A nomogram for predicting survival in patients with advanced (stage III/IV) pancreatic body tail cancer: a SEER-based study

Identification of methylation driver genes for predicting the prognosis of pancreatic cancer patients based on whole-genome DNA methylation sequencing technology

This study was based on the use of whole-genome DNA methylation sequencing technology to identify DNA methylation biomarkers in tumor tissue that can predict the prognosis of patients with pancreatic cancer (PCa). TCGA database was used to download PCa-related DNA methylation and transcriptome atlas data. Methylation driver genes (MDGs) were obtained using the MethylMix package. Candidate genes in the MDGs were screened for prognostic relevance to PCa patients by univariate Cox analysis, and a prognostic risk score model was constructed based on the key MDGs. ROC curve analysis was performed to assess the accuracy of the prognostic risk score model. The effects of PIK3C2B knockdown on malignant phenotypes of PCa cells were investigated in vitro. A total of 2737 differentially expressed genes were identified, with 649 upregulated and 2088 downregulated, using 178 PCa samples and 171 normal samples. MethylMix was employed to identify 71 methylation-driven genes (47 hypermethylated and 24 hypomethylated) from 185 TCGA PCa samples. Cox regression analyses identified eight key MDGs (LEF1, ZIC3, VAV3, TBC1D4, FABP4, MAP3K5, PIK3C2B, IGF1R) associated with prognosis in PCa. Seven of them were hypermethylated, while PIK3C2B was hypomethylated. A prognostic risk prediction model was constructed based on the eight key MDGs, which was found to accurately predict the prognosis of PCa patients. In addition, the malignant phenotypes of PANC-1 cells were decreased after the knockdown of PIK3C2B. Therefore, the prognostic risk prediction model based on the eight key MDGs could accurately predict the prognosis of PCa patients.

Global burden of pancreatic cancer attributable to metabolic risks from 1990 to 2019, with projections of mortality to 2030

OBJECTIVE

Metabolic risks play a key role in the progression of pancreatic cancer. This study aimed to present global, regional and national data on mortality and disability-adjusted life-year (DALY) for pancreatic cancer attributable to metabolic risk and to forecast mortality to 2030 using data from the Global Burden of Disease (GBD).

METHODS

Data on mortality and DALYs due to pancreatic cancer attributable to metabolic risks were obtained from GBD 2019. Metabolic risks include high fasting plasma glucose (FPG) and high body mass index (BMI). Total numbers and age-standardized rates per 100,000 people for mortality and DALYs were reported by age, sex, region and country/territory from 1990 to 2019. The "Bayes age-period-cohort" method was used for projections of mortality to 2030.

RESULTS

Globally, there was a 3.5-fold increase in the number of pancreatic cancer deaths attributable to metabolic risk, from 22,091 in 1990 to 77,215 in 2019. High-income North America and Central Europe had the highest age-standardized mortality rates (ASMRs) of pancreatic cancer attributable to high FPG and high BMI in 2019, respectively. From 1990 to 2019, the global ASMR of pancreatic cancer attributable to high FPG and high BMI increased. Countries with high healthcare access quality had much higher age-standardized DALY rates. In the next 10 years, the ASMR of pancreatic cancer attributable to high FPG and high BMI will continue to increase.

CONCLUSION

Pancreatic cancer mortality and DALYs attributable to metabolic factors remain high, particularly in high-income regions or countries. Studies on the metabolic mechanism of pancreatic cancer and effective treatment strategies are needed.

Construction and application of dynamic online nomogram for prognosis prediction of patients with advanced (Stage III/IV) tongue squamous cell carcinoma

OBJECTIVES

The prognosis of patients with advanced tongue squamous cell carcinoma (ATSCC) is poor, and their overall survival (OS) is relatively short. Currently, the TNM stage system is often used clinically to assess the prognosis of patients, but the evaluation index of the TNM stage system is relatively single and does not specifically demonstrate relevant prognostic data. Therefore, the purpose of this study was to construct a dynamic online nomogram for predicting the prognosis of patients with ATSCC and to provide some reference for personalized clinical treatment of patients.

METHODS

Clinical and prognostic information on patients with pathologically confirmed ATSCC from 2000 to 2018 was extracted from the SEER database and randomly divided into a training cohort and a validation cohort in a 7:3 ratio. Multifactorial and univariate Cox regression analyses were used to identify prognostic risk factors. Dynamic online nomogram were constructed using R software. Area under the curve (AUC), C-index, calibration curve, and decision curve analysis (DCA) with time-dependent ROC curves were used to assess the clinical utility of the nomogram. Kaplan-Meier survival curves were used to compare the prognosis of different patient categories.

RESULTS

A total of 3828 patients with ATSCC were screened in the SEER database.Age,race, primary site, AJCC T,N and M stage, lymph nodes surgery, radiotherapy, chemotherapy and marital status were independent influences on OS(P < 0.05). In the training cohort, the C-index of the OS-related line plot was 0.733 and the AUC for predicting 3-year OS was 0.867. In the validation cohort, the C-index was 0.738 and the AUC for 3-year OS was 0.899. Calibration plots and DCA curves showed good predictive performance of the model in both the training and validation cohorts. Kaplan-Meier survival curves showed that chemotherapy, lymph nodes surgery,married,primary site(tongue base) and radiotherapy had better OS than the non-chemotherapy, non-surgery, single, primary site(tongue anterior), and non-radiotherapy groups, respectively (all P < 0.05).

CONCLUSION

The established dynamic online nomogram has good predictive performance, which helps to personalize and combine the actual clinical patients to comprehensively predict the prognosis of ATSCC patients and may have better clinical application than the TNM stage system.

Nomogram for predicting the preoperative lymph node metastasis in resectable pancreatic cancer

BACKGROUND

Lymph node metastasis (LNM) is a critical prognostic factor in resectable pancreatic cancer (PC) patients, determining treatment strategies. This study aimed to develop a clinical model to adequately and accurately predict the risk of LNM in PC patients.

METHODS

13,200 resectable PC patients were enrolled from the SEER (Surveillance, Epidemiology, and End Results) database, and randomly divided into a training group and an internal validation group at a ratio of 7:3. An independent group (n = 62) obtained from The First Affiliated Hospital of Xinxiang Medical University was enrolled as the external validation group. The univariate and multivariate logistic regression analyses were used to screen independent risk factors for LNM. The minimum Akaike's information criterion (AIC) was performed to select the optimal model parameters and construct a nomogram for assessing the risk of LNM. The performance of the nomogram was assessed by the receiver operating characteristics (ROC) curve, calibration plot, and decision curve analysis (DCA). In addition, an online web calculator was designed to assess the risk of LNM.

RESULT

A total of six risk predictors (including age at diagnosis, race, primary site, grade, histology, and T-stage) were identified and included in the nomogram. The areas under the curves (AUCs) [95% confidential interval (CI)] were 0.711 (95%CI: 0.700-0.722), 0.700 (95%CI: 0.683-0.717), and 0.845 (95%CI: 0.749-0.942) in the training, internal validation and external validation groups, respectively. The calibration curves showed satisfied consistency between nomogram-predicted LNM and actual observed LNM. The concordance indexes (C-indexes) in the training, internal, and external validation sets were 0.689, 0.686, and 0.752, respectively. The DCA curves of the nomogram demonstrated good clinical utility.

CONCLUSION

We constructed a nomogram model for predicting LNM in pancreatic cancer patients, which may help oncologists and surgeons to choose more individualized clinical treatment strategies and make better clinical decisions.

Time trend of pancreatic cancer mortality in the Western Pacific Region: age-period-cohort analysis from 1990 to 2019 and forecasting for 2044

BACKGROUND

Pancreatic cancer poses a serious medical problem worldwide. Countries in the Western Pacific Region are facing public health challenges from cancer. This study assesses the time trends of pancreatic cancer mortality in the Western Pacific Region from 1990 to 2019 and predicts its trend to 2044.

METHODS

Mortality data were obtained from the Global Health Data Exchange. We used an age-period-cohort model to estimate age, period and birth cohort effects on pancreatic cancer mortality from 1990 to 2019 by calculating net drift, local drift, age-specific rate, period rate ratio, and cohort rate ratio. We also predict pancreatic cancer mortality to 2044 in Western Pacific countries.

RESULTS

Overall, there were 178,276 (95% uncertain interval: 157,771 to 198,636) pancreatic cancer deaths in the Western Pacific Region in 2019, accounting for 33.6% of all deaths due to pancreatic cancer worldwide. There were significant increases in pancreatic cancer disability-adjusted life years between 1990 and 2019 in the Western Pacific Region, mainly due to population growth and aging. Pancreatic cancer mortality increased with age. The period effect showed an increasing trend of mortality for both sexes over the study period. Compared to the reference period (2000 to 2004), the rate ratio was elevated in both males and females in the period of 2015 to 2019. There was an overall increasing rate ratio from early birth cohorts to recent cohorts. Deaths may continue to increase in the next 25 years in the ten countries, while most countries have seen their age-standardized rate forecasts fall.

CONCLUSION

The mortality of pancreatic cancer is still high in the Western Pacific Region. Countries/territories should focus on pancreatic cancer prevention and early cancer screening in high-risk populations. Specific public health methods and policies aimed at reducing risk factors for pancreatic cancer are also needed.

A nomogram for predicting survival in Patients with oral tongue keratinized squamous cell carcinoma: A SEER-based study

OBJECTIVE

Oral tongue keratinized squamous cell carcinoma (OTKSCC), a relatively rare form of tongue cancer (TC) in clinical practice, accompanied by features of cell keratosis, is an uncommon histological subtype. However, its specific clinicopathological features and prognosis have not been adequately described. In this study, we aimed to create a nomogram using R language software to predict overall survival (OS) of patients with OTKSCC to assess the prognosis of OTKSCC patients.

METHODS

We extracted clinical and related prognostic data of OTKSCC patients from 1975 to 2019 from the Surveillance, Epidemiology, and End Results database. Independent prognostic factors were selected using univariate and multivariate Cox analyses, and a nomogram was constructed using R software. The C-index, area under the curve (AUC) of receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA) were used to assess the clinical utility of the nomogram. Finally, OS was assessed using the Kaplan-Meier method.

RESULTS

A total of 2450 OTKSCC patients were included in the study. Univariate and multivariate Cox regression analyses were used to identify age, T stage, N stage, surgery, and radiation therapy as independent risk factors (p<0.05). In the training cohort, the calibration index of the nomogram was 0.725, while the AUC values for nomogram, age, T stage, N stage, surgery and radiation therapy were 0.878, 0.639, 0.781, 0.661, 0.724 and 0.354, respectively. At the same time, in the verification queue, the calibration index of the nomogram was 0.726, while the AUC values for nomogram, age, T stage, N stage, surgery and radiation therapy were 0.859,0.612,0.826,0.675,0.758 and 0.303, respectively. Ideal uniformity of the models from the training and validation cohorts was demonstrated in the calibration and DCA curves. Univariate survival analysis showed that age, T stage, N stage, surgery, and radiotherapy were statistically significant for prognosis (p<0.05).

CONCLUSION

Age, T stage, N stage, surgery, and radiation therapy are independently associated with the OS, and the established nomogram is an effective visualization tool for predicting the OS of OTKSCC patients.

A nomogram based on CT texture features to predict the response of patients with advanced pancreatic cancer treated with chemotherapy

OBJECTIVE

This study aimed to evaluate the predictive value of computed tomography (CT) texture features in the treatment response of patients with advanced pancreatic cancer (APC) receiving palliative chemotherapy.

METHODS

This study enrolled 84 patients with APC treated with first-line chemotherapy and conducted texture analysis on primary pancreatic tumors. 59 patients and 25 were randomly assigned to the training and validation cohorts at a ratio of 7:3. The treatment response to chemotherapy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST1.1). The patients were divided into progressive and non-progressive groups. The least absolute shrinkage selection operator (LASSO) was applied for feature selection in the training cohort and a radiomics signature (RS) was calculated. A nomogram was developed based on a multivariate logistic regression model incorporating the RS and carbohydrate antigen 19-9 (CA19-9), and was internally validated using the C-index and calibration plot. We performed the decision curve analysis (DCA) and clinical impact curve analysis to reflect the clinical utility of the nomogram. The nomogram was further externally confirmed in the validation cohort.

RESULTS

The multivariate logistic regression analysis indicated that the RS and CA19-9 were independent predictors (P < 0.05), and a trend was found for chemotherapy between progressive and non-progressive groups. The nomogram incorporating RS, CA19-9 and chemotherapy showed favorable discriminative ability in the training (C-index = 0.802) and validation (C-index = 0.920) cohorts. The nomogram demonstrated favorable clinical utility.

CONCLUSION

The RS of significant texture features was significantly associated with the early treatment effect of patients with APC treated with chemotherapy. Based on the RS, CA19-9 and chemotherapy, the nomogram provided a promising way to predict chemotherapeutic effects for APC patients.

Prognostic Predicting Model of Pancreatic Body Tail Carcinoma Using Clinical and CT Radiomic Data

Objective: To collect the clinical, pathological, and computed tomography (CT) data of 143 accepted surgical cases of pancreatic body tail cancer (PBTC) and to model and predict its prognosis. Methods: The clinical, pathological, and CT data of 143 PBTC patients who underwent surgical resection or endoscopic ultrasound biopsy and were pathologically diagnosed in Xiangyang No.1 People's Hospital Hospital from December 2012 to December 2022 were retrospectively analyzed. The Kaplan-Meier method was adopted to make survival curves based on the 1 to 5 years' follow-up data, and then the log-rank was employed to analyze the survival. According to the median survival of 6 months, the PBTC patients were divided into a group with a good prognosis (survival time ≥ 6 months) and a group with a poor prognosis (survival time < 6 months), and further the training set and test set were set at a ratio of 7/3. Then logistic regression was conducted to find independent risk factors, establish predictive models, and further the models were validated. Results: The Kaplan-Meier analysis showed that age, diabetes, tumor, node, and metastasis stage, CT enhancement mode, peripancreatic lymph node swelling, nerve invasion, surgery in a top hospital, tumor size, carbohydrate antigen 19-9, carcinoembryonic antigen, Radscore 1/2/3 were the influencing factors of PBTC recurrence. The overall average survival was 7.4 months in this study. The multivariate logistic analysis confirmed that nerve invasion, surgery in top hospital, dilation of the main pancreatic duct, and Radscore 2 were independent factors affecting the mortality of PBTC (P < .05). In the test set, the combined model achieved the best predictive performance [AUC 0.944, 95% CI (0.826-0.991)], significantly superior to the clinicopathological model [AUC 0.770, 95% CI (0.615-0.886), P = .0145], and the CT radiomics model [AUC 0.883, 95% CI (0.746-0.961), P = .1311], with a good clinical net benefit confirmed by decision curve. The same results were subsequently validated on the test set. Conclusion: The diagnosis and treatment of PBTC are challenging, and survival is poor. Nevertheless, the combined model benefits the clinical management and prognosis of PBTC.