Development and validation of prognostic nomogram for young patients with gastric cancer

Development and validation of a nomogram to predict cancer-specific survival of elderly patients with unresected gastric cancer who received chemotherapy

This investigation aimed to explore the prognostic factors in elderly patients with unresected gastric cancer (GC) who have received chemotherapy and to develop a nomogram for predicting their cancer-specific survival (CSS). Elderly gastric cancer patients who have received chemotherapy but no surgery in the Surveillance, Epidemiology, and End Results Database between 2004 and 2015 were included in this study. Cox analyses were conducted to identify prognostic factors, leading to the formulation of a nomogram. The nomogram was validated using receiver operating characteristic (ROC) and calibration curves. The findings elucidated six prognostic factors encompassing grade, histology, M stage, radiotherapy, tumor size, and T stage, culminating in the development of a nomogram. The ROC curve indicated that the area under curve of the nomogram used to predict CSS for 3, 4, and 5 years in the training queue as 0.689, 0.708, and 0.731, and in the validation queue, as 0.666, 0.693, and 0.708. The calibration curve indicated a high degree of consistency between actual and predicted CSS for 3, 4, and 5 years. This nomogram created to predict the CSS of elderly patients with unresected GC who have received chemotherapy could significantly enhance treatment accuracy.

The necroptosis-related signature and tumor microenvironment immune characteristics associated with clinical prognosis and drug sensitivity analysis in stomach adenocarcinoma

PURPOSE

Necroptosis plays an important role in the tumorigenesis, development, metastasis, and drug resistance of malignant tumors. This study explored the new model for assessing stomach adenocarcinoma (STAD) prognosis and immunotherapy by combining long noncoding RNAs associated with necroptosis.

METHODS

Patient clinical data and STAD gene expression profiles were curated from The Cancer Genome Atlas (TCGA). Immune-related genes were sourced from a specialized molecular database. Perl software and R software were used for data processing and analysis. Necroptosis-related lncRNAs in STAD were pinpointed via R's correlation algorithms. These lncRNAs, in conjunction with clinical data, informed the construction of a prognostic lncRNA-associated risk score model using univariate and multivariate Cox regression analyses. The model's prognostic capacity was evaluated by Kaplan-Meier survival curves and validated as an independent prognostic variable. Further, a nomogram incorporating this model with clinical parameters was developed, offering refined individual survival predictions. Subsequent analyses of immune infiltration and chemosensitivity within necroptosis-related lncRNA clusters utilized an arsenal of bioinformatic tools, culminating in RT-PCR validation of lncRNA expression.

RESULTS

Through rigorous Cox regression, 21 lncRNAs were implicated in the risk score model. Stratification by median risk scores delineated patients into high- and low-risk cohorts, with the latter demonstrating superior prognostic outcomes. The risk model was corroborated as an independent prognostic indicator for STAD. The integrative nomogram displayed high concordance between predicted and observed survival rates, as evidenced by calibration curves. Differential immune infiltration in risk-defined groups was illuminated by the single sample GSEA (ssGSEA), indicating pronounced immune presence in higher-risk patients. Tumor microenvironment (TME) analysis showed that cluster-C3 had the highest score in the analysis of the three TMEs. Through the differential analysis of immune checkpoints, it was found that almost all immune checkpoint-related genes were expressed differently in various tumor clusters. Among them, CD44 expression was the highest. By comparing all drug sensitivities, we screened out 29 drugs with differences in drug sensitivity across different clusters. Risk score gene expression identification results showed that these lncRNAs were abnormally expressed in gastric cancer cell lines.

CONCLUSIONS

This investigation provides a robust methodological advance in prognosticating and personalizing immunotherapy for STAD, leveraging quantitatively derived tumor cluster risk scores. It posits the use of necroptosis-related lncRNAs as pivotal molecular beacons for guiding therapeutic strategies and enhancing clinical outcomes in STAD.

Development of a prognostic model for long-term survival of young patients with bladder cancer: a retrospective analysis of the SEER Database

OBJECTIVES

This study aims to present the clinical characteristics of young patients with bladder cancer (YBCa), evaluate related risk factors and construct a nomogram based on data acquired from the Surveillance, Epidemiology, and End Results (SEER) Database.

DESIGN

Retrospective analysis of the SEER Database (2004-2015) for primary YBCa.

SETTING AND PARTICIPANTS

Data for YBCa (defined as those aged 40 years or younger) were extracted from the SEER Database, which covers approximately 28% of the US population, using the SEER*Stat software (V.8.4.0.1). A total of 1233 YBCa were identified. Patients were randomly assigned to the training and validation sets. The database included clinicopathological features, demographic information and survival outcomes, such as age, gender, race, year of diagnosis, marital status at diagnosis, primary tumour site, histological type, tumour grade, tumour, node, metastases (TNM) staging, treatment regimen for the primary tumour, cause of death and survival time. A nomogram model was developed using univariate and multivariate analyses. The prediction model was validated using the consistency index (C-index), calibration curve and receiver operating characteristic curve.

PRIMARY OUTCOME MEASURES

3-year, 5-year and 10-year overall survival (OS).

RESULTS

1233 YBCa from 2004 to 2015 were randomly assigned to the training set (n=865) and validation set (n=368). Age, marital status, tumour grade, histological type and TNM staging were included in the nomogram. The C-index of the model was 0.876. The 3-year, 5-year and 10-year OS area under the curve values for the training and validation sets were 0.949, 0.923 and 0.856, and 0.919, 0.890 and 0.904, respectively. Calibration plots showed that the nomogram had a robust predictive accuracy.

CONCLUSIONS

To our knowledge, this is the first study to establish a precise nomogram predicting the 3-year, 5-year and 10-year OS in YBCa based on multivariate analyses. Our nomogram may serve as a valuable reference for future diagnostics and individualised treatments for YBCa. However, external validation is warranted to assess the accuracy and generalisability of our prognostic model.

Development and validation of survival nomograms in elder triple-negative invasive ductal breast carcinoma patients

BACKGROUND

We aimed to develop a nomogram to predict the overall survival of elderly patients with Triple-negative invasive ductal breast carcinoma (TNIDC).

RESEARCH DESIGN AND METHODS

12165 elderly patients with nonmetastatic TNIDC were retrieved from the SEER database from 2010 to 2019 and were randomly assigned to training and validation cohorts. Stepwise Cox regression analysis was used to select variables for the nomogram based on the training cohort. Univariate and multivariate Cox analyses were used to calculate the correlation between variables and prognosis of the patients. Survival analysis was performed for high- and low-risk subgroups based on risk score.

RESULTS

Eleven predictive factors were identified to construct our nomograms. Compared with the TNM stage, the discrimination of the nomogram revealed good prognostic accuracy and clinical applicability as indicated by C-index values of 0.741 (95% CI 0.728-0.754) against 0.708 (95% CI 0.694-0.721) and 0.765 (95% CI 0.747-0.783) against 0.725 (95% CI 0.705-0.744) for the training and validation cohorts, respectively. Differences in OS were also observed between the high- and low-risk groups (p < 0.001).

CONCLUSION

The proposed nomogram provides a convenient and reliable tool for individual evaluations for elderly patients with M0_stage TNIDC. However, the model may only for Americans.

Nomogram for predicting venous thromboembolism after spinal surgery

PURPOSE

This study aimed to establish a nomogram to predict the risk of venous thromboembolism (VTE), identifying potential risk factors, and providing theoretical basis for prevention of VTE after spinal surgery.

METHODS

A retrospective analysis was conducted on 2754 patients who underwent spinal surgery. The general characteristics of the training group were initially screened using univariate logistic analysis, and the LASSO method was used for optimal prediction. Subsequently, multivariate logistic regression analysis was performed to identify independent risk factors for postoperative VTE in the training group, and a nomogram for predict risk of VTE was established. The discrimination, calibration, and clinical usefulness of the nomogram were separately evaluated using the C-index, receiver operating characteristic curve, calibration plot and clinical decision curve, and was validated using data from the validation group finally.

RESULTS

Multivariate logistic regression analysis identified 10 independent risk factors for VTE after spinal surgery. A nomogram was established based on these independent risk factors. The C-index for the training and validation groups indicating high accuracy and stability of the model. The area under the receiver operating characteristic curve indicating excellent discrimination ability; the calibration curves showed outstanding calibration for both the training and validation groups. Decision curve analysis showed the clinical net benefit of using the nomogram could be maximized in the probability threshold range of 0.01-1.

CONCLUSION

Patients undergoing spinal surgery with elevated D-dimer levels, prolonger surgical, and cervical surgery have higher risk of VTE. The nomogram can provide a theoretical basis for clinicians to prevent VTE.

Development and validation of a nomogram for Siewert II esophagogastric junction adenocarcinoma: a retrospective analysis

Background

Due to the complex histological type and anatomical structures, there has been considerable debate on the classification of adenocarcinoma of the esophagogastric junction (AEG), especially Siewert II AEG. Furthermore, neither the American Joint Committee on Cancer (AJCC) 7th tumor-node-metastasis (TNM) [esophageal adenocarcinoma (E) or gastric cancer (G)] nor the AJCC 8th TNM (E or G) accurately predicted the prognosis of patients with Siewert II AEG.

Objective

This study aimed to investigate the factors influencing the survival and prognosis of patients with Siewert II AEG and establish a new and better prognostic predictive model.

Design

A retrospective study.

Methods

Patients with Siewert II AEG, retrieved from the Surveillance, Epidemiology, and End Results (SEER) databases, were assigned to the training set. Patients retrieved from a single tertiary medical center were assigned to the external validation set. Significant variables were selected using univariate and multivariate Cox regression analyses to construct the nomogram. Nomogram models were assessed using the concordance index (C-index), a calibration plot, decision curve analysis (DCA), and external validation.

Results

Age, tumor grade, and size, as well as the T, N, and M stages, were included in the nomograms. For the SEER training set, the C-index of the nomogram was 0.683 (0.665-0.701). The C-index of the nomogram for the external validation set was 0.690 (0.653-0.727). The calibration curve showed good agreement between the nomogram estimations and actual observations in both the training and external validation sets. The DCA showed that the nomogram was clinically useful.

Conclusion

The new predictive model showed significant accuracy in predicting the prognosis of Siewert II AEG.

A Novel Nomogram to Predict Resectable Gastric Cancer Based on Preoperative Circulating Tumor Cell

INTRODUCTION

Circulating tumor cells (CTCs) have been suggested to have an important prognostic role in gastrointestinal tumors. We developed a preoperative CTC-based nomogram to predict the prognosis of patients with resectable gastric cancer after surgery and established a risk stratification system based on the nomogram.

METHODS

From January 2012 to June 2017, we screened 258 patients with gastric cancer treated with surgery from one center as the training cohort and 133 patients with gastric cancer treated with surgery from another as the validation cohort, screened prognostic factors for the training cohort using univariate and multivariate Cox risk proportional models, created predictive overall survival (OS) and a recurrence-free survival (RFS) nomogram, and plotted the receiver operating characteristic curve and calibration curve for this nomogram in the training and validation cohorts. Risk score stratification was performed according to the nomogram, and OS curves were plotted for the low, medium, and high-risk groups using the Kaplan-Meier method.

RESULTS

The CTC positivity rate was 78.5% in all patients. CTC, TNM stage, and Ki-67 were the prognostic factors affecting OS and RFS after gastric cancer surgery. The nomogram consisted of these 3 variables. In the training group, the area under the curve of the nomogram for OS at 1, 3, and 5 years was 0.918, 0.829, and 0.813, respectively, and the area under the curve for RFS was 0.900, 0884, and 0.839, respectively. There was a statistically significant difference in OS among the low, medium, and high-risk groups according to the risk stratification system constructed from nomogram scores ( P < 0.001).

DISCUSSION

Two nomograms based on preoperative CTC were established to predict OS and RFS after resectable gastric cancer. The 2 nomograms had good discrimination and calibration and significant stratification ability of the risk stratification system established according to them.

Enhancing prognostic accuracy: a SEER-based analysis for overall and cancer-specific survival prediction in cervical adenocarcinoma patients

BACKGROUND

Cervical adenocarcinoma (CA) is the second most prevalent histological subtype of cervical cancer, following cervical squamous cell carcinoma (CSCC). As stated in the guidelines provided by the National Comprehensive Cancer Network, they are staged and treated similarly. However, compared with CSCC patients, CA patients are more prone to lymph node metastasis and recurrence with a poorer prognosis. The objective of this research was to discover prognostic indicators and develop nomograms that can be utilized to anticipate the overall survival (OS) and cancer-specific survival (CSS) of patients diagnosed with CA.

METHODS

Using the Surveillance, Epidemiology, and End Result (SEER) database, individuals with CA who received their diagnosis between 2004 and 2015 were identified. A total cohort (n = 4485) was randomly classified into two separate groups in a 3:2 ratio, to form a training cohort (n = 2679) and a testing cohort (n = 1806). Overall survival (OS) was the primary outcome measure and cancer-specific survival (CSS) was the secondary outcome measure. Univariate and multivariate Cox analyses were employed to select significant independent factors and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis was utilized to develop predictive nomogram models. The predictive accuracy and discriminatory ability of the nomogram were assessed by employing metrics such as the calibration curve, receiver operating characteristic (ROC) curve, and the concordance index (C-index).

RESULTS

Age, Tumor Node Metastasis stages (T, N, and M), SEER stage, grade, and tumor size were assessed as common independent predictors of both OS and CSS. The C-index value of the nomograms for predicting OS was 0.832 (95% CI 0.817-0.847) in the training cohort and 0.823 (95% CI 0.805-0.841) in the testing cohort.

CONCLUSION

We developed and verified nomogram models for predicting 1-, 3- and 5-year OS and CSS among patients with cervical adenocarcinoma. These models exhibited excellent performance in prognostic prediction, providing support and assisting clinicians in assessing survival prognosis and devising personalized treatments for CA patients.

Biological and clinical implications of early-onset cancers: A unique subtype

In recent years, the incidence of cancers is continuously increasing in young adults. Early-onset cancer (EOC) is usually defined as patients with cancers under the age of 50, and may represent a unique subgroup due to its special disease features. Overall, EOCs often initiate at a young age, present as a better physical performance but high degree of malignancy. EOCs also share common epidemiological and hereditary risk factors. In this review, we discuss several representative EOCs which were well studied previously. By revealing their clinical and molecular similarities and differences, we consider the group of EOCs as a unique subtype compared to ordinary cancers. In consideration of EOC as a rising threat to human health, more researches on molecular mechanisms, and large-scale, prospective clinical trials should be carried out to further translate into improved outcomes.

The percentages of signet-ring cells (SRCs) affects the prognosis after radical gastrectomy for advanced gastric cancer

PURPOSE

Only recently has the percentage of signet-ring cells (SRCs) been shown to affect the prognosis following gastric cancer surgery. It is uncertain whether the SRC percentage has a role in tumour biology or prognosis of gastric signet-ring cell carcinoma (GSRCC). For this research, we assessed the effect of the SRC percentage on the clinicopathological and prognostic characteristics of gastric cancer (GC) tumours and created and verified a prognostic nomogram to assess the overall survival (OS) of GSRCC patients.

METHODS

In our study, 1100 GC patients with signet-ring cell carcinoma (SRCC) at Zhejiang Cancer Hospital from December 2013 to December 2018 who underwent curative gastric cancer resection were retrospectively analysed. The patients were separated into two groups: those with SRCC (SRC percentage >50%; n = 157) and those with partial signet-ring cell carcinoma (PSRCC) (SRC percentage ≤50%; n = 943). We compared the clinicopathological characteristics of both groups. To estimate OS and determine correlations with the SRC percentage, the Kaplan-Meier method and log-rank test were used. To develop the prognostic nomogram, independent prognostic indicators for OS were identified using Cox regression analyses. Predictions were assessed using the calibration curve and C-index.

RESULTS

Our research showed that there was no discernible difference in OS between the two groups. The preoperative CA242 level, pT stage, pN stage, age, nerve invasion, neoadjuvant chemotherapy, postoperative chemotherapy, and maximum tumour diameter were independent prognostic risk factors for OS for GC (all p < 0.05). However, for advanced GC, the SRC percentage (HR = 1.571, 95% CI 1.072-2.302, p = 0.020) was an independent prognostic factor of OS. Other independent prognostic risk factors were age, pT stage, pN stage, nerve invasion, tumour location, neoadjuvant chemotherapy, postoperative chemotherapy, preoperative CA50 level, and preoperative CEA level (all p < 0.05). On these bases, nomograms were constructed for GC and advanced GC, with C-indexes of 0.806 (95%CI 0.782-0.830) and 0.728 (95%CI 0.697-0.759), respectively.

CONCLUSIONS

In cases of advanced gastric cancer, the SRC percentage served as a standalone prognostic indicator for OS. An effective tool for assessing the prognosis of GSRCC was offered by the nomogram.

Characteristics and prognostic factors of adult patients with osteosarcoma from the SEER database

Osteosarcoma is the most common bone malignancy. There are many studies on the prognostic factors of children and adolescents, but the characteristics and prognostic factors of adult osteosarcoma are rarely studied. The aim of this study was to construct a nomogram for predicting the prognosis of adult osteosarcoma. Information on all osteosarcoma patients aged ≥ 18 years from 2004 to 2015 was downloaded from the surveillance, epidemiology and end results database. A total of 70% of the patients were included in the training set and 30% of the patients were included in the validation set. Univariate log-rank analysis and multivariate cox regression analysis were used to screen independent risk factors affecting the prognosis of adult osteosarcoma. These risk factors were used to construct a nomogram to predict 3-year and 5-year prognosis in adult osteosarcoma. Multivariate cox regression analysis yielded 6 clinicopathological features (age, primary site, tumor size, grade, American Joint Committee on Cancer stage, and surgery) for the prognosis of adult osteosarcoma patients in the training cohort. A nomogram was constructed based on these predictors to assess the prognosis of adult patients with osteosarcoma. Concordance index, receiver operating characteristic and calibration curves analyses also showed satisfactory performance of the nomogram in predicting prognosis. The constructed nomogram is a helpful tool for exactly predicting the prognosis of adult patients with osteosarcoma, which could enable patients to be more accurately managed in clinical practice.

Establishment of a nomogram for predicting prolonged mechanical ventilation in cardiovascular surgery patients

AIMS

This study aimed to develop a nomogram model for predicting prolonged mechanical ventilation (PMV) in patients undergoing cardiovascular surgery.

METHODS AND RESULTS

In total, 693 patients undergoing cardiovascular surgery at an Affiliated Hospital of Nantong University between January 2018 and June 2020 were studied. Postoperative PMV was required in 147 patients (21.2%). Logistic regression analysis showed that delirium [odds ratio (OR), 3.063; 95% confidence interval (CI), 1.991-4.713; P < 0.001], intraoperative blood transfusion (OR, 2.489; 95% CI, 1.565-3.960; P < 0.001), obesity (OR, 2.789; 95% CI, 1.543-5.040; P = 0.001), postoperative serum creatinine level (mmol/L; OR, 1.012; 95% CI, 1.007-1.017; P < 0.001), postoperative serum albumin level (g/L; OR, 0.937; 95% CI, 0.902-0.973; P = 0.001), and postoperative total bilirubin level (μmol/L; OR, 1.020; 95% CI, 1.005-1.034; P = 0.008) were independent risk factors for PMV. The area under the receiver operating characteristic curve for our nomogram was found to be 0.770 (95% CI, 0.727-0.813). The goodness-of-fit test indicated that the model fitted the data well (χ2 = 12.480, P = 0.131). After the model was internally validated, the calibration plot demonstrated good performance of the nomogram, as supported by the Harrell concordance index of 0.760. Decision curve analysis demonstrated that the nomogram was clinically useful in identifying patients at risk for PMV.

CONCLUSION

We established a new nomogram model that may provide an individual prediction of PMV. This model may provide nurses, social workers, physicians, and administrators with an accurate and objective assessment tool to identify patients at high risk for PMV after cardiovascular surgery.

The evaluation of tumor microenvironment infiltration and the identification of angiogenesis-related subgroups in skin cutaneous melanoma

BACKGROUND

There are limited studies on the association between angiogenesis-related genes (ARGs) and the predictive risk of melanoma, even though angiogenic factors, which are essential for tumor growth and metastasis, might be secreted by angiogenesis-related protein in skin cutaneous melanoma (SKCM). To forecast patient outcomes, this study attempts to develop a predictive risk signature linked to angiogenesis in cutaneous melanoma.

METHODS

In 650 patients with SKCM, the expression and mutation of ARGs were examined, and this information was related to the clinical prognosis. SKCM patients were split into two groups based on how well they performed on the ARG. The link between ARGs, risk genes, and immunological microenvironment was examined using a range of algorithmic analysis techniques. Based on these five risk genes, an angiogenesis risk signature was created. We developed a nomogram and examined the sensitivity of antineoplastic medications to help the proposed risk model's clinical applicability.

RESULTS

The risk model developed by ARGs revealed that the prognosis for the two groups was significantly different. The predictive risk score was negatively connected with memory B cells, activated memory CD4 + T cells, M1 macrophages, and CD8 + T cells, and favorably correlated with dendritic cells, mast cells, and neutrophils.

CONCLUSIONS

Our findings offer fresh perspectives on prognostic evaluation and imply that ARG modulation is implicated in SKCM. Potential medications for the treatment of individuals with various SKCM subtypes were predicted by drug sensitivity analysis.

Construction of the XGBoost model for early lung cancer prediction based on metabolic indices

BACKGROUND

Lung cancer is a malignant tumour, and early diagnosis has been shown to improve the survival rate of lung cancer patients. In this study, we assessed the use of plasma metabolites as biomarkers for lung cancer diagnosis. In this work, we used a novel interdisciplinary mechanism, applied for the first time to lung cancer, to detect biomarkers for early lung cancer diagnosis by combining metabolomics and machine learning approaches.

RESULTS

In total, 478 lung cancer patients and 370 subjects with benign lung nodules were enrolled from a hospital in Dalian, Liaoning Province. We selected 47 serum amino acid and carnitine indicators from targeted metabolomics studies using LC‒MS/MS and age and sex demographic indicators of the subjects. After screening by a stepwise regression algorithm, 16 metrics were included. The XGBoost model in the machine learning algorithm showed superior predictive power (AUC = 0.81, accuracy = 75.29%, sensitivity = 74%), with the metabolic biomarkers ornithine and palmitoylcarnitine being potential biomarkers to screen for lung cancer. The machine learning model XGBoost is proposed as an tool for early lung cancer prediction. This study provides strong support for the feasibility of blood-based screening for metabolites and provide a safer, faster and more accurate tool for early diagnosis of lung cancer.

CONCLUSIONS

This study proposes an interdisciplinary approach combining metabolomics with a machine learning model (XGBoost) to predict early the occurrence of lung cancer. The metabolic biomarkers ornithine and palmitoylcarnitine showed significant power for early lung cancer diagnosis.

Two web-based dynamic prediction models for the diagnosis and prognosis of gastric cancer with bone metastases: evidence from the SEER database

Purpose

Our aim was to identify the clinical characteristics and develop and validate diagnostic and prognostic web-based dynamic prediction models for gastric cancer (GC) with bone metastasis (BM) using the SEER database.

Method

Our study retrospectively analyzed and extracted the clinical data of patients aged 18-85 years who were diagnosed with gastric cancer between 2010 and 2015 in the SEER database. We randomly divided all patients into a training set and a validation set according to the ratio of 7 to 3. Independent factors were identified using logistic regression and Cox regression analyses. Furthermore, we developed and validated two web-based clinical prediction models. We evaluated the prediction models using the C-index, ROC, calibration curve, and DCA.

Result

A total of 23,156 patients with gastric cancer were included in this study, of whom 975 developed bone metastases. Age, site, grade, T stage, N stage, brain metastasis, liver metastasis, and lung metastasis were identified as independent risk factors for the development of BM in GC patients. T stage, surgery, and chemotherapy were identified as independent prognostic factors for GC with BM. The AUCs of the diagnostic nomogram were 0.79 and 0.81 in the training and test sets, respectively. The AUCs of the prognostic nomogram at 6, 9, and 12 months were 0.93, 0.86, 0.78, and 0.65, 0.69, 0.70 in the training and test sets, respectively. The calibration curve and DCA showed good performance of the nomogram.

Conclusions

We established two web-based dynamic prediction models in our study. It could be used to predict the risk score and overall survival time of developing bone metastasis in patients with gastric cancer. In addition, we also hope that these two web-based applications will help physicians comprehensively manage gastric cancer patients with bone metastases.

A postsurgical prognostic nomogram for patients with lymph node positive rectosigmoid junction adenocarcinoma

OBJECTIVE

The definition of rectosigmoid junction (RSJ) is still in debate. The treatment and prognosis of patients with rectosigmoid junction cancer (RSJC) and positive lymph nodes (PLN-RSJCs) are mostly based on the American Joint Committee on Cancer (AJCC) staging system. Our study aims to assist clinicians in creating a more intuitive and accurate nomogram model for PLN-RSJCs for the prediction of patient overall survival (OS) after surgery.

METHODS

Based on the Surveillance, Epidemiology, and End Results (SEER) database, we extracted 3384 patients with PLN-RSJCs and randomly divided them into development (n = 2344) and validation (n = 1004) cohorts at a ratio of 7:3. Using univariate and multivariate COX regression analysis, we identified independent risk factors associated with OS in PLN-RSJCs in the development cohort, which were further used to establish a nomogram model. To verify the accuracy of the model, the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and an internal validation cohort have been employed. Decision curve analysis (DCA) was used to assess the clinical applicability and benefits of the generated model. Survival curves of the low- and high-risk groups were calculated using the Kaplan-Meier method together with the log-rank test.

RESULTS

Age, marital, chemotherapy, AJCC stage, T and N stage of TNM system, tumor size, and regional lymph nodes were selected as independent risk factors and included in the nomogram model. The C-index of this nomogram in the development (0.751;0.737-0.765) and validation cohorts (0.750;0.764-0.736) were more significant than that of the AJCC 7th staging system (0.681; 0.665-0.697). The ROC curve with the calculated area under the curve (AUC) in the development cohort was 0.845,0.808 and 0.800 for 1-year, 3-year and 5-year OS, AUC in the validation cohort was 0.815,0.833 and 0.814 for 1-year, 3-year and 5-year, respectively. The calibration plots of both cohorts for 1-year,3-year and 5-year OS all demonstrated good agreement between actual clinical observations and predicted outcomes. In the development cohort, the DCA showed that the nomogram prediction model is more advantageous for clinical application than the AJCC 7th staging system. Kaplan-Meier curves in the low and high groups showed significant difference in patient OS.

CONCLUSIONS

We established an accurate nomogram model for PLN-RSJCs, intended to support clinicians in the treatment and follow-up of patients.

Survival benefit of younger gastric cancer patients in China and the United States: A comparative study

BACKGROUND

The impact of racial and regional disparity on younger patients with gastric cancer (GC) remains unclear.

AIM

To investigate the clinicopathological characteristics, prognostic nomogram, and biological analysis of younger GC patients in China and the United States.

METHODS

From 2000 to 2018, GC patients aged less than 40 years were enrolled from the China National Cancer Center and the Surveillance Epidemiology and End Results database. Biological analysis was performed based on the Gene Expression Omnibus database. Survival analysis was conducted via Kaplan-Meier estimates and Cox proportional hazards models.

RESULTS

A total of 6098 younger GC patients were selected from 2000 to 2018, of which 1159 were enrolled in the China National Cancer Center, and 4939 were collected from the Surveillance Epidemiology and End Results database. Compared with the United States group, younger patients in China revealed better survival outcomes (P < 0.01). For race/ethnicity, younger Chinese cases also enjoyed a better prognosis than that in White and Black datasets (P < 0.01). After stratification by pathological Tumor-Node-Metastasis (pTNM) stage, a survival advantage was observed in China with pathological stage I, III, and IV (all P < 0.01), whereas younger GC patients with stage II showed no difference (P = 0.16). In multivariate analysis, predictors in China involved period of diagnosis, linitis plastica, and pTNM stage, while race, diagnostic period, sex, location, differentiation, linitis plastica, signet ring cell, pTNM stage, surgery, and chemotherapy were confirmed in the United States group. Prognostic nomograms for younger patients were established, with the area under the curve of 0.786 in the China group and of 0.842 in the United States group. Moreover, three gene expression profiles (GSE27342, GSE51105, and GSE38749) were enrolled in further biological analysis, and distinctive molecular characteristics were identified in younger GC patients among different regions.

CONCLUSION

Except for younger cases with pTNM stage II, a survival advantage was observed in the China group with pathological stage I, III, and IV compared to the United States group, which might be partly due to differences in surgical approaches and the improvement of the cancer screening in China. The nomogram model provided an insightful and applicable tool to evaluate the prognosis of younger patients in China and the United States. Furthermore, biological analysis of younger patients was performed among different regions, which might partly explain the histopathological behavior and survival disparity in the subpopulations.

Construction and validation of a novel nomogram to predict cancer-specific survival in patients with gastric adenocarcinoma

Background and aims

Adenocarcinoma is one of the most common pathological types of gastric cancer. The aims of this study were to develop and validate prognostic nomograms that could predict the probability of cancer-specific survival (CSS) for gastric adenocarcinoma (GAC) patients at 1, 3, and 5 years.

Methods

In total, 7747 patients with GAC diagnosed between 2010 and 2015, and 4591 patients diagnosed between 2004 and 2009 from the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. The 7747 patients were used as a prognostic cohort to explore GAC-related prognostic risk factors. Moreover, the 4591 patients were used for external validation. The prognostic cohort was also divided into a training and internal validation sets for construction and internal validation of the nomogram. CSS predictors were screened using least absolute shrinkage and selection operator regression analysis. A prognostic model was built using Cox hazard regression analysis and provided as static and dynamic network-based nomograms.

Results

The primary site, tumor grade, surgery of the primary site, T stage, N stage, and M stage were determined to be independent prognostic factors for CSS and were subsequently included in construction of the nomogram. CSS was accurately estimated using the nomogram at 1, 3, and 5 years. The areas under the curve (AUCs) for the training group at 1, 3, and 5 years were 0.816, 0.853, and 0.863, respectively. Following internal validation, these values were 0.817, 0.851, and 0.861. Further, the AUC of the nomogram was much greater than that of American Joint Committee on Cancer (AJCC) or SEER staging. Moreover, the anticipated and actual CSS values were in good agreement based on decision curves and time-calibrated plots. Then, patients from the two subgroups were divided into high- and low-risk groups based on this nomogram. The survival rate of high-risk patients was considerably lower than that of low-risk patients, according to Kaplan-Meier (K-M) curves (p<0.0001).

Conclusions

A reliable and convenient nomogram in the form of a static nomogram or an online calculator was constructed and validated to assist physicians in quantifying the probability of CSS in GAC patients.

Development and Validation of Prognostic Nomograms for Lung Squamous Cell Carcinoma With Brain Metastasis in Patients Aged 45 Years or Older: A Population-Based Study

PURPOSE

We aimed to establish nomograms to predict the survival in patients aged ≥45  years with lung squamous cell carcinoma and brain metastasis.

METHODS

We collected patients diagnosed as lung squamous cell carcinoma with brain metastasis aged ≥45 years between 2010 and 2019 from the Surveillance, Epidemiology, and End Results database. Prognostic factors were determined by the univariate and multivariate Cox regression analysis, and then the nomogram was constructed to predict cancer-specific survival and overall survival. Nomograms were evaluated by decision curve analysis, the area under the receiver operating characteristic curve, calibration plot, concordance index, and risk group stratification.

RESULTS

In total, 2437 patients were included, with 1706 and 731 in the cohorts of training and validation, respectively. The age, N stage, T stage, liver metastasis, chemotherapy, bone metastasis, along with radiotherapy were significant in predicting the survival, and adopted for the establishment of nomograms. In the training and validation sets, the concordance index were .713(95%CI:0.699-.728) & .700(95%CI:0.677-.722) in predicting cancer-specific survival and .715(95%CI:0.701-.729) & .712(95%CI:0.690-.735) in predicting overall survival, respectively. Besides, the area under the receiver operating characteristic curve for predicting cancer-specific survival and overall survival in the training set were all >.7 at 1-, 2-, and 3- years. Calibration plots proved the survival predicted by nomograms were consistent with the actual values. decision curve analysis revealed better clinical validity of the nomogram in predicting cancer-specific survival and overall survival at 1-year than TNM staging. Patients were stratified into the high-/low-risk groups according to the optimal cutoff value of 100.21 for cancer-specific survival and 91.98 for overall survival. A web-based probability calculator was constructed finally.

CONCLUSION

Two nomograms were developed for the prognostic prediction of lung squamous cell carcinoma patients with brain metastasis aged ≥45 years, providing guidance for decision-making in clinical practice.

A novel prognostic model to predict OS and DFS of stage II/III gastric adenocarcinoma patients in China

Background

The prognosis of advanced gastric adenocarcinoma (GAC) after radical gastrectomy varies greatly. We aimed to build and validate a novel individualized nomogram based on inflammation index and tumor markers for patients with stage II/III GAC.

Methods

A total of 755 individuals with stage II/III GAC who had undergone radical gastrectomy at the First Affiliated Hospital of Zhengzhou University between 2012 and 2017 were included in this retrospective study. The patients were randomly divided into a training cohort (n ​= ​503) and a validation cohort (n ​= ​252). Univariate and multivariate analyses were used to determine independent prognostic factors of overall survival (OS) and disease-free survival (DFS). A nomogram was developed based on these independent factors. The concordance index (C-index) and calibration curves were used to evaluate the predictive accuracy of the nomogram.

Results

Univariate and multivariate analyses demonstrated that older age, poor differentiation, advanced stage, elevated neutrophil-to-lymphocyte ratio (NLR), lower hemoglobin, and high carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) levels were significantly associated with lower OS and DFS and were independent prognostic factors in stage II/III GAC. The nomogram developed based on these factors in the training cohort showed excellent calibration and discrimination (OS: C-index ​= ​0.739, 95% CI ​= ​0.706-0.772; DFS: C-index ​= ​0.735, 95% CI ​= ​0.702-0.769). In the internal validation cohort, the nomogram was also well-calibrated for the prediction of OS and DFS; it was superior to the 8th edition UICC/AJCC TNM staging system (for OS: C-index ​= ​0.746 vs. 0.679, respectively; for DFS: C-index ​= ​0.736 vs. 0.675, respectively; P ​< ​0.001).

Conclusion

The nomogram model could reliably predict OS and DFS in stage II/III gastric cancer patients with radical gastrectomy. It may help physicians make better treatment decisions.

Significance of Identifying Key Genes Involved in HBV-Related Hepatocellular Carcinoma for Primary Care Surveillance of Patients with Cirrhosis

Cirrhosis is frequently the final stage of disease preceding the development of hepatocellular carcinoma (HCC) and is one of the risk factors for HCC. Preventive surveillance for early HCC in patients with cirrhosis is advantageous for achieving early HCC prevention and diagnosis, thereby enhancing patient prognosis and reducing mortality. However, there is no highly sensitive diagnostic marker for the clinical surveillance of HCC in patients with cirrhosis, which significantly restricts its use in primary care for HCC. To increase the accuracy of illness diagnosis, the study of the effective and sensitive genetic biomarkers involved in HCC incidence is crucial. In this study, a set of 120 significantly differentially expressed genes (DEGs) was identified in the GSE121248 dataset. A protein-protein interaction (PPI) network was constructed among the DEGs, and Cytoscape was used to extract hub genes from the network. In TCGA database, the expression levels, correlation analysis, and predictive performance of hub genes were validated. In total, 15 hub genes showed increased expression, and their positive correlation ranged from 0.80 to 0.90, suggesting they may be involved in the same signaling pathway governing HBV-related HCC. The GSE10143, GSE25097, GSE54236, and GSE17548 datasets were used to investigate the expression pattern of these hub genes in the progression from cirrhosis to HCC. Using Cox regression analysis, a prediction model was then developed. The ROC curves, DCA, and calibration analysis demonstrated the superior disease prediction accuracy of this model. In addition, using proteomic analysis, we investigated whether these key hub genes interact with the HBV-encoded oncogene X protein (HBx), the oncogenic protein in HCC. We constructed stable HBx-expressing LO2-HBx and Huh-7-HBx cell lines. Co-immunoprecipitation coupled with mass spectrometry (Co-IP/MS) results demonstrated that CDK1, RRM2, ANLN, and HMMR interacted specifically with HBx in both cell models. Importantly, we investigated 15 potential key genes (CCNB1, CDK1, BUB1B, ECT2, RACGAP1, ANLN, PBK, TOP2A, ASPM, RRM2, NEK2, PRC1, SPP1, HMMR, and DTL) participating in the transformation process of HBV infection to HCC, of which 4 hub genes (CDK1, RRM2, ANLN, and HMMR) probably serve as potential oncogenic HBx downstream target molecules. All these findings of our study provided valuable research direction for the diagnostic gene detection of HBV-related HCC in primary care surveillance for HCC in patients with cirrhosis.

Developing and validating nomograms for predicting the survival in patients with clinical local-advanced gastric cancer

Background

Many patients with gastric cancer are at a locally advanced stage during initial diagnosis. TNM staging is inaccurate in predicting survival. This study aims to develop two more accurate survival prediction models for patients with locally advanced gastric cancer (LAGC) and guide clinical decision-making.

Methods

We recruited 2794 patients diagnosed with LAGC (2010–2015) from the Surveillance, Epidemiology, and End Results (SEER) database and performed external validation using data from 115 patients with LAGC at Yantai Affiliated Hospital of Binzhou Medical University. Univariate and multifactorial survival analyses were screened for meaningful independent prognostic factors and were used to build survival prediction models. Concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were evaluated for nomograms. Finally, the differences and relationships of survival and prognosis between the three different risk groups were described using the Kaplan–Meier method.

Results

Cox proportional risk regression model analysis identified independent prognostic factors for patients with LAGC, and variables associated with overall survival (OS) included age, race, marital status, T-stage, N-stage, grade, histologic type, surgery, and chemotherapy. Variables associated with cancer-specific survival (CSS) included age, race, T-stage, N-stage, grade, histological type, surgery, and chemotherapy. In the training cohort, C-index of nomogram for predicting OS was 0.722 (95% confidence interval [95% CI]: 0.708–0.736] and CSS was 0.728 (95% CI: 0.713–0.743). In the external validation cohort, C-index of nomogram for predicted OS was 0.728 (95% CI:0.672–0.784) and CSS was 0.727 (95% CI:0.668–0.786). The calibration curves showed good concordance between the predicted and actual results. C-index, ROC, and DCA results indicated that our nomograms could more accurately predict OS and CSS than TNM staging and had a higher clinical benefit. Finally, to facilitate clinical use, we set up two web servers based on nomograms.

Conclusion

The nomograms established in this study have better risk assessment ability than the clinical staging system, which can help clinicians predict the individual survival of LAGC patients more accurately and thus develop appropriate treatment strategies.

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.

Relationships of Ferroptosis and Pyroptosis-Related Genes with Clinical Prognosis and Tumor Immune Microenvironment in Head and Neck Squamous Cell Carcinoma

Ferroptosis and pyroptosis are two new programmed cell death (PCD) modes discovered in recent years. However, the potential value of ferroptosis and pyroptosis-related genes (FPRGs) in prognosis prediction and the tumor immune microenvironment of head and neck squamous cell carcinoma (HNSCC) is still unclear. We obtained 21 significant FPRGs based on the training dataset (TCGA- HNSC) using the univariate Cox and differential expression analysis. The TCGA- HNSC (n = 502) dataset was clustered into two group (clusters A and B) based on the 21 significant FPRGs. 1467 differentially expressed genes (DEGs) between cluster A and B were put into univariate Cox and Least absolute shrinkage and selection operator (LASSO) analysis to build a risk model. The predictive capability of the risk model was successfully confirmed by internal validation, external validation, and clinical sample validation. To improve the clinical applicability, a nomogram model combined risk score and clinical information were constructed. Moreover, the patients with lower risk score were characterized by increased immune response and tumor mutation burden (TMB), while the patients with higher risk score were characterized by increased TP53 mutation rate. In conclusion, our comprehensive analysis of the FPRGs revealed their significant role in prognosis prediction and the tumor immune microenvironment. The risk model containing 9 FPRGs could be a potential prognostic markers and effective immunotherapy targets for HNSCC.

Survival nomogram for different grades of gastric cancer patients based on SEER database and external validation cohort

Background

Influencing factors varied among gastric cancer (GC) for different differentiation grades which affect the prognosis accordingly. This study aimed to develop a nomogram to effectively identify the overall survival (OS).

Methods

Totally, 9,568 patients with GC were obtained from the SEER database as the training cohort and internal validation cohort. We then retrospectively enrolled patients diagnosed with GC to construct the external validation cohort from the First Affiliated Hospital of Anhui Medical University. The prognostic factors were integrated into the multivariate Cox regression to construct a nomogram. To test the accuracy of the model, we used the calibration curves, receiver operating characteristics (ROC) curves, C-index, and decision curve analysis (DCA).

Results

Race chemotherapy, tumor size, and other four factors were significantly associated with the prognosis of Grade III GC Patients. On this basis, we developed a nomogram. The discrimination of the nomogram revealed good prognostic accuracy The results of the area under the curve (AUC) calculated by ROC for five-year survival were 0.828 and 0.758 in the training set and external validation cohort, higher than that of the TNM staging system. The calibration plot revealed that the estimated risk was close to the actual risk. DCA also suggested an excellent predictive value of the nomogram. Similar results were obtained in Grade-I and Grade-II GC patients.

Conclusions

The nomogram developed in this study and other findings could help individualize the treatment of GC patients and assist clinicians in their shared decision-making with patients.

Multi‑institutional development and validation of a nomogram to predict prognosis of early-onset gastric cancer patients

Background

Early-onset gastric cancer (EOGC, ≤45 years old) is characterized with increasing incidence and more malignant phenotypes compared with late-onset gastric cancer, which exhibits remarkable immune cell infiltration and is potential immunotherapeutic population. Till now, restricted survival information of EOGC is available due to limited case numbers. This study established a novel nomogram to help evaluate cancer-specific survival (CSS) of EOGC patients who underwent gastrectomy, and may provide evidence for predicting patients’ survival.

Methods

We retrospectively enrolled a cohort containing 555 EOGC cases from five independent medical centers in China, among which 388 cases were randomly selected into a training set while the other 167 cases were assigned into the internal validation set. Asian or Pacific Islander (API) patients diagnosed with EOGC during 1975-2016 were retrieved from the SEER database (n=299) and utilized as the external validation cohort. Univariate and multivariate analyses were conducted to test prognostic significances of clinicopathological factors in the training set. Accordingly, two survival nomogram models were established and compared by concordance index (C-index), calibration curve, receiver operating characteristics (ROC) curves and decision curve analyses (DCA).

Results

The 5-year CSS rate of training cohort was 61.3% with a median survival time as 97.2 months. High consistency was observed on calibration curves in all three cohorts. Preferred nomogram was selected due to its better performance on ROC and DCA results. Accordingly, a novel predicative risk model was introduced to better stratify high-risk EOGC patients with low-risk patients. In brief, the 5-year CSS rates for low-risk groups were 92.9% in training set, 83.1% in internal validation set, 89.9% in combined NQSQS cohort, and 85.3% in SEER-API cohort. In contrast, the 5-year CSS rates decreased to 38.5%, 44.3%, 40.5%, and 36.9% in the high-risk groups of the four cohorts above, respectively. The significant survival difference between high-risk group (HRG) and low-risk group (LRG) indicated the precise accuracy of our risk model. Furthermore, the risk model was validated in patients with different TNM stages, respectively. Finally, an EOGC web-based survival calculator was established with public access, which can help predict prognosis.

Conclusions

Our data provided a precise nomogram on predicting CSS of EOGC patients with potential clinical applicability.

A Simple Nomogram for Predicting Osteoarthritis Severity in Patients with Knee Osteoarthritis

Objective

To explore the influencing factors of knee osteoarthritis (KOA) severity and establish a KOA nomogram model.

Methods

Inpatient data collected in the Department of Joint Surgery, Chengde Medical University Affiliated Hospital from January 2020 to January 2022 were used as the training cohort. Patients with knee osteoarthritis who were admitted to the Third Hospital of Hebei Medical University from February 2022 to May 2022 were taken as the external validation group of the model. In the training group, the least absolute shrinkage and selection operator (LASSO) method was used to screen the factors of KOA severity to determine the best prediction index. Then, after combining the significant factors from the LASSO and multivariate logistic regressions, a prediction model was established. All potential prediction factors were included in the KOA severity prediction model, and the corresponding nomogram was drawn. The consistency index (C-index), area under the receiver operating characteristic (ROC) curve (AUC), GiViTi calibration band, net classification improvement (NRI) index, and integrated discrimination improvement (IDI) index evaluation of a model predicted KOA severity. Decision curve analysis (DCA) and clinical influence curves were used to study the model's potential clinical value. The validation group also used the above evaluation indexes to measure the diagnostic efficiency of the model. Spearman correlation was used to investigate the relationship between nomogram-related markers and osteoarthritis severity.

Results

The total sample included 572 patients with knee osteoarthritis, including 400 patients in the training cohort and 172 patients in the validation cohort. The nomogram's predictive factors were age, pulse, absolute value of lymphocytes, mean corpuscular haemoglobin concentration (MCHC), and blood urea nitrogen (BUN). The C-index and AUC of the model were 0.802. The GiViTi calibration band (P = 0.065), NRI (0.091), and IDI (0.033) showed that the modified model can distinguish between severe KOA and nonsevere KOA. DCA showed that the KOA severity nomogram has clinical application value with threshold probabilities between 0.01 and 0.78. The external verification results also show the stability and diagnosis of the model. Age, pulse, MCHC, and BUN are correlated with osteoarthritis severity.

Conclusions

A nomogram model for predicting KOA severity was established for the first time that can visually identify patients with severe KOA and is novel for indirectly evaluating KOA severity by nonimaging means.

Nomograms predicting cancer-specific survival for stage IV colorectal cancer with synchronous lung metastases

This study aimed to establish a nomogram for the prediction of cancer-specific survival (CSS) of CRC patients with synchronous LM. The final prognostic nomogram based on prognostic factors was evaluated by concordance index (C-index), time-dependent receiver operating characteristic curves, and calibration curves. In the training and validation groups, the C-index for the nomogram was 0.648 and 0.638, and the AUC was 0.793 and 0.785, respectively. The high quality of the calibration curves in the nomogram models for CSS at 1-, 3-, and 5-year was observed. The nomogram model provided a conventional and useful tool to evaluate the 1-, 3-, and 5-year CSS of CRC patients with synchronous LM.

A model established using marital status and other factors from the Surveillance, Epidemiology, and End Results database for early stage gastric cancer

Currently, the postoperative prognosis of early stage gastric cancer (GC) is difficult to accurately predict. In particular, social factors are not frequently used in the prognostic assessment of early stage GC. Therefore, this study aimed to combine the clinical indicators and social factors to establish a predictive model for early stage GC based on a new scoring system. A total of 3647 patients with early stage GC from the Surveillance, Epidemiology, and End Results database were included in this study. A Kaplan-Meier survival analysis was used to compare differences in prognosis between different marital status, as an innovative prognostic indicator. Univariate and multivariate analyses were used to screen available prediction factors and then build a nomogram using the Cox proportional hazard regression model. The univariate analysis and multivariate analysis revealed that age at diagnosis, sex, histology, stage_T, surgery, tumor size, and marital status were independent prognostic factors of overall survival. Both the C-index and calibration curves confirmed that the nomogram had a great predictive effect on patient prognosis in training and testing sets. This nomogram based on clinical indicators and marital status can effectively help patients with early stage GC in the future.

[Development and validation of a prognostic model based on SEER data for patients with esophageal carcinoma after esophagectomy]

OBJECTIVE

To develop a nomogram to predict the long-term survival of patients with esophageal cancer following esophagectomy.

METHODS

We collected the data of 7215 patients with esophageal carcinoma from the Surveillance, Epidemiology, and End Results (SEER) database during the period from 2004 and 2016. Of these patients, 5052 were allocated to the training cohort and the remaining 2163 patients to the internal validation cohort using bootstrap resampling, with another 435 patients treated in the Department of Cardiothoracic Surgery of Jinling Hospital between 2014 and 2016 serving as the external validation cohort.

RESULTS

In the overall cohort, the 1-, 3-, and 5-year cancer-specific mortality rates were 14.6%, 35.7% and 41.6%, respectively. Age (≥80 years vs < 50 years, P < 0.001), gender (male vs female, P < 0.001), tumor site (lower vs middle segment, P=0.013), histology (EAC vs ESCC, P=0.012), tumor grade (poorly vs well differentiated, P < 0.001), TNM stage (Ⅳ vs Ⅰ, P < 0.001), tumor size (> 50 mm vs 0-20 mm, P < 0.001), chemotherapy (yes vs no, P < 0.001), and LNR (> 0.25 vs 0, P < 0.001) were identified as independent risk factors affecting long-term survival of the patients. The nomograms established based on the model for predicting the survival probability of the patients at 1, 3 and 5 years after operation showed a C-index of 0.726 (95% CI: 0.714-0.738) for predicting the overall survival (OS) and of 0.735 (95% CI: 0.727-0.743) for cancer-specific survival (CSS) in the training cohort. In the internal validation cohort, the C-index of the nomograms was 0.752 (95% CI: 0.738-0.76) for OS and 0.804 (95% CI: 0.790-0.817) for CSS, as compared with 0.749 (95% CI: 0.736-0.767) and 0.788 (95%CI: 0.751-0.808), respectively, in the external validation cohort. The nomograms also showed a higher sensitivity than the TNM staging system for predicting long-term prognosis.

CONCLUSION

This prognostic model has a high prediction efficiency and can help to identify the high-risk patients with esophageal carcinoma after surgery and serve as a supplement for the current TNM staging system.

LINC00665 interacts with BACH1 to activate Wnt1 and mediates the M2 polarization of tumor-associated macrophages in GC

Gastric cancer (GC) remains one of the prevalent causes of cancer-related deaths globally. Long non-coding RNAs (lncRNAs) have been associated with different cancers. The polarization of macrophages towards the M2 (alternatively activated) phenotype promotes immunologic tolerance and can induce gastric tumorigenesis. Thus far, lncRNAs have been shown to modulate the differentiation of immune cells. Here, we investigated the biological effects of LINC00665 on the progression of GC and explored the mechanisms underlying its ability to mediate the polarization of macrophages towards the M2 phenotype. We report that the levels of LINC00665 were increased in GC tissues. Furthermore, this increase in LINC00665 expression could be associated with decreased overall survival (OS), progression-free survival (PFS), and post-progression survival (PPS). Using cell-based macrophage polarization models, we demonstrated that LINC00665 upregulation in GC cells facilitated the polarization of macrophages towards the M2 but not M1 (classically activated) phenotype. Furthermore, the loss of LINC00665 prevented the M2 polarization of macrophages. Mechanically, we identified that Wnt1 was the downstream target of LINC00665. Additionally, LINC00665 could directly interact with the transcription factor BTB domain and CNC homology 1 (BACH1). The interaction between LINC00665 and BACH1 resulted in the activation and binding of BACH1 to the Wnt1 promoters. Furthermore, BACH1 silencing could inhibit GC progression, which highlighted a crucial role for BACH1 in LINC00665-mediated Wnt1 activation. In addition, genetic Wnt1 overexpression effectively abolished the repression of Wnt signaling after BACH1 depletion and mediated GC development by supporting M2 macrophage polarization. In conclusion, we report that LINC00665 modulates M2 macrophage polarization and suggest that it may facilitate macrophage-dependent GC progression.

A Novel Overall Survival Nomogram Prediction of Secondary Primary Malignancies after Hypopharyngeal Cancer: A Population-Based Study

Objectives

We aimed to construct a nomogram for predicting the overall survival (OS) of patients with secondary primary malignancies (SPMs) after hypopharyngeal cancer (HPC).

Methods

613 HPC patients were included in the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2018, which were divided into training and validation cohorts. The least absolute shrinkage and selection operation (LASSO) and stepwise Cox regression were used to determine the variables by which a nomogram model was established.

Results

After the LASSO and stepwise Cox regression analysis, the age, year of diagnosis, sites of SPMs, SEER stage of SPMs, surgery for SPMs, and radiotherapy for SPMs were included for model establishment. The ROC curve showed good discrimination for the 3- and 5-year AUC values in the training (0.774 and 0.779, respectively) and validation (0.758 and 0.763, respectively) cohorts. The calibration curve indicated good prognostic accuracy, especially in the 5-year survival prediction for this model. The DCA also demonstrated clinical efficacy over a wide range of threshold probabilities. Lastly, the risk group classified by the individual nomogram values showed significantly different survival outcomes in both training and validation cohorts.

Conclusions

We constructed a nomogram to predict the OS of SPMs after HPC with good clinical values.

Comparison of treatment strategies and survival of early-onset gastric cancer: a population-based study

Treatments for early-onset gastric cancer (EOGC) patients are rarely included in clinical trials, resulting in an unclear impact on survival. This study aimed to investigate the treatment patterns of EOGC patients and their impact on survival. Based on the Surveillance, Epidemiology, and End Results database, we conducted a retrospective analysis of 1639 EOGC patients (< 50 years) diagnosed between 2010 and 2018. Patients with larger tumours, distant metastasis, and AJCC TNM stage in IV were prone to receive nonsurgical treatment. Patients treated with surgery alone had a better prognosis than those receiving SROC or SCRT or nonsurgical treatment. However, analyses stratified by histological type, tumour size and TNM stage showed that patients did not benefit more from SROC and SCRT than from surgery alone. Similar results were observed in the stratified Cox regression risk analysis. Patients who received nonsurgical treatment had the highest risk of overall death [hazard ratio (HR) = 2.443, 95% confidence interval (CI) 1.865–3.200, P < 0.001]. This study indicated that additional radiotherapy, chemotherapy or chemoradiotherapy did not provide a coordinated survival benefit to EOGC patients.

Nomogram for predicting the overall survival of patients with early-onset prostate cancer: A population-based retrospective study

Background

The incidence of early‐onset prostate cancer (PCa) has increased significantly over the past few decades. It is necessary to develop a prognostic nomogram for the prediction of overall survival (OS) in early‐onset PCa patients.

Methods

A total of 23,730 early‐onset PCa patients (younger than 55 years old) between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were enrolled for the current study, and randomly separated into the training cohort and the validation cohort. 361 eligible early‐onset PCa patients from The Cancer Genome Atlas‐Prostate Adenocarcinoma (TCGA‐PRAD) cohort were obtained as the external validation cohort. Independent predictors were selected by univariate and multivariate Cox regression analysis, and a prognostic nomogram was constructed for 1‐, 3‐, and 5‐year OS. The accurate and discriminative abilities of the nomogram were evaluated by the concordance index (C‐index), receiver operating characteristic curve (ROC), calibration plot, net reclassification index (NRI), and integrated discrimination improvement (IDI).

Results

Multivariate Cox analysis showed that race, marital status, TNM stage, prostate‐specific antigen, Gleason score, and surgery were significantly associated with poor prognosis of PCa. A nomogram consisting of these variables was established, which had higher C‐indexes than the TNM system (training cohort: 0.831 vs. 0.746, validation cohort: 0.817 vs. 0.752). Better AUCs of the nomogram than the TNM system at 1, 3, and 5 years were found in both the training cohort and the validation cohort. The 3‐year and 5‐year AUCs of the nomogram in the TCGA‐PRAD cohort were 0.723 and 0.679, respectively. The calibration diagram, NRI, and IDI also showed promising prognostic value in OS.

Conclusions

We developed an effective prognostic nomogram for OS prediction in early‐onset PCa patients, which will further assist both the precise clinical treatment and the assessment of long‐term outcomes.

Significance of a PTEN Mutational Status-Associated Gene Signature in the Progression and Prognosis of Endometrial Carcinoma

Background

PTEN mutations have been reported to be involved in the development and prognosis of endometrial carcinoma (EC). However, a prognostic gene signature associated with PTEN mutational status has not yet been developed. In this study, we generated a PTEN mutation-associated prognostic gene signature for EC.

Methods

We obtained the single-nucleotide variation and transcriptomic profiling data from The Cancer Genome Atlas database as training data and implemented the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm to establish a PTEN mutation-associated prognostic gene signature. The overall survival rates of the high-risk and low-risk groups were determined with the Kaplan-Meier (K-M) method, and the accuracy of risk score prediction was tested by using the receiver operating characteristic (ROC) curve.

Results

The K-M curves revealed that the EC patients with PTEN mutations augured favorable survival outcomes. Differential expression analysis between the EC patients with PTEN mutation and wild-type PTEN identified 224 differentially expressed genes (DEGs). Eighty-four DEGs that manifested prognostic value were fitted into the LASSO-Cox analysis, and a PTEN gene signature with seven mutation-associated prognostic genes that showed robust prognostic ability was constructed; this signature was then successfully validated in the other two datasets from the cBioPortal database as well as with 60 clinical specimens. Furthermore, the PTEN mutation-associated prognostic gene signature proved to be an independent prognostic predictor of EC. Remarkably, the EC patients in the high-risk group were characterized by higher tumor stages and grades as well as lower tumor mutation burden with respect to EC, with a poor survival outcome. Collectively, the PTEN mutation-associated prognostic gene signature that we developed could now be used as a favorable prognostic biomarker for EC.

Conclusion

In summary, we developed and validated a prognostic predictor for EC associated with PTEN mutational status that may be used as a favorable prognostic biomarker and therapeutic target for EC.

Development and Validation of Prognostic Survival Nomograms for Patients with Anal Canal Cancer: A SEER-Based Study

Objective

Anal canal cancer is a rare malignancy with increasing incidence in recent times. This study aimed to develop two nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of patients with anal canal cancer.

Methods

Information of patients with anal canal cancer from 2004 to 2015 was extracted from the surveillance, epidemiology, and end results (SEER) database. Cox analysis was used to select the risk factors for prognosis, and nomograms were constructed using the R software. The C-index, area under the curve (AUC) of time-dependent receiver operating characteristic (ROC) curves, calibration plot and decision curve analysis (DCA) were used to assess the clinical utility of the nomograms.

Results

A total of 2458 patients with malignant tumours of the anal canal were screened out. Sex, age, marital status, histological type, grade, tumour size, AJCC stage, SEER stage and chemotherapy were independent prognostic factors for OS, whereas sex, age, race, histological type, grade, tumour size, AJCC stage, SEER stage and radiotherapy were independent prognostic factors for CSS. In the training cohort, the C-index value for OS nomogram was 0.73 (95% CI, 0.69-0.77), and the AUC values that predicted the 1-, 3- and 5-year survival rates were 0.764, 0.758 and 0.760, respectively, whereas the C-index value for CSS nomogram model was 0.74 (95% CI, 0.69-0.79), and the AUC values were 0.763, 0.769 and 0.763, respectively. The calibration plot and DCA curves demonstrated good prediction performance of the model in both the training and validation cohorts.

Conclusion

The established nomogram is a visualisation tool that can effectively predict the OS and CSS of patients with anal canal cancer.

Development and Validation of a Prognostic Model to Predict Overall Survival for Lung Adenocarcinoma: A Population-Based Study From the SEER Database and the Chinese Multicenter Lung Cancer Database

Background: Lung adenocarcinoma (LUAD) is the most common subtype of non-small-cell lung cancer (NSCLC). The aim of our study was to determine prognostic risk factors and establish a novel nomogram for lung adenocarcinoma patients. Methods: This retrospective cohort study is based on the Surveillance, Epidemiology, and End Results (SEER) database and the Chinese multicenter lung cancer database. We selected 22,368 eligible LUAD patients diagnosed between 2010 and 2015 from the SEER database and screened them based on the inclusion and exclusion criteria. Subsequently, the patients were randomly divided into the training cohort (n = 15,657) and the testing cohort (n = 6711), with a ratio of 7:3. Meanwhile, 736 eligible LUAD patients from the Chinese multicenter lung cancer database diagnosed between 2011 and 2021 were considered as the validation cohort. Results: We established a nomogram based on each independent prognostic factor analysis for 1-, 3-, and 5-year overall survival (OS) . For the training cohort, the area under the curves (AUCs) for predicting the 1-, 3-, and 5-year OS were 0.806, 0.856, and 0.886. For the testing cohort, AUCs for predicting the 1-, 3-, and 5-year OS were 0.804, 0.849, and 0.873. For the validation cohort, AUCs for predicting the 1-, 3-, and 5-year OS were 0.86, 0.874, and 0.861. The calibration curves were observed to be closer to the ideal 45° dotted line with regard to 1-, 3-, and 5-year OS in the training cohort, the testing cohort, and the validation cohort. The decision curve analysis (DCA) plots indicated that the established nomogram had greater net benefits in comparison with the Tumor-Node-Metastasis (TNM) staging system for predicting 1-, 3-, and 5-year OS of lung adenocarcinoma patients. The Kaplan-Meier curves indicated that patients' survival in the low-risk group was better than that in the high-risk group (P < .001). Conclusion: The nomogram performed very well with excellent predictive ability in both the US population and the Chinese population.

Identification of Immune-Related Biomarkers for Sciatica in Peripheral Blood

Objective: Sciatica pertains to neuropathic pain that has been associated with inflammatory response. We aimed to identify significant immune-related biomarkers for sciatica in peripheral blood. Methods: We utilized the GSE150408 expression profiling data from the Gene Expression Omnibus (GEO) database as the training dataset and extracted immune-related genes for further analysis. Differentially expressed immune-related genes (DEIRGs) between healthy controls and patients with sciatica were selected using the "limma" package and verified in clinical specimens by quantitative reverse transcription PCR (RT-qPCR). A diagnostic immune-related gene signature was established using the training model and random forest (RF), generalized linear model (GLM), and support vector machine (SVM) models. Sciatica patient subtypes were identified using the consensus clustering method. Results: Thirteen significant DEIRGs were acquired, of which five (CRP, EREG, FAM19A4, RLN1, and WFIKKN1) were selected to establish a diagnostic immune-related gene signature according to the most appropriate training model, namely, the RF model. A clinical application nomogram model was established based on the expression level of the five DEIRGs. The sciatica patients were divided into two subtypes (C1 and C2) according to the consensus clustering method. Conclusions: Our research established a diagnostic five immune-related gene signature to discriminate sciatica and identified two sciatica subtypes, which may be beneficial to the clinical diagnosis and treatment of sciatica.

New nomograms to predict overall and cancer-specific survival of angiosarcoma

Objective

This study was designed to establish and validate promising and reliable nomograms for predicting the survival of angiosarcoma (AS) patients.

Methods

The Surveillance, Epidemiology, and End Results database was queried to collect the clinical information of 785 AS patients between 2004 and 2015. Data were split into a training cohort (n = 549) and a validation cohort (n = 236) without any preference. Univariate Cox and multivariate Cox regression analyses were performed to analyze the clinical parameters. Independent prognostic factors were then identified. Two nomograms were constructed to predict overall survival (OS) and cancer‐specific survival (CSS) at 3 and 5 years. Finally, the models were evaluated using concordance indices (C‐indices), calibration plots, and decision curve analysis (DCA).

Results

Based on the inclusion and exclusion criteria, 785 individuals were included in this analysis. Univariate and multivariate Cox regression analyses revealed that age, tumor size, and stage were prognostic factors independently associated with the OS of AS. Tumor site, tumor size, and stage were associated with the CSS of AS. Based on the statistical results and clinical significance of variables, nomograms were built. The nomograms for OS and CSS had C‐indices of 0.666 and 0.654, respectively. The calibration curves showed good agreement between the predictive values and the actual values. DCA also indicated that the nomograms were clinically useful.

Conclusion

We established nomograms with good predictive ability that could provide clinicians with better predictions about the clinical outcomes of AS patients.

Development and validation of a SEER-based prognostic nomogram for cervical cancer patients below the age of 45 years

In this study, we established a nomogram for the prognostic prediction of patients with early-onset cervical cancer (EOCC) for both overall survival (OS) and cancer-specific survival (CSS). The Surveillance, Epidemiology, and End Results (SEER) database was used to identify 10,079 patients diagnosed with EOCC between 2004 and 2015; these cases were then randomly divided into training and validation sets. The independent prognostic factors were identified in a retrospective study of 7,055 patients from the training set. A prognostic nomogram was developed using R software according to the results of multivariable Cox regression analysis. Furthermore, the model was externally validated using the data from the remaining 3,024 patients diagnosed at different times and enrolled in the SEER database. For the training set, the C-indexes for OS and CSS prediction were determined to be 0.831 (95 % confidence interval [CI]: 0.815–0.847) and 0.855 (95 % CI: 0.839–0.871), respectively. Receiver operating characteristic (ROC) analysis has revealed that the nomograms were a superior predictor compared with TNM stage and SEER stage. The areas under the curve (AUC) of the nomogram for OS and CSS prediction in the ROC analysis were 0.855 (95 % CI: 0.847–0.864) and 0.782 (95 % CI: 0.760–0.804), respectively. In addition, calibration curves indicated a perfect agreement between the nomogram-predicted and the actual 1-, 3-, and 5-year OS and CSS rates in the validation cohort. Thus, in this study, we established and validated a prognostic nomogram that provides an accurate prediction for 3-, 5-, and 10-year OS and CSS of EOCC patients. This will be useful for clinicians in guiding counseling and clinical trial design for cervical cancer patients.

Development and Validation of Prognostic Nomograms for Elderly Patients with Osteosarcoma

Background

The aim of the current study was to construct prognostic nomograms for individual risk prediction in elderly patients with osteosarcoma.

Methods

Data for 816 elderly patients (≥40 years old) with osteosarcoma between 2004 and 2016 from the Surveillance, Epidemiology, and End Results (SEER) database were randomly assigned to training (N=573) and internal validation (N=243) sets. The essential clinical predictors were identified based on least absolute shrinkage and selection operator (Lasso) Cox regression. Nomograms were constructed to predict the 1-, 3-, and 5-year cancer-specific survival (CSS) and overall survival (OS).

Results

Our LASSO regression analyses of the training set yielded five clinicopathological features (age, chemotherapy, surgery, AJCC stage, and summary stage) in the training cohort for the prognosis of elderly patients with osteosarcoma, while grade was only associated with OS and M stage was only associated with CSS. Construction of nomograms based on these predictors was performed to evaluate the prognosis of elderly patients with osteosarcoma. The C-index, calibration and decision curve analysis also showed the satisfactory performance of these nomograms for prognosis prediction.

Conclusion

The constructed nomograms are helpful tools for exactly predicting the prognosis of elderly patients with osteosarcoma, which could enable patients to be more accurately managed in clinical practice.

Clinicopathological Characteristics and Prognosis of Signet Ring Gastric Cancer: A Population-Based Study

Background

To better define the clinicopathologic characteristics of signet ring cell (SRC) gastric cancer and build a prognostic model for it.

Methods

SRC patient information from 2010 to 2015 were identified using Surveillance, Epidemiology, and End Results (SEER) database. Kaplan-Meier method and log-rank test were used to estimate Overall survival (OS) and to determine associations with histologic subtypes. In COX proportional hazards regression model–based univariate and multivariate analyses, significant variables for construction of a nomogram were screened out. The nomogram was validated by means of the concordance index (CI), calibration plots, and receiver operating characteristics (ROCs) curves.

Results

A total of 11,363 gastric cancer patients were enrolled. On dividing the patients into SRC, well-to-moderately differentiated (WMD) adenocarcinoma, and poorly differentiated (PD) adenocarcinoma, differences among these subgroups emerged. SRC patients were more likely to occur in female and young patients than other histologic subtypes. Larger tumors, stage T4, and node stage N3 were more likely to be found in the SRC group. The survival for SRC patients was better than non-SRC patients in stage I. Univariate and multivariate analyses identified age, tumor site, larger tumor size, advanced T classification, advanced N classification, advanced TNM stage, and surgery of primary site as independent prognostic indicators. Then an OS nomogram was formulated.

Conclusions

SRC had distinct clinicopathological characteristics. The nomogram provided an accurate tool to evaluate the prognosis of SRC.

A novel nomogram based on cardia invasion and chemotherapy to predict postoperative overall survival of gastric cancer patients

Background

We aimed to establish and externally validate a nomogram to predict the 3- and 5-year overall survival (OS) of gastric cancer (GC) patients after surgical resection.

Methods

A total of 6543 patients diagnosed with primary GC during 2004–2016 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. We grouped patients diagnosed during 2004–2012 into a training set (n = 4528) and those diagnosed during 2013–2016 into an external validation set (n = 2015). A nomogram was constructed after univariate and multivariate analysis. Performance was evaluated by Harrell’s C-index, area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), and calibration plot.

Results

The multivariate analysis identified age, race, location, tumor size, T stage, N stage, M stage, and chemotherapy as independent prognostic factors. In multivariate analysis, the hazard ratio (HR) of non-cardia invasion was 0.762 (P < 0.001) and that of chemotherapy was 0.556 (P < 0.001). Our nomogram was found to exhibit excellent discrimination: in the training set, Harrell’s C-index was superior to that of the 8th American Joint Committee on Cancer (AJCC) TNM classification (0.736 vs 0.699, P < 0.001); the C-index was also better in the validation set (0.748 vs 0.707, P < 0.001). The AUCs for 3- and 5-year OS were 0.806 and 0.815 in the training set and 0.775 and 0.783 in the validation set, respectively. The DCA and calibration plot of the model also shows good performance.

Conclusions

We established a well-designed nomogram to accurately predict the OS of primary GC patients after surgical resection. We also further confirmed the prognostic value of cardia invasion and chemotherapy in predicting the survival rate of GC patients.

A Potential Immune-Related Long Non-coding RNA Prognostic Signature for Ovarian Cancer

Ovarian cancer (OC), the most lethal gynecologic malignancy, ranks fifth in cancer deaths among women, largely because of late diagnosis. Recent studies suggest that the expression levels of immune-related long non-coding RNAs (lncRNAs) play a significant role in the prognosis of OC; however, the potential of immune-related lncRNAs as prognostic factors in OC remains unexplored. In this study, we aimed to identify a potential immune-related lncRNA prognostic signature for OC patients. We used RNA sequencing and clinical data from The Cancer Genome Atlas and the Gene Expression Omnibus database to identify immune-related lncRNAs that could serve as useful biomarkers for OC diagnosis and prognosis. Univariate Cox regression analysis was used to identify the immune-related lncRNAs with prognostic value. Functional annotation of the data was performed through the GenCLiP310 website. Seven differentially expressed lncRNAs (AC007406.4, AC008750.1, AL022341.2, AL133351.1, FAM74A7, LINC02229, and HOXB-AS2) were found to be independent prognostic factors for OC patients. The Kaplan-Meier curve indicated that patients in the high-risk group had a poorer survival outcome than those in the low-risk group. The receiver operating characteristic curve revealed that the predictive potential of the immune-related lncRNA signature for OC was robust. The prognostic signature of the seven lncRNAs was successfully validated in the GSE9891, GSE26193 datasets and our clinical specimens. Multivariate analyses suggested that the signature of the seven lncRNAs was an independent prognostic factor for OC patients. Finally, we constructed a nomogram model and a competing endogenous RNA network related to the lncRNA prognostic signature. In conclusion, our study reveals novel immune-related lncRNAs that may serve as independent prognostic factors in OC.

A Prognostic Model for Patients With Gastric Signet Ring Cell Carcinoma

Background:

The aim of our study was to develop a nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with gastric signet ring cell carcinoma (GSRC).

Methods:

GSRC patients from 2004 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to the training and validation sets. Multivariate Cox regression analyses screened for OS and CSS independent risk factors and nomograms were constructed.

Results:

A total of 7,149 eligible GSRC patients were identified, including 4,766 in the training set and 2,383 in the validation set. Multivariate Cox regression analysis showed that gender, marital status, race, AJCC stage, TNM stage, surgery and chemotherapy were independent risk factors for both OS and CSS. Based on the results of the multivariate Cox regression analysis, prognostic nomograms were constructed for OS and CSS. In the training set, the C-index was 0.754 (95% CI = 0.746-0.762) for the OS nomogram and 0.762 (95% CI: 0.753-0.771) for the CSS nomogram. In the internal validation, the C-index for the OS nomogram was 0.758 (95% CI: 0.746-0.770), while the C-index for the CSS nomogram was 0.762 (95% CI: 0.749-0.775). Compared with TNM stage and SEER stage, the nomogram had better predictive ability. In addition, the calibration curves also showed good consistency between the predicted and actual 3-year and 5-year OS and CSS.

Conclusion:

The nomogram can effectively predict OS and CSS in patients with GSRC, which may help clinicians to personalize prognostic assessments and clinical decisions.

Application of competing risk model in the prognostic prediction study of patients with follicular thyroid carcinoma

BACKGROUND

Follicular thyroid carcinoma (FTC) is an indolent carcinoma. The cumulative incidence of death from patients with FTC and the nomogram built based on the competing risks model have not been described.

METHODS

The data from patients diagnosed with primary FTC were identified and extracted from the surveillance, epidemiology, and end results (SEER) program (1988-2015). The cumulative incidence function was utilized to calculate the likelihood of death resulting from thyroid cancer and other causes, respectively. Gray's test was used to examine the difference in the cumulative incidence of death between the groups. A tenfold cross-validation was applied to assess the discrimination and calibration of the model.

RESULTS

A total of 9210 patients diagnosed with primary FTC were included. The median follow-up time was 92 months (1-347 months). The 5-year, 10-year, and 20-year probabilities of death from FTC were 2.84%, 5.23%, and 8.61%, respectively. The age at diagnosis, sex, tumor size, pathological subtypes, tumor extension, lymph node involvement, as well as surgical and radiotherapy methods used, were related to the cumulative incidence of death. Multivariate analysis identified several risk factors for patient survival. The model behaved well in terms of performance. A nomogram based on the model allowed the prediction of the probability of death among patients with FTC.

CONCLUSIONS

The prognosis of FTC is excellent. The likelihood of death caused by thyroid cancer increases with age. Male sex, tumors larger than 4 cm, invasion, extrathyroidal extension, lymph node involvement, and distant metastases increase the risk of dying of thyroid carcinoma. The nomogram constructed on the basis of the model is potentially useful for both clinicians and patients.

Development and validation of prognostic nomogram for lung cancer patients below the age of 45 years

This study aimed to establish a nomogram for the prognostic prediction of patients with early-onset lung cancer (EOLC) in both overall survival (OS) and cancer-specific survival (CSS). EOLC patients diagnosed between 2004 and 2015 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and further divided into training and validation sets randomly. The prognostic nomobgram for predicting 3-, 5- and 10-years OS and CSS was established based on the relative clinical variables determined by the multivariate Cox analysis results. Furthermore, the predictive performance of nomogram was assessed by concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve and decision curve analysis (DCA) curve. A total of 1,822 EOLC patients were selected and randomized into a training cohort (1,275, 70%) and a validation cohort (547, 30%). The nomograms were established based on the statistical results of Cox analysis. In training set, the C-indexes for OS and CSS prediction were 0.797 (95% confidence interval [CI]: 0.773-0.818) and 0.794 (95% CI: 0.771-0.816). Significant agreement in the calibration curves was noticed in the nomogram models. The results of ROC and DCA indicated nomograms possessed better predict performance compared with TNM-stage and SEER-stage. And the area under the curve (AUC) of the nomogram for OS and CSS prediction in ROC analysis were 0.766 (95% CI: 0.745-0.787) and 0.782 (95% CI: 0.760-0.804) respectively. The prognostic nomogram provided an accurate prediction of 3-, 5-, and 10-year OS and CSS of EOLC patients which contributed clinicians to optimize individualized treatment plans.

A Novel Clinical Nomogram for Predicting Cancer-Specific Survival in Adult Patients After Primary Surgery for Epithelial Ovarian Cancer: A Real-World Analysis Based on the Surveillance, Epidemiology, and End Results Database and External Validation in a Tertiary Center

Background

The present study aimed to construct and validate a nomogram that can be used to predict cancer-specific survival (CSS) in patients with epithelial ovarian cancer (EOC).

Methods

A total of 7,129 adult patients with EOC were extracted from the Surveillance, Epidemiology, and End Results database between 2010 and 2015. Patients were randomly divided into the training and validation cohorts (7:3). Cox regression was conducted to evaluate prognostic factors of CSS. The internal validation of the nomogram was performed using concordance index (C-index), AUC, calibration curves, and decision curve analyses (DCAs). Data from 53 adult EOC patients at Shengjing Hospital of China Medical University from 2008 to 2012 were collected for external verification. Kaplan-Meier curves were plotted to compare survival outcomes among risk subgroups.

Results

Age, grade, histological types, stage, residual lesion size, number of regional lymph nodes resected, number of positive lymph nodes, and chemotherapy were independent risk factors for CSS. Based on the above factors, we constructed a nomogram. The C-indices of the training cohort, internal validation cohort, and external verification group were 0.763, 0.750, and 0.920, respectively. The calibration curve indicated good agreement between the nomogram prediction and actual survival. AUC and DCA results indicated great clinical usefulness of the nomogram. The differences in the Kaplan-Meier curves among different risk subgroups were statistically significant.

Conclusions

We constructed a nomogram to predict CSS in adult patients with EOC after primary surgery, which can assist in counseling and guiding treatment decision making.

Development and validation of a Surveillance, Epidemiology, and End Results (SEER)-based prognostic nomogram for predicting survival in elderly patients with gastric cancer after surgery

Background

Elderly gastric cancer (ELGC) remains one of the intensively investigated topics during the last decades. To establish a comprehensive nomogram for effective clinical practice and assessment is of significance. This study is designed to develop a prognostic nomogram for ELGC both in overall survival (OS) and cancer-specific survival (CSS).

Methods

The recruited cases were from the Surveillance, Epidemiology, and End Results (SEER) database and input for the construction of nomogram.

Results

A total of 4,414 individuals were recruited for this study, of which 2,208 were randomly in training group and 2,206 were in validation group. In univariate analysis of OS, significant variables (P<0.05) included age, marital status, grade, American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) stage, bone/brain/liver/lung metastasis and tumor size. In univariate analysis of CSS, significant variables (P<0.05) included age, grade, AJCC TNM stage, bone/brain/liver/lung metastasis and tumor size. In multivariate analysis of OS, sex, age, race, grade, TNM stage, lung metastasis and tumor size were considered as the significant variables and subjected to the establishment of nomogram. In multivariable analysis of CSS, age, grade, TNM, tumor size were considered as the significant variables and input to the establishment of nomogram. Sex, age, race, grade, TNM stage, lung metastasis and tumor size were included for the establishment of nomogram in OS while age, grade, TNM, tumor size were included to the establishment of nomogram in CSS. C-index, decision curve analysis (DCA) and the area under the curve (AUC) showed distinct value of newly established nomogram models. Both OS and CSS nomograms showed higher statistic power over the AJCC stage.

Conclusions

This study established and validated novel nomogram models of OS and CSS for ELGC based on population dataset.

Molecular Subtypes and CD4+ Memory T Cell-Based Signature Associated With Clinical Outcomes in Gastric Cancer

Background

CD4⁺ memory T cells are an important component of the tumor microenvironment (TME) and affect tumor occurrence and progression. Nevertheless, there has been no systematic analysis of the effect of CD4⁺ memory T cells in gastric cancer (GC).

Methods

Three datasets obtained from microarray and the corresponding clinical data of GC patients were retrieved and downloaded from the Gene Expression Omnibus (GEO) database. We uploaded the normalize gene expression data with standard annotation to the CIBERSORT web portal for evaluating the proportion of immune cells in the GC samples. The WGCNA was performed to identify the modules the CD4⁺ memory T cell related module (CD4⁺ MTRM) which was most significantly associated with CD4⁺ memory T cell. Univariate Cox analysis was used to screen prognostic CD4⁺ memory T cell-related genes (CD4⁺ MTRGs) in CD4⁺ MTRM. LASSO analysis and multivariate Cox analysis were then performed to construct a prognostic gene signature whose effect was evaluated by Kaplan-Meier curves and receiver operating characteristic (ROC), Harrell’s concordance index (C-index), and decision curve analyses (DCA). A prognostic nomogram was finally established based on the CD4⁺ MTRGs.

Result

We observed that a high abundance of CD4⁺ memory T cells was associated with better survival in GC patients. CD4⁺ MTRM was used to stratify GC patients into three clusters by unsupervised clustering analysis and ten CD4⁺ MTRGs were identified. Overall survival, five immune checkpoint genes and 17 types of immunocytes were observed to be significantly different among the three clusters. A ten-CD4⁺ MTRG signature was constructed to predict GC patient prognosis. The ten-CD4⁺ MTRG signature could divide GC patients into high- and low-risk groups with distinct OS rates. Multivariate Cox analysis suggested that the ten-CD4⁺ MTRG signature was an independent risk factor in GC. A nomogram incorporating this signature and clinical variables was established, and the C-index was 0.73 (95% CI: 0.697–0.763). Calibration curves and DCA presented high credibility for the OS nomogram.

Conclusion

We identified three molecule subtypes, ten CD4⁺ MTRGs, and generated a prognostic nomogram that reliably predicts OS in GC. These findings have implications for precise prognosis prediction and individualized targeted therapy.