Prognostic Model
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PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260720
Cai-Zhi Yang ◽  
Lei-Hao Hu ◽  
Zhong-Yu Huang ◽  
Li Deng ◽  
Wei Guo ◽  

Globally, non-small cell lung cancer (NSCLC) is the most common malignancy and its prognosis remains poor because of the lack of reliable early diagnostic biomarkers. The competitive endogenous RNA (ceRNA) network plays an important role in the tumorigenesis and prognosis of NSCLC. Tumor immune microenvironment (TIME) is valuable for predicting the response to immunotherapy and determining the prognosis of NSCLC patients. To understand the TIME-related ceRNA network, the RNA profiling datasets from the Genotype-Tissue Expression and The Cancer Genome Atlas databases were analyzed to identify the mRNAs, microRNAs, and lncRNAs associated with the differentially expressed genes. Weighted gene co-expression network analysis revealed that the brown module of mRNAs and the turquoise module of lncRNAs were the most important. Interactions among microRNAs, lncRNAs, and mRNAs were prognosticated using miRcode, miRDB, TargetScan, miRTarBase, and starBase databases. A prognostic model consisting of 13 mRNAs was established using univariate and multivariate Cox regression analyses and validated by the receiver operating characteristic (ROC) curve. The 22 immune infiltrating cell types were analyzed using the CIBERSORT algorithm, and results showed that the high-risk score of this model was related to poor prognosis and an immunosuppressive TIME. A lncRNA–miRNA–mRNA ceRNA network that included 69 differentially expressed lncRNAs (DElncRNAs) was constructed based on the five mRNAs obtained from the prognostic model. ROC survival analysis further showed that the seven DElncRNAs had a substantial prognostic value for the overall survival (OS) in NSCLC patients; the area under the curve was 0.65. In addition, the high-risk group showed drug resistance to several chemotherapeutic and targeted drugs including cisplatin, paclitaxel, docetaxel, gemcitabine, and gefitinib. The differential expression of five mRNAs and seven lncRNAs in the ceRNA network was supported by the results of the HPA database and RT-qPCR analyses. This comprehensive analysis of a ceRNA network identified a set of biomarkers for prognosis and TIME prediction in NSCLC.

2021 ◽  
Jimin Ma ◽  
Yakun Zhu ◽  
Ziming Guo ◽  
Xuefei Yang ◽  
Haitao Fan

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.

2021 ◽  
Vol 8 ◽  
Ke Wang ◽  
Weibo Zhong ◽  
Zining Long ◽  
Yufei Guo ◽  
Chuanfan Zhong ◽  

The effects of 5-methylcytosine in RNA (m5C) in various human cancers have been increasingly studied recently; however, the m5C regulator signature in prostate cancer (PCa) has not been well established yet. In this study, we identified and characterized a series of m5C-related long non-coding RNAs (lncRNAs) in PCa. Univariate Cox regression analysis and least absolute shrinkage and selector operation (LASSO) regression analysis were implemented to construct a m5C-related lncRNA prognostic signature. Consequently, a prognostic m5C-lnc model was established, including 17 lncRNAs: MAFG-AS1, AC012510.1, AC012065.3, AL117332.1, AC132192.2, AP001160.2, AC129510.1, AC084018.2, UBXN10-AS1, AC138956.2, ZNF32-AS2, AC017100.1, AC004943.2, SP2-AS1, Z93930.2, AP001486.2, and LINC01135. The high m5C-lnc score calculated by the model significantly relates to poor biochemical recurrence (BCR)-free survival (p < 0.0001). Receiver operating characteristic (ROC) curves and a decision curve analysis (DCA) further validated the accuracy of the prognostic model. Subsequently, a predictive nomogram combining the prognostic model with clinical features was created, and it exhibited promising predictive efficacy for BCR risk stratification. Next, the competing endogenous RNA (ceRNA) network and lncRNA–protein interaction network were established to explore the potential functions of these 17 lncRNAs mechanically. In addition, functional enrichment analysis revealed that these lncRNAs are involved in many cellular metabolic pathways. Lastly, MAFG-AS1 was selected for experimental validation; it was upregulated in PCa and probably promoted PCa proliferation and invasion in vitro. These results offer some insights into the m5C's effects on PCa and reveal a predictive model with the potential clinical value to improve the prognosis of patients with PCa.

2021 ◽  
Vol 12 ◽  
Tianli Chen ◽  
Yue Wang ◽  
Zhaodi Nan ◽  
Jie Wu ◽  
Ailu Li ◽  

BackgroundMacrophage extracellular traps (METs) and tumor-infiltrating macrophages contribute to the progression of several diseases. But the role of METs and tumor-infiltrating macrophages in colon cancer (CC) has not been illuminated. In this study, we aimed to clarify the prognostic value of METs for CC patients and to explore the interaction between CC cells and METs in vitro and in vivo.MethodsA training cohort consisting of 116 patients and a validation cohort of 94 patients were enrolled in this study. Immunofluorescence (IF) staining was conducted to determine METs formation in CC patients. Cox regression was used to perform prognostic analysis and screen out the best prognostic model. A nomogram was established to predict 5-year overall survival (OS). The correlation between METs with clinicopathological features and inflammatory markers was analyzed. The formation of METs in vitro was detected by SYTOX® green and IF staining, and the effect of METs on CC cells was detected by transwell assays. PAD2-IN-1, a selective inhibitor of peptidylarginine deiminase 2 (PAD2), was introduced to destroy the crosstalk between CC cells and METs in vitro and in vivo.ResultsMETs levels were higher in CC tissues and were an independent prognostic factor for CC patients. The prognostic model consisting of age, tumors local invasion, lymph node metastasis and METs were confirmed to be consistent and accurate for predicting the 5-year OS of CC patients. Besides, METs were correlated with distant metastasis and inflammation. Through in vitro experiments, we confirmed that there was a positive feedback loop between CC cells and METs, in that METs promoted the invasion of CC cells and CC cells enhanced the production of METs, in turn. This interaction could be blocked by PAD2-IN-1 inhibitors. More importantly, animal experiments revealed that PAD2-IN-1 inhibited METs formation and CC liver metastasis in vivo.ConclusionsMETs were the potential biomarker of CC patient prognosis. PAD2-IN-1 inhibited the crosstalk between CC cells and METs in vitro and in vivo, which should be emphasized in CC therapy.

2021 ◽  
Vol 18 (4) ◽  
pp. 26-32
Binit Kumar Jha ◽  
Prabhat Jha ◽  
Bikesh Khambu ◽  
Rajendra Shrestha ◽  
Rajiv Jha ◽  

Introduction: Traumatic brain injury disease of major importance globally. Prognostic models are useful for making decisions in the clinical practice. The aim of this study was to assess the accuracy of International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) score in predicting outcome in moderate to severe TBI at 3 months.  Materials and Methods: All patients admitted to National Trauma Center, National Academy of Medical Sciences with moderate to severe traumatic brain injury from February 2020 to February 2021 were included in the study. IMPACT scores (core/extended core/ lab) were recorded separately at admission. Outcome was measured with Glasgow Outcome Scale (GOS) at the time of discharge and at six months. Correlation between observed and predicted outcomes was evaluated by Pearson’s correlation coefficient (r). Sensitivity and specificity were plotted in the receiver-operating characteristic (ROC) curve, and the area under the curve (AUC) was calculated to determine the discrimination ability of this prognostic model. Results: A total of 112 patients were enrolled in the study. Eighty (71.4 %) patients had moderate and 32 (28.57 %) had severe TBI. The median age was 33 years with male preponderance (M: F=4:1). Thirty three (29.5 %) patients died within 6 months of TBI, and 38 (33.9 %) patients  had an unfavorable outcome. Pearson correlation coefficient showed good correlation between observed and predicted outcomes. Hosmer-Lemeshow test showed good model fit for IMPACT core, IMPACT extended and IMPACT lab in diagnosing mortality and unfavorable outcome in six months (p>0.05). The ROC curve indicated that all 3 models could accurately discriminate between favorable and unfavorable outcomes, as well as between survival and mortality (unfavorable outcome AUC= 0.905, 0.940, 0.955; mortality AUC= 0.875, 0.914, 0.917 respectively) in our patient population. Conclusion: The IMPACT score is a good prognostic model to predict 6-month outcomes in moderate to severe TBI at admission in Nepalese patient population. Among the three IMPACT models, IMPACT lab has the greatest discriminating ability.  

2021 ◽  
Vol 21 (1) ◽  
Peipei Yang ◽  
Wanrong Chen ◽  
Hua Xu ◽  
Junhan Yang ◽  
Jinghang Jiang ◽  

Abstract Background The tumor microenvironment (TME) is critical in the progression and metastasis of skin cutaneous melanoma (SKCM). Differences in tumor-infiltrating immune cells (TICs) and their gene expression have been linked to cancer prognosis. Given that immunotherapy can be effective against SKCM, we aimed to identify key genes that regulate the immunological state of the TME in SKCM. Methods Data from 471 SKCM patients in the The Cancer Genome Atlas were analyzed using ESTIMATE algorithms to generate an ImmuneScore, StromalScore, and EstimateScore for each patient. Patients were classified into low- or high-score groups based on median values, then compared in order to identify differentially expressed genes (DEGs). Then a protein–protein interaction (PPI) network was developed, and a prognostic model was created using uni- and multivariate Cox regression as well as the least absolute shrinkage and selection operator (LASSO). Key DEGs were identified using the web-based tool GEPIA. Profiles of TIC subpopulations in each patient were analyzed using CIBORSORT, and possible correlations between key DEG expression and TICs were explored. Levels of CCL8 were determined in SKCM and normal skin tissue using immunohistochemistry. Results Two scores correlated positively with the prognosis of SKCM patients. Comparison of the low- and high-score groups revealed 1684 up-regulated and 18 down-regulated DEGs, all of which were enriched in immune-related functions. The prognostic model identified CCL8 as a key gene, which CIBERSORT found to correlate with M1 macrophages. Immunohistochemistry revealed strong expression in SKCM tissue, but failed to detect the protein in normal skin tissue. Conclusions CCL8 is a potential prognostic marker for SKCM, and it may become an effective target for melanoma in which M1 macrophages play an important role.

2021 ◽  
Vol 16 (1) ◽  
Hairui Fu ◽  
Bin Liang ◽  
Wei Qin ◽  
Xiaoxiong Qiao ◽  
Qiang Liu

Abstract Background No prognostic model for the survival of fragile hip fracture has been developed for Asians. The goal of this study was to develop a simple and practical prognostic model to predict survival within 1 year after fragile hip fracture in Asians. Methods A single-center retrospective cohort study was designed. Under a multivariable Cox proportional hazards regression model, we used the preoperative characteristics of patients to predict survival within 1 year after hip fracture. We built a full model and then used the least absolute shrinkage and selection operator (LASSO) method to further shrink the model coefficients and achieved variable screening. Finally, we obtained a LASSO model. The model performance was evaluated with Nagelkerke’s R2 and the concordance (c) statistic. We assessed the internal validity with a bootstrapping procedure of 1 000 repetitions. Results A total of 735 eligible patients were admitted to our department for hip fracture from January 2015 to December 2020, but 11 (1.5%) patients were lost to follow-up. Among the remaining patients, 68 (9.3%) died within 1 year after hip fracture. We identified 12 candidate predictors from the preoperative characteristics of the patients. The last model contained nine predictors: surgery, age, albumin, sex, serum creatinine, malignancy, hypertension, ability to live independently, and cardiovascular and cerebrovascular diseases. Among them, surgery, age, and albumin are effective predictors of survival. The discrimination c statistic of the model is 0.814 (95% confidence interval 0.762–0.865); the corrected value through internal validation is 0.795. Conclusions This prognostic model can accurately predict a 1-year survival rate for patients with fragile hip fractures. This information can help clinicians develop a reasonable and personalized treatment plan.

2021 ◽  
Vol 12 ◽  
Zhen Kang ◽  
Wei Li ◽  
Yan-Hong Yu ◽  
Meng Che ◽  
Mao-Lin Yang ◽  

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 < 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.

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