biomarkers remain a promising research area of gliomas. Notably, the aberrant expression of mesenchymal (MES) subtype related long non-coding RNAs (lncRNAs) is significantly associated with the prognosis of glioma patients. In this study, MES-related genes were obtained from The Cancer Genome Atlas (TCGA) and the Ivy Glioblastoma Atlas Project (Ivy GAP) data sets of glioma, and MES-related lncRNAs were acquired by performing co-expression analysis of these genes. Next, Cox regression analysis was used to establish a prognostic model, that integrated ten MES-related lncRNAs. Glioma patients in TCGA were divided into high-risk and low-risk groups based on the median risk score; compared with the low-risk groups, patients in the high-risk group had shorter survival times. Additionally, we measured the specificity and sensitivity of our model with the ROC curve. Univariate and multivariate Cox analyses showed that the prognostic model was an independent prognostic factor for glioma. To verify the predictive power of these candidate lncRNAs, the corresponding RNA-seq data were downloaded from the Chinese Glioma Genome Atlas (CGGA), and similar results were obtained. Next, we performed the immune cell infiltration profile of patients between two risk groups, and gene set enrichment analysis (GSEA) was performed to detect functional annotation. Finally, the protective factors DGCR10 and HAR1B, and risk factor SNHG18 were selected for functional verification. Knockdown of DGCR10 and HAR1B promoted, whereas knockdown of SNHG18 inhibited the migration and invasion of gliomas. Collectively, we successfully constructed a prognostic model based on a ten MES-related lncRNAs signature, which provides a novel target for predicting the prognosis for glioma patients.

scholarly journals BACH1: A Potential Predictor of Survival in Early-Stage Lung Adenocarcinoma

2022 ◽  
Vol 2022 ◽  
pp. 1-16
Author(s):  
Jin Zhou ◽  
Zheming Liu ◽  
Huibo Zhang ◽  
Tianyu Lei ◽  
Jiahui Liu ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Early Stage ◽  
External Validation ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Cox Regression Analysis

Purpose. Recent researches showed the vital role of BACH1 in promoting the metastasis of lung cancer. We aimed to explore the value of BACH1 in predicting the overall survival (OS) of early-stage (stages I-II) lung adenocarcinoma. Patients and Methods. Lung adenocarcinoma cases were screened from the Cancer Genome Atlas (TCGA) database. Functional enrichment analysis was performed to obtain the biological mechanisms of BACH1. Gene set enrichment analysis (GSEA) was performed to identify the difference of biological pathways between high- and low-BACH1 groups. Univariate and multivariate COX regression analysis had been used to screen prognostic factors, which were used to establish the BACH1 expression-based prognostic model in the TCGA dataset. The C-index and time-dependent AUC curve were used to evaluate predictive power of the model. External validation of prognostic value was performed in two independent datasets from Gene Expression Omnibus (GEO). Decision analysis curve was finally used to evaluate clinical usefulness of the BACH1-based model beyond pathologic stage alone. Results. BACH1 was an independent prognostic factor for lung adenocarcinoma. High-expression BACH1 cases had worse OS. BACH1-based prognostic model showed an ideal C-index and t -AUC and validated by two GEO datasets, independently. More importantly, the BACH1-based model indicated positive clinical applicability by DCA curves. Conclusion. Our research confirmed that BACH1 was an important predictor of prognosis in early-stage lung adenocarcinoma. The higher the expression of BACH1, the worse OS of the patients.

Construction and Validation of Prognostic Markers of Liver Cancer Based on Autophagy Genes

2021 ◽  
Vol 21 ◽  
Author(s):  
Dawei Zhou ◽  
Junchen Wan ◽  
Jiang Luo ◽  
Yuhao Tao
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Liver Cancer ◽  
Cancer Patients ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Primary Liver Cancer ◽  
Risk Groups ◽  
Cox Regression Analysis

Background: Liver cancer is one of the most common diseases in the world. At present, the mechanism of autophagy genes in liver cancer is not very clear. Therefore, it is meaningful to study the role and prognostic value of autophagy genes in liver cancer. Objective: The purpose of this study is to conduct a bioinformatics analysis of autophagy genes related to primary liver cancer to establish a prognostic model of primary liver cancer based on autophagy genes. Results: Through difference analysis, 31 differential autophagy genes were screened out and then analyzed by GO and KEGG analysis. At the same time, we built a PPI network. To optimize the evaluation of the prognosis of liver cancer patients, we integrated multiple autophagy genes to establish a prognostic model. By using univariate cox regression analysis, 15 autophagy genes related to prognosis were screened out. Then we included these 15 genes into the Least Absolute Shrinkage and Selection Operator (LASSO), and performed multi-factor cox regression analysis on the 9 selected genes to construct a prognostic model. The risk score of each patient was calculated based on 4 genes(BIRC5, HSP8, SQSTM1, and TMEM74) which participated in the establishing of the model, then the patients were divided into high-risk groups and low-risk groups. In the multivariate cox regression analysis, the risk score was the independent prognostic factors (HR=1.872, 95%CI=1.544-2.196, P<0.001). Survival analysis showed that the survival time of the low-risk group was significantly longer than that of the high-risk group. Combining clinical characteristics and autophagy genes, we constructed a nomogram for predicting prognosis. The external dataset GSE14520 proved that the nomogram has a good prediction for individual patients with primary liver cancer. Conclusion: This study provided potential autophagy-related markers for liver cancer patients to predict their prognosis and revealed part of the molecular mechanism of liver cancer autophagy. At the same time, the certain gene pathways and protein pathways related to autophagy may provide some inspiration for the development of anticancer drugs.

A tool to identify patients with embolic stroke of undetermined source at high recurrence risk

Neurology ◽  
2019 ◽  
Vol 93 (23) ◽  
pp. e2094-e2104 ◽  
Author(s):  
George Ntaios ◽  
Georgios Georgiopoulos ◽  
Kalliopi Perlepe ◽  
Gaia Sirimarco ◽  
Davide Strambo ◽  
...  
Keyword(s):  
High Risk ◽  
Cox Regression ◽  
External Validation ◽  
Low Risk ◽  
Embolic Stroke ◽  
Stroke Recurrence ◽  
Intermediate Risk ◽  
Cox Regression Analysis ◽  
Derivation Cohort ◽  
Risk Patients

ObjectiveA tool to stratify the risk of stroke recurrence in patients with embolic stroke of undetermined source (ESUS) could be useful in research and clinical practice. We aimed to determine whether a score can be developed and externally validated for the identification of patients with ESUS at high risk for stroke recurrence.MethodsWe pooled the data of all consecutive patients with ESUS from 11 prospective stroke registries. We performed multivariable Cox regression analysis to identify predictors of stroke recurrence. Based on the coefficient of each covariate of the fitted multivariable model, we generated an integer-based point scoring system. We validated the score externally assessing its discrimination and calibration.ResultsIn 3 registries (884 patients) that were used as the derivation cohort, age, leukoaraiosis, and multiterritorial infarct were identified as independent predictors of stroke recurrence and were included in the final score, which assigns 1 point per every decade after 35 years of age, 2 points for leukoaraiosis, and 3 points for multiterritorial infarcts (acute or old nonlacunar). The rate of stroke recurrence was 2.1 per 100 patient-years (95% confidence interval [CI] 1.44–3.06) in patients with a score of 0–4 (low risk), 3.74 (95% CI 2.77–5.04) in patients with a score of 5–6 (intermediate risk), and 8.23 (95% CI 5.99–11.3) in patients with a score of 7–12 (high risk). Compared to low-risk patients, the risk of stroke recurrence was significantly higher in intermediate-risk (hazard ratio [HR] 1.78, 95% CI 1.1–2.88) and high-risk patients (HR 4.67, 95% CI 2.83–7.7). The score was well-calibrated in both derivation and external validation cohorts (8 registries, 820 patients) (Hosmer-Lemeshow test χ2: 12.1 [p = 0.357] and χ2: 21.7 [p = 0.753], respectively). The area under the curve of the score was 0.63 (95% CI 0.58–0.68) and 0.60 (95% CI 0.54–0.66), respectively.ConclusionsThe proposed score can assist in the identification of patients with ESUS at high risk for stroke recurrence.

scholarly journals Identification of Immune-Related Genes Associated With Bladder Cancer Based on Immunological Characteristics and Their Correlation With the Prognosis

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhen Kang ◽  
Wei Li ◽  
Yan-Hong Yu ◽  
Meng Che ◽  
Mao-Lin Yang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Regression Analysis ◽  
Prognostic Model ◽  
Cox Regression ◽  
External Validation ◽  
Experimental Studies ◽  
Training Group ◽  
Validation Group ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Background:To identify the immune-related genes of bladder cancer (BLCA) based on immunological characteristics and explore their correlation with the prognosis. Methods:We downloaded the gene and clinical data of BLCA from the Cancer Genome Atlas (TCGA) as the training group, and obtained immune-related genes from the Immport database. We downloaded GSE31684 and GSE39281 from the Gene Expression Omnibus (GEO) as the external validation group. R (version 4.0.5) and Perl were used to analyze all data. Result:Univariate Cox regression analysis and Lasso regression analysis revealed that 9 prognosis-related immunity genes (PIMGs) of differentially expressed immune genes (DEIGs) were significantly associated with the survival of BLCA patients (p &lt; 0.01), of which 5 genes, including NPR2, PDGFRA, VIM, RBP1, RBP1 and TNC, increased the risk of the prognosis, while the rest, including CD3D, GNLY, LCK, and ZAP70, decreased the risk of the prognosis. Then, we used these genes to establish a prognostic model. We drew receiver operator characteristic (ROC) curves in the training group, and estimated the area under the curve (AUC) of 1-, 3- and 5-year survival for this model, which were 0.688, 0.719, and 0.706, respectively. The accuracy of the prognostic model was verified by the calibration chart. Combining clinical factors, we established a nomogram. The ROC curve in the external validation group showed that the nomogram had a good predictive ability for the survival rate, with a high accuracy, and the AUC values of 1-, 3-, and 5-year survival were 0.744, 0.770, and 0.782, respectively. The calibration chart indicated that the nomogram performed similarly with the ideal model. Conclusion:We had identified nine genes, including PDGFRA, VIM, RBP1, RBP1, TNC, CD3D, GNLY, LCK, and ZAP70, which played important roles in the occurrence and development of BLCA. The prognostic model based on these genes had good accuracy in predicting the OS of patients and might be promising candidates of therapeutic targets. This study may provide a new insight for the diagnosis, treatment and prognosis of BLCA from the perspective of immunology. However, further experimental studies are necessary to reveal the underlying mechanisms by which these genes mediate the progression of BLCA.

scholarly journals Development and Validation of a Novel Metabolic-Related Signature Predicting Overall Survival for Pancreatic Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Lymph Node ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
External Validation ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
Cox Regression Analysis

Recently, growing evidence has revealed the significant effect of reprogrammed metabolism on pancreatic cancer in relation to carcinogenesis, progression, and treatment. However, the prognostic value of metabolism-related genes in pancreatic cancer has not been fully revealed. We identified 379 differentially expressed metabolic-related genes (DEMRGs) by comparing 178 pancreatic cancer tissues with 171 normal pancreatic tissues in The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression project (GTEx) databases. Then, we used univariate Cox regression analysis together with Lasso regression for constructing a prognostic model consisting of 15 metabolic genes. The unified risk score formula and cutoff value were taken into account to divide patients into two groups: high risk and low risk, with the former exhibiting a worse prognosis compared with the latter. The external validation results of the International Cancer Genome Consortium (IGCC) cohort and the Gene Expression Omnibus (GEO) cohort further confirm the effectiveness of this prognostic model. As shown in the receiver operating characteristic (ROC) curve, the area under curve (AUC) values of the risk score for overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS) were 0.871, 0.885, and 0.886, respectively. Based on the Gene Set Enrichment Analysis (GSEA), the 15-gene signature can affect some important biological processes and pathways of pancreatic cancer. In addition, the prognostic model was significantly correlated with the tumor immune microenvironment (immune cell infiltration, and immune checkpoint expression, etc.) and clinicopathological features (pathological stage, lymph node, and metastasis, etc.). We also built a nomogram based on three independent prognostic predictors (including individual neoplasm status, lymph node metastasis, and risk score) for the prediction of 1-, 3-, and 5-year OS of pancreatic cancer, which may help to further improve the treatment strategy of pancreatic cancer.

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