Survival nomogram for patients with bone metastatic renal cell carcinoma: A population-based study

Nomogram based on burn characteristics and the National Early Warning Score to predict survival in severely burned patients

BACKGROUND

Extensive burns are associated with a high mortality rate. Early prediction and action can reduce mortality. The National Early Warning Score (NEWS) is considered the best early warning score for predicting mortality. However, there has been no assessment conducted on the clinical prognostic significance of NEWS in individuals suffering from severe burns. The objective of this research was to establish a nomogram based on burn characteristics and the NEWS to predict survival in severely burned patients.

METHODS

A retrospective analysis was performed on 335 patients diagnosed with extensive burns from 2005 to 2021 in the Department of Burn Surgery of Changhai Hospital, the First Affiliated Hospital of Naval Medical University. Univariate and multivariate analyses were used to determine independent prognostic factors. A nomogram was developed using these prognostic factors and its internal validity was assessed through bootstrap resampling.

RESULTS

The results of multivariate analysis showed that the independent factors affecting the prognosis of severe burn patients were age, full-thickness burn, creatinine, inhalation tracheotomy, and the NEWS, all of which were identified to create the nomogram. The Akaike Information Criterion and Bayesian Information Criterion values of the nomogram demonstrated superior goodness-of-fit in predicting severe burns compared to NEWS, with lower scores (195.21 vs. 201.24; 221.91 vs. 224.12, respectively). The bootstrap-adjusted concordance index (C-index) of the nomogram yielded a higher value of 0.923(95 % CI 0.892-0.953), compared to NEWS which had a C-index of 0.699 (95 % CI 0.628-0.770). The calibration curves demonstrated excellent agreement between predicted probabilities and observed outcomes in the nomogram analysis. Furthermore, decision curve analysis indicated promising clinical utility for the proposed nomogram model. By applying an appropriate cutoff value derived from receiver operating characteristics curve analysis, it was observed that the high-risk group identified by the nomogram exhibited a significantly higher mortality rate than the low-risk group.

CONCLUSION

This study introduces an innovative nomogram that predicts the survival rate of individuals with severe burn injuries by combining clinical attributes and laboratory examinations, demonstrating superior efficacy compared to conventional NEWS systems.

Impact of Treatment Strategies on Survival and Within Multivariate Predictive Model for Renal Cell Carcinoma Based on the SEER Database: A Retrospective Cohort Study

BACKGROUND

This project aims to shed light on how various treatment approaches affect RCC patients' chances of survival and create a prediction model for them.

METHODS

Data from the Surveillance, Epidemiology, and End Results database were used in this investigation. OS and RCSS after radiation, chemotherapy, and surgery were investigated using the Kaplan-Meier approach. Fourteen factors, including gender, age, race, and others, were subjected to univariate and multivariate COX analyses. Predicting RCSS at three, five, or ten years is the main goal. Predicting OS at three, five, or ten years is the secondary endpoint. Cox analyses, both univariate and multivariate, were used to identify prognostic factors. Furthermore, a nomogram was developed to precisely forecast patient survival rates at 3-, 5-, and 10-year intervals. DCA, calibration curves, and ROC were used to assess the nomogram's efficacy.

RESULTS

Kaplan-Meier analysis revealed that PN was associated with better survival compared to RN for tumors ≤10 cm. Cox analysis identified 10 independent prognostic factors. These variables included gender, age, race, histological type, histological grade, AJCC stage, N stage, T stage, M stage, and surgical type. Based on these variables, a nomogram for OS and RCSS prediction was created.

CONCLUSION

PN is advised over RN for RCC patients whose tumors are less than 10 cm in diameter since it offers more advantages. The combined nomogram model, which is based on clinicopathological characteristics, therapy data, and demographic variables, may be used to predict the survival of RCC patients and perform prognostic and survival analysis with accuracy.

Development of a New Focal Mouse Model of Bone Metastasis in Renal Cell Carcinoma

BACKGROUND/AIM

Developing animal models of bone metastasis in renal cell carcinoma (RCC) is challenging as immunodeficient mice are required. The aim of this study was to develop a simple immune model of RCC bone metastasis.

MATERIALS AND METHODS

RENCA tumor cells were injected into the right femurs of BALB/c mice. Sixty mice were grouped into each twenty-mouse group according to the tumor cell concentration, and the presence or absence and extent of bone metastasis in the total length of the femur were compared using hematoxylin and eosin staining of the excised tissues.

RESULTS

Bone metastasis was significantly higher in the high concentration group than in the other groups (p<0.05), with 10 mice developing bone metastasis at two weeks and nine mice developing bone metastasis at three weeks. The extent of bone metastasis was significantly greater in the high concentration group than in the other groups (p<0.05). Multiple logistic regression analysis was performed to examine the factors influencing bone metastasis, and only the high concentration was a significant factor (p<0.05).

CONCLUSION

We developed a normal immunity mouse model of local bone metastasis from RCC. This model could prove valuable for research into the treatment of bone metastases in RCC.

A dynamic visualization clinical tool constructed and validated based on the SEER database for screening the optimal surgical candidates for bone metastasis in primary kidney cancer

The implementation of primary tumor resection (PTR) in the treatment of kidney cancer patients (KC) with bone metastases (BM) has been controversial. This study aims to construct the first tool that can accurately predict the likelihood of PTR benefit in KC patients with BM (KCBM) and select the optimal surgical candidates. This study acquired data on all patients diagnosed with KCBM during 2010-2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Propensity score matching (PSM) was utilized to achieve balanced matching of PTR and non-PTR groups to eliminate selection bias and confounding factors. The median overall survival (OS) of the non-PTR group was used as the threshold to categorize the PTR group into PTR-beneficial and PTR-Nonbeneficial subgroups. Kaplan-Meier (K-M) survival analysis was used for comparison of survival differences and median OS between groups. Risk factors associated with PTR-beneficial were identified using univariate and multivariate logistic regression analyses. Receiver operating characteristic (ROC), area under the curve (AUC), calibration curves, and decision curve analysis (DCA) were used to validate the predictive performance and clinical utility of the nomogram. Ultimately, 1963 KCBM patients meeting screening criteria were recruited. Of these, 962 patients received PTR and the remaining 1061 patients did not receive PTR. After 1:1 PSM, there were 308 patients in both PTR and non-PTR groups. The K-M survival analysis results showed noteworthy survival disparities between PTR and non-PTR groups, both before and after PSM (p < 0.001). In the logistic regression results of the PTR group, histological type, T/N stage and lung metastasis were shown to be independent risk factors associated with PTR-beneficial. The web-based nomogram allows clinicians to enter risk variables directly and quickly obtain PTR beneficial probabilities. The validation results showed the excellent predictive performance and clinical utility of the nomograms for accurate screening of optimal surgical candidates for KCBM. This study constructed an easy-to-use nomogram based on conventional clinicopathologic variables to accurately select the optimal surgical candidates for KCBM patients.

Individualized assessment predictive models for risk and overall survival in elderly patients of primary kidney cancer with bone metastases: A large population-based study

Background

Elderly people are at high risk of metastatic kidney cancer (KC), and, the bone is one of the most common metastatic sites for metastatic KC. However, studies on diagnostic and prognostic prediction models for bone metastases (BM) in elderly KC patients are still vacant. Therefore, it is necessary to establish new diagnostic and prognostic nomograms.

Methods

We downloaded the data of all KC patients aged more than 65 years during 2010-2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses were used to study independent risk factors of BM in elderly KC patients. Univariate and multivariate Cox regression analysis for the study of independent prognostic factors in elderly KCBM patients. Survival differences were studied using Kaplan-Meier (K-M) survival analysis. The predictive efficacy and clinical utility of nomograms were assessed by receiver operating characteristic (ROC) curve, the area under curve (AUC), calibration curve, and decision curve analysis (DCA).

Results

A final total of 17,404 elderly KC patients (training set: n = 12,184, validation set: n = 5,220) were included to study the risk of BM. 394 elderly KCBM patients (training set: n = 278, validation set: n = 116) were included to study the overall survival (OS). Age, histological type, tumor size, grade, T/N stage and brain/liver/lung metastasis were identified as independent risk factors for developing BM in elderly KC patients. Surgery, lung/liver metastasis and T stage were identified as independent prognostic factors in elderly KCBM patients. The diagnostic nomogram had AUCs of 0.859 and 0.850 in the training and validation sets, respectively. The AUCs of the prognostic nomogram in predicting OS at 12, 24 and 36 months were: training set (0.742, 0.775, 0.787), and validation set (0.721, 0.827, 0.799), respectively. The calibration curve and DCA also showed excellent clinical utility of the two nomograms.

Conclusion

Two new nomograms were constructed and validated to predict the risk of developing BM in elderly KC patients and 12-, 24-, and 36-months OS in elderly KCBM patients. These models can help surgeons provide more comprehensive and personalized clinical management programs for this population.

Bone metastasis risk and prognosis assessment models for kidney cancer based on machine learning

Background

Bone metastasis is a common adverse event in kidney cancer, often resulting in poor survival. However, tools for predicting KCBM and assessing survival after KCBM have not performed well.

Methods

The study uses machine learning to build models for assessing kidney cancer bone metastasis risk, prognosis, and performance evaluation. We selected 71,414 kidney cancer patients from SEER database between 2010 and 2016. Additionally, 963 patients with kidney cancer from an independent medical center were chosen to validate the performance. In the next step, eight different machine learning methods were applied to develop KCBM diagnosis and prognosis models while the risk factors were identified from univariate and multivariate logistic regression and the prognosis factors were analyzed through Kaplan-Meier survival curve and Cox proportional hazards regression. The performance of the models was compared with current models, including the logistic regression model and the AJCC TNM staging model, applying receiver operating characteristics, decision curve analysis, and the calculation of accuracy and sensitivity in both internal and independent external cohorts.

Results

Our prognosis model achieved an AUC of 0.8269 (95%CI: 0.8083-0.8425) in the internal validation cohort and 0.9123 (95%CI: 0.8979-0.9261) in the external validation cohort. In addition, we tested the performance of the extreme gradient boosting model through decision curve analysis curve, Precision-Recall curve, and Brier score and two models exhibited excellent performance.

Conclusion

Our developed models can accurately predict the risk and prognosis of KCBM and contribute to helping improve decision-making.

Identification of prognostic factors for predicting survival of patients with malignant adrenal tumors: A population-based study

Background

This study aimed to identify the prognostic factors for overall survival (OS) and cancer-specific survival (CSS) in patients with malignant adrenal tumors and establish a predictive nomogram for patient survival.

Methods

The clinical characteristics of patients diagnosed with malignant adrenal tumors between 1988 and 2015 were retrieved from the Surveillance, Epidemiology and End Results (SEER) database. As the external validation set, we included 110 real-world patients from our medical centers. Univariate and multivariate Cox regressions were implemented to determine the prognostic factors of patients. The results from Cox regression were applied to establish the nomogram.

Results

A total of 2,206 eligible patients were included in our study. Patients were randomly assigned to the training set (1,544; 70%) and the validation set (662; 30%). It was determined that gender, age, marital status, histological type, tumor size, SEER stage, surgery, and chemotherapy were prognostic factors that affected patient survival. The OS prediction nomogram contained all the risk factors, while gender was excluded in the CSS prediction nomogram. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) indicated that the nomogram had a better predictive performance than SEER stage. Moreover, the clinical impact curve (CIC) showed that the nomograms functioned as effective predictive models in clinical application. The C-index of nomogram for OS and CSS prediction was 0.773 (95% confidence interval [CI]: 0.761–0.785) and 0.689 (95% CI: 0.675–0.703) in the training set. The calibration curves exhibited significant agreement between the nomogram and actual observation. Additionally, the results from the external validation set also presented that established nomograms functioned well in predicting the survival of patients with malignant adrenal tumors.

Conclusions

The following clinical variables were identified as prognostic factors: age, marital status, histological type, tumor size, SEER stage, surgery, and chemotherapy. The nomogram for patients with malignant adrenal tumors contained the accurate predictive performance of OS and CSS, contributing to optimizing individualized clinical treatments.

The geriatric nutritional risk index predicts complications after nephrectomy for renal cancer

PURPOSE

We examined if malnutrition, as defined by the Geriatric Nutritional Risk Index (GNRI), is independently associated with 30-day postoperative complications in patients undergoing nephrectomy for the treatment of renal cancer.

MATERIALS AND METHODS

Using the American College of Surgeons National Surgical Quality Improvement Program database from 2006-2019, we identified patients ≥65 years old who underwent nephrectomy for renal cancer. The following formula for GNRI was used to define preoperative nutritional status: 1.489 x serum albumin (g/L) + 41.7 x (current body weight [kg]/ ideal body weight [kg]). Based on the GNRI, patients were classified as having no (> 98), moderate (92-98), or severe malnutrition (< 92). After adjusting for potential confounders, multivariable logistic regression analyses were performed to assess the association between GNRI and 30-day postoperative complications. Odds ratios (OR) with 95% confidence intervals (CI) were reported.

RESULTS

A total of 7,683 patients were identified, of which 1,241 (16.2%) and 872 (11.3%) had moderate and severe malnutrition, respectively. Compared to normal nutrition, moderate and severe malnutrition were significantly associated with a greater odds of superficial surgical site infection, progressive renal insufficiency, readmission, extended length of stay, and non-home discharge. Severe malnutrition was also associated with urinary tract infection (OR 2.10, 95% CI 1.31-3.35) and septic shock (OR 2.93, 95% CI 1.21-7.07).

CONCLUSION

Malnutrition, as defined by a GNRI ≤ 98, is an independent predictor of 30-day complications following nephrectomy. The GNRI could be used to counsel elderly patients with renal cancer prior to nephrectomy.

A Simple-To-Use Nomogram for Predicting Early Death in Metastatic Renal Cell Carcinoma: A Population-Based Study

Background

Metastatic renal cell carcinoma (mRCC) is usually considered to have a poor prognosis, which has a high risk of early death (≤3 months). Our aim was to developed a predictive nomogram for early death of mRCC.

Methods

The SEER database was accessed to obtain the related information of 6,005 mRCC patients between 2010 and 2015. They were randomly divided into primary cohort and validation cohort in radio of 7:3. The optimal cut-off point regarding age at diagnosis and tumor size were identified by the X-tile analysis. Univariate and multivariate logistic regression models were applied to determine significant independent risk factors contributed to early death. A practical nomogram was constructed and then verified by using calibration plots, receiver operating characteristics (ROCs) curve, and decision curve analysis (DCA).

Results

There were 6,005 patients with mRCC included in the predictive model, where 1,816 patients went through early death (death within ≤3 months of diagnosis), and among them 1,687 patients died of mRCC. Based on 11 significant risk factors, including age, grade, N-stage, histologic type, metastatic sites (bone, lung, liver and brain) and treatments (surgery, radiation, and chemotherapy), a practical nomogram was developed. The model's excellent effectiveness, discrimination and clinical practicality were proved by the AUC value, calibration plots and DCA, respectively.

Conclusions

The nomogram may play a major part in distinguishing the early death of mRCC patients, which can assist clinicians in individualized medicine.

A Web-Based Prediction Model for Cancer-Specific Survival of Middle-Aged Patients With Non-metastatic Renal Cell Carcinoma: A Population-Based Study

Background

Renal cell carcinoma (RCC) is one of the most common cancers in middle-aged patients. We aimed to establish a new nomogram for predicting cancer-specific survival (CSS) in middle-aged patients with non-metastatic renal cell carcinoma (nmRCC).

Methods

The clinicopathological information of all patients from 2010 to 2018 was downloaded from the SEER database. These patients were randomly assigned to the training set (70%) and validation set (30%). Univariate and multivariate COX regression analyses were used to identify independent risk factors for CSS in middle-aged patients with nmRCC in the training set. Based on these independent risk factors, a new nomogram was constructed to predict 1-, 3-, and 5-year CSS in middle-aged patients with nmRCC. Then, we used the consistency index (C-index), calibration curve, and area under receiver operating curve (AUC) to validate the accuracy and discrimination of the model. Decision curve analysis (DCA) was used to validate the clinical application value of the model.

Results

A total of 27,073 patients were included in the study. These patients were randomly divided into a training set (N = 18,990) and a validation set (N = 8,083). In the training set, univariate and multivariate Cox regression analysis indicated that age, sex, histological tumor grade, T stage, tumor size, and surgical method are independent risk factors for CSS of patients. A new nomogram was constructed to predict patients' 1-, 3-, and 5-year CSS. The C-index of the training set and validation set were 0.818 (95% CI: 0.802-0.834) and 0.802 (95% CI: 0.777-0.827), respectively. The 1 -, 3 -, and 5-year AUC for the training and validation set ranged from 77.7 to 80.0. The calibration curves of the training set and the validation set indicated that the predicted value is highly consistent with the actual observation value, indicating that the model has good accuracy. DCA also suggested that the model has potential clinical application value.

Conclusion

We found that independent risk factors for CSS in middle-aged patients with nmRCC were age, sex, histological tumor grade, T stage, tumor size, and surgery. We have constructed a new nomogram to predict the CSS of middle-aged patients with nmRCC. This model has good accuracy and reliability and can assist doctors and patients in clinical decision making.

Development and Validation of a Nomogram to Predict Distant Metastasis in Elderly Patients With Renal Cell Carcinoma

Background

Renal cell carcinoma (RCC) is the most common renal malignant tumor in elderly patients. The prognosis of renal cell carcinoma with distant metastasis is poor. We aim to construct a nomogram to predict the risk of distant metastasis in elderly patients with RCC to help doctors and patients with early intervention and improve the survival rate.

Methods

The clinicopathological information of patients was downloaded from SEER to identify all elderly patients with RCC over 65 years old from 2010 to 2018. Univariate and multivariate logistic regression analyzed the training cohort's independent risk factors for distant metastasis. A nomogram was established to predict the distant metastasis of elderly patients with RCC based on these risk factors. We used the consistency index (C-index), calibration curve, and area under the receiver operating curve (AUC) to evaluate the accuracy and discrimination of the prediction model. Decision curve analysis (DCA) was used to assess the clinical application value of the model.

Results

A total of 36,365 elderly patients with RCC were included in the study. They were randomly divided into the training cohort (N = 25,321) and the validation cohort (N = 11,044). In the training cohort, univariate and multivariate logistic regression analysis suggested that race, tumor histological type, histological grade, T stage, N stage, tumor size, surgery, radiotherapy, and chemotherapy were independent risk factors for distant metastasis elderly patients with RCC. A nomogram was constructed to predict the risk of distant metastasis in elderly patients with RCC. The training and validation cohort's C-indexes are 0.949 and 0.954, respectively, indicating that the nomogram has excellent accuracy. AUC of the training and validation cohorts indicated excellent predictive ability. DCA suggested that the nomogram had a better clinical application value than the traditional TN staging.

Conclusion

This study constructed a new nomogram to predict the risk of distant metastasis in elderly patients with RCC. The nomogram has excellent accuracy and reliability, which can help doctors and patients actively monitor and follow up patients to prevent distant metastasis of tumors.

Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer

Background

Bone is a common target of metastasis in kidney cancer, and accurately predicting the risk of bone metastases (BMs) facilitates risk stratification and precision medicine in kidney cancer.

Methods

Patients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database to comprise the training group from 2010 to 2017, and the validation group was drawn from our academic medical center. Univariate and multivariate logistic regression analyses explored the statistical relationships between the included variables and BM. Statistically significant risk factors were applied to develop a nomogram. Calibration plots, receiver operating characteristic (ROC) curves, probability density functions (PDF), and clinical utility curves (CUC) were used to verify the predictive performance. Kaplan-Meier (KM) curves demonstrated survival differences between two subgroups of kidney cancer with and without BMs. A convenient web calculator was provided for users via “shiny” package.

Results

A total of 43,503 patients were recruited in this study, of which 42,650 were training group cases and 853 validation group cases. The variables included in the nomogram were sex, pathological grade, T-stage, N-stage, sequence number, brain metastases, liver metastasis, pulmonary metastasis, histological type, primary site, and laterality. The calibration plots confirmed good agreement between the prediction model and the actual results. The area under the curve (AUC) values in the training and validation groups were 0.952 (95% CI, 0.950–0.954) and 0.836 (95% CI, 0.809–0.860), respectively. Based on CUC, we recommend a threshold probability of 5% to guide the diagnosis of BMs.

Conclusions

The comprehensive predictive tool consisting of nomogram and web calculator contributes to risk stratification which helped clinicians identify high-risk cases and provide personalized treatment options.

Combination of Total Psoas Index and Albumin-Globulin Score for the Prognosis Prediction of Bladder Cancer Patients After Radical Cystectomy: A Population-Based Study

Background

Sarcopenia as the loss of skeletal muscle mass is related with poor postoperative survival. This work purposed to evaluate the prognostic prediction of the total psoas index (TPI), albumin–globulin score (AGS), and the combination of TPI and AGS (CTA) in bladder cancer (BCa) patients after radical cystectomy.

Methods

BCa patients that received radical cystectomy between 2012 and 2020 were retrieved from our medical center. The calculation of TPI was based on the plain computed tomography images. The predictive effects of TPI, AGS, and CTA grade on survival of BCa patients were analyzed and compared with the albumin–globulin ratio (AGR) through the receiver operating characteristic (ROC) curves. A nomogram was further established based on the Cox regression results from CTA grade and clinicopathological characteristics, which are verified by the decision curve analysis (DCA).

Results

A total of 112 eligible patients diagnosed as BCa were included in this study for retrospective analysis. The patients with lower TPI or higher AGS grade (1/2) contained poorer overall survival (OS) and disease-free survival (DFS). Divided by CTA grade, there were 35 (31.25%) patients in grade 1 associated with the best postoperative prognosis, which was accompanied with increased TPI and decreased AGS. The CTA grade could better predict postoperative outcomes compared with TPI, AGR, and AGS for the highest area under the curve (AUC; 0.674 of OS and 0.681 of DFS). The 3- and 5-year OS and DFS nomograms were conducted based on CTA grade and clinical variables, with a higher predictive performance than the TNM stage.

Conclusion

This study revealed that the novel index CTA functioned as an effective prognostic predictor for postoperative OS and DFS of BCa patients after radical cystectomy. Preoperative assessment of CTA would contribute to optimizing clinical therapies.

Identification of CDK2-Related Immune Forecast Model and ceRNA in Lung Adenocarcinoma, a Pan-Cancer Analysis

Background

Tumor microenvironment (TME) plays important roles in different cancers. Our study aimed to identify molecules with significant prognostic values and construct a relevant Nomogram, immune model, competing endogenous RNA (ceRNA) in lung adenocarcinoma (LUAD).

Methods

“GEO2R,” “limma” R packages were used to identify all differentially expressed mRNAs from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Genes with P-value <0.01, LogFC>2 or <-2 were included for further analyses. The function analysis of 250 overlapping mRNAs was shown by DAVID and Metascape software. By UALCAN, Oncomine and R packages, we explored the expression levels, survival analyses of CDK2 in 33 cancers. “Survival,” “survminer,” “rms” R packages were used to construct a Nomogram model of age, gender, stage, T, M, N. Univariate and multivariate Cox regression were used to establish prognosis-related immune forecast model in LUAD. CeRNA network was constructed by various online databases. The Genomics of Drug Sensitivity in Cancer (GDSC) database was used to explore correlations between CDK2 expression and IC50 of anti-tumor drugs.

Results

A total of 250 differentially expressed genes (DEGs) were identified to participate in many cancer-related pathways, such as activation of immune response, cell adhesion, migration, P13K-AKT signaling pathway. The target molecule CDK2 had prognostic value for the survival of patients in LUAD (P = 5.8e-15). Through Oncomine, TIMER, UALCAN, PrognoScan databases, the expression level of CDK2 in LUAD was higher than normal tissues. Pan-cancer analysis revealed that the expression, stage and survival of CDK2 in 33 cancers, which were statistically significant. Through TISIDB database, we selected 13 immunodepressants, 21 immunostimulants associated with CDK2 and explored 48 genes related to these 34 immunomodulators in cBioProtal database (P < 0.05). Gene Set Enrichment Analysis (GSEA) and Metascape indicated that 49 mRNAs were involved in PUJANA ATM PCC NETWORK (ES = 0.557, P = 0, FDR = 0), SIGNAL TRANSDUCTION (ES = –0.459, P = 0, FDR = 0), immune system process, cell proliferation. Forest map and Nomogram model showed the prognosis of patients with LUAD (Log-Rank = 1.399e-08, Concordance Index = 0.7). Cox regression showed that four mRNAs (SIT1, SNAI3, ASB2, and CDK2) were used to construct the forecast model to predict the prognosis of patients (P < 0.05). LUAD patients were divided into two different risk groups (low and high) had a statistical significance (P = 6.223e-04). By “survival ROC” R package, the total risk score of this prognostic model was AUC = 0.729 (SIT1 = 0.484, SNAI3 = 0.485, ASB2 = 0.267, CDK2 = 0.579). CytoHubba selected ceRNA mechanism medicated by potential biomarkers, 6 lncRNAs-7miRNAs-CDK2. The expression of CDK2 was associated with IC50 of 89 antitumor drugs, and we showed the top 20 drugs with P < 0.05.

Conclusion

In conclusion, our study identified CDK2 related immune forecast model, Nomogram model, forest map, ceRNA network, IC50 of anti-tumor drugs, to predict the prognosis and guide targeted therapy for LUAD patients.

Prognostic Value of the Systemic Inflammatory Response Index in Patients Undergoing Radical Cystectomy for Bladder Cancer: A Population-Based Study

PURPOSE

The aim of this study was to evaluate the prognostic significance of the systemic inflammatory response index (SIRI) in patients with bladder cancer (BCa) treated with radical cystectomy (RC) and develop a survival predictive model through establishing a nomogram.

MATERIALS AND METHODS

A total of 203 BCa patients who underwent RC were included in this study. The relationship between the SIRI and overall survival (OS), disease-free survival (DFS), and clinicopathological features were evaluated. Cox regression analysis was performed to investigate the effect of the factors on the OS and DFS. The results were applied in the establishment of a nomogram. Receiver operating characteristic (ROC) curves, decision curve analysis (DCA) curves, and calibration curves were performed to assess the predictive performance and accuracy of the nomogram, respectively.

RESULTS

According to the classification of the SIRI, 81 patients (39.9%) were assigned to SIRI grade 1, 94 patients (46.3%) to SIRI grade 2, and the remaining 28 patients (13.8%) to SIRI grade 3. Multivariate Cox regression revealed that a higher SIRI grade was significantly associated with a poor prognosis and served as an independent prognostic factor for the OS [Grade 2 vs Grade 1, odds ratio = 2.54, 95% confidence interval (CI),1.39-4.64, P = 0.002; Grade 3 vs Grade 1, odds ratio = 4.79, 95%CI: 2.41-9.50, P < 0.001] and DFS [Grade 2 vs Grade 1, odds ratio = 2.19, 95% CI, 1.12-4.31, P = 0.023; Grade 3 vs Grade 2, odds ratio = 3.36, 95%CI, 1.53-7.35, P = 0.002]. The ROC and DCA analysis indicated that the nomogram based on the SIRI contained a better predictive performance compared with the TNM stage (AUC = 0.750 and 0.791; all P < 0.05). The ROC analysis showed that nomograms can better predict the 3- and 5-year OS and DFS. The calibration curves exhibited a significant agreement between the nomogram and the actual observation.

CONCLUSION

SIRI as a novel independent prognostic index and potential prognostic biomarker can effectively improve the traditional clinicopathological analysis and optimize individualized clinical treatments for BCa patients after RC.