Nomogram application to predict overall and cancer-specific survival in osteosarcoma

Comparison of survival between palliative surgery and no surgery for advanced tongue squamous cell carcinoma: an analysis of SEER data

The aim of this study was to investigate the survival effect of palliative surgery in advanced tongue squamous cell carcinoma (TSCC). A retrospective analysis of data in the SEER database for 6151 patients with stage III/IV TSCC (American Joint Committee on Cancer (AJCC) staging), diagnosed between 2004 and 2015, was performed. The patients were divided into two groups: palliative surgery and no surgery. Kaplan-Meier and Cox proportional hazards regression analyses were applied to determine risk factors for overall survival (OS) and cancer-specific survival (CSS). A further analysis was performed using 1:1 propensity score matching (PSM) to balance 13 patient variables (sex, age at diagnosis, race, marital status, primary tumour site, SEER stage, AJCC stage, pathological differentiation grade, tumour size, lymph node metastasis, previous lymph node removal, radiotherapy, and chemotherapy). Among the 6151 patients, 706 underwent palliative surgery; the other 5445 did not undergo any kind of surgery. Those who underwent palliative surgery had a higher 5-year survival rate. After PSM, 1274 patients were included in the matched cohort. Multivariate Cox regression analysis showed that patients who underwent palliative surgery had a lower risk of death than those who did not (OS: hazard ratio 0.58, 95% confidence interval 0.49-0.69, P < 0.001; CSS: hazard ratio 0.60, 95% confidence interval 0.49-0.74, P < 0.001). In this comparative study it was found that compared with no surgery, palliative surgery has a positive impact on the survival rate of patients with advanced TSCC.

Survival analysis and prognostic model establishment of secondary osteosarcoma: a SEER-based study

Background

Surgical excision is considered one of the most effective treatments for secondary osteosarcoma (SO). It remains unclear whether the survival of patients with secondary osteosarcoma (SO) could be associated with their surgical willingness.

Materials and methods

The statistics of the patients diagnosed with SO between 1975 and 2008 were gathered from the surveillance epidemiology and end results (SEER) database. The patients were divided into three subgroups according to their surgical compliance. The authors used the multivariable Logistic regression analysis and cox regression method to reveal the influence of surgical compliance on prognosis and the risk factors of surgical compliance. Additionally, the authors formulated a nomogram model to predict the overall survival (OS) of patients. The concordance index (C-index) was used to evaluate the accuracy and practicability of the above prediction model.

Results

Sixty-three (9.2%) of the 688 patients with SO who were recommended for surgical treatment refused to undergo surgery. Lower surgical compliance can be ascribed to an earlier time of diagnosis and refusal of chemotherapy. The lower overall survival (OS) {[hazard ratio (HR)] 1.733, [CI] 1.205-2.494, P value [P]=0.003} of not surgical compliant patients was verified by the multivariate cox regression method, compared with surgical compliant patients. In addition, the discernibility of the nomogram model was proven to be relatively high (C-index=0.748), by which we can calibrate 3-year- and 5-year OS prediction plots to obtain good concordance to the actual situation.

Conclusions

Surgical compliance was proved to be an independent prognostic factor in the survival of patients with SO.

Integrative transcriptome analysis identifies MYBL2 as a poor prognosis marker for osteosarcoma and a pan-cancer marker of immune infiltration

MYBL2 (MYB proto-oncogene like 2) is an emerging prognostic marker for malignant tumors, and its potential role in osteosarcoma and its relationship with immune infiltration in pan-cancer is yet to be elucidated. We constructed a transcription factor activity profile of osteosarcoma using the single-cell regulatory network inference algorithm based on single-cell RNA sequencing data obtained from the Gene Expression Omnibus. Subsequently, we calculated the extent of MYBL2 activation in malignant proliferative osteoblasts. We also explored the association between MYBL2 and chemotherapy resistance in osteosarcoma. Furthermore, we systematically correlated MYBL2 with immunological signatures in the tumor microenvironment in pan-cancer, including immune cell infiltration, immune checkpoints, and tumor immunotherapy prognosis. Finally, we developed and validated a risk score (MRGS), derived an osteosarcoma risk score nomogram based on MRGS, and tested its ability to predict prognosis. MYBL2 and gene enrichment analyses in osteosarcoma and pan-cancer revealed that MYBL2 was positively correlated with cell proliferation and tumor immune pathways. MYBL2 expression positively correlated with SLC19A1 in pan-cancer and osteosarcoma cell lines. Pan-cancer immune infiltration analysis revealed that MYBL2 was correlated with myeloid-derived suppressor cells, Th2 cell infiltration, CD276, RELT gene expression, and tumor mutation burden. In summary, MYBL2 regulates proliferation, progression, and immune infiltration in osteosarcoma and pan-cancer. Therefore, we found that MYBL2 could be used as a potential marker for predicting the osteosarcoma prognosis. Patients with osteosarcoma and high MYBL2 expression are theoretically more sensitive to methotrexate. An osteosarcoma prognostic nomogram can provide new ideas in the search for osteosarcoma prognostic markers.

Establishment and validation of a competitive risk model for predicting cancer-specific survival in patients with osteosarcoma: a population-based study

BACKGROUND

Osteosarcoma is the most common primary bone tumor with a poor prognosis. The aim of this study was to establish a competitive risk model nomogram to predict cancer-specific survival in patients with osteosarcoma.

METHODS

Patient data was obtained from the Surveillance, Epidemiology, and End Results database in the United States. A sub-distribution proportional hazards model was used to analyze independent risk factors affecting cancer-specific mortality (CSM) in osteosarcoma patients. Based on these risk factors, a competitive risk model was constructed to predict 1-year, 3-year, and 5-year cancer-specific survival (CSS) in osteosarcoma patients. The reliability and accuracy of the nomogram were evaluated using the concordance index (C-index), the area under the receiver operating characteristic curve (AUC), and calibration curves.

RESULTS

A total of 2900 osteosarcoma patients were included. The analysis showed that age, primary tumor site, M stage, surgery, chemotherapy, and median household income were independent risk factors influencing CSM in patients. The competitive risk model was constructed to predict CSS in osteosarcoma patients. In the training and validation sets, the C-index of the model was 0.756 (95% CI 0.725-0.787) and 0.737 (95% CI 0.717-0.757), respectively, and the AUC was greater than 0.7 for both. The calibration curves also demonstrated a high consistency between the predicted survival rates and the actual survival rates, confirming the accuracy and reliability of the model.

CONCLUSION

We established a competitive risk model to predict 1-year, 3-year, and 5-year CSS in osteosarcoma patients. The model demonstrated good predictive performance and can assist clinicians and patients in making clinical decisions and formulating follow-up strategies.

Long non-coding HOXA-AS3 contributes to osteosarcoma progression through the miR-1286/TEAD1 axis

Long non-coding RNA (lncRNA) HOXA cluster antisense RNA 3 (HOXA-AS3) regulates the progression of several types of human malignancy. However, the role and potential mechanism of HOXA-AS3 in osteosarcoma (OS) remain unknown. In this study, upregulation of HOXA-AS3 was observed in OS tissues and cell lines and associated with poor clinical outcomes. Silencing of HOXA-AS3 significantly inhibited the proliferation, migration and invasion of OS cells in vitro and suppressed the tumorigenesis of OS cells in vivo. Furthermore, knockdown of HOXA-AS3 inhibited the proliferation and migration of human umbilical vein endothelial cells (HUVECs) and epithelial-to-mesenchymal transition (EMT) in OS. Further investigation of this mechanism revealed that HOXA-AS3 could directly upregulate the expression of TEAD1 via its competing endogenous RNA (ceRNA) activity on miR-1286. This study clarified the oncogenic roles of the HOXA-AS3/miR-1286/TEAD1 axis in OS progression, suggesting a novel therapeutic target for OS.

Predicting metastasis at initial diagnosis and radiotherapy effectiveness in patients with metastatic osteosarcoma

Osteosarcoma is a primary malignant bone tumor affecting mostly children and adolescents. The overall 10 year survivals of patients with metastatic osteosarcoma are typically less than 20% in the literature and remain concerning. We aimed to develop a nomogram for predicting the risk of metastasis at initial diagnosis in patients with osteosarcoma and evaluate the effectiveness of radiotherapy in patients with metastatic osteosarcoma. Clinical and demographic data of patients with osteosarcoma were collected from the surveillance, epidemiology, and end results database. We randomly split our analytical sample into the training and validation cohorts, then established and validated a nomogram for predicting the risk of osteosarcoma metastasis at initial diagnosis. The effectiveness of radiotherapy was evaluated by performing propensity score matching in patients underwent surgery + chemotherapy and those underwent surgery + chemotherapy + radiotherapy, among patients with metastatic osteosarcoma. 1439 patients met the inclusion criteria and were included in this study. 343 of 1439 had osteosarcoma metastasis by the time of initial presentation. A nomogram for predicting the likelihood of osteosarcoma metastasis by the time of initial presentation was developed. In both unmatched and matched samples, the radiotherapy group demonstrated a superior survival profile comparing with the non-radiotherapy group. Our study established a novel nomogram to evaluate the risk of osteosarcoma with metastasis, and demonstrated that radiotherapy combined with chemotherapy and surgical resection could improve 10-year survival in patients with metastasis. These findings may guide the clinical decision-making for orthopedic surgeons.

Development and validation of a nomogram to predict long-term cancer-specific survival for patients with osteosarcoma

The present work aimed to establish a new model to accurately estimate overall survival (OS) as well as cancer-specific survival (CSS) of osteosarcoma. Osteosarcoma cases were collected from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2017 and randomized as training or validation sets. Then, the OS- and CSS-related variables were discovered through multivariate Cox regression analysis to develop new nomograms to predict the 1-, 3- and 5-year OS and CSS. Besides, consistency index (C-index), decision curve analysis (DCA), along with calibration curve were adopted for assessing the predicting ability of our constructed nomograms after calibrating for 1-, 3- and 5-year OS and CSS. Altogether, 1727 osteosarcoma cases were enrolled in the present study and randomly divided as training (n = 1149, 70%) or validation (n = 576, 30%) set. As shown by univariate as well as multivariate Cox regression analyses, age, grade, T stage, M stage, surgery, chemotherapy, and histological type were identified to be the adverse factors to independently predict OS and CSS among the osteosarcoma cases. Besides, based on results of multivariate Cox regression analysis, we constructed the OS and CSS prediction nomograms. The C-index in training set was 0.806 (95% CI 0.769-0.836) for OS nomogram and 0.807 (95% CI 0.769-0.836) for CSS nomogram. In the meantime, C-index value in validation set was 0.818 (95% CI 0.789-0.847) for OS nomogram, while 0.804 (95% CI 0.773-0.835) for CSS nomogram. Besides, those calibration curves regarding the 3- and 5-year CSS of our constructed nomogram were highly consistent between the predicted values and the measurements in the training set as well as the external validation set. Our constructed nomogram outperformed the TNM stage in prediction. Our constructed nomogram is facile, creditable, and feasible; it efficiently predicts OS and CSS for osteosarcoma cases and can assist clinicians in assessing the prognosis for individuals and making decisions.

The application of clinical variable-based nomogram in predicting overall survival in malignant phyllodes tumors of the breast

Background: We aimed to explore prognostic risk factors in patients with malignant phyllodes tumors (PTs) of the breast and construct a survival prediction model. Methods: The Surveillance, Epidemiology, and End Results database was used to collect information on patients with malignant breast PTs from 2004 to 2015. The patients were randomly divided into training and validation groups using R software. Univariate and multivariate Cox regression analyses were used to screen out independent risk factors. Then, a nomogram model was developed in the training group and validated in the validation group, and the prediction performance and concordance were evaluated. Results: The study included 508 patients with malignant PTs of the breast, including 356 in the training group and 152 in the validation group. Univariate and multivariate Cox proportional hazard regression analyses showed that age, tumor size, tumor stage, regional lymph node metastasis (N), distant metastasis (M) and tumor grade were independent risk factors for the 5-year survival rate of patients with breast PTs in the training group (p < 0.05). These factors were used to construct the nomogram prediction model. The results showed that the C-indices of the training and validation groups were 0.845 (95% confidence interval [CI] 0.802-0.888) and 0.784 (95% CI 0.688-0.880), respectively. The calibration curves of the two groups were close to the ideal 45° reference line and showed good performance and concordance. Receiver operating characteristic and decision curve analysis curves showed that the nomogram has better predictive accuracy than other clinical factors. Conclusion: The nomogram prediction model constructed in this study has good predictive value. It can effectively assess the survival rates of patients with malignant breast PTs, which will aid in the personalized management and treatment of clinical patients.

Establishment and validation of systematic prognostic nomograms in patients over 60 years of age with osteosarcoma: A multicenter external verification study

BACKGROUND

The aim of this study was to develop and validate systematic nomograms to predict cancer specific survival (CSS) and overall survival (OS) in osteosarcoma patients aged over 60 years.

METHODS

We used data from the Surveillance, Epidemiology, and End Results (SEER) database and identified 982 patients with osteosarcoma over 60 years of age diagnosed between 2004 and 2015. Overall, 306 patients met the requirements for the training group. Next, we enrolled 56 patients who met the study requirements from multiple medical centers as the external validation group to validate and analyze our model. We collected all available variables and finally selected eight that were statistically associated with CSS and OS through Cox regression analysis. Integrating the identified variables, we constructed 3- and 5-year OS and CSS nomograms, respectively, which were further evaluated by calculating the C-index. A calibration curve was used to evaluate the accuracy of the model. Receiver operating characteristic (ROC) curves measured the predictive capacity of the nomograms. The Kaplan-Meier analysis was used for all patient-based variables to explore the influence of various factors on patient survival. Finally, a decision curve analysis (DCA) curve was used to analyze whether our model would be suitable for application in clinical practice.

RESULTS

Cox regression analysis of clinical variables identified age, sex, marital status, tumor grade, tumor laterality, tumor size, M-stage, and surgical treatment as prognostic factors. Nomograms showed good predictive capacity for OS and CSS. We calculated that the C-index of the OS nomogram of the training population was 0.827 (95% CI 0.778-0.876), while that of the CSS nomogram was 0.722 (95% CI 0.665-0.779). The C-index of the OS nomogram evaluated on the external validation population was 0.716 (95% CI 0.575-0.857), while that of the CSS nomogram was 0.642 (95% CI 0.50-0.788). Furthermore, the calibration curve of our prediction models indicated the nomograms could accurately predict patient outcome.

CONCLUSIONS

The constructed nomogram is a useful tool for accurately predicting OS and CSS at 3 and 5 years for patients over 60 years of age with osteosarcoma and can assist clinicians in making appropriate decisions in practice.

Predictive models for the risk and prognosis of bone metastasis in patients with newly-diagnosed esophageal cancer: A retrospective cohort study

Background

Esophageal cancer (EC) is a common malignant tumor worldwide, and patients with both EC and bone metastasis (BM) have a poor prognosis. We aimed to determine the risk and prognostic factors for BM in patients with newly diagnosed EC and to conduct two nomograms to predict the probability of BM and overall survival after BM.

Methods

Data from patients with EC from 2010 to 2015 were reviewed in the Surveillance, Epidemiology, and End Results (SEER) database. We divided participants into training and validation cohorts using univariate and multivariate logistic regression analyses and Cox regression models to explore the risk and prognostic factors of BM, respectively. Moreover, two nomograms were developed for predicting the risk and prognosis of BM in patients with EC. Then we used receiver operating characteristic curves, decision curve analysis, and calibration curves to evaluate the nomogram models. The overall survival of patients with EC and BM was analyzed using the Kaplan-Meier method.

Results

A total of 10,730 patients with EC were involved, 735 of whom had BM at the time of diagnosis. Histologic type, sex, age, N stage, primary site, liver, lung, and brain metastases, and tumor differentiation grade were identified as independent BM risk factors. Histological type, chemotherapy, brain, liver, and lung metastases were identified as prognostic risk factors for patients with EC and BM. We developed diagnostic and prognostic nomograms according to the results. Receiver operating characteristic curves, calibration, and Kaplan-Meier curves, and decision curve analysis all indicated that both nomograms had great clinical predictive ability and good clinical application potential.

Conclusions

Two novel nomograms were constructed to predict the risk and prognosis of BM in patients with EC. These prediction models can effectively assist clinicians in clinical decision-making based on their good accuracy and reliability.

Comparison of nomogram with random survival forest for prediction of survival in patients with spindle cell carcinoma

Purpose

Spindle cell carcinoma (SpCC) is a relatively rare tumor with an unfavorable prognosis. This study aimed to develop and validate a prediction model for the individual survival of patients with SpCC using Cox regression and the random survival forest (RSF) model.

Methods

Patients diagnosed with SpCC between 2004 and 2016 were selected from the Surveillance, Epidemiology, and End Results (SEER) database, and randomly divided into training and validating cohorts. Cox regression and RSF were used to identify prognostic predictors and build prediction models. A nomogram based on Cox regression was constructed to predict the 1-, 3-, and 5-year survival of patients with SpCC. Internal validation was conducted using the bootstrapping method. We evaluated the discrimination accuracy and calibration of the model using Harrell's C-index and calibration plot, respectively.

Results

Two hundred and fifty patients diagnosed with SpCC with required information were enrolled in this study. Multivariate Cox regression and RSF identified age, primary site, grade, SEER stage, tumor size, and treatment as significant prognostic predictors of SpCC. The bootstrapped and validated C-indices were 0.812 and 0.783 for nomogram, and 0.790 and 0.768 for RSF, respectively. Calibration plot of the nomogram showed an agreement between the prediction and actual observation.

Conclusions

The nomogram developed in this study is a promising tool with a simplified presentation that can easily be used and interpreted by clinicians for evaluating the survival of each patient with SpCC; its performance was comparable to that of RSF. Application of such models are needed to help oncologists identify the high-risk patients and improve clinical decision making of SpCC treatment.

A novel risk score model based on glycolysis-related genes and a prognostic model for predicting overall survival of osteosarcoma patients

This study aims to construct a novel risk score model based on glycolysis-related genes in osteosarcoma and to build and validate a prognostic model for predicting overall survival of patients with osteosarcoma. The transcriptome data and corresponding clinical data of patients with osteosarcoma were obtained from The Cancer Genome Atlas (TCGA) as the training set, and from Gene Expression Omnibus (GEO) database as the validation set. Univariate Cox regression analysis was used to screen the prognostic glycolysis-related genes. The risk coefficient of each glycolysis-related gene was calculated using LASSO regression analysis. Using the median risk score as the cut-off point, patients were divided into high-risk and low-risk groups. Kaplan-Meier survival analysis was used to determine whether there was a significant difference in the overall survival between the two groups. The nomogram was constructed according to the results of multivariate Cox regression. The C-index was calculated, the calibration chart, clinical decision curve and receiver operating characteristic curve were drawn to evaluate the predictive performance of the nomogram. We performed Gene Ontology and Kyoto encyclopedia of genes and genomics enrichment analysis to explore the potential mechanism of prognostic-related glycolysis genes in osteosarcoma. A total of 88 and 53 cases were obtained from the TCGA and GEO database, respectively. A total of 10 key glycolytic genes related to prognosis were screened out. The Kaplan-Meier survival curve revealed that the overall survival of the high-risk group was significantly shorter than that of the low-risk group. The C indices of the training set and the verification set were 0.882 and 0.828, respectively. Our findings will provide further understanding of clinical prognostic outcomes of osteosarcoma patients.

LINC01140 regulates osteosarcoma proliferation and invasion by targeting the miR-139-5p/HOXA9 axis

Osteosarcoma is one of the commonest metastatic tumor in children and teenagers, and has a hopeless, prognosis. Long non-coding RNA (lncRNA) acts momentous roles as a regulator on the proliferation and migration of cancer. Here, we performed GEO database analysis and qPCR to identify differentially expressed lncRNAs in osteosarcoma cells. Knockdown of lncRNA LINC01140 was used to detect the effect of LINC01140 on the proliferation, invasion, and epithelial-mesenchymal transition (EMT) of osteosarcoma cells. Bioinformatics analysis and qPCR identified the LINC01140/miR-139-5p/Homeobox A9 (HOXA9) regulatory axis. RNA immunoprecipitation assay, Dual-luciferase assay, and rescue experiments confirmed the interaction of LINC01140/miR-139-5p/HOXA9 in osteosarcoma. LINC01140 was overexpressed in osteosarcoma and knocking down LINC01140 restrained the proliferation and invasion of osteosarcoma cells and EMT. In Saos2 and MG63 cells, LINC01140 sponged miR-139-5p, and a miR-139-5p inhibitor overturned the suppression of LINC01140 knockdown on the proliferation and migration of osteosarcoma cells. Moreover, miR-139-5p depressed the invasion, proliferation, and EMT of osteosarcoma cells via targeting HOXA9. Our results indicate that LINC01140 downregulation inhibits the invasion, proliferation, and EMT in osteosarcoma cells through targeting the miR-139-5p/HOXA9 axis. Therefore, LINC01140 is a potential therapeutic target for osteosarcoma.

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LINC01140 is a newly discovered lncRNA associated with osteosarcoma growth and metastasis in this study.

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GEO and dbDEMC databases were used to analyze LINC01140 as a ceRNA to regulate the growth and metastasis of osteosarcoma.

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LINC01140/miR-139-5p/HOXA9 axis was confirmed to be related to the proliferation, migration and invasion of osteosarcoma.

Risk stratification system and web-based nomogram constructed for predicting the overall survival of primary osteosarcoma patients after surgical resection

Background

Previous prediction models of osteosarcoma have not focused on survival in patients undergoing surgery, nor have they distinguished and compared prognostic differences among amputation, radical and local resection. This study aimed to establish and validate the first reliable prognostic nomogram to accurately predict overall survival (OS) after surgical resection in patients with osteosarcoma. On this basis, we constructed a risk stratification system and a web-based nomogram.

Methods

We enrolled all patients with primary osteosarcoma who underwent surgery between 2004 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. In patients with primary osteosarcoma after surgical resection, univariate and multivariate cox proportional hazards regression analyses were utilized to identify independent prognostic factors and construct a novel nomogram for the 1-, 3-, and 5-year OS. Then the nomogram's predictive performance and clinical utility were evaluated by the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

Result

This study recruited 1,396 patients in all, with 837 serving as the training set (60%) and 559 as the validation set (40%). After COX regression analysis, we identified seven independent prognostic factors to develop the nomogram, including age, primary site, histological type, disease stage, AJCC stage, tumor size, and surgical method. The C-index indicated that this nomogram is considerably more accurate than the AJCC stage in predicting OS [Training set (HR: 0.741, 95% CI: 0.726–0.755) vs. (HR: 0.632, 95% CI: 0.619–0.645); Validation set (HR: 0.735, 95% CI: 0.718–0.753) vs. (HR: 0.635, 95% CI: 0.619–0.652)]. Moreover, the area under ROC curves, the calibration curves, and DCA demonstrated that this nomogram was significantly superior to the AJCC stage, with better predictive performance and more net clinical benefits.

Conclusion

This study highlighted that radical surgery was the first choice for patients with primary osteosarcoma since it provided the best survival prognosis. We have established and validated a novel nomogram that could objectively predict the overall survival of patients with primary osteosarcoma after surgical resection. Furthermore, a risk stratification system and a web-based nomogram could be applied in clinical practice to assist in therapeutic decision-making.

A Web-Based Prediction Model for Overall Survival of Elderly Patients With Malignant Bone Tumors: A Population-Based Study

Background: Malignant bone tumors (MBT) are one of the causes of death in elderly patients. The purpose of our study is to establish a nomogram to predict the overall survival (OS) of elderly patients with MBT.

Methods: The clinicopathological data of all elderly patients with MBT from 2004 to 2018 were downloaded from the SEER database. They were randomly assigned to the training set (70%) and validation set (30%). Univariate and multivariate Cox regression analysis was used to identify independent risk factors for elderly patients with MBT. A nomogram was built based on these risk factors to predict the 1-, 3-, and 5-year OS of elderly patients with MBT. Then, used the consistency index (C-index), calibration curve, and the area under the receiver operating curve (AUC) to evaluate the accuracy and discrimination of the prediction model was. Decision curve analysis (DCA) was used to assess the clinical potential application value of the nomogram. Based on the scores on the nomogram, patients were divided into high- and low-risk groups. The Kaplan-Meier (K-M) curve was used to test the difference in survival between the two patients.

Results: A total of 1,641 patients were included, and they were randomly assigned to the training set (N = 1,156) and the validation set (N = 485). The univariate and multivariate analysis of the training set suggested that age, sex, race, primary site, histologic type, grade, stage, M stage, surgery, and tumor size were independent risk factors for elderly patients with MBT. The C-index of the training set and the validation set were 0.779 [0.759–0.799] and 0.801 [0.772–0.830], respectively. The AUC of the training and validation sets also showed similar results. The calibration curves of the training and validation sets indicated that the observed and predicted values were highly consistent. DCA suggested that the nomogram had potential clinical value compared with traditional TNM staging.

Conclusion: We had established a new nomogram to predict the 1-, 3-, 5-year OS of elderly patients with MBT. This predictive model can help doctors and patients develop treatment plans and follow-up strategies.

A predictive model with a risk-classification system for cancer-specific survival in patients with primary osteosarcoma of long bone

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Seven clinical factors were significantly related to the prognosis of patients with long bone osteosarcoma.

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The established nomogram can help surgeons evaluate the prognosis of osteosarcoma patients in the most common sites.

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High-risk individuals can be identified through risk-stratification system.

Background

Osteosarcoma (OS), most commonly occurring in long bone, is a group of malignant tumors with high incidence in adolescents. No individualized model has been developed to predict the prognosis of primary long bone osteosarcoma (PLBOS) and the current AJCC TNM staging system lacks accuracy in prognosis prediction. We aimed to develop a nomogram based on the clinicopathological factors affecting the prognosis of PLBOS patients to help clinicians predict the cancer-specific survival (CSS) of PLBOS patients.

Method

We studied 1199 PLBOS patients from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015 and randomly divided the dataset into training and validation cohorts at a proportion of 7:3. Independent prognostic factors determined by stepwise multivariate Cox analysis were included in the nomogram and risk-stratification system. C-index, calibration curve, and decision curve analysis (DCA) were used to verify the performance of the nomogram.

Results

Age, Histological type, Surgery of primary site, Tumor size, Local extension, Regional lymph node (LN) invasion, and Distant metastasis were identified as independent prognostic factors. C-indexes, calibration curves and DCAs of the nomogram indicating that the nomogram had good discrimination and validity. The risk-stratification system based on the nomogram showed significant differences (P < 0.05) in CSS among different risk groups.

Conclusion

We established a nomogram with risk-stratification system to predict CSS in PLBOS patients and demonstrated that the nomogram had good performance. This model can help clinicians evaluate prognoses, identify high-risk individuals, and give individualized treatment recommendation of PLBOS patients.

Knockdown of lncRNA LINC00662 suppresses malignant behaviour of osteosarcoma cells via competition with miR-30b-3p to regulate ELK1 expression

Purpose

Osteosarcoma is a type of bone malignancy that mainly occurred in teenagers. This investigation is aimed to clarify the effect of long non-coding RNA (lncRNA) LINC00662 on the proliferation, migration, and invasion in osteosarcoma and explore the underlying action mechanisms.

Methods

The mRNA expression of LINC00662 was determined by real-time quantitative polymerase chain reaction. Cell proliferation, migration, and invasion were evaluated by 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, wound healing, and transwell assays, respectively. A dual-luciferase reporter assay was used to validate the target relationships Between microRNA (miR)-30b-3p and LINC00662/ ETS domain-containing protein 1 (ELK1). Western blotting was performed to determine the protein expression of ELK1. Xenograft model was established to evaluate the effects of LINC00662 silencing on tumor growth in vivo.

Results

LncRNA LINC00662 and ELK1 were significantly increased, while miR-30b-3p was reduced in osteosarcoma tissues. The results of functional experiments indicated that transfection of small hairpin (sh)-LINC00662 and miR-30b-3p mimics repressed the migration, invasion, and proliferation of osteosarcoma cells. LncRNA LINC00662 also appeared to sponge miR-30b-3p in order to affect the expression of ELK1. Simultaneously, there were weak negative correlations between the expression of miR-30b-3p and LINC00662/ELK1 in osteosarcoma tissues. Rescue experiments suggested that ELK1 overexpression and downregulation of miR-30b-3p reversed the suppressive effects of sh-LINC00662 on the cell migration, invasion, and proliferation in osteosarcoma.

Conclusions

The current study indicated that knockdown of LINC00662 repressed cell migration, invasion, and proliferation through sponging miR-30b-3p to regulate the expression of ELK1 in osteosarcoma. These results may uncover a promising target for the treatment of osteosarcoma.

Prognostic Nomogram and a Risk Classification System for Predicting Overall Survival of Elderly Patients with Fibrosarcoma: A Population-Based Study

Background

The objective of this study was to develop a nomogram model and risk classification system to predict overall survival in elderly patients with fibrosarcoma.

Methods

The study retrospectively collected data from the Surveillance, Epidemiology, and End Results (SEER) database relating to elderly patients diagnosed with fibrosarcoma between 1975 and 2015. Independent prognostic factors were identified using univariate and multivariate Cox regression analyses on the training set to construct a nomogram model for predicting the overall survival of patients at 3, 5, and 10 years. The receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the discrimination and predictive accuracy of the model. Decision curve analysis was used for assessing the clinical utility of the model.

Result

A total of 357 elderly fibrosarcoma patients from the SEER database were included in our analysis, randomly classified into a training set (252) and a validation set (105). The multivariate Cox regression analysis of the training set demonstrated that age, surgery, grade, chemotherapy, and tumor stage were independent prognostic factors. The ROC showed good model discrimination, with AUC values of 0.837, 0.808, and 0.806 for 3, 5, and 10 years in the training set and 0.769, 0.779, and 0.770 for 3, 5, and 10 years in the validation set, respectively. The calibration curves and decision curve analysis showed that the model has high predictive accuracy and a high clinical application. In addition, a risk classification system was constructed to differentiate patients into three different mortality risk groups accurately.

Conclusion

The nomogram model and risk classification system constructed by us help optimize patients' treatment decisions to improve prognosis.

Bone Metastasis in Renal Cell Carcinoma Patients: Risk and Prognostic Factors and Nomograms

Background

Bone metastasis (BM) is one of the common sites of renal cell carcinoma (RCC), and patients with BM have a poorer prognosis. We aimed to develop two nomograms to quantify the risk of BM and predict the prognosis of RCC patients with BM.

Methods

We reviewed patients with diagnosed RCC with BM in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. Multivariate logistic regression analysis was used to determine independent factors to predict BM in RCC patients. Univariate and multivariate Cox proportional hazards regression analyses were used to determine independent prognostic factors for BM in RCC patients. Two nomograms were established and evaluated by calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).

Results

The study included 37,554 patients diagnosed with RCC in the SEER database, 537 of whom were BM patients. BM's risk factors included sex, tumor size, liver metastasis, lung metastasis, brain metastasis, N stage, T stage, histologic type, and grade in RCC patients. Currently, independent prognostic factors for RCC with BM included grade, histologic type, N stage, surgery, brain metastasis, and lung metastasis. The calibration curve, ROC curve, and DCA showed good performance for diagnostic and prognostic nomograms.

Conclusions

Nomograms were established to predict the risk of BM in RCC and the prognosis of RCC with BM, separately. These nomograms strengthen each patient's prognosis-based decision making, which is critical in improving the prognosis of patients.

Construction and validation of nomogram to predict distant metastasis in osteosarcoma: a retrospective study

Background

Osteosarcoma is most common malignant bone tumors. OS patients with metastasis have a poor prognosis. There are few tools to assess metastasis; we want to establish a nomogram to evaluate metastasis of osteosarcoma.

Methods

Data from the Surveillance, Epidemiology, and End Results (SEER) database of patients with osteosarcoma were retrieved for retrospective analysis. We identify risk factors through univariate logistic regression and multivariate logistic regression analysis. Based on the results of multivariate analysis, we established a nomogram to predict metastasis of patients with osteosarcoma and used the concordance index (C-index) and calibration curves to test models.

Results

One thousand fifteen cases were obtained from the SEER database. In the univariate and multivariate logistic regression analysis, age, primary site, grade, T stage, and surgery are risk factors. The nomogram for metastasis was constructed based on these factors. The C-index of the training and validation cohort was 0.754 and 0.716. This means that the nomogram predictions of patients with metastasis are correct, and the calibration plots also show the good prediction performance of the nomogram.

Conclusion

We successfully develop the nomogram which can reliably predict metastasis in different patients with osteosarcoma and it only required basic information of patients. The nomogram that we developed can help clinicians better predict the metastasis with OS and determine postoperative treatment strategies.

Derivation and validation of risk groups in patients with osteosarcoma utilizing regression tree analysis

BACKGROUND

For patients with osteosarcoma, apart from stage and primary site, we lack reliable prognostic factors for risk stratification at diagnosis. There is a need for further defined, discrete prognostic groups using presenting clinical features.

METHODS

We analyzed a cohort of 3069 patients less than 50 years of age, diagnosed with primary osteosarcoma of the bone between 1986 and 2013 from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly split into test and validation cohorts. Optimal cut points for age, tumor size, and grade were identified using classification and regression tree analysis. Manual recursive partitioning was used to identify discrete prognostic groups within the test cohort. These groups were applied to the validation cohort, and overall survival was analyzed using Cox models, Kaplan Meier methods, and log-rank tests.

RESULTS

After applying recursive partitioning to the test cohort, our initial model included six groups. Application of these groups to the validation cohort resulted in four final groups. Key risk factors included presence of metastases, tumor site, tumor grade, age, and tumor size. Patients with localized axial tumors were identified as having similar outcomes to patients with metastases. Age and tumor size were only prognostically important in patients with extremity tumors when assessed in the validation cohort.

CONCLUSIONS

This analysis supports prior reports that patients with axial tumors are a high-risk group, and demonstrates the importance of age and tumor size in patients with appendicular tumors. Biologic and genetic markers are needed to further define subgroups in osteosarcoma.

Pretreatment Prediction of Relapse Risk in Patients with Osteosarcoma Using Radiomics Nomogram Based on CT: A Retrospective Multicenter Study

OBJECTIVE

To develop and externally validate a CT-based radiomics nomogram for pretreatment prediction of relapse in osteosarcoma patients within one year.

MATERIALS AND METHODS

In this multicenter retrospective study, a total of 80 patients (training cohort: 63 patients from three hospitals; validation cohort: 17 patients from three other hospitals) with osteosarcoma, undergoing pretreatment CT between August 2010 and December 2018, were identified from multicenter databases. Radiomics features were extracted and selected from tumor regions on CT image, and then, the radiomics signature was constructed. The radiomics nomogram that incorporated the radiomics signature and clinical-based risk factors was developed to predict relapse risk with a multivariate Cox regression model using the training cohort and validated using the external validation cohort. The performance of the nomogram was assessed concerning discrimination, calibration, reclassification, and clinical usefulness.

RESULTS

Kaplan-Meier curves based on the radiomics signature showed a significant difference between the high-risk and the low-risk groups in both training and validation cohorts (P < 0.001 and P = 0.015, respectively). The radiomics nomogram achieved good discriminant results in the training cohort (C-index: 0.779) and the validation cohort (C-index: 0.710) as well as good calibration. Decision curve analysis revealed that the proposed model significantly improved the clinical benefit compared with the clinical-based nomogram (P < 0.001).

CONCLUSIONS

This multicenter study demonstrates that a radiomics nomogram incorporated the radiomics signature and clinical-based risk factors can increase the predictive value of the osteosarcoma relapse risk, which supports the clinical application in different institutions.

The role of endoscopic tumor length in resected esophageal squamous cell carcinoma: a retrospective study

Background

In esophageal squamous cell carcinoma (ESCC), tumor status is assessed on the basis of latitudinal invasion. Endoscopic tumor length (ETL) may represent the longitudinal scope of the primary tumor, and whether it affects tumor stage or prognosis is not entirely clear. In this study, we evaluated the role of ETL in patients with resected ESCC.

Methods

The relationships of ETL with pathological parameters (pT status and pN status) and overall survival (OS) were analyzed using data from patients with resected ESCC who were treated at Fujian Cancer Hospital between January 1997 and December 2013. Odds ratios (ORs) and hazard ratios (HRs) were fitted with locally weighted scatterplot smoothing, and the structural breakpoints for ETL were determined using the Chow test.

Results

A total of 721 patients with resected ESCC were enrolled. As the ETL increased in these patients, a rise in the risk of advanced pT status, nodal metastasis, and mortality was observed. Cutpoint analysis showed a breakpoint of 7.0 cm. A negative impact of ETL ≥7.0 cm was also found (adjusted HR, 1.335; 95% CI, 1.004–1.774). Seven independent prognostic factors, including sex, age, number of nodes dissected, T stage, N stage, tumor location, and ETL, were identified and entered into the nomogram. The calibration curves for 1-, 3-, and 5-year OS showed optimal agreement between nomogram prediction and actual observation (c-index: 0.688).

Conclusions

Longer tumor length, with ETL ≥7.0 cm as the breakpoint, is a negative prognostic factor in patients with ESCC.

Development of an immune-related prognostic index associated with osteosarcoma

Tumor immunity is closely associated with the prognosis of tumors, including osteosarcoma (OS). The aim of the present study was to construct an immune-related prognostic index (PI) to predict the prognosis of OS. Herein, OS expression data were sourced from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. We divided the OS patients into nonmetastatic and metastatic groups, allowing differentially immune-related genes (DIRGs) to be selected. After DIRGs were further investigated by enrichment analysis, four keys prognostic IRGs (CD79A, CSF3R, MTNR1B and NPPC) were identified using a Cox proportional hazards model. Then, an immune-related prognostic index was constructed. Finally, gene set enrichment analysis (GSEA) was employed to further explore the underlying mechanisms. The difference in tumor-infiltrating immune cell (TIIC) abundance was also discussed. In our study, eight upregulated genes and 30 downregulated genes were identified. Several Gene Ontology (GO) terms and the most significantly enriched KEGG pathways were immune-associated functions and pathways. Four genes, including CD79A, CSF3R, MTNR1B and NPPC, were used to establish a risk assessment model for evaluating OS prognosis. GSEA revealed that the risk score was related to cytokine receptor interaction and to the chemokine and B cell receptor signaling pathways. Furthermore, high risk markedly related to the infiltration of several immune cell types, including M2 macrophages, naïve CD4 T cells, and CD8 T cells. In sum, we developed a survival model for OS. The underlying molecular mechanisms of the high-risk group may affect immune-related biological processes and TIICs.Abbreviations TARGET: Therapeutically Applicable Research To Generate Effective Treatments; PI: Prognostic index; OS: Osteosarcoma; DIRGs: Differentially immune-related genes; GSEA: Gene set enrichment analysis; TIIC: Tumor-infiltrating immune cell.

A nomogram for determining the disease-specific survival in invasive lobular carcinoma of the breast: A population study

We aimed to establish and validate a nomogram for predicting the disease-specific survival of invasive lobular carcinoma (ILC) patients.The Surveillance, Epidemiology, and End Results program database was used to identify ILC from 2010 to 2015, in which the data was extracted from 18 registries in the US. Multivariate Cox regression analysis was performed to identify independent prognostic factors and a nomogram was constructed to predict the 3-year and 5-year survival rates of ILC patients based on Cox regression. Predictive values were compared between the new model and the American Joint Committee on Cancer staging system using the concordance index, calibration plots, integrated discrimination improvement, net reclassification improvement, and decision-curve analyses.In total, 4155 patients were identified. After multivariate Cox regression analysis, nomogram was established based on a new model containing the predictive variables of age, the primary tumor site, histology grade, American Joint Committee on Cancer TNM (tumor node metastasis) stages II, III, and IV, breast cancer subtype, therapy modality (surgery and chemotherapy). The concordance index for the training and validation cohorts were higher for the new model (0.781 and 0.832, respectively) than for the old model (0.733 and 0.779). The new model had good performance in the calibration plots. Net reclassification improvement and integrated discrimination improvement were also improved. Finally, decision-curve analyses demonstrated that the nomogram was clinically useful.We have developed a reliable nomogram for determining the prognosis and treatment outcomes of ILC. The new model facilitates the choosing of superior medical examinations and the optimizing of therapeutic regimens with cooperation among oncologists.

LncRNA NR_027471 Functions as a ceRNA for miRNA-8055 Leading to Suppression of Osteosarcoma by Regulating the Expression of TP53INP1

Osteosarcoma is a malignancy with high aggressiveness and poor prognosis, which occurs mainly in children. The therapeutic strategy against osteosarcoma includes surgery combined with chemotherapy and radiotherapy. Although the treatment of osteosarcoma has been improved in recent years, there is a large proportion of patients with incurable osteosarcoma. Investigation of the mechanism of osteosarcoma progression would be of great help in discovering therapeutic targets for this disease. Long non-coding RNAs play critical roles in the pathogenesis of different types of cancer. The current study showed that long non-coding RNA NR_027471 was downregulated in osteosarcoma cells. In vitro and in vivo studies indicated that upregulation of NR_027471 impeded the viability, proliferation, and invasion of osteosarcoma, as well as induced cell cycle arrest at G1. In addition, binding of miR-8055 to NR_027471 was demonstrated, thereby influencing the expression of tumor protein p53 inducible nuclear protein 1 (TP53INP1). Knockdown of NR_027471 promoted epithelial–mesenchymal transition by inhibiting E-cadherin and increasing the expression of zinc finger E-box-binding homeobox 1 (ZEB1), Snail, and fibronectin. These results suggested that overexpression of NR_027471 upregulated TP53INP1 by sponging to miR-8055, leading to suppression of osteosarcoma cell proliferation and progression.

The impact of previous cancer on overall survival of bladder cancer patients and the establishment of nomogram for overall survival prediction

To investigate the role of previous cancer on overall survival in patients with bladder cancer (BCa) and to establish an effective prognostic tool for individualized overall survival prediction.A total of 78,660 patients diagnosed with BCa between 2000 and 2013 were selected from the Surveillance, Epidemiology, and End Results (SEER) database, among which 8915 patients had a history of other cancers. We compared the overall survival between patients with and without previous cancer after propensity score matching and we further established a nomogram for overall survival prediction.Univariate and multivariate Cox analyses were used to determine independent prognostic factors. The calibration curve and concordance index (C-index) were used to assess the accuracy of the nomogram. Cox proportional hazards models and Kaplan-Meier analysis were used to compare survival outcomes.BCa patients with previous cancer had worse overall survival compared with those without previous cancer (HR = 1.37; 95%CI = 1.32-1.42, P < .001). Cancers in lung prior to BCa had the most adverse impact on overall survival (HR = 2.35; 95%CI = 2.10-2.63; P < .001), and the minimal impact was located in prostate (HR = 1.16; 95%CI = 1.10-1.22; P < .001) for male and in gynecological (HR = 1.15; 95%CI = 1.02-1.30; P = .027) for female. The shorter interval time between 2 cancers and the higher stage of the previous cancer development, the higher risk of death. Age, race, sex, marital status, surgery, radiation, grade, stage, type of previous cancer as the independent prognostic factors were selected into the nomogram. The favorable calibration curve and C-index value (0.784, 95%CI = 0.782-0.786) indicated the nomogram could accurately predict the 1-, 3-, and 5-year overall survival rate of BCa patients.Previous cancer has a negative impact on the overall survival of BCa patients and requires more effective clinical management. The nomogram provides accurate survival prediction for BCa patients and might be helpful for clinical treatment selection and follow-up strategy adjustment.

Nomograms for predicting overall survival and cancer-specific survival of chondroblastic osteosarcoma patients

BACKGROUND

The establishment of precise and personalized prediction systems for chondroblastic osteosarcoma patients is important for guiding the treatment.

METHODS

The univariate logrank test and multivariate Cox regression analysis were performed to identify independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS). Nomograms were constructed to estimate the OS and CSS based on these factors. Internal and external validation was performed. The predictive power of the nomograms was determined by C-index and calibration plots.

RESULTS

A total of 401 chondroblastic osteosarcoma cases were identified. Univariate and multivariate analysis revealed that age at diagnosis, histological grade, tumor size, Surveillance, Epidemiology, and End Results stage, and surgical resection were independent prognostic factors for OS and CSS. The five factors were incorporated to construct the nomograms for estimating the 3- and 5-year OS and CSS. The C-index values for the internal validation of the OS and CSS nomogram were 0.732 and 0.746, respectively, and for the external validation were 0.780 and 0.808, respectively. The calibration curves revealed that the predicted OS and CSS could well match the actual survival rate.

CONCLUSIONS

The nomograms for predicting 3- and 5-year OS and CSS were constructed and were proved to be accurate and reliable by the internal and external validation.

Comprehensive analysis of prognostic tumor microenvironment-related genes in osteosarcoma patients

Background

Tumor microenvironment (TME) plays an important role in malignant tumors. Our study aimed to investigate the effect of the TME and related genes in osteosarcoma patients.

Methods

Gene expression profiles and clinical data of osteosarcoma patients were downloaded from the TARGET dataset. ESTIMATE algorithm was used to quantify the immune score. Then, the association between immune score and prognosis was studied. Afterward, a differential analysis was performed based on the high- and low-immune scores to determine TME-related genes. Additionally, Cox analyses were performed to construct two prognostic signatures for overall survival (OS) and disease-free survival (DFS), respectively. Two datasets obtained from the GEO database were used to validate signatures.

Results

Eighty-five patients were included in our research. The survival analysis indicated that patients with higher immune score have a favorable OS and DFS. Moreover, 769 genes were determined as TME-related genes. The unsupervised clustering analysis revealed two clusters were significantly related to immune score and T cells CD4 memory fraction. In addition, two signatures were generated based on three and two TME-related genes, respectively. Both two signatures can significantly divide patients into low- and high-risk groups and were validated in two GEO datasets. Afterward, the risk score and metastatic status were identified as independent prognostic factors for both OS and DFS and two nomograms were generated. The C-indexes of OS nomogram and DFS nomogram were 0.791 and 0.711, respectively.

Conclusion

TME was associated with the prognosis of osteosarcoma patients. Prognostic models based on TME-related genes can effectively predict OS and DFS of osteosarcoma patients.

Prognostic Factors in Patients With Osteosarcoma With the Surveillance, Epidemiology, and End Results Database

Background:

Osteosarcoma is a rare type of bone tumor, and this study aimed to assess the clinicopathologic features and prognoses of osteosarcoma patients.

Methods:

Clinicopathologic and survival data of 1025 patients between 2010 and 2016, 230 between 2008 and 2009 were downloaded and analyzed from the SEER database. Patients’ survival was analyzed using the Kaplan-Meier analysis; prognostic factors were assessed using the Cox regression hazards model. The 1-, 3-, and 5-year survival rates were estimated with nomogram. Competitive risk models were used to identify prognostic risk factors related to endpoint events of osteosarcoma patients.

Results:

Overall, 722 samples were obtained from the extremities, 134 from the axial bones, and 119 from the cranial and mandible in SEER (2010-2016 cohort). After the preliminary diagnosis, the median survival time of patients with osteosarcoma was 39 months, and the 1-, 3-, and 5-year survival rates were 87.3%, 67.2%, and 58.0%, respectively (P < 0.001). The competitive risk model revealed no competitive risks of the endpoint event.

Conclusion:

Our study found out the prognostic factors in patients with Osteosarcoma by Cox regression hazards model, after that, nomogram was established to predict the 1-, 3-, and 5-year survival rates, which may help oncologists to understand the highly malignant tumor.

LncRNA NR_136400 Suppresses Cell Proliferation and Invasion by Acting as a ceRNA of TUSC5 That Is Modulated by miR-8081 in Osteosarcoma

Long non-coding RNAs (lncRNAs) are emerging as important regulators of the processes involved in cancer development and progression. The molecular mechanism by which lncRNAs regulate the progression of osteosarcoma has not been clearly elucidated. The role of NR_136400, which is an uncharacterized lncRNA, has not been previously reported in osteosarcoma (OS). In the present study, we demonstrated that NR_136400 was downregulated in OS cells and that its downregulation promoted OS cell proliferation, apoptosis, and invasion. NR_136400 downregulation facilitated EMT by inhibiting the expression of E-cadherin and elevating the expression of ZEB1, Snail, and fibronectin. In vivo experiments using a xenograft tumor mouse model revealed that NR_136400 downregulation promoted tumor growth in OS. Mechanistic investigations demonstrated that NR_136400 competitively bound to miR-8081 and then upregulated the protein expression of TUSC5. Taken together, a newly identified regulatory mechanism of the lncRNA NR_136400/miR-8081/TUSC5 axis was systematically studied in OS, providing a promising target for therapeutic treatment.

LncRNA KCNQ1OT1 sponges miR-34c-5p to promote osteosarcoma growth via ALDOA enhanced aerobic glycolysis

Metabolic switch from oxidative phosphorylation to aerobic glycolysis, which is also called the Warburg effect, is a hallmark of osteosarcoma (OS) and leads to the enhancement of cell chemoresistance, growth, metastasis, and invasion. Emerging evidence indicates that long non-coding RNA (lncRNA) plays a crucial role in the Warburg effect of cancer cells. Here, we report that lncRNA KCNQ1OT1 was upregulated in OS. Meanwhile, functional experiments demonstrated that the KCNQ1OT1 facilitated proliferation and suppressed apoptosis of OS cells. In addition, KCNQ1OT1 contributed to the Warburg effect by stimulating aldolase A (ALDOA) expression. Furthermore, using bioinformatics analysis, luciferase reporter, RNA immunoprecipitation, and RNA pull-down assay, we identified that KCNQ1OT1 functions as a competing endogenous RNA (ceRNA) by sponging miR-34c-5p, which inhibited ALDOA expression by directly targeting its 3'UTR. Taken together, these data identified a key role of KCNQ1OT1 in glucose metabolism reprogramming of OS. Targeting the KCNQ1OT1/miR-34c-5p/ALDOA axis may be a potential therapeutic target in OS treatment.

Prognostic nomograms and Aggtrmmns scoring system for predicting overall survival and cancer-specific survival of patients with kidney cancer

Background

Currently, the prognosis of kidney cancer depends mainly on the pathological grade or tumor stage. Clinicians have few effective tools that can personalize and adequately evaluate the prognosis of kidney cancer patients.

Methods

A total of 70 481 kidney cancer patients were selected from the Surveillance, Epidemiology, and End Results database, among which patients diagnosed in 2005‐2011 (n = 42 890) were used to establish nomograms for overall survival (OS) and cancer‐specific survival (CSS), and those diagnosed in 2012‐2015 (n = 24 591) were used for external validation. Univariate and multivariate Cox analyses were used to determine independent prognostic factors. Concordance index (C‐index), receiver operating characteristic curve, and calibration curve were used to evaluate the predictive capacity of the nomograms. We further reduced subgroup classification and used propensity score matching to balance clinical informations, and analyzed the effect of other variables on survival. We established a new kidney cancer prognostic score system based on the effect of all available variables on survival. Cox proportional hazard model and Kaplan‐Meier curves were used for survival comparison.

Results

Age, gender, marital status, surgery, grade, T stage, and M stage were included as independent risk factors in the nomograms. The favorable area under the curve (AUC) value (for OS, AUC = 0.812‐0.858; and for CSS, AUC = 0.890‐0.921), internal (for OS, C‐index = 0.776; and for CSS, C‐index = 0.856), and external (for OS, C‐index = 0.814‐0.841; and for CSS, C‐index = 0.894‐0.904) validation indicated that the proposed nomograms could accurately predict 1‐, 3‐, and 5‐year OS and CSS of kidney cancer patients. The Aggtrmmns prognostic scoring system based on age, gender, race, marital status, grade, TNM stage, and surgery of kidney cancer patients could stage patients more explicitly than the AJCC staging system.

Conclusion

The nomogram and Aggtrmmns scoring system can predict OS and CSS in kidney cancer patients effectively, which may help clinicians personalize prognostic assessments and clinical decisions.

Prognostic factors in osteosarcoma: A study level meta-analysis and systematic review of current practice

Background

A consensus has not yet been reached regarding the abilities of gender, age, tumor size, tumor location, histologic subtypes, and surgery in the prediction of survival in osteosarcoma. We aimed to disclose their prognostic significance by conducting a meta-analysis of all the published data from the last decade.

Materials and Methods

Electronic database searches were conducted in PubMed, Embase, and Web of Science for relevant articles published within the last ten years. The pooled hazard ratio (HR) and corresponding 95% confidence interval (CI) were obtained to evaluate the prognostic values of the target factors.

Results

A total of 18,126 patients from 40 studies were eventually included. Results indicated that gender (male vs. female: 1.21, 95% CI, 1.11–1.32; female vs. male: 0.85, 95% CI, 0.75–0.98), age (12–20 vs. ≤12: 1.37, 95% CI, 1.13–1.65; ≥20 vs. <20: 1.29, 95% CI, 1.08–1.55; ≥40 vs. <40: 1.63, 95% CI, 1.21–2.20; ≥50 vs. <25: 2.60, 95% CI, 1.92–3.53; ≥60 vs. <60: 1.11, 95% CI, 1.06–1.18), tumor location (non-extremities vs. extremities: 2.10, 95% CI, 1.76–2.51; proximal vs. distal femur: 3.68, 95% CI: 1.51–8.96; proximal vs. distal humerus: 3.15, 95% CI: 1.53–6.49), tumor size (≥5 vs. <5: 1.42, 95% CI, 1.09–1.86; >8 vs. ≤8: 1.55, 95% CI, 1.07–2.24; >9 vs. ≤9: 1.44, 95% CI, 1.05–1.96), chemotherapy response (poor vs. good: 2.45, 95% CI, 2.02–2.97; good vs. poor: 0.41, 95% CI, 0.34–0.48), and surgery (yes vs. no: 0.45, 95% CI, 0.36–0.57; amputation vs. salvage: 2.34, 95% CI, 1.47–3.74) were significantly associated with overall survival in osteosarcoma patients.

Conclusion

The meta-analysis demonstrated that male patients, older age, large tumor size, non-extremity osteosarcoma, proximal osteosarcoma, poor chemotherapy response, no surgical treatment, and amputation surgery were correlated with a poor prognosis in osteosarcoma patients.

Long noncoding RNA SNHG14 promotes osteosarcoma progression via miR-433-3p/FBXO22 axis

Long noncoding RNA small nucleolus RNA host gene 14 (SNHG14) has been shown to exert oncogenic functions in seceral cancers, but its role in osteosarcoma still unclear. In this present study, we found that SNHG14 was significantly upregulated in osteosarcoma tissues and cell lines. Knockdown SNHG14 expression significantly inhibited osteosarcoma cell proliferation through inducing apoptosis. Further functional assays revealed that SNHG14 knockdown dramatically suppressed cell migration and invasion. Bioinformatics analysis and luciferase assays identified that microRNA-433-3p (miR-433-3p) was a direct target of SNHG14, and directly targeted F-box only protein 22 (FBXO22). Mechanistic analysis demonstrated that SNHG14 acted as a ceRNA in modulating osteosarcoma proliferation, migration and invasion by decoying miR-433-3p to upregulate FBXO22 expression. We also observed that knockdown of FBXO22 and SNHG14 and overexpression of miR-433-3p both dramatically suppressed osteosarcoma cell proliferation, migration and invasion, but induced cell apoptosis. Moreover, the suppresive effect of SNHG14 knockdown on osteosarcoma cell proliferation, invasion and migration was attenuated by miR-433-3p inhibitor. Our findings demonstrated that SNHG14 promoted osteosarcoma progression by acting as a ceRNA for miR-433-3p to regulate FBXO22 expression, suggesting that SNHG14 may serve as a potential therapeutic target in osteosarcoma patients.

Prognostic Factors and Nomograms to Predict Overall and Cancer-Specific Survival for Children with Wilms' Tumor

Objective

This study is aimed at constructing and verifying nomograms that forecast overall survival (OS) and cancer-specific survival (CSS) of children with Wilms' tumor (WT).

Patients and methods

Clinical information of 1613 WT patients who were under 18 years old between 1988 and 2010 was collected from the Surveillance, Epidemiology, and End Results (SEER) database. Using these data, we performed univariate as well as multivariate Cox's regression analyses to determine independent prognostic factors for WT. Then, nomograms to predict 3- and 5-year OS and CSS rates were constructed based on the identified prognostic factors. The nomograms were validated externally and internally. The nomograms' reliability was evaluated utilizing receiver operating characteristic (ROC) curves and concordance indices (C-indices).

Results

1613 WT patients under 18 were involved in the study and randomly divided into the training (n = 1210) and validation (n = 403) cohorts. Age at diagnosis, tumor laterality, tumor size, tumor stage, and use of surgery were determined as independent prognostic factors for OS and CSS in WT and were further applied to construct prognostic nomograms. The C-index and area under the receiver operating characteristic curve (AUC) revealed the great performance of our nomograms. Internal and external calibration plots also showed excellent agreement between actual survival and nomogram prediction.

Conclusion

Precise and convenient nomograms were developed for forecasting OS and CSS of children with WT. These nomograms were able to offer accurate and individualized prognosis and assisted clinicians in performing suitable therapy.

Functional variant of IL33 is associated with survival of osteosarcoma patients

Objectives

Previous genome-wide association study showed that GLDC/IL33 loci were associated with overall survival in patients with osteosarcoma (OS). We performed a replication study to explore whether variants of GLDC/IL33 are associated with the survival of OS patients and to further verify their functional role in the gene expression.

Methods

A total of 216 patients with OS were enrolled. The overall survival time was calculated from the date of diagnosis till the date of last follow-up or mortality. Two SNPs were genotyped, including rs55933544 and rs74438701. OS specimens were obtained from 72 patients during surgery. The gene expression level of IL33 and GLDC was evaluated by qPCR. Patients were classified into two groups according to the 5-year overall survival (death/survival). The chi-square test was used to analyze difference of genotype frequency. The Student t-test was used to compare the gene expression level between different genotypes. Cumulative survival time was calculated by the Kaplan–Meier method and analyzed by the log-rank test.

Results

Genotype TT of rs55933544 was significantly associated with the event of death (0.176 vs. 0.061, p < 0.001). Patients with no risk allele T of rs55933544 showed a 5-year overall survival of 81.4% (110/141), which was significantly higher than an overall survival of 55.0% (29/54) for patients with one risk allele and 44.8% (12/21) for patients with two risk alleles (p < 0.01). Genotype TT of rs55933544 were indicative of remarkably lower expression of IL33 than genotype CC (0.00041 ± 0.00025 vs. 0.00065 ± 0.00031, p = 0.04). Patients with low IL33 expression presented remarkably worse survival as compared with the patients with high IL33 expression (p < .01)

Conclusions

Variant rs55933544 was associated with the survival time of OS patients. IL33 may contribute to a poor prognosis of OS. Further investigation into the biological mechanisms by which IL33 influences the overall survival can shed light on the improvement of clinical outcome for OS patients.

Nomograms and risk classification systems predicting overall and cancer-specific survival in primary malignant cardiac tumor

BACKGROUND

Primary malignant cardiac tumors (PMCTs) are fatal, but up to now, there is still a lack of survival prediction model for prognosis evaluation. We developed nomograms to predict overall survival (OS) and cancer-specific survival (CSS) for PMCTs by the Surveillance, Epidemiology, and End Result (SEER) database.

METHODS

A total of 506 PMCTs participants were identified in the SEER database from 1973 to 2014 and were randomly assigned into the training cohort (N = 354) and the validation cohort (N = 152). The prognostic factors for PMCTs were identified by Kaplan-Meier and multivariate Cox analysis and further incorporated to build OS and CSS nomograms. The nomograms were internally and externally validated via concordance indexes (C-index) and calibration curves.

RESULTS

The independent prognostic factors for OS and CSS in PMCTs were associated with age at diagnosis, histopathology, tumor stage, cancer-directed surgery, and chemotherapy (all P < .05). In the internal validation, the C-index values were 0.71 (95% confidence interval [CI]: 0.68-0.75) for OS nomogram, and 0.70 (95% CI: 0.67-0.74) for CSS nomogram. In the external validation, the C-index values were 0.71 (95% CI: 0.66-0.77) for OS nomogram, and 0.71 (95% CI: 0.65-0.77) for CSS nomogram. The calibration curves of internal and external validation showed consistency between the nomograms and the actual observation. The risk stratification of PMCTs was significant distinction (P < .05).

CONCLUSION

We developed and validated credible nomograms to predict OS and CSS in PMCTs. These nomograms can be offered to clinicians to more precisely estimate the survival and identify risk stratification of PMCTs.

Long noncoding RNA TTN-AS1 enhances the malignant characteristics of osteosarcoma by acting as a competing endogenous RNA on microRNA-376a thereby upregulating dickkopf-1

The expression levels and detailed functions of TTN-AS1 in osteosarcoma (OS) have not yet been explored. This study aimed to measure TTN-AS1 expression in OS tissues and cell lines, investigate its specific roles in the aggressive characteristics of OS cells in vitro and in vivo, and elucidate the regulatory mechanisms of TTN-AS1 action. TTN-AS1 expression was high in OS tissue samples and cell lines; TTN-AS1 overexpression correlated with the clinical stage, distant metastasis, and shorter overall survival of the patients. A TTN-AS1 knockdown inhibited OS cell proliferation, migration, and invasion and induced apoptosis in vitro and slowed tumor growth in vivo. Mechanism investigation revealed that TTN-AS1 acts as a competing endogenous RNA on microRNA-376a-3p (miR-376a) in OS cells. Dickkopf-1 (DKK1) mRNA was identified as a direct target of miR-376a in OS cells. Resumption of DKK1 expression reversed the tumor-suppressive activities of miR-376a overexpression in OS cells. The knockdown of miR-376a counteracted the reduction in the malignant characteristics of OS cells by the downregulation of TTN-AS1. In conclusion, TTN-AS1 functions as a competing endogenous RNA targeting miR-376a and increases the malignancy of OS cells in vitro and in vivo by upregulating DKK1.

Prognostic nomograms to predict overall survival and cancer-specific survival in patients with pelvic chondrosarcoma

Background

The pelvis is the most common site of chondrosarcoma (CS), and the prognosis for patients with pelvic CS is worse than that for patients with CS in the extremities. However, clinicians have had few tools for estimating the likelihood of survival in patients with pelvic CS. Our aim was to develop nomograms to predict survival of patients with pelvic CS.

Methods

Data from the Surveillance, Epidemiology, and End Results (SEER) database of patients with pelvic CS between 2004 and 2016 were retrieved for retrospective analysis. Univariate and multivariate Cox analyses were used to identify independent prognostic factors. On the basis of the results of the multivariate analyses, nomograms were constructed to predict the likelihood of 3‐ and 5‐year overall survival (OS) and cancer‐specific survival (CSS) of patients with pelvic CS. The concordance index (C‐index) and calibration curves were used to test the models.

Results

In univariate and multivariate analyses of OS, sex, pathologic grade, tumor size, tumor stage, and surgery were identified as the independent risk factors. In univariate and multivariate analyses of CSS, pathologic grade, tumor size, tumor stage, and surgery were identified as the independent risk factors. These characteristics except surgery were integrated in the nomograms for predicting 3‐ and 5‐year OS and CSS, and the C‐indexes were 0.758 and 0.786, respectively.

Conclusion

The nomograms precisely and individually predict OS and CSS of patients with pelvic CS and could aid in personalized prognostic evaluation and individualized clinical decision‐making.

miR-522 stimulates TGF-β/Smad signaling pathway and promotes osteosarcoma tumorigenesis by targeting PPM1A

Osteosarcoma (OS) is identified as an aggressive malignancy of the skeletal system and normally occurs among young people. It is well accepted that microRNAs are implicated in biological activities of diverse tumors. Although miR-522 has been proved to elicit oncogenic properties in a wide range of human cancers, the physiological function and latent mechanism of miR-522 in OS tumorigenesis remain largely to be probed. In the current study, we certified that miR-522 was highly expressed in OS cells and presented carcinogenic function by contributing to cell proliferation, migration, and EMT progression whereas dampening cell apoptosis. In addition, miR-522 provoked TGF-β/Smad pathway through targeting PPM1A. Finally, the results of mechanism experiments elucidated that miR-522 stimulated TGF-β/Smad pathway to induce the development of OS via targeting PPM1A, which exposed that miR-522 may become a promising curative target for OS patients.

Development and Validation of a Prognostic Nomogram for Extremity Soft Tissue Leiomyosarcoma

Background: Extremity soft tissue leiomyosarcoma (LMS) is a rare disease with a poor prognosis. The aim of this study is to develop nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of patients with extremity soft tissue LMS.

Methods: Based on the Surveillance, Epidemiology, and End Results (SEER) database, 1,528 cases of extremity soft tissue LMS diagnosed between 1983 and 2015 were included. Cox proportional hazards regression modeling was used to analyze prognosis and obtain independent predictors. The independent predictors were integrated to develop nomograms predicting 5- and 10-year OS and CSS. Nomogram performance was evaluated by a concordance index (C-index) and calibration plots using R software version 3.5.0.

Results: Multivariate analysis revealed that age ≥60 years, high tumor grade, distant metastasis, tumor size ≥5 cm, and lack of surgery were significantly associated with decreased OS and CSS. These five predictors were used to construct nomograms for predicting 5- and 10-year OS and CSS. Internal and external calibration plots for the probability of 5- and 10-year OS and CSS showed excellent agreement between nomogram prediction and observed outcomes. The C-index values for internal validation of OS and CSS prediction were 0.776 (95% CI 0.752–0.801) and 0.835 (95% CI 0.810–0.860), respectively, whereas those for external validation were 0.748 (95% CI 0.721–0.775) and 0.814 (95% CI 0.785–0.843), respectively.

Conclusions: The proposed nomogram is a reliable and robust tool for accurate prognostic prediction in patients with extremity soft tissue LMS.

miR-552-5p facilitates osteosarcoma cell proliferation and metastasis by targeting WIF1

miR-552 promotes tumor growth and metastasis in colorectal cancer. However, the function of miR-552 in osteosarcoma remains unclear. The current study investigated the role and mechanism of miR-552-5p in the proliferation, migration and invasion of osteosarcoma cells. miR-552-5p was significantly upregulated in osteosarcoma tissues and cell lines compared with adjacent normal tissues and normal osteoblast cells. Knockdown of miR-552-5p significantly reduced the proliferation of MG63 and U2OS cells, and inhibited cell migration and invasion. Wnt inhibitory factor 1 (WIF1) was the direct target gene of miR-552-5p in osteosarcoma cells. Overexpression of miR-552-5p markedly decreased the expression of WIF1 in MG63 and U2OS cells. A negative association was identified between the expression levels of miR-552-5p and WIF1 in osteosarcoma tissues. Furthermore, the expression of WIF1 was downregulated in osteosarcoma tissues and cell lines. Finally, knockdown of WIF1 in MG63 and U2OS cells treated with miR-552-5p inhibitors rescued their ability to proliferate, migrate and invade. Overall, the results indicated that miR-552-5p promoted osteosarcoma development and progression by inhibiting WIF1. Therefore, miR-552-5p may serve as a new therapeutic target for treatment of patients with osteosarcoma.