A Prognostic Nomogram based on Risk Assessment for Invasive Micropapillary Carcinoma of the Breast after Surgery

Abstract Background: Invasive micropapillary carcinoma (IMPC) is one of the rare subtypes of breast cancer. This study aimed to explore a novel predictive nomogram model for IMPC prognosis.Methods: A total of 1855 IMPC patients diagnosed after surgery between 2004 and 2014 were identified from the Surveillance, Epidemiology and End Results (SEER) database to build and validate nomograms. All the patients included were divided into a training group (n=1300) and a validation group (n=555). A nomogram was created based on univariate and multivariate Cox proportional hazards regression analysis. In addition, receiver operating characteristic (ROC) curves were used to demonstrate the accuracy of the prognostic model. Decision curve analysis (DCA) was performed to evaluate the safety of the model in the range of clinical applications, while calibration curves were used to validate the prediction consistency.Results: Cox regression analysis indicated that age ≥62 at diagnosis, negative ER status, and tumor stage were considered adverse independent factors for overall survival (OS), while patients who were married, white or of other races, received chemotherapy or radiotherapy, had a better postoperative prognosis. The nomogram accurately predicted OS with high internal and external validation consistency index (C index) (0.756 and 0.742, respectively). The areas under the ROC curve (AUCs) of the training group were 0.787, 0.774 and 0.764 for 3 years, 5 years and 10 years, respectively, while those of the validation group were 0.756, 0.766 and 0.762, respectively. The results of both DCA and calibration curves demonstrated the good performance of the model.Conclusion: A novel nomogram for IMPC of the breast patients after surgery was developed to estimate 3- and 5-OS based on independent risk factors. This model has good accuracy and consistency in predicting prognosis and has clinical application value.

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Development and Validation of a Nomogram Based on Nutritional Indicators and Tumor Markers for Prognosis Prediction of Pancreatic Ductal Adenocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.682969 ◽

2021 ◽

Vol 11 ◽

Author(s):

Haoran Li ◽

Fang Zhou ◽

Zhifei Cao ◽

Yuchen Tang ◽

Yujie Huang ◽

...

Keyword(s):

Regression Analysis ◽

Tumor Markers ◽

Lymphocyte Count ◽

Cox Regression ◽

External Validation ◽

Training Group ◽

Ductal Adenocarcinoma ◽

Validation Group ◽

Internal Validation ◽

Cox Regression Analysis

PurposeThis study aimed to develop and validate a nomogram with preoperative nutritional indicators and tumor markers for predicting prognosis of patients with pancreatic ductal adenocarcinoma (PDAC).MethodsWe performed a bicentric, retrospective study including 155 eligible patients with PDAC. Patients were divided into a training group (n = 95), an internal validation group (n = 34), an external validation group (n = 26), and an entire validation group (n = 60). Cox regression analysis was conducted in the training group to identify independent prognostic factors to construct a nomogram for overall survival (OS) prediction. The performance of the nomogram was assessed in validation groups and through comparison with controlling nutritional status (CONUT) and prognostic nutrition index (PNI).ResultsThe least absolute shrinkage and selection operator (LASSO) regression, univariate and multivariate Cox regression analysis revealed that serum albumin and lymphocyte count were independent protective factors while CA19-9 and diabetes were independent risk factors. The concordance index (C-index) of the nomogram in the training, internal validation, external validation and entire validation groups were 0.777, 0.769, 0.759 and 0.774 respectively. The areas under curve (AUC) of the nomogram in each group were 0.861, 0.845, 0.773, and 0.814. C-index and AUC of the nomogram were better than those of CONUT and PNI in the training and validation groups. The net reclassification index (NRI), integrated discrimination improvement (IDI) and decision curve analysis showed improvement of accuracy of the nomogram in predicting OS and better net benefit in guiding clinical decisions in comparison with CONUT and PNI.ConclusionsThe nomogram incorporating four preoperative nutritional and tumor markers including serum albumin concentration, lymphocyte count, CA19-9 and diabetes mellitus could predict the prognosis more accurately than CONUT and PNI and may serve as a clinical decision support tool to determine what treatment options to choose.

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

Frontiers in Genetics ◽

10.3389/fgene.2021.763590 ◽

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 < 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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Prognostic Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Patients with Angiosarcoma, a SEER population-based study

10.21203/rs.3.rs-424876/v1 ◽

2021 ◽

Author(s):

Ting Jiang ◽

Zixiang Ye ◽

TianYu Shao ◽

YiYang Luo ◽

BinBin Wang

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

Proportional Hazards ◽

Cox Regression ◽

Predictive Ability ◽

Training Group ◽

Cox Proportional Hazards ◽

Cancer Specific Survival ◽

Cox Regression Analysis ◽

Cancer Management

Abstract Backgrounds: Angiosarcoma (AS) is a kind of highly aggressive cancer with high occurrence and mortality rates. This study aimed to establish a comprehensive and validated prognostic nomogram with various clinical indicators in patients with AS.Methods: Data of patients with AS diagnosed after surgery between 2010 and 2015 was retrieved from the Surveillance Epidemiology and End Results (SEER) database. Univariate and multivariate Cox proportional hazards regression analysis were performed to identify the independent prognostic factors associated with survival to construct the predictive nomogram of 3- and 5-year overall survival (OS) and cancer-specific survival (CSS) rates. Concordance-index(C-index), calibration plots and receiver operating characteristic (ROC) curves were applied to evaluate the predictive ability of the nomograms. The further decision curve analysis (DCA) was drawn to confirm the clinical usefulness of the nomograms.Results: 323 patients in total with AS were divided into the training group (N =226) and the validation group (N = 97). After the multivariate Cox regression analysis, gender, age, AJCC stage group 7th ed, T, N and M stage 7th ed, histologic grade and primary site were statistically identified as independent factors with OS and CSS (P<0.05). The C-index of the nomograms for OS and CCS in the training cohort was 0.760 (95%CI: 0.674–0.847) and 0.793 (95%CI: 0.687–0.898), meanwhile, the C-index of those in the validation cohort was 0.790 (95%CI: 0.725–0.855) and 0.888 (95%CI: 0.799–0.976) respectively. The results of calibration plots and ROC curve showed the nomograms qualified to measure the risk and prognosis. DCA exhibited good clinical utility of nomograms.Conclusion: Our study has developed novel and practical nomograms for predicting prognosis in patients with AS contributing to cancer management.

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Identification and validation of a prognostic 5-protein signature for biochemical recurrence following radical prostatectomy in prostate cancer

10.21203/rs.3.rs-29488/v1 ◽

2020 ◽

Author(s):

Xiangkun Wu ◽

Wenjie Li ◽

Daojun Lv ◽

Yongda Liu ◽

Di Gu

Keyword(s):

Prostate Cancer ◽

Regression Analysis ◽

Biochemical Recurrence ◽

Cox Regression ◽

Characteristic Curve ◽

Cox Proportional Hazards ◽

The Cancer Genome Atlas ◽

Medical Decision ◽

Cox Regression Analysis ◽

Protein Signature

Abstract Background : Biochemical recurrence (BCR) is considered as an indicator for prostate cancer (PCa)-specific recurrence and mortality. However, lack of effective prediction model to assess the prognosis of patients for optimization of treatment. The aim of this work was to construct a protein-based nomogram that could predict BCR for PCa.Materials and methods: Univariate Cox regression analysis was conducted to identify candidate proteins from the Cancer Genome Atlas (TCGA) database. LASSO Cox regression was further conducted to pick out the most significant prognostic proteins and formulate the proteins signature for predicting BCR. Additionally, a nomogram was constructed by multivariate Cox proportional hazards regression.Results: We established a 5‐protein-based signature which was well used to identify PCa patients into high‐ and low‐risk groups. Kaplan-Meier analysis demonstrated patients with higher BCR generally had significantly worse survival than those with lower BCR (p<0.0001). Time-dependent receiver operating characteristic curve expounded that ours signature had excellent prognostic efficiency for 1‐, 3‐ and 5‐year BCR (area under curve in training set: 0.691, 0.797, 0.808 and 0.74, 0.739, 0.82 in the test set). Univariable and multivariate Cox regression analysis showed that this 5‐protein signature was an independent of several clinical signatures including age, Gleason score, T stage, N status, PSA and residual tumor. Moreover, a nomogram was constructed and calibration plots confirmed the its predictive value in 3-, 5- and 10-year BCR overall survival.Conclusion: Our study identified a 5-protein-based signature and constructed a prognostic nomogram that reliably predicts BCR in prostate cancer. The findings might be of paramount importance in tumor prognosis and medical decision-making.

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Nomogram for Predicting Overall Survival in Patients With Invasive Micropapillary Carcinoma After Breast-Conserving Surgery: A Population‐Based Analysis

10.21203/rs.3.rs-1156745/v1 ◽

2021 ◽

Author(s):

Yuting Zhao ◽

Shouyu Li ◽

Lutong Yan ◽

Zejian Yang ◽

Na Chai ◽

...

Keyword(s):

Overall Survival ◽

Cox Regression ◽

Risk Group ◽

Breast Conserving Surgery ◽

High Risk Group ◽

Good Prediction ◽

Invasive Micropapillary Carcinoma ◽

Operating Characteristics ◽

Micropapillary Carcinoma ◽

Calibration Curves

Abstract Background: Due to the rarity of invasive micropapillary carcinoma (IMPC) of the breast, no randomized trial has investigated the prediction of overall survival (OS) for patients with IMPC after breast-conserving surgery (BCS). This study aimed to construct a nomogram for predicting OS in IMPC patients after BCS. Methods: Using the Surveillance, Epidemiology, and End Results (SEER) database, 481 eligible cases diagnosed with IMPC were collected. OS in IMPC patients after BCS were assessed through multivariable Cox analyses, Harrell’s concordance indexes (C-indexes), receiver operating characteristics (ROCs) curves, calibration curves, decision curve analyses (DCA), and survival analyses. Results: 336 patients were randomly assigned into training cohort and 145 cases in validation cohort. The multivariate Cox regression analyses revealed that age at diagnosis, American Joint Committee on Cancer (AJCC) stage, marital status, hormone receptor status and chemotherapy were significant prognostic factors for OS in conservatively operated IMPC patients. The nomogram had a good prediction performance with the C-indices 0.771 (95%CI, 0.712-0.830) and 0.715 (95%CI, 0.603-0.827) in training and validation cohorts, respectively, and good consistency between the predicted and observed probability, with calibration curves plotted and the slope was close to 1. Based on calculation of the model, participants in low-risk group had a better OS in comparison with those in high-risk group (P < 0.001). Conclusions: A nomogram was developed to predict individualized risk of OS for IMPC patients after BCS. By risk stratification, this model is expected to guide treatment decision making in improving long-term follow-up strategies for IMPC patients.

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PPARG, GNG12, and CD19 are Potential Independent Predictors of Central Nerve Recurrence in Childhood Acute Lymphoblastic Leukemia

10.21203/rs.3.rs-963371/v1 ◽

2021 ◽

Author(s):

Shan Zhang ◽

Yansong Tu ◽

Qianmiao Wu ◽

Huijun Chen ◽

Huaijun Tu ◽

...

Keyword(s):

Regression Analysis ◽

Acute Lymphoblastic Leukemia ◽

Phase I ◽

Cox Regression ◽

Lymphoblastic Leukemia ◽

Validation Group ◽

Hub Genes ◽

Cox Regression Analysis ◽

Cox Analysis ◽

Target Database

Abstract Objective: To identify biomarkers that can predict the recurrence of the central nervous system (CNS) in children with acute lymphoblastic leukemia (ALL), and establish a prediction model. Materials and Methods: The transcriptome and clinical data collected by the Children's Oncology Group (COG) collaboration group in the Phase II study (use for test group) and Phase I study (use for validation group) of ALL in children were downloaded from the TARGET database. Transcriptome data were analyzed by bioinformatics method to identify core (hub) genes and establish a risk assessment model. Univariate Cox analysis was performed on each clinical data, and multivariate Cox regression analysis was performed on the obtained results and risk score. The children ALL phase I samples collected by the COG collaboration group in the TARGET database were used for verification. Results: A total of 1230 differentially expressed genes were screened out between the CNS relapsed and non-relapsed groups. Univariate multivariate Cox analysis of 10 hub genes identified showed that PPARG (HR=0.78, 95%CI=0.67-0.91, p=0.007), CD19 (HR=1.15, 95%CI=1.05-1.26, p=0.003) and GNG12 (HR=1.25, 95%CI=1.04-1.51, p=0.017) had statistical differences. The risk score was statistically significant in univariate (HR=3.06, 95%CI=1.30-7.19, p=0.011) and multivariate (HR=1.81, 95%CI=1.16-2.32, p=0.046) Cox regression analysis. The survival analysis results of the high and low-risk groups were different when the validation group was substituted into the model (p=0.018). In addition, the CNS involvement grading status at first diagnosis CNS3 vs. CNS1 (HR=5.74, 95%CI=2.01-16.4, p=0.001), T cell vs B cell (HR=1.63, 95% CI=1.06-2.49, p=0.026) were also statistically significant. Conclusions: PPARG, GNG12, and CD19 may be predictors of CNS relapse in childhood ALL.

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An immune-relevant signature of nine genes as a prognostic biomarker in patients with gastric carcinoma

Open Medicine ◽

10.1515/med-2020-0142 ◽

2020 ◽

Vol 15 (1) ◽

pp. 850-859

Author(s):

Bing Wang ◽

Yang Zhang

Keyword(s):

Regression Analysis ◽

High Risk ◽

Gastric Carcinoma ◽

Proportional Hazards ◽

Cox Regression ◽

Malignant Tumors ◽

Risk Group ◽

Cox Proportional Hazards ◽

Rank Test ◽

Cox Regression Analysis

AbstractBackgroundAs one of the most common malignant tumors worldwide, the morbidity and mortality of gastric carcinoma (GC) are gradually increasing. The aim of this study was to construct a signature according to immune-relevant genes to predict the survival outcome of GC patients using The Cancer Genome Altas (TCGA).MethodsUnivariate Cox regression analysis was used to assess the relationship between immune-relevant genes regarding the prognosis of patients with GC. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to select prognostic immune-relevant genes and to establish the signature for the prognostic evaluation of patients with GC. Multivariate Cox regression analysis and Kaplan–Meier survival analysis were used to assess the independent prognostic ability of the immune-relevant gene signature.ResultsA total of 113 prognostic immune-relevant genes were identified using univariate Cox proportional hazards regression analysis. A signature of nine immune-relevant genes was constructed using the LASSO Cox regression. The GC samples were assigned to two groups (low- and high risk) according to the optimal cutoff value of the signature score. Compared with the patients in the high-risk group, patients in the low-risk group had a significantly better prognosis in the TCGA and GSE84437 cohorts (log-rank test P < 0.001). Multivariate Cox regression analysis demonstrated that the signature of nine immune-relevant genes might serve as an independent predictor of GC.ConclusionsOur results showed that the signature of nine immune-relevant genes may potentially serve as a prognostic prediction for patients with GC, which may contribute to the decision-making of personalized treatment for the patients.

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

BioMed Research International ◽

10.1155/2020/3858373 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13 ◽

Cited By ~ 1

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.

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The lncRNAs RP1-261G23.7, RP11-69E11.4 and SATB2-AS1 are a novel clinical signature for predicting recurrent osteosarcoma

Bioscience Reports ◽

10.1042/bsr20191251 ◽

2020 ◽

Vol 40 (1) ◽

Author(s):

Tang Ying ◽

Jin-ling Dong ◽

Cen Yuan ◽

Peng Li ◽

Qingshan Guo

Keyword(s):

Regression Analysis ◽

Signaling Pathway ◽

Cox Regression ◽

Wnt Signaling Pathway ◽

Cox Proportional Hazards ◽

Recurrence Time ◽

Rank Test ◽

Recurrence Rates ◽

Cox Regression Analysis ◽

Log Rank Test

Abstract Background: Osteosarcoma is the most common primary bone malignancy in children and adolescents. In order to find factors related to its recurrence, and thus improve recovery prospects, a powerful clinical signature is needed. Long noncoding RNAs (lncRNAs) are essential in osteosarcoma processes and development, and here we report significant lncRNAs to aid in earlier diagnosis of osteosarcoma. Methods: A univariate Cox proportional hazards regression analysis and a multivariate Cox regression analysis were used to analyze osteosarcoma patients’ lncRNA expression data from the Therapeutically Applicable Research To Generate Effective Treatments (TARGET), a public database. Results: A lncRNA signature consisting of three lncRNAs (RP1-261G23.7, RP11-69E11.4 and SATB2-AS1) was selected. The signature was used to sort patients into high-risk and low-risk groups with meaningful recurrence rates (median recurrence time 16.80 vs. >128.22 months, log-rank test, P<0.001) in the training group, and predictive ability was validated in a test dataset (median 16.32 vs. >143.80 months, log-rank test, P=0.006). A multivariate Cox regression analysis showed that the significant lncRNA was an independent prognostic factor for osteosarcoma patients. Functional analysis suggests that these lncRNAs were related to the PI3K-Akt signaling pathway, the Wnt signaling pathway, and the G-protein coupled receptor signaling pathway, all of which have various, important roles in osteosarcoma development. The significant 3-lncRNA set could be a novel prediction biomarker that could aid in treatment and also predict the likelihood of recurrence of osteosarcoma in patients.

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Development and Validation of a 23-Gene Signature for Prognosis Prediction in Lung Adenocarcinoma

10.21203/rs.3.rs-272282/v1 ◽

2021 ◽

Author(s):

Jichang Liu ◽

Yadong Wang ◽

Weiqing Zhong ◽

Yong Liu ◽

Kai Wang ◽

...

Keyword(s):

Regression Analysis ◽

Lung Adenocarcinoma ◽

Protein Interactions ◽

Cox Regression ◽

External Validation ◽

Predictive Performance ◽

Gene Signature ◽

Lasso Regression ◽

Cox Regression Analysis ◽

Prognosis Prediction

Abstract Background: Lung cancer remains the most fatal tumorous disease in the worldwide. Among that, lung adenocarcinoma (LUAD) was the most common histological type. A precise and concise prognostic model was urgently needed of LUAD. We developed a 23-gene signature for prognosis prediction based on EMT, immune and stromal datasets.Methods: Univariate Cox regression analysis was performed to select genes which were significantly associated with overall survival (OS) of the TCGA LUAD cohorts. LASSO regression and multivariate Cox regression analysis was used to build the multi-gene signature. Enrichment analyses and a protein-protein interactions (PPI) network were performed to show the interaction and functions of the signature. A nomogram was developed based on risk score and other clinical features. Predictive performance of the signature was externally validated in two independent datasets from Gene Expression Omnibus (GSE37745 and GSE13213).Results: A total of 1334 EMT, immune and stromal associated genes were obtained. After LASSO regression and multivariate Cox regression analysis, a 23-gene signature for risk stratification was built. K-M curves showed that the patients with high risk had a poorer outcome. Finally, a nomogram was built to predict prognosis. The predictive performance of the 23-gene signature was confirmed in internal and external validation.Conclusion: We developed and verified a 23-gene signature based on EMT, immune and stromal gene sets. It provided a convenient and concise tool for risk stratificationand individual medicine.

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